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"""simple docstring"""
import math
class lowerCamelCase :
'''simple docstring'''
def __init__( self: List[Any] , snake_case: int=0 ) -> int: # a graph with Node 0,1,...,N-1
snake_case_ :List[str] = n
snake_case_ :int = [
[math.inf for j in range(0 , snake_case )] for i in range(0 , snake_case )
] # adjacency matrix for weight
snake_case_ :str = [
[math.inf for j in range(0 , snake_case )] for i in range(0 , snake_case )
] # dp[i][j] stores minimum distance from i to j
def lowerCAmelCase_ ( self: Optional[int] , snake_case: str , snake_case: Optional[Any] , snake_case: str ) -> Tuple:
snake_case_ :List[Any] = w
def lowerCAmelCase_ ( self: List[str] ) -> str:
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
snake_case_ :Any = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def lowerCAmelCase_ ( self: int , snake_case: List[Any] , snake_case: Optional[Any] ) -> Union[str, Any]:
return self.dp[u][v]
if __name__ == "__main__":
__a = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 66 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class lowercase ( UpperCamelCase__,UpperCamelCase__ ):
_a = 1
@register_to_config
def __init__( self , _a=2000 , _a=0.1 , _a=20 , _a=1e-3 ) -> List[Any]:
_A : Dict = None
_A : List[Any] = None
_A : Dict = None
def a__ ( self , _a , _a = None ) -> Union[str, Any]:
_A : Union[str, Any] = torch.linspace(1 , self.config.sampling_eps , _a , device=_a )
def a__ ( self , _a , _a , _a , _a=None ) -> Dict:
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
_A : Any = (
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
_A : List[Any] = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
_A : List[str] = std.flatten()
while len(std.shape ) < len(score.shape ):
_A : List[Any] = std.unsqueeze(-1 )
_A : int = -score / std
# compute
_A : Tuple = -1.0 / len(self.timesteps )
_A : str = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
_A : List[str] = beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
_A : Union[str, Any] = beta_t.unsqueeze(-1 )
_A : Tuple = -0.5 * beta_t * x
_A : Tuple = torch.sqrt(_a )
_A : Dict = drift - diffusion**2 * score
_A : Dict = x + drift * dt
# add noise
_A : Any = randn_tensor(x.shape , layout=x.layout , generator=_a , device=x.device , dtype=x.dtype )
_A : str = x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self ) -> Optional[Any]:
return self.config.num_train_timesteps
| 26 | 0 |
'''simple docstring'''
from ....utils import logging
lowercase__ : List[str] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE (a__ ):
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=None , _UpperCAmelCase=2048):
'''simple docstring'''
__A : Dict = config.__dict__
__A : Any = modal_hidden_size
if num_labels:
__A : Dict = num_labels
| 190 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
CTRLTokenizer,
GPTaTokenizer,
GPTaTokenizerFast,
PreTrainedTokenizerFast,
RobertaTokenizer,
RobertaTokenizerFast,
is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
TOKENIZER_MAPPING,
get_tokenizer_config,
tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
DUMMY_DIFF_TOKENIZER_IDENTIFIER,
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tokenizers,
slow,
)
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class SCREAMING_SNAKE_CASE (unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Dict = 0
@slow
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
__A : List[str] = AutoTokenizer.from_pretrained(_UpperCAmelCase)
self.assertIsNotNone(_UpperCAmelCase)
self.assertIsInstance(_UpperCAmelCase , (BertTokenizer, BertTokenizerFast))
self.assertGreater(len(_UpperCAmelCase) , 0)
for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
__A : Any = AutoTokenizer.from_pretrained(_UpperCAmelCase)
self.assertIsNotNone(_UpperCAmelCase)
self.assertIsInstance(_UpperCAmelCase , (GPTaTokenizer, GPTaTokenizerFast))
self.assertGreater(len(_UpperCAmelCase) , 0)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Optional[int] = AutoTokenizer.from_pretrained(_UpperCAmelCase)
self.assertIsInstance(_UpperCAmelCase , (BertTokenizer, BertTokenizerFast))
self.assertEqual(tokenizer.vocab_size , 12)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Optional[Any] = AutoTokenizer.from_pretrained(_UpperCAmelCase)
self.assertIsInstance(_UpperCAmelCase , (RobertaTokenizer, RobertaTokenizerFast))
self.assertEqual(tokenizer.vocab_size , 20)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Tuple = AutoConfig.from_pretrained(_UpperCAmelCase)
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase)
# Check that tokenizer_type ≠ model_type
__A : Optional[Any] = AutoTokenizer.from_pretrained(_UpperCAmelCase , config=_UpperCAmelCase)
self.assertIsInstance(_UpperCAmelCase , (BertTokenizer, BertTokenizerFast))
self.assertEqual(tokenizer.vocab_size , 12)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('./tests/fixtures/vocab.txt' , os.path.join(_UpperCAmelCase , 'vocab.txt'))
__A : Union[str, Any] = AutoTokenizer.from_pretrained(_UpperCAmelCase , tokenizer_type='bert' , use_fast=_UpperCAmelCase)
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase)
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('./tests/fixtures/vocab.json' , os.path.join(_UpperCAmelCase , 'vocab.json'))
shutil.copy('./tests/fixtures/merges.txt' , os.path.join(_UpperCAmelCase , 'merges.txt'))
__A : str = AutoTokenizer.from_pretrained(_UpperCAmelCase , tokenizer_type='gpt2' , use_fast=_UpperCAmelCase)
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase)
@require_tokenizers
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('./tests/fixtures/vocab.txt' , os.path.join(_UpperCAmelCase , 'vocab.txt'))
__A : List[str] = AutoTokenizer.from_pretrained(_UpperCAmelCase , tokenizer_type='bert')
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase)
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('./tests/fixtures/vocab.json' , os.path.join(_UpperCAmelCase , 'vocab.json'))
shutil.copy('./tests/fixtures/merges.txt' , os.path.join(_UpperCAmelCase , 'merges.txt'))
__A : List[str] = AutoTokenizer.from_pretrained(_UpperCAmelCase , tokenizer_type='gpt2')
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
with pytest.raises(_UpperCAmelCase):
AutoTokenizer.from_pretrained('./' , tokenizer_type='xxx')
@require_tokenizers
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
__A : List[Any] = tokenizer_class.from_pretrained('wietsedv/bert-base-dutch-cased')
self.assertIsInstance(_UpperCAmelCase , (BertTokenizer, BertTokenizerFast))
if isinstance(_UpperCAmelCase , _UpperCAmelCase):
self.assertEqual(tokenizer.basic_tokenizer.do_lower_case , _UpperCAmelCase)
else:
self.assertEqual(tokenizer.do_lower_case , _UpperCAmelCase)
self.assertEqual(tokenizer.model_max_length , 512)
@require_tokenizers
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
with self.assertRaisesRegex(
_UpperCAmelCase , 'julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier' , ):
__A : str = tokenizer_class.from_pretrained('julien-c/herlolip-not-exists')
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Any = TOKENIZER_MAPPING.values()
__A : Union[str, Any] = []
for slow_tok, fast_tok in tokenizers:
if slow_tok is not None:
tokenizer_names.append(slow_tok.__name__)
if fast_tok is not None:
tokenizer_names.append(fast_tok.__name__)
for tokenizer_name in tokenizer_names:
# must find the right class
tokenizer_class_from_name(_UpperCAmelCase)
@require_tokenizers
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased' , use_fast=_UpperCAmelCase) , _UpperCAmelCase)
self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased') , _UpperCAmelCase)
@require_tokenizers
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : List[str] = AutoTokenizer.from_pretrained('distilbert-base-uncased' , do_lower_case=_UpperCAmelCase)
__A : str = 'Hello, world. How are you?'
__A : List[str] = tokenizer.tokenize(_UpperCAmelCase)
self.assertEqual('[UNK]' , tokens[0])
__A : Dict = AutoTokenizer.from_pretrained('microsoft/mpnet-base' , do_lower_case=_UpperCAmelCase)
__A : List[Any] = tokenizer.tokenize(_UpperCAmelCase)
self.assertEqual('[UNK]' , tokens[0])
@require_tokenizers
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Union[str, Any] = AutoTokenizer.from_pretrained('robot-test/dummy-tokenizer-fast-with-model-config')
self.assertEqual(type(_UpperCAmelCase) , _UpperCAmelCase)
self.assertEqual(tokenizer.model_max_length , 512)
self.assertEqual(tokenizer.vocab_size , 3_0000)
self.assertEqual(tokenizer.unk_token , '[UNK]')
self.assertEqual(tokenizer.padding_side , 'right')
self.assertEqual(tokenizer.truncation_side , 'right')
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Optional[Any] = AutoTokenizer.from_pretrained(_UpperCAmelCase)
self.assertIsInstance(_UpperCAmelCase , (BertTokenizer, BertTokenizerFast))
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_UpperCAmelCase)
__A : Any = AutoTokenizer.from_pretrained(_UpperCAmelCase)
self.assertIsInstance(_UpperCAmelCase , tokenizer.__class__)
self.assertEqual(tokenizera.vocab_size , 12)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Dict = AutoTokenizer.from_pretrained('ctrl')
# There is no fast CTRL so this always gives us a slow tokenizer.
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : List[Any] = get_tokenizer_config('bert-base-cased')
__A : Optional[int] = config.pop('_commit_hash' , _UpperCAmelCase)
# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
self.assertEqual(_UpperCAmelCase , {'do_lower_case': False})
# This model does not have a tokenizer_config so we get back an empty dict.
__A : Dict = get_tokenizer_config(_UpperCAmelCase)
self.assertDictEqual(_UpperCAmelCase , {})
# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
__A : Any = AutoTokenizer.from_pretrained(_UpperCAmelCase)
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_UpperCAmelCase)
__A : Any = get_tokenizer_config(_UpperCAmelCase)
# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
self.assertEqual(config['tokenizer_class'] , 'BertTokenizer')
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
try:
AutoConfig.register('custom' , _UpperCAmelCase)
AutoTokenizer.register(_UpperCAmelCase , slow_tokenizer_class=_UpperCAmelCase)
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_UpperCAmelCase):
AutoTokenizer.register(_UpperCAmelCase , slow_tokenizer_class=_UpperCAmelCase)
__A : Optional[Any] = CustomTokenizer.from_pretrained(_UpperCAmelCase)
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_UpperCAmelCase)
__A : int = AutoTokenizer.from_pretrained(_UpperCAmelCase)
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase)
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
@require_tokenizers
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
try:
AutoConfig.register('custom' , _UpperCAmelCase)
# Can register in two steps
AutoTokenizer.register(_UpperCAmelCase , slow_tokenizer_class=_UpperCAmelCase)
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, None))
AutoTokenizer.register(_UpperCAmelCase , fast_tokenizer_class=_UpperCAmelCase)
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast))
del TOKENIZER_MAPPING._extra_content[CustomConfig]
# Can register in one step
AutoTokenizer.register(
_UpperCAmelCase , slow_tokenizer_class=_UpperCAmelCase , fast_tokenizer_class=_UpperCAmelCase)
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast))
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_UpperCAmelCase):
AutoTokenizer.register(_UpperCAmelCase , fast_tokenizer_class=_UpperCAmelCase)
# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
# and that model does not have a tokenizer.json
with tempfile.TemporaryDirectory() as tmp_dir:
__A : Optional[int] = BertTokenizerFast.from_pretrained(_UpperCAmelCase)
bert_tokenizer.save_pretrained(_UpperCAmelCase)
__A : Dict = CustomTokenizerFast.from_pretrained(_UpperCAmelCase)
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_UpperCAmelCase)
__A : Union[str, Any] = AutoTokenizer.from_pretrained(_UpperCAmelCase)
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase)
__A : Any = AutoTokenizer.from_pretrained(_UpperCAmelCase , use_fast=_UpperCAmelCase)
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase)
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
with self.assertRaises(_UpperCAmelCase):
__A : List[str] = AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer')
# If remote code is disabled, we can't load this config.
with self.assertRaises(_UpperCAmelCase):
__A : Dict = AutoTokenizer.from_pretrained(
'hf-internal-testing/test_dynamic_tokenizer' , trust_remote_code=_UpperCAmelCase)
__A : str = AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer' , trust_remote_code=_UpperCAmelCase)
self.assertTrue(tokenizer.special_attribute_present)
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_UpperCAmelCase)
__A : Dict = AutoTokenizer.from_pretrained(_UpperCAmelCase , trust_remote_code=_UpperCAmelCase)
self.assertTrue(reloaded_tokenizer.special_attribute_present)
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizerFast')
self.assertEqual(reloaded_tokenizer.__class__.__name__ , 'NewTokenizerFast')
# Test we can also load the slow version
__A : Union[str, Any] = AutoTokenizer.from_pretrained(
'hf-internal-testing/test_dynamic_tokenizer' , trust_remote_code=_UpperCAmelCase , use_fast=_UpperCAmelCase)
self.assertTrue(tokenizer.special_attribute_present)
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer')
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_UpperCAmelCase)
__A : Union[str, Any] = AutoTokenizer.from_pretrained(_UpperCAmelCase , trust_remote_code=_UpperCAmelCase , use_fast=_UpperCAmelCase)
self.assertEqual(reloaded_tokenizer.__class__.__name__ , 'NewTokenizer')
self.assertTrue(reloaded_tokenizer.special_attribute_present)
else:
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer')
self.assertEqual(reloaded_tokenizer.__class__.__name__ , 'NewTokenizer')
@require_tokenizers
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
class SCREAMING_SNAKE_CASE (a__ ):
lowerCAmelCase = False
class SCREAMING_SNAKE_CASE (a__ ):
lowerCAmelCase = NewTokenizer
lowerCAmelCase = False
try:
AutoConfig.register('custom' , _UpperCAmelCase)
AutoTokenizer.register(_UpperCAmelCase , slow_tokenizer_class=_UpperCAmelCase)
AutoTokenizer.register(_UpperCAmelCase , fast_tokenizer_class=_UpperCAmelCase)
# If remote code is not set, the default is to use local
__A : List[Any] = AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer')
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizerFast')
self.assertFalse(tokenizer.special_attribute_present)
__A : Dict = AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer' , use_fast=_UpperCAmelCase)
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer')
self.assertFalse(tokenizer.special_attribute_present)
# If remote code is disabled, we load the local one.
__A : Optional[Any] = AutoTokenizer.from_pretrained(
'hf-internal-testing/test_dynamic_tokenizer' , trust_remote_code=_UpperCAmelCase)
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizerFast')
self.assertFalse(tokenizer.special_attribute_present)
__A : Any = AutoTokenizer.from_pretrained(
'hf-internal-testing/test_dynamic_tokenizer' , trust_remote_code=_UpperCAmelCase , use_fast=_UpperCAmelCase)
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer')
self.assertFalse(tokenizer.special_attribute_present)
# If remote is enabled, we load from the Hub
__A : int = AutoTokenizer.from_pretrained(
'hf-internal-testing/test_dynamic_tokenizer' , trust_remote_code=_UpperCAmelCase)
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizerFast')
self.assertTrue(tokenizer.special_attribute_present)
__A : Optional[Any] = AutoTokenizer.from_pretrained(
'hf-internal-testing/test_dynamic_tokenizer' , trust_remote_code=_UpperCAmelCase , use_fast=_UpperCAmelCase)
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer')
self.assertTrue(tokenizer.special_attribute_present)
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : int = AutoTokenizer.from_pretrained(
'hf-internal-testing/test_dynamic_tokenizer_legacy' , trust_remote_code=_UpperCAmelCase)
self.assertTrue(tokenizer.special_attribute_present)
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizerFast')
# Test we can also load the slow version
__A : int = AutoTokenizer.from_pretrained(
'hf-internal-testing/test_dynamic_tokenizer_legacy' , trust_remote_code=_UpperCAmelCase , use_fast=_UpperCAmelCase)
self.assertTrue(tokenizer.special_attribute_present)
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer')
else:
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer')
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
with self.assertRaisesRegex(
_UpperCAmelCase , 'bert-base is not a local folder and is not a valid model identifier'):
__A : Union[str, Any] = AutoTokenizer.from_pretrained('bert-base')
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
with self.assertRaisesRegex(
_UpperCAmelCase , R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
__A : Union[str, Any] = AutoTokenizer.from_pretrained(_UpperCAmelCase , revision='aaaaaa')
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Optional[int] = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert')
with RequestCounter() as counter:
__A : Union[str, Any] = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert')
self.assertEqual(counter.get_request_count , 0)
self.assertEqual(counter.head_request_count , 1)
self.assertEqual(counter.other_request_count , 0)
| 190 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase : Optional[Any] = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : List[str] = [
'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'InformerForPrediction',
'InformerModel',
'InformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
_UpperCamelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 220 |
"""simple docstring"""
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
_UpperCamelCase : List[Any] = 4
_UpperCamelCase : Optional[Any] = 3
class a ( a_ ):
pass
def _SCREAMING_SNAKE_CASE ( __snake_case : List[str] ):
'''simple docstring'''
for shard in shards:
for i in range(__snake_case ):
yield {"i": i, "shard": shard}
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = int(os.environ['RANK'] )
lowercase = int(os.environ['WORLD_SIZE'] )
lowercase = ArgumentParser()
parser.add_argument('--streaming' , type=__snake_case )
parser.add_argument('--local_rank' , type=__snake_case )
parser.add_argument('--num_workers' , type=__snake_case , default=0 )
lowercase = parser.parse_args()
lowercase = args.streaming
lowercase = args.num_workers
lowercase = {'shards': [f'shard_{shard_idx}' for shard_idx in range(__snake_case )]}
lowercase = IterableDataset.from_generator(__snake_case , gen_kwargs=__snake_case )
if not streaming:
lowercase = Dataset.from_list(list(__snake_case ) )
lowercase = split_dataset_by_node(__snake_case , rank=__snake_case , world_size=__snake_case )
lowercase = torch.utils.data.DataLoader(__snake_case , num_workers=__snake_case )
lowercase = NUM_SHARDS * NUM_ITEMS_PER_SHARD
lowercase = full_size // world_size
expected_local_size += int(rank < (full_size % world_size) )
lowercase = sum(1 for _ in dataloader )
if local_size != expected_local_size:
raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}' )
if __name__ == "__main__":
main()
| 220 | 1 |
lowercase_ = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
lowercase_ = frozenset(["prompt", "negative_prompt"])
lowercase_ = frozenset([])
lowercase_ = frozenset(["image"])
lowercase_ = frozenset(
[
"image",
"height",
"width",
"guidance_scale",
]
)
lowercase_ = frozenset(["image"])
lowercase_ = frozenset(
[
"prompt",
"image",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
]
)
lowercase_ = frozenset(["prompt", "image", "negative_prompt"])
lowercase_ = frozenset(
[
# Text guided image variation with an image mask
"prompt",
"image",
"mask_image",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
]
)
lowercase_ = frozenset(["prompt", "image", "mask_image", "negative_prompt"])
lowercase_ = frozenset(
[
# image variation with an image mask
"image",
"mask_image",
"height",
"width",
"guidance_scale",
]
)
lowercase_ = frozenset(["image", "mask_image"])
lowercase_ = frozenset(
[
"example_image",
"image",
"mask_image",
"height",
"width",
"guidance_scale",
]
)
lowercase_ = frozenset(["example_image", "image", "mask_image"])
lowercase_ = frozenset(["class_labels"])
lowercase_ = frozenset(["class_labels"])
lowercase_ = frozenset(["batch_size"])
lowercase_ = frozenset([])
lowercase_ = frozenset(["batch_size"])
lowercase_ = frozenset([])
lowercase_ = frozenset(
[
"prompt",
"audio_length_in_s",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
lowercase_ = frozenset(["prompt", "negative_prompt"])
lowercase_ = frozenset(["input_tokens"])
lowercase_ = frozenset(["input_tokens"])
| 354 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ) -> int:
'''simple docstring'''
def update_area_of_max_square(SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
A__ = update_area_of_max_square(SCREAMING_SNAKE_CASE__ , col + 1 )
A__ = update_area_of_max_square(row + 1 , col + 1 )
A__ = update_area_of_max_square(row + 1 , SCREAMING_SNAKE_CASE__ )
if mat[row][col]:
A__ = 1 + min([right, diagonal, down] )
A__ = max(largest_square_area[0] , SCREAMING_SNAKE_CASE__ )
return sub_problem_sol
else:
return 0
A__ = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ) -> int:
'''simple docstring'''
def update_area_of_max_square_using_dp_array(
SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
A__ = update_area_of_max_square_using_dp_array(SCREAMING_SNAKE_CASE__ , col + 1 , SCREAMING_SNAKE_CASE__ )
A__ = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , SCREAMING_SNAKE_CASE__ )
A__ = update_area_of_max_square_using_dp_array(row + 1 , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if mat[row][col]:
A__ = 1 + min([right, diagonal, down] )
A__ = max(largest_square_area[0] , SCREAMING_SNAKE_CASE__ )
A__ = sub_problem_sol
return sub_problem_sol
else:
return 0
A__ = [0]
A__ = [[-1] * cols for _ in range(SCREAMING_SNAKE_CASE__ )]
update_area_of_max_square_using_dp_array(0 , 0 , SCREAMING_SNAKE_CASE__ )
return largest_square_area[0]
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ) -> int:
'''simple docstring'''
A__ = [[0] * (cols + 1) for _ in range(rows + 1 )]
A__ = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
A__ = dp_array[row][col + 1]
A__ = dp_array[row + 1][col + 1]
A__ = dp_array[row + 1][col]
if mat[row][col] == 1:
A__ = 1 + min(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
A__ = max(dp_array[row][col] , SCREAMING_SNAKE_CASE__ )
else:
A__ = 0
return largest_square_area
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ) -> int:
'''simple docstring'''
A__ = [0] * (cols + 1)
A__ = [0] * (cols + 1)
A__ = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
A__ = current_row[col + 1]
A__ = next_row[col + 1]
A__ = next_row[col]
if mat[row][col] == 1:
A__ = 1 + min(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
A__ = max(current_row[col] , SCREAMING_SNAKE_CASE__ )
else:
A__ = 0
A__ = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 282 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file # noqa: F401
from .default import default_command_parser
from .update import update_command_parser
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Dict=None ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser(add_help=__UpperCamelCase , allow_abbrev=__UpperCamelCase )
# The main config parser
SCREAMING_SNAKE_CASE__ = config_command_parser(__UpperCamelCase )
# The subparser to add commands to
SCREAMING_SNAKE_CASE__ = config_parser.add_subparsers(title="""subcommands""" , dest="""subcommand""" )
# Then add other parsers with the parent parser
default_command_parser(__UpperCamelCase , parents=[parent_parser] )
update_command_parser(__UpperCamelCase , parents=[parent_parser] )
return config_parser
def __SCREAMING_SNAKE_CASE ( ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = get_config_parser()
SCREAMING_SNAKE_CASE__ = config_parser.parse_args()
if not hasattr(__UpperCamelCase , """func""" ):
config_parser.print_help()
exit(1 )
# Run
args.func(__UpperCamelCase )
if __name__ == "__main__":
main()
| 219 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
__lowerCamelCase : Union[str, Any] = '''3'''
print('''Python version:''', sys.version)
print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
print('''Torch version:''', None)
try:
import transformers
print('''transformers version:''', transformers.__version__)
except ImportError:
print('''transformers version:''', None)
| 219 | 1 |
import os
def SCREAMING_SNAKE_CASE_ ( ) -> List[str]:
"""simple docstring"""
UpperCamelCase :Dict = os.path.dirname(os.path.realpath(__magic_name__ ) )
UpperCamelCase :List[str] = os.path.join(__magic_name__ , """triangle.txt""" )
with open(__magic_name__ ) as f:
UpperCamelCase :List[Any] = f.readlines()
UpperCamelCase :Union[str, Any] = []
for line in triangle:
UpperCamelCase :Dict = []
for number in line.strip().split(""" """ ):
numbers_from_line.append(int(__magic_name__ ) )
a.append(__magic_name__ )
for i in range(1 , len(__magic_name__ ) ):
for j in range(len(a[i] ) ):
UpperCamelCase :Dict = a[i - 1][j] if j != len(a[i - 1] ) else 0
UpperCamelCase :Any = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(__magic_name__ , __magic_name__ )
return max(a[-1] )
if __name__ == "__main__":
print(solution())
| 62 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase_ : str = get_tests_dir('''fixtures/spiece.model''')
@require_sentencepiece
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( _a , unittest.TestCase ):
snake_case__ : List[Any] = DebertaVaTokenizer
snake_case__ : Any = DebertaVaTokenizerFast
snake_case__ : Union[str, Any] = True
snake_case__ : Tuple = True
def _A ( self : Union[str, Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
UpperCamelCase :Tuple = DebertaVaTokenizer(__lowerCamelCase , unk_token="""<unk>""" )
tokenizer.save_pretrained(self.tmpdirname )
def _A ( self : int , __lowerCamelCase : Union[str, Any] ):
UpperCamelCase :str = """this is a test"""
UpperCamelCase :Dict = """this is a test"""
return input_text, output_text
def _A ( self : Tuple ):
UpperCamelCase :Optional[Any] = """<pad>"""
UpperCamelCase :Optional[int] = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase ) , __lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase ) , __lowerCamelCase )
def _A ( self : int ):
UpperCamelCase :Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<pad>""" )
self.assertEqual(vocab_keys[1] , """<unk>""" )
self.assertEqual(vocab_keys[-1] , """[PAD]""" )
self.assertEqual(len(__lowerCamelCase ) , 30_001 )
def _A ( self : Optional[int] ):
self.assertEqual(self.get_tokenizer().vocab_size , 30_000 )
def _A ( self : str ):
# fmt: off
UpperCamelCase :Optional[int] = """ \tHeLLo!how \n Are yoU? """
UpperCamelCase :Any = ["""▁hello""", """!""", """how""", """▁are""", """▁you""", """?"""]
# fmt: on
UpperCamelCase :Optional[Any] = DebertaVaTokenizer(__lowerCamelCase , do_lower_case=__lowerCamelCase )
UpperCamelCase :Optional[int] = tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :Any = DebertaVaTokenizerFast(__lowerCamelCase , do_lower_case=__lowerCamelCase )
UpperCamelCase :List[Any] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
@unittest.skip("""There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.""" )
def _A ( self : Dict ):
pass
@unittest.skip("""There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.""" )
def _A ( self : Optional[Any] ):
pass
def _A ( self : Optional[int] ):
# fmt: off
UpperCamelCase :Union[str, Any] = """I was born in 92000, and this is falsé."""
UpperCamelCase :int = ["""▁""", """<unk>""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """▁""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """▁""", """.""", ]
# fmt: on
UpperCamelCase :int = DebertaVaTokenizer(__lowerCamelCase , split_by_punct=__lowerCamelCase )
UpperCamelCase :int = tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :Optional[int] = DebertaVaTokenizerFast(__lowerCamelCase , split_by_punct=__lowerCamelCase )
UpperCamelCase :Any = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def _A ( self : int ):
# fmt: off
UpperCamelCase :Union[str, Any] = """I was born in 92000, and this is falsé."""
UpperCamelCase :Any = ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """▁""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """▁""", """.""", ]
# fmt: on
UpperCamelCase :Tuple = DebertaVaTokenizer(__lowerCamelCase , do_lower_case=__lowerCamelCase , split_by_punct=__lowerCamelCase )
UpperCamelCase :Any = tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :Union[str, Any] = DebertaVaTokenizerFast(__lowerCamelCase , do_lower_case=__lowerCamelCase , split_by_punct=__lowerCamelCase )
UpperCamelCase :int = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def _A ( self : Any ):
# fmt: off
UpperCamelCase :Union[str, Any] = """I was born in 92000, and this is falsé."""
UpperCamelCase :List[Any] = ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """.""", ]
# fmt: on
UpperCamelCase :Tuple = DebertaVaTokenizer(__lowerCamelCase , do_lower_case=__lowerCamelCase , split_by_punct=__lowerCamelCase )
UpperCamelCase :Any = tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :Union[str, Any] = DebertaVaTokenizerFast(__lowerCamelCase , do_lower_case=__lowerCamelCase , split_by_punct=__lowerCamelCase )
UpperCamelCase :Dict = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def _A ( self : str ):
# fmt: off
UpperCamelCase :List[str] = """I was born in 92000, and this is falsé."""
UpperCamelCase :int = ["""▁""", """<unk>""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """▁""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """▁""", """.""", ]
# fmt: on
UpperCamelCase :List[str] = DebertaVaTokenizer(__lowerCamelCase , do_lower_case=__lowerCamelCase , split_by_punct=__lowerCamelCase )
UpperCamelCase :Tuple = tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :List[str] = DebertaVaTokenizerFast(__lowerCamelCase , do_lower_case=__lowerCamelCase , split_by_punct=__lowerCamelCase )
UpperCamelCase :Optional[int] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def _A ( self : Optional[Any] ):
# fmt: off
UpperCamelCase :Optional[Any] = """ \tHeLLo!how \n Are yoU? """
UpperCamelCase :Dict = ["""▁""", """<unk>""", """e""", """<unk>""", """o""", """!""", """how""", """▁""", """<unk>""", """re""", """▁yo""", """<unk>""", """?"""]
# fmt: on
UpperCamelCase :int = DebertaVaTokenizer(__lowerCamelCase , do_lower_case=__lowerCamelCase , split_by_punct=__lowerCamelCase )
UpperCamelCase :Optional[Any] = tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :Any = DebertaVaTokenizerFast(__lowerCamelCase , do_lower_case=__lowerCamelCase , split_by_punct=__lowerCamelCase )
UpperCamelCase :Tuple = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def _A ( self : int ):
UpperCamelCase :int = self.get_tokenizer()
UpperCamelCase :str = self.get_rust_tokenizer()
UpperCamelCase :Dict = """I was born in 92000, and this is falsé."""
UpperCamelCase :List[str] = tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) )
UpperCamelCase :Optional[int] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :List[str] = tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
UpperCamelCase :Optional[int] = rust_tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :int = self.get_rust_tokenizer()
UpperCamelCase :Tuple = tokenizer.encode(__lowerCamelCase )
UpperCamelCase :Dict = rust_tokenizer.encode(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def _A ( self : Dict ):
UpperCamelCase :Optional[int] = """This is a test"""
UpperCamelCase :str = [13, 1, 4_398, 25, 21, 1_289]
UpperCamelCase :int = ["""▁""", """T""", """his""", """▁is""", """▁a""", """▁test"""]
UpperCamelCase :Any = ["""▁""", """<unk>""", """his""", """▁is""", """▁a""", """▁test"""]
UpperCamelCase :str = DebertaVaTokenizer(__lowerCamelCase , keep_accents=__lowerCamelCase )
UpperCamelCase :Union[str, Any] = DebertaVaTokenizerFast(__lowerCamelCase , keep_accents=__lowerCamelCase )
UpperCamelCase :Optional[Any] = tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :Union[str, Any] = tokenizer.tokenize(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :Any = tokenizer.convert_ids_to_tokens(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :List[Any] = rust_tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :Optional[Any] = rust_tokenizer.tokenize(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :int = rust_tokenizer.convert_ids_to_tokens(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
# fmt: off
UpperCamelCase :Optional[Any] = """I was born in 92000, and this is falsé."""
UpperCamelCase :Any = [13, 1, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]
UpperCamelCase :Union[str, Any] = ["""▁""", """I""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """.""", ]
UpperCamelCase :Optional[Any] = ["""▁""", """<unk>""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """.""", ]
# fmt: on
UpperCamelCase :str = tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :Any = tokenizer.tokenize(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :Union[str, Any] = tokenizer.convert_ids_to_tokens(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :List[Any] = rust_tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :int = rust_tokenizer.tokenize(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase :Dict = rust_tokenizer.convert_ids_to_tokens(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def _A ( self : Optional[int] ):
UpperCamelCase :str = DebertaVaTokenizer(__lowerCamelCase )
UpperCamelCase :Union[str, Any] = tokenizer.encode("""sequence builders""" )
UpperCamelCase :Any = tokenizer.encode("""multi-sequence build""" )
UpperCamelCase :Optional[int] = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase )
UpperCamelCase :str = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase , __lowerCamelCase )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , __lowerCamelCase )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , __lowerCamelCase , )
@slow
def _A ( self : List[Any] ):
# fmt: off
UpperCamelCase :Union[str, Any] = {"""input_ids""": [[1, 39_867, 36, 19_390, 486, 27, 35_052, 81_436, 18, 60_685, 1_225, 7, 35_052, 81_436, 18, 9_367, 16_899, 18, 15_937, 53, 594, 773, 18, 16_287, 30_465, 36, 15_937, 6, 41_139, 38, 36_979, 60_763, 191, 6, 34_132, 99, 6, 50_538, 390, 43_230, 6, 34_132, 2_779, 20_850, 14, 699, 1_072, 1_194, 36, 382, 10_901, 53, 7, 699, 1_072, 2_084, 36, 20_422, 630, 53, 19, 105, 3_049, 1_896, 1_053, 16_899, 1_506, 11, 37_978, 4_243, 7, 1_237, 31_869, 200, 16_566, 654, 6, 35_052, 81_436, 7, 55_630, 13_593, 4, 2], [1, 26, 15_011, 13, 667, 8, 1_053, 18, 23_611, 1_237, 72_356, 12_820, 34, 104_134, 1_209, 35, 13_313, 6_627, 21, 202, 347, 7, 164, 2_399, 11, 46, 4_485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1_232, 2_864, 15_785, 14_951, 105, 5, 8_581, 1_250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__lowerCamelCase , model_name="""microsoft/deberta-v2-xlarge""" , revision="""ad6e42c1532ddf3a15c39246b63f5559d558b670""" , )
| 62 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_lowercase = {
'''vocab_file''': {
'''squeezebert/squeezebert-uncased''': (
'''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'''
),
'''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt''',
'''squeezebert/squeezebert-mnli-headless''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''squeezebert/squeezebert-uncased''': (
'''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'''
),
'''squeezebert/squeezebert-mnli''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'''
),
'''squeezebert/squeezebert-mnli-headless''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'''
),
},
}
_lowercase = {
'''squeezebert/squeezebert-uncased''': 5_12,
'''squeezebert/squeezebert-mnli''': 5_12,
'''squeezebert/squeezebert-mnli-headless''': 5_12,
}
_lowercase = {
'''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
'''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
'''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
_lowerCamelCase: Dict = VOCAB_FILES_NAMES
_lowerCamelCase: Dict = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase: List[Any] = PRETRAINED_INIT_CONFIGURATION
_lowerCamelCase: Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase: List[str] = SqueezeBertTokenizer
def __init__( self : List[Any] ,A_ : Optional[Any]=None ,A_ : Any=None ,A_ : Optional[Any]=True ,A_ : str="[UNK]" ,A_ : Optional[int]="[SEP]" ,A_ : Dict="[PAD]" ,A_ : Tuple="[CLS]" ,A_ : Dict="[MASK]" ,A_ : Tuple=True ,A_ : Tuple=None ,**A_ : int ,) -> Union[str, Any]:
super().__init__(
A_ ,tokenizer_file=A_ ,do_lower_case=A_ ,unk_token=A_ ,sep_token=A_ ,pad_token=A_ ,cls_token=A_ ,mask_token=A_ ,tokenize_chinese_chars=A_ ,strip_accents=A_ ,**A_ ,)
A = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' ,A_ ) != do_lower_case
or normalizer_state.get('strip_accents' ,A_ ) != strip_accents
or normalizer_state.get('handle_chinese_chars' ,A_ ) != tokenize_chinese_chars
):
A = getattr(A_ ,normalizer_state.pop('type' ) )
A = do_lower_case
A = strip_accents
A = tokenize_chinese_chars
A = normalizer_class(**A_ )
A = do_lower_case
def _SCREAMING_SNAKE_CASE ( self : List[Any] ,A_ : List[str] ,A_ : Optional[int]=None ) -> str:
A = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _SCREAMING_SNAKE_CASE ( self : str ,A_ : List[int] ,A_ : Optional[List[int]] = None ) -> List[int]:
A = [self.sep_token_id]
A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _SCREAMING_SNAKE_CASE ( self : int ,A_ : str ,A_ : Optional[str] = None ) -> Tuple[str]:
A = self._tokenizer.model.save(A_ ,name=A_ )
return tuple(A_ )
| 74 |
def _a ( a :int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
a = gray_code_sequence_string(a )
#
# convert them to integers
for i in range(len(a ) ):
a = int(sequence[i] , 2 )
return sequence
def _a ( a :int ) -> list:
# The approach is a recursive one
# Base case achieved when either n = 0 or n=1
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
a = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
a = gray_code_sequence_string(bit_count - 1 )
a = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
a = '''0''' + smaller_sequence[i]
sequence.append(a )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
a = '''1''' + smaller_sequence[i]
sequence.append(a )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 0 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def lowerCamelCase ( SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
if subparsers is not None:
__UpperCamelCase :List[Any] = subparsers.add_parser('''env''' )
else:
__UpperCamelCase :List[str] = argparse.ArgumentParser('''Accelerate env command''' )
parser.add_argument(
'''--config_file''' , default=SCREAMING_SNAKE_CASE , help='''The config file to use for the default values in the launching script.''' )
if subparsers is not None:
parser.set_defaults(func=SCREAMING_SNAKE_CASE )
return parser
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Tuple = torch.__version__
__UpperCamelCase :Optional[int] = torch.cuda.is_available()
__UpperCamelCase :Tuple = is_xpu_available()
__UpperCamelCase :Optional[Any] = is_npu_available()
__UpperCamelCase :int = '''Not found'''
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE ):
__UpperCamelCase :List[str] = load_config_from_file(args.config_file ).to_dict()
__UpperCamelCase :List[str] = {
'''`Accelerate` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''Numpy version''': np.__version__,
'''PyTorch version (GPU?)''': f"""{pt_version} ({pt_cuda_available})""",
'''PyTorch XPU available''': str(SCREAMING_SNAKE_CASE ),
'''PyTorch NPU available''': str(SCREAMING_SNAKE_CASE ),
'''System RAM''': f"""{psutil.virtual_memory().total / 1_024 ** 3:.2f} GB""",
}
if pt_cuda_available:
__UpperCamelCase :Optional[Any] = torch.cuda.get_device_name()
print('''\nCopy-and-paste the text below in your GitHub issue\n''' )
print('''\n'''.join([f"""- {prop}: {val}""" for prop, val in info.items()] ) )
print('''- `Accelerate` default config:''' if args.config_file is None else '''- `Accelerate` config passed:''' )
__UpperCamelCase :str = (
'''\n'''.join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()] )
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
else f"""\t{accelerate_config}"""
)
print(SCREAMING_SNAKE_CASE )
__UpperCamelCase :Tuple = accelerate_config
return info
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Union[str, Any] = env_command_parser()
__UpperCamelCase :List[Any] = parser.parse_args()
env_command(SCREAMING_SNAKE_CASE )
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 105 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
__lowercase = logging.get_logger(__name__)
logging.set_verbosity_info()
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if "xprophetnet" in prophetnet_checkpoint_path:
__UpperCamelCase :Dict = XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
__UpperCamelCase , __UpperCamelCase :int = XLMProphetNetForConditionalGeneration.from_pretrained(
SCREAMING_SNAKE_CASE , output_loading_info=SCREAMING_SNAKE_CASE )
else:
__UpperCamelCase :Union[str, Any] = ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
__UpperCamelCase , __UpperCamelCase :Union[str, Any] = ProphetNetForConditionalGeneration.from_pretrained(
SCREAMING_SNAKE_CASE , output_loading_info=SCREAMING_SNAKE_CASE )
__UpperCamelCase :Dict = ['''key_proj''', '''value_proj''', '''query_proj''']
__UpperCamelCase :Optional[Any] = {
'''self_attn''': '''ngram_self_attn''',
'''cross_attn''': '''encoder_attn''',
'''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
'''feed_forward_layer_norm''': '''final_layer_norm''',
'''feed_forward''': '''''',
'''intermediate''': '''fc1''',
'''output''': '''fc2''',
'''key_proj''': '''k_proj''',
'''query_proj''': '''q_proj''',
'''value_proj''': '''v_proj''',
'''word_embeddings''': '''embed_tokens''',
'''embeddings_layer_norm''': '''emb_layer_norm''',
'''relative_pos_embeddings''': '''relative_linear''',
'''ngram_embeddings''': '''ngram_input_embed''',
'''position_embeddings''': '''embed_positions''',
}
for key in loading_info["missing_keys"]:
__UpperCamelCase :Tuple = key.split('''.''' )
if attributes[0] == "lm_head":
__UpperCamelCase :Union[str, Any] = prophet
__UpperCamelCase :Any = prophet_old
else:
__UpperCamelCase :Any = prophet.prophetnet
__UpperCamelCase :int = prophet_old.model
__UpperCamelCase :Optional[Any] = False
for attribute in attributes:
if attribute in mapping:
__UpperCamelCase :str = mapping[attribute]
if not hasattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and len(SCREAMING_SNAKE_CASE ) > 0:
__UpperCamelCase :Optional[int] = attribute
elif hasattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__UpperCamelCase :List[Any] = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
__UpperCamelCase :Tuple = old_model.weight
logger.info(f"""{attribute} is initialized.""" )
__UpperCamelCase :Union[str, Any] = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
__UpperCamelCase :Union[str, Any] = old_model.bias
logger.info(f"""{attribute} is initialized""" )
__UpperCamelCase :List[Any] = True
break
elif attribute in special_keys and hasattr(SCREAMING_SNAKE_CASE , '''in_proj_weight''' ):
__UpperCamelCase :str = old_model.in_proj_weight.shape[0] // 3
__UpperCamelCase :Optional[Any] = getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
__UpperCamelCase :Optional[int] = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
__UpperCamelCase :Tuple = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
__UpperCamelCase :List[Any] = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
__UpperCamelCase :Dict = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
__UpperCamelCase :Tuple = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
__UpperCamelCase :Dict = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
__UpperCamelCase :Optional[int] = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
__UpperCamelCase :Optional[int] = nn.Parameter(old_model.embed_positions.weight[:512, :] )
__UpperCamelCase :List[Any] = True
break
if attribute.isdigit():
__UpperCamelCase :List[Any] = model[int(SCREAMING_SNAKE_CASE )]
__UpperCamelCase :Optional[int] = old_model[int(SCREAMING_SNAKE_CASE )]
else:
__UpperCamelCase :Optional[Any] = getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if old_attribute == "":
__UpperCamelCase :Any = old_model
else:
if not hasattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise ValueError(f"""{old_model} does not have {old_attribute}""" )
__UpperCamelCase :Any = getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if not is_key_init:
raise ValueError(f"""{key} was not correctly initialized!""" )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
prophet.save_pretrained(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__lowercase = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 105 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase_ :
def __init__( self , UpperCamelCase_ , UpperCamelCase_=13 , UpperCamelCase_=32 , UpperCamelCase_=2 , UpperCamelCase_=3 , UpperCamelCase_=16 , UpperCamelCase_=[32, 64, 1_28] , UpperCamelCase_=[1, 2, 1] , UpperCamelCase_=[2, 2, 4] , UpperCamelCase_=2 , UpperCamelCase_=2.0 , UpperCamelCase_=True , UpperCamelCase_=0.0 , UpperCamelCase_=0.0 , UpperCamelCase_=0.1 , UpperCamelCase_="gelu" , UpperCamelCase_=False , UpperCamelCase_=True , UpperCamelCase_=0.0_2 , UpperCamelCase_=1E-5 , UpperCamelCase_=True , UpperCamelCase_=None , UpperCamelCase_=True , UpperCamelCase_=10 , UpperCamelCase_=8 , UpperCamelCase_=["stage1", "stage2"] , UpperCamelCase_=[1, 2] , ) -> Optional[int]:
__lowercase : Dict = parent
__lowercase : Optional[Any] = batch_size
__lowercase : Optional[Any] = image_size
__lowercase : Any = patch_size
__lowercase : str = num_channels
__lowercase : Any = embed_dim
__lowercase : Dict = hidden_sizes
__lowercase : Dict = depths
__lowercase : Any = num_heads
__lowercase : str = window_size
__lowercase : str = mlp_ratio
__lowercase : Tuple = qkv_bias
__lowercase : int = hidden_dropout_prob
__lowercase : List[Any] = attention_probs_dropout_prob
__lowercase : Optional[int] = drop_path_rate
__lowercase : Any = hidden_act
__lowercase : int = use_absolute_embeddings
__lowercase : List[Any] = patch_norm
__lowercase : Optional[int] = layer_norm_eps
__lowercase : str = initializer_range
__lowercase : Dict = is_training
__lowercase : List[str] = scope
__lowercase : Dict = use_labels
__lowercase : Union[str, Any] = type_sequence_label_size
__lowercase : List[Any] = encoder_stride
__lowercase : List[Any] = out_features
__lowercase : str = out_indices
def _lowerCamelCase ( self ) -> Union[str, Any]:
__lowercase : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase : Dict = None
if self.use_labels:
__lowercase : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase : List[str] = self.get_config()
return config, pixel_values, labels
def _lowerCamelCase ( self ) -> Any:
return FocalNetConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , hidden_sizes=self.hidden_sizes , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Tuple:
__lowercase : Optional[int] = FocalNetModel(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__lowercase : Optional[Any] = model(UpperCamelCase_ )
__lowercase : Union[str, Any] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
__lowercase : str = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Any:
__lowercase : Tuple = FocalNetBackbone(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__lowercase : Any = model(UpperCamelCase_ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size, 8, 8] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[:-1] )
# verify backbone works with out_features=None
__lowercase : Any = None
__lowercase : int = FocalNetBackbone(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__lowercase : int = model(UpperCamelCase_ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Optional[Any]:
__lowercase : str = FocalNetForMaskedImageModeling(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__lowercase : Optional[int] = model(UpperCamelCase_ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
__lowercase : Tuple = 1
__lowercase : Optional[Any] = FocalNetForMaskedImageModeling(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__lowercase : Union[str, Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowercase : Tuple = model(UpperCamelCase_ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Optional[int]:
__lowercase : int = self.type_sequence_label_size
__lowercase : Optional[Any] = FocalNetForImageClassification(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__lowercase : int = model(UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__lowercase : List[Any] = 1
__lowercase : Optional[int] = FocalNetForImageClassification(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__lowercase : Optional[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowercase : Optional[int] = model(UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _lowerCamelCase ( self ) -> str:
__lowercase : List[Any] = self.prepare_config_and_inputs()
__lowercase ,__lowercase ,__lowercase : Optional[Any] = config_and_inputs
__lowercase : str = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( snake_case , snake_case , unittest.TestCase ):
UpperCamelCase =(
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
UpperCamelCase =(
{"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase =False
UpperCamelCase =False
UpperCamelCase =False
UpperCamelCase =False
UpperCamelCase =False
def _lowerCamelCase ( self ) -> List[Any]:
__lowercase : List[str] = FocalNetModelTester(self )
__lowercase : int = ConfigTester(self , config_class=UpperCamelCase_ , embed_dim=37 , has_text_modality=UpperCamelCase_ )
def _lowerCamelCase ( self ) -> List[str]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _lowerCamelCase ( self ) -> str:
return
def _lowerCamelCase ( self ) -> List[Any]:
__lowercase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase_ )
def _lowerCamelCase ( self ) -> Union[str, Any]:
__lowercase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*UpperCamelCase_ )
def _lowerCamelCase ( self ) -> List[Any]:
__lowercase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase_ )
def _lowerCamelCase ( self ) -> List[Any]:
__lowercase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_ )
@unittest.skip(reason='''FocalNet does not use inputs_embeds''' )
def _lowerCamelCase ( self ) -> List[Any]:
pass
@unittest.skip(reason='''FocalNet does not use feedforward chunking''' )
def _lowerCamelCase ( self ) -> Any:
pass
def _lowerCamelCase ( self ) -> Optional[int]:
__lowercase ,__lowercase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
__lowercase : Tuple = model_class(UpperCamelCase_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowercase : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCamelCase_ , nn.Linear ) )
def _lowerCamelCase ( self ) -> Any:
__lowercase ,__lowercase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
__lowercase : Optional[int] = model_class(UpperCamelCase_ )
__lowercase : List[str] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase : Tuple = [*signature.parameters.keys()]
__lowercase : Tuple = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCamelCase_ )
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> int:
__lowercase : Any = model_class(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
with torch.no_grad():
__lowercase : int = model(**self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ ) )
__lowercase : Dict = outputs.hidden_states
__lowercase : Tuple = getattr(
self.model_tester , '''expected_num_hidden_layers''' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(UpperCamelCase_ ) , UpperCamelCase_ )
# FocalNet has a different seq_length
__lowercase : str = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__lowercase : Tuple = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
__lowercase : int = outputs.reshaped_hidden_states
self.assertEqual(len(UpperCamelCase_ ) , UpperCamelCase_ )
__lowercase ,__lowercase ,__lowercase ,__lowercase : Tuple = reshaped_hidden_states[0].shape
__lowercase : Dict = (
reshaped_hidden_states[0].view(UpperCamelCase_ , UpperCamelCase_ , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def _lowerCamelCase ( self ) -> str:
__lowercase ,__lowercase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase : Tuple = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
__lowercase : Any = True
self.check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase : int = True
self.check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
def _lowerCamelCase ( self ) -> List[str]:
__lowercase ,__lowercase : int = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase : Optional[Any] = 3
__lowercase : str = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
__lowercase : Any = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__lowercase : List[Any] = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__lowercase : List[Any] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
__lowercase : Optional[int] = True
self.check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase : int = True
self.check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , (padded_height, padded_width) )
@slow
def _lowerCamelCase ( self ) -> List[str]:
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase : int = FocalNetModel.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
def _lowerCamelCase ( self ) -> Tuple:
__lowercase ,__lowercase : int = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase : Optional[int] = _config_zero_init(UpperCamelCase_ )
for model_class in self.all_model_classes:
__lowercase : Dict = model_class(config=UpperCamelCase_ )
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@require_vision
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
@cached_property
def _lowerCamelCase ( self ) -> Union[str, Any]:
# TODO update organization
return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny''' ) if is_vision_available() else None
@slow
def _lowerCamelCase ( self ) -> Tuple:
__lowercase : Tuple = FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny''' ).to(UpperCamelCase_ )
__lowercase : Any = self.default_image_processor
__lowercase : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
__lowercase : Union[str, Any] = image_processor(images=UpperCamelCase_ , return_tensors='''pt''' ).to(UpperCamelCase_ )
# forward pass
with torch.no_grad():
__lowercase : List[Any] = model(**UpperCamelCase_ )
# verify the logits
__lowercase : Dict = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , UpperCamelCase_ )
__lowercase : List[Any] = torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1] ).to(UpperCamelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCamelCase_ , atol=1E-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item() , 2_81 )
@require_torch
class UpperCAmelCase_ ( snake_case , unittest.TestCase ):
UpperCamelCase =(FocalNetBackbone,) if is_torch_available() else ()
UpperCamelCase =FocalNetConfig
UpperCamelCase =False
def _lowerCamelCase ( self ) -> Dict:
__lowercase : Optional[int] = FocalNetModelTester(self )
| 249 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
a_ = datasets.utils.logging.get_logger(__name__)
@dataclass
class UpperCAmelCase_ ( datasets.BuilderConfig ):
UpperCamelCase =1_00_00
UpperCamelCase =None
UpperCamelCase =None
class UpperCAmelCase_ ( datasets.ArrowBasedBuilder ):
UpperCamelCase =ParquetConfig
def _lowerCamelCase ( self ) -> List[str]:
return datasets.DatasetInfo(features=self.config.features )
def _lowerCamelCase ( self , UpperCamelCase_ ) -> Tuple:
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
__lowercase : Optional[Any] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(UpperCamelCase_ , (str, list, tuple) ):
__lowercase : str = data_files
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
__lowercase : Union[str, Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
__lowercase : int = [dl_manager.iter_files(UpperCamelCase_ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
__lowercase : int = []
for split_name, files in data_files.items():
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
__lowercase : List[str] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
__lowercase : Any = [dl_manager.iter_files(UpperCamelCase_ ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(UpperCamelCase_ ):
with open(UpperCamelCase_ , '''rb''' ) as f:
__lowercase : Any = datasets.Features.from_arrow_schema(pq.read_schema(UpperCamelCase_ ) )
break
splits.append(datasets.SplitGenerator(name=UpperCamelCase_ , gen_kwargs={'''files''': files} ) )
return splits
def _lowerCamelCase ( self , UpperCamelCase_ ) -> pa.Table:
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
__lowercase : Tuple = table_cast(UpperCamelCase_ , self.info.features.arrow_schema )
return pa_table
def _lowerCamelCase ( self , UpperCamelCase_ ) -> Tuple:
__lowercase : Union[str, Any] = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""" )
for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase_ ) ):
with open(UpperCamelCase_ , '''rb''' ) as f:
__lowercase : Union[str, Any] = pq.ParquetFile(UpperCamelCase_ )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
__lowercase : Dict = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield F"""{file_idx}_{batch_idx}""", self._cast_table(UpperCamelCase_ )
except ValueError as e:
logger.error(F"""Failed to read file '{file}' with error {type(UpperCamelCase_ )}: {e}""" )
raise
| 249 | 1 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class UpperCAmelCase_ :
def __init__( self : Dict , A : List[str] , A : Dict=1_3 , A : List[Any]=7 , A : Dict=True , A : Tuple=True , A : Tuple=False , A : List[str]=True , A : Any=9_9 , A : Optional[Any]=3_2 , A : Union[str, Any]=5 , A : str=4 , A : Tuple=3_7 , A : Union[str, Any]="gelu" , A : Optional[Any]=0.1 , A : Dict=0.1 , A : Optional[int]=5_1_2 , A : Optional[int]=1_6 , A : Dict=2 , A : Dict=0.02 , A : List[Any]=3 , A : Tuple=4 , A : Any=None , ):
_UpperCAmelCase : Union[str, Any] = parent
_UpperCAmelCase : Any = batch_size
_UpperCAmelCase : Any = seq_length
_UpperCAmelCase : str = is_training
_UpperCAmelCase : Optional[int] = use_input_mask
_UpperCAmelCase : List[Any] = use_token_type_ids
_UpperCAmelCase : Any = use_labels
_UpperCAmelCase : Any = vocab_size
_UpperCAmelCase : Any = hidden_size
_UpperCAmelCase : Union[str, Any] = num_hidden_layers
_UpperCAmelCase : List[str] = num_attention_heads
_UpperCAmelCase : Optional[Any] = intermediate_size
_UpperCAmelCase : Optional[int] = hidden_act
_UpperCAmelCase : Dict = hidden_dropout_prob
_UpperCAmelCase : List[Any] = attention_probs_dropout_prob
_UpperCAmelCase : Any = max_position_embeddings
_UpperCAmelCase : Optional[Any] = type_vocab_size
_UpperCAmelCase : int = type_sequence_label_size
_UpperCAmelCase : Dict = initializer_range
_UpperCAmelCase : Optional[Any] = num_labels
_UpperCAmelCase : List[Any] = num_choices
_UpperCAmelCase : List[str] = scope
def snake_case_ ( self : str ):
_UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : Optional[Any] = None
if self.use_input_mask:
_UpperCAmelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Optional[int] = None
if self.use_token_type_ids:
_UpperCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase : List[Any] = None
_UpperCAmelCase : Dict = None
_UpperCAmelCase : Any = None
if self.use_labels:
_UpperCAmelCase : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase : Union[str, Any] = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase : Tuple = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case_ ( self : Optional[int] ):
return LlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
def snake_case_ ( self : Optional[Any] , A : int , A : Tuple , A : Union[str, Any] , A : List[str] , A : int , A : str , A : Optional[Any] ):
_UpperCAmelCase : Tuple = LlamaModel(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Any = model(A , attention_mask=A )
_UpperCAmelCase : int = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case_ ( self : Tuple , A : Optional[Any] , A : Tuple , A : Optional[Any] , A : List[str] , A : Union[str, Any] , A : Optional[Any] , A : Union[str, Any] , A : Union[str, Any] , A : Any , ):
_UpperCAmelCase : int = True
_UpperCAmelCase : int = LlamaModel(A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , )
_UpperCAmelCase : int = model(
A , attention_mask=A , encoder_hidden_states=A , )
_UpperCAmelCase : Optional[Any] = model(A , attention_mask=A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case_ ( self : List[str] , A : Tuple , A : Any , A : int , A : Optional[Any] , A : Tuple , A : Any , A : List[str] , A : Any , A : Dict , ):
_UpperCAmelCase : Any = LlamaForCausalLM(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : str = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case_ ( self : Any , A : Tuple , A : str , A : List[str] , A : Optional[Any] , A : Any , A : Dict , A : Dict , A : List[str] , A : Optional[Any] , ):
_UpperCAmelCase : Optional[int] = True
_UpperCAmelCase : Tuple = True
_UpperCAmelCase : Optional[Any] = LlamaForCausalLM(config=A )
model.to(A )
model.eval()
# first forward pass
_UpperCAmelCase : str = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , use_cache=A , )
_UpperCAmelCase : Union[str, Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_UpperCAmelCase : Any = ids_tensor((self.batch_size, 3) , config.vocab_size )
_UpperCAmelCase : str = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
_UpperCAmelCase : str = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase : Union[str, Any] = torch.cat([input_mask, next_mask] , dim=-1 )
_UpperCAmelCase : Dict = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , output_hidden_states=A , )["hidden_states"][0]
_UpperCAmelCase : str = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , past_key_values=A , output_hidden_states=A , )["hidden_states"][0]
# select random slice
_UpperCAmelCase : Union[str, Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase : Dict = output_from_no_past[:, -3:, random_slice_idx].detach()
_UpperCAmelCase : Union[str, Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1e-3 ) )
def snake_case_ ( self : Dict ):
_UpperCAmelCase : Any = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : List[str] = config_and_inputs
_UpperCAmelCase : Optional[int] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : str = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
__SCREAMING_SNAKE_CASE : Optional[int] = (LlamaForCausalLM,) if is_torch_available() else ()
__SCREAMING_SNAKE_CASE : Optional[Any] = (
{
'feature-extraction': LlamaModel,
'text-classification': LlamaForSequenceClassification,
'text-generation': LlamaForCausalLM,
'zero-shot': LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
__SCREAMING_SNAKE_CASE : Dict = False
__SCREAMING_SNAKE_CASE : int = False
def snake_case_ ( self : Dict ):
_UpperCAmelCase : str = LlamaModelTester(self )
_UpperCAmelCase : Optional[Any] = ConfigTester(self , config_class=A , hidden_size=3_7 )
def snake_case_ ( self : Optional[int] ):
self.config_tester.run_common_tests()
def snake_case_ ( self : Tuple ):
_UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def snake_case_ ( self : Any ):
_UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase : List[Any] = type
self.model_tester.create_and_check_model(*A )
def snake_case_ ( self : Dict ):
_UpperCAmelCase , _UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : str = 3
_UpperCAmelCase : Any = input_dict["input_ids"]
_UpperCAmelCase : List[Any] = input_ids.ne(1 ).to(A )
_UpperCAmelCase : Optional[int] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_UpperCAmelCase : Any = LlamaForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : List[Any] = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def snake_case_ ( self : Tuple ):
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : List[Any] = 3
_UpperCAmelCase : List[str] = "single_label_classification"
_UpperCAmelCase : Optional[Any] = input_dict["input_ids"]
_UpperCAmelCase : Any = input_ids.ne(1 ).to(A )
_UpperCAmelCase : Union[str, Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_UpperCAmelCase : Tuple = LlamaForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : int = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def snake_case_ ( self : Dict ):
_UpperCAmelCase , _UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : List[str] = 3
_UpperCAmelCase : List[Any] = "multi_label_classification"
_UpperCAmelCase : Union[str, Any] = input_dict["input_ids"]
_UpperCAmelCase : Dict = input_ids.ne(1 ).to(A )
_UpperCAmelCase : List[Any] = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_UpperCAmelCase : str = LlamaForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : List[Any] = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip("LLaMA buffers include complex numbers, which breaks this test" )
def snake_case_ ( self : str ):
pass
@parameterized.expand([("linear",), ("dynamic",)] )
def snake_case_ ( self : Tuple , A : Tuple ):
_UpperCAmelCase , _UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : str = ids_tensor([1, 1_0] , config.vocab_size )
_UpperCAmelCase : Optional[Any] = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
_UpperCAmelCase : Union[str, Any] = LlamaModel(A )
original_model.to(A )
original_model.eval()
_UpperCAmelCase : Dict = original_model(A ).last_hidden_state
_UpperCAmelCase : Dict = original_model(A ).last_hidden_state
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
_UpperCAmelCase : Any = {"type": scaling_type, "factor": 10.0}
_UpperCAmelCase : List[Any] = LlamaModel(A )
scaled_model.to(A )
scaled_model.eval()
_UpperCAmelCase : List[str] = scaled_model(A ).last_hidden_state
_UpperCAmelCase : str = scaled_model(A ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(A , A , atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(A , A , atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(A , A , atol=1e-5 ) )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!" )
@slow
def snake_case_ ( self : Optional[Any] ):
_UpperCAmelCase : int = [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
_UpperCAmelCase : Any = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf" , device_map="auto" )
_UpperCAmelCase : int = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
_UpperCAmelCase : Optional[Any] = torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]] )
torch.testing.assert_close(out.mean(-1 ) , A , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
_UpperCAmelCase : Optional[int] = torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :3_0] , A , atol=1e-5 , rtol=1e-5 )
@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!" )
@slow
def snake_case_ ( self : Any ):
_UpperCAmelCase : List[str] = [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
_UpperCAmelCase : Optional[Any] = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf" , device_map="auto" )
_UpperCAmelCase : Tuple = model(torch.tensor(A ) )
# Expected mean on dim = -1
_UpperCAmelCase : Dict = torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]] )
torch.testing.assert_close(out.mean(-1 ) , A , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
_UpperCAmelCase : Dict = torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273] )
# fmt: on
torch.testing.assert_close(out[0, 0, :3_0] , A , atol=1e-5 , rtol=1e-5 )
@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!" )
@slow
def snake_case_ ( self : Dict ):
_UpperCAmelCase : Tuple = [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
_UpperCAmelCase : Optional[int] = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf" , device_map="auto" )
_UpperCAmelCase : Optional[Any] = model(torch.tensor(A ) )
# Expected mean on dim = -1
_UpperCAmelCase : str = torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]] )
torch.testing.assert_close(out.mean(-1 ) , A , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
_UpperCAmelCase : Any = torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) , A , atol=1e-2 , rtol=1e-2 )
@unittest.skip(
"Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test" )
@slow
def snake_case_ ( self : Optional[Any] ):
_UpperCAmelCase : List[str] = [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
_UpperCAmelCase : Union[str, Any] = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf" , device_map="auto" )
_UpperCAmelCase : str = model(torch.tensor(A ) )
_UpperCAmelCase : Tuple = torch.tensor(
[[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]] , dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) , A , atol=1e-2 , rtol=1e-2 )
# fmt: off
_UpperCAmelCase : List[str] = torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312] )
# fmt: on
torch.testing.assert_close(out[0, 0, :3_0] , A , atol=1e-5 , rtol=1e-5 )
@unittest.skip("Model is curently gated" )
@slow
def snake_case_ ( self : Union[str, Any] ):
_UpperCAmelCase : Optional[int] = "Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"
_UpperCAmelCase : str = "Simply put, the theory of relativity states that "
_UpperCAmelCase : str = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf" )
_UpperCAmelCase : Any = tokenizer.encode(A , return_tensors="pt" )
_UpperCAmelCase : List[str] = LlamaForCausalLM.from_pretrained(
"meta-llama/Llama-2-13b-chat-hf" , device_map="sequential" , use_safetensors=A )
# greedy generation outputs
_UpperCAmelCase : str = model.generate(A , max_new_tokens=6_4 , top_p=A , temperature=1 , do_sample=A )
_UpperCAmelCase : Any = tokenizer.decode(generated_ids[0] , skip_special_tokens=A )
self.assertEqual(A , A )
| 202 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
_lowerCAmelCase : List[str] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
_lowerCAmelCase : int = {
"vocab_file": {
"google/electra-small-generator": (
"https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"
),
"google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt",
"google/electra-large-generator": (
"https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"
),
"google/electra-small-discriminator": (
"https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"
),
"google/electra-base-discriminator": (
"https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"
),
"google/electra-large-discriminator": (
"https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"google/electra-small-generator": (
"https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"
),
"google/electra-base-generator": (
"https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"
),
"google/electra-large-generator": (
"https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"
),
"google/electra-small-discriminator": (
"https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"
),
"google/electra-base-discriminator": (
"https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"
),
"google/electra-large-discriminator": (
"https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"
),
},
}
_lowerCAmelCase : List[Any] = {
"google/electra-small-generator": 5_12,
"google/electra-base-generator": 5_12,
"google/electra-large-generator": 5_12,
"google/electra-small-discriminator": 5_12,
"google/electra-base-discriminator": 5_12,
"google/electra-large-discriminator": 5_12,
}
_lowerCAmelCase : Optional[Any] = {
"google/electra-small-generator": {"do_lower_case": True},
"google/electra-base-generator": {"do_lower_case": True},
"google/electra-large-generator": {"do_lower_case": True},
"google/electra-small-discriminator": {"do_lower_case": True},
"google/electra-base-discriminator": {"do_lower_case": True},
"google/electra-large-discriminator": {"do_lower_case": True},
}
class UpperCAmelCase_ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE : Any = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE : List[str] = PRETRAINED_INIT_CONFIGURATION
__SCREAMING_SNAKE_CASE : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE : Tuple = ElectraTokenizer
def __init__( self : Dict , A : Dict=None , A : Optional[int]=None , A : Dict=True , A : Optional[Any]="[UNK]" , A : Any="[SEP]" , A : str="[PAD]" , A : Tuple="[CLS]" , A : Optional[Any]="[MASK]" , A : Any=True , A : Tuple=None , **A : Any , ):
super().__init__(
A , tokenizer_file=A , do_lower_case=A , unk_token=A , sep_token=A , pad_token=A , cls_token=A , mask_token=A , tokenize_chinese_chars=A , strip_accents=A , **A , )
_UpperCAmelCase : List[str] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , A ) != do_lower_case
or normalizer_state.get("strip_accents" , A ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , A ) != tokenize_chinese_chars
):
_UpperCAmelCase : Union[str, Any] = getattr(A , normalizer_state.pop("type" ) )
_UpperCAmelCase : Dict = do_lower_case
_UpperCAmelCase : Optional[int] = strip_accents
_UpperCAmelCase : Any = tokenize_chinese_chars
_UpperCAmelCase : Optional[Any] = normalizer_class(**A )
_UpperCAmelCase : int = do_lower_case
def snake_case_ ( self : Tuple , A : str , A : int=None ):
_UpperCAmelCase : Dict = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def snake_case_ ( self : Any , A : List[int] , A : Optional[List[int]] = None ):
_UpperCAmelCase : Any = [self.sep_token_id]
_UpperCAmelCase : Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def snake_case_ ( self : Any , A : str , A : Optional[str] = None ):
_UpperCAmelCase : List[Any] = self._tokenizer.model.save(A , name=A )
return tuple(A )
| 202 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = [
'''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''NllbMoeForConditionalGeneration''',
'''NllbMoeModel''',
'''NllbMoePreTrainedModel''',
'''NllbMoeTop2Router''',
'''NllbMoeSparseMLP''',
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
__lowerCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 89 |
'''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
__lowerCAmelCase = None
__lowerCAmelCase = '''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__lowerCAmelCase = [
np.dtype('''|b1'''),
np.dtype('''|u1'''),
np.dtype('''<u2'''),
np.dtype('''>u2'''),
np.dtype('''<i2'''),
np.dtype('''>i2'''),
np.dtype('''<u4'''),
np.dtype('''>u4'''),
np.dtype('''<i4'''),
np.dtype('''>i4'''),
np.dtype('''<f4'''),
np.dtype('''>f4'''),
np.dtype('''<f8'''),
np.dtype('''>f8'''),
]
@dataclass
class __magic_name__ :
lowerCAmelCase : bool = True
lowerCAmelCase : Optional[str] = None
# Automatically constructed
lowerCAmelCase : ClassVar[str] = "PIL.Image.Image"
lowerCAmelCase : ClassVar[Any] = pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
lowerCAmelCase : str = field(default='Image' , init=_UpperCamelCase , repr=_UpperCamelCase )
def __call__( self : Union[str, Any] ):
return self.pa_type
def __lowercase ( self : Any ,_UpperCAmelCase : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
_a : Optional[Any] = np.array(_UpperCAmelCase )
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
return {"path": value, "bytes": None}
elif isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
return {"path": None, "bytes": value}
elif isinstance(_UpperCAmelCase ,np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(_UpperCAmelCase )
elif isinstance(_UpperCAmelCase ,PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(_UpperCAmelCase )
elif value.get('path' ) is not None and os.path.isfile(value['path'] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get('path' )}
elif value.get('bytes' ) is not None or value.get('path' ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get('bytes' ), "path": value.get('path' )}
else:
raise ValueError(
F"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )
def __lowercase ( self : Optional[Any] ,_UpperCAmelCase : dict ,_UpperCAmelCase : Optional[int]=None ):
if not self.decode:
raise RuntimeError('Decoding is disabled for this feature. Please use Image(decode=True) instead.' )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support decoding images, please install \'Pillow\'.' )
if token_per_repo_id is None:
_a : Dict = {}
_a , _a : str = value['path'], value['bytes']
if bytes_ is None:
if path is None:
raise ValueError(F"""An image should have one of 'path' or 'bytes' but both are None in {value}.""" )
else:
if is_local_path(_UpperCAmelCase ):
_a : Any = PIL.Image.open(_UpperCAmelCase )
else:
_a : List[Any] = path.split('::' )[-1]
try:
_a : str = string_to_dict(_UpperCAmelCase ,config.HUB_DATASETS_URL )['repo_id']
_a : Optional[Any] = token_per_repo_id.get(_UpperCAmelCase )
except ValueError:
_a : int = None
with xopen(_UpperCAmelCase ,'rb' ,use_auth_token=_UpperCAmelCase ) as f:
_a : Tuple = BytesIO(f.read() )
_a : Union[str, Any] = PIL.Image.open(bytes_ )
else:
_a : Optional[int] = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def __lowercase ( self : int ):
from .features import Value
return (
self
if self.decode
else {
"bytes": Value('binary' ),
"path": Value('string' ),
}
)
def __lowercase ( self : str ,_UpperCAmelCase : Union[pa.StringArray, pa.StructArray, pa.ListArray] ):
if pa.types.is_string(storage.type ):
_a : Union[str, Any] = pa.array([None] * len(_UpperCAmelCase ) ,type=pa.binary() )
_a : Union[str, Any] = pa.StructArray.from_arrays([bytes_array, storage] ,['bytes', 'path'] ,mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
_a : List[str] = pa.array([None] * len(_UpperCAmelCase ) ,type=pa.string() )
_a : Any = pa.StructArray.from_arrays([storage, path_array] ,['bytes', 'path'] ,mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index('bytes' ) >= 0:
_a : Union[str, Any] = storage.field('bytes' )
else:
_a : Tuple = pa.array([None] * len(_UpperCAmelCase ) ,type=pa.binary() )
if storage.type.get_field_index('path' ) >= 0:
_a : Union[str, Any] = storage.field('path' )
else:
_a : Dict = pa.array([None] * len(_UpperCAmelCase ) ,type=pa.string() )
_a : Optional[Any] = pa.StructArray.from_arrays([bytes_array, path_array] ,['bytes', 'path'] ,mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
_a : List[str] = pa.array(
[encode_np_array(np.array(_UpperCAmelCase ) )['bytes'] if arr is not None else None for arr in storage.to_pylist()] ,type=pa.binary() ,)
_a : int = pa.array([None] * len(_UpperCAmelCase ) ,type=pa.string() )
_a : Optional[Any] = pa.StructArray.from_arrays(
[bytes_array, path_array] ,['bytes', 'path'] ,mask=bytes_array.is_null() )
return array_cast(_UpperCAmelCase ,self.pa_type )
def __lowercase ( self : Dict ,_UpperCAmelCase : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(_UpperCAmelCase : Tuple ):
with xopen(_UpperCAmelCase ,'rb' ) as f:
_a : int = f.read()
return bytes_
_a : Any = pa.array(
[
(path_to_bytes(x['path'] ) if x['bytes'] is None else x['bytes']) if x is not None else None
for x in storage.to_pylist()
] ,type=pa.binary() ,)
_a : Optional[Any] = pa.array(
[os.path.basename(_UpperCAmelCase ) if path is not None else None for path in storage.field('path' ).to_pylist()] ,type=pa.string() ,)
_a : Dict = pa.StructArray.from_arrays([bytes_array, path_array] ,['bytes', 'path'] ,mask=bytes_array.is_null() )
return array_cast(_UpperCAmelCase ,self.pa_type )
def __lowerCamelCase ( ) -> List[str]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
_a : Dict = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def __lowerCamelCase ( lowerCAmelCase_ ) -> bytes:
_a : Optional[int] = BytesIO()
if image.format in list_image_compression_formats():
_a : Optional[Any] = image.format
else:
_a : str = 'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
image.save(lowerCAmelCase_ , format=lowerCAmelCase_ )
return buffer.getvalue()
def __lowerCamelCase ( lowerCAmelCase_ ) -> dict:
if hasattr(lowerCAmelCase_ , 'filename' ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(lowerCAmelCase_ )}
def __lowerCamelCase ( lowerCAmelCase_ ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
_a : List[Any] = array.dtype
_a : Optional[int] = dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
_a : Union[str, Any] = dtype.kind
_a : Union[str, Any] = dtype.itemsize
_a : List[Any] = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
_a : Optional[int] = np.dtype('|u1' )
if dtype_kind not in ["u", "i"]:
raise TypeError(
f"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.""" )
if dtype is not dest_dtype:
warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
_a : Union[str, Any] = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
_a : str = dtype_byteorder + dtype_kind + str(lowerCAmelCase_ )
_a : List[Any] = np.dtype(lowerCAmelCase_ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
f"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}""" )
_a : Union[str, Any] = PIL.Image.fromarray(array.astype(lowerCAmelCase_ ) )
return {"path": None, "bytes": image_to_bytes(lowerCAmelCase_ )}
def __lowerCamelCase ( lowerCAmelCase_ ) -> List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
if objs:
_a , _a : Optional[Any] = first_non_null_value(lowerCAmelCase_ )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(lowerCAmelCase_ , np.ndarray ):
_a : List[str] = no_op_if_value_is_null(lowerCAmelCase_ )
return [obj_to_image_dict_func(lowerCAmelCase_ ) for obj in objs]
elif isinstance(lowerCAmelCase_ , PIL.Image.Image ):
_a : List[str] = no_op_if_value_is_null(lowerCAmelCase_ )
return [obj_to_image_dict_func(lowerCAmelCase_ ) for obj in objs]
else:
return objs
else:
return objs
| 89 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase__ : Dict = logging.get_logger(__name__)
lowercase__ : int = {
'microsoft/swin-tiny-patch4-window7-224': (
'https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class a__ ( UpperCamelCase__ , UpperCamelCase__ ):
a : Any = """swin"""
a : Any = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self , A=224 , A=4 , A=3 , A=96 , A=[2, 2, 6, 2] , A=[3, 6, 12, 24] , A=7 , A=4.0 , A=True , A=0.0 , A=0.0 , A=0.1 , A="gelu" , A=False , A=0.0_2 , A=1e-5 , A=32 , A=None , A=None , **A , ) -> Dict:
'''simple docstring'''
super().__init__(**__a )
a = image_size
a = patch_size
a = num_channels
a = embed_dim
a = depths
a = len(__a )
a = num_heads
a = window_size
a = mlp_ratio
a = qkv_bias
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = drop_path_rate
a = hidden_act
a = use_absolute_embeddings
a = layer_norm_eps
a = initializer_range
a = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
a = int(embed_dim * 2 ** (len(__a ) - 1) )
a = ["stem"] + [F'''stage{idx}''' for idx in range(1 , len(__a ) + 1 )]
a , a = get_aligned_output_features_output_indices(
out_features=__a , out_indices=__a , stage_names=self.stage_names )
class a__ ( UpperCamelCase__ ):
a : Tuple = version.parse("""1.11""" )
@property
def lowerCAmelCase_ ( self ) -> Any:
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def lowerCAmelCase_ ( self ) -> Any:
'''simple docstring'''
return 1e-4
| 368 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> list[int]:
a = 2
a = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__UpperCamelCase)
if n > 1:
factors.append(__UpperCamelCase)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 180 | 0 |
"""simple docstring"""
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer
from ...test_tokenization_common import TokenizerTesterMixin
@require_phonemizer
class _SCREAMING_SNAKE_CASE( A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Dict = WavaVecaPhonemeCTCTokenizer
SCREAMING_SNAKE_CASE_ : str = False
def _UpperCamelCase ( self ) -> Any:
"""simple docstring"""
super().setUp()
__SCREAMING_SNAKE_CASE :str = (
'''<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː '''
'''ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː '''
'''ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 '''
'''oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ '''
'''pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ '''
'''yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ '''
'''əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '''
'''ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ '''
'''ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ '''
'''uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ '''
'''ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ '''
'''ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ '''
'''ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4'''
).split(''' ''' )
__SCREAMING_SNAKE_CASE :Union[str, Any] = dict(zip(SCREAMING_SNAKE_CASE__ ,range(len(SCREAMING_SNAKE_CASE__ ) ) ) )
__SCREAMING_SNAKE_CASE :Tuple = {'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}
__SCREAMING_SNAKE_CASE :Optional[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file ,'''w''' ,encoding='''utf-8''' ) as fp:
fp.write(json.dumps(SCREAMING_SNAKE_CASE__ ) + '''\n''' )
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__=False ,SCREAMING_SNAKE_CASE__=20 ,SCREAMING_SNAKE_CASE__=5 ) -> Tuple[str, list]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :List[Any] = [(i, tokenizer.decode([i] ,clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )) for i in range(len(SCREAMING_SNAKE_CASE__ ) )]
__SCREAMING_SNAKE_CASE :Union[str, Any] = list(filter(lambda SCREAMING_SNAKE_CASE__ : [t[0]] == tokenizer.encode(t[1] ,do_phonemize=SCREAMING_SNAKE_CASE__ ) ,SCREAMING_SNAKE_CASE__ ) )
if max_length is not None and len(SCREAMING_SNAKE_CASE__ ) > max_length:
__SCREAMING_SNAKE_CASE :Optional[int] = toks[:max_length]
if min_length is not None and len(SCREAMING_SNAKE_CASE__ ) < min_length and len(SCREAMING_SNAKE_CASE__ ) > 0:
while len(SCREAMING_SNAKE_CASE__ ) < min_length:
__SCREAMING_SNAKE_CASE :List[Any] = toks + toks
# toks_str = [t[1] for t in toks]
__SCREAMING_SNAKE_CASE :Union[str, Any] = [t[0] for t in toks]
# Ensure consistency
__SCREAMING_SNAKE_CASE :Tuple = tokenizer.decode(SCREAMING_SNAKE_CASE__ ,clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
if " " not in output_txt and len(SCREAMING_SNAKE_CASE__ ) > 1:
__SCREAMING_SNAKE_CASE :List[str] = (
tokenizer.decode([toks_ids[0]] ,clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
+ ''' '''
+ tokenizer.decode(toks_ids[1:] ,clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
)
if with_prefix_space:
__SCREAMING_SNAKE_CASE :Any = ''' ''' + output_txt
__SCREAMING_SNAKE_CASE :Dict = tokenizer.encode(SCREAMING_SNAKE_CASE__ ,add_special_tokens=SCREAMING_SNAKE_CASE__ )
return output_txt, output_ids
def _UpperCamelCase ( self ,**SCREAMING_SNAKE_CASE__ ) -> Tuple:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname ,**SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Tuple = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )
# check adding a single token
tokenizer.add_tokens('''xxx''' )
__SCREAMING_SNAKE_CASE :Optional[Any] = tokenizer('''m xxx ɪ''' ,do_phonemize=SCREAMING_SNAKE_CASE__ ).input_ids
self.assertEqual(SCREAMING_SNAKE_CASE__ ,[13, 3_92, 17] ) # xxx should be last token
tokenizer.add_tokens(['''aaa''', '''bbb''', '''ccc'''] )
__SCREAMING_SNAKE_CASE :Optional[int] = tokenizer('''m aaa ɪ ccc''' ,do_phonemize=SCREAMING_SNAKE_CASE__ ).input_ids
self.assertEqual(SCREAMING_SNAKE_CASE__ ,[13, 3_93, 17, 3_95] ) # aaa and ccc should be after xxx and 2 after aaa
__SCREAMING_SNAKE_CASE :str = tokenizer('''maɪ c''' ,do_phonemize=SCREAMING_SNAKE_CASE__ ).input_ids
self.assertEqual(SCREAMING_SNAKE_CASE__ ,[3, 2_00] ) # mai should be <unk> (=3)
def _UpperCamelCase ( self ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Tuple = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )
__SCREAMING_SNAKE_CASE :int = '''Hello how are you'''
__SCREAMING_SNAKE_CASE :str = tokenizer.phonemize(SCREAMING_SNAKE_CASE__ ,phonemizer_lang='''en-us''' )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,'''h ə l oʊ h aʊ ɑːɹ j uː''' )
def _UpperCamelCase ( self ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Any = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )
__SCREAMING_SNAKE_CASE :Optional[Any] = '''Hello how are you'''
__SCREAMING_SNAKE_CASE :Optional[int] = tokenizer.phonemize(SCREAMING_SNAKE_CASE__ ,phonemizer_lang='''en-us''' )
self.assertEqual(tokenizer(SCREAMING_SNAKE_CASE__ ).input_ids ,tokenizer(SCREAMING_SNAKE_CASE__ ,do_phonemize=SCREAMING_SNAKE_CASE__ ).input_ids )
def _UpperCamelCase ( self ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Union[str, Any] = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )
__SCREAMING_SNAKE_CASE :Dict = '''Hello how are you'''
__SCREAMING_SNAKE_CASE :str = tokenizer.phonemize(SCREAMING_SNAKE_CASE__ ,phonemizer_lang='''en-us''' )
__SCREAMING_SNAKE_CASE :str = tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE__ ).input_ids )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Any = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )
__SCREAMING_SNAKE_CASE :List[Any] = [
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
[24, 22, 5, 24, 22, 5, 77],
]
__SCREAMING_SNAKE_CASE :List[str] = tokenizer.decode(sample_ids[0] )
__SCREAMING_SNAKE_CASE :Any = tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,batch_tokens[0] )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ'''] )
def _UpperCamelCase ( self ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Tuple = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' ,word_delimiter_token='''|''' )
tokenizer.add_tokens('''|''' )
__SCREAMING_SNAKE_CASE :List[str] = '''Hello how are you'''
__SCREAMING_SNAKE_CASE :Any = tokenizer.phonemize(SCREAMING_SNAKE_CASE__ ,phonemizer_lang='''en-us''' )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,'''h ə l oʊ | h aʊ | ɑːɹ | j uː |''' )
def _UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Any = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' ,word_delimiter_token='''|''' )
tokenizer.add_tokens('''|''' )
__SCREAMING_SNAKE_CASE :Union[str, Any] = '''Hello how are you'''
__SCREAMING_SNAKE_CASE :int = tokenizer.phonemize(SCREAMING_SNAKE_CASE__ ,phonemizer_lang='''en-us''' )
self.assertEqual(tokenizer(SCREAMING_SNAKE_CASE__ ).input_ids ,tokenizer(SCREAMING_SNAKE_CASE__ ,do_phonemize=SCREAMING_SNAKE_CASE__ ).input_ids )
def _UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Any = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' ,word_delimiter_token='''|''' )
tokenizer.add_tokens('''|''' )
# fmt: off
__SCREAMING_SNAKE_CASE :Any = [
[11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
[tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
]
# fmt: on
# decode with word_del_token filter
__SCREAMING_SNAKE_CASE :str = tokenizer.decode(sample_ids[0] )
__SCREAMING_SNAKE_CASE :int = tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,batch_tokens[0] )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ'''] )
# decode with no word_del_token filter
__SCREAMING_SNAKE_CASE :Union[str, Any] = tokenizer.decode(sample_ids[0] ,filter_word_delimiter_token=SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Union[str, Any] = tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ ,filter_word_delimiter_token=SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,batch_tokens[0] )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,['''k s ɾ | ɾ l | ɭʲ''', '''| j ð | s j ð s oːɹ'''] )
def _UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Any = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' ,word_delimiter_token='''|''' )
tokenizer.add_tokens('''|''' )
__SCREAMING_SNAKE_CASE :Union[str, Any] = '''Hello how are you'''
__SCREAMING_SNAKE_CASE :Optional[int] = tokenizer.phonemize(SCREAMING_SNAKE_CASE__ ,phonemizer_lang='''en-us''' )
__SCREAMING_SNAKE_CASE :Optional[Any] = tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE__ ).input_ids ,filter_word_delimiter_token=SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Optional[int] = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' ,word_delimiter_token='''|''' )
tokenizer.add_tokens('''|''' )
__SCREAMING_SNAKE_CASE :Tuple = '''Hello how are you'''
__SCREAMING_SNAKE_CASE :List[Any] = tokenizer.phonemize(SCREAMING_SNAKE_CASE__ ,phonemizer_lang='''en-us''' )
__SCREAMING_SNAKE_CASE :Optional[Any] = tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE__ ).input_ids ,filter_word_delimiter_token=SCREAMING_SNAKE_CASE__ )
self.assertEqual(''' '''.join([p.strip() for p in phonemes.split(''' |''' )] ).strip() ,SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :List[str] = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' ,word_delimiter_token=SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Dict = '''Hello how are you'''
__SCREAMING_SNAKE_CASE :str = tokenizer(SCREAMING_SNAKE_CASE__ ,phonemizer_lang='''en-us''' ).input_ids
__SCREAMING_SNAKE_CASE :List[str] = tokenizer(SCREAMING_SNAKE_CASE__ ,phonemizer_lang='''fr-fr''' ).input_ids
self.assertNotEqual(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Dict = tokenizer.decode(SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Tuple = tokenizer.decode(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,'''h ə l oʊ h aʊ ɑːɹ j uː''' )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,'''ɛ l o h aʊ a ʁ j u''' )
def _UpperCamelCase ( self ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :List[Any] = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )
__SCREAMING_SNAKE_CASE :Union[str, Any] = '''Hello how Are you'''
__SCREAMING_SNAKE_CASE :Tuple = '''hello how are you'''
__SCREAMING_SNAKE_CASE :Union[str, Any] = tokenizer(SCREAMING_SNAKE_CASE__ ).input_ids
__SCREAMING_SNAKE_CASE :Optional[Any] = tokenizer(SCREAMING_SNAKE_CASE__ ).input_ids
self.assertEqual(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :str = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )
tokenizer.add_tokens(['''!''', '''?'''] )
tokenizer.add_special_tokens({'''cls_token''': '''$$$'''} )
# fmt: off
__SCREAMING_SNAKE_CASE :Union[str, Any] = [
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 3_92, 3_92, 3_93, 3_92, 3_92, 3_93, 3_94, 3_94],
[24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 3_94, 3_94],
]
# fmt: on
__SCREAMING_SNAKE_CASE :List[Any] = tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,['''k s ɾ ɾ l ɭʲ!?!? $$$''', '''j ð s j ð s oːɹ $$$'''] )
@staticmethod
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :str = [d[key] for d in offsets]
return retrieved_list
def _UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Union[str, Any] = self.get_tokenizer(word_delimiter_token='''|''' )
tokenizer.add_tokens('''|''' )
# fmt: off
# ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
__SCREAMING_SNAKE_CASE :Optional[int] = [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
# fmt: on
__SCREAMING_SNAKE_CASE :Dict = tokenizer.decode(SCREAMING_SNAKE_CASE__ ,output_char_offsets=SCREAMING_SNAKE_CASE__ ,filter_word_delimiter_token=SCREAMING_SNAKE_CASE__ )
# check Wav2Vec2CTCTokenizerOutput keys for char
self.assertEqual(len(outputs.keys() ) ,2 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''char_offsets''' in outputs )
self.assertTrue(isinstance(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) )
# check that order of chars is correct and identical for both outputs
self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''char_offsets'''] ,'''char''' ) ) ,outputs.text )
self.assertListEqual(
self.get_from_offsets(outputs['''char_offsets'''] ,'''char''' ) ,['''k''', '''s''', '''ɾ''', '''ɾ''', '''|''', '''ɾ''', '''l''', '''|''', '''ɭʲ'''] )
# check that offsets are actually correct for char
# 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
# 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
self.assertListEqual(
self.get_from_offsets(outputs['''char_offsets'''] ,'''start_offset''' ) ,[0, 1, 4, 7, 9, 11, 12, 15, 16] )
self.assertListEqual(
self.get_from_offsets(outputs['''char_offsets'''] ,'''end_offset''' ) ,[1, 4, 6, 9, 10, 12, 15, 16, 17] )
def _UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Dict = self.get_tokenizer(word_delimiter_token='''|''' )
def check_list_tuples_equal(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ):
self.assertTrue(isinstance(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) )
self.assertTrue(isinstance(outputs_list[0] ,SCREAMING_SNAKE_CASE__ ) )
# transform list to ModelOutput
__SCREAMING_SNAKE_CASE :int = WavaVecaPhonemeCTCTokenizerOutput(
{k: [d[k] for d in outputs_list] for k in outputs_list[0]} )
self.assertListEqual(outputs_batch['''text'''] ,outputs_batch_a['''text'''] )
def recursive_check(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ):
if isinstance(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ):
[recursive_check(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) for la, la in zip(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )]
self.assertEqual(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
if "char_offsets" in outputs_batch:
recursive_check(outputs_batch['''char_offsets'''] ,outputs_batch_a['''char_offsets'''] )
# fmt: off
__SCREAMING_SNAKE_CASE :Dict = [
[11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
[24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
]
# fmt: on
# We assume that `decode` works as expected. All we will check now is
# the output type is correct and the output is identical to `decode`
# char
__SCREAMING_SNAKE_CASE :str = tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ ,output_char_offsets=SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Optional[Any] = [tokenizer.decode(SCREAMING_SNAKE_CASE__ ,output_char_offsets=SCREAMING_SNAKE_CASE__ ) for ids in sample_ids]
check_list_tuples_equal(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
@unittest.skip('''Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes''' )
def _UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
pass
@unittest.skip('''Wav2Vec2PhonemeTokenizer always puts spaces between phonemes''' )
def _UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
pass
@unittest.skip('''encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency''' )
def _UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip('''Wav2Vec2PhonemeModel has no max model length => no testing''' )
def _UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
pass
def _UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Optional[Any] = self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE__ )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
__SCREAMING_SNAKE_CASE :Optional[int] = tokenizer.vocab_size
__SCREAMING_SNAKE_CASE :Any = len(SCREAMING_SNAKE_CASE__ )
self.assertNotEqual(SCREAMING_SNAKE_CASE__ ,0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
__SCREAMING_SNAKE_CASE :List[str] = ['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
__SCREAMING_SNAKE_CASE :Union[str, Any] = tokenizer.add_tokens(SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Any = tokenizer.vocab_size
__SCREAMING_SNAKE_CASE :Tuple = len(SCREAMING_SNAKE_CASE__ )
self.assertNotEqual(SCREAMING_SNAKE_CASE__ ,0 )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,len(SCREAMING_SNAKE_CASE__ ) )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,all_size + len(SCREAMING_SNAKE_CASE__ ) )
__SCREAMING_SNAKE_CASE :Optional[int] = tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l''' ,add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertGreaterEqual(len(SCREAMING_SNAKE_CASE__ ) ,4 )
self.assertGreater(tokens[0] ,tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] ,tokenizer.vocab_size - 1 )
__SCREAMING_SNAKE_CASE :Optional[Any] = {'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
__SCREAMING_SNAKE_CASE :List[str] = tokenizer.add_special_tokens(SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Dict = tokenizer.vocab_size
__SCREAMING_SNAKE_CASE :Dict = len(SCREAMING_SNAKE_CASE__ )
self.assertNotEqual(SCREAMING_SNAKE_CASE__ ,0 )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,len(SCREAMING_SNAKE_CASE__ ) )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,all_size_a + len(SCREAMING_SNAKE_CASE__ ) )
__SCREAMING_SNAKE_CASE :List[str] = tokenizer.encode(
'''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l''' ,add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertGreaterEqual(len(SCREAMING_SNAKE_CASE__ ) ,6 )
self.assertGreater(tokens[0] ,tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] ,tokens[1] )
self.assertGreater(tokens[-3] ,tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] ,tokens[-4] )
self.assertEqual(tokens[0] ,tokenizer.eos_token_id )
self.assertEqual(tokens[-3] ,tokenizer.pad_token_id )
@unittest.skip('''The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.''' )
def _UpperCamelCase ( self ) -> str:
"""simple docstring"""
pass
@unittest.skip('''The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.''' )
def _UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
pass
def _UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Optional[int] = self.get_tokenizers(fast=SCREAMING_SNAKE_CASE__ ,do_lower_case=SCREAMING_SNAKE_CASE__ )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
__SCREAMING_SNAKE_CASE :Dict = ['''ð''', '''ɪ''', '''s''', '''ɪ''', '''z''', '''ɐ''', '''t''', '''ɛ''', '''k''', '''s''', '''t''']
__SCREAMING_SNAKE_CASE :List[Any] = tokenizer.convert_tokens_to_string(SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(output['''text'''] ,SCREAMING_SNAKE_CASE__ )
| 191 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ = {
"configuration_blip_2": [
"BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Blip2Config",
"Blip2QFormerConfig",
"Blip2VisionConfig",
],
"processing_blip_2": ["Blip2Processor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
"BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Blip2Model",
"Blip2QFormerModel",
"Blip2PreTrainedModel",
"Blip2ForConditionalGeneration",
"Blip2VisionModel",
]
if TYPE_CHECKING:
from .configuration_blip_a import (
BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlipaConfig,
BlipaQFormerConfig,
BlipaVisionConfig,
)
from .processing_blip_a import BlipaProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip_a import (
BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipaForConditionalGeneration,
BlipaModel,
BlipaPreTrainedModel,
BlipaQFormerModel,
BlipaVisionModel,
)
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 191 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
"studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json",
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
snake_case : Optional[Any] = 'luke'
def __init__( self , __lowerCAmelCase=50267 , __lowerCAmelCase=500000 , __lowerCAmelCase=768 , __lowerCAmelCase=256 , __lowerCAmelCase=12 , __lowerCAmelCase=12 , __lowerCAmelCase=3072 , __lowerCAmelCase="gelu" , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.1 , __lowerCAmelCase=512 , __lowerCAmelCase=2 , __lowerCAmelCase=0.02 , __lowerCAmelCase=1E-12 , __lowerCAmelCase=True , __lowerCAmelCase=None , __lowerCAmelCase=1 , __lowerCAmelCase=0 , __lowerCAmelCase=2 , **__lowerCAmelCase , ):
super().__init__(pad_token_id=lowercase__ , bos_token_id=lowercase__ , eos_token_id=lowercase__ , **lowercase__ )
UpperCamelCase__ = vocab_size
UpperCamelCase__ = entity_vocab_size
UpperCamelCase__ = hidden_size
UpperCamelCase__ = entity_emb_size
UpperCamelCase__ = num_hidden_layers
UpperCamelCase__ = num_attention_heads
UpperCamelCase__ = hidden_act
UpperCamelCase__ = intermediate_size
UpperCamelCase__ = hidden_dropout_prob
UpperCamelCase__ = attention_probs_dropout_prob
UpperCamelCase__ = max_position_embeddings
UpperCamelCase__ = type_vocab_size
UpperCamelCase__ = initializer_range
UpperCamelCase__ = layer_norm_eps
UpperCamelCase__ = use_entity_aware_attention
UpperCamelCase__ = classifier_dropout
| 358 |
def _UpperCamelCase (a__ :dict ):
"""simple docstring"""
UpperCamelCase__ = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
UpperCamelCase__ = set()
return any(
node not in visited and depth_first_search(a__ , a__ , a__ , a__ )
for node in graph )
def _UpperCamelCase (a__ :dict , a__ :int , a__ :set , a__ :set ):
"""simple docstring"""
visited.add(a__ )
rec_stk.add(a__ )
for node in graph[vertex]:
if node not in visited:
if depth_first_search(a__ , a__ , a__ , a__ ):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(a__ )
return False
if __name__ == "__main__":
from doctest import testmod
testmod()
| 87 | 0 |
import sys
import turtle
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : tuple[float, float] , __magic_name__ : tuple[float, float] ) -> tuple[float, float]:
"""simple docstring"""
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : tuple[float, float] , __magic_name__ : tuple[float, float] , __magic_name__ : tuple[float, float] , __magic_name__ : int , ) -> None:
"""simple docstring"""
my_pen.up()
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.down()
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.goto(vertexa[0] , vertexa[1] )
if depth == 0:
return
triangle(__magic_name__ , get_mid(__magic_name__ , __magic_name__ ) , get_mid(__magic_name__ , __magic_name__ ) , depth - 1 )
triangle(__magic_name__ , get_mid(__magic_name__ , __magic_name__ ) , get_mid(__magic_name__ , __magic_name__ ) , depth - 1 )
triangle(__magic_name__ , get_mid(__magic_name__ , __magic_name__ ) , get_mid(__magic_name__ , __magic_name__ ) , depth - 1 )
if __name__ == "__main__":
if len(sys.argv) != 2:
raise ValueError(
'''Correct format for using this script: '''
'''python fractals.py <int:depth_for_fractal>'''
)
UpperCAmelCase_ : str = turtle.Turtle()
my_pen.ht()
my_pen.speed(5)
my_pen.pencolor('''red''')
UpperCAmelCase_ : int = [(-1_75, -1_25), (0, 1_75), (1_75, -1_25)] # vertices of triangle
triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))
| 38 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class a_ :
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = True , _lowerCamelCase = False ) ->Any:
SCREAMING_SNAKE_CASE : str = scheduler
SCREAMING_SNAKE_CASE : List[str] = optimizers if isinstance(_lowerCamelCase , (list, tuple) ) else [optimizers]
SCREAMING_SNAKE_CASE : Union[str, Any] = split_batches
SCREAMING_SNAKE_CASE : List[Any] = step_with_optimizer
SCREAMING_SNAKE_CASE : List[str] = GradientState()
def __lowerCAmelCase ( self , *_lowerCamelCase , **_lowerCamelCase ) ->Optional[Any]:
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*_lowerCamelCase , **_lowerCamelCase )
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*_lowerCamelCase , **_lowerCamelCase )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
SCREAMING_SNAKE_CASE : List[str] = AcceleratorState().num_processes
for _ in range(_lowerCamelCase ):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler , '''total_steps''' ):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*_lowerCamelCase , **_lowerCamelCase )
else:
self.scheduler.step(*_lowerCamelCase , **_lowerCamelCase )
def __lowerCAmelCase ( self ) ->Union[str, Any]:
return self.scheduler.get_last_lr()
def __lowerCAmelCase ( self ) ->List[str]:
return self.scheduler.state_dict()
def __lowerCAmelCase ( self , _lowerCamelCase ) ->Optional[Any]:
self.scheduler.load_state_dict(_lowerCamelCase )
def __lowerCAmelCase ( self ) ->Any:
return self.scheduler.get_lr()
def __lowerCAmelCase ( self , *_lowerCamelCase , **_lowerCamelCase ) ->List[str]:
return self.scheduler.print_lr(*_lowerCamelCase , **_lowerCamelCase )
| 313 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
"facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
"facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
"facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
"facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
"facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
"facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
"facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
"facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}
class __lowerCAmelCase ( A ):
UpperCamelCase = '''xmod'''
def __init__( self : Optional[int] , A : int=3_05_22 , A : Tuple=7_68 , A : Optional[Any]=12 , A : Tuple=12 , A : str=30_72 , A : List[str]="gelu" , A : Any=0.1 , A : int=0.1 , A : Dict=5_12 , A : Optional[Any]=2 , A : Optional[Any]=0.0_2 , A : List[Any]=1E-12 , A : int=1 , A : Tuple=0 , A : Optional[Any]=2 , A : int="absolute" , A : Union[str, Any]=True , A : List[Any]=None , A : Optional[Any]=False , A : List[str]=2 , A : int=False , A : str=True , A : Optional[Any]=True , A : Tuple=("en_XX",) , A : Optional[int]=None , **A : List[str] , ) -> Dict:
"""simple docstring"""
super().__init__(pad_token_id=A , bos_token_id=A , eos_token_id=A , **A)
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = position_embedding_type
_UpperCAmelCase = use_cache
_UpperCAmelCase = classifier_dropout
_UpperCAmelCase = pre_norm
_UpperCAmelCase = adapter_reduction_factor
_UpperCAmelCase = adapter_layer_norm
_UpperCAmelCase = adapter_reuse_layer_norm
_UpperCAmelCase = ln_before_adapter
_UpperCAmelCase = list(A)
_UpperCAmelCase = default_language
class __lowerCAmelCase ( A ):
@property
def _lowerCamelCase ( self : Optional[Any]) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
_UpperCAmelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_UpperCAmelCase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
])
| 290 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def A ( ) -> tuple[list[int], int]:
'''simple docstring'''
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )]
_UpperCAmelCase = randint(-5_000 , 5_000 )
return (arr, r)
UpperCAmelCase__ = make_dataset()
def A ( _UpperCAmelCase : list[int] , _UpperCAmelCase : int ) -> tuple[int, ...]:
'''simple docstring'''
for triplet in permutations(_UpperCAmelCase , 3 ):
if sum(_UpperCAmelCase ) == target:
return tuple(sorted(_UpperCAmelCase ) )
return (0, 0, 0)
def A ( _UpperCAmelCase : list[int] , _UpperCAmelCase : int ) -> tuple[int, int, int]:
'''simple docstring'''
arr.sort()
_UpperCAmelCase = len(_UpperCAmelCase )
for i in range(n - 1 ):
_UpperCAmelCase , _UpperCAmelCase = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def A ( ) -> tuple[float, float]:
'''simple docstring'''
_UpperCAmelCase = '\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
_UpperCAmelCase = '\ntriplet_sum1(*dataset)\n'
_UpperCAmelCase = '\ntriplet_sum2(*dataset)\n'
_UpperCAmelCase = repeat(setup=_UpperCAmelCase , stmt=_UpperCAmelCase , repeat=5 , number=10_000 )
_UpperCAmelCase = repeat(setup=_UpperCAmelCase , stmt=_UpperCAmelCase , repeat=5 , number=10_000 )
return (min(_UpperCAmelCase ), min(_UpperCAmelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCAmelCase__ = solution_times()
print(f"""The time for naive implementation is {times[0]}.""")
print(f"""The time for optimized implementation is {times[1]}.""")
| 290 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('''TEST_SAGEMAKER''' , '''False''')) is not True , reason='''Skipping test because should only be run when releasing minor transformers version''' , )
@pytest.mark.usefixtures('''sm_env''')
@parameterized_class(
[
{
'''framework''': '''pytorch''',
'''script''': '''run_glue.py''',
'''model_name_or_path''': '''distilbert-base-cased''',
'''instance_type''': '''ml.g4dn.xlarge''',
'''results''': {'''train_runtime''': 650, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
},
{
'''framework''': '''tensorflow''',
'''script''': '''run_tf.py''',
'''model_name_or_path''': '''distilbert-base-cased''',
'''instance_type''': '''ml.g4dn.xlarge''',
'''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
},
])
class _a ( unittest.TestCase):
def UpperCAmelCase__( self : List[str] )-> int:
if self.framework == "pytorch":
subprocess.run(
F'cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'.split() , encoding='''utf-8''' , check=_SCREAMING_SNAKE_CASE , )
assert hasattr(self , '''env''' )
def UpperCAmelCase__( self : Dict , _SCREAMING_SNAKE_CASE : Optional[int]=1 )-> int:
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=F'{self.env.base_job_name}-single' , instance_count=_SCREAMING_SNAKE_CASE , instance_type=self.instance_type , debugger_hook_config=_SCREAMING_SNAKE_CASE , hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path} , metric_definitions=self.env.metric_definitions , py_version='''py36''' , )
def UpperCAmelCase__( self : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any] )-> Dict:
TrainingJobAnalytics(_SCREAMING_SNAKE_CASE ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv' )
def UpperCAmelCase__( self : Dict )-> str:
# create estimator
lowerCAmelCase__ : Any = self.create_estimator()
# run training
estimator.fit()
# result dataframe
lowerCAmelCase__ : Dict = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
lowerCAmelCase__ : Dict = list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''] )
lowerCAmelCase__ : Tuple = list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
lowerCAmelCase__ : Union[str, Any] = (
Session().describe_training_job(estimator.latest_training_job.name ).get('''TrainingTimeInSeconds''' , 99_9999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy )
assert all(t <= self.results['''eval_loss'''] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F'{estimator.latest_training_job.name}.json' , '''w''' ) as outfile:
json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss} , _SCREAMING_SNAKE_CASE )
| 131 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
lowerCamelCase = collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowerCamelCase = '''https://storage.googleapis.com/cvdf-datasets/mnist/'''
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : Dict = numpy.dtype(numpy.uintaa ).newbyteorder('''>''' )
return numpy.frombuffer(bytestream.read(4 ) , dtype=_a )[0]
@deprecated(_a , '''Please use tf.data to implement this functionality.''' )
def lowerCamelCase_ ( _a ):
"""simple docstring"""
print('''Extracting''' , f.name )
with gzip.GzipFile(fileobj=_a ) as bytestream:
lowerCAmelCase__ : Any = _readaa(_a )
if magic != 2_051:
raise ValueError(
'''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name) )
lowerCAmelCase__ : Any = _readaa(_a )
lowerCAmelCase__ : Tuple = _readaa(_a )
lowerCAmelCase__ : List[Any] = _readaa(_a )
lowerCAmelCase__ : Union[str, Any] = bytestream.read(rows * cols * num_images )
lowerCAmelCase__ : List[Any] = numpy.frombuffer(_a , dtype=numpy.uinta )
lowerCAmelCase__ : int = data.reshape(_a , _a , _a , 1 )
return data
@deprecated(_a , '''Please use tf.one_hot on tensors.''' )
def lowerCamelCase_ ( _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : List[Any] = labels_dense.shape[0]
lowerCAmelCase__ : Optional[Any] = numpy.arange(_a ) * num_classes
lowerCAmelCase__ : str = numpy.zeros((num_labels, num_classes) )
lowerCAmelCase__ : Optional[Any] = 1
return labels_one_hot
@deprecated(_a , '''Please use tf.data to implement this functionality.''' )
def lowerCamelCase_ ( _a , _a=False , _a=10 ):
"""simple docstring"""
print('''Extracting''' , f.name )
with gzip.GzipFile(fileobj=_a ) as bytestream:
lowerCAmelCase__ : Optional[int] = _readaa(_a )
if magic != 2_049:
raise ValueError(
'''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name) )
lowerCAmelCase__ : Union[str, Any] = _readaa(_a )
lowerCAmelCase__ : Tuple = bytestream.read(_a )
lowerCAmelCase__ : Dict = numpy.frombuffer(_a , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(_a , _a )
return labels
class _a :
@deprecated(
_SCREAMING_SNAKE_CASE , '''Please use alternatives such as official/mnist/_DataSet.py'''
''' from tensorflow/models.''' , )
def __init__( self : Dict , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Tuple=False , _SCREAMING_SNAKE_CASE : Any=False , _SCREAMING_SNAKE_CASE : Optional[Any]=dtypes.floataa , _SCREAMING_SNAKE_CASE : List[str]=True , _SCREAMING_SNAKE_CASE : List[str]=None , )-> List[Any]:
lowerCAmelCase__ , lowerCAmelCase__ : List[Any] = random_seed.get_seed(_SCREAMING_SNAKE_CASE )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
lowerCAmelCase__ : Optional[int] = dtypes.as_dtype(_SCREAMING_SNAKE_CASE ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype )
if fake_data:
lowerCAmelCase__ : int = 1_0000
lowerCAmelCase__ : List[Any] = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), F'images.shape: {images.shape} labels.shape: {labels.shape}'
lowerCAmelCase__ : List[Any] = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
lowerCAmelCase__ : Tuple = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
lowerCAmelCase__ : Any = images.astype(numpy.floataa )
lowerCAmelCase__ : Any = numpy.multiply(_SCREAMING_SNAKE_CASE , 1.0 / 255.0 )
lowerCAmelCase__ : Tuple = images
lowerCAmelCase__ : Tuple = labels
lowerCAmelCase__ : List[Any] = 0
lowerCAmelCase__ : Tuple = 0
@property
def UpperCAmelCase__( self : Tuple )-> Dict:
return self._images
@property
def UpperCAmelCase__( self : Tuple )-> Optional[int]:
return self._labels
@property
def UpperCAmelCase__( self : Tuple )-> Dict:
return self._num_examples
@property
def UpperCAmelCase__( self : Tuple )-> Any:
return self._epochs_completed
def UpperCAmelCase__( self : List[Any] , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Dict=False , _SCREAMING_SNAKE_CASE : Optional[int]=True )-> List[str]:
if fake_data:
lowerCAmelCase__ : Dict = [1] * 784
lowerCAmelCase__ : Union[str, Any] = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(_SCREAMING_SNAKE_CASE )],
[fake_label for _ in range(_SCREAMING_SNAKE_CASE )],
)
lowerCAmelCase__ : str = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
lowerCAmelCase__ : Union[str, Any] = numpy.arange(self._num_examples )
numpy.random.shuffle(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : List[Any] = self.images[perma]
lowerCAmelCase__ : Tuple = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
lowerCAmelCase__ : Any = self._num_examples - start
lowerCAmelCase__ : List[str] = self._images[start : self._num_examples]
lowerCAmelCase__ : Tuple = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
lowerCAmelCase__ : Union[str, Any] = numpy.arange(self._num_examples )
numpy.random.shuffle(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : str = self.images[perm]
lowerCAmelCase__ : List[Any] = self.labels[perm]
# Start next epoch
lowerCAmelCase__ : Dict = 0
lowerCAmelCase__ : Union[str, Any] = batch_size - rest_num_examples
lowerCAmelCase__ : Any = self._index_in_epoch
lowerCAmelCase__ : Optional[Any] = self._images[start:end]
lowerCAmelCase__ : Optional[Any] = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
lowerCAmelCase__ : Dict = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(_a , '''Please write your own downloading logic.''' )
def lowerCamelCase_ ( _a , _a , _a ):
"""simple docstring"""
if not gfile.Exists(_a ):
gfile.MakeDirs(_a )
lowerCAmelCase__ : str = os.path.join(_a , _a )
if not gfile.Exists(_a ):
urllib.request.urlretrieve(_a , _a ) # noqa: S310
with gfile.GFile(_a ) as f:
lowerCAmelCase__ : Optional[Any] = f.size()
print('''Successfully downloaded''' , _a , _a , '''bytes.''' )
return filepath
@deprecated(
_a , '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')''' )
def lowerCamelCase_ ( _a , _a=False , _a=False , _a=dtypes.floataa , _a=True , _a=5_000 , _a=None , _a=DEFAULT_SOURCE_URL , ):
"""simple docstring"""
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=_a , one_hot=_a , dtype=_a , seed=_a )
lowerCAmelCase__ : Tuple = fake()
lowerCAmelCase__ : Union[str, Any] = fake()
lowerCAmelCase__ : Tuple = fake()
return _Datasets(train=_a , validation=_a , test=_a )
if not source_url: # empty string check
lowerCAmelCase__ : Optional[Any] = DEFAULT_SOURCE_URL
lowerCAmelCase__ : Tuple = '''train-images-idx3-ubyte.gz'''
lowerCAmelCase__ : Dict = '''train-labels-idx1-ubyte.gz'''
lowerCAmelCase__ : List[str] = '''t10k-images-idx3-ubyte.gz'''
lowerCAmelCase__ : Optional[int] = '''t10k-labels-idx1-ubyte.gz'''
lowerCAmelCase__ : Optional[Any] = _maybe_download(
_a , _a , source_url + train_images_file )
with gfile.Open(_a , '''rb''' ) as f:
lowerCAmelCase__ : Optional[Any] = _extract_images(_a )
lowerCAmelCase__ : Any = _maybe_download(
_a , _a , source_url + train_labels_file )
with gfile.Open(_a , '''rb''' ) as f:
lowerCAmelCase__ : Any = _extract_labels(_a , one_hot=_a )
lowerCAmelCase__ : Any = _maybe_download(
_a , _a , source_url + test_images_file )
with gfile.Open(_a , '''rb''' ) as f:
lowerCAmelCase__ : str = _extract_images(_a )
lowerCAmelCase__ : Dict = _maybe_download(
_a , _a , source_url + test_labels_file )
with gfile.Open(_a , '''rb''' ) as f:
lowerCAmelCase__ : int = _extract_labels(_a , one_hot=_a )
if not 0 <= validation_size <= len(_a ):
lowerCAmelCase__ : Dict = (
'''Validation size should be between 0 and '''
f'{len(_a )}. Received: {validation_size}.'
)
raise ValueError(_a )
lowerCAmelCase__ : List[str] = train_images[:validation_size]
lowerCAmelCase__ : Any = train_labels[:validation_size]
lowerCAmelCase__ : Optional[Any] = train_images[validation_size:]
lowerCAmelCase__ : Optional[int] = train_labels[validation_size:]
lowerCAmelCase__ : Optional[Any] = {'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}
lowerCAmelCase__ : List[str] = _DataSet(_a , _a , **_a )
lowerCAmelCase__ : Dict = _DataSet(_a , _a , **_a )
lowerCAmelCase__ : Dict = _DataSet(_a , _a , **_a )
return _Datasets(train=_a , validation=_a , test=_a )
| 131 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ = {
'''configuration_xlm_roberta''': [
'''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaConfig''',
'''XLMRobertaOnnxConfig''',
],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = ['''XLMRobertaTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = ['''XLMRobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
'''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLMRobertaForCausalLM''',
'''XLMRobertaForMaskedLM''',
'''XLMRobertaForMultipleChoice''',
'''XLMRobertaForQuestionAnswering''',
'''XLMRobertaForSequenceClassification''',
'''XLMRobertaForTokenClassification''',
'''XLMRobertaModel''',
'''XLMRobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
'''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLMRobertaForCausalLM''',
'''TFXLMRobertaForMaskedLM''',
'''TFXLMRobertaForMultipleChoice''',
'''TFXLMRobertaForQuestionAnswering''',
'''TFXLMRobertaForSequenceClassification''',
'''TFXLMRobertaForTokenClassification''',
'''TFXLMRobertaModel''',
'''TFXLMRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
'''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FlaxXLMRobertaForMaskedLM''',
'''FlaxXLMRobertaForCausalLM''',
'''FlaxXLMRobertaForMultipleChoice''',
'''FlaxXLMRobertaForQuestionAnswering''',
'''FlaxXLMRobertaForSequenceClassification''',
'''FlaxXLMRobertaForTokenClassification''',
'''FlaxXLMRobertaModel''',
'''FlaxXLMRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaConfig,
XLMRobertaOnnxConfig,
)
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta import XLMRobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaForCausalLM,
XLMRobertaForMaskedLM,
XLMRobertaForMultipleChoice,
XLMRobertaForQuestionAnswering,
XLMRobertaForSequenceClassification,
XLMRobertaForTokenClassification,
XLMRobertaModel,
XLMRobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm_roberta import (
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMRobertaForCausalLM,
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
TFXLMRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xlm_roberta import (
FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxXLMRobertaForCausalLM,
FlaxXLMRobertaForMaskedLM,
FlaxXLMRobertaForMultipleChoice,
FlaxXLMRobertaForQuestionAnswering,
FlaxXLMRobertaForSequenceClassification,
FlaxXLMRobertaForTokenClassification,
FlaxXLMRobertaModel,
FlaxXLMRobertaPreTrainedModel,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 350 |
'''simple docstring'''
from manim import *
class lowerCAmelCase__ ( UpperCAmelCase__ ):
def lowerCAmelCase__ ( self : List[Any] ) ->str:
'''simple docstring'''
_UpperCAmelCase : Dict = Rectangle(height=0.5 , width=0.5 )
_UpperCAmelCase : Optional[Any] = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0 )
_UpperCAmelCase : Optional[Any] = [mem.copy() for i in range(6 )]
_UpperCAmelCase : Optional[Any] = [mem.copy() for i in range(6 )]
_UpperCAmelCase : Dict = VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__ , buff=0 )
_UpperCAmelCase : str = VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__ , buff=0 )
_UpperCAmelCase : Optional[Any] = VGroup(lowerCamelCase__ , lowerCamelCase__ ).arrange(lowerCamelCase__ , buff=0 )
_UpperCAmelCase : int = Text("CPU" , font_size=24 )
_UpperCAmelCase : Any = Group(lowerCamelCase__ , lowerCamelCase__ ).arrange(lowerCamelCase__ , buff=0.5 , aligned_edge=lowerCamelCase__ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(lowerCamelCase__ )
_UpperCAmelCase : Optional[Any] = [mem.copy() for i in range(1 )]
_UpperCAmelCase : str = VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__ , buff=0 )
_UpperCAmelCase : int = Text("GPU" , font_size=24 )
_UpperCAmelCase : str = Group(lowerCamelCase__ , lowerCamelCase__ ).arrange(lowerCamelCase__ , buff=0.5 , aligned_edge=lowerCamelCase__ )
gpu.align_to(lowerCamelCase__ , lowerCamelCase__ )
gpu.set_x(gpu.get_x() - 1 )
self.add(lowerCamelCase__ )
_UpperCAmelCase : List[str] = [mem.copy() for i in range(6 )]
_UpperCAmelCase : Any = VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__ , buff=0 )
_UpperCAmelCase : Optional[int] = Text("Model" , font_size=24 )
_UpperCAmelCase : Tuple = Group(lowerCamelCase__ , lowerCamelCase__ ).arrange(lowerCamelCase__ , buff=0.5 , aligned_edge=lowerCamelCase__ )
model.move_to([3, -1.0, 0] )
self.play(
Create(lowerCamelCase__ , run_time=1 ) , Create(lowerCamelCase__ , run_time=1 ) , Create(lowerCamelCase__ , run_time=1 ) , )
_UpperCAmelCase : int = MarkupText(
F"""First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM.""" , font_size=24 , )
_UpperCAmelCase : Any = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_UpperCAmelCase : Union[str, Any] = MarkupText(
F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(lowerCamelCase__ , run_time=2.5 ) , Write(lowerCamelCase__ ) , Write(lowerCamelCase__ ) )
self.add(lowerCamelCase__ )
_UpperCAmelCase : int = []
_UpperCAmelCase : List[str] = []
_UpperCAmelCase : Dict = []
for i, rect in enumerate(lowerCamelCase__ ):
_UpperCAmelCase : int = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0.0 ).set_fill(lowerCamelCase__ , opacity=0.7 )
cpu_target.move_to(lowerCamelCase__ )
cpu_target.generate_target()
_UpperCAmelCase : Dict = 0.4_6 / 4
_UpperCAmelCase : Any = 0.4_6 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.0_2 , direction=lowerCamelCase__ )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=lowerCamelCase__ , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=lowerCamelCase__ , buff=0.0 )
cpu_targs.append(lowerCamelCase__ )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(lowerCamelCase__ ) )
second_animations.append(MoveToTarget(lowerCamelCase__ , run_time=1.5 ) )
self.play(*lowerCamelCase__ )
self.play(*lowerCamelCase__ )
self.wait()
| 322 | 0 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class _a :
def __init__( self: Union[str, Any] , UpperCamelCase_: Tuple , UpperCamelCase_: List[Any]=2 , UpperCamelCase_: Optional[Any]=8 , UpperCamelCase_: Tuple=True , UpperCamelCase_: Union[str, Any]=True , UpperCamelCase_: Union[str, Any]=True , UpperCamelCase_: Dict=True , UpperCamelCase_: Any=99 , UpperCamelCase_: int=16 , UpperCamelCase_: Optional[int]=5 , UpperCamelCase_: Union[str, Any]=2 , UpperCamelCase_: Any=36 , UpperCamelCase_: List[str]="gelu" , UpperCamelCase_: List[str]=0.0 , UpperCamelCase_: Tuple=0.0 , UpperCamelCase_: Any=512 , UpperCamelCase_: int=16 , UpperCamelCase_: Union[str, Any]=2 , UpperCamelCase_: str=0.02 , UpperCamelCase_: List[Any]=3 , UpperCamelCase_: List[str]=4 , UpperCamelCase_: str=None , ) -> int:
"""simple docstring"""
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = seq_length
lowercase__ = is_training
lowercase__ = use_input_mask
lowercase__ = use_token_type_ids
lowercase__ = use_labels
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = type_vocab_size
lowercase__ = type_sequence_label_size
lowercase__ = initializer_range
lowercase__ = num_labels
lowercase__ = num_choices
lowercase__ = scope
def lowerCamelCase_ ( self: int ) -> List[str]:
"""simple docstring"""
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ = None
if self.use_input_mask:
lowercase__ = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ = None
if self.use_token_type_ids:
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase__ = None
lowercase__ = None
lowercase__ = None
if self.use_labels:
lowercase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase__ = ids_tensor([self.batch_size] , self.num_choices )
lowercase__ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase_ ( self: Tuple ) -> Union[str, Any]:
"""simple docstring"""
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCAmelCase , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self: Any ) -> List[Any]:
"""simple docstring"""
lowercase__ = self.get_config()
lowercase__ = 300
return config
def lowerCamelCase_ ( self: Union[str, Any] ) -> Tuple:
"""simple docstring"""
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) = self.prepare_config_and_inputs()
lowercase__ = True
lowercase__ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def lowerCamelCase_ ( self: int , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: List[Any] , UpperCamelCase_: Any , UpperCamelCase_: List[Any] , UpperCamelCase_: Tuple , UpperCamelCase_: Optional[int] , UpperCamelCase_: Tuple ) -> Any:
"""simple docstring"""
lowercase__ = MraModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowercase__ = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
lowercase__ = model(__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
lowercase__ = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase_ ( self: Dict , UpperCamelCase_: List[Any] , UpperCamelCase_: str , UpperCamelCase_: Tuple , UpperCamelCase_: Tuple , UpperCamelCase_: Optional[Any] , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: Any , UpperCamelCase_: Optional[int] , UpperCamelCase_: Union[str, Any] , ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = True
lowercase__ = MraModel(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowercase__ = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , encoder_attention_mask=__UpperCAmelCase , )
lowercase__ = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , )
lowercase__ = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase_ ( self: Tuple , UpperCamelCase_: List[Any] , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: str , UpperCamelCase_: Dict , UpperCamelCase_: str , UpperCamelCase_: Any , UpperCamelCase_: List[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = MraForMaskedLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowercase__ = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase_ ( self: List[Any] , UpperCamelCase_: Optional[int] , UpperCamelCase_: int , UpperCamelCase_: Optional[Any] , UpperCamelCase_: Any , UpperCamelCase_: Tuple , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: Dict ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = MraForQuestionAnswering(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowercase__ = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , start_positions=__UpperCAmelCase , end_positions=__UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase_ ( self: str , UpperCamelCase_: List[Any] , UpperCamelCase_: List[str] , UpperCamelCase_: Optional[int] , UpperCamelCase_: List[Any] , UpperCamelCase_: Optional[int] , UpperCamelCase_: List[str] , UpperCamelCase_: Union[str, Any] ) -> Dict:
"""simple docstring"""
lowercase__ = self.num_labels
lowercase__ = MraForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowercase__ = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self: Tuple , UpperCamelCase_: Dict , UpperCamelCase_: List[str] , UpperCamelCase_: str , UpperCamelCase_: str , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: str , UpperCamelCase_: Optional[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.num_labels
lowercase__ = MraForTokenClassification(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowercase__ = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase_ ( self: int , UpperCamelCase_: Any , UpperCamelCase_: Tuple , UpperCamelCase_: Dict , UpperCamelCase_: str , UpperCamelCase_: Optional[Any] , UpperCamelCase_: List[Any] , UpperCamelCase_: Tuple ) -> List[Any]:
"""simple docstring"""
lowercase__ = self.num_choices
lowercase__ = MraForMultipleChoice(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowercase__ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase_ ( self: List[Any] ) -> Dict:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) = config_and_inputs
lowercase__ = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _a ( lowerCAmelCase__ , unittest.TestCase ):
_lowercase : Dict = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
_lowercase : List[str] = False
_lowercase : Dict = False
_lowercase : Optional[int] = False
_lowercase : Any = False
_lowercase : int = ()
def lowerCamelCase_ ( self: List[str] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = MraModelTester(self )
lowercase__ = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def lowerCamelCase_ ( self: Union[str, Any] ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self: str ) -> str:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase_ ( self: int ) -> Tuple:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase__ = type
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase_ ( self: Any ) -> Tuple:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )
def lowerCamelCase_ ( self: str ) -> str:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase )
def lowerCamelCase_ ( self: Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )
def lowerCamelCase_ ( self: int ) -> str:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )
def lowerCamelCase_ ( self: Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )
@slow
def lowerCamelCase_ ( self: Optional[Any] ) -> str:
"""simple docstring"""
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = MraModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
@unittest.skip(reason='''MRA does not output attentions''' )
def lowerCamelCase_ ( self: Tuple ) -> List[Any]:
"""simple docstring"""
return
@require_torch
class _a ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self: Dict ) -> List[Any]:
"""simple docstring"""
lowercase__ = MraModel.from_pretrained('''uw-madison/mra-base-512-4''' )
lowercase__ = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
lowercase__ = model(__UpperCAmelCase )[0]
lowercase__ = torch.Size((1, 256, 768) )
self.assertEqual(output.shape , __UpperCAmelCase )
lowercase__ = torch.tensor(
[[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCamelCase_ ( self: Any ) -> Optional[int]:
"""simple docstring"""
lowercase__ = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''' )
lowercase__ = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
lowercase__ = model(__UpperCAmelCase )[0]
lowercase__ = 50_265
lowercase__ = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape , __UpperCAmelCase )
lowercase__ = torch.tensor(
[[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCamelCase_ ( self: int ) -> int:
"""simple docstring"""
lowercase__ = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''' )
lowercase__ = torch.arange(4_096 ).unsqueeze(0 )
with torch.no_grad():
lowercase__ = model(__UpperCAmelCase )[0]
lowercase__ = 50_265
lowercase__ = torch.Size((1, 4_096, vocab_size) )
self.assertEqual(output.shape , __UpperCAmelCase )
lowercase__ = torch.tensor(
[[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 110 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
a_ = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["""memory_attention""", """encoder_attn"""],
["""attention""", """attn"""],
["""/""", """."""],
[""".LayerNorm.gamma""", """_layer_norm.weight"""],
[""".LayerNorm.beta""", """_layer_norm.bias"""],
["""r.layer_""", """r.layers."""],
["""output_proj""", """out_proj"""],
["""ffn.dense_1.""", """fc2."""],
["""ffn.dense.""", """fc1."""],
["""ffn_layer_norm""", """final_layer_norm"""],
["""kernel""", """weight"""],
["""encoder_layer_norm.""", """encoder.layer_norm."""],
["""decoder_layer_norm.""", """decoder.layer_norm."""],
["""embeddings.weights""", """shared.weight"""],
]
def a__ ( _UpperCamelCase : int ):
for pegasus_name, hf_name in PATTERNS:
__lowerCamelCase = k.replace(_UpperCamelCase ,_UpperCamelCase )
return k
def a__ ( _UpperCamelCase : dict ,_UpperCamelCase : dict ):
__lowerCamelCase = DEFAULTS.copy()
cfg_kwargs.update(_UpperCamelCase )
__lowerCamelCase = PegasusConfig(**_UpperCamelCase )
__lowerCamelCase = PegasusForConditionalGeneration(_UpperCamelCase )
__lowerCamelCase = torch_model.model.state_dict()
__lowerCamelCase = {}
for k, v in tf_weights.items():
__lowerCamelCase = rename_state_dict_key(_UpperCamelCase )
if new_k not in sd:
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if "dense" in k or "proj" in new_k:
__lowerCamelCase = v.T
__lowerCamelCase = torch.tensor(_UpperCamelCase ,dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
# make sure embedding.padding_idx is respected
__lowerCamelCase = torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1] )
__lowerCamelCase = mapping['''shared.weight''']
__lowerCamelCase = mapping['''shared.weight''']
__lowerCamelCase = {k: torch.zeros_like(_UpperCamelCase ) for k, v in sd.items() if k.endswith('''bias''' ) and k not in mapping}
mapping.update(**_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = torch_model.model.load_state_dict(_UpperCamelCase ,strict=_UpperCamelCase )
__lowerCamelCase = [
k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
]
assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], F"""no matches found for the following tf keys {extra}"""
return torch_model
def a__ ( _UpperCamelCase : str="./ckpt/aeslc/model.ckpt-32000" ):
__lowerCamelCase = tf.train.list_variables(_UpperCamelCase )
__lowerCamelCase = {}
__lowerCamelCase = ['''Adafactor''', '''global_step''']
for name, shape in tqdm(_UpperCamelCase ,desc='''converting tf checkpoint to dict''' ):
__lowerCamelCase = any(pat in name for pat in ignore_name )
if skip_key:
continue
__lowerCamelCase = tf.train.load_variable(_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase = array
return tf_weights
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ):
# save tokenizer first
__lowerCamelCase = Path(_UpperCamelCase ).parent.name
__lowerCamelCase = task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
__lowerCamelCase = PegasusTokenizer.from_pretrained('''sshleifer/pegasus''' ,model_max_length=_UpperCamelCase )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(_UpperCamelCase )
# convert model
__lowerCamelCase = get_tf_weights_as_numpy(_UpperCamelCase )
__lowerCamelCase = task_specific_params[F"""summarization_{dataset}"""]
if dataset == "large":
__lowerCamelCase = task_specific_params
__lowerCamelCase = convert_pegasus(_UpperCamelCase ,_UpperCamelCase )
torch_model.save_pretrained(_UpperCamelCase )
__lowerCamelCase = torch_model.state_dict()
sd.pop('''model.decoder.embed_positions.weight''' )
sd.pop('''model.encoder.embed_positions.weight''' )
torch.save(_UpperCamelCase ,Path(_UpperCamelCase ) / '''pytorch_model.bin''' )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
parser.add_argument("""save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
a_ = parser.parse_args()
if args.save_dir is None:
a_ = Path(args.tf_ckpt_path).parent.name
a_ = os.path.join("""pegasus""", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 330 | 0 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
def get_masked_lm_array(lowerCamelCase__ ):
lowerCamelCase_ = F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
lowerCamelCase_ = tf.train.load_variable(lowerCamelCase__ , lowerCamelCase__ )
if "kernel" in name:
lowerCamelCase_ = array.transpose()
return torch.from_numpy(lowerCamelCase__ )
def get_encoder_array(lowerCamelCase__ ):
lowerCamelCase_ = F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
lowerCamelCase_ = tf.train.load_variable(lowerCamelCase__ , lowerCamelCase__ )
if "kernel" in name:
lowerCamelCase_ = array.transpose()
return torch.from_numpy(lowerCamelCase__ )
def get_encoder_layer_array(lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
lowerCamelCase_ = tf.train.load_variable(lowerCamelCase__ , lowerCamelCase__ )
if "kernel" in name:
lowerCamelCase_ = array.transpose()
return torch.from_numpy(lowerCamelCase__ )
def get_encoder_attention_layer_array(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = F'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
lowerCamelCase_ = tf.train.load_variable(lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase_ = array.reshape(lowerCamelCase__ )
if "kernel" in name:
lowerCamelCase_ = array.transpose()
return torch.from_numpy(lowerCamelCase__ )
print(F'Loading model based on config from {config_path}...' )
lowerCamelCase_ = BertConfig.from_json_file(lowerCamelCase__ )
lowerCamelCase_ = BertForMaskedLM(lowerCamelCase__ )
# Layers
for layer_index in range(0 , config.num_hidden_layers ):
lowerCamelCase_ = model.bert.encoder.layer[layer_index]
# Self-attention
lowerCamelCase_ = layer.attention.self
lowerCamelCase_ = get_encoder_attention_layer_array(
lowerCamelCase__ , "_query_dense/kernel" , self_attn.query.weight.data.shape )
lowerCamelCase_ = get_encoder_attention_layer_array(
lowerCamelCase__ , "_query_dense/bias" , self_attn.query.bias.data.shape )
lowerCamelCase_ = get_encoder_attention_layer_array(
lowerCamelCase__ , "_key_dense/kernel" , self_attn.key.weight.data.shape )
lowerCamelCase_ = get_encoder_attention_layer_array(
lowerCamelCase__ , "_key_dense/bias" , self_attn.key.bias.data.shape )
lowerCamelCase_ = get_encoder_attention_layer_array(
lowerCamelCase__ , "_value_dense/kernel" , self_attn.value.weight.data.shape )
lowerCamelCase_ = get_encoder_attention_layer_array(
lowerCamelCase__ , "_value_dense/bias" , self_attn.value.bias.data.shape )
# Self-attention Output
lowerCamelCase_ = layer.attention.output
lowerCamelCase_ = get_encoder_attention_layer_array(
lowerCamelCase__ , "_output_dense/kernel" , self_output.dense.weight.data.shape )
lowerCamelCase_ = get_encoder_attention_layer_array(
lowerCamelCase__ , "_output_dense/bias" , self_output.dense.bias.data.shape )
lowerCamelCase_ = get_encoder_layer_array(lowerCamelCase__ , "_attention_layer_norm/gamma" )
lowerCamelCase_ = get_encoder_layer_array(lowerCamelCase__ , "_attention_layer_norm/beta" )
# Intermediate
lowerCamelCase_ = layer.intermediate
lowerCamelCase_ = get_encoder_layer_array(lowerCamelCase__ , "_intermediate_dense/kernel" )
lowerCamelCase_ = get_encoder_layer_array(lowerCamelCase__ , "_intermediate_dense/bias" )
# Output
lowerCamelCase_ = layer.output
lowerCamelCase_ = get_encoder_layer_array(lowerCamelCase__ , "_output_dense/kernel" )
lowerCamelCase_ = get_encoder_layer_array(lowerCamelCase__ , "_output_dense/bias" )
lowerCamelCase_ = get_encoder_layer_array(lowerCamelCase__ , "_output_layer_norm/gamma" )
lowerCamelCase_ = get_encoder_layer_array(lowerCamelCase__ , "_output_layer_norm/beta" )
# Embeddings
lowerCamelCase_ = get_encoder_array("_position_embedding_layer/embeddings" )
lowerCamelCase_ = get_encoder_array("_type_embedding_layer/embeddings" )
lowerCamelCase_ = get_encoder_array("_embedding_norm_layer/gamma" )
lowerCamelCase_ = get_encoder_array("_embedding_norm_layer/beta" )
# LM Head
lowerCamelCase_ = model.cls.predictions.transform
lowerCamelCase_ = get_masked_lm_array("dense/kernel" )
lowerCamelCase_ = get_masked_lm_array("dense/bias" )
lowerCamelCase_ = get_masked_lm_array("layer_norm/gamma" )
lowerCamelCase_ = get_masked_lm_array("layer_norm/beta" )
lowerCamelCase_ = get_masked_lm_array("embedding_table" )
# Pooling
lowerCamelCase_ = BertPooler(config=lowerCamelCase__ )
lowerCamelCase_ = get_encoder_array("_pooler_layer/kernel" )
lowerCamelCase_ = get_encoder_array("_pooler_layer/bias" )
# Export final model
model.save_pretrained(lowerCamelCase__ )
# Integration test - should load without any errors ;)
lowerCamelCase_ = BertForMaskedLM.from_pretrained(lowerCamelCase__ )
print(new_model.eval() )
print("Model conversion was done sucessfully!" )
if __name__ == "__main__":
__A =argparse.ArgumentParser()
parser.add_argument(
'''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow Token Dropping checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
type=str,
required=True,
help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''',
type=str,
required=True,
help='''Path to the output PyTorch model.''',
)
__A =parser.parse_args()
convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 47 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A =logging.get_logger(__name__)
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = YolosConfig()
# size of the architecture
if "yolos_ti" in yolos_name:
lowerCamelCase_ = 1_9_2
lowerCamelCase_ = 7_6_8
lowerCamelCase_ = 1_2
lowerCamelCase_ = 3
lowerCamelCase_ = [8_0_0, 1_3_3_3]
lowerCamelCase_ = False
elif yolos_name == "yolos_s_dWr":
lowerCamelCase_ = 3_3_0
lowerCamelCase_ = 1_4
lowerCamelCase_ = 6
lowerCamelCase_ = 1_3_2_0
elif "yolos_s" in yolos_name:
lowerCamelCase_ = 3_8_4
lowerCamelCase_ = 1_5_3_6
lowerCamelCase_ = 1_2
lowerCamelCase_ = 6
elif "yolos_b" in yolos_name:
lowerCamelCase_ = [8_0_0, 1_3_4_4]
lowerCamelCase_ = 9_1
lowerCamelCase_ = "huggingface/label-files"
lowerCamelCase_ = "coco-detection-id2label.json"
lowerCamelCase_ = json.load(open(hf_hub_download(lowerCamelCase__ , lowerCamelCase__ , repo_type="dataset" ) , "r" ) )
lowerCamelCase_ = {int(lowerCamelCase__ ): v for k, v in idalabel.items()}
lowerCamelCase_ = idalabel
lowerCamelCase_ = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = False ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCamelCase_ = state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
lowerCamelCase_ = state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
lowerCamelCase_ = in_proj_weight[: config.hidden_size, :]
lowerCamelCase_ = in_proj_bias[: config.hidden_size]
lowerCamelCase_ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCamelCase_ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCamelCase_ = in_proj_weight[-config.hidden_size :, :]
lowerCamelCase_ = in_proj_bias[-config.hidden_size :]
def lowerCamelCase_ ( lowerCamelCase__ ):
if "backbone" in name:
lowerCamelCase_ = name.replace("backbone" , "vit" )
if "cls_token" in name:
lowerCamelCase_ = name.replace("cls_token" , "embeddings.cls_token" )
if "det_token" in name:
lowerCamelCase_ = name.replace("det_token" , "embeddings.detection_tokens" )
if "mid_pos_embed" in name:
lowerCamelCase_ = name.replace("mid_pos_embed" , "encoder.mid_position_embeddings" )
if "pos_embed" in name:
lowerCamelCase_ = name.replace("pos_embed" , "embeddings.position_embeddings" )
if "patch_embed.proj" in name:
lowerCamelCase_ = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "blocks" in name:
lowerCamelCase_ = name.replace("blocks" , "encoder.layer" )
if "attn.proj" in name:
lowerCamelCase_ = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
lowerCamelCase_ = name.replace("attn" , "attention.self" )
if "norm1" in name:
lowerCamelCase_ = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
lowerCamelCase_ = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
lowerCamelCase_ = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
lowerCamelCase_ = name.replace("mlp.fc2" , "output.dense" )
if "class_embed" in name:
lowerCamelCase_ = name.replace("class_embed" , "class_labels_classifier" )
if "bbox_embed" in name:
lowerCamelCase_ = name.replace("bbox_embed" , "bbox_predictor" )
if "vit.norm" in name:
lowerCamelCase_ = name.replace("vit.norm" , "vit.layernorm" )
return name
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
for key in orig_state_dict.copy().keys():
lowerCamelCase_ = orig_state_dict.pop(lowerCamelCase__ )
if "qkv" in key:
lowerCamelCase_ = key.split("." )
lowerCamelCase_ = int(key_split[2] )
lowerCamelCase_ = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
if "weight" in key:
lowerCamelCase_ = val[:dim, :]
lowerCamelCase_ = val[
dim : dim * 2, :
]
lowerCamelCase_ = val[-dim:, :]
else:
lowerCamelCase_ = val[:dim]
lowerCamelCase_ = val[dim : dim * 2]
lowerCamelCase_ = val[-dim:]
else:
lowerCamelCase_ = val
return orig_state_dict
def lowerCamelCase_ ( ):
lowerCamelCase_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
lowerCamelCase_ = Image.open(requests.get(lowerCamelCase__ , stream=lowerCamelCase__ ).raw )
return im
@torch.no_grad()
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = False ):
lowerCamelCase_ = get_yolos_config(lowerCamelCase__ )
# load original state_dict
lowerCamelCase_ = torch.load(lowerCamelCase__ , map_location="cpu" )["model"]
# load 🤗 model
lowerCamelCase_ = YolosForObjectDetection(lowerCamelCase__ )
model.eval()
lowerCamelCase_ = convert_state_dict(lowerCamelCase__ , lowerCamelCase__ )
model.load_state_dict(lowerCamelCase__ )
# Check outputs on an image, prepared by YolosImageProcessor
lowerCamelCase_ = 8_0_0 if yolos_name != "yolos_ti" else 5_1_2
lowerCamelCase_ = YolosImageProcessor(format="coco_detection" , size=lowerCamelCase__ )
lowerCamelCase_ = image_processor(images=prepare_img() , return_tensors="pt" )
lowerCamelCase_ = model(**lowerCamelCase__ )
lowerCamelCase_ , lowerCamelCase_ = outputs.logits, outputs.pred_boxes
lowerCamelCase_ , lowerCamelCase_ = None, None
if yolos_name == "yolos_ti":
lowerCamelCase_ = torch.tensor(
[[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]] )
lowerCamelCase_ = torch.tensor(
[[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]] )
elif yolos_name == "yolos_s_200_pre":
lowerCamelCase_ = torch.tensor(
[[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]] )
lowerCamelCase_ = torch.tensor(
[[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]] )
elif yolos_name == "yolos_s_300_pre":
lowerCamelCase_ = torch.tensor(
[[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]] )
lowerCamelCase_ = torch.tensor(
[[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]] )
elif yolos_name == "yolos_s_dWr":
lowerCamelCase_ = torch.tensor(
[[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]] )
lowerCamelCase_ = torch.tensor(
[[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]] )
elif yolos_name == "yolos_base":
lowerCamelCase_ = torch.tensor(
[[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]] )
lowerCamelCase_ = torch.tensor(
[[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]] )
else:
raise ValueError(F'Unknown yolos_name: {yolos_name}' )
assert torch.allclose(logits[0, :3, :3] , lowerCamelCase__ , atol=1e-4 )
assert torch.allclose(pred_boxes[0, :3, :3] , lowerCamelCase__ , atol=1e-4 )
Path(lowerCamelCase__ ).mkdir(exist_ok=lowerCamelCase__ )
print(F'Saving model {yolos_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(lowerCamelCase__ )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(lowerCamelCase__ )
if push_to_hub:
lowerCamelCase_ = {
"yolos_ti": "yolos-tiny",
"yolos_s_200_pre": "yolos-small",
"yolos_s_300_pre": "yolos-small-300",
"yolos_s_dWr": "yolos-small-dwr",
"yolos_base": "yolos-base",
}
print("Pushing to the hub..." )
lowerCamelCase_ = model_mapping[yolos_name]
image_processor.push_to_hub(lowerCamelCase__ , organization="hustvl" )
model.push_to_hub(lowerCamelCase__ , organization="hustvl" )
if __name__ == "__main__":
__A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--yolos_name''',
default='''yolos_s_200_pre''',
type=str,
help=(
'''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
),
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
__A =parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 47 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class a__ ( unittest.TestCase ):
"""simple docstring"""
def _snake_case (self ):
__lowerCAmelCase = tempfile.mkdtemp()
# fmt: off
__lowerCAmelCase = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
__lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
__lowerCAmelCase = {
'''do_resize''': True,
'''size''': {'''height''': 18, '''width''': 18},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
__lowerCAmelCase = os.path.join(self.tmpdirname , __lowercase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(__lowercase , __lowercase )
def _snake_case (self , **__lowercase ):
return BertTokenizer.from_pretrained(self.tmpdirname , **__lowercase )
def _snake_case (self , **__lowercase ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__lowercase )
def _snake_case (self ):
shutil.rmtree(self.tmpdirname )
def _snake_case (self ):
__lowerCAmelCase = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
__lowerCAmelCase = [Image.fromarray(np.moveaxis(__lowercase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _snake_case (self ):
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = self.get_image_processor()
__lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=__lowercase , image_processor=__lowercase )
processor.save_pretrained(self.tmpdirname )
__lowerCAmelCase = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowercase )
def _snake_case (self ):
__lowerCAmelCase = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__lowerCAmelCase = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__lowerCAmelCase = self.get_image_processor(do_normalize=__lowercase , padding_value=1.0 )
__lowerCAmelCase = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__lowercase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowercase )
def _snake_case (self ):
__lowerCAmelCase = self.get_image_processor()
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=__lowercase , image_processor=__lowercase )
__lowerCAmelCase = self.prepare_image_inputs()
__lowerCAmelCase = image_processor(__lowercase , return_tensors='''np''' )
__lowerCAmelCase = processor(images=__lowercase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _snake_case (self ):
__lowerCAmelCase = self.get_image_processor()
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=__lowercase , image_processor=__lowercase )
__lowerCAmelCase = '''lower newer'''
__lowerCAmelCase = processor(text=__lowercase )
__lowerCAmelCase = tokenizer(__lowercase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _snake_case (self ):
__lowerCAmelCase = self.get_image_processor()
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=__lowercase , image_processor=__lowercase )
__lowerCAmelCase = '''lower newer'''
__lowerCAmelCase = self.prepare_image_inputs()
__lowerCAmelCase = processor(text=__lowercase , images=__lowercase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(__lowercase ):
processor()
def _snake_case (self ):
__lowerCAmelCase = self.get_image_processor()
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=__lowercase , image_processor=__lowercase )
__lowerCAmelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__lowerCAmelCase = processor.batch_decode(__lowercase )
__lowerCAmelCase = tokenizer.batch_decode(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
def _snake_case (self ):
__lowerCAmelCase = self.get_image_processor()
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=__lowercase , image_processor=__lowercase )
__lowerCAmelCase = '''lower newer'''
__lowerCAmelCase = self.prepare_image_inputs()
__lowerCAmelCase = processor(text=__lowercase , images=__lowercase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 174 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_UpperCAmelCase : Any = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
"""processing_trocr""": ["""TrOCRProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Dict = [
"""TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TrOCRForCausalLM""",
"""TrOCRPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
_UpperCAmelCase : Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 174 | 1 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader
from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
ROUGE_KEYS,
LegacySeqaSeqDataset,
SeqaSeqDataset,
assert_all_frozen,
calculate_bleu,
calculate_rouge,
check_output_dir,
flatten_list,
freeze_embeds,
freeze_params,
get_git_info,
label_smoothed_nll_loss,
lmap,
pickle_save,
save_git_info,
save_json,
use_task_specific_params,
)
# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
__snake_case = logging.getLogger(__name__)
class lowercase__ ( _UpperCAmelCase ):
A__ : Tuple ="""summarization"""
A__ : Optional[int] =["""loss"""]
A__ : Optional[Any] =ROUGE_KEYS
A__ : str ="""rouge2"""
def __init__( self : List[str] , UpperCAmelCase_ : str , **UpperCAmelCase_ : Optional[Any] ):
if hparams.sortish_sampler and hparams.gpus > 1:
SCREAMING_SNAKE_CASE__ = False
elif hparams.max_tokens_per_batch is not None:
if hparams.gpus > 1:
raise NotImplementedError('Dynamic Batch size does not work for multi-gpu training' )
if hparams.sortish_sampler:
raise ValueError('--sortish_sampler and --max_tokens_per_batch may not be used simultaneously' )
super().__init__(UpperCAmelCase_ , num_labels=UpperCAmelCase_ , mode=self.mode , **UpperCAmelCase_ )
use_task_specific_params(self.model , 'summarization' )
save_git_info(self.hparams.output_dir )
SCREAMING_SNAKE_CASE__ = Path(self.output_dir ) / 'metrics.json'
SCREAMING_SNAKE_CASE__ = Path(self.output_dir ) / 'hparams.pkl'
pickle_save(self.hparams , self.hparams_save_path )
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = defaultdict(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = self.config.model_type
SCREAMING_SNAKE_CASE__ = self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size
SCREAMING_SNAKE_CASE__ = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
SCREAMING_SNAKE_CASE__ = {
'train': self.hparams.n_train,
'val': self.hparams.n_val,
'test': self.hparams.n_test,
}
SCREAMING_SNAKE_CASE__ = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
SCREAMING_SNAKE_CASE__ = {
'train': self.hparams.max_target_length,
'val': self.hparams.val_max_target_length,
'test': self.hparams.test_max_target_length,
}
assert self.target_lens["train"] <= self.target_lens["val"], F'target_lens: {self.target_lens}'
assert self.target_lens["train"] <= self.target_lens["test"], F'target_lens: {self.target_lens}'
if self.hparams.freeze_embeds:
freeze_embeds(self.model )
if self.hparams.freeze_encoder:
freeze_params(self.model.get_encoder() )
assert_all_frozen(self.model.get_encoder() )
SCREAMING_SNAKE_CASE__ = get_git_info()['repo_sha']
SCREAMING_SNAKE_CASE__ = hparams.num_workers
SCREAMING_SNAKE_CASE__ = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE__ = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
SCREAMING_SNAKE_CASE__ = self.decoder_start_token_id
SCREAMING_SNAKE_CASE__ = (
SeqaSeqDataset if hasattr(self.tokenizer , 'prepare_seq2seq_batch' ) else LegacySeqaSeqDataset
)
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
if self.hparams.eval_max_gen_length is not None:
SCREAMING_SNAKE_CASE__ = self.hparams.eval_max_gen_length
else:
SCREAMING_SNAKE_CASE__ = self.model.config.max_length
SCREAMING_SNAKE_CASE__ = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def A_ ( self : List[str] , UpperCAmelCase_ : Dict[str, torch.Tensor] ):
SCREAMING_SNAKE_CASE__ = {
k: self.tokenizer.batch_decode(v.tolist() ) if 'mask' not in k else v.shape for k, v in batch.items()
}
save_json(UpperCAmelCase_ , Path(self.output_dir ) / 'text_batch.json' )
save_json({k: v.tolist() for k, v in batch.items()} , Path(self.output_dir ) / 'tok_batch.json' )
SCREAMING_SNAKE_CASE__ = True
return readable_batch
def A_ ( self : List[str] , UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : str ):
return self.model(UpperCAmelCase_ , **UpperCAmelCase_ )
def A_ ( self : Dict , UpperCAmelCase_ : List[int] ):
SCREAMING_SNAKE_CASE__ = self.tokenizer.batch_decode(
UpperCAmelCase_ , skip_special_tokens=UpperCAmelCase_ , clean_up_tokenization_spaces=UpperCAmelCase_ )
return lmap(str.strip , UpperCAmelCase_ )
def A_ ( self : List[Any] , UpperCAmelCase_ : dict ):
SCREAMING_SNAKE_CASE__ = self.tokenizer.pad_token_id
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = batch['input_ids'], batch['attention_mask']
SCREAMING_SNAKE_CASE__ = batch['labels']
if isinstance(self.model , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE__ = self.model._shift_right(UpperCAmelCase_ )
else:
SCREAMING_SNAKE_CASE__ = shift_tokens_right(UpperCAmelCase_ , UpperCAmelCase_ )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
SCREAMING_SNAKE_CASE__ = decoder_input_ids
self.save_readable_batch(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = self(UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , decoder_input_ids=UpperCAmelCase_ , use_cache=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = outputs['logits']
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
SCREAMING_SNAKE_CASE__ = nn.CrossEntropyLoss(ignore_index=UpperCAmelCase_ )
assert lm_logits.shape[-1] == self.vocab_size
SCREAMING_SNAKE_CASE__ = ce_loss_fct(lm_logits.view(-1 , lm_logits.shape[-1] ) , tgt_ids.view(-1 ) )
else:
SCREAMING_SNAKE_CASE__ = nn.functional.log_softmax(UpperCAmelCase_ , dim=-1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = label_smoothed_nll_loss(
UpperCAmelCase_ , UpperCAmelCase_ , self.hparams.label_smoothing , ignore_index=UpperCAmelCase_ )
return (loss,)
@property
def A_ ( self : Dict ):
return self.tokenizer.pad_token_id
def A_ ( self : List[str] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : List[str] ):
SCREAMING_SNAKE_CASE__ = self._step(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = dict(zip(self.loss_names , UpperCAmelCase_ ) )
# tokens per batch
SCREAMING_SNAKE_CASE__ = batch['input_ids'].ne(self.pad ).sum() + batch['labels'].ne(self.pad ).sum()
SCREAMING_SNAKE_CASE__ = batch['input_ids'].shape[0]
SCREAMING_SNAKE_CASE__ = batch['input_ids'].eq(self.pad ).sum()
SCREAMING_SNAKE_CASE__ = batch['input_ids'].eq(self.pad ).float().mean()
# TODO(SS): make a wandb summary metric for this
return {"loss": loss_tensors[0], "log": logs}
def A_ ( self : Tuple , UpperCAmelCase_ : int , UpperCAmelCase_ : Tuple ):
return self._generative_step(UpperCAmelCase_ )
def A_ ( self : Optional[Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Tuple="val" ):
self.step_count += 1
SCREAMING_SNAKE_CASE__ = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
SCREAMING_SNAKE_CASE__ = losses['loss']
SCREAMING_SNAKE_CASE__ = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ['gen_time', 'gen_len']
}
SCREAMING_SNAKE_CASE__ = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
SCREAMING_SNAKE_CASE__ = torch.tensor(UpperCAmelCase_ ).type_as(UpperCAmelCase_ )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = {F'{prefix}_avg_{k}': x for k, x in losses.items()}
SCREAMING_SNAKE_CASE__ = self.step_count
self.metrics[prefix].append(UpperCAmelCase_ ) # callback writes this to self.metrics_save_path
SCREAMING_SNAKE_CASE__ = flatten_list([x['preds'] for x in outputs] )
return {
"log": all_metrics,
"preds": preds,
F'{prefix}_loss': loss,
F'{prefix}_{self.val_metric}': metric_tensor,
}
def A_ ( self : List[str] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Dict ):
return calculate_rouge(UpperCAmelCase_ , UpperCAmelCase_ )
def A_ ( self : str , UpperCAmelCase_ : dict ):
SCREAMING_SNAKE_CASE__ = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
SCREAMING_SNAKE_CASE__ = self.model.generate(
batch['input_ids'] , attention_mask=batch['attention_mask'] , use_cache=UpperCAmelCase_ , decoder_start_token_id=self.decoder_start_token_id , num_beams=self.eval_beams , max_length=self.eval_max_length , )
SCREAMING_SNAKE_CASE__ = (time.time() - ta) / batch['input_ids'].shape[0]
SCREAMING_SNAKE_CASE__ = self.ids_to_clean_text(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = self.ids_to_clean_text(batch['labels'] )
SCREAMING_SNAKE_CASE__ = self._step(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = dict(zip(self.loss_names , UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE__ = self.calc_generative_metrics(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = np.mean(lmap(UpperCAmelCase_ , UpperCAmelCase_ ) )
base_metrics.update(gen_time=UpperCAmelCase_ , gen_len=UpperCAmelCase_ , preds=UpperCAmelCase_ , target=UpperCAmelCase_ , **UpperCAmelCase_ )
return base_metrics
def A_ ( self : List[Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : int ):
return self._generative_step(UpperCAmelCase_ )
def A_ ( self : Any , UpperCAmelCase_ : List[str] ):
return self.validation_epoch_end(UpperCAmelCase_ , prefix='test' )
def A_ ( self : Optional[int] , UpperCAmelCase_ : List[Any] ):
SCREAMING_SNAKE_CASE__ = self.n_obs[type_path]
SCREAMING_SNAKE_CASE__ = self.target_lens[type_path]
SCREAMING_SNAKE_CASE__ = self.dataset_class(
self.tokenizer , type_path=UpperCAmelCase_ , n_obs=UpperCAmelCase_ , max_target_length=UpperCAmelCase_ , **self.dataset_kwargs , )
return dataset
def A_ ( self : Optional[Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : int , UpperCAmelCase_ : bool = False ):
SCREAMING_SNAKE_CASE__ = self.get_dataset(UpperCAmelCase_ )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
SCREAMING_SNAKE_CASE__ = dataset.make_sortish_sampler(UpperCAmelCase_ , distributed=self.hparams.gpus > 1 )
return DataLoader(
UpperCAmelCase_ , batch_size=UpperCAmelCase_ , collate_fn=dataset.collate_fn , shuffle=UpperCAmelCase_ , num_workers=self.num_workers , sampler=UpperCAmelCase_ , )
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
SCREAMING_SNAKE_CASE__ = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch , distributed=self.hparams.gpus > 1 )
return DataLoader(
UpperCAmelCase_ , batch_sampler=UpperCAmelCase_ , collate_fn=dataset.collate_fn , num_workers=self.num_workers , )
else:
return DataLoader(
UpperCAmelCase_ , batch_size=UpperCAmelCase_ , collate_fn=dataset.collate_fn , shuffle=UpperCAmelCase_ , num_workers=self.num_workers , sampler=UpperCAmelCase_ , )
def A_ ( self : Any ):
SCREAMING_SNAKE_CASE__ = self.get_dataloader('train' , batch_size=self.hparams.train_batch_size , shuffle=UpperCAmelCase_ )
return dataloader
def A_ ( self : str ):
return self.get_dataloader('val' , batch_size=self.hparams.eval_batch_size )
def A_ ( self : int ):
return self.get_dataloader('test' , batch_size=self.hparams.eval_batch_size )
@staticmethod
def A_ ( UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Any ):
BaseTransformer.add_model_specific_args(UpperCAmelCase_ , UpperCAmelCase_ )
add_generic_args(UpperCAmelCase_ , UpperCAmelCase_ )
parser.add_argument(
'--max_source_length' , default=1024 , type=UpperCAmelCase_ , help=(
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
) , )
parser.add_argument(
'--max_target_length' , default=56 , type=UpperCAmelCase_ , help=(
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
) , )
parser.add_argument(
'--val_max_target_length' , default=142 , type=UpperCAmelCase_ , help=(
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
) , )
parser.add_argument(
'--test_max_target_length' , default=142 , type=UpperCAmelCase_ , help=(
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
) , )
parser.add_argument('--freeze_encoder' , action='store_true' )
parser.add_argument('--freeze_embeds' , action='store_true' )
parser.add_argument('--sortish_sampler' , action='store_true' , default=UpperCAmelCase_ )
parser.add_argument('--overwrite_output_dir' , action='store_true' , default=UpperCAmelCase_ )
parser.add_argument('--max_tokens_per_batch' , type=UpperCAmelCase_ , default=UpperCAmelCase_ )
parser.add_argument('--logger_name' , type=UpperCAmelCase_ , choices=['default', 'wandb', 'wandb_shared'] , default='default' )
parser.add_argument('--n_train' , type=UpperCAmelCase_ , default=-1 , required=UpperCAmelCase_ , help='# examples. -1 means use all.' )
parser.add_argument('--n_val' , type=UpperCAmelCase_ , default=500 , required=UpperCAmelCase_ , help='# examples. -1 means use all.' )
parser.add_argument('--n_test' , type=UpperCAmelCase_ , default=-1 , required=UpperCAmelCase_ , help='# examples. -1 means use all.' )
parser.add_argument(
'--task' , type=UpperCAmelCase_ , default='summarization' , required=UpperCAmelCase_ , help='# examples. -1 means use all.' )
parser.add_argument('--label_smoothing' , type=UpperCAmelCase_ , default=0.0 , required=UpperCAmelCase_ )
parser.add_argument('--src_lang' , type=UpperCAmelCase_ , default='' , required=UpperCAmelCase_ )
parser.add_argument('--tgt_lang' , type=UpperCAmelCase_ , default='' , required=UpperCAmelCase_ )
parser.add_argument('--eval_beams' , type=UpperCAmelCase_ , default=UpperCAmelCase_ , required=UpperCAmelCase_ )
parser.add_argument(
'--val_metric' , type=UpperCAmelCase_ , default=UpperCAmelCase_ , required=UpperCAmelCase_ , choices=['bleu', 'rouge2', 'loss', None] )
parser.add_argument('--eval_max_gen_length' , type=UpperCAmelCase_ , default=UpperCAmelCase_ , help='never generate more than n tokens' )
parser.add_argument('--save_top_k' , type=UpperCAmelCase_ , default=1 , required=UpperCAmelCase_ , help='How many checkpoints to save' )
parser.add_argument(
'--early_stopping_patience' , type=UpperCAmelCase_ , default=-1 , required=UpperCAmelCase_ , help=(
'-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'
' val_check_interval will effect it.'
) , )
return parser
class lowercase__ ( _UpperCAmelCase ):
A__ : Optional[Any] ="""translation"""
A__ : Dict =["""loss"""]
A__ : Optional[int] =["""bleu"""]
A__ : Union[str, Any] ="""bleu"""
def __init__( self : Tuple , UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Optional[int] ):
super().__init__(UpperCAmelCase_ , **UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = hparams.src_lang
SCREAMING_SNAKE_CASE__ = hparams.tgt_lang
def A_ ( self : List[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : Dict ):
return calculate_bleu(UpperCAmelCase_ , UpperCAmelCase_ )
def _lowercase ( UpperCamelCase_ , UpperCamelCase_=None ) -> SummarizationModule:
'''simple docstring'''
Path(args.output_dir ).mkdir(exist_ok=UpperCamelCase_ )
check_output_dir(UpperCamelCase_ , expected_items=3 )
if model is None:
if "summarization" in args.task:
SCREAMING_SNAKE_CASE__ = SummarizationModule(UpperCamelCase_ )
else:
SCREAMING_SNAKE_CASE__ = TranslationModule(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ = Path(args.data_dir ).name
if (
args.logger_name == "default"
or args.fast_dev_run
or str(args.output_dir ).startswith('/tmp' )
or str(args.output_dir ).startswith('/var' )
):
SCREAMING_SNAKE_CASE__ = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
SCREAMING_SNAKE_CASE__ = os.environ.get('WANDB_PROJECT' , UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ = WandbLogger(name=model.output_dir.name , project=UpperCamelCase_ )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
SCREAMING_SNAKE_CASE__ = WandbLogger(name=model.output_dir.name , project=F'hf_{dataset}' )
if args.early_stopping_patience >= 0:
SCREAMING_SNAKE_CASE__ = get_early_stopping_callback(model.val_metric , args.early_stopping_patience )
else:
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = args.val_metric == 'loss'
SCREAMING_SNAKE_CASE__ = generic_train(
UpperCamelCase_ , UpperCamelCase_ , logging_callback=SeqaSeqLoggingCallback() , checkpoint_callback=get_checkpoint_callback(
args.output_dir , model.val_metric , args.save_top_k , UpperCamelCase_ ) , early_stopping_callback=UpperCamelCase_ , logger=UpperCamelCase_ , )
pickle_save(model.hparams , model.output_dir / 'hparams.pkl' )
if not args.do_predict:
return model
SCREAMING_SNAKE_CASE__ = ''
SCREAMING_SNAKE_CASE__ = sorted(glob.glob(os.path.join(args.output_dir , '*.ckpt' ) , recursive=UpperCamelCase_ ) )
if checkpoints:
SCREAMING_SNAKE_CASE__ = checkpoints[-1]
SCREAMING_SNAKE_CASE__ = checkpoints[-1]
trainer.logger.log_hyperparams(model.hparams )
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
__snake_case = pl.Trainer.add_argparse_args(parser)
__snake_case = SummarizationModule.add_model_specific_args(parser, os.getcwd())
__snake_case = parser.parse_args()
main(args)
| 169 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class lowercase__ :
@staticmethod
def A_ ( *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : Optional[Any] ):
pass
@is_pipeline_test
@require_vision
class lowercase__ ( unittest.TestCase ):
@require_torch
def A_ ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE__ = pipeline(
model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification' , )
SCREAMING_SNAKE_CASE__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
SCREAMING_SNAKE_CASE__ = image_classifier(UpperCAmelCase_ , candidate_labels=['a', 'b', 'c'] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(UpperCAmelCase_ ) , [
[{'score': 0.333, 'label': 'a'}, {'score': 0.333, 'label': 'b'}, {'score': 0.333, 'label': 'c'}],
[{'score': 0.333, 'label': 'a'}, {'score': 0.333, 'label': 'c'}, {'score': 0.333, 'label': 'b'}],
] , )
SCREAMING_SNAKE_CASE__ = image_classifier([image] * 5 , candidate_labels=['A', 'B', 'C'] , batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ ) , [
[
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
],
[
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
],
[
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
],
[
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
],
[
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
],
] , )
@require_tf
def A_ ( self : List[Any] ):
SCREAMING_SNAKE_CASE__ = pipeline(
model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification' , framework='tf' )
SCREAMING_SNAKE_CASE__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
SCREAMING_SNAKE_CASE__ = image_classifier(UpperCAmelCase_ , candidate_labels=['a', 'b', 'c'] )
self.assertEqual(
nested_simplify(UpperCAmelCase_ ) , [{'score': 0.333, 'label': 'a'}, {'score': 0.333, 'label': 'b'}, {'score': 0.333, 'label': 'c'}] , )
SCREAMING_SNAKE_CASE__ = image_classifier([image] * 5 , candidate_labels=['A', 'B', 'C'] , batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ ) , [
[
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
],
[
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
],
[
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
],
[
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
],
[
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
{'score': 0.333, 'label': ANY(UpperCAmelCase_ )},
],
] , )
@slow
@require_torch
def A_ ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE__ = pipeline(
task='zero-shot-image-classification' , model='openai/clip-vit-base-patch32' , )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
SCREAMING_SNAKE_CASE__ = image_classifier(UpperCAmelCase_ , candidate_labels=['cat', 'plane', 'remote'] )
self.assertEqual(
nested_simplify(UpperCAmelCase_ ) , [
{'score': 0.511, 'label': 'remote'},
{'score': 0.485, 'label': 'cat'},
{'score': 0.004, 'label': 'plane'},
] , )
SCREAMING_SNAKE_CASE__ = image_classifier([image] * 5 , candidate_labels=['cat', 'plane', 'remote'] , batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ ) , [
[
{'score': 0.511, 'label': 'remote'},
{'score': 0.485, 'label': 'cat'},
{'score': 0.004, 'label': 'plane'},
],
]
* 5 , )
@slow
@require_tf
def A_ ( self : Tuple ):
SCREAMING_SNAKE_CASE__ = pipeline(
task='zero-shot-image-classification' , model='openai/clip-vit-base-patch32' , framework='tf' )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
SCREAMING_SNAKE_CASE__ = image_classifier(UpperCAmelCase_ , candidate_labels=['cat', 'plane', 'remote'] )
self.assertEqual(
nested_simplify(UpperCAmelCase_ ) , [
{'score': 0.511, 'label': 'remote'},
{'score': 0.485, 'label': 'cat'},
{'score': 0.004, 'label': 'plane'},
] , )
SCREAMING_SNAKE_CASE__ = image_classifier([image] * 5 , candidate_labels=['cat', 'plane', 'remote'] , batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ ) , [
[
{'score': 0.511, 'label': 'remote'},
{'score': 0.485, 'label': 'cat'},
{'score': 0.004, 'label': 'plane'},
],
]
* 5 , )
| 169 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case_ = LongformerTokenizer
snake_case_ = True
snake_case_ = LongformerTokenizerFast
snake_case_ = True
def UpperCamelCase_ ( self : Any ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__A = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
__A = dict(zip(A ,range(len(A ) ) ) )
__A = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
__A = {"unk_token": "<unk>"}
__A = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] )
__A = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp:
fp.write(json.dumps(A ) + "\n" )
with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp:
fp.write("\n".join(A ) )
def UpperCamelCase_ ( self : Tuple ,**A : Dict ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname ,**A )
def UpperCamelCase_ ( self : int ,**A : Optional[Any] ):
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,**A )
def UpperCamelCase_ ( self : Any ,A : Tuple ):
__A = "lower newer"
__A = "lower newer"
return input_text, output_text
def UpperCamelCase_ ( self : Dict ):
__A = self.tokenizer_class(self.vocab_file ,self.merges_file ,**self.special_tokens_map )
__A = "lower newer"
__A = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
__A = tokenizer.tokenize(A ) # , add_prefix_space=True)
self.assertListEqual(A ,A )
__A = tokens + [tokenizer.unk_token]
__A = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) ,A )
def UpperCamelCase_ ( self : Optional[Any] ):
__A = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("Hello world!" ,add_special_tokens=A ) ,[0, 3_14_14, 2_32, 3_28, 2] )
self.assertListEqual(
tokenizer.encode("Hello world! cécé herlolip 418" ,add_special_tokens=A ) ,[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2] ,)
@slow
def UpperCamelCase_ ( self : Union[str, Any] ):
__A = self.tokenizer_class.from_pretrained("allenai/longformer-base-4096" )
__A = tokenizer.encode("sequence builders" ,add_special_tokens=A )
__A = tokenizer.encode("multi-sequence build" ,add_special_tokens=A )
__A = tokenizer.encode(
"sequence builders" ,add_special_tokens=A ,add_prefix_space=A )
__A = tokenizer.encode(
"sequence builders" ,"multi-sequence build" ,add_special_tokens=A ,add_prefix_space=A )
__A = tokenizer.build_inputs_with_special_tokens(A )
__A = tokenizer.build_inputs_with_special_tokens(A ,A )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def UpperCamelCase_ ( self : Optional[Any] ):
__A = self.get_tokenizer()
__A = "Encode this sequence."
__A = tokenizer.byte_encoder[" ".encode("utf-8" )[0]]
# Testing encoder arguments
__A = tokenizer.encode(A ,add_special_tokens=A ,add_prefix_space=A )
__A = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(A ,A )
__A = tokenizer.encode(A ,add_special_tokens=A ,add_prefix_space=A )
__A = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(A ,A )
tokenizer.add_special_tokens({"bos_token": "<s>"} )
__A = tokenizer.encode(A ,add_special_tokens=A )
__A = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(A ,A )
# Testing spaces after special tokens
__A = "<mask>"
tokenizer.add_special_tokens(
{"mask_token": AddedToken(A ,lstrip=A ,rstrip=A )} ) # mask token has a left space
__A = tokenizer.convert_tokens_to_ids(A )
__A = "Encode <mask> sequence"
__A = "Encode <mask>sequence"
__A = tokenizer.encode(A )
__A = encoded.index(A )
__A = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(A ,A )
__A = tokenizer.encode(A )
__A = encoded.index(A )
__A = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(A ,A )
def UpperCamelCase_ ( self : int ):
pass
def UpperCamelCase_ ( self : str ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__A = self.rust_tokenizer_class.from_pretrained(A ,**A )
__A = self.tokenizer_class.from_pretrained(A ,**A )
__A = "A, <mask> AllenNLP sentence."
__A = tokenizer_r.encode_plus(A ,add_special_tokens=A ,return_token_type_ids=A )
__A = tokenizer_p.encode_plus(A ,add_special_tokens=A ,return_token_type_ids=A )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"] ) ,sum(tokens_p["token_type_ids"] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) ,sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) ,)
__A = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
__A = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
A ,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
A ,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
def UpperCamelCase_ ( self : int ):
for trim_offsets, add_prefix_space in itertools.product([True, False] ,repeat=2 ):
__A = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname ,use_fast=A ,add_prefix_space=A ,trim_offsets=A )
__A = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
__A = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["add_prefix_space"] ,A )
self.assertEqual(post_processor_state["add_prefix_space"] ,A )
self.assertEqual(post_processor_state["trim_offsets"] ,A )
def UpperCamelCase_ ( self : Any ):
# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
# `trim_offsets`
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__A = "hello" # `hello` is a token in the vocabulary of `pretrained_name`
__A = f'''{text_of_1_token} {text_of_1_token}'''
__A = self.rust_tokenizer_class.from_pretrained(
A ,use_fast=A ,add_prefix_space=A ,trim_offsets=A )
__A = tokenizer_r(A ,return_offsets_mapping=A ,add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] ,(0, len(A )) )
self.assertEqual(
encoding.offset_mapping[1] ,(len(A ) + 1, len(A ) + 1 + len(A )) ,)
__A = self.rust_tokenizer_class.from_pretrained(
A ,use_fast=A ,add_prefix_space=A ,trim_offsets=A )
__A = tokenizer_r(A ,return_offsets_mapping=A ,add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] ,(0, len(A )) )
self.assertEqual(
encoding.offset_mapping[1] ,(len(A ) + 1, len(A ) + 1 + len(A )) ,)
__A = self.rust_tokenizer_class.from_pretrained(
A ,use_fast=A ,add_prefix_space=A ,trim_offsets=A )
__A = tokenizer_r(A ,return_offsets_mapping=A ,add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] ,(0, len(A )) )
self.assertEqual(
encoding.offset_mapping[1] ,(len(A ), len(A ) + 1 + len(A )) ,)
__A = self.rust_tokenizer_class.from_pretrained(
A ,use_fast=A ,add_prefix_space=A ,trim_offsets=A )
__A = tokenizer_r(A ,return_offsets_mapping=A ,add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] ,(0, len(A )) )
self.assertEqual(
encoding.offset_mapping[1] ,(len(A ), len(A ) + 1 + len(A )) ,)
__A = f''' {text}'''
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
__A = self.rust_tokenizer_class.from_pretrained(
A ,use_fast=A ,add_prefix_space=A ,trim_offsets=A )
__A = tokenizer_r(A ,return_offsets_mapping=A ,add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] ,(1, 1 + len(A )) )
self.assertEqual(
encoding.offset_mapping[1] ,(1 + len(A ) + 1, 1 + len(A ) + 1 + len(A )) ,)
__A = self.rust_tokenizer_class.from_pretrained(
A ,use_fast=A ,add_prefix_space=A ,trim_offsets=A )
__A = tokenizer_r(A ,return_offsets_mapping=A ,add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] ,(0, 1 + len(A )) )
self.assertEqual(
encoding.offset_mapping[1] ,(1 + len(A ), 1 + len(A ) + 1 + len(A )) ,)
__A = self.rust_tokenizer_class.from_pretrained(
A ,use_fast=A ,add_prefix_space=A ,trim_offsets=A )
__A = tokenizer_r(A ,return_offsets_mapping=A ,add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] ,(0, 1 + len(A )) )
self.assertEqual(
encoding.offset_mapping[1] ,(1 + len(A ), 1 + len(A ) + 1 + len(A )) ,)
| 15 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
snake_case_ : Any = "0.12" # assumed parallelism: 8
@require_flax
@is_staging_test
class __a (unittest.TestCase ):
@classmethod
def UpperCAmelCase__ ( cls : Tuple ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = TOKEN
HfFolder.save_token(__magic_name__ )
@classmethod
def UpperCAmelCase__ ( cls : List[Any] ) -> Optional[Any]:
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id='''test-model-flax''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-model-flax-org''' )
except HTTPError:
pass
def UpperCAmelCase__ ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
UpperCAmelCase_ : Optional[Any] = FlaxBertModel(__magic_name__ )
model.push_to_hub('''test-model-flax''' , use_auth_token=self._token )
UpperCAmelCase_ : Tuple = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" )
UpperCAmelCase_ : Tuple = flatten_dict(unfreeze(model.params ) )
UpperCAmelCase_ : List[Any] = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
UpperCAmelCase_ : Tuple = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1E-3 , msg=F"""{key} not identical""" )
# Reset repo
delete_repo(token=self._token , repo_id='''test-model-flax''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(__magic_name__ , repo_id='''test-model-flax''' , push_to_hub=__magic_name__ , use_auth_token=self._token )
UpperCAmelCase_ : str = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" )
UpperCAmelCase_ : str = flatten_dict(unfreeze(model.params ) )
UpperCAmelCase_ : List[str] = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
UpperCAmelCase_ : List[Any] = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1E-3 , msg=F"""{key} not identical""" )
def UpperCAmelCase__ ( self : Dict ) -> Any:
"""simple docstring"""
UpperCAmelCase_ : str = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
UpperCAmelCase_ : Tuple = FlaxBertModel(__magic_name__ )
model.push_to_hub('''valid_org/test-model-flax-org''' , use_auth_token=self._token )
UpperCAmelCase_ : str = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' )
UpperCAmelCase_ : List[Any] = flatten_dict(unfreeze(model.params ) )
UpperCAmelCase_ : Dict = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
UpperCAmelCase_ : List[str] = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1E-3 , msg=F"""{key} not identical""" )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-model-flax-org''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
__magic_name__ , repo_id='''valid_org/test-model-flax-org''' , push_to_hub=__magic_name__ , use_auth_token=self._token )
UpperCAmelCase_ : Tuple = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' )
UpperCAmelCase_ : List[str] = flatten_dict(unfreeze(model.params ) )
UpperCAmelCase_ : List[str] = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
UpperCAmelCase_ : str = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1E-3 , msg=F"""{key} not identical""" )
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : Optional[Any], SCREAMING_SNAKE_CASE__ : Any ) -> List[Any]:
UpperCAmelCase_ : List[Any] = True
UpperCAmelCase_ : Union[str, Any] = flatten_dict(modela.params )
UpperCAmelCase_ : List[Any] = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1E-4:
UpperCAmelCase_ : List[str] = False
return models_are_equal
@require_flax
class __a (unittest.TestCase ):
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ : Dict = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
UpperCAmelCase_ : Optional[Any] = FlaxBertModel(__magic_name__ )
UpperCAmelCase_ : Optional[int] = '''bert'''
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(__magic_name__ , __magic_name__ ) )
with self.assertRaises(__magic_name__ ):
UpperCAmelCase_ : Optional[int] = FlaxBertModel.from_pretrained(__magic_name__ )
UpperCAmelCase_ : List[str] = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertTrue(check_models_equal(__magic_name__ , __magic_name__ ) )
def UpperCAmelCase__ ( self : List[Any] ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ : int = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
UpperCAmelCase_ : Union[str, Any] = FlaxBertModel(__magic_name__ )
UpperCAmelCase_ : Optional[int] = '''bert'''
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(__magic_name__ , __magic_name__ ) , max_shard_size='''10KB''' )
with self.assertRaises(__magic_name__ ):
UpperCAmelCase_ : Union[str, Any] = FlaxBertModel.from_pretrained(__magic_name__ )
UpperCAmelCase_ : Any = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertTrue(check_models_equal(__magic_name__ , __magic_name__ ) )
def UpperCAmelCase__ ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ : Tuple = '''bert'''
UpperCAmelCase_ : str = '''hf-internal-testing/tiny-random-bert-subfolder'''
with self.assertRaises(__magic_name__ ):
UpperCAmelCase_ : int = FlaxBertModel.from_pretrained(__magic_name__ )
UpperCAmelCase_ : List[Any] = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def UpperCAmelCase__ ( self : Any ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ : str = '''bert'''
UpperCAmelCase_ : str = '''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
with self.assertRaises(__magic_name__ ):
UpperCAmelCase_ : Optional[int] = FlaxBertModel.from_pretrained(__magic_name__ )
UpperCAmelCase_ : str = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertIsNotNone(__magic_name__ )
| 125 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __lowerCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
torch.manual_seed(0 )
__magic_name__ : Dict = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def __lowerCAmelCase ( self : int ) -> List[str]:
__magic_name__ : Optional[int] = self.dummy_uncond_unet
__magic_name__ : Optional[int] = PNDMScheduler()
__magic_name__ : Optional[int] = PNDMPipeline(unet=_A , scheduler=_A )
pndm.to(_A )
pndm.set_progress_bar_config(disable=_A )
__magic_name__ : Optional[int] = torch.manual_seed(0 )
__magic_name__ : Dict = pndm(generator=_A , num_inference_steps=20 , output_type='numpy' ).images
__magic_name__ : str = torch.manual_seed(0 )
__magic_name__ : Tuple = pndm(generator=_A , num_inference_steps=20 , output_type='numpy' , return_dict=_A )[0]
__magic_name__ : Dict = image[0, -3:, -3:, -1]
__magic_name__ : Tuple = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__magic_name__ : Any = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class _lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self : Any ) -> List[str]:
__magic_name__ : Optional[Any] = 'google/ddpm-cifar10-32'
__magic_name__ : List[str] = UNetaDModel.from_pretrained(_A )
__magic_name__ : Optional[Any] = PNDMScheduler()
__magic_name__ : int = PNDMPipeline(unet=_A , scheduler=_A )
pndm.to(_A )
pndm.set_progress_bar_config(disable=_A )
__magic_name__ : Optional[Any] = torch.manual_seed(0 )
__magic_name__ : Any = pndm(generator=_A , output_type='numpy' ).images
__magic_name__ : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__magic_name__ : Optional[int] = np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 275 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : str , lowerCAmelCase : Any , lowerCAmelCase : Any=False ):
"""simple docstring"""
if isinstance(lowerCAmelCase , lowerCAmelCase ) and isinstance(lowerCAmelCase , lowerCAmelCase ):
__magic_name__ : str = len(set_a.intersection(lowerCAmelCase ) )
if alternative_union:
__magic_name__ : List[str] = len(lowerCAmelCase ) + len(lowerCAmelCase )
else:
__magic_name__ : Any = len(set_a.union(lowerCAmelCase ) )
return intersection / union
if isinstance(lowerCAmelCase , (list, tuple) ) and isinstance(lowerCAmelCase , (list, tuple) ):
__magic_name__ : str = [element for element in set_a if element in set_b]
if alternative_union:
__magic_name__ : Dict = len(lowerCAmelCase ) + len(lowerCAmelCase )
return len(lowerCAmelCase ) / union
else:
__magic_name__ : Any = set_a + [element for element in set_b if element not in set_a]
return len(lowerCAmelCase ) / len(lowerCAmelCase )
return len(lowerCAmelCase ) / len(lowerCAmelCase )
return None
if __name__ == "__main__":
lowerCAmelCase :Dict = {'''a''', '''b''', '''c''', '''d''', '''e'''}
lowerCAmelCase :Tuple = {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
print(jaccard_similarity(set_a, set_b))
| 275 | 1 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig
__A =logging.get_logger(__name__)
# General docstring
__A ='''ResNetConfig'''
# Base docstring
__A ='''microsoft/resnet-50'''
__A =[1, 2_0_4_8, 7, 7]
# Image classification docstring
__A ='''microsoft/resnet-50'''
__A ='''tiger cat'''
__A =[
'''microsoft/resnet-50''',
# See all resnet models at https://huggingface.co/models?filter=resnet
]
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase , lowercase , lowercase = 3 , lowercase = 1 , lowercase = "relu" ) -> List[Any]:
super().__init__()
lowerCamelCase_ = nn.Convad(
lowercase , lowercase , kernel_size=lowercase , stride=lowercase , padding=kernel_size // 2 , bias=lowercase )
lowerCamelCase_ = nn.BatchNormad(lowercase )
lowerCamelCase_ = ACTaFN[activation] if activation is not None else nn.Identity()
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Tensor:
lowerCamelCase_ = self.convolution(lowercase )
lowerCamelCase_ = self.normalization(lowercase )
lowerCamelCase_ = self.activation(lowercase )
return hidden_state
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase ) -> List[str]:
super().__init__()
lowerCamelCase_ = ResNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=7 , stride=2 , activation=config.hidden_act )
lowerCamelCase_ = nn.MaxPoolad(kernel_size=3 , stride=2 , padding=1 )
lowerCamelCase_ = config.num_channels
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Tensor:
lowerCamelCase_ = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
lowerCamelCase_ = self.embedder(lowercase )
lowerCamelCase_ = self.pooler(lowercase )
return embedding
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase , lowercase , lowercase = 2 ) -> Dict:
super().__init__()
lowerCamelCase_ = nn.Convad(lowercase , lowercase , kernel_size=1 , stride=lowercase , bias=lowercase )
lowerCamelCase_ = nn.BatchNormad(lowercase )
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Tensor:
lowerCamelCase_ = self.convolution(lowercase )
lowerCamelCase_ = self.normalization(lowercase )
return hidden_state
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase , lowercase , lowercase = 1 , lowercase = "relu" ) -> List[Any]:
super().__init__()
lowerCamelCase_ = in_channels != out_channels or stride != 1
lowerCamelCase_ = (
ResNetShortCut(lowercase , lowercase , stride=lowercase ) if should_apply_shortcut else nn.Identity()
)
lowerCamelCase_ = nn.Sequential(
ResNetConvLayer(lowercase , lowercase , stride=lowercase ) , ResNetConvLayer(lowercase , lowercase , activation=lowercase ) , )
lowerCamelCase_ = ACTaFN[activation]
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Union[str, Any]:
lowerCamelCase_ = hidden_state
lowerCamelCase_ = self.layer(lowercase )
lowerCamelCase_ = self.shortcut(lowercase )
hidden_state += residual
lowerCamelCase_ = self.activation(lowercase )
return hidden_state
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase , lowercase , lowercase = 1 , lowercase = "relu" , lowercase = 4 ) -> List[Any]:
super().__init__()
lowerCamelCase_ = in_channels != out_channels or stride != 1
lowerCamelCase_ = out_channels // reduction
lowerCamelCase_ = (
ResNetShortCut(lowercase , lowercase , stride=lowercase ) if should_apply_shortcut else nn.Identity()
)
lowerCamelCase_ = nn.Sequential(
ResNetConvLayer(lowercase , lowercase , kernel_size=1 ) , ResNetConvLayer(lowercase , lowercase , stride=lowercase ) , ResNetConvLayer(lowercase , lowercase , kernel_size=1 , activation=lowercase ) , )
lowerCamelCase_ = ACTaFN[activation]
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Optional[Any]:
lowerCamelCase_ = hidden_state
lowerCamelCase_ = self.layer(lowercase )
lowerCamelCase_ = self.shortcut(lowercase )
hidden_state += residual
lowerCamelCase_ = self.activation(lowercase )
return hidden_state
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase , lowercase , lowercase , lowercase = 2 , lowercase = 2 , ) -> Union[str, Any]:
super().__init__()
lowerCamelCase_ = ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer
lowerCamelCase_ = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(lowercase , lowercase , stride=lowercase , activation=config.hidden_act ) , *[layer(lowercase , lowercase , activation=config.hidden_act ) for _ in range(depth - 1 )] , )
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Tensor:
lowerCamelCase_ = input
for layer in self.layers:
lowerCamelCase_ = layer(lowercase )
return hidden_state
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase ) -> Union[str, Any]:
super().__init__()
lowerCamelCase_ = nn.ModuleList([] )
# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
self.stages.append(
ResNetStage(
lowercase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
lowerCamelCase_ = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(lowercase , config.depths[1:] ):
self.stages.append(ResNetStage(lowercase , lowercase , lowercase , depth=lowercase ) )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = False , lowercase = True ) -> BaseModelOutputWithNoAttention:
lowerCamelCase_ = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowerCamelCase_ = hidden_states + (hidden_state,)
lowerCamelCase_ = stage_module(lowercase )
if output_hidden_states:
lowerCamelCase_ = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(
last_hidden_state=lowercase , hidden_states=lowercase , )
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
lowerCAmelCase__ = ResNetConfig
lowerCAmelCase__ = 'resnet'
lowerCAmelCase__ = 'pixel_values'
lowerCAmelCase__ = True
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Tuple:
if isinstance(lowercase , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="fan_out" , nonlinearity="relu" )
elif isinstance(lowercase , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase=False ) -> Tuple:
if isinstance(lowercase , lowercase ):
lowerCamelCase_ = value
__A =R'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
__A =R'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
'The bare ResNet model outputting raw features without any specific head on top.' , snake_case_ , )
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
def __init__( self , lowercase ) -> Union[str, Any]:
super().__init__(lowercase )
lowerCamelCase_ = config
lowerCamelCase_ = ResNetEmbeddings(lowercase )
lowerCamelCase_ = ResNetEncoder(lowercase )
lowerCamelCase_ = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowercase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=lowercase , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None , lowercase = None ) -> BaseModelOutputWithPoolingAndNoAttention:
lowerCamelCase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCamelCase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowerCamelCase_ = self.embedder(lowercase )
lowerCamelCase_ = self.encoder(
lowercase , output_hidden_states=lowercase , return_dict=lowercase )
lowerCamelCase_ = encoder_outputs[0]
lowerCamelCase_ = self.pooler(lowercase )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=lowercase , pooler_output=lowercase , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
'\n ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , snake_case_ , )
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
def __init__( self , lowercase ) -> str:
super().__init__(lowercase )
lowerCamelCase_ = config.num_labels
lowerCamelCase_ = ResNetModel(lowercase )
# classification head
lowerCamelCase_ = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowercase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=lowercase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def SCREAMING_SNAKE_CASE_( self , lowercase = None , lowercase = None , lowercase = None , lowercase = None , ) -> ImageClassifierOutputWithNoAttention:
lowerCamelCase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowerCamelCase_ = self.resnet(lowercase , output_hidden_states=lowercase , return_dict=lowercase )
lowerCamelCase_ = outputs.pooler_output if return_dict else outputs[1]
lowerCamelCase_ = self.classifier(lowercase )
lowerCamelCase_ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowerCamelCase_ = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowerCamelCase_ = "single_label_classification"
else:
lowerCamelCase_ = "multi_label_classification"
if self.config.problem_type == "regression":
lowerCamelCase_ = MSELoss()
if self.num_labels == 1:
lowerCamelCase_ = loss_fct(logits.squeeze() , labels.squeeze() )
else:
lowerCamelCase_ = loss_fct(lowercase , lowercase )
elif self.config.problem_type == "single_label_classification":
lowerCamelCase_ = CrossEntropyLoss()
lowerCamelCase_ = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowerCamelCase_ = BCEWithLogitsLoss()
lowerCamelCase_ = loss_fct(lowercase , lowercase )
if not return_dict:
lowerCamelCase_ = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=lowercase , logits=lowercase , hidden_states=outputs.hidden_states )
@add_start_docstrings(
'\n ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n ' , snake_case_ , )
class _SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ ):
def __init__( self , lowercase ) -> Optional[int]:
super().__init__(lowercase )
super()._init_backbone(lowercase )
lowerCamelCase_ = [config.embedding_size] + config.hidden_sizes
lowerCamelCase_ = ResNetEmbeddings(lowercase )
lowerCamelCase_ = ResNetEncoder(lowercase )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowercase )
@replace_return_docstrings(output_type=lowercase , config_class=_CONFIG_FOR_DOC )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None , lowercase = None ) -> BackboneOutput:
lowerCamelCase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowerCamelCase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCamelCase_ = self.embedder(lowercase )
lowerCamelCase_ = self.encoder(lowercase , output_hidden_states=lowercase , return_dict=lowercase )
lowerCamelCase_ = outputs.hidden_states
lowerCamelCase_ = ()
for idx, stage in enumerate(self.stage_names ):
if stage in self.out_features:
feature_maps += (hidden_states[idx],)
if not return_dict:
lowerCamelCase_ = (feature_maps,)
if output_hidden_states:
output += (outputs.hidden_states,)
return output
return BackboneOutput(
feature_maps=lowercase , hidden_states=outputs.hidden_states if output_hidden_states else None , attentions=lowercase , )
| 19 |
import math
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(lowerCamelCase__ )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:
return 1 # any number raised to 0 is 1
raise AssertionError("This should never happen" )
if __name__ == "__main__": # Main function
# Read two numbers from input and typecast them to int using map function.
# Here x is the base and y is the power.
__A ='''Enter the base and the power separated by a comma: '''
__A, __A =map(int, input(prompt).split(''','''))
__A, __A =map(int, input(prompt).split(''','''))
# We find the log of each number, using the function res(), which takes two
# arguments.
__A =res(xa, ya)
__A =res(xa, ya)
# We check for the largest number
if resa > resa:
print('''Largest number is''', xa, '''^''', ya)
elif resa > resa:
print('''Largest number is''', xa, '''^''', ya)
else:
print('''Both are equal''')
| 19 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> Tuple:
lowercase : str = len(lowerCAmelCase__ )
while cur > 1:
# Find the maximum number in arr
lowercase : Dict = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
lowercase : Tuple = arr[mi::-1] + arr[mi + 1 : len(lowerCAmelCase__ )]
# Reverse whole list
lowercase : Optional[int] = arr[cur - 1 :: -1] + arr[cur : len(lowerCAmelCase__ )]
cur -= 1
return arr
if __name__ == "__main__":
lowercase : Optional[int] = input("""Enter numbers separated by a comma:\n""").strip()
lowercase : str = [int(item) for item in user_input.split(""",""")]
print(pancake_sort(unsorted))
| 352 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> int:
assert (
isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and number_of_steps > 0
), f"number_of_steps needs to be positive integer, your input {number_of_steps}"
if number_of_steps == 1:
return 1
lowercase , lowercase : Tuple = 1, 1
for _ in range(number_of_steps - 1 ):
lowercase , lowercase : str = current + previous, current
return current
if __name__ == "__main__":
import doctest
doctest.testmod()
| 285 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : Any, SCREAMING_SNAKE_CASE__ : List[str] = None ) -> list[list[str]]:
UpperCAmelCase_ : Union[str, Any] = word_bank or []
# create a table
UpperCAmelCase_ : int = len(SCREAMING_SNAKE_CASE__ ) + 1
UpperCAmelCase_ : list[list[list[str]]] = []
for _ in range(SCREAMING_SNAKE_CASE__ ):
table.append([] )
# seed value
UpperCAmelCase_ : int = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(SCREAMING_SNAKE_CASE__ ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(SCREAMING_SNAKE_CASE__ )] == word:
UpperCAmelCase_ : list[list[str]] = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(SCREAMING_SNAKE_CASE__ )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(SCREAMING_SNAKE_CASE__ )]:
combination.reverse()
return table[len(SCREAMING_SNAKE_CASE__ )]
if __name__ == "__main__":
print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
print(
all_construct(
"hexagonosaurus",
["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
)
)
| 125 |
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor
if is_flax_available():
import jax
from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __init__( self : Tuple , A : str , A : List[str]=1_0_0 , A : List[str]=1_3 , A : Union[str, Any]=3_0 , A : Union[str, Any]=2 , A : List[Any]=3 , A : Any=True , A : Tuple=True , A : Tuple=3_2 , A : str=5 , A : Any=4 , A : List[str]=3_7 , A : Tuple="gelu" , A : Union[str, Any]=0.1 , A : Tuple=0.1 , A : Union[str, Any]=1_0 , A : List[str]=0.02 , A : Dict=3 , ) ->int:
lowerCamelCase__ : int = parent
lowerCamelCase__ : Tuple = vocab_size
lowerCamelCase__ : Dict = batch_size
lowerCamelCase__ : str = image_size
lowerCamelCase__ : Any = patch_size
lowerCamelCase__ : str = num_channels
lowerCamelCase__ : List[Any] = is_training
lowerCamelCase__ : Tuple = use_labels
lowerCamelCase__ : Dict = hidden_size
lowerCamelCase__ : Optional[int] = num_hidden_layers
lowerCamelCase__ : str = num_attention_heads
lowerCamelCase__ : Tuple = intermediate_size
lowerCamelCase__ : str = hidden_act
lowerCamelCase__ : str = hidden_dropout_prob
lowerCamelCase__ : Any = attention_probs_dropout_prob
lowerCamelCase__ : Tuple = type_sequence_label_size
lowerCamelCase__ : List[Any] = initializer_range
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
lowerCamelCase__ : List[Any] = (image_size // patch_size) ** 2
lowerCamelCase__ : Tuple = num_patches + 1
def __lowerCamelCase ( self : Optional[int] ) ->List[Any]:
lowerCamelCase__ : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase__ : List[str] = None
if self.use_labels:
lowerCamelCase__ : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase__ : Any = BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=A , initializer_range=self.initializer_range , )
return config, pixel_values, labels
def __lowerCamelCase ( self : List[Any] , A : str , A : List[Any] , A : Any ) ->Tuple:
lowerCamelCase__ : Union[str, Any] = FlaxBeitModel(config=A )
lowerCamelCase__ : int = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCamelCase ( self : Union[str, Any] , A : List[str] , A : Optional[int] , A : Dict ) ->Optional[int]:
lowerCamelCase__ : Dict = FlaxBeitForMaskedImageModeling(config=A )
lowerCamelCase__ : Optional[Any] = model(A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def __lowerCamelCase ( self : Union[str, Any] , A : Optional[Any] , A : Optional[int] , A : List[Any] ) ->Any:
lowerCamelCase__ : Tuple = self.type_sequence_label_size
lowerCamelCase__ : Tuple = FlaxBeitForImageClassification(config=A )
lowerCamelCase__ : Any = model(A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCamelCase__ : Union[str, Any] = 1
lowerCamelCase__ : Optional[int] = FlaxBeitForImageClassification(A )
lowerCamelCase__ : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCamelCase__ : List[str] = model(A )
def __lowerCamelCase ( self : Optional[Any] ) ->List[str]:
lowerCamelCase__ : List[Any] = self.prepare_config_and_inputs()
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) : str = config_and_inputs
lowerCamelCase__ : Optional[int] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_flax
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ,unittest.TestCase ):
_UpperCAmelCase : int = (
(FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
)
def __lowerCamelCase ( self : str ) ->None:
lowerCamelCase__ : Dict = FlaxBeitModelTester(self )
lowerCamelCase__ : Union[str, Any] = ConfigTester(self , config_class=A , has_text_modality=A , hidden_size=3_7 )
def __lowerCamelCase ( self : List[str] ) ->Any:
self.config_tester.run_common_tests()
def __lowerCamelCase ( self : str ) ->List[Any]:
lowerCamelCase__ , lowerCamelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__ : List[str] = model_class(A )
lowerCamelCase__ : int = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase__ : str = [*signature.parameters.keys()]
lowerCamelCase__ : Union[str, Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , A )
def __lowerCamelCase ( self : int ) ->List[Any]:
lowerCamelCase__ , lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowerCamelCase__ : Union[str, Any] = self._prepare_for_class(A , A )
lowerCamelCase__ : Optional[int] = model_class(A )
@jax.jit
def model_jitted(A : str , **A : Optional[int] ):
return model(pixel_values=A , **A )
with self.subTest('''JIT Enabled''' ):
lowerCamelCase__ : str = model_jitted(**A ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
lowerCamelCase__ : Dict = model_jitted(**A ).to_tuple()
self.assertEqual(len(A ) , len(A ) )
for jitted_output, output in zip(A , A ):
self.assertEqual(jitted_output.shape , output.shape )
def __lowerCamelCase ( self : Tuple ) ->Tuple:
lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def __lowerCamelCase ( self : Dict ) ->Any:
lowerCamelCase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A )
def __lowerCamelCase ( self : Any ) ->str:
lowerCamelCase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
@slow
def __lowerCamelCase ( self : Optional[int] ) ->Tuple:
for model_class_name in self.all_model_classes:
lowerCamelCase__ : List[str] = model_class_name.from_pretrained('''microsoft/beit-base-patch16-224''' )
lowerCamelCase__ : Union[str, Any] = model(np.ones((1, 3, 2_2_4, 2_2_4) ) )
self.assertIsNotNone(A )
def _a ( ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase__ : str = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@require_flax
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def __lowerCamelCase ( self : List[Any] ) ->Dict:
return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224''' ) if is_vision_available() else None
@slow
def __lowerCamelCase ( self : str ) ->str:
lowerCamelCase__ : List[str] = FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k''' )
lowerCamelCase__ : Optional[Any] = self.default_image_processor
lowerCamelCase__ : str = prepare_img()
lowerCamelCase__ : Optional[int] = image_processor(images=A , return_tensors='''np''' ).pixel_values
# prepare bool_masked_pos
lowerCamelCase__ : List[str] = np.ones((1, 1_9_6) , dtype=A )
# forward pass
lowerCamelCase__ : Optional[int] = model(pixel_values=A , bool_masked_pos=A )
lowerCamelCase__ : Optional[Any] = outputs.logits
# verify the logits
lowerCamelCase__ : str = (1, 1_9_6, 8_1_9_2)
self.assertEqual(logits.shape , A )
lowerCamelCase__ : Any = np.array(
[[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]] )
self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] , A , atol=1e-2 ) )
@slow
def __lowerCamelCase ( self : Dict ) ->List[Any]:
lowerCamelCase__ : Any = FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224''' )
lowerCamelCase__ : Dict = self.default_image_processor
lowerCamelCase__ : List[str] = prepare_img()
lowerCamelCase__ : int = image_processor(images=A , return_tensors='''np''' )
# forward pass
lowerCamelCase__ : List[str] = model(**A )
lowerCamelCase__ : Optional[int] = outputs.logits
# verify the logits
lowerCamelCase__ : Union[str, Any] = (1, 1_0_0_0)
self.assertEqual(logits.shape , A )
lowerCamelCase__ : Any = np.array([-1.23_85, -1.09_87, -1.01_08] )
self.assertTrue(np.allclose(logits[0, :3] , A , atol=1e-4 ) )
lowerCamelCase__ : Union[str, Any] = 2_8_1
self.assertEqual(logits.argmax(-1 ).item() , A )
@slow
def __lowerCamelCase ( self : int ) ->Tuple:
lowerCamelCase__ : List[Any] = FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k''' )
lowerCamelCase__ : Any = self.default_image_processor
lowerCamelCase__ : Union[str, Any] = prepare_img()
lowerCamelCase__ : Optional[Any] = image_processor(images=A , return_tensors='''np''' )
# forward pass
lowerCamelCase__ : Union[str, Any] = model(**A )
lowerCamelCase__ : Any = outputs.logits
# verify the logits
lowerCamelCase__ : List[str] = (1, 2_1_8_4_1)
self.assertEqual(logits.shape , A )
lowerCamelCase__ : str = np.array([1.68_81, -0.27_87, 0.59_01] )
self.assertTrue(np.allclose(logits[0, :3] , A , atol=1e-4 ) )
lowerCamelCase__ : List[Any] = 2_3_9_6
self.assertEqual(logits.argmax(-1 ).item() , A )
| 142 | 0 |
from collections.abc import Generator
from math import sin
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bytes:
if len(UpperCAmelCase__ ) != 32:
raise ValueError("""Input must be of length 32""" )
A_ = b""""""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bytes:
if i < 0:
raise ValueError("""Input must be non-negative""" )
A_ = format(UpperCAmelCase__, """08x""" )[-8:]
A_ = b""""""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8""" )
return little_endian_hex
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bytes:
A_ = b""""""
for char in message:
bit_string += format(UpperCAmelCase__, """08b""" ).encode("""utf-8""" )
A_ = format(len(UpperCAmelCase__ ), """064b""" ).encode("""utf-8""" )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(UpperCAmelCase__ ) % 5_12 != 4_48:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )
return bit_string
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Generator[list[int], None, None]:
if len(UpperCAmelCase__ ) % 5_12 != 0:
raise ValueError("""Input must have length that's a multiple of 512""" )
for pos in range(0, len(UpperCAmelCase__ ), 5_12 ):
A_ = bit_string[pos : pos + 5_12]
A_ = []
for i in range(0, 5_12, 32 ):
block_words.append(int(to_little_endian(block[i : i + 32] ), 2 ) )
yield block_words
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if i < 0:
raise ValueError("""Input must be non-negative""" )
A_ = format(UpperCAmelCase__, """032b""" )
A_ = """"""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(UpperCAmelCase__, 2 )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> int:
return (a + b) % 2**32
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> int:
if i < 0:
raise ValueError("""Input must be non-negative""" )
if shift < 0:
raise ValueError("""Shift must be non-negative""" )
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bytes:
A_ = preprocess(UpperCAmelCase__ )
A_ = [int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]
# Starting states
A_ = 0X67_45_23_01
A_ = 0XEF_CD_AB_89
A_ = 0X98_BA_DC_FE
A_ = 0X10_32_54_76
A_ = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(UpperCAmelCase__ ):
A_ = aa
A_ = ba
A_ = ca
A_ = da
# Hash current chunk
for i in range(64 ):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
A_ = d ^ (b & (c ^ d))
A_ = i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
A_ = c ^ (d & (b ^ c))
A_ = (5 * i + 1) % 16
elif i <= 47:
A_ = b ^ c ^ d
A_ = (3 * i + 5) % 16
else:
A_ = c ^ (b | not_aa(UpperCAmelCase__ ))
A_ = (7 * i) % 16
A_ = (f + a + added_consts[i] + block_words[g]) % 2**32
A_ = d
A_ = c
A_ = b
A_ = sum_aa(UpperCAmelCase__, left_rotate_aa(UpperCAmelCase__, shift_amounts[i] ) )
# Add hashed chunk to running total
A_ = sum_aa(UpperCAmelCase__, UpperCAmelCase__ )
A_ = sum_aa(UpperCAmelCase__, UpperCAmelCase__ )
A_ = sum_aa(UpperCAmelCase__, UpperCAmelCase__ )
A_ = sum_aa(UpperCAmelCase__, UpperCAmelCase__ )
A_ = reformat_hex(UpperCAmelCase__ ) + reformat_hex(UpperCAmelCase__ ) + reformat_hex(UpperCAmelCase__ ) + reformat_hex(UpperCAmelCase__ )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 370 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class A__ ( unittest.TestCase ):
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def snake_case_ ( self ) -> List[str]:
'''simple docstring'''
A_ = 1
A_ = 3
A_ = (32, 32)
A_ = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(UpperCamelCase__ )
return image
@property
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(0 )
A_ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
return model
@property
def snake_case_ ( self ) -> List[str]:
'''simple docstring'''
torch.manual_seed(0 )
A_ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
return model
@property
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0 )
A_ = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5006 , )
return RobertaSeriesModelWithTransformation(UpperCamelCase__ )
@property
def snake_case_ ( self ) -> int:
'''simple docstring'''
def extract(*UpperCamelCase__ , **UpperCamelCase__ ):
class A__ :
def __init__( self ) -> Dict:
'''simple docstring'''
A_ = torch.ones([0] )
def snake_case_ ( self , UpperCamelCase__ ) -> str:
'''simple docstring'''
self.pixel_values.to(UpperCamelCase__ )
return self
return Out()
return extract
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
A_ = """cpu""" # ensure determinism for the device-dependent torch.Generator
A_ = self.dummy_cond_unet
A_ = PNDMScheduler(skip_prk_steps=UpperCamelCase__ )
A_ = self.dummy_vae
A_ = self.dummy_text_encoder
A_ = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""" )
A_ = 77
A_ = self.dummy_image.to(UpperCamelCase__ )
A_ = init_image / 2 + 0.5
# make sure here that pndm scheduler skips prk
A_ = AltDiffusionImgaImgPipeline(
unet=UpperCamelCase__ , scheduler=UpperCamelCase__ , vae=UpperCamelCase__ , text_encoder=UpperCamelCase__ , tokenizer=UpperCamelCase__ , safety_checker=UpperCamelCase__ , feature_extractor=self.dummy_extractor , )
A_ = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=UpperCamelCase__ )
A_ = alt_pipe.to(UpperCamelCase__ )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
A_ = """A painting of a squirrel eating a burger"""
A_ = torch.Generator(device=UpperCamelCase__ ).manual_seed(0 )
A_ = alt_pipe(
[prompt] , generator=UpperCamelCase__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , image=UpperCamelCase__ , )
A_ = output.images
A_ = torch.Generator(device=UpperCamelCase__ ).manual_seed(0 )
A_ = alt_pipe(
[prompt] , generator=UpperCamelCase__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , image=UpperCamelCase__ , return_dict=UpperCamelCase__ , )[0]
A_ = image[0, -3:, -3:, -1]
A_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A_ = np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5e-3
@unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" )
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
A_ = self.dummy_cond_unet
A_ = PNDMScheduler(skip_prk_steps=UpperCamelCase__ )
A_ = self.dummy_vae
A_ = self.dummy_text_encoder
A_ = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""" )
A_ = 77
A_ = self.dummy_image.to(UpperCamelCase__ )
# put models in fp16
A_ = unet.half()
A_ = vae.half()
A_ = bert.half()
# make sure here that pndm scheduler skips prk
A_ = AltDiffusionImgaImgPipeline(
unet=UpperCamelCase__ , scheduler=UpperCamelCase__ , vae=UpperCamelCase__ , text_encoder=UpperCamelCase__ , tokenizer=UpperCamelCase__ , safety_checker=UpperCamelCase__ , feature_extractor=self.dummy_extractor , )
A_ = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=UpperCamelCase__ )
A_ = alt_pipe.to(UpperCamelCase__ )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
A_ = """A painting of a squirrel eating a burger"""
A_ = torch.manual_seed(0 )
A_ = alt_pipe(
[prompt] , generator=UpperCamelCase__ , num_inference_steps=2 , output_type="""np""" , image=UpperCamelCase__ , ).images
assert image.shape == (1, 32, 32, 3)
@unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" )
def snake_case_ ( self ) -> Any:
'''simple docstring'''
A_ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
# resize to resolution that is divisible by 8 but not 16 or 32
A_ = init_image.resize((760, 504) )
A_ = """BAAI/AltDiffusion"""
A_ = AltDiffusionImgaImgPipeline.from_pretrained(
UpperCamelCase__ , safety_checker=UpperCamelCase__ , )
pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
pipe.enable_attention_slicing()
A_ = """A fantasy landscape, trending on artstation"""
A_ = torch.manual_seed(0 )
A_ = pipe(
prompt=UpperCamelCase__ , image=UpperCamelCase__ , strength=0.75 , guidance_scale=7.5 , generator=UpperCamelCase__ , output_type="""np""" , )
A_ = output.images[0]
A_ = image[255:258, 383:386, -1]
assert image.shape == (504, 760, 3)
A_ = np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class A__ ( unittest.TestCase ):
def snake_case_ ( self ) -> List[str]:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
A_ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
A_ = init_image.resize((768, 512) )
A_ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy""" )
A_ = """BAAI/AltDiffusion"""
A_ = AltDiffusionImgaImgPipeline.from_pretrained(
UpperCamelCase__ , safety_checker=UpperCamelCase__ , )
pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
pipe.enable_attention_slicing()
A_ = """A fantasy landscape, trending on artstation"""
A_ = torch.manual_seed(0 )
A_ = pipe(
prompt=UpperCamelCase__ , image=UpperCamelCase__ , strength=0.75 , guidance_scale=7.5 , generator=UpperCamelCase__ , output_type="""np""" , )
A_ = output.images[0]
assert image.shape == (512, 768, 3)
# img2img is flaky across GPUs even in fp32, so using MAE here
assert np.abs(expected_image - image ).max() < 1e-2
| 101 | 0 |
'''simple docstring'''
import qiskit
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> qiskit.result.counts.Counts:
'''simple docstring'''
snake_case_ = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
snake_case_ = qiskit.QuantumCircuit(__UpperCAmelCase, __UpperCAmelCase )
# Map the quantum measurement to the classical bits
circuit.measure([0], [0] )
# Execute the circuit on the simulator
snake_case_ = qiskit.execute(__UpperCAmelCase, __UpperCAmelCase, shots=1000 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(__UpperCAmelCase )
if __name__ == "__main__":
print(f'''Total count for various states are: {single_qubit_measure(1, 1)}''')
| 56 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
__lowercase : int = IFInpaintingPipeline
__lowercase : str = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
__lowercase : Optional[int] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__lowercase : Optional[int] = PipelineTesterMixin.required_optional_params - {"latents"}
def __UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
return self._get_dummy_components()
def __UpperCamelCase ( self , A_ , A_=0 ) -> List[Any]:
"""simple docstring"""
if str(A_ ).startswith('mps' ):
UpperCamelCase = torch.manual_seed(A_ )
else:
UpperCamelCase = torch.Generator(device=A_ ).manual_seed(A_ )
UpperCamelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(A_ ) ).to(A_ )
UpperCamelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(A_ ) ).to(A_ )
UpperCamelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def __UpperCamelCase ( self ) -> str:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def __UpperCamelCase ( self ) -> str:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def __UpperCamelCase ( self ) -> str:
"""simple docstring"""
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def __UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
self._test_save_load_local()
def __UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 222 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_ ) -> None:
lowerCAmelCase__ = value
lowerCAmelCase__ = None
lowerCAmelCase__ = None
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_ ) -> None:
lowerCAmelCase__ = tree
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ ) -> int:
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self ) -> Iterator[int]:
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 228 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
__UpperCAmelCase = logging.get_logger(__name__)
def _snake_case ( A , A , A ) -> Optional[Any]:
return [
int(1000 * (box[0] / width) ),
int(1000 * (box[1] / height) ),
int(1000 * (box[2] / width) ),
int(1000 * (box[3] / height) ),
]
def _snake_case ( A , A , A ) -> Union[str, Any]:
lowerCAmelCase__ = to_pil_image(A )
lowerCAmelCase__ , lowerCAmelCase__ = pil_image.size
lowerCAmelCase__ = pytesseract.image_to_data(A , lang=A , output_type='''dict''' , config=A )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']
# filter empty words and corresponding coordinates
lowerCAmelCase__ = [idx for idx, word in enumerate(A ) if not word.strip()]
lowerCAmelCase__ = [word for idx, word in enumerate(A ) if idx not in irrelevant_indices]
lowerCAmelCase__ = [coord for idx, coord in enumerate(A ) if idx not in irrelevant_indices]
lowerCAmelCase__ = [coord for idx, coord in enumerate(A ) if idx not in irrelevant_indices]
lowerCAmelCase__ = [coord for idx, coord in enumerate(A ) if idx not in irrelevant_indices]
lowerCAmelCase__ = [coord for idx, coord in enumerate(A ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
lowerCAmelCase__ = []
for x, y, w, h in zip(A , A , A , A ):
lowerCAmelCase__ = [x, y, x + w, y + h]
actual_boxes.append(A )
# finally, normalize the bounding boxes
lowerCAmelCase__ = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(A , A , A ) )
assert len(A ) == len(A ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class a__ ( a__ ):
'''simple docstring'''
lowercase__ : Any = ["pixel_values"]
def __init__( self , lowerCamelCase_ = True , lowerCamelCase_ = None , lowerCamelCase_ = PILImageResampling.BILINEAR , lowerCamelCase_ = True , lowerCamelCase_ = 1 / 2_55 , lowerCamelCase_ = True , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = True , lowerCamelCase_ = None , lowerCamelCase_ = "" , **lowerCamelCase_ , ) -> None:
super().__init__(**lowerCamelCase_ )
lowerCAmelCase__ = size if size is not None else {'''height''': 2_24, '''width''': 2_24}
lowerCAmelCase__ = get_size_dict(lowerCamelCase_ )
lowerCAmelCase__ = do_resize
lowerCAmelCase__ = size
lowerCAmelCase__ = resample
lowerCAmelCase__ = do_rescale
lowerCAmelCase__ = rescale_value
lowerCAmelCase__ = do_normalize
lowerCAmelCase__ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCAmelCase__ = image_std if image_std is not None else IMAGENET_STANDARD_STD
lowerCAmelCase__ = apply_ocr
lowerCAmelCase__ = ocr_lang
lowerCAmelCase__ = tesseract_config
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = PILImageResampling.BILINEAR , lowerCamelCase_ = None , **lowerCamelCase_ , ) -> np.ndarray:
lowerCAmelCase__ = get_size_dict(lowerCamelCase_ )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
lowerCAmelCase__ = (size['''height'''], size['''width'''])
return resize(lowerCamelCase_ , size=lowerCamelCase_ , resample=lowerCamelCase_ , data_format=lowerCamelCase_ , **lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = None , **lowerCamelCase_ , ) -> np.ndarray:
return rescale(lowerCamelCase_ , scale=lowerCamelCase_ , data_format=lowerCamelCase_ , **lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = None , **lowerCamelCase_ , ) -> np.ndarray:
return normalize(lowerCamelCase_ , mean=lowerCamelCase_ , std=lowerCamelCase_ , data_format=lowerCamelCase_ , **lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_=None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = ChannelDimension.FIRST , **lowerCamelCase_ , ) -> PIL.Image.Image:
lowerCAmelCase__ = do_resize if do_resize is not None else self.do_resize
lowerCAmelCase__ = size if size is not None else self.size
lowerCAmelCase__ = get_size_dict(lowerCamelCase_ )
lowerCAmelCase__ = resample if resample is not None else self.resample
lowerCAmelCase__ = do_rescale if do_rescale is not None else self.do_rescale
lowerCAmelCase__ = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCAmelCase__ = do_normalize if do_normalize is not None else self.do_normalize
lowerCAmelCase__ = image_mean if image_mean is not None else self.image_mean
lowerCAmelCase__ = image_std if image_std is not None else self.image_std
lowerCAmelCase__ = apply_ocr if apply_ocr is not None else self.apply_ocr
lowerCAmelCase__ = ocr_lang if ocr_lang is not None else self.ocr_lang
lowerCAmelCase__ = tesseract_config if tesseract_config is not None else self.tesseract_config
lowerCAmelCase__ = make_list_of_images(lowerCamelCase_ )
if not valid_images(lowerCamelCase_ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.''' )
# All transformations expect numpy arrays.
lowerCAmelCase__ = [to_numpy_array(lowerCamelCase_ ) for image in images]
# Tesseract OCR to get words + normalized bounding boxes
if apply_ocr:
requires_backends(self , '''pytesseract''' )
lowerCAmelCase__ = []
lowerCAmelCase__ = []
for image in images:
lowerCAmelCase__ , lowerCAmelCase__ = apply_tesseract(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
words_batch.append(lowerCamelCase_ )
boxes_batch.append(lowerCamelCase_ )
if do_resize:
lowerCAmelCase__ = [self.resize(image=lowerCamelCase_ , size=lowerCamelCase_ , resample=lowerCamelCase_ ) for image in images]
if do_rescale:
lowerCAmelCase__ = [self.rescale(image=lowerCamelCase_ , scale=lowerCamelCase_ ) for image in images]
if do_normalize:
lowerCAmelCase__ = [self.normalize(image=lowerCamelCase_ , mean=lowerCamelCase_ , std=lowerCamelCase_ ) for image in images]
lowerCAmelCase__ = [to_channel_dimension_format(lowerCamelCase_ , lowerCamelCase_ ) for image in images]
lowerCAmelCase__ = BatchFeature(data={'''pixel_values''': images} , tensor_type=lowerCamelCase_ )
if apply_ocr:
lowerCAmelCase__ = words_batch
lowerCAmelCase__ = boxes_batch
return data
| 228 | 1 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class snake_case_( unittest.TestCase ):
def lowerCamelCase__ ( self : List[str] ):
lowerCAmelCase : Dict = torch.nn.Linear(1_0 , 1_0 )
lowerCAmelCase : str = torch.optim.SGD(model.parameters() , 0.1 )
lowerCAmelCase : str = Accelerator()
lowerCAmelCase : int = accelerator.prepare(UpperCamelCase_ )
try:
pickle.loads(pickle.dumps(UpperCamelCase_ ) )
except Exception as e:
self.fail(F'''Accelerated optimizer pickling failed with {e}''' )
AcceleratorState._reset_state()
| 60 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['ConvNextFeatureExtractor']
a_ = ['ConvNextImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConvNextForImageClassification',
'ConvNextModel',
'ConvNextPreTrainedModel',
'ConvNextBackbone',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'TFConvNextForImageClassification',
'TFConvNextModel',
'TFConvNextPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 249 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class SCREAMING_SNAKE_CASE :
_UpperCamelCase : int
_UpperCamelCase : Node | None = None
_UpperCamelCase : Node | None = None
def __UpperCamelCase () -> Node | None:
lowercase__ = Node(1 )
lowercase__ = Node(2 )
lowercase__ = Node(3 )
lowercase__ = Node(4 )
lowercase__ = Node(5 )
return tree
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> list[int]:
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> list[int]:
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> list[int]:
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> int:
return (max(height(root.left ) , height(root.right ) ) + 1) if root else 0
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> Sequence[Node | None]:
lowercase__ = []
if root is None:
return output
lowercase__ = deque([root] )
while process_queue:
lowercase__ = process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Sequence[Node | None]:
lowercase__ = []
def populate_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left , level - 1 )
populate_output(root.right , level - 1 )
populate_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return output
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Sequence[Node | None]:
lowercase__ = []
def populate_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right , level - 1 )
populate_output(root.left , level - 1 )
populate_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return output
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> Sequence[Node | None] | list[Any]:
if root is None:
return []
lowercase__ = []
lowercase__ = 0
lowercase__ = height(_SCREAMING_SNAKE_CASE )
for h in range(1 , height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
lowercase__ = 1
else:
output.append(get_nodes_from_right_to_left(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
lowercase__ = 0
return output
def __UpperCamelCase () -> None: # Main function for testing.
lowercase__ = make_tree()
print(F"""In-order Traversal: {inorder(_SCREAMING_SNAKE_CASE )}""" )
print(F"""Pre-order Traversal: {preorder(_SCREAMING_SNAKE_CASE )}""" )
print(F"""Post-order Traversal: {postorder(_SCREAMING_SNAKE_CASE )}""" , '\n' )
print(F"""Height of Tree: {height(_SCREAMING_SNAKE_CASE )}""" , '\n' )
print('Complete Level Order Traversal: ' )
print(level_order(_SCREAMING_SNAKE_CASE ) , '\n' )
print('Level-wise order Traversal: ' )
for level in range(1 , height(_SCREAMING_SNAKE_CASE ) + 1 ):
print(F"""Level {level}:""" , get_nodes_from_left_to_right(_SCREAMING_SNAKE_CASE , level=_SCREAMING_SNAKE_CASE ) )
print('\nZigZag order Traversal: ' )
print(zigzag(_SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 269 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ = logging.get_logger(__name__)
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> Any:
lowercase__ = MobileNetVaConfig(layer_norm_eps=0.0_0_1 )
if "_quant" in model_name:
raise ValueError('Quantized models are not supported.' )
lowercase__ = re.match(R'^mobilenet_v1_([^_]*)_([^_]*)$' , _SCREAMING_SNAKE_CASE )
if matches:
lowercase__ = float(matches[1] )
lowercase__ = int(matches[2] )
# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
# the usual 1000. The first class (index 0) is "background".
lowercase__ = 1001
lowercase__ = 'imagenet-1k-id2label.json'
lowercase__ = 'huggingface/label-files'
lowercase__ = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type='dataset' ) , 'r' ) )
lowercase__ = {int(_SCREAMING_SNAKE_CASE ) + 1: v for k, v in idalabel.items()}
lowercase__ = 'background'
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
return config
def __UpperCamelCase () -> int:
lowercase__ = 'http://images.cocodataset.org/val2017/000000039769.jpg'
lowercase__ = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> Union[str, Any]:
lowercase__ = get_mobilenet_va_config(_SCREAMING_SNAKE_CASE )
# Load 🤗 model
lowercase__ = MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
# Load weights from TensorFlow checkpoint
load_tf_weights_in_mobilenet_va(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by MobileNetV1ImageProcessor
lowercase__ = MobileNetVaImageProcessor(
crop_size={'width': config.image_size, 'height': config.image_size} , size={'shortest_edge': config.image_size + 32} , )
lowercase__ = image_processor(images=prepare_img() , return_tensors='pt' )
lowercase__ = model(**_SCREAMING_SNAKE_CASE )
lowercase__ = outputs.logits
assert logits.shape == (1, 1001)
if model_name == "mobilenet_v1_1.0_224":
lowercase__ = torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5] )
elif model_name == "mobilenet_v1_0.75_192":
lowercase__ = torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3] )
else:
lowercase__ = None
if expected_logits is not None:
assert torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
print('Pushing to the hub...' )
lowercase__ = 'google/' + model_name
image_processor.push_to_hub(_SCREAMING_SNAKE_CASE )
model.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""mobilenet_v1_1.0_224""",
type=str,
help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
)
parser.add_argument(
"""--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
lowercase_ = parser.parse_args()
convert_movilevit_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 269 | 1 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
A =logging.getLogger(__name__)
require_version('pytorch_lightning>=1.0.4')
A ={
'base': AutoModel,
'sequence-classification': AutoModelForSequenceClassification,
'question-answering': AutoModelForQuestionAnswering,
'pretraining': AutoModelForPreTraining,
'token-classification': AutoModelForTokenClassification,
'language-modeling': AutoModelWithLMHead,
'summarization': AutoModelForSeqaSeqLM,
'translation': AutoModelForSeqaSeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
A ={
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
A =sorted(arg_to_scheduler.keys())
A ='{' + ', '.join(arg_to_scheduler_choices) + '}'
class _a ( pl.LightningModule ):
def __init__( self : List[str] , lowercase : argparse.Namespace , lowercase : List[Any]=None , lowercase : Dict="base" , lowercase : Optional[int]=None , lowercase : Dict=None , lowercase : Tuple=None , **lowercase : Optional[int] , ):
'''simple docstring'''
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(lowercase )
UpperCAmelCase = 0
UpperCAmelCase = Path(self.hparams.output_dir )
UpperCAmelCase = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
UpperCAmelCase = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path , **({'''num_labels''': num_labels} if num_labels is not None else {}) , cache_dir=lowercase , **lowercase , )
else:
UpperCAmelCase = config
UpperCAmelCase = ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
for p in extra_model_params:
if getattr(self.hparams , lowercase , lowercase ):
assert hasattr(self.config , lowercase ), f"model config doesn't have a `{p}` attribute"
setattr(self.config , lowercase , getattr(self.hparams , lowercase ) )
if tokenizer is None:
UpperCAmelCase = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=lowercase , )
else:
UpperCAmelCase = tokenizer
UpperCAmelCase = MODEL_MODES[mode]
if model is None:
UpperCAmelCase = self.model_type.from_pretrained(
self.hparams.model_name_or_path , from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path ) , config=self.config , cache_dir=lowercase , )
else:
UpperCAmelCase = model
def A ( self : List[Any] , *lowercase : List[str] , **lowercase : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.model_type.from_pretrained(*lowercase , **lowercase )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = arg_to_scheduler[self.hparams.lr_scheduler]
UpperCAmelCase = get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps() )
UpperCAmelCase = {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
return scheduler
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.model
UpperCAmelCase = ['''bias''', '''LayerNorm.weight''']
UpperCAmelCase = [
{
'''params''': [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
], # check this named paramters
'''weight_decay''': self.hparams.weight_decay,
},
{
'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
if self.hparams.adafactor:
UpperCAmelCase = Adafactor(
lowercase , lr=self.hparams.learning_rate , scale_parameter=lowercase , relative_step=lowercase )
else:
UpperCAmelCase = AdamW(
lowercase , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon )
UpperCAmelCase = optimizer
UpperCAmelCase = self.get_lr_scheduler()
return [optimizer], [scheduler]
def A ( self : List[Any] , lowercase : int , lowercase : List[str] ):
'''simple docstring'''
return self.validation_step(lowercase , lowercase )
def A ( self : List[Any] , lowercase : Tuple ):
'''simple docstring'''
return self.validation_end(lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = max(1 , self.hparams.gpus ) # TODO: consider num_tpu_cores
UpperCAmelCase = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def A ( self : List[str] , lowercase : Any ):
'''simple docstring'''
if stage == "test":
UpperCAmelCase = len(self.test_dataloader().dataset )
else:
UpperCAmelCase = self.get_dataloader('''train''' , self.hparams.train_batch_size , shuffle=lowercase )
UpperCAmelCase = len(self.train_dataloader().dataset )
def A ( self : List[str] , lowercase : str , lowercase : int , lowercase : bool = False ):
'''simple docstring'''
raise NotImplementedError('''You must implement this for your task''' )
def A ( self : Union[str, Any] ):
'''simple docstring'''
return self.train_loader
def A ( self : Optional[Any] ):
'''simple docstring'''
return self.get_dataloader('''dev''' , self.hparams.eval_batch_size , shuffle=lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
return self.get_dataloader('''test''' , self.hparams.eval_batch_size , shuffle=lowercase )
def A ( self : Any , lowercase : Union[str, Any] ):
'''simple docstring'''
return os.path.join(
self.hparams.data_dir , '''cached_{}_{}_{}'''.format(
lowercase , list(filter(lowercase , self.hparams.model_name_or_path.split('''/''' ) ) ).pop() , str(self.hparams.max_seq_length ) , ) , )
@pl.utilities.rank_zero_only
def A ( self : List[str] , lowercase : Dict[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.output_dir.joinpath('''best_tfmr''' )
UpperCAmelCase = self.step_count
self.model.save_pretrained(lowercase )
self.tokenizer.save_pretrained(lowercase )
@staticmethod
def A ( lowercase : Optional[int] , lowercase : List[str] ):
'''simple docstring'''
parser.add_argument(
'''--model_name_or_path''' , default=lowercase , type=lowercase , required=lowercase , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--config_name''' , default='''''' , type=lowercase , help='''Pretrained config name or path if not the same as model_name''' )
parser.add_argument(
'''--tokenizer_name''' , default=lowercase , type=lowercase , help='''Pretrained tokenizer name or path if not the same as model_name''' , )
parser.add_argument(
'''--cache_dir''' , default=str(Path(lowercase ).parent / '''test_run''' / '''cache''' ) , type=lowercase , help='''Where do you want to store the pre-trained models downloaded from huggingface.co''' , )
parser.add_argument(
'''--encoder_layerdrop''' , type=lowercase , help='''Encoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--decoder_layerdrop''' , type=lowercase , help='''Decoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--dropout''' , type=lowercase , help='''Dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--attention_dropout''' , type=lowercase , help='''Attention dropout probability (Optional). Goes into model.config''' , )
parser.add_argument('''--learning_rate''' , default=5E-5 , type=lowercase , help='''The initial learning rate for Adam.''' )
parser.add_argument(
'''--lr_scheduler''' , default='''linear''' , choices=lowercase , metavar=lowercase , type=lowercase , help='''Learning rate scheduler''' , )
parser.add_argument('''--weight_decay''' , default=0.0 , type=lowercase , help='''Weight decay if we apply some.''' )
parser.add_argument('''--adam_epsilon''' , default=1E-8 , type=lowercase , help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--warmup_steps''' , default=0 , type=lowercase , help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--num_workers''' , default=4 , type=lowercase , help='''kwarg passed to DataLoader''' )
parser.add_argument('''--num_train_epochs''' , dest='''max_epochs''' , default=3 , type=lowercase )
parser.add_argument('''--train_batch_size''' , default=32 , type=lowercase )
parser.add_argument('''--eval_batch_size''' , default=32 , type=lowercase )
parser.add_argument('''--adafactor''' , action='''store_true''' )
class _a ( pl.Callback ):
def A ( self : Dict , lowercase : Optional[Any] , lowercase : List[Any] ):
'''simple docstring'''
if (
trainer.is_global_zero and trainer.global_rank == 0
): # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
pl_module.model.rag.retriever.init_retrieval() # better to use hook functions.
class _a ( pl.Callback ):
def A ( self : Optional[int] , lowercase : Union[str, Any] , lowercase : Any ):
'''simple docstring'''
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(lowercase )
class _a ( pl.Callback ):
def A ( self : Optional[int] , lowercase : Optional[int] , lowercase : Dict ):
'''simple docstring'''
UpperCAmelCase = trainer.lr_schedulers[0]['''scheduler''']
UpperCAmelCase = {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
pl_module.logger.log_metrics(lowercase )
def A ( self : Tuple , lowercase : pl.Trainer , lowercase : pl.LightningModule ):
'''simple docstring'''
rank_zero_info('''***** Validation results *****''' )
UpperCAmelCase = trainer.callback_metrics
# Log results
for key in sorted(lowercase ):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
def A ( self : Dict , lowercase : pl.Trainer , lowercase : pl.LightningModule ):
'''simple docstring'''
rank_zero_info('''***** Test results *****''' )
UpperCAmelCase = trainer.callback_metrics
# Log and save results to file
UpperCAmelCase = os.path.join(pl_module.hparams.output_dir , '''test_results.txt''' )
with open(lowercase , '''w''' ) as writer:
for key in sorted(lowercase ):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
writer.write('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
def snake_case_ (_a : int , _a : Optional[Any] ):
# To allow all pl args uncomment the following line
# parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument(
'''--output_dir''' , default=str(Path(_a ).parent / '''test_run''' / '''model_checkpoints''' ) , type=_a , help='''The output directory where the model predictions and checkpoints will be written.''' , )
parser.add_argument(
'''--fp16''' , action='''store_true''' , help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''' , )
parser.add_argument(
'''--fp16_opt_level''' , type=_a , default='''O2''' , help=(
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
'''See details at https://nvidia.github.io/apex/amp.html'''
) , )
parser.add_argument('''--n_tpu_cores''' , dest='''tpu_cores''' , type=_a )
parser.add_argument('''--max_grad_norm''' , dest='''gradient_clip_val''' , default=1.0 , type=_a , help='''Max gradient norm''' )
parser.add_argument('''--do_train''' , action='''store_true''' , help='''Whether to run training.''' )
parser.add_argument('''--do_predict''' , action='''store_true''' , help='''Whether to run predictions on the test set.''' )
parser.add_argument(
'''--gradient_accumulation_steps''' , dest='''accumulate_grad_batches''' , type=_a , default=1 , help='''Number of updates steps to accumulate before performing a backward/update pass.''' , )
parser.add_argument('''--seed''' , type=_a , default=4_2 , help='''random seed for initialization''' )
parser.add_argument(
'''--data_dir''' , default=str(Path(_a ).parent / '''test_run''' / '''dummy-train-data''' ) , type=_a , help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.''' , )
def snake_case_ (_a : BaseTransformer , _a : argparse.Namespace , _a : List[Any]=None , _a : Tuple=True , _a : int=[] , _a : Any=None , _a : int=None , **_a : Optional[Any] , ):
pl.seed_everything(args.seed )
# init model
UpperCAmelCase = Path(model.hparams.output_dir )
odir.mkdir(exist_ok=_a )
# add custom checkpoints
if checkpoint_callback is None:
UpperCAmelCase = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir , prefix='''checkpoint''' , monitor='''val_loss''' , mode='''min''' , save_top_k=1 )
if early_stopping_callback:
extra_callbacks.append(_a )
if logging_callback is None:
UpperCAmelCase = LoggingCallback()
UpperCAmelCase = {}
if args.fpaa:
UpperCAmelCase = 1_6
if args.gpus > 1:
UpperCAmelCase = '''auto'''
UpperCAmelCase = '''ddp'''
UpperCAmelCase = args.accumulate_grad_batches
UpperCAmelCase = None
UpperCAmelCase = '''auto'''
UpperCAmelCase = pl.Trainer.from_argparse_args(
_a , weights_summary=_a , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=_a , val_check_interval=1 , num_sanity_val_steps=2 , **_a , )
if args.do_train:
trainer.fit(_a )
else:
print('''RAG modeling tests with new set functions successfuly executed!''' )
return trainer
| 34 |
'''simple docstring'''
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
A =logging.get_logger(__name__)
A =TypeVar('DatasetType', Dataset, IterableDataset)
def snake_case_ (_a : List[DatasetType] , _a : Optional[List[float]] = None , _a : Optional[int] = None , _a : Optional[DatasetInfo] = None , _a : Optional[NamedSplit] = None , _a : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ):
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('''Unable to interleave an empty list of datasets.''' )
for i, dataset in enumerate(_a ):
if not isinstance(_a , (Dataset, IterableDataset) ):
if isinstance(_a , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(_a )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a ).__name__}." )
if i == 0:
UpperCAmelCase , UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(_a , _a ) else (IterableDataset, Dataset)
)
elif not isinstance(_a , _a ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy." )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
_a , _a , _a , info=_a , split=_a , stopping_strategy=_a )
else:
return _interleave_iterable_datasets(
_a , _a , _a , info=_a , split=_a , stopping_strategy=_a )
def snake_case_ (_a : List[DatasetType] , _a : Optional[DatasetInfo] = None , _a : Optional[NamedSplit] = None , _a : int = 0 , ):
if not dsets:
raise ValueError('''Unable to concatenate an empty list of datasets.''' )
for i, dataset in enumerate(_a ):
if not isinstance(_a , (Dataset, IterableDataset) ):
if isinstance(_a , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(_a )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a ).__name__}." )
if i == 0:
UpperCAmelCase , UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(_a , _a ) else (IterableDataset, Dataset)
)
elif not isinstance(_a , _a ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(_a , info=_a , split=_a , axis=_a )
else:
return _concatenate_iterable_datasets(_a , info=_a , split=_a , axis=_a )
| 34 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class lowerCamelCase__ ( __magic_name__ ):
'''simple docstring'''
def __init__( self ) -> Optional[Any]:
_lowerCAmelCase =[]
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ) -> List[Any]:
self.events.append("""on_init_end""" )
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ) -> Any:
self.events.append("""on_train_begin""" )
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ) -> Any:
self.events.append("""on_train_end""" )
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ) -> str:
self.events.append("""on_epoch_begin""" )
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ) -> int:
self.events.append("""on_epoch_end""" )
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ) -> Dict:
self.events.append("""on_step_begin""" )
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ) -> Dict:
self.events.append("""on_step_end""" )
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ) -> Optional[Any]:
self.events.append("""on_evaluate""" )
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ) -> Optional[int]:
self.events.append("""on_predict""" )
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ) -> List[str]:
self.events.append("""on_save""" )
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ) -> Any:
self.events.append("""on_log""" )
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ) -> List[Any]:
self.events.append("""on_prediction_step""" )
@require_torch
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase ( self ) -> List[str]:
_lowerCAmelCase =tempfile.mkdtemp()
def _lowerCAmelCase ( self ) -> Any:
shutil.rmtree(self.output_dir )
def _lowerCAmelCase ( self , __UpperCAmelCase=0 , __UpperCAmelCase=0 , __UpperCAmelCase=64 , __UpperCAmelCase=64 , __UpperCAmelCase=None , __UpperCAmelCase=False , **__UpperCAmelCase ) -> List[Any]:
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
_lowerCAmelCase =RegressionDataset(length=__UpperCAmelCase )
_lowerCAmelCase =RegressionDataset(length=__UpperCAmelCase )
_lowerCAmelCase =RegressionModelConfig(a=__UpperCAmelCase , b=__UpperCAmelCase )
_lowerCAmelCase =RegressionPreTrainedModel(__UpperCAmelCase )
_lowerCAmelCase =TrainingArguments(self.output_dir , disable_tqdm=__UpperCAmelCase , report_to=[] , **__UpperCAmelCase )
return Trainer(
__UpperCAmelCase , __UpperCAmelCase , train_dataset=__UpperCAmelCase , eval_dataset=__UpperCAmelCase , callbacks=__UpperCAmelCase , )
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ) -> Any:
self.assertEqual(len(__UpperCAmelCase ) , len(__UpperCAmelCase ) )
# Order doesn't matter
_lowerCAmelCase =sorted(__UpperCAmelCase , key=lambda __UpperCAmelCase : cb.__name__ if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else cb.__class__.__name__ )
_lowerCAmelCase =sorted(__UpperCAmelCase , key=lambda __UpperCAmelCase : cb.__name__ if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else cb.__class__.__name__ )
for cba, cba in zip(__UpperCAmelCase , __UpperCAmelCase ):
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and isinstance(__UpperCAmelCase , __UpperCAmelCase ):
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
elif isinstance(__UpperCAmelCase , __UpperCAmelCase ) and not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
self.assertEqual(__UpperCAmelCase , cba.__class__ )
elif not isinstance(__UpperCAmelCase , __UpperCAmelCase ) and isinstance(__UpperCAmelCase , __UpperCAmelCase ):
self.assertEqual(cba.__class__ , __UpperCAmelCase )
else:
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
def _lowerCAmelCase ( self , __UpperCAmelCase ) -> Optional[int]:
_lowerCAmelCase =["""on_init_end""", """on_train_begin"""]
_lowerCAmelCase =0
_lowerCAmelCase =len(trainer.get_eval_dataloader() )
_lowerCAmelCase =["""on_prediction_step"""] * len(trainer.get_eval_dataloader() ) + ["""on_log""", """on_evaluate"""]
for _ in range(trainer.state.num_train_epochs ):
expected_events.append("""on_epoch_begin""" )
for _ in range(__UpperCAmelCase ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append("""on_log""" )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append("""on_save""" )
expected_events.append("""on_epoch_end""" )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def _lowerCAmelCase ( self ) -> Union[str, Any]:
_lowerCAmelCase =self.get_trainer()
_lowerCAmelCase =DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , __UpperCAmelCase )
# Callbacks passed at init are added to the default callbacks
_lowerCAmelCase =self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(__UpperCAmelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks , __UpperCAmelCase )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
_lowerCAmelCase =self.get_trainer(disable_tqdm=__UpperCAmelCase )
_lowerCAmelCase =DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , __UpperCAmelCase )
def _lowerCAmelCase ( self ) -> Union[str, Any]:
_lowerCAmelCase =DEFAULT_CALLBACKS.copy() + [ProgressCallback]
_lowerCAmelCase =self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(__UpperCAmelCase )
expected_callbacks.remove(__UpperCAmelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks , __UpperCAmelCase )
_lowerCAmelCase =self.get_trainer()
_lowerCAmelCase =trainer.pop_callback(__UpperCAmelCase )
self.assertEqual(cb.__class__ , __UpperCAmelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks , __UpperCAmelCase )
trainer.add_callback(__UpperCAmelCase )
expected_callbacks.insert(0 , __UpperCAmelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks , __UpperCAmelCase )
# We can also add, pop, or remove by instance
_lowerCAmelCase =self.get_trainer()
_lowerCAmelCase =trainer.callback_handler.callbacks[0]
trainer.remove_callback(__UpperCAmelCase )
expected_callbacks.remove(__UpperCAmelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks , __UpperCAmelCase )
_lowerCAmelCase =self.get_trainer()
_lowerCAmelCase =trainer.callback_handler.callbacks[0]
_lowerCAmelCase =trainer.pop_callback(__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks , __UpperCAmelCase )
trainer.add_callback(__UpperCAmelCase )
expected_callbacks.insert(0 , __UpperCAmelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks , __UpperCAmelCase )
def _lowerCAmelCase ( self ) -> Optional[Any]:
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action="""ignore""" , category=__UpperCAmelCase )
_lowerCAmelCase =self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
_lowerCAmelCase =trainer.callback_handler.callbacks[-2].events
self.assertEqual(__UpperCAmelCase , self.get_expected_events(__UpperCAmelCase ) )
# Independent log/save/eval
_lowerCAmelCase =self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
_lowerCAmelCase =trainer.callback_handler.callbacks[-2].events
self.assertEqual(__UpperCAmelCase , self.get_expected_events(__UpperCAmelCase ) )
_lowerCAmelCase =self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
_lowerCAmelCase =trainer.callback_handler.callbacks[-2].events
self.assertEqual(__UpperCAmelCase , self.get_expected_events(__UpperCAmelCase ) )
_lowerCAmelCase =self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy="""steps""" )
trainer.train()
_lowerCAmelCase =trainer.callback_handler.callbacks[-2].events
self.assertEqual(__UpperCAmelCase , self.get_expected_events(__UpperCAmelCase ) )
_lowerCAmelCase =self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy="""epoch""" )
trainer.train()
_lowerCAmelCase =trainer.callback_handler.callbacks[-2].events
self.assertEqual(__UpperCAmelCase , self.get_expected_events(__UpperCAmelCase ) )
# A bit of everything
_lowerCAmelCase =self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=10 , eval_steps=5 , evaluation_strategy="""steps""" , )
trainer.train()
_lowerCAmelCase =trainer.callback_handler.callbacks[-2].events
self.assertEqual(__UpperCAmelCase , self.get_expected_events(__UpperCAmelCase ) )
# warning should be emitted for duplicated callbacks
with patch("""transformers.trainer_callback.logger.warning""" ) as warn_mock:
_lowerCAmelCase =self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(__UpperCAmelCase ) in warn_mock.call_args[0][0]
| 363 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
__A = datasets.logging.get_logger(__name__)
__A = '\\n@InProceedings{moosavi2019minimum,\n author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n year = {2019},\n booktitle = {Proceedings of the 57th Annual Meeting of\n the Association for Computational Linguistics (Volume 1: Long Papers)},\n publisher = {Association for Computational Linguistics},\n address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n author = {Amit Bagga and Breck Baldwin},\n title = {Algorithms for Scoring Coreference Chains},\n booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n year = {1998},\n pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n author = {Xiaoqiang Luo},\n title = {On coreference resolution performance metrics},\n booktitle = {In Proc. of HLT/EMNLP},\n year = {2005},\n pages = {25--32},\n publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",\n author = "Moosavi, Nafise Sadat and\n Strube, Michael",\n booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",\n month = aug,\n year = "2016",\n address = "Berlin, Germany",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/P16-1060",\n doi = "10.18653/v1/P16-1060",\n pages = "632--642",\n}\n\n'
__A = '\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn Type Description\n1 Document ID This is a variation on the document filename\n2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3 Word number\n4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.\n5 Part-of-Speech\n6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.\n7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"\n8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7.\n9 Word sense This is the word sense of the word in Column 3.\n10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11 Named Entities These columns identifies the spans representing various named entities.\n12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN Coreference Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n'
__A = '\nCalculates coreference evaluation metrics.\nArgs:\n predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n Each prediction is a word with its annotations as a string made of columns joined with spaces.\n Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n See the details on the format in the description of the metric.\n references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n Each reference is a word with its annotations as a string made of columns joined with spaces.\n Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n See the details on the format in the description of the metric.\n keep_singletons: After extracting all mentions of key or system files,\n mentions whose corresponding coreference chain is of size one,\n are considered as singletons. The default evaluation mode will include\n singletons in evaluations if they are included in the key or the system files.\n By setting \'keep_singletons=False\', all singletons in the key and system files\n will be excluded from the evaluation.\n NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.\n min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.\n Minimum spans are determined using the MINA algorithm.\n\nReturns:\n \'mentions\': mentions\n \'muc\': MUC metric [Vilain et al, 1995]\n \'bcub\': B-cubed [Bagga and Baldwin, 1998]\n \'ceafe\': CEAFe [Luo et al., 2005]\n \'lea\': LEA [Moosavi and Strube, 2016]\n \'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n >>> coval = datasets.load_metric(\'coval\')\n >>> words = [\'bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -\',\n ... \'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)\',\n ... \'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)\',\n ... \'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -\',\n ... \'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -\',\n ... \'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -\']\n >>> references = [words]\n >>> predictions = [words]\n >>> results = coval.compute(predictions=predictions, references=references)\n >>> print(results) # doctest:+ELLIPSIS\n {\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}\n'
def _lowerCamelCase(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase=False , __UpperCamelCase=False , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase="dummy_doc" ) -> Dict:
_lowerCAmelCase ={doc: key_lines}
_lowerCAmelCase ={doc: sys_lines}
_lowerCAmelCase ={}
_lowerCAmelCase =0
_lowerCAmelCase =0
_lowerCAmelCase =0
_lowerCAmelCase =0
_lowerCAmelCase =0
_lowerCAmelCase =0
_lowerCAmelCase , _lowerCAmelCase =reader.get_doc_mentions(__UpperCamelCase , key_doc_lines[doc] , __UpperCamelCase )
key_singletons_num += singletons_num
if NP_only or min_span:
_lowerCAmelCase =reader.set_annotated_parse_trees(__UpperCamelCase , key_doc_lines[doc] , __UpperCamelCase , __UpperCamelCase )
_lowerCAmelCase , _lowerCAmelCase =reader.get_doc_mentions(__UpperCamelCase , sys_doc_lines[doc] , __UpperCamelCase )
sys_singletons_num += singletons_num
if NP_only or min_span:
_lowerCAmelCase =reader.set_annotated_parse_trees(__UpperCamelCase , key_doc_lines[doc] , __UpperCamelCase , __UpperCamelCase )
if remove_nested:
_lowerCAmelCase , _lowerCAmelCase =reader.remove_nested_coref_mentions(__UpperCamelCase , __UpperCamelCase )
key_nested_coref_num += nested_mentions
key_removed_nested_clusters += removed_clusters
_lowerCAmelCase , _lowerCAmelCase =reader.remove_nested_coref_mentions(__UpperCamelCase , __UpperCamelCase )
sys_nested_coref_num += nested_mentions
sys_removed_nested_clusters += removed_clusters
_lowerCAmelCase =reader.get_mention_assignments(__UpperCamelCase , __UpperCamelCase )
_lowerCAmelCase =reader.get_mention_assignments(__UpperCamelCase , __UpperCamelCase )
_lowerCAmelCase =(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)
if remove_nested:
logger.info(
"""Number of removed nested coreferring mentions in the key """
F'''annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}''' )
logger.info(
"""Number of resulting singleton clusters in the key """
F'''annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}''' )
if not keep_singletons:
logger.info(
F'''{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '''
"""files, respectively""" )
return doc_coref_infos
def _lowerCamelCase(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
_lowerCAmelCase =get_coref_infos(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
_lowerCAmelCase ={}
_lowerCAmelCase =0
_lowerCAmelCase =0
for name, metric in metrics:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =evaluator.evaluate_documents(__UpperCamelCase , __UpperCamelCase , beta=1 )
if name in ["muc", "bcub", "ceafe"]:
conll += fa
conll_subparts_num += 1
output_scores.update({F'''{name}/recall''': recall, F'''{name}/precision''': precision, F'''{name}/f1''': fa} )
logger.info(
name.ljust(10 ) , F'''Recall: {recall * 100:.2f}''' , F''' Precision: {precision * 100:.2f}''' , F''' F1: {fa * 100:.2f}''' , )
if conll_subparts_num == 3:
_lowerCAmelCase =(conll / 3) * 100
logger.info(F'''CoNLL score: {conll:.2f}''' )
output_scores.update({"""conll_score""": conll} )
return output_scores
def _lowerCamelCase(__UpperCamelCase ) -> Tuple:
_lowerCAmelCase =False
for line in key_lines:
if not line.startswith("""#""" ):
if len(line.split() ) > 6:
_lowerCAmelCase =line.split()[5]
if not parse_col == "-":
_lowerCAmelCase =True
break
else:
break
return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase__ ( datasets.Metric ):
'''simple docstring'''
def _lowerCAmelCase ( self ) -> str:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""string""" ) ),
"""references""": datasets.Sequence(datasets.Value("""string""" ) ),
} ) , codebase_urls=["""https://github.com/ns-moosavi/coval"""] , reference_urls=[
"""https://github.com/ns-moosavi/coval""",
"""https://www.aclweb.org/anthology/P16-1060""",
"""http://www.conll.cemantix.org/2012/data.html""",
] , )
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=False , __UpperCAmelCase=False ) -> Optional[Any]:
_lowerCAmelCase =[
("""mentions""", evaluator.mentions),
("""muc""", evaluator.muc),
("""bcub""", evaluator.b_cubed),
("""ceafe""", evaluator.ceafe),
("""lea""", evaluator.lea),
]
if min_span:
_lowerCAmelCase =util.check_gold_parse_annotation(__UpperCAmelCase )
if not has_gold_parse:
raise NotImplementedError("""References should have gold parse annotation to use 'min_span'.""" )
# util.parse_key_file(key_file)
# key_file = key_file + ".parsed"
_lowerCAmelCase =evaluate(
key_lines=__UpperCAmelCase , sys_lines=__UpperCAmelCase , metrics=__UpperCAmelCase , NP_only=__UpperCAmelCase , remove_nested=__UpperCAmelCase , keep_singletons=__UpperCAmelCase , min_span=__UpperCAmelCase , )
return score
| 341 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class lowerCAmelCase__ ( lowercase, unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = KandinskyInpaintPipeline
lowerCamelCase__ = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
lowerCamelCase__ = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
lowerCamelCase__ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
lowerCamelCase__ = False
@property
def A_ ( self ):
return 32
@property
def A_ ( self ):
return 32
@property
def A_ ( self ):
return self.time_input_dim
@property
def A_ ( self ):
return self.time_input_dim * 4
@property
def A_ ( self ):
return 100
@property
def A_ ( self ):
_lowerCamelCase : Tuple = XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base' )
return tokenizer
@property
def A_ ( self ):
torch.manual_seed(0 )
_lowerCamelCase : str = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1005 , )
_lowerCamelCase : str = MultilingualCLIP(lowercase )
_lowerCamelCase : Optional[Any] = text_encoder.eval()
return text_encoder
@property
def A_ ( self ):
torch.manual_seed(0 )
_lowerCamelCase : List[str] = {
'in_channels': 9,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'text_image',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'text_image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
_lowerCamelCase : Union[str, Any] = UNetaDConditionModel(**lowercase )
return model
@property
def A_ ( self ):
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def A_ ( self ):
torch.manual_seed(0 )
_lowerCamelCase : str = VQModel(**self.dummy_movq_kwargs )
return model
def A_ ( self ):
_lowerCamelCase : Optional[int] = self.dummy_text_encoder
_lowerCamelCase : Optional[Any] = self.dummy_tokenizer
_lowerCamelCase : Optional[int] = self.dummy_unet
_lowerCamelCase : Tuple = self.dummy_movq
_lowerCamelCase : Dict = DDIMScheduler(
num_train_timesteps=1000 , beta_schedule='linear' , beta_start=0.0_00_85 , beta_end=0.0_12 , clip_sample=lowercase , set_alpha_to_one=lowercase , steps_offset=1 , prediction_type='epsilon' , thresholding=lowercase , )
_lowerCamelCase : List[str] = {
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def A_ ( self , lowercase , lowercase=0 ):
_lowerCamelCase : List[Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(lowercase ) ).to(lowercase )
_lowerCamelCase : List[str] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(lowercase )
# create init_image
_lowerCamelCase : int = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowercase ) ).to(lowercase )
_lowerCamelCase : Union[str, Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_lowerCamelCase : List[str] = Image.fromarray(np.uinta(lowercase ) ).convert('RGB' ).resize((256, 256) )
# create mask
_lowerCamelCase : Tuple = np.ones((64, 64) , dtype=np.floataa )
_lowerCamelCase : Optional[int] = 0
if str(lowercase ).startswith('mps' ):
_lowerCamelCase : List[Any] = torch.manual_seed(lowercase )
else:
_lowerCamelCase : List[Any] = torch.Generator(device=lowercase ).manual_seed(lowercase )
_lowerCamelCase : str = {
'prompt': 'horse',
'image': init_image,
'mask_image': mask,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 2,
'guidance_scale': 4.0,
'output_type': 'np',
}
return inputs
def A_ ( self ):
_lowerCamelCase : List[Any] = 'cpu'
_lowerCamelCase : Tuple = self.get_dummy_components()
_lowerCamelCase : List[str] = self.pipeline_class(**lowercase )
_lowerCamelCase : Optional[int] = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : int = pipe(**self.get_dummy_inputs(lowercase ) )
_lowerCamelCase : List[Any] = output.images
_lowerCamelCase : Dict = pipe(
**self.get_dummy_inputs(lowercase ) , return_dict=lowercase , )[0]
_lowerCamelCase : Optional[int] = image[0, -3:, -3:, -1]
_lowerCamelCase : str = image_from_tuple[0, -3:, -3:, -1]
print(F'''image.shape {image.shape}''' )
assert image.shape == (1, 64, 64, 3)
_lowerCamelCase : List[Any] = np.array(
[0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
def A_ ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def A_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self ):
_lowerCamelCase : List[str] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy' )
_lowerCamelCase : Optional[int] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
_lowerCamelCase : str = np.ones((768, 768) , dtype=np.floataa )
_lowerCamelCase : Any = 0
_lowerCamelCase : Any = 'a hat'
_lowerCamelCase : Optional[int] = KandinskyPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-prior' , torch_dtype=torch.floataa )
pipe_prior.to(lowercase )
_lowerCamelCase : List[Any] = KandinskyInpaintPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-inpaint' , torch_dtype=torch.floataa )
_lowerCamelCase : Optional[int] = pipeline.to(lowercase )
pipeline.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : Optional[Any] = torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCamelCase, _lowerCamelCase : Optional[Any] = pipe_prior(
lowercase , generator=lowercase , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
_lowerCamelCase : int = pipeline(
lowercase , image=lowercase , mask_image=lowercase , image_embeds=lowercase , negative_image_embeds=lowercase , generator=lowercase , num_inference_steps=100 , height=768 , width=768 , output_type='np' , )
_lowerCamelCase : Dict = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(lowercase , lowercase )
| 96 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
__a : Optional[Any] = logging.get_logger(__name__)
__a : List[str] = TypeVar("""DatasetType""", Dataset, IterableDataset)
def UpperCAmelCase ( lowercase , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = "first_exhausted" , ):
"""simple docstring"""
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('''Unable to interleave an empty list of datasets.''' )
for i, dataset in enumerate(lowercase ):
if not isinstance(lowercase , (Dataset, IterableDataset) ):
if isinstance(lowercase , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(lowercase )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase ).__name__}." )
if i == 0:
__lowercase , __lowercase = (
(Dataset, IterableDataset) if isinstance(lowercase , lowercase ) else (IterableDataset, Dataset)
)
elif not isinstance(lowercase , lowercase ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy." )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
lowercase , lowercase , lowercase , info=lowercase , split=lowercase , stopping_strategy=lowercase )
else:
return _interleave_iterable_datasets(
lowercase , lowercase , lowercase , info=lowercase , split=lowercase , stopping_strategy=lowercase )
def UpperCAmelCase ( lowercase , lowercase = None , lowercase = None , lowercase = 0 , ):
"""simple docstring"""
if not dsets:
raise ValueError('''Unable to concatenate an empty list of datasets.''' )
for i, dataset in enumerate(lowercase ):
if not isinstance(lowercase , (Dataset, IterableDataset) ):
if isinstance(lowercase , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(lowercase )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase ).__name__}." )
if i == 0:
__lowercase , __lowercase = (
(Dataset, IterableDataset) if isinstance(lowercase , lowercase ) else (IterableDataset, Dataset)
)
elif not isinstance(lowercase , lowercase ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(lowercase , info=lowercase , split=lowercase , axis=lowercase )
else:
return _concatenate_iterable_datasets(lowercase , info=lowercase , split=lowercase , axis=lowercase )
| 210 | 0 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def A ( a_ ) -> str:
__UpperCamelCase : int =args.pruning_method
__UpperCamelCase : List[Any] =args.threshold
__UpperCamelCase : Union[str, Any] =args.model_name_or_path.rstrip('/' )
__UpperCamelCase : Any =args.target_model_path
print(F'Load fine-pruned model from {model_name_or_path}' )
__UpperCamelCase : Tuple =torch.load(os.path.join(a_ ,'pytorch_model.bin' ) )
__UpperCamelCase : Dict ={}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
__UpperCamelCase : Optional[Any] =tensor
print(F'Copied layer {name}' )
elif "classifier" in name or "qa_output" in name:
__UpperCamelCase : List[str] =tensor
print(F'Copied layer {name}' )
elif "bias" in name:
__UpperCamelCase : int =tensor
print(F'Copied layer {name}' )
else:
if pruning_method == "magnitude":
__UpperCamelCase : str =MagnitudeBinarizer.apply(inputs=a_ ,threshold=a_ )
__UpperCamelCase : List[str] =tensor * mask
print(F'Pruned layer {name}' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
__UpperCamelCase : List[Any] =name[:-6]
__UpperCamelCase : str =model[F'{prefix_}mask_scores']
__UpperCamelCase : int =TopKBinarizer.apply(a_ ,a_ )
__UpperCamelCase : Union[str, Any] =tensor * mask
print(F'Pruned layer {name}' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
__UpperCamelCase : List[Any] =name[:-6]
__UpperCamelCase : List[str] =model[F'{prefix_}mask_scores']
__UpperCamelCase : Union[str, Any] =ThresholdBinarizer.apply(a_ ,a_ ,a_ )
__UpperCamelCase : Optional[int] =tensor * mask
print(F'Pruned layer {name}' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
__UpperCamelCase : Dict =name[:-6]
__UpperCamelCase : Optional[int] =model[F'{prefix_}mask_scores']
__UpperCamelCase , __UpperCamelCase : Optional[Any] =-0.1, 1.1
__UpperCamelCase : List[Any] =torch.sigmoid(a_ )
__UpperCamelCase : str =s * (r - l) + l
__UpperCamelCase : Any =s_bar.clamp(min=0.0 ,max=1.0 )
__UpperCamelCase : Any =tensor * mask
print(F'Pruned layer {name}' )
else:
raise ValueError('Unknown pruning method' )
if target_model_path is None:
__UpperCamelCase : str =os.path.join(
os.path.dirname(a_ ) ,F'bertarized_{os.path.basename(a_ )}' )
if not os.path.isdir(a_ ):
shutil.copytree(a_ ,a_ )
print(F'\nCreated folder {target_model_path}' )
torch.save(a_ ,os.path.join(a_ ,'pytorch_model.bin' ) )
print('\nPruned model saved! See you later!' )
if __name__ == "__main__":
A_ :Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
'''--pruning_method''',
choices=['''l0''', '''magnitude''', '''topK''', '''sigmoied_threshold'''],
type=str,
required=True,
help=(
'''Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'''
''' sigmoied_threshold = Soft movement pruning)'''
),
)
parser.add_argument(
'''--threshold''',
type=float,
required=False,
help=(
'''For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
'''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
'''Not needed for `l0`'''
),
)
parser.add_argument(
'''--model_name_or_path''',
type=str,
required=True,
help='''Folder containing the model that was previously fine-pruned''',
)
parser.add_argument(
'''--target_model_path''',
default=None,
type=str,
required=False,
help='''Folder containing the model that was previously fine-pruned''',
)
A_ :Optional[Any] = parser.parse_args()
main(args)
| 245 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 245 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase = {'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = ['XLNetTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = ['XLNetTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = [
'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLNetForMultipleChoice',
'XLNetForQuestionAnswering',
'XLNetForQuestionAnsweringSimple',
'XLNetForSequenceClassification',
'XLNetForTokenClassification',
'XLNetLMHeadModel',
'XLNetModel',
'XLNetPreTrainedModel',
'load_tf_weights_in_xlnet',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = [
'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFXLNetForMultipleChoice',
'TFXLNetForQuestionAnsweringSimple',
'TFXLNetForSequenceClassification',
'TFXLNetForTokenClassification',
'TFXLNetLMHeadModel',
'TFXLNetMainLayer',
'TFXLNetModel',
'TFXLNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet import XLNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet_fast import XLNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlnet import (
XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetLMHeadModel,
XLNetModel,
XLNetPreTrainedModel,
load_tf_weights_in_xlnet,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlnet import (
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLNetForMultipleChoice,
TFXLNetForQuestionAnsweringSimple,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetLMHeadModel,
TFXLNetMainLayer,
TFXLNetModel,
TFXLNetPreTrainedModel,
)
else:
import sys
_UpperCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 208 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCamelCase = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = [
'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BioGptForCausalLM',
'BioGptForTokenClassification',
'BioGptForSequenceClassification',
'BioGptModel',
'BioGptPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
from .tokenization_biogpt import BioGptTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_biogpt import (
BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptPreTrainedModel,
)
else:
import sys
_UpperCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 208 | 1 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ : Union[str, Any] = logging.get_logger()
def _lowerCamelCase ( lowerCamelCase_ : int , lowerCamelCase_ : str , lowerCamelCase_ : LevitConfig , lowerCamelCase_ : Path , lowerCamelCase_ : bool = True ):
print(F'''Converting {name}...''' )
with torch.no_grad():
if hidden_sizes == 128:
if name[-1] == "S":
UpperCAmelCase_ : int = timm.create_model('levit_128s' , pretrained=A__ )
else:
UpperCAmelCase_ : Dict = timm.create_model('levit_128' , pretrained=A__ )
if hidden_sizes == 192:
UpperCAmelCase_ : Optional[int] = timm.create_model('levit_192' , pretrained=A__ )
if hidden_sizes == 256:
UpperCAmelCase_ : str = timm.create_model('levit_256' , pretrained=A__ )
if hidden_sizes == 384:
UpperCAmelCase_ : Optional[int] = timm.create_model('levit_384' , pretrained=A__ )
from_model.eval()
UpperCAmelCase_ : List[Any] = LevitForImageClassificationWithTeacher(A__ ).eval()
UpperCAmelCase_ : int = OrderedDict()
UpperCAmelCase_ : List[str] = from_model.state_dict()
UpperCAmelCase_ : List[Any] = list(from_model.state_dict().keys() )
UpperCAmelCase_ : List[str] = list(our_model.state_dict().keys() )
print(len(A__ ) , len(A__ ) )
for i in range(len(A__ ) ):
UpperCAmelCase_ : Optional[int] = weights[og_keys[i]]
our_model.load_state_dict(A__ )
UpperCAmelCase_ : str = torch.randn((2, 3, 224, 224) )
UpperCAmelCase_ : Dict = from_model(A__ )
UpperCAmelCase_ : List[Any] = our_model(A__ ).logits
assert torch.allclose(A__ , A__ ), "The model logits don't match the original one."
UpperCAmelCase_ : Optional[int] = name
print(A__ )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
UpperCAmelCase_ : int = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(F'''Pushed {checkpoint_name}''' )
def _lowerCamelCase ( lowerCamelCase_ : Path , lowerCamelCase_ : str = None , lowerCamelCase_ : bool = True ):
UpperCAmelCase_ : int = """imagenet-1k-id2label.json"""
UpperCAmelCase_ : str = 1000
UpperCAmelCase_ : List[Any] = (1, num_labels)
UpperCAmelCase_ : str = """huggingface/label-files"""
UpperCAmelCase_ : Optional[int] = num_labels
UpperCAmelCase_ : Dict = json.load(open(hf_hub_download(A__ , A__ , repo_type='dataset' ) , 'r' ) )
UpperCAmelCase_ : int = {int(A__ ): v for k, v in idalabel.items()}
UpperCAmelCase_ : Any = idalabel
UpperCAmelCase_ : Tuple = {v: k for k, v in idalabel.items()}
UpperCAmelCase_ : int = partial(A__ , num_labels=A__ , idalabel=A__ , labelaid=A__ )
UpperCAmelCase_ : Dict = {
"""levit-128S""": 128,
"""levit-128""": 128,
"""levit-192""": 192,
"""levit-256""": 256,
"""levit-384""": 384,
}
UpperCAmelCase_ : int = {
"""levit-128S""": ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 6, 8] , depths=[2, 3, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
"""levit-128""": ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 8, 12] , depths=[4, 4, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
"""levit-192""": ImageNetPreTrainedConfig(
hidden_sizes=[192, 288, 384] , num_attention_heads=[3, 5, 6] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
"""levit-256""": ImageNetPreTrainedConfig(
hidden_sizes=[256, 384, 512] , num_attention_heads=[4, 6, 8] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
"""levit-384""": ImageNetPreTrainedConfig(
hidden_sizes=[384, 512, 768] , num_attention_heads=[6, 9, 12] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0.1 , ),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name] , A__ , names_to_config[model_name] , A__ , A__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name] , A__ , A__ , A__ , A__ )
return config, expected_shape
if __name__ == "__main__":
snake_case__ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help='''The name of the model you wish to convert, it must be one of the supported Levit* architecture,''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''levit-dump-folder/''',
type=Path,
required=False,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
parser.add_argument(
'''--no-push_to_hub''',
dest='''push_to_hub''',
action='''store_false''',
help='''Do not push model and image processor to the hub''',
)
snake_case__ : Any = parser.parse_args()
snake_case__ : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 351 |
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : str ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = [0] * len(lowerCamelCase_ )
for i in range(1 , len(lowerCamelCase_ ) ):
# use last results for better performance - dynamic programming
UpperCAmelCase_ : List[Any] = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
UpperCAmelCase_ : str = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
UpperCAmelCase_ : Any = j
return prefix_result
def _lowerCamelCase ( lowerCamelCase_ : str ):
"""simple docstring"""
return max(prefix_function(lowerCamelCase_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 274 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
for i in range(len(_snake_case ) - 1 , 0 , -1 ):
lowerCAmelCase : int = False
for j in range(_snake_case , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
lowerCAmelCase, lowerCAmelCase : Tuple = unsorted[j - 1], unsorted[j]
lowerCAmelCase : Optional[Any] = True
for j in range(_snake_case ):
if unsorted[j] > unsorted[j + 1]:
lowerCAmelCase, lowerCAmelCase : Any = unsorted[j + 1], unsorted[j]
lowerCAmelCase : int = True
if not swapped:
break
return unsorted
if __name__ == "__main__":
import doctest
doctest.testmod()
snake_case__ : List[str] = input('''Enter numbers separated by a comma:\n''').strip()
snake_case__ : str = [int(item) for item in user_input.split(''',''')]
print(f"""{cocktail_shaker_sort(unsorted) = }""")
| 60 |
"""simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __A (unittest.TestCase):
'''simple docstring'''
def lowerCAmelCase ( self : Optional[int] ) ->Dict:
"""simple docstring"""
snake_case_ = """ylacombe/bark-small"""
snake_case_ = tempfile.mkdtemp()
snake_case_ = """en_speaker_1"""
snake_case_ = """This is a test string"""
snake_case_ = """speaker_embeddings_path.json"""
snake_case_ = """speaker_embeddings"""
def lowerCAmelCase ( self : List[str] , **UpperCAmelCase_ : str ) ->Optional[int]:
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.checkpoint , **UpperCAmelCase_ )
def lowerCAmelCase ( self : Any ) ->Dict:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowerCAmelCase ( self : List[Any] ) ->Dict:
"""simple docstring"""
snake_case_ = self.get_tokenizer()
snake_case_ = BarkProcessor(tokenizer=UpperCAmelCase_ )
processor.save_pretrained(self.tmpdirname )
snake_case_ = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def lowerCAmelCase ( self : Dict ) ->int:
"""simple docstring"""
snake_case_ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
snake_case_ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
snake_case_ = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token="""(BOS)""" , eos_token="""(EOS)""" , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def lowerCAmelCase ( self : Optional[Any] ) ->Any:
"""simple docstring"""
snake_case_ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
snake_case_ = 35
snake_case_ = 2
snake_case_ = 8
snake_case_ = {
"""semantic_prompt""": np.ones(UpperCAmelCase_ ),
"""coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len) ),
"""fine_prompt""": np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
snake_case_ = processor(text=self.input_string , voice_preset=UpperCAmelCase_ )
snake_case_ = inputs["""history_prompt"""]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(UpperCAmelCase_ , np.array([] ) ).tolist() )
# test loading voice preset from npz file
snake_case_ = os.path.join(self.tmpdirname , """file.npz""" )
np.savez(UpperCAmelCase_ , **UpperCAmelCase_ )
snake_case_ = processor(text=self.input_string , voice_preset=UpperCAmelCase_ )
snake_case_ = inputs["""history_prompt"""]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(UpperCAmelCase_ , np.array([] ) ).tolist() )
# test loading voice preset from the hub
snake_case_ = processor(text=self.input_string , voice_preset=self.voice_preset )
def lowerCAmelCase ( self : Tuple ) ->Dict:
"""simple docstring"""
snake_case_ = self.get_tokenizer()
snake_case_ = BarkProcessor(tokenizer=UpperCAmelCase_ )
snake_case_ = processor(text=self.input_string )
snake_case_ = tokenizer(
self.input_string , padding="""max_length""" , max_length=256 , add_special_tokens=UpperCAmelCase_ , return_attention_mask=UpperCAmelCase_ , return_token_type_ids=UpperCAmelCase_ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 347 | 0 |
import math
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int ) -> int:
"""simple docstring"""
if not isinstance(__magic_name__ , __magic_name__ ):
UpperCamelCase :Optional[Any] = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__magic_name__ )
if number < 1:
UpperCamelCase :Union[str, Any] = f"""Input value of [number={number}] must be > 0"""
raise ValueError(__magic_name__ )
elif number == 1:
return 3
elif number == 2:
return 5
else:
UpperCamelCase :Optional[Any] = int(math.log(number // 3 , 2 ) ) + 2
UpperCamelCase :Optional[Any] = [3, 5]
UpperCamelCase :Union[str, Any] = 2
UpperCamelCase :Optional[Any] = 3
for block in range(1 , __magic_name__ ):
for _ in range(__magic_name__ ):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
UpperCAmelCase_ : Optional[int] = 0
try:
UpperCAmelCase_ : int = proth(number)
except ValueError:
print(F'''ValueError: there is no {number}th Proth number''')
continue
print(F'''The {number}th Proth number: {value}''')
| 360 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class _SCREAMING_SNAKE_CASE ( _a ):
snake_case__ : Union[str, Any] = """char"""
snake_case__ : Optional[int] = """bpe"""
snake_case__ : Dict = """wp"""
UpperCAmelCase_ : List[Any] = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class _SCREAMING_SNAKE_CASE ( _a ):
snake_case__ : List[Any] = ["""image_processor""", """char_tokenizer"""]
snake_case__ : Dict = """ViTImageProcessor"""
snake_case__ : List[str] = """MgpstrTokenizer"""
def __init__( self : Optional[int] , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : Dict=None , **__lowerCamelCase : Any ):
UpperCamelCase :Optional[Any] = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , __lowerCamelCase , )
UpperCamelCase :Optional[int] = kwargs.pop("""feature_extractor""" )
UpperCamelCase :List[str] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
UpperCamelCase :Optional[int] = tokenizer
UpperCamelCase :int = AutoTokenizer.from_pretrained("""gpt2""" )
UpperCamelCase :int = AutoTokenizer.from_pretrained("""bert-base-uncased""" )
super().__init__(__lowerCamelCase , __lowerCamelCase )
def __call__( self : str , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : Dict=None , __lowerCamelCase : str=None , **__lowerCamelCase : Dict ):
if images is None and text is None:
raise ValueError("""You need to specify either an `images` or `text` input to process.""" )
if images is not None:
UpperCamelCase :Tuple = self.image_processor(__lowerCamelCase , return_tensors=__lowerCamelCase , **__lowerCamelCase )
if text is not None:
UpperCamelCase :Any = self.char_tokenizer(__lowerCamelCase , return_tensors=__lowerCamelCase , **__lowerCamelCase )
if text is None:
return inputs
elif images is None:
return encodings
else:
UpperCamelCase :Dict = encodings["""input_ids"""]
return inputs
def _A ( self : Tuple , __lowerCamelCase : str ):
UpperCamelCase , UpperCamelCase , UpperCamelCase :int = sequences
UpperCamelCase :Tuple = char_preds.size(0 )
UpperCamelCase , UpperCamelCase :str = self._decode_helper(__lowerCamelCase , """char""" )
UpperCamelCase , UpperCamelCase :List[Any] = self._decode_helper(__lowerCamelCase , """bpe""" )
UpperCamelCase , UpperCamelCase :List[Any] = self._decode_helper(__lowerCamelCase , """wp""" )
UpperCamelCase :Any = []
UpperCamelCase :str = []
for i in range(__lowerCamelCase ):
UpperCamelCase :Union[str, Any] = [char_scores[i], bpe_scores[i], wp_scores[i]]
UpperCamelCase :Any = [char_strs[i], bpe_strs[i], wp_strs[i]]
UpperCamelCase :str = scores.index(max(__lowerCamelCase ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
UpperCamelCase :Optional[Any] = {}
UpperCamelCase :Dict = final_strs
UpperCamelCase :Union[str, Any] = final_scores
UpperCamelCase :List[str] = char_strs
UpperCamelCase :Tuple = bpe_strs
UpperCamelCase :Optional[Any] = wp_strs
return out
def _A ( self : int , __lowerCamelCase : List[Any] , __lowerCamelCase : List[str] ):
if format == DecodeType.CHARACTER:
UpperCamelCase :List[str] = self.char_decode
UpperCamelCase :Union[str, Any] = 1
UpperCamelCase :Optional[Any] = """[s]"""
elif format == DecodeType.BPE:
UpperCamelCase :Union[str, Any] = self.bpe_decode
UpperCamelCase :str = 2
UpperCamelCase :int = """#"""
elif format == DecodeType.WORDPIECE:
UpperCamelCase :int = self.wp_decode
UpperCamelCase :Any = 102
UpperCamelCase :int = """[SEP]"""
else:
raise ValueError(F"""Format {format} is not supported.""" )
UpperCamelCase , UpperCamelCase :int = [], []
UpperCamelCase :Any = pred_logits.size(0 )
UpperCamelCase :List[Any] = pred_logits.size(1 )
UpperCamelCase , UpperCamelCase :Optional[int] = pred_logits.topk(1 , dim=-1 , largest=__lowerCamelCase , sorted=__lowerCamelCase )
UpperCamelCase :Optional[Any] = preds_index.view(-1 , __lowerCamelCase )[:, 1:]
UpperCamelCase :int = decoder(__lowerCamelCase )
UpperCamelCase , UpperCamelCase :Optional[int] = torch.nn.functional.softmax(__lowerCamelCase , dim=2 ).max(dim=2 )
UpperCamelCase :Tuple = preds_max_prob[:, 1:]
for index in range(__lowerCamelCase ):
UpperCamelCase :Tuple = preds_str[index].find(__lowerCamelCase )
UpperCamelCase :List[Any] = preds_str[index][:pred_eos]
UpperCamelCase :List[Any] = preds_index[index].cpu().tolist()
UpperCamelCase :Optional[Any] = pred_index.index(__lowerCamelCase ) if eos_token in pred_index else -1
UpperCamelCase :List[str] = preds_max_prob[index][: pred_eos_index + 1]
UpperCamelCase :List[str] = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(__lowerCamelCase )
conf_scores.append(__lowerCamelCase )
return dec_strs, conf_scores
def _A ( self : Optional[Any] , __lowerCamelCase : str ):
UpperCamelCase :Dict = [seq.replace(""" """ , """""" ) for seq in self.char_tokenizer.batch_decode(__lowerCamelCase )]
return decode_strs
def _A ( self : Union[str, Any] , __lowerCamelCase : str ):
return self.bpe_tokenizer.batch_decode(__lowerCamelCase )
def _A ( self : int , __lowerCamelCase : Optional[int] ):
UpperCamelCase :Any = [seq.replace(""" """ , """""" ) for seq in self.wp_tokenizer.batch_decode(__lowerCamelCase )]
return decode_strs
| 62 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : Union[str, Any] = ['''pixel_values''']
def __init__( self : List[str] , SCREAMING_SNAKE_CASE__ : bool = True , SCREAMING_SNAKE_CASE__ : Optional[Dict[str, int]] = None , SCREAMING_SNAKE_CASE__ : PILImageResampling = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE__ : bool = True , SCREAMING_SNAKE_CASE__ : Dict[str, int] = None , SCREAMING_SNAKE_CASE__ : bool = True , SCREAMING_SNAKE_CASE__ : Union[int, float] = 1 / 2_5_5 , SCREAMING_SNAKE_CASE__ : bool = True , SCREAMING_SNAKE_CASE__ : Optional[Union[float, List[float]]] = None , SCREAMING_SNAKE_CASE__ : Optional[Union[float, List[float]]] = None , **SCREAMING_SNAKE_CASE__ : Union[str, Any] , ) -> None:
super().__init__(**SCREAMING_SNAKE_CASE__ )
a_ : str = size if size is not None else {'shortest_edge': 2_5_6}
a_ : Any = get_size_dict(SCREAMING_SNAKE_CASE__ , default_to_square=SCREAMING_SNAKE_CASE__ )
a_ : Dict = crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
a_ : Optional[int] = get_size_dict(SCREAMING_SNAKE_CASE__ )
a_ : List[str] = do_resize
a_ : Dict = size
a_ : Optional[Any] = resample
a_ : Optional[int] = do_center_crop
a_ : Dict = crop_size
a_ : int = do_rescale
a_ : int = rescale_factor
a_ : Tuple = do_normalize
a_ : int = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
a_ : Tuple = image_std if image_std is not None else IMAGENET_STANDARD_STD
def SCREAMING_SNAKE_CASE ( self : List[Any] , SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : Dict[str, int] , SCREAMING_SNAKE_CASE__ : PILImageResampling = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE__ : Optional[Union[str, ChannelDimension]] = None , **SCREAMING_SNAKE_CASE__ : Union[str, Any] , ) -> np.ndarray:
a_ : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE__ , default_to_square=SCREAMING_SNAKE_CASE__ )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
a_ : Tuple = get_resize_output_image_size(SCREAMING_SNAKE_CASE__ , size=size['shortest_edge'] , default_to_square=SCREAMING_SNAKE_CASE__ )
return resize(SCREAMING_SNAKE_CASE__ , size=SCREAMING_SNAKE_CASE__ , resample=SCREAMING_SNAKE_CASE__ , data_format=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : List[Any] , SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : Dict[str, int] , SCREAMING_SNAKE_CASE__ : Optional[Union[str, ChannelDimension]] = None , **SCREAMING_SNAKE_CASE__ : Union[str, Any] , ) -> np.ndarray:
a_ : str = get_size_dict(SCREAMING_SNAKE_CASE__ )
return center_crop(SCREAMING_SNAKE_CASE__ , size=(size['height'], size['width']) , data_format=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Dict , SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : Optional[Union[str, ChannelDimension]] = None , **SCREAMING_SNAKE_CASE__ : Optional[int] ) -> np.ndarray:
return rescale(SCREAMING_SNAKE_CASE__ , scale=SCREAMING_SNAKE_CASE__ , data_format=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : Union[float, List[float]] , SCREAMING_SNAKE_CASE__ : Union[float, List[float]] , SCREAMING_SNAKE_CASE__ : Optional[Union[str, ChannelDimension]] = None , **SCREAMING_SNAKE_CASE__ : List[str] , ) -> np.ndarray:
return normalize(SCREAMING_SNAKE_CASE__ , mean=SCREAMING_SNAKE_CASE__ , std=SCREAMING_SNAKE_CASE__ , data_format=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Dict , SCREAMING_SNAKE_CASE__ : ImageInput , SCREAMING_SNAKE_CASE__ : Optional[bool] = None , SCREAMING_SNAKE_CASE__ : Dict[str, int] = None , SCREAMING_SNAKE_CASE__ : PILImageResampling = None , SCREAMING_SNAKE_CASE__ : bool = None , SCREAMING_SNAKE_CASE__ : Dict[str, int] = None , SCREAMING_SNAKE_CASE__ : Optional[bool] = None , SCREAMING_SNAKE_CASE__ : Optional[float] = None , SCREAMING_SNAKE_CASE__ : Optional[bool] = None , SCREAMING_SNAKE_CASE__ : Optional[Union[float, List[float]]] = None , SCREAMING_SNAKE_CASE__ : Optional[Union[float, List[float]]] = None , SCREAMING_SNAKE_CASE__ : Optional[Union[str, TensorType]] = None , SCREAMING_SNAKE_CASE__ : Union[str, ChannelDimension] = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE__ : Union[str, Any] , ) -> Union[str, Any]:
a_ : List[str] = do_resize if do_resize is not None else self.do_resize
a_ : Dict = size if size is not None else self.size
a_ : Dict = get_size_dict(SCREAMING_SNAKE_CASE__ , default_to_square=SCREAMING_SNAKE_CASE__ )
a_ : List[Any] = resample if resample is not None else self.resample
a_ : List[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
a_ : int = crop_size if crop_size is not None else self.crop_size
a_ : Optional[int] = get_size_dict(SCREAMING_SNAKE_CASE__ )
a_ : Dict = do_rescale if do_rescale is not None else self.do_rescale
a_ : Dict = rescale_factor if rescale_factor is not None else self.rescale_factor
a_ : Any = do_normalize if do_normalize is not None else self.do_normalize
a_ : str = image_mean if image_mean is not None else self.image_mean
a_ : Dict = image_std if image_std is not None else self.image_std
a_ : Optional[int] = make_list_of_images(SCREAMING_SNAKE_CASE__ )
if not valid_images(SCREAMING_SNAKE_CASE__ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
a_ : Any = [to_numpy_array(SCREAMING_SNAKE_CASE__ ) for image in images]
if do_resize:
a_ : str = [self.resize(image=SCREAMING_SNAKE_CASE__ , size=SCREAMING_SNAKE_CASE__ , resample=SCREAMING_SNAKE_CASE__ ) for image in images]
if do_center_crop:
a_ : int = [self.center_crop(image=SCREAMING_SNAKE_CASE__ , size=SCREAMING_SNAKE_CASE__ ) for image in images]
if do_rescale:
a_ : Optional[Any] = [self.rescale(image=SCREAMING_SNAKE_CASE__ , scale=SCREAMING_SNAKE_CASE__ ) for image in images]
if do_normalize:
a_ : List[Any] = [self.normalize(image=SCREAMING_SNAKE_CASE__ , mean=SCREAMING_SNAKE_CASE__ , std=SCREAMING_SNAKE_CASE__ ) for image in images]
a_ : Dict = [to_channel_dimension_format(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for image in images]
a_ : Tuple = {'pixel_values': images}
return BatchFeature(data=SCREAMING_SNAKE_CASE__ , tensor_type=SCREAMING_SNAKE_CASE__ )
| 32 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _SCREAMING_SNAKE_CASE ( _a , _a , unittest.TestCase ):
snake_case__ : Any = StableDiffusionXLImgaImgPipeline
snake_case__ : Tuple = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
snake_case__ : Tuple = PipelineTesterMixin.required_optional_params - {"""latents"""}
snake_case__ : Any = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
snake_case__ : List[str] = IMAGE_TO_IMAGE_IMAGE_PARAMS
snake_case__ : Tuple = IMAGE_TO_IMAGE_IMAGE_PARAMS
def _A ( self : int ):
torch.manual_seed(0 )
UpperCamelCase :Any = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , attention_head_dim=(2, 4) , use_linear_projection=__lowerCamelCase , addition_embed_type="""text_time""" , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
UpperCamelCase :Tuple = EulerDiscreteScheduler(
beta_start=0.00085 , beta_end=0.012 , steps_offset=1 , beta_schedule="""scaled_linear""" , timestep_spacing="""leading""" , )
torch.manual_seed(0 )
UpperCamelCase :Union[str, Any] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
UpperCamelCase :Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , hidden_act="""gelu""" , projection_dim=32 , )
UpperCamelCase :Any = CLIPTextModel(__lowerCamelCase )
UpperCamelCase :List[Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=__lowerCamelCase )
UpperCamelCase :List[Any] = CLIPTextModelWithProjection(__lowerCamelCase )
UpperCamelCase :int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=__lowerCamelCase )
UpperCamelCase :Union[str, Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""text_encoder_2""": text_encoder_a,
"""tokenizer_2""": tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def _A ( self : Tuple , __lowerCamelCase : Any , __lowerCamelCase : Optional[Any]=0 ):
UpperCamelCase :Tuple = floats_tensor((1, 3, 32, 32) , rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
UpperCamelCase :List[str] = image / 2 + 0.5
if str(__lowerCamelCase ).startswith("""mps""" ):
UpperCamelCase :Any = torch.manual_seed(__lowerCamelCase )
else:
UpperCamelCase :List[Any] = torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
UpperCamelCase :str = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 5.0,
"""output_type""": """numpy""",
"""strength""": 0.75,
}
return inputs
def _A ( self : str ):
UpperCamelCase :List[str] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase :Optional[Any] = self.get_dummy_components()
UpperCamelCase :List[Any] = StableDiffusionXLImgaImgPipeline(**__lowerCamelCase )
UpperCamelCase :Any = sd_pipe.to(__lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )
UpperCamelCase :Optional[Any] = self.get_dummy_inputs(__lowerCamelCase )
UpperCamelCase :Union[str, Any] = sd_pipe(**__lowerCamelCase ).images
UpperCamelCase :Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCamelCase :List[Any] = np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _A ( self : Dict ):
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def _A ( self : Optional[Any] ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def _A ( self : Union[str, Any] ):
pass
def _A ( self : Optional[int] ):
UpperCamelCase :Union[str, Any] = self.get_dummy_components()
UpperCamelCase :Dict = StableDiffusionXLImgaImgPipeline(**__lowerCamelCase )
UpperCamelCase :List[Any] = sd_pipe.to(__lowerCamelCase )
UpperCamelCase :List[str] = sd_pipe.to(__lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )
# forward without prompt embeds
UpperCamelCase :List[Any] = self.get_dummy_inputs(__lowerCamelCase )
UpperCamelCase :int = 3 * ["""this is a negative prompt"""]
UpperCamelCase :Union[str, Any] = negative_prompt
UpperCamelCase :Union[str, Any] = 3 * [inputs["""prompt"""]]
UpperCamelCase :Dict = sd_pipe(**__lowerCamelCase )
UpperCamelCase :Union[str, Any] = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
UpperCamelCase :Union[str, Any] = self.get_dummy_inputs(__lowerCamelCase )
UpperCamelCase :Optional[int] = 3 * ["""this is a negative prompt"""]
UpperCamelCase :Union[str, Any] = 3 * [inputs.pop("""prompt""" )]
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) :Union[str, Any] = sd_pipe.encode_prompt(__lowerCamelCase , negative_prompt=__lowerCamelCase )
UpperCamelCase :Dict = sd_pipe(
**__lowerCamelCase , prompt_embeds=__lowerCamelCase , negative_prompt_embeds=__lowerCamelCase , pooled_prompt_embeds=__lowerCamelCase , negative_pooled_prompt_embeds=__lowerCamelCase , )
UpperCamelCase :Union[str, Any] = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def _A ( self : Tuple ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _A ( self : List[Any] , __lowerCamelCase : Any , __lowerCamelCase : Dict="cpu" , __lowerCamelCase : List[Any]=torch.floataa , __lowerCamelCase : Tuple=0 ):
UpperCamelCase :Optional[int] = torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
UpperCamelCase :Optional[Any] = np.random.RandomState(__lowerCamelCase ).standard_normal((1, 4, 64, 64) )
UpperCamelCase :Dict = torch.from_numpy(__lowerCamelCase ).to(device=__lowerCamelCase , dtype=__lowerCamelCase )
UpperCamelCase :str = {
"""prompt""": """a photograph of an astronaut riding a horse""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _A ( self : Optional[Any] ):
UpperCamelCase :Any = DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base""" )
pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
UpperCamelCase :Optional[Any] = self.get_inputs(__lowerCamelCase )
UpperCamelCase :Optional[int] = pipe(**__lowerCamelCase ).images
UpperCamelCase :Dict = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
UpperCamelCase :Union[str, Any] = np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 38 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class A__ ( _snake_case ):
lowercase = "decision_transformer"
lowercase = ["past_key_values"]
lowercase = {
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self , UpperCamelCase__=17 , UpperCamelCase__=4 , UpperCamelCase__=128 , UpperCamelCase__=4096 , UpperCamelCase__=True , UpperCamelCase__=1 , UpperCamelCase__=1024 , UpperCamelCase__=3 , UpperCamelCase__=1 , UpperCamelCase__=None , UpperCamelCase__="relu" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=1e-5 , UpperCamelCase__=0.02 , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=50256 , UpperCamelCase__=50256 , UpperCamelCase__=False , UpperCamelCase__=False , **UpperCamelCase__ , ) -> str:
'''simple docstring'''
A_ = state_dim
A_ = act_dim
A_ = hidden_size
A_ = max_ep_len
A_ = action_tanh
A_ = vocab_size
A_ = n_positions
A_ = n_layer
A_ = n_head
A_ = n_inner
A_ = activation_function
A_ = resid_pdrop
A_ = embd_pdrop
A_ = attn_pdrop
A_ = layer_norm_epsilon
A_ = initializer_range
A_ = scale_attn_weights
A_ = use_cache
A_ = scale_attn_by_inverse_layer_idx
A_ = reorder_and_upcast_attn
A_ = bos_token_id
A_ = eos_token_id
super().__init__(bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__ )
| 367 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if not postfix_notation:
return 0
A_ = {"""+""", """-""", """*""", """/"""}
A_ = []
for token in postfix_notation:
if token in operations:
A_ , A_ = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(UpperCAmelCase__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 101 | 0 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
__snake_case =[
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
]
# Define blinker example
__snake_case =[[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def a_ ( lowerCamelCase : list[list[int]] ):
lowerCAmelCase = []
for i in range(len(lowerCamelCase ) ):
lowerCAmelCase = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
lowerCAmelCase = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(lowerCamelCase ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(lowerCamelCase ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(lowerCamelCase ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
lowerCAmelCase = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(lowerCamelCase )
return next_generation
def a_ ( lowerCamelCase : list[list[int]] , lowerCamelCase : int ):
lowerCAmelCase = []
for _ in range(lowerCamelCase ):
# Create output image
lowerCAmelCase = Image.new('RGB' , (len(cells[0] ), len(lowerCamelCase )) )
lowerCAmelCase = img.load()
# Save cells to image
for x in range(len(lowerCamelCase ) ):
for y in range(len(cells[0] ) ):
lowerCAmelCase = 255 - cells[y][x] * 255
lowerCAmelCase = (colour, colour, colour)
# Save image
images.append(lowerCamelCase )
lowerCAmelCase = new_generation(lowerCamelCase )
return images
if __name__ == "__main__":
__snake_case =generate_images(GLIDER, 16)
images[0].save("""out.gif""", save_all=True, append_images=images[1:])
| 4 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
UpperCamelCase = logging.get_logger(__name__)
if is_vision_available():
import PIL
class snake_case_ ( __A ):
__A : str = ["pixel_values"]
def __init__( self : int , lowercase_ : bool = True , lowercase_ : Dict[str, int] = None , lowercase_ : PILImageResampling = PILImageResampling.BICUBIC , lowercase_ : bool = True , lowercase_ : Dict[str, int] = None , lowercase_ : bool = True , lowercase_ : Union[int, float] = 1 / 2_55 , lowercase_ : bool = True , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : bool = True , **lowercase_ : Union[str, Any] , ) -> None:
super().__init__(**lowercase_ )
lowercase__ : Tuple = size if size is not None else {"shortest_edge": 2_24}
lowercase__ : Tuple = get_size_dict(lowercase_ , default_to_square=lowercase_ )
lowercase__ : List[str] = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
lowercase__ : Tuple = get_size_dict(lowercase_ , default_to_square=lowercase_ , param_name="crop_size" )
lowercase__ : Dict = do_resize
lowercase__ : List[Any] = size
lowercase__ : int = resample
lowercase__ : Union[str, Any] = do_center_crop
lowercase__ : Optional[int] = crop_size
lowercase__ : List[str] = do_rescale
lowercase__ : int = rescale_factor
lowercase__ : List[Any] = do_normalize
lowercase__ : Union[str, Any] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
lowercase__ : str = image_std if image_std is not None else OPENAI_CLIP_STD
lowercase__ : Dict = do_convert_rgb
def __UpperCamelCase ( self : Optional[Any] , lowercase_ : np.ndarray , lowercase_ : Dict[str, int] , lowercase_ : PILImageResampling = PILImageResampling.BICUBIC , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : Union[str, Any] , ) -> np.ndarray:
lowercase__ : str = get_size_dict(lowercase_ , default_to_square=lowercase_ )
if "shortest_edge" not in size:
raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
lowercase__ : Dict = get_resize_output_image_size(lowercase_ , size=size["shortest_edge"] , default_to_square=lowercase_ )
return resize(lowercase_ , size=lowercase_ , resample=lowercase_ , data_format=lowercase_ , **lowercase_ )
def __UpperCamelCase ( self : int , lowercase_ : np.ndarray , lowercase_ : Dict[str, int] , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : int , ) -> np.ndarray:
lowercase__ : Optional[Any] = get_size_dict(lowercase_ )
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(lowercase_ , size=(size["height"], size["width"]) , data_format=lowercase_ , **lowercase_ )
def __UpperCamelCase ( self : Optional[Any] , lowercase_ : np.ndarray , lowercase_ : Union[int, float] , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : Optional[Any] , ) -> Any:
return rescale(lowercase_ , scale=lowercase_ , data_format=lowercase_ , **lowercase_ )
def __UpperCamelCase ( self : str , lowercase_ : np.ndarray , lowercase_ : Union[float, List[float]] , lowercase_ : Union[float, List[float]] , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : str , ) -> np.ndarray:
return normalize(lowercase_ , mean=lowercase_ , std=lowercase_ , data_format=lowercase_ , **lowercase_ )
def __UpperCamelCase ( self : Optional[Any] , lowercase_ : ImageInput , lowercase_ : bool = None , lowercase_ : Dict[str, int] = None , lowercase_ : PILImageResampling = None , lowercase_ : bool = None , lowercase_ : int = None , lowercase_ : bool = None , lowercase_ : float = None , lowercase_ : bool = None , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : bool = None , lowercase_ : Optional[Union[str, TensorType]] = None , lowercase_ : Optional[ChannelDimension] = ChannelDimension.FIRST , **lowercase_ : Union[str, Any] , ) -> PIL.Image.Image:
lowercase__ : int = do_resize if do_resize is not None else self.do_resize
lowercase__ : Dict = size if size is not None else self.size
lowercase__ : List[Any] = get_size_dict(lowercase_ , param_name="size" , default_to_square=lowercase_ )
lowercase__ : Dict = resample if resample is not None else self.resample
lowercase__ : int = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase__ : Dict = crop_size if crop_size is not None else self.crop_size
lowercase__ : List[str] = get_size_dict(lowercase_ , param_name="crop_size" , default_to_square=lowercase_ )
lowercase__ : int = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ : Dict = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ : Union[str, Any] = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ : int = image_mean if image_mean is not None else self.image_mean
lowercase__ : List[str] = image_std if image_std is not None else self.image_std
lowercase__ : Union[str, Any] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
lowercase__ : Union[str, Any] = make_list_of_images(lowercase_ )
if not valid_images(lowercase_ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
lowercase__ : Dict = [convert_to_rgb(lowercase_ ) for image in images]
# All transformations expect numpy arrays.
lowercase__ : Optional[Any] = [to_numpy_array(lowercase_ ) for image in images]
if do_resize:
lowercase__ : List[Any] = [self.resize(image=lowercase_ , size=lowercase_ , resample=lowercase_ ) for image in images]
if do_center_crop:
lowercase__ : int = [self.center_crop(image=lowercase_ , size=lowercase_ ) for image in images]
if do_rescale:
lowercase__ : str = [self.rescale(image=lowercase_ , scale=lowercase_ ) for image in images]
if do_normalize:
lowercase__ : Optional[int] = [self.normalize(image=lowercase_ , mean=lowercase_ , std=lowercase_ ) for image in images]
lowercase__ : Optional[Any] = [to_channel_dimension_format(lowercase_ , lowercase_ ) for image in images]
lowercase__ : List[str] = {"pixel_values": images}
return BatchFeature(data=lowercase_ , tensor_type=lowercase_ )
| 87 | 0 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFCvtForImageClassification, TFCvtModel
from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A ( __UpperCAmelCase ):
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(UpperCamelCase__, '''embed_dim''' ) )
self.parent.assertTrue(hasattr(UpperCamelCase__, '''num_heads''' ) )
class A :
def __init__( self, UpperCamelCase__, UpperCamelCase__=13, UpperCamelCase__=64, UpperCamelCase__=3, UpperCamelCase__=[16, 48, 96], UpperCamelCase__=[1, 3, 6], UpperCamelCase__=[1, 2, 10], UpperCamelCase__=[7, 3, 3], UpperCamelCase__=[4, 2, 2], UpperCamelCase__=[2, 1, 1], UpperCamelCase__=[2, 2, 2], UpperCamelCase__=[False, False, True], UpperCamelCase__=[0.0, 0.0, 0.0], UpperCamelCase__=0.02, UpperCamelCase__=1E-12, UpperCamelCase__=True, UpperCamelCase__=True, UpperCamelCase__=2, ):
"""simple docstring"""
lowerCAmelCase_ = parent
lowerCAmelCase_ = batch_size
lowerCAmelCase_ = image_size
lowerCAmelCase_ = patch_sizes
lowerCAmelCase_ = patch_stride
lowerCAmelCase_ = patch_padding
lowerCAmelCase_ = is_training
lowerCAmelCase_ = use_labels
lowerCAmelCase_ = num_labels
lowerCAmelCase_ = num_channels
lowerCAmelCase_ = embed_dim
lowerCAmelCase_ = num_heads
lowerCAmelCase_ = stride_kv
lowerCAmelCase_ = depth
lowerCAmelCase_ = cls_token
lowerCAmelCase_ = attention_drop_rate
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = layer_norm_eps
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ = None
if self.use_labels:
# create a random int32 tensor of given shape
lowerCAmelCase_ = ids_tensor([self.batch_size], self.num_labels )
lowerCAmelCase_ = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
return CvtConfig(
image_size=self.image_size, num_labels=self.num_labels, num_channels=self.num_channels, embed_dim=self.embed_dim, num_heads=self.num_heads, patch_sizes=self.patch_sizes, patch_padding=self.patch_padding, patch_stride=self.patch_stride, stride_kv=self.stride_kv, depth=self.depth, cls_token=self.cls_token, attention_drop_rate=self.attention_drop_rate, initializer_range=self.initializer_range, )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = TFCvtModel(config=UpperCamelCase__ )
lowerCAmelCase_ = model(UpperCamelCase__, training=UpperCamelCase__ )
lowerCAmelCase_ = (self.image_size, self.image_size)
lowerCAmelCase_ , lowerCAmelCase_ = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
lowerCAmelCase_ = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
lowerCAmelCase_ = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.embed_dim[-1], height, width) )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = self.num_labels
lowerCAmelCase_ = TFCvtForImageClassification(UpperCamelCase__ )
lowerCAmelCase_ = model(UpperCamelCase__, labels=UpperCamelCase__, training=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = config_and_inputs
lowerCAmelCase_ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class A ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
__snake_case = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
__snake_case = (
{'feature-extraction': TFCvtModel, 'image-classification': TFCvtForImageClassification}
if is_tf_available()
else {}
)
__snake_case = False
__snake_case = False
__snake_case = False
__snake_case = False
__snake_case = False
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = TFCvtModelTester(self )
lowerCAmelCase_ = TFCvtConfigTester(self, config_class=UpperCamelCase__, has_text_modality=UpperCamelCase__, hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
self.config_tester.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
@unittest.skip(reason='''Cvt does not output attentions''' )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='''Cvt does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='''Cvt does not support input and output embeddings''' )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0, reason='''TF does not support backprop for grouped convolutions on CPU.''', )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0, reason='''TF does not support backprop for grouped convolutions on CPU.''', )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
super().test_keras_fit()
@unittest.skip(reason='''Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8''' )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = tf.keras.mixed_precision.Policy('''mixed_float16''' )
tf.keras.mixed_precision.set_global_policy(UpperCamelCase__ )
super().test_keras_fit()
tf.keras.mixed_precision.set_global_policy('''float32''' )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ , lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ = model_class(UpperCamelCase__ )
lowerCAmelCase_ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ = [*signature.parameters.keys()]
lowerCAmelCase_ = ['''pixel_values''']
self.assertListEqual(arg_names[:1], UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
def check_hidden_states_output(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
lowerCAmelCase_ = model_class(UpperCamelCase__ )
lowerCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__, UpperCamelCase__ ) )
lowerCAmelCase_ = outputs.hidden_states
lowerCAmelCase_ = len(self.model_tester.depth )
self.assertEqual(len(UpperCamelCase__ ), UpperCamelCase__ )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ), [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
], )
lowerCAmelCase_ , lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ = True
check_hidden_states_output(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase_ = True
check_hidden_states_output(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ = TFCvtModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
def __UpperCamelCase ( ):
lowerCAmelCase_ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class A ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
lowerCAmelCase_ = self.default_image_processor
lowerCAmelCase_ = prepare_img()
lowerCAmelCase_ = image_processor(images=UpperCamelCase__, return_tensors='''tf''' )
# forward pass
lowerCAmelCase_ = model(**UpperCamelCase__ )
# verify the logits
lowerCAmelCase_ = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape, UpperCamelCase__ )
lowerCAmelCase_ = tf.constant([0.9_285, 0.9_015, -0.3_150] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy(), UpperCamelCase__, atol=1E-4 ) )
| 167 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPSegProcessor, ViTImageProcessor
@require_vision
class A ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = tempfile.mkdtemp()
# fmt: off
lowerCAmelCase_ = ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
# fmt: on
lowerCAmelCase_ = dict(zip(UpperCamelCase__, range(len(UpperCamelCase__ ) ) ) )
lowerCAmelCase_ = ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
lowerCAmelCase_ = {'''unk_token''': '''<unk>'''}
lowerCAmelCase_ = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCAmelCase_ = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write(json.dumps(UpperCamelCase__ ) + '''\n''' )
with open(self.merges_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(UpperCamelCase__ ) )
lowerCAmelCase_ = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''do_normalize''': True,
'''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
'''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
lowerCAmelCase_ = os.path.join(self.tmpdirname, UpperCamelCase__ )
with open(self.image_processor_file, '''w''', encoding='''utf-8''' ) as fp:
json.dump(UpperCamelCase__, UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self, **UpperCamelCase__ ):
"""simple docstring"""
return CLIPTokenizer.from_pretrained(self.tmpdirname, **UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self, **UpperCamelCase__ ):
"""simple docstring"""
return CLIPTokenizerFast.from_pretrained(self.tmpdirname, **UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self, **UpperCamelCase__ ):
"""simple docstring"""
return ViTImageProcessor.from_pretrained(self.tmpdirname, **UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = [np.random.randint(255, size=(3, 30, 400), dtype=np.uinta )]
lowerCAmelCase_ = [Image.fromarray(np.moveaxis(UpperCamelCase__, 0, -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.get_tokenizer()
lowerCAmelCase_ = self.get_rust_tokenizer()
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = CLIPSegProcessor(tokenizer=UpperCamelCase__, image_processor=UpperCamelCase__ )
processor_slow.save_pretrained(self.tmpdirname )
lowerCAmelCase_ = CLIPSegProcessor.from_pretrained(self.tmpdirname, use_fast=UpperCamelCase__ )
lowerCAmelCase_ = CLIPSegProcessor(tokenizer=UpperCamelCase__, image_processor=UpperCamelCase__ )
processor_fast.save_pretrained(self.tmpdirname )
lowerCAmelCase_ = CLIPSegProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer, UpperCamelCase__ )
self.assertIsInstance(processor_fast.tokenizer, UpperCamelCase__ )
self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor, UpperCamelCase__ )
self.assertIsInstance(processor_fast.image_processor, UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = CLIPSegProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase_ = self.get_tokenizer(bos_token='''(BOS)''', eos_token='''(EOS)''' )
lowerCAmelCase_ = self.get_image_processor(do_normalize=UpperCamelCase__, padding_value=1.0 )
lowerCAmelCase_ = CLIPSegProcessor.from_pretrained(
self.tmpdirname, bos_token='''(BOS)''', eos_token='''(EOS)''', do_normalize=UpperCamelCase__, padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer, UpperCamelCase__ )
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor, UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = self.get_tokenizer()
lowerCAmelCase_ = CLIPSegProcessor(tokenizer=UpperCamelCase__, image_processor=UpperCamelCase__ )
lowerCAmelCase_ = self.prepare_image_inputs()
lowerCAmelCase_ = image_processor(UpperCamelCase__, return_tensors='''np''' )
lowerCAmelCase_ = processor(images=UpperCamelCase__, return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1E-2 )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = self.get_tokenizer()
lowerCAmelCase_ = CLIPSegProcessor(tokenizer=UpperCamelCase__, image_processor=UpperCamelCase__ )
lowerCAmelCase_ = '''lower newer'''
lowerCAmelCase_ = processor(text=UpperCamelCase__ )
lowerCAmelCase_ = tokenizer(UpperCamelCase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key] )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = self.get_tokenizer()
lowerCAmelCase_ = CLIPSegProcessor(tokenizer=UpperCamelCase__, image_processor=UpperCamelCase__ )
lowerCAmelCase_ = '''lower newer'''
lowerCAmelCase_ = self.prepare_image_inputs()
lowerCAmelCase_ = processor(text=UpperCamelCase__, images=UpperCamelCase__ )
self.assertListEqual(list(inputs.keys() ), ['''input_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(UpperCamelCase__ ):
processor()
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = self.get_tokenizer()
lowerCAmelCase_ = CLIPSegProcessor(tokenizer=UpperCamelCase__, image_processor=UpperCamelCase__ )
lowerCAmelCase_ = self.prepare_image_inputs()
lowerCAmelCase_ = self.prepare_image_inputs()
lowerCAmelCase_ = processor(images=UpperCamelCase__, visual_prompt=UpperCamelCase__ )
self.assertListEqual(list(inputs.keys() ), ['''pixel_values''', '''conditional_pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(UpperCamelCase__ ):
processor()
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = self.get_tokenizer()
lowerCAmelCase_ = CLIPSegProcessor(tokenizer=UpperCamelCase__, image_processor=UpperCamelCase__ )
lowerCAmelCase_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCAmelCase_ = processor.batch_decode(UpperCamelCase__ )
lowerCAmelCase_ = tokenizer.batch_decode(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__, UpperCamelCase__ )
| 167 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : List[str] = {
"""configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Optional[int] = [
"""TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TimesformerModel""",
"""TimesformerForVideoClassification""",
"""TimesformerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
lowerCAmelCase : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 13 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowercase (SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Any=False ) -> str:
SCREAMING_SNAKE_CASE = OmegaConf.load(SCREAMING_SNAKE_CASE_ )
if display:
print(yaml.dump(OmegaConf.to_container(SCREAMING_SNAKE_CASE_ ) ) )
return config
def lowercase (SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Tuple=None , SCREAMING_SNAKE_CASE_ : Tuple=None ) -> Any:
if conf_path is None:
SCREAMING_SNAKE_CASE = './model_checkpoints/vqgan_only.yaml'
SCREAMING_SNAKE_CASE = load_config(SCREAMING_SNAKE_CASE_ , display=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = VQModel(**config.model.params )
if ckpt_path is None:
SCREAMING_SNAKE_CASE = './model_checkpoints/vqgan_only.pt'
SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location=SCREAMING_SNAKE_CASE_ )
if ".ckpt" in ckpt_path:
SCREAMING_SNAKE_CASE = sd['state_dict']
model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
del sd
return model
def lowercase (SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : int ) -> List[Any]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = model.encode(SCREAMING_SNAKE_CASE_ )
print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}' )
SCREAMING_SNAKE_CASE = model.decode(SCREAMING_SNAKE_CASE_ )
return xrec
def lowercase (SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Any=False ) -> Dict:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = string.rsplit('.' , 1 )
if reload:
SCREAMING_SNAKE_CASE = importlib.import_module(SCREAMING_SNAKE_CASE_ )
importlib.reload(SCREAMING_SNAKE_CASE_ )
return getattr(importlib.import_module(SCREAMING_SNAKE_CASE_ , package=SCREAMING_SNAKE_CASE_ ) , cls )
def lowercase (SCREAMING_SNAKE_CASE_ : Any ) -> Dict:
if "target" not in config:
raise KeyError('Expected key `target` to instantiate.' )
return get_obj_from_str(config['target'] )(**config.get('params' , {} ) )
def lowercase (SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : List[Any]=True , SCREAMING_SNAKE_CASE_ : int=True ) -> Any:
SCREAMING_SNAKE_CASE = instantiate_from_config(SCREAMING_SNAKE_CASE_ )
if sd is not None:
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def lowercase (SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[str] ) -> Any:
# load the specified checkpoint
if ckpt:
SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location='cpu' )
SCREAMING_SNAKE_CASE = pl_sd['global_step']
print(F'loaded model from global step {global_step}.' )
else:
SCREAMING_SNAKE_CASE = {'state_dict': None}
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = load_model_from_config(config.model , pl_sd['state_dict'] , gpu=SCREAMING_SNAKE_CASE_ , eval_mode=SCREAMING_SNAKE_CASE_ )['model']
return model, global_step
| 113 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class lowercase ( _a ):
"""simple docstring"""
UpperCAmelCase = """philschmid/bart-large-cnn-samsum"""
UpperCAmelCase = (
"""This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
"""and returns a summary of the text."""
)
UpperCAmelCase = """summarizer"""
UpperCAmelCase = AutoTokenizer
UpperCAmelCase = AutoModelForSeqaSeqLM
UpperCAmelCase = ["""text"""]
UpperCAmelCase = ["""text"""]
def _snake_case ( self ,a_ ) -> Dict:
return self.pre_processor(__lowerCAmelCase ,return_tensors="""pt""" ,truncation=__lowerCAmelCase )
def _snake_case ( self ,a_ ) -> Optional[Any]:
return self.model.generate(**__lowerCAmelCase )[0]
def _snake_case ( self ,a_ ) -> Optional[int]:
return self.pre_processor.decode(__lowerCAmelCase ,skip_special_tokens=__lowerCAmelCase ,clean_up_tokenization_spaces=__lowerCAmelCase )
| 371 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 100 , )-> float:
'''simple docstring'''
_UpperCAmelCase : str = x_start
_UpperCAmelCase : Union[str, Any] = fnc(lowerCAmelCase_ )
_UpperCAmelCase : Tuple = 0.0
for _ in range(lowerCAmelCase_ ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
_UpperCAmelCase : Any = (x_end - x_start) / steps + xa
_UpperCAmelCase : List[Any] = fnc(lowerCAmelCase_ )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
_UpperCAmelCase : Any = xa
_UpperCAmelCase : str = fxa
return area
if __name__ == "__main__":
def snake_case_ ( lowerCAmelCase_ )-> Any:
'''simple docstring'''
return x**3 + x**2
print("""f(x) = x^3 + x^2""")
print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
A_ : List[str] = 1_0
while i <= 1_0_0_0_0_0:
print(f"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
i *= 1_0
| 349 | 0 |
'''simple docstring'''
import logging
from transformers import PretrainedConfig
_lowerCAmelCase = logging.getLogger(__name__)
_lowerCAmelCase = {
"bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}
class lowerCAmelCase_( _lowerCAmelCase ):
'''simple docstring'''
__lowercase : int = "bertabs"
def __init__( self ,__UpperCAmelCase=3_0522 ,__UpperCAmelCase=512 ,__UpperCAmelCase=6 ,__UpperCAmelCase=512 ,__UpperCAmelCase=8 ,__UpperCAmelCase=512 ,__UpperCAmelCase=0.2 ,__UpperCAmelCase=6 ,__UpperCAmelCase=768 ,__UpperCAmelCase=8 ,__UpperCAmelCase=2048 ,__UpperCAmelCase=0.2 ,**__UpperCAmelCase ,) -> Union[str, Any]:
super().__init__(**__UpperCAmelCase )
lowerCAmelCase__ : Any = vocab_size
lowerCAmelCase__ : Optional[Any] = max_pos
lowerCAmelCase__ : List[Any] = enc_layers
lowerCAmelCase__ : Tuple = enc_hidden_size
lowerCAmelCase__ : Dict = enc_heads
lowerCAmelCase__ : Optional[int] = enc_ff_size
lowerCAmelCase__ : Optional[Any] = enc_dropout
lowerCAmelCase__ : Any = dec_layers
lowerCAmelCase__ : int = dec_hidden_size
lowerCAmelCase__ : Tuple = dec_heads
lowerCAmelCase__ : List[str] = dec_ff_size
lowerCAmelCase__ : List[str] = dec_dropout
| 37 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
_snake_case : Any = models.Sequential()
# Step 1 - Convolution
# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
# (3,3) is the kernel size (filter matrix)
classifier.add(
layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="relu")
)
# Step 2 - Pooling
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Adding a second convolutional layer
classifier.add(layers.ConvaD(32, (3, 3), activation="relu"))
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Step 3 - Flattening
classifier.add(layers.Flatten())
# Step 4 - Full connection
classifier.add(layers.Dense(units=128, activation="relu"))
classifier.add(layers.Dense(units=1, activation="sigmoid"))
# Compiling the CNN
classifier.compile(
optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
)
# Part 2 - Fitting the CNN to the images
# Load Trained model weights
# from keras.models import load_model
# regressor=load_model('cnn.h5')
_snake_case : int = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
)
_snake_case : Optional[Any] = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)
_snake_case : List[str] = train_datagen.flow_from_directory(
"dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
)
_snake_case : Any = test_datagen.flow_from_directory(
"dataset/test_set", target_size=(64, 64), batch_size=32, class_mode="binary"
)
classifier.fit_generator(
training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
)
classifier.save("cnn.h5")
# Part 3 - Making new predictions
_snake_case : Optional[Any] = tf.keras.preprocessing.image.load_img(
"dataset/single_prediction/image.png", target_size=(64, 64)
)
_snake_case : int = tf.keras.preprocessing.image.img_to_array(test_image)
_snake_case : Tuple = np.expand_dims(test_image, axis=0)
_snake_case : Any = classifier.predict(test_image)
# training_set.class_indices
if result[0][0] == 0:
_snake_case : Any = "Normal"
if result[0][0] == 1:
_snake_case : List[str] = "Abnormality detected"
| 123 | 0 |
"""simple docstring"""
from __future__ import annotations
def A_ ( snake_case_ : str ,snake_case_ : list[str] | None = None ,snake_case_ : dict[str, float] | None = None ,snake_case_ : bool = False ,):
'''simple docstring'''
UpperCamelCase : List[str] = cipher_alphabet or [chr(snake_case_ ) for i in range(9_7 ,1_2_3 )]
# If the argument is None or the user provided an empty dictionary
if not frequencies_dict:
# Frequencies of letters in the english language (how much they show up)
UpperCamelCase : str = {
"""a""": 0.08497,
"""b""": 0.01492,
"""c""": 0.02202,
"""d""": 0.04253,
"""e""": 0.11162,
"""f""": 0.02228,
"""g""": 0.02015,
"""h""": 0.06094,
"""i""": 0.07546,
"""j""": 0.00153,
"""k""": 0.01292,
"""l""": 0.04025,
"""m""": 0.02406,
"""n""": 0.06749,
"""o""": 0.07507,
"""p""": 0.01929,
"""q""": 0.00095,
"""r""": 0.07587,
"""s""": 0.06327,
"""t""": 0.09356,
"""u""": 0.02758,
"""v""": 0.00978,
"""w""": 0.02560,
"""x""": 0.00150,
"""y""": 0.01994,
"""z""": 0.00077,
}
else:
# Custom frequencies dictionary
UpperCamelCase : Dict = frequencies_dict
if not case_sensitive:
UpperCamelCase : Tuple = ciphertext.lower()
# Chi squared statistic values
UpperCamelCase : dict[int, tuple[float, str]] = {}
# cycle through all of the shifts
for shift in range(len(snake_case_ ) ):
UpperCamelCase : Tuple = """"""
# decrypt the message with the shift
for letter in ciphertext:
try:
# Try to index the letter in the alphabet
UpperCamelCase : Union[str, Any] = (alphabet_letters.index(letter.lower() ) - shift) % len(
snake_case_ )
decrypted_with_shift += (
alphabet_letters[new_key].upper()
if case_sensitive and letter.isupper()
else alphabet_letters[new_key]
)
except ValueError:
# Append the character if it isn't in the alphabet
decrypted_with_shift += letter
UpperCamelCase : Any = 0.0
# Loop through each letter in the decoded message with the shift
for letter in decrypted_with_shift:
if case_sensitive:
UpperCamelCase : Union[str, Any] = letter.lower()
if letter in frequencies:
# Get the amount of times the letter occurs in the message
UpperCamelCase : str = decrypted_with_shift.lower().count(snake_case_ )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
UpperCamelCase : str = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
UpperCamelCase : str = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
else:
if letter.lower() in frequencies:
# Get the amount of times the letter occurs in the message
UpperCamelCase : Any = decrypted_with_shift.count(snake_case_ )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
UpperCamelCase : Union[str, Any] = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
UpperCamelCase : str = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
# Add the data to the chi_squared_statistic_values dictionary
UpperCamelCase : Optional[int] = (
chi_squared_statistic,
decrypted_with_shift,
)
# Get the most likely cipher by finding the cipher with the smallest chi squared
# statistic
def chi_squared_statistic_values_sorting_key(snake_case_ : int ) -> tuple[float, str]:
return chi_squared_statistic_values[key]
UpperCamelCase : int = min(
snake_case_ ,key=snake_case_ ,)
# Get all the data from the most likely cipher (key, decoded message)
(
(
UpperCamelCase
) , (
UpperCamelCase
) ,
) : Optional[int] = chi_squared_statistic_values[most_likely_cipher]
# Return the data on the most likely shift
return (
most_likely_cipher,
most_likely_cipher_chi_squared_value,
decoded_most_likely_cipher,
)
| 27 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( snake_case_ : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
UpperCamelCase : Any = BeautifulSoup(requests.get(snake_case_ ).text ,"""html.parser""" )
UpperCamelCase : Optional[int] = soup.findAll("""h1""" )
UpperCamelCase : List[Any] = soup.findAll("""div""" ,{"""class""": """maincounter-number"""} )
keys += soup.findAll("""span""" ,{"""class""": """panel-title"""} )
values += soup.findAll("""div""" ,{"""class""": """number-table-main"""} )
return {key.text.strip(): value.text.strip() for key, value in zip(snake_case_ ,snake_case_ )}
if __name__ == "__main__":
print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
for key, value in world_covidaa_stats().items():
print(F'''{key}\n{value}\n''')
| 27 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
A__ : str = logging.get_logger(__name__)
A__ : Any = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A__ : str = {
'''vocab_file''': {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'''
),
'''google/realm-cc-news-pretrained-encoder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'''
),
'''google/realm-cc-news-pretrained-scorer''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'''
),
'''google/realm-cc-news-pretrained-openqa''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'''
),
'''google/realm-orqa-nq-openqa''': '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt''',
'''google/realm-orqa-nq-reader''': '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt''',
'''google/realm-orqa-wq-openqa''': '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt''',
'''google/realm-orqa-wq-reader''': '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt''',
},
'''tokenizer_file''': {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'''
),
'''google/realm-cc-news-pretrained-encoder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'''
),
'''google/realm-cc-news-pretrained-scorer''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'''
),
'''google/realm-cc-news-pretrained-openqa''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'''
),
'''google/realm-orqa-nq-openqa''': (
'''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'''
),
'''google/realm-orqa-nq-reader''': (
'''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'''
),
'''google/realm-orqa-wq-openqa''': (
'''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'''
),
'''google/realm-orqa-wq-reader''': (
'''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'''
),
},
}
A__ : Union[str, Any] = {
'''google/realm-cc-news-pretrained-embedder''': 512,
'''google/realm-cc-news-pretrained-encoder''': 512,
'''google/realm-cc-news-pretrained-scorer''': 512,
'''google/realm-cc-news-pretrained-openqa''': 512,
'''google/realm-orqa-nq-openqa''': 512,
'''google/realm-orqa-nq-reader''': 512,
'''google/realm-orqa-wq-openqa''': 512,
'''google/realm-orqa-wq-reader''': 512,
}
A__ : Dict = {
'''google/realm-cc-news-pretrained-embedder''': {'''do_lower_case''': True},
'''google/realm-cc-news-pretrained-encoder''': {'''do_lower_case''': True},
'''google/realm-cc-news-pretrained-scorer''': {'''do_lower_case''': True},
'''google/realm-cc-news-pretrained-openqa''': {'''do_lower_case''': True},
'''google/realm-orqa-nq-openqa''': {'''do_lower_case''': True},
'''google/realm-orqa-nq-reader''': {'''do_lower_case''': True},
'''google/realm-orqa-wq-openqa''': {'''do_lower_case''': True},
'''google/realm-orqa-wq-reader''': {'''do_lower_case''': True},
}
class __snake_case ( UpperCamelCase_ ):
_a = VOCAB_FILES_NAMES
_a = PRETRAINED_VOCAB_FILES_MAP
_a = PRETRAINED_INIT_CONFIGURATION
_a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a = RealmTokenizer
def __init__( self : int , A_ : Optional[int]=None , A_ : Optional[Any]=None , A_ : Optional[Any]=True , A_ : Optional[int]="[UNK]" , A_ : List[Any]="[SEP]" , A_ : List[Any]="[PAD]" , A_ : Optional[Any]="[CLS]" , A_ : Dict="[MASK]" , A_ : List[Any]=True , A_ : List[str]=None , **A_ : List[str] , ):
super().__init__(
A_ , tokenizer_file=A_ , do_lower_case=A_ , unk_token=A_ , sep_token=A_ , pad_token=A_ , cls_token=A_ , mask_token=A_ , tokenize_chinese_chars=A_ , strip_accents=A_ , **A_ , )
lowerCAmelCase_ : Optional[int] = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get('''lowercase''' , A_) != do_lower_case
or normalizer_state.get('''strip_accents''' , A_) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , A_) != tokenize_chinese_chars
):
lowerCAmelCase_ : int = getattr(A_ , normalizer_state.pop('''type'''))
lowerCAmelCase_ : str = do_lower_case
lowerCAmelCase_ : Dict = strip_accents
lowerCAmelCase_ : Optional[Any] = tokenize_chinese_chars
lowerCAmelCase_ : Union[str, Any] = normalizer_class(**A_)
lowerCAmelCase_ : Any = do_lower_case
def UpperCAmelCase__ ( self : Optional[Any] , A_ : Optional[Any] , **A_ : Tuple):
lowerCAmelCase_ : List[str] = PaddingStrategy.MAX_LENGTH
lowerCAmelCase_ : str = text
lowerCAmelCase_ : int = kwargs.pop('''text_pair''' , A_)
lowerCAmelCase_ : str = kwargs.pop('''return_tensors''' , A_)
lowerCAmelCase_ : int = {
'''input_ids''': [],
'''attention_mask''': [],
'''token_type_ids''': [],
}
for idx, candidate_text in enumerate(A_):
if batch_text_pair is not None:
lowerCAmelCase_ : List[Any] = batch_text_pair[idx]
else:
lowerCAmelCase_ : List[Any] = None
lowerCAmelCase_ : int = super().__call__(A_ , A_ , return_tensors=A_ , **A_)
lowerCAmelCase_ : Optional[Any] = encoded_candidates.get('''input_ids''')
lowerCAmelCase_ : List[str] = encoded_candidates.get('''attention_mask''')
lowerCAmelCase_ : Optional[Any] = encoded_candidates.get('''token_type_ids''')
if encoded_input_ids is not None:
output_data["input_ids"].append(A_)
if encoded_attention_mask is not None:
output_data["attention_mask"].append(A_)
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(A_)
lowerCAmelCase_ : List[str] = {key: item for key, item in output_data.items() if len(A_) != 0}
return BatchEncoding(A_ , tensor_type=A_)
def UpperCAmelCase__ ( self : List[str] , A_ : Tuple , A_ : List[Any]=None):
lowerCAmelCase_ : Optional[Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCAmelCase__ ( self : Tuple , A_ : List[int] , A_ : Optional[List[int]] = None):
lowerCAmelCase_ : Tuple = [self.sep_token_id]
lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]
def UpperCAmelCase__ ( self : List[str] , A_ : str , A_ : Optional[str] = None):
lowerCAmelCase_ : List[str] = self._tokenizer.model.save(A_ , name=A_)
return tuple(A_)
| 103 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__snake_case = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
__snake_case = {
'''facebook/blenderbot_small-90M''': 512,
}
class lowercase ( A__ ):
"""simple docstring"""
_a = VOCAB_FILES_NAMES
_a = PRETRAINED_VOCAB_FILES_MAP
_a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a = BlenderbotSmallTokenizer
def __init__( self , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_=False , UpperCamelCase_=True , **UpperCamelCase_ , ):
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=UpperCamelCase_ , merges=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ , trim_offsets=UpperCamelCase_ , ) , bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , **UpperCamelCase_ , )
UpperCamelCase__ :Union[str, Any] = add_prefix_space
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_=None ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
'''simple docstring'''
UpperCamelCase__ :Optional[int] = [self.sep_token_id]
UpperCamelCase__ :Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 97 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
Resize,
ToTensor,
)
import transformers
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForImageClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A : Dict = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
A : str = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
A : Optional[int] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
with open(_UpperCamelCase , "rb" ) as f:
__lowerCAmelCase = Image.open(_UpperCamelCase )
return im.convert("RGB" )
@dataclass
class _UpperCamelCase :
'''simple docstring'''
__UpperCAmelCase : Optional[str] =field(
default=lowerCAmelCase__ ,metadata={
"""help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
} ,)
__UpperCAmelCase : Optional[str] =field(
default=lowerCAmelCase__ ,metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
__UpperCAmelCase : Optional[str] =field(default=lowerCAmelCase__ ,metadata={"""help""": """A folder containing the training data."""} )
__UpperCAmelCase : Optional[str] =field(default=lowerCAmelCase__ ,metadata={"""help""": """A folder containing the validation data."""} )
__UpperCAmelCase : Optional[float] =field(
default=0.15 ,metadata={"""help""": """Percent to split off of train for validation."""} )
__UpperCAmelCase : Optional[int] =field(
default=lowerCAmelCase__ ,metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} ,)
__UpperCAmelCase : Optional[int] =field(
default=lowerCAmelCase__ ,metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} ,)
def snake_case ( self ):
if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
raise ValueError(
"You must specify either a dataset name from the hub or a train and/or validation directory." )
@dataclass
class _UpperCamelCase :
'''simple docstring'''
__UpperCAmelCase : str =field(
default="""google/vit-base-patch16-224-in21k""" ,metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} ,)
__UpperCAmelCase : Optional[str] =field(
default=lowerCAmelCase__ ,metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(lowerCAmelCase__ )} ,)
__UpperCAmelCase : Optional[str] =field(
default=lowerCAmelCase__ ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__UpperCAmelCase : Optional[str] =field(
default=lowerCAmelCase__ ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
__UpperCAmelCase : str =field(
default="""main""" ,metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} ,)
__UpperCAmelCase : str =field(default=lowerCAmelCase__ ,metadata={"""help""": """Name or path of preprocessor config."""} )
__UpperCAmelCase : bool =field(
default=lowerCAmelCase__ ,metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} ,)
__UpperCAmelCase : bool =field(
default=lowerCAmelCase__ ,metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} ,)
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = torch.stack([example["pixel_values"] for example in examples] )
__lowerCAmelCase = torch.tensor([example["labels"] for example in examples] )
return {"pixel_values": pixel_values, "labels": labels}
def _lowerCamelCase ( ):
'''simple docstring'''
__lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_image_classification" , _UpperCamelCase , _UpperCamelCase )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
__lowerCAmelCase = training_args.get_process_log_level()
logger.setLevel(_UpperCamelCase )
transformers.utils.logging.set_verbosity(_UpperCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(f"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
__lowerCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome." )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )
# Set seed before initializing model.
set_seed(training_args.seed )
# Initialize our dataset and prepare it for the 'image-classification' task.
if data_args.dataset_name is not None:
__lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir , task="image-classification" , use_auth_token=True if model_args.use_auth_token else None , )
else:
__lowerCAmelCase = {}
if data_args.train_dir is not None:
__lowerCAmelCase = os.path.join(data_args.train_dir , "**" )
if data_args.validation_dir is not None:
__lowerCAmelCase = os.path.join(data_args.validation_dir , "**" )
__lowerCAmelCase = load_dataset(
"imagefolder" , data_files=_UpperCamelCase , cache_dir=model_args.cache_dir , task="image-classification" , )
# If we don't have a validation split, split off a percentage of train as validation.
__lowerCAmelCase = None if "validation" in dataset.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , _UpperCamelCase ) and data_args.train_val_split > 0.0:
__lowerCAmelCase = dataset["train"].train_test_split(data_args.train_val_split )
__lowerCAmelCase = split["train"]
__lowerCAmelCase = split["test"]
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
__lowerCAmelCase = dataset["train"].features["labels"].names
__lowerCAmelCase , __lowerCAmelCase = {}, {}
for i, label in enumerate(_UpperCamelCase ):
__lowerCAmelCase = str(_UpperCamelCase )
__lowerCAmelCase = label
# Load the accuracy metric from the datasets package
__lowerCAmelCase = evaluate.load("accuracy" )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_UpperCamelCase ):
return metric.compute(predictions=np.argmax(p.predictions , axis=1 ) , references=p.label_ids )
__lowerCAmelCase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(_UpperCamelCase ) , labelaid=_UpperCamelCase , idalabel=_UpperCamelCase , finetuning_task="image-classification" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
__lowerCAmelCase = AutoModelForImageClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=_UpperCamelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
__lowerCAmelCase = AutoImageProcessor.from_pretrained(
model_args.image_processor_name or model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Define torchvision transforms to be applied to each image.
if "shortest_edge" in image_processor.size:
__lowerCAmelCase = image_processor.size["shortest_edge"]
else:
__lowerCAmelCase = (image_processor.size["height"], image_processor.size["width"])
__lowerCAmelCase = Normalize(mean=image_processor.image_mean , std=image_processor.image_std )
__lowerCAmelCase = Compose(
[
RandomResizedCrop(_UpperCamelCase ),
RandomHorizontalFlip(),
ToTensor(),
normalize,
] )
__lowerCAmelCase = Compose(
[
Resize(_UpperCamelCase ),
CenterCrop(_UpperCamelCase ),
ToTensor(),
normalize,
] )
def train_transforms(_UpperCamelCase ):
__lowerCAmelCase = [
_train_transforms(pil_img.convert("RGB" ) ) for pil_img in example_batch["image"]
]
return example_batch
def val_transforms(_UpperCamelCase ):
__lowerCAmelCase = [_val_transforms(pil_img.convert("RGB" ) ) for pil_img in example_batch["image"]]
return example_batch
if training_args.do_train:
if "train" not in dataset:
raise ValueError("--do_train requires a train dataset" )
if data_args.max_train_samples is not None:
__lowerCAmelCase = (
dataset["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
dataset["train"].set_transform(_UpperCamelCase )
if training_args.do_eval:
if "validation" not in dataset:
raise ValueError("--do_eval requires a validation dataset" )
if data_args.max_eval_samples is not None:
__lowerCAmelCase = (
dataset["validation"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
dataset["validation"].set_transform(_UpperCamelCase )
# Initalize our trainer
__lowerCAmelCase = Trainer(
model=_UpperCamelCase , args=_UpperCamelCase , train_dataset=dataset["train"] if training_args.do_train else None , eval_dataset=dataset["validation"] if training_args.do_eval else None , compute_metrics=_UpperCamelCase , tokenizer=_UpperCamelCase , data_collator=_UpperCamelCase , )
# Training
if training_args.do_train:
__lowerCAmelCase = None
if training_args.resume_from_checkpoint is not None:
__lowerCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__lowerCAmelCase = last_checkpoint
__lowerCAmelCase = trainer.train(resume_from_checkpoint=_UpperCamelCase )
trainer.save_model()
trainer.log_metrics("train" , train_result.metrics )
trainer.save_metrics("train" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
__lowerCAmelCase = trainer.evaluate()
trainer.log_metrics("eval" , _UpperCamelCase )
trainer.save_metrics("eval" , _UpperCamelCase )
# Write model card and (optionally) push to hub
__lowerCAmelCase = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "image-classification",
"dataset": data_args.dataset_name,
"tags": ["image-classification", "vision"],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_UpperCamelCase )
else:
trainer.create_model_card(**_UpperCamelCase )
if __name__ == "__main__":
main()
| 259 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : Optional[int] = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : Any ="""xlnet"""
__UpperCAmelCase : Tuple =["""mems"""]
__UpperCAmelCase : List[str] ={
"""n_token""": """vocab_size""", # Backward compatibility
"""hidden_size""": """d_model""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , __a=3_20_00 , __a=10_24 , __a=24 , __a=16 , __a=40_96 , __a="gelu" , __a=True , __a="bi" , __a=0.0_2 , __a=1e-1_2 , __a=0.1 , __a=5_12 , __a=None , __a=True , __a=False , __a=False , __a=-1 , __a=False , __a="last" , __a=True , __a="tanh" , __a=0.1 , __a=5 , __a=5 , __a=5 , __a=1 , __a=2 , **__a , ):
__lowerCAmelCase = vocab_size
__lowerCAmelCase = d_model
__lowerCAmelCase = n_layer
__lowerCAmelCase = n_head
if d_model % n_head != 0:
raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0" )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})" )
__lowerCAmelCase = d_model // n_head
__lowerCAmelCase = ff_activation
__lowerCAmelCase = d_inner
__lowerCAmelCase = untie_r
__lowerCAmelCase = attn_type
__lowerCAmelCase = initializer_range
__lowerCAmelCase = layer_norm_eps
__lowerCAmelCase = dropout
__lowerCAmelCase = mem_len
__lowerCAmelCase = reuse_len
__lowerCAmelCase = bi_data
__lowerCAmelCase = clamp_len
__lowerCAmelCase = same_length
__lowerCAmelCase = summary_type
__lowerCAmelCase = summary_use_proj
__lowerCAmelCase = summary_activation
__lowerCAmelCase = summary_last_dropout
__lowerCAmelCase = start_n_top
__lowerCAmelCase = end_n_top
__lowerCAmelCase = bos_token_id
__lowerCAmelCase = pad_token_id
__lowerCAmelCase = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
"The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
" instead." , __a , )
__lowerCAmelCase = kwargs["use_cache"]
__lowerCAmelCase = use_mems_eval
__lowerCAmelCase = use_mems_train
super().__init__(pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a )
@property
def snake_case ( self ):
logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit." )
return -1
@max_position_embeddings.setter
def snake_case ( self , __a ):
# Message copied from Transformer-XL documentation
raise NotImplementedError(
f"The model {self.model_type} is one of the few models that has no sequence length limit." )
| 259 | 1 |
from __future__ import annotations
from collections.abc import Callable
A__ : List[str] = list[list[float | int]]
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = len(__lowerCAmelCase )
lowercase__ = [[0 for _ in range(size + 1 )] for _ in range(__lowerCAmelCase )]
lowercase__ = 42
lowercase__ = 42
lowercase__ = 42
lowercase__ = 42
lowercase__ = 42
lowercase__ = 42
for row in range(__lowerCAmelCase ):
for col in range(__lowerCAmelCase ):
lowercase__ = matrix[row][col]
lowercase__ = vector[row][0]
lowercase__ = 0
lowercase__ = 0
while row < size and col < size:
# pivoting
lowercase__ = max((abs(augmented[rowa][col] ), rowa) for rowa in range(__lowerCAmelCase , __lowerCAmelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
lowercase__ , lowercase__ = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , __lowerCAmelCase ):
lowercase__ = augmented[rowa][col] / augmented[row][col]
lowercase__ = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , __lowerCAmelCase ):
for row in range(__lowerCAmelCase ):
lowercase__ = augmented[row][col] / augmented[col][col]
for cola in range(__lowerCAmelCase , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(__lowerCAmelCase )
]
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = len(__lowerCAmelCase )
lowercase__ = [[0 for _ in range(__lowerCAmelCase )] for _ in range(__lowerCAmelCase )]
lowercase__ = [[0] for _ in range(__lowerCAmelCase )]
lowercase__ = 42
lowercase__ = 42
lowercase__ = 42
lowercase__ = 42
for x_val, y_val in enumerate(__lowerCAmelCase ):
for col in range(__lowerCAmelCase ):
lowercase__ = (x_val + 1) ** (size - col - 1)
lowercase__ = y_val
lowercase__ = solve(__lowerCAmelCase , __lowerCAmelCase )
def interpolated_func(lowerCamelCase_ ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(__lowerCAmelCase ) )
return interpolated_func
def a ( lowerCamelCase_ ):
'''simple docstring'''
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def a ( lowerCamelCase_ = question_function , lowerCamelCase_ = 10 ):
'''simple docstring'''
lowercase__ = [func(__lowerCAmelCase ) for x_val in range(1 , order + 1 )]
lowercase__ = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
lowercase__ = 0
lowercase__ = 42
lowercase__ = 42
for poly in polynomials:
lowercase__ = 1
while func(__lowerCAmelCase ) == poly(__lowerCAmelCase ):
x_val += 1
ret += poly(__lowerCAmelCase )
return ret
if __name__ == "__main__":
print(F"{solution() = }")
| 207 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed
snake_case : List[Any] = logging.getLogger(__name__)
def __lowercase ( __lowerCAmelCase : str=2 , __lowerCAmelCase : Optional[int]=3 , __lowerCAmelCase : List[str]=1_6 , __lowerCAmelCase : int = 1_0 , __lowerCAmelCase : int = 2 ):
def get_dataset(__lowerCAmelCase : Dict ):
a__ = torch.randn(batch_size * n_batches , 1 )
return TensorDataset(__lowerCAmelCase , a * x + b + 0.1 * torch.randn(batch_size * n_batches , 1 ) )
a__ = get_dataset(__lowerCAmelCase )
a__ = get_dataset(__lowerCAmelCase )
a__ = DataLoader(__lowerCAmelCase , shuffle=__lowerCAmelCase , batch_size=__lowerCAmelCase , num_workers=4 )
a__ = DataLoader(__lowerCAmelCase , shuffle=__lowerCAmelCase , batch_size=__lowerCAmelCase , num_workers=4 )
return (train_dataloader, valid_dataloader)
def __lowercase ( __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : str , __lowerCAmelCase : int , __lowerCAmelCase : Optional[int]=None ):
a__ = []
for epoch in range(__lowerCAmelCase ):
# Train quickly
model.train()
for batch in dataloader:
a__ , a__ = batch
a__ = model(__lowerCAmelCase )
a__ = torch.nn.functional.mse_loss(__lowerCAmelCase , __lowerCAmelCase )
accelerator.backward(__lowerCAmelCase )
optimizer.step()
optimizer.zero_grad()
rands.append(random.random() ) # Introduce some randomness
if scheduler is not None:
scheduler.step()
return rands
class snake_case_ (nn.Module ):
def __init__( self :Any ) -> Union[str, Any]:
super().__init__()
a__ = nn.Parameter(torch.randn(1 ) )
a__ = nn.Parameter(torch.randn(1 ) )
def lowerCamelCase__( self :List[str] ,__snake_case :Union[str, Any] ) -> str:
return x * self.a + self.b
class snake_case_ (unittest.TestCase ):
def lowerCamelCase__( self :Tuple ) -> Dict:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
a__ = DummyModel()
a__ = torch.optim.Adam(params=model.parameters() ,lr=1E-3 )
a__ , a__ = dummy_dataloaders()
a__ = ProjectConfiguration(total_limit=1 ,project_dir=__snake_case ,automatic_checkpoint_naming=__snake_case )
# Train baseline
a__ = Accelerator(project_config=__snake_case )
a__ , a__ , a__ , a__ = accelerator.prepare(
__snake_case ,__snake_case ,__snake_case ,__snake_case )
# Save initial
accelerator.save_state()
# Save second state
accelerator.save_state()
self.assertEqual(len(os.listdir(accelerator.project_dir ) ) ,1 )
def lowerCamelCase__( self :List[Any] ) -> str:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
a__ = DummyModel()
a__ = torch.optim.Adam(params=model.parameters() ,lr=1E-3 )
a__ , a__ = dummy_dataloaders()
# Train baseline
a__ = Accelerator()
a__ , a__ , a__ , a__ = accelerator.prepare(
__snake_case ,__snake_case ,__snake_case ,__snake_case )
# Save initial
a__ = os.path.join(__snake_case ,'initial' )
accelerator.save_state(__snake_case )
((a__) , (a__)) = model.a.item(), model.b.item()
a__ = optimizer.state_dict()
a__ = train(3 ,__snake_case ,__snake_case ,__snake_case ,__snake_case )
((a__) , (a__)) = model.a.item(), model.b.item()
a__ = optimizer.state_dict()
# Train partially
set_seed(42 )
a__ = DummyModel()
a__ = torch.optim.Adam(params=model.parameters() ,lr=1E-3 )
a__ , a__ = dummy_dataloaders()
a__ = Accelerator()
a__ , a__ , a__ , a__ = accelerator.prepare(
__snake_case ,__snake_case ,__snake_case ,__snake_case )
accelerator.load_state(__snake_case )
((a__) , (a__)) = model.a.item(), model.b.item()
a__ = optimizer.state_dict()
self.assertEqual(__snake_case ,__snake_case )
self.assertEqual(__snake_case ,__snake_case )
self.assertEqual(__snake_case ,__snake_case )
a__ = train(2 ,__snake_case ,__snake_case ,__snake_case ,__snake_case )
# Save everything
a__ = os.path.join(__snake_case ,'checkpoint' )
accelerator.save_state(__snake_case )
# Load everything back in and make sure all states work
accelerator.load_state(__snake_case )
test_rands += train(1 ,__snake_case ,__snake_case ,__snake_case ,__snake_case )
((a__) , (a__)) = model.a.item(), model.b.item()
a__ = optimizer.state_dict()
self.assertEqual(__snake_case ,__snake_case )
self.assertEqual(__snake_case ,__snake_case )
self.assertEqual(__snake_case ,__snake_case )
self.assertEqual(__snake_case ,__snake_case )
def lowerCamelCase__( self :str ) -> List[str]:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
a__ = DummyModel()
a__ = torch.optim.Adam(params=model.parameters() ,lr=1E-3 )
a__ , a__ = dummy_dataloaders()
a__ = ProjectConfiguration(automatic_checkpoint_naming=__snake_case )
# Train baseline
a__ = Accelerator(project_dir=__snake_case ,project_config=__snake_case )
a__ , a__ , a__ , a__ = accelerator.prepare(
__snake_case ,__snake_case ,__snake_case ,__snake_case )
# Save initial
accelerator.save_state()
((a__) , (a__)) = model.a.item(), model.b.item()
a__ = optimizer.state_dict()
a__ = train(3 ,__snake_case ,__snake_case ,__snake_case ,__snake_case )
((a__) , (a__)) = model.a.item(), model.b.item()
a__ = optimizer.state_dict()
# Train partially
set_seed(42 )
a__ = DummyModel()
a__ = torch.optim.Adam(params=model.parameters() ,lr=1E-3 )
a__ , a__ = dummy_dataloaders()
a__ = ProjectConfiguration(iteration=1 ,automatic_checkpoint_naming=__snake_case )
a__ = Accelerator(project_dir=__snake_case ,project_config=__snake_case )
a__ , a__ , a__ , a__ = accelerator.prepare(
__snake_case ,__snake_case ,__snake_case ,__snake_case )
accelerator.load_state(os.path.join(__snake_case ,'checkpoints' ,'checkpoint_0' ) )
((a__) , (a__)) = model.a.item(), model.b.item()
a__ = optimizer.state_dict()
self.assertEqual(__snake_case ,__snake_case )
self.assertEqual(__snake_case ,__snake_case )
self.assertEqual(__snake_case ,__snake_case )
a__ = train(2 ,__snake_case ,__snake_case ,__snake_case ,__snake_case )
# Save everything
accelerator.save_state()
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(__snake_case ,'checkpoints' ,'checkpoint_1' ) )
test_rands += train(1 ,__snake_case ,__snake_case ,__snake_case ,__snake_case )
((a__) , (a__)) = model.a.item(), model.b.item()
a__ = optimizer.state_dict()
self.assertEqual(__snake_case ,__snake_case )
self.assertEqual(__snake_case ,__snake_case )
self.assertEqual(__snake_case ,__snake_case )
self.assertEqual(__snake_case ,__snake_case )
def lowerCamelCase__( self :Union[str, Any] ) -> List[str]:
a__ = torch.tensor([1, 2, 3] )
a__ = torch.tensor([2, 3, 4] )
a__ = DummyModel()
a__ = torch.optim.Adam(net.parameters() )
a__ = Accelerator()
with self.assertRaises(__snake_case ) as ve:
accelerator.register_for_checkpointing(__snake_case ,__snake_case ,__snake_case ,__snake_case )
a__ = str(ve.exception )
self.assertTrue('Item at index 0' in message )
self.assertTrue('Item at index 1' in message )
self.assertFalse('Item at index 2' in message )
self.assertFalse('Item at index 3' in message )
def lowerCamelCase__( self :List[Any] ) -> Dict:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
a__ = DummyModel()
a__ = torch.optim.Adam(params=model.parameters() ,lr=1E-3 )
a__ = torch.optim.lr_scheduler.StepLR(__snake_case ,step_size=1 ,gamma=0.99 )
a__ , a__ = dummy_dataloaders()
a__ = ProjectConfiguration(automatic_checkpoint_naming=__snake_case )
# Train baseline
a__ = Accelerator(project_dir=__snake_case ,project_config=__snake_case )
a__ , a__ , a__ , a__ , a__ = accelerator.prepare(
__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case )
# Save initial
accelerator.save_state()
a__ = scheduler.state_dict()
train(3 ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case )
self.assertNotEqual(__snake_case ,scheduler.state_dict() )
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(__snake_case ,'checkpoints' ,'checkpoint_0' ) )
self.assertEqual(__snake_case ,scheduler.state_dict() )
def lowerCamelCase__( self :Optional[int] ) -> List[str]:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
a__ = DummyModel()
a__ = ProjectConfiguration(automatic_checkpoint_naming=__snake_case ,total_limit=2 )
# Train baseline
a__ = Accelerator(project_dir=__snake_case ,project_config=__snake_case )
a__ = accelerator.prepare(__snake_case )
# Save 3 states:
for _ in range(11 ):
accelerator.save_state()
self.assertTrue(not os.path.exists(os.path.join(__snake_case ,'checkpoints' ,'checkpoint_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__snake_case ,'checkpoints' ,'checkpoint_9' ) ) )
self.assertTrue(os.path.exists(os.path.join(__snake_case ,'checkpoints' ,'checkpoint_10' ) ) )
@require_cuda
def lowerCamelCase__( self :Dict ) -> str:
a__ = ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__ )]
execute_subprocess_async(__snake_case ,env=os.environ.copy() )
if __name__ == "__main__":
snake_case : Tuple = '''/tmp/accelerate/state_checkpointing'''
snake_case : str = DummyModel()
snake_case : List[Any] = torch.optim.Adam(params=model.parameters(), lr=1e-3)
snake_case : Union[str, Any] = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
snake_case , snake_case : str = dummy_dataloaders()
snake_case : Union[str, Any] = ProjectConfiguration(automatic_checkpoint_naming=True)
# Train baseline
snake_case : Dict = Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='''no''')
if accelerator.process_index == 0:
if os.path.exists(savedir):
shutil.rmtree(savedir)
os.makedirs(savedir)
snake_case , snake_case , snake_case , snake_case , snake_case : List[str] = accelerator.prepare(
model, optimizer, train_dataloader, valid_dataloader, scheduler
)
snake_case , snake_case : Any = accelerator.prepare(model, optimizer)
train(3, model, train_dataloader, optimizer, accelerator, scheduler)
# Check that the intial optimizer is loaded on the GPU
for group in optimizer.param_groups:
snake_case : Any = group['''params'''][0].device
break
assert param_device.type == accelerator.device.type
snake_case : Union[str, Any] = model.cpu()
accelerator.wait_for_everyone()
accelerator.save_state()
accelerator.wait_for_everyone()
# Check CPU state
accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''cpu''')
for group in optimizer.param_groups:
snake_case : int = group['''params'''][0].device
break
assert (
param_device.type == torch.device('''cpu''').type
), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"
# Check device state
model.to(accelerator.device)
accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''on_device''')
for group in optimizer.param_groups:
snake_case : Optional[int] = group['''params'''][0].device
break
assert (
param_device.type == accelerator.device.type
), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"
# Check error
with pytest.raises(TypeError, match='''Unsupported optimizer map location passed'''):
accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''invalid''')
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
shutil.rmtree(savedir)
accelerator.wait_for_everyone()
| 240 | 0 |
"""simple docstring"""
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
def lowercase__( __SCREAMING_SNAKE_CASE : List[str] ) -> Dict:
print('Loading config file...' )
def flatten_yaml_as_dict(__SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[str]="" , __SCREAMING_SNAKE_CASE : Union[str, Any]="." ):
lowercase_ : Optional[Any] = []
for k, v in d.items():
lowercase_ : str = parent_key + sep + k if parent_key else k
if isinstance(__SCREAMING_SNAKE_CASE , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , sep=__SCREAMING_SNAKE_CASE ).items() )
else:
items.append((new_key, v) )
return dict(__SCREAMING_SNAKE_CASE )
lowercase_ : Dict = argparse.Namespace()
with open(__SCREAMING_SNAKE_CASE , 'r' ) as yaml_file:
try:
lowercase_ : int = yaml.load(__SCREAMING_SNAKE_CASE , Loader=yaml.FullLoader )
lowercase_ : List[str] = flatten_yaml_as_dict(__SCREAMING_SNAKE_CASE )
for k, v in flat_cfg.items():
setattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
except yaml.YAMLError as exc:
logger.error('Error while loading config file: {}. Error message: {}'.format(__SCREAMING_SNAKE_CASE , str(__SCREAMING_SNAKE_CASE ) ) )
return config
def lowercase__( __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[int]:
lowercase_ : Optional[Any] = MobileViTVaConfig()
lowercase_ : Optional[int] = False
# dataset
if task_name.startswith('imagenet1k_' ):
lowercase_ : int = 10_00
if int(task_name.strip().split('_' )[-1] ) == 3_84:
lowercase_ : Optional[Any] = 3_84
else:
lowercase_ : Dict = 2_56
lowercase_ : List[Any] = 'imagenet-1k-id2label.json'
elif task_name.startswith('imagenet21k_to_1k_' ):
lowercase_ : Optional[Any] = 2_10_00
if int(task_name.strip().split('_' )[-1] ) == 3_84:
lowercase_ : Tuple = 3_84
else:
lowercase_ : List[Any] = 2_56
lowercase_ : Optional[int] = 'imagenet-22k-id2label.json'
elif task_name.startswith('ade20k_' ):
lowercase_ : Tuple = 1_51
lowercase_ : str = 5_12
lowercase_ : Optional[int] = 'ade20k-id2label.json'
lowercase_ : Optional[int] = True
elif task_name.startswith('voc_' ):
lowercase_ : Any = 21
lowercase_ : Optional[Any] = 5_12
lowercase_ : Dict = 'pascal-voc-id2label.json'
lowercase_ : Tuple = True
# orig_config
lowercase_ : List[str] = load_orig_config_file(__SCREAMING_SNAKE_CASE )
assert getattr(__SCREAMING_SNAKE_CASE , 'model.classification.name' , -1 ) == "mobilevit_v2", "Invalid model"
lowercase_ : int = getattr(__SCREAMING_SNAKE_CASE , 'model.classification.mitv2.width_multiplier' , 1.0 )
assert (
getattr(__SCREAMING_SNAKE_CASE , 'model.classification.mitv2.attn_norm_layer' , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
lowercase_ : Union[str, Any] = getattr(__SCREAMING_SNAKE_CASE , 'model.classification.activation.name' , 'swish' )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
lowercase_ : List[str] = getattr(__SCREAMING_SNAKE_CASE , 'model.segmentation.output_stride' , 16 )
if "_deeplabv3" in task_name:
lowercase_ : Optional[int] = getattr(__SCREAMING_SNAKE_CASE , 'model.segmentation.deeplabv3.aspp_rates' , [12, 24, 36] )
lowercase_ : int = getattr(__SCREAMING_SNAKE_CASE , 'model.segmentation.deeplabv3.aspp_out_channels' , 5_12 )
lowercase_ : Optional[Any] = getattr(__SCREAMING_SNAKE_CASE , 'model.segmentation.deeplabv3.aspp_dropout' , 0.1 )
# id2label
lowercase_ : List[str] = 'huggingface/label-files'
lowercase_ : Optional[int] = json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , repo_type='dataset' ) , 'r' ) )
lowercase_ : Any = {int(__SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
lowercase_ : int = idalabel
lowercase_ : Optional[int] = {v: k for k, v in idalabel.items()}
return config
def lowercase__( __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : str ) -> Any:
lowercase_ : List[Any] = dct.pop(__SCREAMING_SNAKE_CASE )
lowercase_ : Union[str, Any] = val
def lowercase__( __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any]=False ) -> Tuple:
if base_model:
lowercase_ : Dict = ''
else:
lowercase_ : Optional[int] = 'mobilevitv2.'
lowercase_ : Optional[int] = []
for k in state_dict.keys():
if k[:8] == "encoder.":
lowercase_ : List[Any] = k[8:]
else:
lowercase_ : List[Any] = k
if ".block." in k:
lowercase_ : Optional[Any] = k_new.replace('.block.' , '.' )
if ".conv." in k:
lowercase_ : List[str] = k_new.replace('.conv.' , '.convolution.' )
if ".norm." in k:
lowercase_ : List[Any] = k_new.replace('.norm.' , '.normalization.' )
if "conv_1." in k:
lowercase_ : str = k_new.replace('conv_1.' , F'''{model_prefix}conv_stem.''' )
for i in [1, 2]:
if F'''layer_{i}.''' in k:
lowercase_ : List[Any] = k_new.replace(F'''layer_{i}.''' , F'''{model_prefix}encoder.layer.{i-1}.layer.''' )
if ".exp_1x1." in k:
lowercase_ : Dict = k_new.replace('.exp_1x1.' , '.expand_1x1.' )
if ".red_1x1." in k:
lowercase_ : Any = k_new.replace('.red_1x1.' , '.reduce_1x1.' )
for i in [3, 4, 5]:
if F'''layer_{i}.0.''' in k:
lowercase_ : Union[str, Any] = k_new.replace(F'''layer_{i}.0.''' , F'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.''' )
if F'''layer_{i}.1.local_rep.0.''' in k:
lowercase_ : int = k_new.replace(F'''layer_{i}.1.local_rep.0.''' , F'''{model_prefix}encoder.layer.{i-1}.conv_kxk.''' )
if F'''layer_{i}.1.local_rep.1.''' in k:
lowercase_ : Any = k_new.replace(F'''layer_{i}.1.local_rep.1.''' , F'''{model_prefix}encoder.layer.{i-1}.conv_1x1.''' )
for i in [3, 4, 5]:
if i == 3:
lowercase_ : Dict = [0, 1]
elif i == 4:
lowercase_ : Optional[Any] = [0, 1, 2, 3]
elif i == 5:
lowercase_ : List[str] = [0, 1, 2]
for j in j_in:
if F'''layer_{i}.1.global_rep.{j}.''' in k:
lowercase_ : int = k_new.replace(
F'''layer_{i}.1.global_rep.{j}.''' , F'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.''' )
if F'''layer_{i}.1.global_rep.{j+1}.''' in k:
lowercase_ : List[str] = k_new.replace(
F'''layer_{i}.1.global_rep.{j+1}.''' , F'''{model_prefix}encoder.layer.{i-1}.layernorm.''' )
if F'''layer_{i}.1.conv_proj.''' in k:
lowercase_ : Optional[int] = k_new.replace(F'''layer_{i}.1.conv_proj.''' , F'''{model_prefix}encoder.layer.{i-1}.conv_projection.''' )
if "pre_norm_attn.0." in k:
lowercase_ : Any = k_new.replace('pre_norm_attn.0.' , 'layernorm_before.' )
if "pre_norm_attn.1." in k:
lowercase_ : Optional[Any] = k_new.replace('pre_norm_attn.1.' , 'attention.' )
if "pre_norm_ffn.0." in k:
lowercase_ : Union[str, Any] = k_new.replace('pre_norm_ffn.0.' , 'layernorm_after.' )
if "pre_norm_ffn.1." in k:
lowercase_ : Optional[Any] = k_new.replace('pre_norm_ffn.1.' , 'ffn.conv1.' )
if "pre_norm_ffn.3." in k:
lowercase_ : Dict = k_new.replace('pre_norm_ffn.3.' , 'ffn.conv2.' )
if "classifier.1." in k:
lowercase_ : str = k_new.replace('classifier.1.' , 'classifier.' )
if "seg_head." in k:
lowercase_ : Optional[Any] = k_new.replace('seg_head.' , 'segmentation_head.' )
if ".aspp_layer." in k:
lowercase_ : List[str] = k_new.replace('.aspp_layer.' , '.' )
if ".aspp_pool." in k:
lowercase_ : Optional[Any] = k_new.replace('.aspp_pool.' , '.' )
rename_keys.append((k, k_new) )
return rename_keys
def lowercase__( __SCREAMING_SNAKE_CASE : Dict ) -> Dict:
lowercase_ : Optional[int] = []
for k in state_dict.keys():
if k.startswith('seg_head.aux_head.' ):
keys_to_ignore.append(__SCREAMING_SNAKE_CASE )
for k in keys_to_ignore:
state_dict.pop(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def lowercase__( ) -> Optional[Any]:
lowercase_ : Dict = 'http://images.cocodataset.org/val2017/000000039769.jpg'
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
lowercase_ : Optional[int] = Image.open(requests.get(__SCREAMING_SNAKE_CASE , stream=__SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def lowercase__( __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Any ) -> Any:
lowercase_ : int = get_mobilevitva_config(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# load original state_dict
lowercase_ : List[Any] = torch.load(__SCREAMING_SNAKE_CASE , map_location='cpu' )
# load huggingface model
if task_name.startswith('ade20k_' ) or task_name.startswith('voc_' ):
lowercase_ : str = MobileViTVaForSemanticSegmentation(__SCREAMING_SNAKE_CASE ).eval()
lowercase_ : Optional[Any] = False
else:
lowercase_ : Tuple = MobileViTVaForImageClassification(__SCREAMING_SNAKE_CASE ).eval()
lowercase_ : int = False
# remove and rename some keys of load the original model
lowercase_ : Tuple = checkpoint
remove_unused_keys(__SCREAMING_SNAKE_CASE )
lowercase_ : Optional[int] = create_rename_keys(__SCREAMING_SNAKE_CASE , base_model=__SCREAMING_SNAKE_CASE )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# load modified state_dict
model.load_state_dict(__SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by MobileViTImageProcessor
lowercase_ : List[Any] = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
lowercase_ : Dict = image_processor(images=prepare_img() , return_tensors='pt' )
lowercase_ : str = model(**__SCREAMING_SNAKE_CASE )
# verify classification model
if task_name.startswith('imagenet' ):
lowercase_ : int = outputs.logits
lowercase_ : Any = logits.argmax(-1 ).item()
print('Predicted class:' , model.config.idalabel[predicted_class_idx] )
if task_name.startswith('imagenet1k_256' ) and config.width_multiplier == 1.0:
# expected_logits for base variant
lowercase_ : Optional[int] = torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01] )
assert torch.allclose(logits[0, :3] , __SCREAMING_SNAKE_CASE , atol=1E-4 )
Path(__SCREAMING_SNAKE_CASE ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
print(F'''Saving model {task_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(__SCREAMING_SNAKE_CASE )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task",
default="imagenet1k_256",
type=str,
help=(
"Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
"\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n "
),
choices=[
"imagenet1k_256",
"imagenet1k_384",
"imagenet21k_to_1k_256",
"imagenet21k_to_1k_384",
"ade20k_deeplabv3",
"voc_deeplabv3",
],
)
parser.add_argument(
"--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
)
parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
__SCREAMING_SNAKE_CASE =parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 367 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase ( unittest.TestCase ):
def _UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
lowercase_ : Union[str, Any] = [[1, 2, 4], [1, 2, 3, 4]]
lowercase_ : List[Any] = DisjunctiveConstraint(__UpperCamelCase )
self.assertTrue(isinstance(dc.token_ids ,__UpperCamelCase ) )
with self.assertRaises(__UpperCamelCase ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(__UpperCamelCase ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def _UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
lowercase_ : List[Any] = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(__UpperCamelCase ):
DisjunctiveConstraint(__UpperCamelCase ) # fails here
def _UpperCAmelCase ( self ) -> str:
'''simple docstring'''
lowercase_ : Optional[int] = [[1, 2, 3], [1, 2, 4]]
lowercase_ : Dict = DisjunctiveConstraint(__UpperCamelCase )
lowercase_ , lowercase_ , lowercase_ : Union[str, Any] = dc.update(1 )
lowercase_ : str = stepped is True and completed is False and reset is False
self.assertTrue(__UpperCamelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
lowercase_ , lowercase_ , lowercase_ : Optional[Any] = dc.update(2 )
lowercase_ : Any = stepped is True and completed is False and reset is False
self.assertTrue(__UpperCamelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
lowercase_ , lowercase_ , lowercase_ : Tuple = dc.update(3 )
lowercase_ : Union[str, Any] = stepped is True and completed is True and reset is False
self.assertTrue(__UpperCamelCase )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def _UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
lowercase_ : List[str] = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
lowercase_ : Union[str, Any] = DisjunctiveConstraint(__UpperCamelCase )
lowercase_ , lowercase_ , lowercase_ : Optional[int] = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
lowercase_ , lowercase_ , lowercase_ : int = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
lowercase_ , lowercase_ , lowercase_ : str = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
lowercase_ , lowercase_ , lowercase_ : List[str] = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
lowercase_ , lowercase_ , lowercase_ : Optional[int] = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
lowercase_ , lowercase_ , lowercase_ : int = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
lowercase_ , lowercase_ , lowercase_ : Dict = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 321 | 0 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
_A = logging.get_logger(__name__)
_A = {
'b0': efficientnet.EfficientNetBa,
'b1': efficientnet.EfficientNetBa,
'b2': efficientnet.EfficientNetBa,
'b3': efficientnet.EfficientNetBa,
'b4': efficientnet.EfficientNetBa,
'b5': efficientnet.EfficientNetBa,
'b6': efficientnet.EfficientNetBa,
'b7': efficientnet.EfficientNetBa,
}
_A = {
'b0': {
'hidden_dim': 1280,
'width_coef': 1.0,
'depth_coef': 1.0,
'image_size': 224,
'dropout_rate': 0.2,
'dw_padding': [],
},
'b1': {
'hidden_dim': 1280,
'width_coef': 1.0,
'depth_coef': 1.1,
'image_size': 240,
'dropout_rate': 0.2,
'dw_padding': [16],
},
'b2': {
'hidden_dim': 1408,
'width_coef': 1.1,
'depth_coef': 1.2,
'image_size': 260,
'dropout_rate': 0.3,
'dw_padding': [5, 8, 16],
},
'b3': {
'hidden_dim': 1536,
'width_coef': 1.2,
'depth_coef': 1.4,
'image_size': 300,
'dropout_rate': 0.3,
'dw_padding': [5, 18],
},
'b4': {
'hidden_dim': 1792,
'width_coef': 1.4,
'depth_coef': 1.8,
'image_size': 380,
'dropout_rate': 0.4,
'dw_padding': [6],
},
'b5': {
'hidden_dim': 2048,
'width_coef': 1.6,
'depth_coef': 2.2,
'image_size': 456,
'dropout_rate': 0.4,
'dw_padding': [13, 27],
},
'b6': {
'hidden_dim': 2304,
'width_coef': 1.8,
'depth_coef': 2.6,
'image_size': 528,
'dropout_rate': 0.5,
'dw_padding': [31],
},
'b7': {
'hidden_dim': 2560,
'width_coef': 2.0,
'depth_coef': 3.1,
'image_size': 600,
'dropout_rate': 0.5,
'dw_padding': [18],
},
}
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : List[Any] ):
__UpperCamelCase =EfficientNetConfig()
__UpperCamelCase =CONFIG_MAP[model_name]['hidden_dim']
__UpperCamelCase =CONFIG_MAP[model_name]['width_coef']
__UpperCamelCase =CONFIG_MAP[model_name]['depth_coef']
__UpperCamelCase =CONFIG_MAP[model_name]['image_size']
__UpperCamelCase =CONFIG_MAP[model_name]['dropout_rate']
__UpperCamelCase =CONFIG_MAP[model_name]['dw_padding']
__UpperCamelCase ='huggingface/label-files'
__UpperCamelCase ='imagenet-1k-id2label.json'
__UpperCamelCase =10_00
__UpperCamelCase =json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , repo_type='dataset' ) , 'r' ) )
__UpperCamelCase ={int(SCREAMING_SNAKE_CASE__ ): v for k, v in idalabel.items()}
__UpperCamelCase =idalabel
__UpperCamelCase ={v: k for k, v in idalabel.items()}
return config
def _UpperCAmelCase ( ):
__UpperCamelCase ='http://images.cocodataset.org/val2017/000000039769.jpg'
__UpperCamelCase =Image.open(requests.get(SCREAMING_SNAKE_CASE__ , stream=SCREAMING_SNAKE_CASE__ ).raw )
return im
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
__UpperCamelCase =CONFIG_MAP[model_name]['image_size']
__UpperCamelCase =EfficientNetImageProcessor(
size={'height': size, 'width': size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.47853944, 0.4732864, 0.47434163] , do_center_crop=SCREAMING_SNAKE_CASE__ , )
return preprocessor
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : str ):
__UpperCamelCase =[v.split('_' )[0].split('block' )[1] for v in original_param_names if v.startswith('block' )]
__UpperCamelCase =sorted(set(SCREAMING_SNAKE_CASE__ ) )
__UpperCamelCase =len(SCREAMING_SNAKE_CASE__ )
__UpperCamelCase ={b: str(SCREAMING_SNAKE_CASE__ ) for b, i in zip(SCREAMING_SNAKE_CASE__ , range(SCREAMING_SNAKE_CASE__ ) )}
__UpperCamelCase =[]
rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight') )
rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight') )
rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias') )
rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean') )
rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var') )
for b in block_names:
__UpperCamelCase =block_name_mapping[b]
rename_keys.append((F'block{b}_expand_conv/kernel:0', F'encoder.blocks.{hf_b}.expansion.expand_conv.weight') )
rename_keys.append((F'block{b}_expand_bn/gamma:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.weight') )
rename_keys.append((F'block{b}_expand_bn/beta:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.bias') )
rename_keys.append(
(F'block{b}_expand_bn/moving_mean:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean') )
rename_keys.append(
(F'block{b}_expand_bn/moving_variance:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_var') )
rename_keys.append(
(F'block{b}_dwconv/depthwise_kernel:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight') )
rename_keys.append((F'block{b}_bn/gamma:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight') )
rename_keys.append((F'block{b}_bn/beta:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias') )
rename_keys.append(
(F'block{b}_bn/moving_mean:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean') )
rename_keys.append(
(F'block{b}_bn/moving_variance:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var') )
rename_keys.append((F'block{b}_se_reduce/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight') )
rename_keys.append((F'block{b}_se_reduce/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias') )
rename_keys.append((F'block{b}_se_expand/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.weight') )
rename_keys.append((F'block{b}_se_expand/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.bias') )
rename_keys.append(
(F'block{b}_project_conv/kernel:0', F'encoder.blocks.{hf_b}.projection.project_conv.weight') )
rename_keys.append((F'block{b}_project_bn/gamma:0', F'encoder.blocks.{hf_b}.projection.project_bn.weight') )
rename_keys.append((F'block{b}_project_bn/beta:0', F'encoder.blocks.{hf_b}.projection.project_bn.bias') )
rename_keys.append(
(F'block{b}_project_bn/moving_mean:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_mean') )
rename_keys.append(
(F'block{b}_project_bn/moving_variance:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_var') )
rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight') )
rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight') )
rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias') )
rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean') )
rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var') )
__UpperCamelCase ={}
for item in rename_keys:
if item[0] in original_param_names:
__UpperCamelCase ='efficientnet.' + item[1]
__UpperCamelCase ='classifier.weight'
__UpperCamelCase ='classifier.bias'
return key_mapping
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Tuple ):
for key, value in tf_params.items():
if "normalization" in key:
continue
__UpperCamelCase =key_mapping[key]
if "_conv" in key and "kernel" in key:
__UpperCamelCase =torch.from_numpy(SCREAMING_SNAKE_CASE__ ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
__UpperCamelCase =torch.from_numpy(SCREAMING_SNAKE_CASE__ ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
__UpperCamelCase =torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE__ ) )
else:
__UpperCamelCase =torch.from_numpy(SCREAMING_SNAKE_CASE__ )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(SCREAMING_SNAKE_CASE__ )
@torch.no_grad()
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
__UpperCamelCase =model_classes[model_name](
include_top=SCREAMING_SNAKE_CASE__ , weights='imagenet' , input_tensor=SCREAMING_SNAKE_CASE__ , input_shape=SCREAMING_SNAKE_CASE__ , pooling=SCREAMING_SNAKE_CASE__ , classes=10_00 , classifier_activation='softmax' , )
__UpperCamelCase =original_model.trainable_variables
__UpperCamelCase =original_model.non_trainable_variables
__UpperCamelCase ={param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
__UpperCamelCase =param.numpy()
__UpperCamelCase =list(tf_params.keys() )
# Load HuggingFace model
__UpperCamelCase =get_efficientnet_config(SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =EfficientNetForImageClassification(SCREAMING_SNAKE_CASE__ ).eval()
__UpperCamelCase =hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('Converting parameters...' )
__UpperCamelCase =rename_keys(SCREAMING_SNAKE_CASE__ )
replace_params(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Initialize preprocessor and preprocess input image
__UpperCamelCase =convert_image_processor(SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =preprocessor(images=prepare_img() , return_tensors='pt' )
# HF model inference
hf_model.eval()
with torch.no_grad():
__UpperCamelCase =hf_model(**SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =outputs.logits.detach().numpy()
# Original model inference
__UpperCamelCase =False
__UpperCamelCase =CONFIG_MAP[model_name]['image_size']
__UpperCamelCase =prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
__UpperCamelCase =image.img_to_array(SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =np.expand_dims(SCREAMING_SNAKE_CASE__ , axis=0 )
__UpperCamelCase =original_model.predict(SCREAMING_SNAKE_CASE__ )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , atol=1E-3 ), "The predicted logits are not the same."
print('Model outputs match!' )
if save_model:
# Create folder to save model
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
os.mkdir(SCREAMING_SNAKE_CASE__ )
# Save converted model and image processor
hf_model.save_pretrained(SCREAMING_SNAKE_CASE__ )
preprocessor.save_pretrained(SCREAMING_SNAKE_CASE__ )
if push_to_hub:
# Push model and image processor to hub
print(F'Pushing converted {model_name} to the hub...' )
__UpperCamelCase =F'efficientnet-{model_name}'
preprocessor.push_to_hub(SCREAMING_SNAKE_CASE__ )
hf_model.push_to_hub(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='b0',
type=str,
help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='hf_model',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--save_model', action='store_true', help='Save model to local')
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
_A = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 62 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"""tensor(bool)""": np.bool_,
"""tensor(int8)""": np.inta,
"""tensor(uint8)""": np.uinta,
"""tensor(int16)""": np.intaa,
"""tensor(uint16)""": np.uintaa,
"""tensor(int32)""": np.intaa,
"""tensor(uint32)""": np.uintaa,
"""tensor(int64)""": np.intaa,
"""tensor(uint64)""": np.uintaa,
"""tensor(float16)""": np.floataa,
"""tensor(float)""": np.floataa,
"""tensor(double)""": np.floataa,
}
class UpperCAmelCase :
def __init__(self : Optional[Any] , snake_case__ : Optional[Any]=None , **snake_case__ : Optional[Any] ) -> List[str]:
'''simple docstring'''
logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future." )
snake_case : Optional[Any] = model
snake_case : Dict = kwargs.get("model_save_dir" , snake_case__ )
snake_case : int = kwargs.get("latest_model_name" , snake_case__ )
def __call__(self : Tuple , **snake_case__ : str ) -> List[str]:
'''simple docstring'''
snake_case : Union[str, Any] = {k: np.array(snake_case__ ) for k, v in kwargs.items()}
return self.model.run(snake_case__ , snake_case__ )
@staticmethod
def _SCREAMING_SNAKE_CASE (snake_case__ : Union[str, Path] , snake_case__ : Optional[int]=None , snake_case__ : Optional[int]=None ) -> Any:
'''simple docstring'''
if provider is None:
logger.info("No onnxruntime provider specified, using CPUExecutionProvider" )
snake_case : Optional[int] = "CPUExecutionProvider"
return ort.InferenceSession(snake_case__ , providers=[provider] , sess_options=snake_case__ )
def _SCREAMING_SNAKE_CASE (self : List[Any] , snake_case__ : Union[str, Path] , snake_case__ : Optional[str] = None , **snake_case__ : Any ) -> List[Any]:
'''simple docstring'''
snake_case : Tuple = file_name if file_name is not None else ONNX_WEIGHTS_NAME
snake_case : Any = self.model_save_dir.joinpath(self.latest_model_name )
snake_case : str = Path(snake_case__ ).joinpath(snake_case__ )
try:
shutil.copyfile(snake_case__ , snake_case__ )
except shutil.SameFileError:
pass
# copy external weights (for models >2GB)
snake_case : List[str] = self.model_save_dir.joinpath(snake_case__ )
if src_path.exists():
snake_case : Tuple = Path(snake_case__ ).joinpath(snake_case__ )
try:
shutil.copyfile(snake_case__ , snake_case__ )
except shutil.SameFileError:
pass
def _SCREAMING_SNAKE_CASE (self : str , snake_case__ : Union[str, os.PathLike] , **snake_case__ : Optional[int] , ) -> str:
'''simple docstring'''
if os.path.isfile(snake_case__ ):
logger.error(f"""Provided path ({save_directory}) should be a directory, not a file""" )
return
os.makedirs(snake_case__ , exist_ok=snake_case__ )
# saving model weights/files
self._save_pretrained(snake_case__ , **snake_case__ )
@classmethod
def _SCREAMING_SNAKE_CASE (cls : Tuple , snake_case__ : Union[str, Path] , snake_case__ : Optional[Union[bool, str, None]] = None , snake_case__ : Optional[Union[str, None]] = None , snake_case__ : bool = False , snake_case__ : Optional[str] = None , snake_case__ : Optional[str] = None , snake_case__ : Optional[str] = None , snake_case__ : Optional["ort.SessionOptions"] = None , **snake_case__ : Tuple , ) -> Tuple:
'''simple docstring'''
snake_case : List[str] = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(snake_case__ ):
snake_case : Any = OnnxRuntimeModel.load_model(
os.path.join(snake_case__ , snake_case__ ) , provider=snake_case__ , sess_options=snake_case__ )
snake_case : Union[str, Any] = Path(snake_case__ )
# load model from hub
else:
# download model
snake_case : Dict = hf_hub_download(
repo_id=snake_case__ , filename=snake_case__ , use_auth_token=snake_case__ , revision=snake_case__ , cache_dir=snake_case__ , force_download=snake_case__ , )
snake_case : List[Any] = Path(snake_case__ ).parent
snake_case : Union[str, Any] = Path(snake_case__ ).name
snake_case : Dict = OnnxRuntimeModel.load_model(snake_case__ , provider=snake_case__ , sess_options=snake_case__ )
return cls(model=snake_case__ , **snake_case__ )
@classmethod
def _SCREAMING_SNAKE_CASE (cls : Optional[Any] , snake_case__ : Union[str, Path] , snake_case__ : bool = True , snake_case__ : Optional[str] = None , snake_case__ : Optional[str] = None , **snake_case__ : Dict , ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Dict = None
if len(str(snake_case__ ).split("@" ) ) == 2:
snake_case , snake_case : int = model_id.split("@" )
return cls._from_pretrained(
model_id=snake_case__ , revision=snake_case__ , cache_dir=snake_case__ , force_download=snake_case__ , use_auth_token=snake_case__ , **snake_case__ , )
| 59 | 0 |
class _a :
'''simple docstring'''
def __init__( self ):
A__ : str = {}
def __A ( self ):
print(self.vertex )
for i in self.vertex:
print(A__ , """ -> """ , """ -> """.join([str(A__ ) for j in self.vertex[i]] ) )
def __A ( self , A__ , A__ ):
# check if vertex is already present,
if from_vertex in self.vertex:
self.vertex[from_vertex].append(A__ )
else:
# else make a new vertex
A__ : List[Any] = [to_vertex]
def __A ( self ):
# visited array for storing already visited nodes
A__ : List[Any] = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(A__ , A__ )
def __A ( self , A__ , A__ ):
# mark start vertex as visited
A__ : int = True
print(A__ , end=""" """ )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(A__ , A__ )
if __name__ == "__main__":
A_ : Union[str, Any] = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print('DFS:')
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 362 |
from __future__ import annotations
def UpperCamelCase (lowercase_: list[int] , lowercase_: list[int] , lowercase_: int ) -> tuple[float, list[float]]:
A__ : Tuple = list(range(len(lowercase_ ) ) )
A__ : Union[str, Any] = [v / w for v, w in zip(lowercase_ , lowercase_ )]
index.sort(key=lambda lowercase_ : ratio[i] , reverse=lowercase_ )
A__ : float = 0
A__ : list[float] = [0] * len(lowercase_ )
for i in index:
if weight[i] <= capacity:
A__ : Optional[int] = 1
max_value += value[i]
capacity -= weight[i]
else:
A__ : Union[str, Any] = capacity / weight[i]
max_value += value[i] * capacity / weight[i]
break
return max_value, fractions
if __name__ == "__main__":
import doctest
doctest.testmod()
| 141 | 0 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
_a = logging.get_logger(__name__)
_a = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_a = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
_a = {'''allegro/herbert-base-cased''': 5_1_4}
_a = {}
class A_ ( snake_case__ ):
_lowercase : List[str] = VOCAB_FILES_NAMES
_lowercase : str = PRETRAINED_VOCAB_FILES_MAP
_lowercase : Optional[Any] = PRETRAINED_INIT_CONFIGURATION
_lowercase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowercase : List[Any] = HerbertTokenizer
def __init__( self : Optional[Any] , UpperCAmelCase : Optional[int]=None , UpperCAmelCase : Dict=None , UpperCAmelCase : Optional[int]=None , UpperCAmelCase : List[Any]="<s>" , UpperCAmelCase : Optional[Any]="<unk>" , UpperCAmelCase : Optional[int]="<pad>" , UpperCAmelCase : Tuple="<mask>" , UpperCAmelCase : List[Any]="</s>" , **UpperCAmelCase : Union[str, Any] , ) -> int:
super().__init__(
UpperCAmelCase , UpperCAmelCase , tokenizer_file=UpperCAmelCase , cls_token=UpperCAmelCase , unk_token=UpperCAmelCase , pad_token=UpperCAmelCase , mask_token=UpperCAmelCase , sep_token=UpperCAmelCase , **UpperCAmelCase , )
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : List[int] , UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
__lowerCAmelCase: Any = [self.cls_token_id]
__lowerCAmelCase: Optional[int] = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCAmelCase ( self : List[str] , UpperCAmelCase : List[int] , UpperCAmelCase : Optional[List[int]] = None , UpperCAmelCase : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCAmelCase , token_ids_a=UpperCAmelCase , already_has_special_tokens=UpperCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(UpperCAmelCase )) + [1]
return [1] + ([0] * len(UpperCAmelCase )) + [1] + ([0] * len(UpperCAmelCase )) + [1]
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : List[int] , UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
__lowerCAmelCase: List[str] = [self.sep_token_id]
__lowerCAmelCase: Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : str , UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
__lowerCAmelCase: List[str] = self._tokenizer.model.save(UpperCAmelCase , name=UpperCAmelCase )
return tuple(UpperCAmelCase )
| 322 |
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: List[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE )
if number < 0:
return False
__lowerCAmelCase: str = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 322 | 1 |
import requests
def lowerCamelCase_ ( UpperCamelCase__ : Dict , UpperCamelCase__ : Dict ) -> List[str]:
"""simple docstring"""
__lowerCamelCase = {'Content-Type': 'application/json'}
__lowerCamelCase = requests.post(lowercase__ , json={'text': message_body} , headers=lowercase__ )
if response.status_code != 200:
__lowerCamelCase = (
'Request to slack returned an error '
F"""{response.status_code}, the response is:\n{response.text}"""
)
raise ValueError(lowercase__ )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message("<YOUR MESSAGE BODY>", "<SLACK CHANNEL URL>")
| 362 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''sew-d'''
def __init__( self , lowerCamelCase__=32 , lowerCamelCase__=768 , lowerCamelCase__=12 , lowerCamelCase__=12 , lowerCamelCase__=3_072 , lowerCamelCase__=2 , lowerCamelCase__=512 , lowerCamelCase__=256 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=("p2c", "c2p") , lowerCamelCase__="layer_norm" , lowerCamelCase__="gelu_python" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=0.0 , lowerCamelCase__=0.1 , lowerCamelCase__=0.02 , lowerCamelCase__=1e-7 , lowerCamelCase__=1e-5 , lowerCamelCase__="group" , lowerCamelCase__="gelu" , lowerCamelCase__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , lowerCamelCase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , lowerCamelCase__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , lowerCamelCase__=False , lowerCamelCase__=128 , lowerCamelCase__=16 , lowerCamelCase__=True , lowerCamelCase__=0.05 , lowerCamelCase__=10 , lowerCamelCase__=2 , lowerCamelCase__=0.0 , lowerCamelCase__=10 , lowerCamelCase__=0 , lowerCamelCase__="mean" , lowerCamelCase__=False , lowerCamelCase__=False , lowerCamelCase__=256 , lowerCamelCase__=0 , lowerCamelCase__=1 , lowerCamelCase__=2 , **lowerCamelCase__ , ) -> Any:
'''simple docstring'''
super().__init__(**lowerCamelCase__ , pad_token_id=lowerCamelCase__ , bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ )
__lowerCamelCase = hidden_size
__lowerCamelCase = feat_extract_norm
__lowerCamelCase = feat_extract_activation
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = conv_bias
__lowerCamelCase = num_conv_pos_embeddings
__lowerCamelCase = num_conv_pos_embedding_groups
__lowerCamelCase = len(self.conv_dim )
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = intermediate_size
__lowerCamelCase = squeeze_factor
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = position_buckets
__lowerCamelCase = share_att_key
__lowerCamelCase = relative_attention
__lowerCamelCase = norm_rel_ebd
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = hidden_act
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = feat_proj_dropout
__lowerCamelCase = final_dropout
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = feature_layer_norm_eps
__lowerCamelCase = initializer_range
__lowerCamelCase = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect.'
'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
f"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
f"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowerCamelCase = apply_spec_augment
__lowerCamelCase = mask_time_prob
__lowerCamelCase = mask_time_length
__lowerCamelCase = mask_time_min_masks
__lowerCamelCase = mask_feature_prob
__lowerCamelCase = mask_feature_length
__lowerCamelCase = mask_feature_min_masks
# ctc loss
__lowerCamelCase = ctc_loss_reduction
__lowerCamelCase = ctc_zero_infinity
# sequence classification
__lowerCamelCase = use_weighted_layer_sum
__lowerCamelCase = classifier_proj_size
@property
def lowercase_ ( self ) -> Any:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 348 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class A ( unittest.TestCase ):
"""simple docstring"""
@slow
def snake_case__ ( self : Any )-> List[str]:
'''simple docstring'''
A__ = XLMRobertaModel.from_pretrained('xlm-roberta-base' )
A__ = torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]] )
# The dog is cute and lives in the garden house
A__ = torch.Size((1, 1_2, 7_6_8) ) # batch_size, sequence_length, embedding_vector_dim
A__ = torch.tensor(
[[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
A__ = model(lowercase_ )['last_hidden_state'].detach()
self.assertEqual(output.shape,lowercase_ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1],lowercase_,atol=1E-3 ) )
@slow
def snake_case__ ( self : Any )-> int:
'''simple docstring'''
A__ = XLMRobertaModel.from_pretrained('xlm-roberta-large' )
A__ = torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]] )
# The dog is cute and lives in the garden house
A__ = torch.Size((1, 1_2, 1_0_2_4) ) # batch_size, sequence_length, embedding_vector_dim
A__ = torch.tensor(
[[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
A__ = model(lowercase_ )['last_hidden_state'].detach()
self.assertEqual(output.shape,lowercase_ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1],lowercase_,atol=1E-3 ) )
| 7 |
"""simple docstring"""
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
SCREAMING_SNAKE_CASE_ = 1E-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_=16 , snake_case_=13 , snake_case_=7 , snake_case_=14 , snake_case_=10 , snake_case_=19 , snake_case_=5 , snake_case_=4 , snake_case_=True , snake_case_=16 , snake_case_=2 , snake_case_=4 , snake_case_=4 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=[1, 2, 3, 4, 5] , snake_case_=25 , snake_case_=5 , ) -> Tuple:
__lowerCAmelCase = d_model
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = prediction_length
__lowerCAmelCase = context_length
__lowerCAmelCase = cardinality
__lowerCAmelCase = num_time_features
__lowerCAmelCase = lags_sequence
__lowerCAmelCase = embedding_dimension
__lowerCAmelCase = is_training
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = context_length
__lowerCAmelCase = prediction_length + label_length
__lowerCAmelCase = label_length
__lowerCAmelCase = moving_average
__lowerCAmelCase = autocorrelation_factor
def A__ ( self ) -> List[Any]:
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def A__ ( self , snake_case_ ) -> Any:
__lowerCAmelCase = config.context_length + max(config.lags_sequence )
__lowerCAmelCase = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
__lowerCAmelCase = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
__lowerCAmelCase = floats_tensor([self.batch_size, _past_length] )
__lowerCAmelCase = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
__lowerCAmelCase = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
__lowerCAmelCase = floats_tensor([self.batch_size, config.prediction_length] )
__lowerCAmelCase = {
"""past_values""": past_values,
"""static_categorical_features""": static_categorical_features,
"""past_time_features""": past_time_features,
"""past_observed_mask""": past_observed_mask,
"""future_time_features""": future_time_features,
"""future_values""": future_values,
}
return inputs_dict
def A__ ( self ) -> Union[str, Any]:
__lowerCAmelCase = self.get_config()
__lowerCAmelCase = self.prepare_autoformer_inputs_dict(snake_case_ )
return config, inputs_dict
def A__ ( self ) -> int:
__lowerCAmelCase , __lowerCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def A__ ( self , snake_case_ , snake_case_ ) -> int:
__lowerCAmelCase = AutoformerModel(config=snake_case_ ).to(snake_case_ ).eval()
__lowerCAmelCase = model(**snake_case_ )
__lowerCAmelCase = outputs.encoder_last_hidden_state
__lowerCAmelCase = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
__lowerCAmelCase = model.get_encoder()
encoder.save_pretrained(snake_case_ )
__lowerCAmelCase = AutoformerEncoder.from_pretrained(snake_case_ ).to(snake_case_ )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = model.create_network_inputs(**snake_case_ )
__lowerCAmelCase , __lowerCAmelCase = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
__lowerCAmelCase = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
__lowerCAmelCase = encoder(inputs_embeds=snake_case_ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
__lowerCAmelCase = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
__lowerCAmelCase = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
__lowerCAmelCase = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
__lowerCAmelCase = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
__lowerCAmelCase = model.get_decoder()
decoder.save_pretrained(snake_case_ )
__lowerCAmelCase = AutoformerDecoder.from_pretrained(snake_case_ ).to(snake_case_ )
__lowerCAmelCase = decoder(
trend=snake_case_ , inputs_embeds=snake_case_ , encoder_hidden_states=snake_case_ , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class lowerCAmelCase_ ( A__ , A__ , unittest.TestCase ):
'''simple docstring'''
_snake_case = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
_snake_case = (AutoformerForPrediction,) if is_torch_available() else ()
_snake_case = {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
_snake_case = False
_snake_case = False
_snake_case = False
_snake_case = False
_snake_case = False
_snake_case = False
def A__ ( self ) -> Optional[int]:
__lowerCAmelCase = AutoformerModelTester(self )
__lowerCAmelCase = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ )
def A__ ( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def A__ ( self ) -> Dict:
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
__lowerCAmelCase = model_class(snake_case_ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(snake_case_ )
__lowerCAmelCase , __lowerCAmelCase = model_class.from_pretrained(snake_case_ , output_loading_info=snake_case_ )
self.assertEqual(info["""missing_keys"""] , [] )
def A__ ( self ) -> Optional[Any]:
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*snake_case_ )
@unittest.skip(reason="""Model has no tokens embeddings""" )
def A__ ( self ) -> Any:
pass
def A__ ( self ) -> str:
__lowerCAmelCase = inspect.signature(getattr(snake_case_ , """forward""" ) )
# The main input is the name of the argument after `self`
__lowerCAmelCase = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , snake_case_ )
def A__ ( self ) -> Any:
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase = model_class(snake_case_ )
__lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCAmelCase = [*signature.parameters.keys()]
__lowerCAmelCase = [
"""past_values""",
"""past_time_features""",
"""past_observed_mask""",
"""static_categorical_features""",
"""static_real_features""",
"""future_values""",
"""future_time_features""",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("""future_observed_mask""" )
expected_arg_names.extend(
[
"""decoder_attention_mask""",
"""head_mask""",
"""decoder_head_mask""",
"""cross_attn_head_mask""",
"""encoder_outputs""",
"""past_key_values""",
"""output_hidden_states""",
"""output_attentions""",
"""use_cache""",
"""return_dict""",
] )
self.assertListEqual(arg_names[: len(snake_case_ )] , snake_case_ )
def A__ ( self ) -> Optional[int]:
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase = True
__lowerCAmelCase = getattr(self.model_tester , """seq_length""" , snake_case_ )
__lowerCAmelCase = getattr(self.model_tester , """decoder_seq_length""" , snake_case_ )
__lowerCAmelCase = getattr(self.model_tester , """encoder_seq_length""" , snake_case_ )
__lowerCAmelCase = getattr(self.model_tester , """d_model""" , snake_case_ )
__lowerCAmelCase = getattr(self.model_tester , """num_attention_heads""" , snake_case_ )
__lowerCAmelCase = d_model // num_attention_heads
for model_class in self.all_model_classes:
__lowerCAmelCase = True
__lowerCAmelCase = False
__lowerCAmelCase = True
__lowerCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
__lowerCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
__lowerCAmelCase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__lowerCAmelCase = True
__lowerCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
__lowerCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
__lowerCAmelCase = outputs.encoder_attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
__lowerCAmelCase = len(snake_case_ )
__lowerCAmelCase = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(snake_case_ , snake_case_ )
# decoder attentions
__lowerCAmelCase = outputs.decoder_attentions
self.assertIsInstance(snake_case_ , (list, tuple) )
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
__lowerCAmelCase = outputs.cross_attentions
self.assertIsInstance(snake_case_ , (list, tuple) )
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
__lowerCAmelCase = True
__lowerCAmelCase = True
__lowerCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
__lowerCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
self.assertEqual(out_len + 2 , len(snake_case_ ) )
__lowerCAmelCase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def A__ ( self ) -> int:
super().test_retain_grad_hidden_states_attentions()
def lowercase (_lowerCAmelCase="train-batch.pt" ):
__lowerCAmelCase = hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch""" , filename=_lowerCAmelCase , repo_type="""dataset""" )
__lowerCAmelCase = torch.load(_lowerCAmelCase , map_location=_lowerCAmelCase )
return batch
@require_torch
@slow
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def A__ ( self ) -> int:
__lowerCAmelCase = AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly""" ).to(snake_case_ )
__lowerCAmelCase = prepare_batch()
with torch.no_grad():
__lowerCAmelCase = model(
past_values=batch["""past_values"""] , past_time_features=batch["""past_time_features"""] , past_observed_mask=batch["""past_observed_mask"""] , static_categorical_features=batch["""static_categorical_features"""] , future_values=batch["""future_values"""] , future_time_features=batch["""future_time_features"""] , )[0]
__lowerCAmelCase = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , snake_case_ )
__lowerCAmelCase = torch.tensor(
[[0.3_593, -1.3_398, 0.6_330], [0.2_279, 1.5_396, -0.1_792], [0.0_450, 1.3_225, -0.2_335]] , device=snake_case_ )
self.assertTrue(torch.allclose(output[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def A__ ( self ) -> List[str]:
__lowerCAmelCase = AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly""" ).to(snake_case_ )
__lowerCAmelCase = prepare_batch("""val-batch.pt""" )
with torch.no_grad():
__lowerCAmelCase = model(
past_values=batch["""past_values"""] , past_time_features=batch["""past_time_features"""] , past_observed_mask=batch["""past_observed_mask"""] , static_categorical_features=batch["""static_categorical_features"""] , ).encoder_last_hidden_state
__lowerCAmelCase = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , snake_case_ )
__lowerCAmelCase = torch.tensor(
[[-0.0_734, -0.9_036, 0.8_358], [4.7_186, 2.4_113, 1.9_581], [1.7_953, 2.3_558, 1.2_970]] , device=snake_case_ )
self.assertTrue(torch.allclose(output[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def A__ ( self ) -> Any:
__lowerCAmelCase = AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly""" ).to(snake_case_ )
__lowerCAmelCase = prepare_batch("""val-batch.pt""" )
with torch.no_grad():
__lowerCAmelCase = model.generate(
static_categorical_features=batch["""static_categorical_features"""] , past_time_features=batch["""past_time_features"""] , past_values=batch["""past_values"""] , future_time_features=batch["""future_time_features"""] , past_observed_mask=batch["""past_observed_mask"""] , )
__lowerCAmelCase = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , snake_case_ )
__lowerCAmelCase = torch.tensor([3_130.6_763, 4_056.5_293, 7_053.0_786] , device=snake_case_ )
__lowerCAmelCase = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , snake_case_ , rtol=1e-1 ) )
| 301 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def A_ ( snake_case_ : int ):
'''simple docstring'''
if num <= 0:
UpperCamelCase : Optional[int] = f'{num}: Invalid input, please enter a positive integer.'
raise ValueError(__UpperCamelCase )
UpperCamelCase : str = [True] * (num + 1)
UpperCamelCase : int = []
UpperCamelCase : List[str] = 2
UpperCamelCase : int = int(math.sqrt(__UpperCamelCase ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(__UpperCamelCase )
# Set multiples of start be False
for i in range(start * start ,num + 1 ,__UpperCamelCase ):
if sieve[i] is True:
UpperCamelCase : List[Any] = False
start += 1
for j in range(end + 1 ,num + 1 ):
if sieve[j] is True:
prime.append(__UpperCamelCase )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input('''Enter a positive integer: ''').strip())))
| 368 |
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : float = b
if function(snake_case_ ) == 0: # one of the a or b is a root for the function
return a
elif function(snake_case_ ) == 0:
return b
elif (
function(snake_case_ ) * function(snake_case_ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("""could not find root in given interval.""" )
else:
UpperCamelCase : float = start + (end - start) / 2.0
while abs(start - mid ) > 1_0**-7: # until precisely equals to 10^-7
if function(snake_case_ ) == 0:
return mid
elif function(snake_case_ ) * function(snake_case_ ) < 0:
UpperCamelCase : Dict = mid
else:
UpperCamelCase : List[str] = mid
UpperCamelCase : Tuple = start + (end - start) / 2.0
return mid
def A_ ( snake_case_ : float ):
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 27 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCAmelCase = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')
@dataclass
class A :
UpperCamelCase_ : Optional[str] =field(
default='''cifar10''' , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
UpperCamelCase_ : Optional[str] =field(
default=A_ , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
UpperCamelCase_ : Optional[str] =field(
default=A_ , metadata={'''help''': '''The column name of the images in the files.'''} )
UpperCamelCase_ : Optional[str] =field(default=A_ , metadata={'''help''': '''A folder containing the training data.'''} )
UpperCamelCase_ : Optional[str] =field(default=A_ , metadata={'''help''': '''A folder containing the validation data.'''} )
UpperCamelCase_ : Optional[float] =field(
default=0.15 , metadata={'''help''': '''Percent to split off of train for validation.'''} )
UpperCamelCase_ : Optional[int] =field(
default=A_ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase_ : Optional[int] =field(
default=A_ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def _A (self ):
__lowercase= {}
if self.train_dir is not None:
__lowercase= self.train_dir
if self.validation_dir is not None:
__lowercase= self.validation_dir
__lowercase= data_files if data_files else None
@dataclass
class A :
UpperCamelCase_ : str =field(
default=A_ , metadata={
'''help''': (
'''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
)
} , )
UpperCamelCase_ : Optional[str] =field(
default=A_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''} )
UpperCamelCase_ : Optional[str] =field(
default=A_ , metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
} , )
UpperCamelCase_ : Optional[str] =field(
default=A_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''} )
UpperCamelCase_ : str =field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase_ : str =field(default=A_ , metadata={'''help''': '''Name or path of preprocessor config.'''} )
UpperCamelCase_ : bool =field(
default=A_ , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
UpperCamelCase_ : float =field(
default=0.75 , metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''} )
UpperCamelCase_ : bool =field(
default=A_ , metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''} )
@dataclass
class A ( A_ ):
UpperCamelCase_ : float =field(
default=1e-3 , metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''} )
def _lowerCamelCase( lowercase__ ) -> List[str]:
'''simple docstring'''
__lowercase= torch.stack([example['pixel_values'] for example in examples] )
return {"pixel_values": pixel_values}
def _lowerCamelCase( ) -> List[Any]:
'''simple docstring'''
__lowercase= HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__lowercase, __lowercase, __lowercase= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowercase, __lowercase, __lowercase= parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_mae' , lowercase__ , lowercase__ )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
__lowercase= training_args.get_process_log_level()
logger.setLevel(lowercase__ )
transformers.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ F'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(F'Training/evaluation parameters {training_args}' )
# Detecting last checkpoint.
__lowercase= None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowercase= get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. '
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Initialize our dataset.
__lowercase= load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
__lowercase= None if 'validation' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , lowercase__ ) and data_args.train_val_split > 0.0:
__lowercase= ds['train'].train_test_split(data_args.train_val_split )
__lowercase= split['train']
__lowercase= split['test']
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowercase= {
'cache_dir': model_args.cache_dir,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.config_name:
__lowercase= ViTMAEConfig.from_pretrained(model_args.config_name , **lowercase__ )
elif model_args.model_name_or_path:
__lowercase= ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **lowercase__ )
else:
__lowercase= ViTMAEConfig()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.config_overrides is not None:
logger.info(F'Overriding config: {model_args.config_overrides}' )
config.update_from_string(model_args.config_overrides )
logger.info(F'New config: {config}' )
# adapt config
config.update(
{
'mask_ratio': model_args.mask_ratio,
'norm_pix_loss': model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
__lowercase= ViTImageProcessor.from_pretrained(model_args.image_processor_name , **lowercase__ )
elif model_args.model_name_or_path:
__lowercase= ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **lowercase__ )
else:
__lowercase= ViTImageProcessor()
# create model
if model_args.model_name_or_path:
__lowercase= ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=lowercase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('Training new model from scratch' )
__lowercase= ViTMAEForPreTraining(lowercase__ )
if training_args.do_train:
__lowercase= ds['train'].column_names
else:
__lowercase= ds['validation'].column_names
if data_args.image_column_name is not None:
__lowercase= data_args.image_column_name
elif "image" in column_names:
__lowercase= 'image'
elif "img" in column_names:
__lowercase= 'img'
else:
__lowercase= column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
__lowercase= image_processor.size['shortest_edge']
else:
__lowercase= (image_processor.size['height'], image_processor.size['width'])
__lowercase= Compose(
[
Lambda(lambda lowercase__ : img.convert('RGB' ) if img.mode != "RGB" else img ),
RandomResizedCrop(lowercase__ , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(lowercase__ ):
__lowercase= [transforms(lowercase__ ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
__lowercase= ds['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(lowercase__ )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
__lowercase= (
ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(lowercase__ )
# Compute absolute learning rate
__lowercase= (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
__lowercase= training_args.base_learning_rate * total_train_batch_size / 2_5_6
# Initialize our trainer
__lowercase= Trainer(
model=lowercase__ , args=lowercase__ , train_dataset=ds['train'] if training_args.do_train else None , eval_dataset=ds['validation'] if training_args.do_eval else None , tokenizer=lowercase__ , data_collator=lowercase__ , )
# Training
if training_args.do_train:
__lowercase= None
if training_args.resume_from_checkpoint is not None:
__lowercase= training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__lowercase= last_checkpoint
__lowercase= trainer.train(resume_from_checkpoint=lowercase__ )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
__lowercase= trainer.evaluate()
trainer.log_metrics('eval' , lowercase__ )
trainer.save_metrics('eval' , lowercase__ )
# Write model card and (optionally) push to hub
__lowercase= {
'tasks': 'masked-auto-encoding',
'dataset': data_args.dataset_name,
'tags': ['masked-auto-encoding'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase__ )
else:
trainer.create_model_card(**lowercase__ )
def _lowerCamelCase( lowercase__ ) -> Dict:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 295 |
from __future__ import annotations
def _lowerCamelCase( lowercase__ , lowercase__ ) -> Any:
'''simple docstring'''
if len(lowercase__ ) <= 1 or n <= 1:
return
insert_next(lowercase__ , n - 1 )
rec_insertion_sort(lowercase__ , n - 1 )
def _lowerCamelCase( lowercase__ , lowercase__ ) -> Any:
'''simple docstring'''
if index >= len(lowercase__ ) or collection[index - 1] <= collection[index]:
return
# Swaps adjacent elements since they are not in ascending order
__lowercase, __lowercase= (
collection[index],
collection[index - 1],
)
insert_next(lowercase__ , index + 1 )
if __name__ == "__main__":
lowerCAmelCase = input('''Enter integers separated by spaces: ''')
lowerCAmelCase = [int(num) for num in numbers.split()]
rec_insertion_sort(number_list, len(number_list))
print(number_list)
| 295 | 1 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'microsoft/conditional-detr-resnet-50': (
'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
),
}
class _a ( _lowerCamelCase):
_a = '''conditional_detr'''
_a = ['''past_key_values''']
_a = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self : List[Any] , _SCREAMING_SNAKE_CASE : Optional[Any]=True , _SCREAMING_SNAKE_CASE : List[Any]=None , _SCREAMING_SNAKE_CASE : Tuple=3 , _SCREAMING_SNAKE_CASE : Dict=300 , _SCREAMING_SNAKE_CASE : Optional[int]=6 , _SCREAMING_SNAKE_CASE : List[Any]=2048 , _SCREAMING_SNAKE_CASE : List[str]=8 , _SCREAMING_SNAKE_CASE : Tuple=6 , _SCREAMING_SNAKE_CASE : Union[str, Any]=2048 , _SCREAMING_SNAKE_CASE : Optional[int]=8 , _SCREAMING_SNAKE_CASE : str=0.0 , _SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , _SCREAMING_SNAKE_CASE : str=True , _SCREAMING_SNAKE_CASE : Tuple="relu" , _SCREAMING_SNAKE_CASE : Any=256 , _SCREAMING_SNAKE_CASE : Optional[Any]=0.1 , _SCREAMING_SNAKE_CASE : int=0.0 , _SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , _SCREAMING_SNAKE_CASE : List[str]=0.02 , _SCREAMING_SNAKE_CASE : Dict=1.0 , _SCREAMING_SNAKE_CASE : Tuple=False , _SCREAMING_SNAKE_CASE : Dict="sine" , _SCREAMING_SNAKE_CASE : Union[str, Any]="resnet50" , _SCREAMING_SNAKE_CASE : Union[str, Any]=True , _SCREAMING_SNAKE_CASE : Tuple=False , _SCREAMING_SNAKE_CASE : List[str]=2 , _SCREAMING_SNAKE_CASE : List[Any]=5 , _SCREAMING_SNAKE_CASE : Dict=2 , _SCREAMING_SNAKE_CASE : List[Any]=1 , _SCREAMING_SNAKE_CASE : Dict=1 , _SCREAMING_SNAKE_CASE : int=2 , _SCREAMING_SNAKE_CASE : Dict=5 , _SCREAMING_SNAKE_CASE : Any=2 , _SCREAMING_SNAKE_CASE : List[str]=0.25 , **_SCREAMING_SNAKE_CASE : Tuple , )-> Optional[int]:
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowerCAmelCase__ : List[str] = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
lowerCAmelCase__ : List[Any] = backbone_config.get('''model_type''' )
lowerCAmelCase__ : Optional[int] = CONFIG_MAPPING[backbone_model_type]
lowerCAmelCase__ : Optional[Any] = config_class.from_dict(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : str = use_timm_backbone
lowerCAmelCase__ : int = backbone_config
lowerCAmelCase__ : Dict = num_channels
lowerCAmelCase__ : List[Any] = num_queries
lowerCAmelCase__ : List[str] = d_model
lowerCAmelCase__ : Union[str, Any] = encoder_ffn_dim
lowerCAmelCase__ : Optional[int] = encoder_layers
lowerCAmelCase__ : Any = encoder_attention_heads
lowerCAmelCase__ : Any = decoder_ffn_dim
lowerCAmelCase__ : Union[str, Any] = decoder_layers
lowerCAmelCase__ : List[str] = decoder_attention_heads
lowerCAmelCase__ : Union[str, Any] = dropout
lowerCAmelCase__ : Optional[int] = attention_dropout
lowerCAmelCase__ : Any = activation_dropout
lowerCAmelCase__ : Union[str, Any] = activation_function
lowerCAmelCase__ : List[str] = init_std
lowerCAmelCase__ : Union[str, Any] = init_xavier_std
lowerCAmelCase__ : Union[str, Any] = encoder_layerdrop
lowerCAmelCase__ : int = decoder_layerdrop
lowerCAmelCase__ : Tuple = encoder_layers
lowerCAmelCase__ : Dict = auxiliary_loss
lowerCAmelCase__ : Union[str, Any] = position_embedding_type
lowerCAmelCase__ : Dict = backbone
lowerCAmelCase__ : Tuple = use_pretrained_backbone
lowerCAmelCase__ : List[Any] = dilation
# Hungarian matcher
lowerCAmelCase__ : List[str] = class_cost
lowerCAmelCase__ : Dict = bbox_cost
lowerCAmelCase__ : Dict = giou_cost
# Loss coefficients
lowerCAmelCase__ : List[Any] = mask_loss_coefficient
lowerCAmelCase__ : Any = dice_loss_coefficient
lowerCAmelCase__ : Optional[Any] = cls_loss_coefficient
lowerCAmelCase__ : Dict = bbox_loss_coefficient
lowerCAmelCase__ : Optional[int] = giou_loss_coefficient
lowerCAmelCase__ : Any = focal_alpha
super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@property
def UpperCAmelCase__( self : Tuple )-> Dict:
return self.encoder_attention_heads
@property
def UpperCAmelCase__( self : Optional[Any] )-> Tuple:
return self.d_model
def UpperCAmelCase__( self : Any )-> List[str]:
lowerCAmelCase__ : Tuple = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
lowerCAmelCase__ : Dict = self.backbone_config.to_dict()
lowerCAmelCase__ : Tuple = self.__class__.model_type
return output
class _a ( _lowerCamelCase):
_a = version.parse('''1.11''')
@property
def UpperCAmelCase__( self : Any )-> Union[str, Any]:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
] )
@property
def UpperCAmelCase__( self : str )-> Any:
return 1E-5
@property
def UpperCAmelCase__( self : Any )-> Dict:
return 12
| 353 |
def lowerCamelCase_ ( _a = 4_000_000 ):
"""simple docstring"""
lowerCAmelCase__ : str = []
lowerCAmelCase__ , lowerCAmelCase__ : Optional[Any] = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_a )
lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = b, a + b
return sum(_a )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 211 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__snake_case : List[Any] = logging.get_logger(__name__)
__snake_case : Union[str, Any] = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
__snake_case : Optional[int] = {
"""vocab_file""": {
"""yjernite/retribert-base-uncased""": (
"""https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""yjernite/retribert-base-uncased""": (
"""https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"""
),
},
}
__snake_case : Any = {
"""yjernite/retribert-base-uncased""": 512,
}
__snake_case : Optional[Any] = {
"""yjernite/retribert-base-uncased""": {"""do_lower_case""": True},
}
class A__ ( a_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE = RetriBertTokenizer
SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
def __init__( self: Tuple , _SCREAMING_SNAKE_CASE: str=None , _SCREAMING_SNAKE_CASE: List[Any]=None , _SCREAMING_SNAKE_CASE: List[Any]=True , _SCREAMING_SNAKE_CASE: List[Any]="[UNK]" , _SCREAMING_SNAKE_CASE: Tuple="[SEP]" , _SCREAMING_SNAKE_CASE: List[str]="[PAD]" , _SCREAMING_SNAKE_CASE: Optional[int]="[CLS]" , _SCREAMING_SNAKE_CASE: Union[str, Any]="[MASK]" , _SCREAMING_SNAKE_CASE: Union[str, Any]=True , _SCREAMING_SNAKE_CASE: Any=None , **_SCREAMING_SNAKE_CASE: List[str] , ) -> Tuple:
"""simple docstring"""
super().__init__(
_SCREAMING_SNAKE_CASE , tokenizer_file=_SCREAMING_SNAKE_CASE , do_lower_case=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , tokenize_chinese_chars=_SCREAMING_SNAKE_CASE , strip_accents=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : Dict = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get("lowercase" , _SCREAMING_SNAKE_CASE) != do_lower_case
or normalizer_state.get("strip_accents" , _SCREAMING_SNAKE_CASE) != strip_accents
or normalizer_state.get("handle_chinese_chars" , _SCREAMING_SNAKE_CASE) != tokenize_chinese_chars
):
__lowerCAmelCase : Dict = getattr(_SCREAMING_SNAKE_CASE , normalizer_state.pop("type"))
__lowerCAmelCase : Union[str, Any] = do_lower_case
__lowerCAmelCase : Tuple = strip_accents
__lowerCAmelCase : Dict = tokenize_chinese_chars
__lowerCAmelCase : List[str] = normalizer_class(**_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[Any] = do_lower_case
def _SCREAMING_SNAKE_CASE ( self: Tuple , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: List[Any]=None) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Any = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _SCREAMING_SNAKE_CASE ( self: int , _SCREAMING_SNAKE_CASE: List[int] , _SCREAMING_SNAKE_CASE: Optional[List[int]] = None) -> List[int]:
"""simple docstring"""
__lowerCAmelCase : List[str] = [self.sep_token_id]
__lowerCAmelCase : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]
def _SCREAMING_SNAKE_CASE ( self: Tuple , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Optional[str] = None) -> Tuple[str]:
"""simple docstring"""
__lowerCAmelCase : Any = self._tokenizer.model.save(_SCREAMING_SNAKE_CASE , name=_SCREAMING_SNAKE_CASE)
return tuple(_SCREAMING_SNAKE_CASE)
| 269 |
def a_ ( _A = 1000 ) -> int:
"""simple docstring"""
return sum(e for e in range(3 , _A ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 307 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"caidas/swin2sr-classicalsr-x2-64": (
"https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"
),
}
class lowerCamelCase ( A_ ):
UpperCAmelCase__ : Optional[Any] = "swin2sr"
UpperCAmelCase__ : int = {
"hidden_size": "embed_dim",
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__(self : List[str] , _A : int=6_4 , _A : str=1 , _A : Dict=3 , _A : Dict=1_8_0 , _A : Tuple=[6, 6, 6, 6, 6, 6] , _A : List[str]=[6, 6, 6, 6, 6, 6] , _A : str=8 , _A : Tuple=2.0 , _A : List[Any]=True , _A : List[str]=0.0 , _A : Optional[int]=0.0 , _A : List[str]=0.1 , _A : Optional[int]="gelu" , _A : str=False , _A : int=0.02 , _A : int=1E-5 , _A : Union[str, Any]=2 , _A : Optional[int]=1.0 , _A : List[Any]="1conv" , _A : List[str]="pixelshuffle" , **_A : Union[str, Any] , ) -> List[str]:
super().__init__(**_A )
snake_case = image_size
snake_case = patch_size
snake_case = num_channels
snake_case = embed_dim
snake_case = depths
snake_case = len(_A )
snake_case = num_heads
snake_case = window_size
snake_case = mlp_ratio
snake_case = qkv_bias
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = drop_path_rate
snake_case = hidden_act
snake_case = use_absolute_embeddings
snake_case = layer_norm_eps
snake_case = initializer_range
snake_case = upscale
snake_case = img_range
snake_case = resi_connection
snake_case = upsampler
| 137 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
_A = logging.get_logger(__name__)
def lowercase_ ( A__ , A__ ) -> int:
"""simple docstring"""
snake_case = RobertaPreLayerNormConfig.from_pretrained(
A__ , architectures=["RobertaPreLayerNormForMaskedLM"] )
# convert state_dict
snake_case = torch.load(hf_hub_download(repo_id=A__ , filename="pytorch_model.bin" ) )
snake_case = {}
for tensor_key, tensor_value in original_state_dict.items():
# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
if tensor_key.startswith("roberta." ):
snake_case = "roberta_prelayernorm." + tensor_key[len("roberta." ) :]
# The original implementation contains weights which are not used, remove them from the state_dict
if tensor_key.endswith(".self.LayerNorm.weight" ) or tensor_key.endswith(".self.LayerNorm.bias" ):
continue
snake_case = tensor_value
snake_case = RobertaPreLayerNormForMaskedLM.from_pretrained(
pretrained_model_name_or_path=A__ , config=A__ , state_dict=A__ )
model.save_pretrained(A__ )
# convert tokenizer
snake_case = AutoTokenizer.from_pretrained(A__ )
tokenizer.save_pretrained(A__ )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint-repo",
default=None,
type=str,
required=True,
help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
_A = parser.parse_args()
convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)
| 137 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = ['''XGLMTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = ['''XGLMTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
'''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XGLMForCausalLM''',
'''XGLMModel''',
'''XGLMPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
'''FlaxXGLMForCausalLM''',
'''FlaxXGLMModel''',
'''FlaxXGLMPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
'''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXGLMForCausalLM''',
'''TFXGLMModel''',
'''TFXGLMPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm import XGLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm_fast import XGLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
else:
import sys
A_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 64 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : list[int] , snake_case__ : int ):
"""simple docstring"""
if len(snake_case__ ) < k or k < 0:
raise ValueError("""Invalid Input""" )
_snake_case : Optional[int] = sum(array[:k] )
for i in range(len(snake_case__ ) - k ):
_snake_case : Optional[Any] = current_sum - array[i] + array[i + k]
_snake_case : List[str] = max(snake_case__ , snake_case__ )
return max_sum
if __name__ == "__main__":
from doctest import testmod
from random import randint
testmod()
A_ = [randint(-10_00, 10_00) for i in range(1_00)]
A_ = randint(0, 1_10)
print(F'''The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}''')
| 64 | 1 |
import argparse
import datetime
def _lowerCAmelCase ( A__: str ):
'''simple docstring'''
UpperCAmelCase = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday''',
'''3''': '''Wednesday''',
'''4''': '''Thursday''',
'''5''': '''Friday''',
'''6''': '''Saturday''',
}
UpperCAmelCase = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(A__ ) < 11:
raise ValueError('''Must be 10 characters long''' )
# Get month
UpperCAmelCase = int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 13:
raise ValueError('''Month must be between 1 - 12''' )
UpperCAmelCase = date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('''Date separator must be \'-\' or \'/\'''' )
# Get day
UpperCAmelCase = int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 32:
raise ValueError('''Date must be between 1 - 31''' )
# Get second separator
UpperCAmelCase = date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('''Date separator must be \'-\' or \'/\'''' )
# Get year
UpperCAmelCase = int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 45 < y < 8500:
raise ValueError(
'''Year out of range. There has to be some sort of limit...right?''' )
# Get datetime obj for validation
UpperCAmelCase = datetime.date(int(A__ ) , int(A__ ) , int(A__ ) )
# Start math
if m <= 2:
UpperCAmelCase = y - 1
UpperCAmelCase = m + 12
# maths var
UpperCAmelCase = int(str(A__ )[:2] )
UpperCAmelCase = int(str(A__ )[2:] )
UpperCAmelCase = int(2.6 * m - 5.39 )
UpperCAmelCase = int(c / 4 )
UpperCAmelCase = int(k / 4 )
UpperCAmelCase = int(d + k )
UpperCAmelCase = int(t + u + v + x )
UpperCAmelCase = int(z - (2 * c) )
UpperCAmelCase = round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError('''The date was evaluated incorrectly. Contact developer.''' )
# Response
UpperCAmelCase = F"""Your date {date_input}, is a {days[str(A__ )]}!"""
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
__magic_name__ = argparse.ArgumentParser(
description=(
"Find out what day of the week nearly any date is or was. Enter "
"date as a string in the mm-dd-yyyy or mm/dd/yyyy format"
)
)
parser.add_argument(
"date_input", type=str, help="Date as a string (mm-dd-yyyy or mm/dd/yyyy)"
)
__magic_name__ = parser.parse_args()
zeller(args.date_input)
| 152 |
import operator as op
def _lowerCAmelCase ( A__: List[str] ):
'''simple docstring'''
UpperCAmelCase = []
UpperCAmelCase = lambda A__ , A__ : int(x / y ) # noqa: E731 integer division operation
UpperCAmelCase = {
'''^''': op.pow,
'''*''': op.mul,
'''/''': div,
'''+''': op.add,
'''-''': op.sub,
} # operators & their respective operation
# print table header
print('''Symbol'''.center(8 ) , '''Action'''.center(12 ) , '''Stack''' , sep=''' | ''' )
print('''-''' * (30 + len(A__ )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(A__ ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ('''push(''' + x + ''')''').ljust(12 ) , ''','''.join(A__ ) , sep=''' | ''' )
else:
UpperCAmelCase = stack.pop() # pop stack
# output in tabular format
print(''''''.rjust(8 ) , ('''pop(''' + b + ''')''').ljust(12 ) , ''','''.join(A__ ) , sep=''' | ''' )
UpperCAmelCase = stack.pop() # pop stack
# output in tabular format
print(''''''.rjust(8 ) , ('''pop(''' + a + ''')''').ljust(12 ) , ''','''.join(A__ ) , sep=''' | ''' )
stack.append(
str(opr[x](int(A__ ) , int(A__ ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ('''push(''' + a + x + b + ''')''').ljust(12 ) , ''','''.join(A__ ) , sep=''' | ''' , )
return int(stack[0] )
if __name__ == "__main__":
__magic_name__ = input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
print("\n\tResult = ", solve(Postfix))
| 152 | 1 |
from __future__ import annotations
from collections.abc import Iterator
class a__ :
def __init__( self : int,_A : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = value
SCREAMING_SNAKE_CASE_ : Node | None = None
SCREAMING_SNAKE_CASE_ : Node | None = None
class a__ :
def __init__( self : Optional[int],_A : Node ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = tree
def __UpperCamelCase ( self : Any,_A : Node | None ):
"""simple docstring"""
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self : Dict ):
"""simple docstring"""
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 18 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase : List[str] = logging.get_logger(__name__)
__lowerCamelCase : Tuple = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
__lowerCamelCase : List[Any] = {
'''vocab_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
},
'''merges_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
},
}
__lowerCamelCase : int = {
'''allenai/longformer-base-4096''': 40_96,
'''allenai/longformer-large-4096''': 40_96,
'''allenai/longformer-large-4096-finetuned-triviaqa''': 40_96,
'''allenai/longformer-base-4096-extra.pos.embd.only''': 40_96,
'''allenai/longformer-large-4096-extra.pos.embd.only''': 40_96,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _snake_case ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
SCREAMING_SNAKE_CASE_ : str = bs[:]
SCREAMING_SNAKE_CASE_ : Optional[int] = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowerCAmelCase )
cs.append(2**8 + n )
n += 1
SCREAMING_SNAKE_CASE_ : List[str] = [chr(lowerCAmelCase ) for n in cs]
return dict(zip(lowerCAmelCase , lowerCAmelCase ) )
def _snake_case ( lowerCAmelCase : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = set()
SCREAMING_SNAKE_CASE_ : Tuple = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
SCREAMING_SNAKE_CASE_ : List[str] = char
return pairs
class a__ ( A__ ):
A = VOCAB_FILES_NAMES
A = PRETRAINED_VOCAB_FILES_MAP
A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A = ['input_ids', 'attention_mask']
def __init__( self : Union[str, Any],_A : List[Any],_A : Tuple,_A : str="replace",_A : Optional[int]="<s>",_A : Dict="</s>",_A : Any="</s>",_A : Optional[Any]="<s>",_A : Union[str, Any]="<unk>",_A : int="<pad>",_A : Dict="<mask>",_A : int=False,**_A : Dict,):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = AddedToken(_A,lstrip=_A,rstrip=_A ) if isinstance(_A,_A ) else bos_token
SCREAMING_SNAKE_CASE_ : Optional[int] = AddedToken(_A,lstrip=_A,rstrip=_A ) if isinstance(_A,_A ) else eos_token
SCREAMING_SNAKE_CASE_ : str = AddedToken(_A,lstrip=_A,rstrip=_A ) if isinstance(_A,_A ) else sep_token
SCREAMING_SNAKE_CASE_ : Union[str, Any] = AddedToken(_A,lstrip=_A,rstrip=_A ) if isinstance(_A,_A ) else cls_token
SCREAMING_SNAKE_CASE_ : List[str] = AddedToken(_A,lstrip=_A,rstrip=_A ) if isinstance(_A,_A ) else unk_token
SCREAMING_SNAKE_CASE_ : Optional[Any] = AddedToken(_A,lstrip=_A,rstrip=_A ) if isinstance(_A,_A ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE_ : Dict = AddedToken(_A,lstrip=_A,rstrip=_A ) if isinstance(_A,_A ) else mask_token
super().__init__(
errors=_A,bos_token=_A,eos_token=_A,unk_token=_A,sep_token=_A,cls_token=_A,pad_token=_A,mask_token=_A,add_prefix_space=_A,**_A,)
with open(_A,encoding="utf-8" ) as vocab_handle:
SCREAMING_SNAKE_CASE_ : Tuple = json.load(_A )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = {v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE_ : Any = errors # how to handle errors in decoding
SCREAMING_SNAKE_CASE_ : Optional[Any] = bytes_to_unicode()
SCREAMING_SNAKE_CASE_ : str = {v: k for k, v in self.byte_encoder.items()}
with open(_A,encoding="utf-8" ) as merges_handle:
SCREAMING_SNAKE_CASE_ : int = merges_handle.read().split("\n" )[1:-1]
SCREAMING_SNAKE_CASE_ : List[str] = [tuple(merge.split() ) for merge in bpe_merges]
SCREAMING_SNAKE_CASE_ : Optional[int] = dict(zip(_A,range(len(_A ) ) ) )
SCREAMING_SNAKE_CASE_ : Any = {}
SCREAMING_SNAKE_CASE_ : List[str] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
SCREAMING_SNAKE_CASE_ : List[Any] = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
def __UpperCamelCase ( self : List[str] ):
"""simple docstring"""
return len(self.encoder )
def __UpperCamelCase ( self : Tuple ):
"""simple docstring"""
return dict(self.encoder,**self.added_tokens_encoder )
def __UpperCamelCase ( self : Any,_A : int ):
"""simple docstring"""
if token in self.cache:
return self.cache[token]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = tuple(_A )
SCREAMING_SNAKE_CASE_ : str = get_pairs(_A )
if not pairs:
return token
while True:
SCREAMING_SNAKE_CASE_ : Tuple = min(_A,key=lambda _A : self.bpe_ranks.get(_A,float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = bigram
SCREAMING_SNAKE_CASE_ : int = []
SCREAMING_SNAKE_CASE_ : Dict = 0
while i < len(_A ):
try:
SCREAMING_SNAKE_CASE_ : Tuple = word.index(_A,_A )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
SCREAMING_SNAKE_CASE_ : str = j
if word[i] == first and i < len(_A ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
SCREAMING_SNAKE_CASE_ : Dict = tuple(_A )
SCREAMING_SNAKE_CASE_ : List[str] = new_word
if len(_A ) == 1:
break
else:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = get_pairs(_A )
SCREAMING_SNAKE_CASE_ : List[str] = " ".join(_A )
SCREAMING_SNAKE_CASE_ : Any = word
return word
def __UpperCamelCase ( self : Dict,_A : Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = []
for token in re.findall(self.pat,_A ):
SCREAMING_SNAKE_CASE_ : Any = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_A ).split(" " ) )
return bpe_tokens
def __UpperCamelCase ( self : Optional[int],_A : str ):
"""simple docstring"""
return self.encoder.get(_A,self.encoder.get(self.unk_token ) )
def __UpperCamelCase ( self : Tuple,_A : str ):
"""simple docstring"""
return self.decoder.get(_A )
def __UpperCamelCase ( self : List[str],_A : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = "".join(_A )
SCREAMING_SNAKE_CASE_ : Tuple = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8",errors=self.errors )
return text
def __UpperCamelCase ( self : List[Any],_A : str,_A : Optional[str] = None ):
"""simple docstring"""
if not os.path.isdir(_A ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
SCREAMING_SNAKE_CASE_ : Tuple = os.path.join(
_A,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
SCREAMING_SNAKE_CASE_ : Any = os.path.join(
_A,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(_A,"w",encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder,indent=2,sort_keys=_A,ensure_ascii=_A ) + "\n" )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 0
with open(_A,"w",encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),key=lambda _A : kv[1] ):
if index != token_index:
logger.warning(
F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
" Please check that the tokenizer is not corrupted!" )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = token_index
writer.write(" ".join(_A ) + "\n" )
index += 1
return vocab_file, merge_file
def __UpperCamelCase ( self : Optional[Any],_A : List[int],_A : Optional[List[int]] = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE_ : str = [self.cls_token_id]
SCREAMING_SNAKE_CASE_ : Tuple = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __UpperCamelCase ( self : str,_A : List[int],_A : Optional[List[int]] = None,_A : bool = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_A,token_ids_a=_A,already_has_special_tokens=_A )
if token_ids_a is None:
return [1] + ([0] * len(_A )) + [1]
return [1] + ([0] * len(_A )) + [1, 1] + ([0] * len(_A )) + [1]
def __UpperCamelCase ( self : Any,_A : List[int],_A : Optional[List[int]] = None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = [self.sep_token_id]
SCREAMING_SNAKE_CASE_ : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __UpperCamelCase ( self : Any,_A : Union[str, Any],_A : Any=False,**_A : Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = kwargs.pop("add_prefix_space",self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(_A ) > 0 and not text[0].isspace()):
SCREAMING_SNAKE_CASE_ : str = " " + text
return (text, kwargs)
| 18 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( lowerCAmelCase = 1000 ):
'''simple docstring'''
return sum(e for e in range(3 , SCREAMING_SNAKE_CASE_ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F'{solution() = }')
| 364 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
lowerCAmelCase_ : Any = {
'''google/tapas-base-finetuned-sqa''': (
'''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wtq''': (
'''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wikisql-supervised''': (
'''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-tabfact''': (
'''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
),
}
class UpperCamelCase_ ( a_ ):
_A : List[str] = 'tapas'
def __init__( self , snake_case__=3_05_22 , snake_case__=7_68 , snake_case__=12 , snake_case__=12 , snake_case__=30_72 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=10_24 , snake_case__=[3, 2_56, 2_56, 2, 2_56, 2_56, 10] , snake_case__=0.02 , snake_case__=1e-12 , snake_case__=0 , snake_case__=10.0 , snake_case__=0 , snake_case__=1.0 , snake_case__=None , snake_case__=1.0 , snake_case__=False , snake_case__=None , snake_case__=1.0 , snake_case__=1.0 , snake_case__=False , snake_case__=False , snake_case__="ratio" , snake_case__=None , snake_case__=None , snake_case__=64 , snake_case__=32 , snake_case__=False , snake_case__=True , snake_case__=False , snake_case__=False , snake_case__=True , snake_case__=False , snake_case__=None , snake_case__=None , **snake_case__ , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(pad_token_id=snake_case__ , **snake_case__ )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = hidden_act
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = type_vocab_sizes
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
# Fine-tuning task hyperparameters
UpperCAmelCase = positive_label_weight
UpperCAmelCase = num_aggregation_labels
UpperCAmelCase = aggregation_loss_weight
UpperCAmelCase = use_answer_as_supervision
UpperCAmelCase = answer_loss_importance
UpperCAmelCase = use_normalized_answer_loss
UpperCAmelCase = huber_loss_delta
UpperCAmelCase = temperature
UpperCAmelCase = aggregation_temperature
UpperCAmelCase = use_gumbel_for_cells
UpperCAmelCase = use_gumbel_for_aggregation
UpperCAmelCase = average_approximation_function
UpperCAmelCase = cell_selection_preference
UpperCAmelCase = answer_loss_cutoff
UpperCAmelCase = max_num_rows
UpperCAmelCase = max_num_columns
UpperCAmelCase = average_logits_per_cell
UpperCAmelCase = select_one_column
UpperCAmelCase = allow_empty_column_selection
UpperCAmelCase = init_cell_selection_weights_to_zero
UpperCAmelCase = reset_position_index_per_cell
UpperCAmelCase = disable_per_token_loss
# Aggregation hyperparameters
UpperCAmelCase = aggregation_labels
UpperCAmelCase = no_aggregation_label_index
if isinstance(self.aggregation_labels , snake_case__ ):
UpperCAmelCase = {int(snake_case__ ): v for k, v in aggregation_labels.items()}
| 248 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( a_: int ):
if not isinstance(a_, a_ ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_persistence() does not accept negative values" )
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : Tuple = str(a_ )
while len(a_ ) != 1:
_UpperCAmelCase : List[str] = [int(a_ ) for i in num_string]
_UpperCAmelCase : Tuple = 1
for i in range(0, len(a_ ) ):
total *= numbers[i]
_UpperCAmelCase : Dict = str(a_ )
steps += 1
return steps
def __UpperCAmelCase ( a_: int ):
if not isinstance(a_, a_ ):
raise ValueError("additive_persistence() only accepts integral values" )
if num < 0:
raise ValueError("additive_persistence() does not accept negative values" )
_UpperCAmelCase : List[str] = 0
_UpperCAmelCase : List[str] = str(a_ )
while len(a_ ) != 1:
_UpperCAmelCase : str = [int(a_ ) for i in num_string]
_UpperCAmelCase : Optional[int] = 0
for i in range(0, len(a_ ) ):
total += numbers[i]
_UpperCAmelCase : Tuple = str(a_ )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 145 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__a = logging.get_logger(__name__)
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = ['''input_features''', '''attention_mask''']
def __init__( self : List[Any] , lowerCAmelCase__ : Union[str, Any]=8_0 , lowerCAmelCase__ : Tuple=1_6_0_0_0 , lowerCAmelCase__ : Union[str, Any]=8_0 , lowerCAmelCase__ : List[Any]=0.0 , lowerCAmelCase__ : str=True , lowerCAmelCase__ : Optional[Any]=True , lowerCAmelCase__ : List[Any]=True , **lowerCAmelCase__ : int , ) -> int:
"""simple docstring"""
super().__init__(feature_size=lowerCAmelCase__ , sampling_rate=lowerCAmelCase__ , padding_value=lowerCAmelCase__ , **lowerCAmelCase__ )
_UpperCAmelCase : str = num_mel_bins
_UpperCAmelCase : Optional[int] = do_ceptral_normalize
_UpperCAmelCase : List[str] = normalize_means
_UpperCAmelCase : str = normalize_vars
_UpperCAmelCase : Union[str, Any] = True
def _lowerCAmelCase ( self : Any , lowerCAmelCase__ : np.ndarray , ) -> np.ndarray:
"""simple docstring"""
_UpperCAmelCase : Tuple = waveform * (2**1_5) # Kaldi compliance: 16-bit signed integers
_UpperCAmelCase : Optional[Any] = torch.from_numpy(lowerCAmelCase__ ).unsqueeze(0 )
_UpperCAmelCase : List[Any] = ta_kaldi.fbank(lowerCAmelCase__ , num_mel_bins=self.num_mel_bins , sample_frequency=self.sampling_rate )
return features.numpy()
@staticmethod
def _lowerCAmelCase ( lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[bool] = True , lowerCAmelCase__ : Optional[bool] = True , lowerCAmelCase__ : float = 0.0 , ) -> np.ndarray:
"""simple docstring"""
if normalize_means:
_UpperCAmelCase : Optional[Any] = x[:input_length].mean(axis=0 )
_UpperCAmelCase : Dict = np.subtract(lowerCAmelCase__ , lowerCAmelCase__ )
if normalize_vars:
_UpperCAmelCase : Any = x[:input_length].std(axis=0 )
_UpperCAmelCase : Optional[int] = np.divide(lowerCAmelCase__ , lowerCAmelCase__ )
if input_length < x.shape[0]:
_UpperCAmelCase : str = padding_value
# make sure array is in float32
_UpperCAmelCase : Union[str, Any] = x.astype(np.floataa )
return x
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : List[np.ndarray] , lowerCAmelCase__ : Optional[np.ndarray] = None ) -> List[np.ndarray]:
"""simple docstring"""
_UpperCAmelCase : Tuple = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [
self.utterance_cmvn(lowerCAmelCase__ , lowerCAmelCase__ , self.normalize_means , self.normalize_vars , self.padding_value )
for x, n in zip(lowerCAmelCase__ , lowerCAmelCase__ )
]
def __call__( self : List[Any] , lowerCAmelCase__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , lowerCAmelCase__ : Union[bool, str, PaddingStrategy] = False , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : bool = False , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : Optional[Union[str, TensorType]] = None , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : Optional[bool] = None , **lowerCAmelCase__ : Optional[Any] , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
F""" {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
_UpperCAmelCase : Any = isinstance(lowerCAmelCase__ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F"""Only mono-channel audio is supported for input to {self}""" )
_UpperCAmelCase : List[Any] = is_batched_numpy or (
isinstance(lowerCAmelCase__ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_UpperCAmelCase : Any = [np.asarray(lowerCAmelCase__ , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(lowerCAmelCase__ , np.ndarray ):
_UpperCAmelCase : Dict = np.asarray(lowerCAmelCase__ , dtype=np.floataa )
elif isinstance(lowerCAmelCase__ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_UpperCAmelCase : Any = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_UpperCAmelCase : List[Any] = [raw_speech]
# extract fbank features
_UpperCAmelCase : Tuple = [self._extract_fbank_features(lowerCAmelCase__ ) for waveform in raw_speech]
# convert into correct format for padding
_UpperCAmelCase : Optional[Any] = BatchFeature({"input_features": features} )
_UpperCAmelCase : Optional[Any] = self.pad(
lowerCAmelCase__ , padding=lowerCAmelCase__ , max_length=lowerCAmelCase__ , truncation=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , **lowerCAmelCase__ , )
# make sure list is in array format
_UpperCAmelCase : Optional[Any] = padded_inputs.get("input_features" )
if isinstance(input_features[0] , lowerCAmelCase__ ):
_UpperCAmelCase : int = [np.asarray(lowerCAmelCase__ , dtype=np.floataa ) for feature in input_features]
_UpperCAmelCase : Optional[int] = padded_inputs.get("attention_mask" )
if attention_mask is not None:
_UpperCAmelCase : Dict = [np.asarray(lowerCAmelCase__ , dtype=np.intaa ) for array in attention_mask]
# Utterance-level cepstral mean and variance normalization
if self.do_ceptral_normalize:
_UpperCAmelCase : List[str] = (
np.array(lowerCAmelCase__ , dtype=np.intaa )
if self._get_padding_strategies(lowerCAmelCase__ , max_length=lowerCAmelCase__ ) is not PaddingStrategy.DO_NOT_PAD
else None
)
_UpperCAmelCase : str = self.normalize(
padded_inputs["input_features"] , attention_mask=lowerCAmelCase__ )
if return_tensors is not None:
_UpperCAmelCase : Any = padded_inputs.convert_to_tensors(lowerCAmelCase__ )
return padded_inputs
| 145 | 1 |
def UpperCAmelCase_( a__ ):
"""simple docstring"""
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''') )
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = credit_card_number
SCREAMING_SNAKE_CASE : Tuple = 0
SCREAMING_SNAKE_CASE : Dict = len(a__ ) - 2
for i in range(a__ , -1 , -2 ):
# double the value of every second digit
SCREAMING_SNAKE_CASE : Optional[int] = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
SCREAMING_SNAKE_CASE : Union[str, Any] = cc_number[:i] + str(a__ ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(a__ ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = F"""{credit_card_number} is an invalid credit card number because"""
if not credit_card_number.isdigit():
print(F"""{error_message} it has nonnumerical characters.""" )
return False
if not 13 <= len(a__ ) <= 16:
print(F"""{error_message} of its length.""" )
return False
if not validate_initial_digits(a__ ):
print(F"""{error_message} of its first two digits.""" )
return False
if not luhn_validation(a__ ):
print(F"""{error_message} it fails the Luhn check.""" )
return False
print(F"""{credit_card_number} is a valid credit card number.""" )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number('''4111111111111111''')
validate_credit_card_number('''32323''')
| 359 |
import math
from collections.abc import Iterator
from itertools import takewhile
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(a__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def UpperCAmelCase_( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = 2
while True:
if is_prime(a__ ):
yield num
num += 1
def UpperCAmelCase_( a__ = 2_000_000 ):
"""simple docstring"""
return sum(takewhile(lambda a__ : x < n , prime_generator() ) )
if __name__ == "__main__":
print(F"{solution() = }")
| 19 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
_a = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
for attribute in key.split("." ):
UpperCAmelCase_ : List[str] = getattr(__lowerCamelCase, __lowerCamelCase )
if weight_type is not None:
UpperCAmelCase_ : List[str] = getattr(__lowerCamelCase, __lowerCamelCase ).shape
else:
UpperCAmelCase_ : Optional[Any] = hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
UpperCAmelCase_ : List[str] = value
elif weight_type == "weight_g":
UpperCAmelCase_ : List[str] = value
elif weight_type == "weight_v":
UpperCAmelCase_ : int = value
elif weight_type == "bias":
UpperCAmelCase_ : int = value
else:
UpperCAmelCase_ : Optional[int] = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : str = []
UpperCAmelCase_ : int = fairseq_model.state_dict()
UpperCAmelCase_ : int = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
UpperCAmelCase_ : int = False
if "conv_layers" in name:
load_conv_layer(
__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, hf_model.config.feat_extract_norm == "group", )
UpperCAmelCase_ : int = True
else:
for key, mapped_key in MAPPING.items():
UpperCAmelCase_ : List[str] = "hubert." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key
if key in name or (key.split("w2v_model." )[-1] == name.split("." )[0] and not is_finetuned):
UpperCAmelCase_ : Union[str, Any] = True
if "*" in mapped_key:
UpperCAmelCase_ : Tuple = name.split(__lowerCamelCase )[0].split("." )[-2]
UpperCAmelCase_ : List[Any] = mapped_key.replace("*", __lowerCamelCase )
if "weight_g" in name:
UpperCAmelCase_ : Optional[Any] = "weight_g"
elif "weight_v" in name:
UpperCAmelCase_ : int = "weight_v"
elif "weight" in name:
UpperCAmelCase_ : List[Any] = "weight"
elif "bias" in name:
UpperCAmelCase_ : Union[str, Any] = "bias"
else:
UpperCAmelCase_ : Optional[int] = None
set_recursively(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
continue
if not is_used:
unused_weights.append(__lowerCamelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : Optional[Any] = full_name.split("conv_layers." )[-1]
UpperCAmelCase_ : Tuple = name.split("." )
UpperCAmelCase_ : int = int(items[0] )
UpperCAmelCase_ : Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
UpperCAmelCase_ : int = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
UpperCAmelCase_ : Optional[Any] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
UpperCAmelCase_ : Optional[int] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
UpperCAmelCase_ : Any = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__lowerCamelCase )
@torch.no_grad()
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase=None, __lowerCamelCase=None, __lowerCamelCase=True ):
if config_path is not None:
UpperCAmelCase_ : Any = HubertConfig.from_pretrained(__lowerCamelCase )
else:
UpperCAmelCase_ : Dict = HubertConfig()
if is_finetuned:
if dict_path:
UpperCAmelCase_ : str = Dictionary.load(__lowerCamelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
UpperCAmelCase_ : str = target_dict.pad_index
UpperCAmelCase_ : int = target_dict.bos_index
UpperCAmelCase_ : List[str] = target_dict.eos_index
UpperCAmelCase_ : Optional[Any] = len(target_dict.symbols )
UpperCAmelCase_ : int = os.path.join(__lowerCamelCase, "vocab.json" )
if not os.path.isdir(__lowerCamelCase ):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__lowerCamelCase ) )
return
os.makedirs(__lowerCamelCase, exist_ok=__lowerCamelCase )
with open(__lowerCamelCase, "w", encoding="utf-8" ) as vocab_handle:
json.dump(target_dict.indices, __lowerCamelCase )
UpperCAmelCase_ : List[Any] = WavaVecaCTCTokenizer(
__lowerCamelCase, unk_token=target_dict.unk_word, pad_token=target_dict.pad_word, bos_token=target_dict.bos_word, eos_token=target_dict.eos_word, word_delimiter_token="|", do_lower_case=__lowerCamelCase, )
UpperCAmelCase_ : int = True if config.feat_extract_norm == "layer" else False
UpperCAmelCase_ : Dict = WavaVecaFeatureExtractor(
feature_size=1, sampling_rate=1_6000, padding_value=0, do_normalize=__lowerCamelCase, return_attention_mask=__lowerCamelCase, )
UpperCAmelCase_ : Tuple = WavaVecaProcessor(feature_extractor=__lowerCamelCase, tokenizer=__lowerCamelCase )
processor.save_pretrained(__lowerCamelCase )
UpperCAmelCase_ : List[Any] = HubertForCTC(__lowerCamelCase )
else:
UpperCAmelCase_ : str = HubertModel(__lowerCamelCase )
if is_finetuned:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Tuple = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
else:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : str = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
UpperCAmelCase_ : int = model[0].eval()
recursively_load_weights(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
hf_wavavec.save_pretrained(__lowerCamelCase )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
_a = parser.parse_args()
convert_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 61 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ = {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FNetConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = ["""FNetTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = ["""FNetTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
"""FNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FNetForMaskedLM""",
"""FNetForMultipleChoice""",
"""FNetForNextSentencePrediction""",
"""FNetForPreTraining""",
"""FNetForQuestionAnswering""",
"""FNetForSequenceClassification""",
"""FNetForTokenClassification""",
"""FNetLayer""",
"""FNetModel""",
"""FNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 86 | 0 |
"""simple docstring"""
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class _snake_case ( _lowercase ):
def __init__( self: Optional[Any] , __lowerCamelCase: NestedDataStructureLike[PathLike] , __lowerCamelCase: Optional[NamedSplit] = None , __lowerCamelCase: Optional[Features] = None , __lowerCamelCase: str = None , __lowerCamelCase: bool = False , __lowerCamelCase: bool = False , __lowerCamelCase: Optional[int] = None , **__lowerCamelCase: Tuple , ) -> str:
super().__init__(
__lowerCamelCase , split=__lowerCamelCase , features=__lowerCamelCase , cache_dir=__lowerCamelCase , keep_in_memory=__lowerCamelCase , streaming=__lowerCamelCase , num_proc=__lowerCamelCase , **__lowerCamelCase , )
__UpperCAmelCase : Union[str, Any] = path_or_paths if isinstance(__lowerCamelCase , __lowerCamelCase ) else {self.split: path_or_paths}
__UpperCAmelCase : int = Text(
cache_dir=__lowerCamelCase , data_files=__lowerCamelCase , features=__lowerCamelCase , **__lowerCamelCase , )
def _lowerCamelCase ( self: List[Any] ) -> Optional[Any]:
# Build iterable dataset
if self.streaming:
__UpperCAmelCase : List[str] = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
__UpperCAmelCase : Any = None
__UpperCAmelCase : Any = None
__UpperCAmelCase : Dict = None
__UpperCAmelCase : str = None
self.builder.download_and_prepare(
download_config=__lowerCamelCase , download_mode=__lowerCamelCase , verification_mode=__lowerCamelCase , base_path=__lowerCamelCase , num_proc=self.num_proc , )
__UpperCAmelCase : Dict = self.builder.as_dataset(
split=self.split , verification_mode=__lowerCamelCase , in_memory=self.keep_in_memory )
return dataset
| 369 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def _UpperCamelCase ( snake_case__ ) -> Tuple:
__UpperCAmelCase : Union[str, Any] = 384
if "tiny" in model_name:
__UpperCAmelCase : Union[str, Any] = [3, 3, 9, 3]
__UpperCAmelCase : List[Any] = [96, 192, 384, 768]
if "small" in model_name:
__UpperCAmelCase : Tuple = [3, 3, 27, 3]
__UpperCAmelCase : Any = [96, 192, 384, 768]
if "base" in model_name:
__UpperCAmelCase : str = [3, 3, 27, 3]
__UpperCAmelCase : str = [128, 256, 512, 1024]
__UpperCAmelCase : str = 512
if "large" in model_name:
__UpperCAmelCase : Dict = [3, 3, 27, 3]
__UpperCAmelCase : int = [192, 384, 768, 1536]
__UpperCAmelCase : Dict = 768
if "xlarge" in model_name:
__UpperCAmelCase : List[Any] = [3, 3, 27, 3]
__UpperCAmelCase : Tuple = [256, 512, 1024, 2048]
__UpperCAmelCase : int = 1024
# set label information
__UpperCAmelCase : List[Any] = 150
__UpperCAmelCase : str = "huggingface/label-files"
__UpperCAmelCase : List[Any] = "ade20k-id2label.json"
__UpperCAmelCase : str = json.load(open(hf_hub_download(snake_case__, snake_case__, repo_type="dataset" ), "r" ) )
__UpperCAmelCase : str = {int(snake_case__ ): v for k, v in idalabel.items()}
__UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()}
__UpperCAmelCase : int = ConvNextConfig(
depths=snake_case__, hidden_sizes=snake_case__, out_features=["stage1", "stage2", "stage3", "stage4"] )
__UpperCAmelCase : int = UperNetConfig(
backbone_config=snake_case__, auxiliary_in_channels=snake_case__, num_labels=snake_case__, idalabel=snake_case__, labelaid=snake_case__, )
return config
def _UpperCamelCase ( snake_case__ ) -> Tuple:
__UpperCAmelCase : Optional[int] = []
# fmt: off
# stem
rename_keys.append(("backbone.downsample_layers.0.0.weight", "backbone.embeddings.patch_embeddings.weight") )
rename_keys.append(("backbone.downsample_layers.0.0.bias", "backbone.embeddings.patch_embeddings.bias") )
rename_keys.append(("backbone.downsample_layers.0.1.weight", "backbone.embeddings.layernorm.weight") )
rename_keys.append(("backbone.downsample_layers.0.1.bias", "backbone.embeddings.layernorm.bias") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f'''backbone.stages.{i}.{j}.gamma''', f'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.norm.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.norm.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''') )
if i > 0:
rename_keys.append((f'''backbone.downsample_layers.{i}.0.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''') )
rename_keys.append((f'''backbone.downsample_layers.{i}.0.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''') )
rename_keys.append((f'''backbone.downsample_layers.{i}.1.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''') )
rename_keys.append((f'''backbone.downsample_layers.{i}.1.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.bias''') )
rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight''') )
rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias''') )
# decode head
rename_keys.extend(
[
("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
] )
# fmt: on
return rename_keys
def _UpperCamelCase ( snake_case__, snake_case__, snake_case__ ) -> Any:
__UpperCAmelCase : Union[str, Any] = dct.pop(snake_case__ )
__UpperCAmelCase : Optional[int] = val
def _UpperCamelCase ( snake_case__, snake_case__, snake_case__ ) -> Union[str, Any]:
__UpperCAmelCase : Dict = {
"upernet-convnext-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth",
"upernet-convnext-small": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth",
"upernet-convnext-base": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth",
"upernet-convnext-large": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth",
"upernet-convnext-xlarge": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth",
}
__UpperCAmelCase : Union[str, Any] = model_name_to_url[model_name]
__UpperCAmelCase : str = torch.hub.load_state_dict_from_url(snake_case__, map_location="cpu" )["state_dict"]
__UpperCAmelCase : Dict = get_upernet_config(snake_case__ )
__UpperCAmelCase : str = UperNetForSemanticSegmentation(snake_case__ )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
__UpperCAmelCase : str = state_dict.pop(snake_case__ )
if "bn" in key:
__UpperCAmelCase : int = key.replace("bn", "batch_norm" )
__UpperCAmelCase : Union[str, Any] = val
# rename keys
__UpperCAmelCase : Optional[Any] = create_rename_keys(snake_case__ )
for src, dest in rename_keys:
rename_key(snake_case__, snake_case__, snake_case__ )
model.load_state_dict(snake_case__ )
# verify on image
__UpperCAmelCase : int = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
__UpperCAmelCase : Optional[int] = Image.open(requests.get(snake_case__, stream=snake_case__ ).raw ).convert("RGB" )
__UpperCAmelCase : str = SegformerImageProcessor()
__UpperCAmelCase : Any = processor(snake_case__, return_tensors="pt" ).pixel_values
with torch.no_grad():
__UpperCAmelCase : Union[str, Any] = model(snake_case__ )
if model_name == "upernet-convnext-tiny":
__UpperCAmelCase : Any = torch.tensor(
[[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]] )
elif model_name == "upernet-convnext-small":
__UpperCAmelCase : Optional[Any] = torch.tensor(
[[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]] )
elif model_name == "upernet-convnext-base":
__UpperCAmelCase : Dict = torch.tensor(
[[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]] )
elif model_name == "upernet-convnext-large":
__UpperCAmelCase : Tuple = torch.tensor(
[[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]] )
elif model_name == "upernet-convnext-xlarge":
__UpperCAmelCase : Union[str, Any] = torch.tensor(
[[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]] )
print("Logits:", outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3], snake_case__, atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(snake_case__ )
print(f'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(snake_case__ )
if push_to_hub:
print(f'''Pushing model and processor for {model_name} to hub''' )
model.push_to_hub(f'''openmmlab/{model_name}''' )
processor.push_to_hub(f'''openmmlab/{model_name}''' )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''upernet-convnext-tiny''',
type=str,
choices=[F'upernet-convnext-{size}' for size in ['''tiny''', '''small''', '''base''', '''large''', '''xlarge''']],
help='''Name of the ConvNext UperNet model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
_snake_case = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 342 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowerCAmelCase__ :
def __init__( self : Dict , _lowerCamelCase : int , _lowerCamelCase : Optional[int]=3 , _lowerCamelCase : List[str]=32 , _lowerCamelCase : Optional[int]=3 , _lowerCamelCase : Dict=10 , _lowerCamelCase : Tuple=[10, 20, 30, 40] , _lowerCamelCase : int=[1, 1, 2, 1] , _lowerCamelCase : int=True , _lowerCamelCase : Optional[int]=True , _lowerCamelCase : Optional[int]="relu" , _lowerCamelCase : List[Any]=3 , _lowerCamelCase : Dict=None , ):
_snake_case = parent
_snake_case = batch_size
_snake_case = image_size
_snake_case = num_channels
_snake_case = embeddings_size
_snake_case = hidden_sizes
_snake_case = depths
_snake_case = is_training
_snake_case = use_labels
_snake_case = hidden_act
_snake_case = num_labels
_snake_case = scope
_snake_case = len(_lowerCamelCase )
def lowercase ( self : Optional[int] ):
_snake_case = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_snake_case = None
if self.use_labels:
_snake_case = ids_tensor([self.batch_size] , self.num_labels )
_snake_case = self.get_config()
return config, pixel_values, labels
def lowercase ( self : Tuple ):
return ResNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def lowercase ( self : List[Any] , _lowerCamelCase : List[str] , _lowerCamelCase : str , _lowerCamelCase : List[Any] ):
_snake_case = TFResNetModel(config=_lowerCamelCase )
_snake_case = model(_lowerCamelCase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowercase ( self : Dict , _lowerCamelCase : str , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Tuple ):
_snake_case = self.num_labels
_snake_case = TFResNetForImageClassification(_lowerCamelCase )
_snake_case = model(_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase ( self : Tuple ):
_snake_case = self.prepare_config_and_inputs()
_snake_case , _snake_case , _snake_case = config_and_inputs
_snake_case = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class lowerCAmelCase__ ( A_ , A_ , unittest.TestCase ):
__a = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
__a = (
{"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
if is_tf_available()
else {}
)
__a = False
__a = False
__a = False
__a = False
__a = False
def lowercase ( self : List[Any] ):
_snake_case = TFResNetModelTester(self )
_snake_case = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def lowercase ( self : Tuple ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowercase ( self : List[Any] ):
return
@unittest.skip(reason='''ResNet does not use inputs_embeds''' )
def lowercase ( self : Any ):
pass
@unittest.skip(reason='''ResNet does not support input and output embeddings''' )
def lowercase ( self : List[str] ):
pass
def lowercase ( self : int ):
_snake_case , _snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case = model_class(_lowerCamelCase )
_snake_case = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_snake_case = [*signature.parameters.keys()]
_snake_case = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def lowercase ( self : List[str] ):
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def lowercase ( self : Union[str, Any] ):
def check_hidden_states_output(_lowerCamelCase : int , _lowerCamelCase : List[Any] , _lowerCamelCase : str ):
_snake_case = model_class(_lowerCamelCase )
_snake_case = model(**self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
_snake_case = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_snake_case = self.model_tester.num_stages
self.assertEqual(len(_lowerCamelCase ) , expected_num_stages + 1 )
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_snake_case , _snake_case = self.model_tester.prepare_config_and_inputs_for_common()
_snake_case = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
_snake_case = layer_type
_snake_case = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_snake_case = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def lowercase ( self : Union[str, Any] ):
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )
@slow
def lowercase ( self : List[str] ):
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case = TFResNetModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def _UpperCAmelCase ( ) -> Union[str, Any]:
_snake_case = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class lowerCAmelCase__ ( unittest.TestCase ):
@cached_property
def lowercase ( self : Dict ):
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def lowercase ( self : List[Any] ):
_snake_case = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
_snake_case = self.default_image_processor
_snake_case = prepare_img()
_snake_case = image_processor(images=_lowerCamelCase , return_tensors='''tf''' )
# forward pass
_snake_case = model(**_lowerCamelCase )
# verify the logits
_snake_case = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
_snake_case = tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , _lowerCamelCase , atol=1e-4 ) )
| 288 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {'vocab_file': 'sentencepiece.model'}
UpperCAmelCase__ = {
'vocab_file': {
'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
},
}
UpperCAmelCase__ = {
'google/rembert': 256,
}
class lowerCAmelCase__ ( A_ ):
__a = VOCAB_FILES_NAMES
__a = PRETRAINED_VOCAB_FILES_MAP
__a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Union[str, Any] , _lowerCamelCase : Any , _lowerCamelCase : Union[str, Any]=False , _lowerCamelCase : Any=True , _lowerCamelCase : Optional[Any]=True , _lowerCamelCase : int="[CLS]" , _lowerCamelCase : Optional[int]="[SEP]" , _lowerCamelCase : Optional[int]="[UNK]" , _lowerCamelCase : Optional[Any]="[SEP]" , _lowerCamelCase : str="[PAD]" , _lowerCamelCase : List[Any]="[CLS]" , _lowerCamelCase : Any="[MASK]" , **_lowerCamelCase : Optional[int] , ):
super().__init__(
do_lower_case=_lowerCamelCase , remove_space=_lowerCamelCase , keep_accents=_lowerCamelCase , bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , sep_token=_lowerCamelCase , pad_token=_lowerCamelCase , cls_token=_lowerCamelCase , mask_token=_lowerCamelCase , **_lowerCamelCase , )
_snake_case = do_lower_case
_snake_case = remove_space
_snake_case = keep_accents
_snake_case = vocab_file
_snake_case = spm.SentencePieceProcessor()
self.sp_model.Load(_lowerCamelCase )
@property
def lowercase ( self : int ):
return len(self.sp_model )
def lowercase ( self : Any ):
_snake_case = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : List[str] ):
_snake_case = self.__dict__.copy()
_snake_case = None
return state
def __setstate__( self : List[str] , _lowerCamelCase : Tuple ):
_snake_case = d
_snake_case = spm.SentencePieceProcessor()
self.sp_model.Load(self.vocab_file )
def lowercase ( self : str , _lowerCamelCase : List[str] , _lowerCamelCase : Tuple=False ):
_snake_case = self.sp_model.EncodeAsPieces(_lowerCamelCase )
return pieces
def lowercase ( self : str , _lowerCamelCase : str ):
return self.sp_model.PieceToId(_lowerCamelCase )
def lowercase ( self : List[str] , _lowerCamelCase : int ):
return self.sp_model.IdToPiece(_lowerCamelCase )
def lowercase ( self : Union[str, Any] , _lowerCamelCase : Any ):
_snake_case = self.sp_model.decode_pieces(_lowerCamelCase )
return out_string
def lowercase ( self : Optional[Any] , _lowerCamelCase : List[int] , _lowerCamelCase : Optional[List[int]] = None ):
_snake_case = [self.sep_token_id]
_snake_case = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowercase ( self : Tuple , _lowerCamelCase : List[int] , _lowerCamelCase : Optional[List[int]] = None , _lowerCamelCase : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(_lowerCamelCase )) + [1] + ([0] * len(_lowerCamelCase )) + [1]
return [1] + ([0] * len(_lowerCamelCase )) + [1]
def lowercase ( self : Optional[int] , _lowerCamelCase : List[int] , _lowerCamelCase : Optional[List[int]] = None ):
_snake_case = [self.sep_token_id]
_snake_case = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase ( self : List[str] , _lowerCamelCase : str , _lowerCamelCase : Optional[str] = None ):
if not os.path.isdir(_lowerCamelCase ):
logger.error('''Vocabulary path ({}) should be a directory'''.format(_lowerCamelCase ) )
return
_snake_case = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ):
copyfile(self.vocab_file , _lowerCamelCase )
return (out_vocab_file,)
| 288 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class __lowerCAmelCase ( unittest.TestCase):
def SCREAMING_SNAKE_CASE ( self: Any ):
lowercase :Union[str, Any] = tempfile.mkdtemp()
lowercase :int = BlipImageProcessor()
lowercase :List[Any] = GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model" )
lowercase :Any = BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert" )
lowercase :List[Any] = InstructBlipProcessor(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE ( self: Dict , **_lowerCAmelCase: List[str] ):
return AutoProcessor.from_pretrained(self.tmpdirname , **_lowerCAmelCase ).tokenizer
def SCREAMING_SNAKE_CASE ( self: Optional[int] , **_lowerCAmelCase: Optional[int] ):
return AutoProcessor.from_pretrained(self.tmpdirname , **_lowerCAmelCase ).image_processor
def SCREAMING_SNAKE_CASE ( self: Tuple , **_lowerCAmelCase: Optional[int] ):
return AutoProcessor.from_pretrained(self.tmpdirname , **_lowerCAmelCase ).qformer_tokenizer
def SCREAMING_SNAKE_CASE ( self: int ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE ( self: str ):
lowercase :Optional[int] = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
lowercase :Dict = [Image.fromarray(np.moveaxis(_lowerCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE ( self: Optional[int] ):
lowercase :Union[str, Any] = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
lowercase :Dict = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
lowercase :Optional[int] = self.get_image_processor(do_normalize=_lowerCAmelCase , padding_value=1.0 )
lowercase :Optional[Any] = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=_lowerCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowerCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCAmelCase )
self.assertIsInstance(processor.qformer_tokenizer , _lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Optional[Any] ):
lowercase :str = self.get_image_processor()
lowercase :Optional[Any] = self.get_tokenizer()
lowercase :Any = self.get_qformer_tokenizer()
lowercase :Any = InstructBlipProcessor(
tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase , qformer_tokenizer=_lowerCAmelCase )
lowercase :Union[str, Any] = self.prepare_image_inputs()
lowercase :Tuple = image_processor(_lowerCAmelCase , return_tensors="np" )
lowercase :List[Any] = processor(images=_lowerCAmelCase , return_tensors="np" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def SCREAMING_SNAKE_CASE ( self: Any ):
lowercase :str = self.get_image_processor()
lowercase :Union[str, Any] = self.get_tokenizer()
lowercase :Any = self.get_qformer_tokenizer()
lowercase :Union[str, Any] = InstructBlipProcessor(
tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase , qformer_tokenizer=_lowerCAmelCase )
lowercase :str = 'lower newer'
lowercase :List[str] = processor(text=_lowerCAmelCase )
lowercase :List[str] = tokenizer(_lowerCAmelCase , return_token_type_ids=_lowerCAmelCase )
lowercase :Dict = qformer_tokenizer(_lowerCAmelCase , return_token_type_ids=_lowerCAmelCase )
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] , encoded_processor[key] )
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] , encoded_processor["qformer_" + key] )
def SCREAMING_SNAKE_CASE ( self: Tuple ):
lowercase :Union[str, Any] = self.get_image_processor()
lowercase :Union[str, Any] = self.get_tokenizer()
lowercase :Tuple = self.get_qformer_tokenizer()
lowercase :List[Any] = InstructBlipProcessor(
tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase , qformer_tokenizer=_lowerCAmelCase )
lowercase :Optional[Any] = 'lower newer'
lowercase :Any = self.prepare_image_inputs()
lowercase :Union[str, Any] = processor(text=_lowerCAmelCase , images=_lowerCAmelCase )
self.assertListEqual(
list(inputs.keys() ) , ["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"] , )
# test if it raises when no input is passed
with pytest.raises(_lowerCAmelCase ):
processor()
def SCREAMING_SNAKE_CASE ( self: int ):
lowercase :str = self.get_image_processor()
lowercase :Optional[int] = self.get_tokenizer()
lowercase :List[Any] = self.get_qformer_tokenizer()
lowercase :Optional[int] = InstructBlipProcessor(
tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase , qformer_tokenizer=_lowerCAmelCase )
lowercase :Tuple = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowercase :Tuple = processor.batch_decode(_lowerCAmelCase )
lowercase :Dict = tokenizer.batch_decode(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Tuple ):
lowercase :List[str] = self.get_image_processor()
lowercase :Tuple = self.get_tokenizer()
lowercase :Optional[int] = self.get_qformer_tokenizer()
lowercase :Union[str, Any] = InstructBlipProcessor(
tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase , qformer_tokenizer=_lowerCAmelCase )
lowercase :Any = 'lower newer'
lowercase :List[str] = self.prepare_image_inputs()
lowercase :Union[str, Any] = processor(text=_lowerCAmelCase , images=_lowerCAmelCase )
self.assertListEqual(
list(inputs.keys() ) , ["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"] , )
| 351 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : str = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
# See all LED models at https://huggingface.co/models?filter=LED
_UpperCAmelCase : List[str] = {
"vocab_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
},
"merges_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
},
"tokenizer_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
},
}
_UpperCAmelCase : Optional[Any] = {
"allenai/led-base-16384": 16384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def UpperCAmelCase__ ( ):
lowercase :int = (
list(range(ord("!" ), ord("~" ) + 1 ) ) + list(range(ord("¡" ), ord("¬" ) + 1 ) ) + list(range(ord("®" ), ord("ÿ" ) + 1 ) )
)
lowercase :Dict = bs[:]
lowercase :List[Any] = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowerCamelCase )
cs.append(2**8 + n )
n += 1
lowercase :List[str] = [chr(lowerCamelCase ) for n in cs]
return dict(zip(lowerCamelCase, lowerCamelCase ) )
def UpperCAmelCase__ ( lowerCamelCase ):
lowercase :List[Any] = set()
lowercase :Any = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowercase :List[str] = char
return pairs
class __lowerCAmelCase ( lowerCAmelCase):
_a = VOCAB_FILES_NAMES
_a = PRETRAINED_VOCAB_FILES_MAP
_a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a = ['''input_ids''', '''attention_mask''']
def __init__( self: Optional[int] , _lowerCAmelCase: Tuple , _lowerCAmelCase: Tuple , _lowerCAmelCase: Union[str, Any]="replace" , _lowerCAmelCase: int="<s>" , _lowerCAmelCase: int="</s>" , _lowerCAmelCase: int="</s>" , _lowerCAmelCase: Optional[int]="<s>" , _lowerCAmelCase: Optional[int]="<unk>" , _lowerCAmelCase: Any="<pad>" , _lowerCAmelCase: Optional[Any]="<mask>" , _lowerCAmelCase: Union[str, Any]=False , **_lowerCAmelCase: Dict , ):
lowercase :Union[str, Any] = AddedToken(_lowerCAmelCase , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else bos_token
lowercase :List[str] = AddedToken(_lowerCAmelCase , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else eos_token
lowercase :Any = AddedToken(_lowerCAmelCase , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else sep_token
lowercase :Tuple = AddedToken(_lowerCAmelCase , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else cls_token
lowercase :List[Any] = AddedToken(_lowerCAmelCase , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else unk_token
lowercase :str = AddedToken(_lowerCAmelCase , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowercase :Tuple = AddedToken(_lowerCAmelCase , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else mask_token
super().__init__(
errors=_lowerCAmelCase , bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , unk_token=_lowerCAmelCase , sep_token=_lowerCAmelCase , cls_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , mask_token=_lowerCAmelCase , add_prefix_space=_lowerCAmelCase , **_lowerCAmelCase , )
with open(_lowerCAmelCase , encoding="utf-8" ) as vocab_handle:
lowercase :List[str] = json.load(_lowerCAmelCase )
lowercase :Union[str, Any] = {v: k for k, v in self.encoder.items()}
lowercase :Dict = errors # how to handle errors in decoding
lowercase :Any = bytes_to_unicode()
lowercase :str = {v: k for k, v in self.byte_encoder.items()}
with open(_lowerCAmelCase , encoding="utf-8" ) as merges_handle:
lowercase :List[Any] = merges_handle.read().split("\n" )[1:-1]
lowercase :Tuple = [tuple(merge.split() ) for merge in bpe_merges]
lowercase :Optional[int] = dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase ) ) ) )
lowercase :Tuple = {}
lowercase :List[Any] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowercase :Optional[int] = re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def SCREAMING_SNAKE_CASE ( self: int ):
return len(self.encoder )
def SCREAMING_SNAKE_CASE ( self: Dict ):
return dict(self.encoder , **self.added_tokens_encoder )
def SCREAMING_SNAKE_CASE ( self: int , _lowerCAmelCase: Optional[int] ):
if token in self.cache:
return self.cache[token]
lowercase :Tuple = tuple(_lowerCAmelCase )
lowercase :List[str] = get_pairs(_lowerCAmelCase )
if not pairs:
return token
while True:
lowercase :List[str] = min(_lowerCAmelCase , key=lambda _lowerCAmelCase : self.bpe_ranks.get(_lowerCAmelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
lowercase , lowercase :List[Any] = bigram
lowercase :str = []
lowercase :Tuple = 0
while i < len(_lowerCAmelCase ):
try:
lowercase :List[str] = word.index(_lowerCAmelCase , _lowerCAmelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowercase :Optional[Any] = j
if word[i] == first and i < len(_lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowercase :Dict = tuple(_lowerCAmelCase )
lowercase :Optional[Any] = new_word
if len(_lowerCAmelCase ) == 1:
break
else:
lowercase :List[str] = get_pairs(_lowerCAmelCase )
lowercase :str = " ".join(_lowerCAmelCase )
lowercase :Optional[Any] = word
return word
def SCREAMING_SNAKE_CASE ( self: Any , _lowerCAmelCase: Optional[int] ):
lowercase :str = []
for token in re.findall(self.pat , _lowerCAmelCase ):
lowercase :str = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCAmelCase ).split(" " ) )
return bpe_tokens
def SCREAMING_SNAKE_CASE ( self: Optional[int] , _lowerCAmelCase: str ):
return self.encoder.get(_lowerCAmelCase , self.encoder.get(self.unk_token ) )
def SCREAMING_SNAKE_CASE ( self: Optional[int] , _lowerCAmelCase: Tuple ):
return self.decoder.get(_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Tuple , _lowerCAmelCase: List[str] ):
lowercase :Optional[int] = "".join(_lowerCAmelCase )
lowercase :List[Any] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def SCREAMING_SNAKE_CASE ( self: List[Any] , _lowerCAmelCase: str , _lowerCAmelCase: Optional[str] = None ):
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
lowercase :List[str] = os.path.join(
_lowerCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowercase :Optional[int] = os.path.join(
_lowerCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(_lowerCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_lowerCAmelCase , ensure_ascii=_lowerCAmelCase ) + "\n" )
lowercase :Tuple = 0
with open(_lowerCAmelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!" )
lowercase :Tuple = token_index
writer.write(" ".join(_lowerCAmelCase ) + "\n" )
index += 1
return vocab_file, merge_file
def SCREAMING_SNAKE_CASE ( self: int , _lowerCAmelCase: List[int] , _lowerCAmelCase: Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowercase :Tuple = [self.cls_token_id]
lowercase :int = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self: List[Any] , _lowerCAmelCase: List[int] , _lowerCAmelCase: Optional[List[int]] = None , _lowerCAmelCase: bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCAmelCase , token_ids_a=_lowerCAmelCase , already_has_special_tokens=_lowerCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(_lowerCAmelCase )) + [1]
return [1] + ([0] * len(_lowerCAmelCase )) + [1, 1] + ([0] * len(_lowerCAmelCase )) + [1]
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] , _lowerCAmelCase: List[int] , _lowerCAmelCase: Optional[List[int]] = None ):
lowercase :List[str] = [self.sep_token_id]
lowercase :Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def SCREAMING_SNAKE_CASE ( self: Tuple , _lowerCAmelCase: Optional[Any] , _lowerCAmelCase: int=False , **_lowerCAmelCase: Dict ):
lowercase :Tuple = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(_lowerCAmelCase ) > 0 and not text[0].isspace()):
lowercase :List[Any] = " " + text
return (text, kwargs)
def SCREAMING_SNAKE_CASE ( self: List[Any] , _lowerCAmelCase: Union[Dict[str, EncodedInput], BatchEncoding] , _lowerCAmelCase: Optional[int] = None , _lowerCAmelCase: PaddingStrategy = PaddingStrategy.DO_NOT_PAD , _lowerCAmelCase: Optional[int] = None , _lowerCAmelCase: Optional[bool] = None , ):
lowercase :Tuple = super()._pad(
encoded_inputs=_lowerCAmelCase , max_length=_lowerCAmelCase , padding_strategy=_lowerCAmelCase , pad_to_multiple_of=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , )
# Load from model defaults
if return_attention_mask is None:
lowercase :Union[str, Any] = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
lowercase :Any = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
lowercase :Optional[int] = len(encoded_inputs["global_attention_mask"] ) != len(_lowerCAmelCase )
if needs_to_be_padded:
lowercase :Optional[int] = len(_lowerCAmelCase ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
lowercase :List[Any] = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
lowercase :int = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 158 | 0 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
BertTokenizer,
ProcessorMixin,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available
sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
from test_module.custom_processing import CustomProcessor # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
lowercase : Optional[Any] = get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
lowercase : Optional[Any] = get_tests_dir("""fixtures/vocab.json""")
lowercase : int = get_tests_dir("""fixtures""")
class __snake_case ( unittest.TestCase ):
_a : Union[str, Any]= ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : List[Any] = 0
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : Union[str, Any] = AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h""" )
self.assertIsInstance(snake_case ,snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase : Optional[int] = WavaVecaConfig()
lowercase : Any = AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h""" )
# save in new folder
model_config.save_pretrained(snake_case )
processor.save_pretrained(snake_case )
lowercase : Optional[Any] = AutoProcessor.from_pretrained(snake_case )
self.assertIsInstance(snake_case ,snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
# copy relevant files
copyfile(snake_case ,os.path.join(snake_case ,snake_case ) )
copyfile(snake_case ,os.path.join(snake_case ,"""vocab.json""" ) )
lowercase : str = AutoProcessor.from_pretrained(snake_case )
self.assertIsInstance(snake_case ,snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase : Optional[int] = WavaVecaFeatureExtractor()
lowercase : Optional[int] = AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h""" )
lowercase : List[str] = WavaVecaProcessor(snake_case ,snake_case )
# save in new folder
processor.save_pretrained(snake_case )
# drop `processor_class` in tokenizer
with open(os.path.join(snake_case ,snake_case ) ,"""r""" ) as f:
lowercase : Optional[int] = json.load(snake_case )
config_dict.pop("""processor_class""" )
with open(os.path.join(snake_case ,snake_case ) ,"""w""" ) as f:
f.write(json.dumps(snake_case ) )
lowercase : str = AutoProcessor.from_pretrained(snake_case )
self.assertIsInstance(snake_case ,snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase : List[str] = WavaVecaFeatureExtractor()
lowercase : List[Any] = AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h""" )
lowercase : List[str] = WavaVecaProcessor(snake_case ,snake_case )
# save in new folder
processor.save_pretrained(snake_case )
# drop `processor_class` in feature extractor
with open(os.path.join(snake_case ,snake_case ) ,"""r""" ) as f:
lowercase : str = json.load(snake_case )
config_dict.pop("""processor_class""" )
with open(os.path.join(snake_case ,snake_case ) ,"""w""" ) as f:
f.write(json.dumps(snake_case ) )
lowercase : Tuple = AutoProcessor.from_pretrained(snake_case )
self.assertIsInstance(snake_case ,snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase : Tuple = WavaVecaConfig(processor_class="""Wav2Vec2Processor""" )
model_config.save_pretrained(snake_case )
# copy relevant files
copyfile(snake_case ,os.path.join(snake_case ,"""vocab.json""" ) )
# create emtpy sample processor
with open(os.path.join(snake_case ,snake_case ) ,"""w""" ) as f:
f.write("""{}""" )
lowercase : str = AutoProcessor.from_pretrained(snake_case )
self.assertIsInstance(snake_case ,snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
with self.assertRaises(snake_case ):
lowercase : Optional[int] = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(snake_case ):
lowercase : Tuple = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" ,trust_remote_code=snake_case )
lowercase : Dict = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" ,trust_remote_code=snake_case )
self.assertTrue(processor.special_attribute_present )
self.assertEqual(processor.__class__.__name__ ,"""NewProcessor""" )
lowercase : Optional[Any] = processor.feature_extractor
self.assertTrue(feature_extractor.special_attribute_present )
self.assertEqual(feature_extractor.__class__.__name__ ,"""NewFeatureExtractor""" )
lowercase : Optional[Any] = processor.tokenizer
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ ,"""NewTokenizerFast""" )
# Test we can also load the slow version
lowercase : Optional[Any] = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" ,trust_remote_code=snake_case ,use_fast=snake_case )
lowercase : List[Any] = new_processor.tokenizer
self.assertTrue(new_tokenizer.special_attribute_present )
self.assertEqual(new_tokenizer.__class__.__name__ ,"""NewTokenizer""" )
else:
self.assertEqual(tokenizer.__class__.__name__ ,"""NewTokenizer""" )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
try:
AutoConfig.register("""custom""" ,snake_case )
AutoFeatureExtractor.register(snake_case ,snake_case )
AutoTokenizer.register(snake_case ,slow_tokenizer_class=snake_case )
AutoProcessor.register(snake_case ,snake_case )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(snake_case ):
AutoProcessor.register(snake_case ,snake_case )
# Now that the config is registered, it can be used as any other config with the auto-API
lowercase : Tuple = CustomFeatureExtractor.from_pretrained(snake_case )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase : List[str] = os.path.join(snake_case ,"""vocab.txt""" )
with open(snake_case ,"""w""" ,encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
lowercase : int = CustomTokenizer(snake_case )
lowercase : Union[str, Any] = CustomProcessor(snake_case ,snake_case )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(snake_case )
lowercase : str = AutoProcessor.from_pretrained(snake_case )
self.assertIsInstance(snake_case ,snake_case )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
class __snake_case ( lowerCAmelCase ):
_a : List[Any]= False
class __snake_case ( lowerCAmelCase ):
_a : Optional[int]= False
class __snake_case ( lowerCAmelCase ):
_a : List[Any]= "AutoFeatureExtractor"
_a : Union[str, Any]= "AutoTokenizer"
_a : str= False
try:
AutoConfig.register("""custom""" ,snake_case )
AutoFeatureExtractor.register(snake_case ,snake_case )
AutoTokenizer.register(snake_case ,slow_tokenizer_class=snake_case )
AutoProcessor.register(snake_case ,snake_case )
# If remote code is not set, the default is to use local classes.
lowercase : Optional[Any] = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" )
self.assertEqual(processor.__class__.__name__ ,"""NewProcessor""" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote code is disabled, we load the local ones.
lowercase : Dict = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" ,trust_remote_code=snake_case )
self.assertEqual(processor.__class__.__name__ ,"""NewProcessor""" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub.
lowercase : Union[str, Any] = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" ,trust_remote_code=snake_case )
self.assertEqual(processor.__class__.__name__ ,"""NewProcessor""" )
self.assertTrue(processor.special_attribute_present )
self.assertTrue(processor.feature_extractor.special_attribute_present )
self.assertTrue(processor.tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : Dict = AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
self.assertEqual(processor.__class__.__name__ ,"""BertTokenizerFast""" )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : List[Any] = AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext""" )
self.assertEqual(processor.__class__.__name__ ,"""ConvNextImageProcessor""" )
@is_staging_test
class __snake_case ( unittest.TestCase ):
_a : Dict= ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
@classmethod
def _SCREAMING_SNAKE_CASE ( cls ):
'''simple docstring'''
lowercase : List[Any] = TOKEN
HfFolder.save_token(snake_case )
@classmethod
def _SCREAMING_SNAKE_CASE ( cls ):
'''simple docstring'''
try:
delete_repo(token=cls._token ,repo_id="""test-processor""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id="""valid_org/test-processor-org""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id="""test-dynamic-processor""" )
except HTTPError:
pass
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : Optional[Any] = WavaVecaProcessor.from_pretrained(snake_case )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(snake_case ,"""test-processor""" ) ,push_to_hub=snake_case ,use_auth_token=self._token )
lowercase : List[str] = WavaVecaProcessor.from_pretrained(f"{USER}/test-processor" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(snake_case ,getattr(new_processor.feature_extractor ,snake_case ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() ,processor.tokenizer.get_vocab() )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : str = WavaVecaProcessor.from_pretrained(snake_case )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(snake_case ,"""test-processor-org""" ) ,push_to_hub=snake_case ,use_auth_token=self._token ,organization="""valid_org""" ,)
lowercase : Optional[int] = WavaVecaProcessor.from_pretrained("""valid_org/test-processor-org""" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(snake_case ,getattr(new_processor.feature_extractor ,snake_case ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() ,processor.tokenizer.get_vocab() )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
CustomFeatureExtractor.register_for_auto_class()
CustomTokenizer.register_for_auto_class()
CustomProcessor.register_for_auto_class()
lowercase : Dict = CustomFeatureExtractor.from_pretrained(snake_case )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase : List[Any] = os.path.join(snake_case ,"""vocab.txt""" )
with open(snake_case ,"""w""" ,encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
lowercase : List[Any] = CustomTokenizer(snake_case )
lowercase : Optional[Any] = CustomProcessor(snake_case ,snake_case )
with tempfile.TemporaryDirectory() as tmp_dir:
create_repo(f"{USER}/test-dynamic-processor" ,token=self._token )
lowercase : Dict = Repository(snake_case ,clone_from=f"{USER}/test-dynamic-processor" ,token=self._token )
processor.save_pretrained(snake_case )
# This has added the proper auto_map field to the feature extractor config
self.assertDictEqual(
processor.feature_extractor.auto_map ,{
"""AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor""",
"""AutoProcessor""": """custom_processing.CustomProcessor""",
} ,)
# This has added the proper auto_map field to the tokenizer config
with open(os.path.join(snake_case ,"""tokenizer_config.json""" ) ) as f:
lowercase : Dict = json.load(snake_case )
self.assertDictEqual(
tokenizer_config["""auto_map"""] ,{
"""AutoTokenizer""": ["""custom_tokenization.CustomTokenizer""", None],
"""AutoProcessor""": """custom_processing.CustomProcessor""",
} ,)
# The code has been copied from fixtures
self.assertTrue(os.path.isfile(os.path.join(snake_case ,"""custom_feature_extraction.py""" ) ) )
self.assertTrue(os.path.isfile(os.path.join(snake_case ,"""custom_tokenization.py""" ) ) )
self.assertTrue(os.path.isfile(os.path.join(snake_case ,"""custom_processing.py""" ) ) )
repo.push_to_hub()
lowercase : Any = AutoProcessor.from_pretrained(f"{USER}/test-dynamic-processor" ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
self.assertEqual(new_processor.__class__.__name__ ,"""CustomProcessor""" )
| 20 |
"""simple docstring"""
from __future__ import annotations
_lowercase : Dict = 1.6_021E-19 # units = C
def snake_case__ ( __lowerCamelCase : float , __lowerCamelCase : float , __lowerCamelCase : float , ):
"""simple docstring"""
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif conductivity < 0:
raise ValueError('''Conductivity cannot be negative''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative''' )
elif mobility < 0:
raise ValueError('''mobility cannot be negative''' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 238 | 0 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
UpperCamelCase_ = _symbol_database.Default()
UpperCamelCase_ = _descriptor_pool.Default().AddSerializedFile(
b"\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"
)
UpperCamelCase_ = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, "sentencepiece_model_pb2", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
UpperCamelCase_ = None
UpperCamelCase_ = b"H\003"
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
UpperCamelCase_ = 4_5
UpperCamelCase_ = 1_5_8_1
UpperCamelCase_ = 1_5_1_7
UpperCamelCase_ = 1_5_7_0
UpperCamelCase_ = 1_5_8_4
UpperCamelCase_ = 1_7_9_3
UpperCamelCase_ = 1_7_9_5
UpperCamelCase_ = 1_9_1_6
UpperCamelCase_ = 1_8_6_4
UpperCamelCase_ = 1_9_0_5
UpperCamelCase_ = 1_9_1_9
UpperCamelCase_ = 2_4_2_9
UpperCamelCase_ = 2_2_0_8
UpperCamelCase_ = 2_4_1_8
UpperCamelCase_ = 2_3_2_3
UpperCamelCase_ = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 246 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_torch_available():
import torch
UpperCamelCase_ = logging.get_logger(__name__)
@add_end_docstrings(
SCREAMING_SNAKE_CASE , R'''
top_k (`int`, defaults to 5):
The number of predictions to return.
targets (`str` or `List[str]`, *optional*):
When passed, the model will limit the scores to the passed targets instead of looking up in the whole
vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
token will be used (with a warning, and that might be slower).
''' , )
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
if self.framework == "tf":
SCREAMING_SNAKE_CASE : List[Any] = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
elif self.framework == "pt":
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.nonzero(input_ids == self.tokenizer.mask_token_id, as_tuple=A )
else:
raise ValueError('Unsupported framework' )
return masked_index
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.get_masked_index(A )
SCREAMING_SNAKE_CASE : Dict = np.prod(masked_index.shape )
if numel < 1:
raise PipelineException(
'fill-mask', self.model.base_model_prefix, F"No mask_token ({self.tokenizer.mask_token}) found on the input", )
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
if isinstance(A, A ):
for model_input in model_inputs:
self._ensure_exactly_one_mask_token(model_input['input_ids'][0] )
else:
for input_ids in model_inputs["input_ids"]:
self._ensure_exactly_one_mask_token(A )
def UpperCamelCase_ ( self, A, A=None, **A ):
'''simple docstring'''
if return_tensors is None:
SCREAMING_SNAKE_CASE : Dict = self.framework
SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer(A, return_tensors=A )
self.ensure_exactly_one_mask_token(A )
return model_inputs
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = self.model(**A )
SCREAMING_SNAKE_CASE : List[str] = model_inputs['input_ids']
return model_outputs
def UpperCamelCase_ ( self, A, A=5, A=None ):
'''simple docstring'''
if target_ids is not None and target_ids.shape[0] < top_k:
SCREAMING_SNAKE_CASE : List[str] = target_ids.shape[0]
SCREAMING_SNAKE_CASE : Union[str, Any] = model_outputs['input_ids'][0]
SCREAMING_SNAKE_CASE : Union[str, Any] = model_outputs['logits']
if self.framework == "tf":
SCREAMING_SNAKE_CASE : Dict = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0]
SCREAMING_SNAKE_CASE : Tuple = outputs.numpy()
SCREAMING_SNAKE_CASE : Any = outputs[0, masked_index, :]
SCREAMING_SNAKE_CASE : List[Any] = stable_softmax(A, axis=-1 )
if target_ids is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = tf.gather_nd(tf.squeeze(A, 0 ), target_ids.reshape(-1, 1 ) )
SCREAMING_SNAKE_CASE : Optional[int] = tf.expand_dims(A, 0 )
SCREAMING_SNAKE_CASE : Optional[Any] = tf.math.top_k(A, k=A )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = topk.values.numpy(), topk.indices.numpy()
else:
SCREAMING_SNAKE_CASE : Optional[Any] = torch.nonzero(input_ids == self.tokenizer.mask_token_id, as_tuple=A ).squeeze(-1 )
# Fill mask pipeline supports only one ${mask_token} per sample
SCREAMING_SNAKE_CASE : Optional[int] = outputs[0, masked_index, :]
SCREAMING_SNAKE_CASE : Any = logits.softmax(dim=-1 )
if target_ids is not None:
SCREAMING_SNAKE_CASE : int = probs[..., target_ids]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = probs.topk(A )
SCREAMING_SNAKE_CASE : Optional[int] = []
SCREAMING_SNAKE_CASE : List[str] = values.shape[0] == 1
for i, (_values, _predictions) in enumerate(zip(values.tolist(), predictions.tolist() ) ):
SCREAMING_SNAKE_CASE : Union[str, Any] = []
for v, p in zip(_values, _predictions ):
# Copy is important since we're going to modify this array in place
SCREAMING_SNAKE_CASE : Tuple = input_ids.numpy().copy()
if target_ids is not None:
SCREAMING_SNAKE_CASE : Any = target_ids[p].tolist()
SCREAMING_SNAKE_CASE : List[Any] = p
# Filter padding out:
SCREAMING_SNAKE_CASE : List[str] = tokens[np.where(tokens != self.tokenizer.pad_token_id )]
# Originally we skip special tokens to give readable output.
# For multi masks though, the other [MASK] would be removed otherwise
# making the output look odd, so we add them back
SCREAMING_SNAKE_CASE : List[Any] = self.tokenizer.decode(A, skip_special_tokens=A )
SCREAMING_SNAKE_CASE : List[Any] = {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p] ), 'sequence': sequence}
row.append(A )
result.append(A )
if single_mask:
return result[0]
return result
def UpperCamelCase_ ( self, A, A=None ):
'''simple docstring'''
if isinstance(A, A ):
SCREAMING_SNAKE_CASE : List[Any] = [targets]
try:
SCREAMING_SNAKE_CASE : Union[str, Any] = self.tokenizer.get_vocab()
except Exception:
SCREAMING_SNAKE_CASE : str = {}
SCREAMING_SNAKE_CASE : List[str] = []
for target in targets:
SCREAMING_SNAKE_CASE : Dict = vocab.get(A, A )
if id_ is None:
SCREAMING_SNAKE_CASE : Dict = self.tokenizer(
A, add_special_tokens=A, return_attention_mask=A, return_token_type_ids=A, max_length=1, truncation=A, )['input_ids']
if len(A ) == 0:
logger.warning(
F"The specified target token `{target}` does not exist in the model vocabulary. "
'We cannot replace it with anything meaningful, ignoring it' )
continue
SCREAMING_SNAKE_CASE : List[Any] = input_ids[0]
# XXX: If users encounter this pass
# it becomes pretty slow, so let's make sure
# The warning enables them to fix the input to
# get faster performance.
logger.warning(
F"The specified target token `{target}` does not exist in the model vocabulary. "
F"Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`." )
target_ids.append(id_ )
SCREAMING_SNAKE_CASE : List[str] = list(set(A ) )
if len(A ) == 0:
raise ValueError('At least one target must be provided when passed.' )
SCREAMING_SNAKE_CASE : Any = np.array(A )
return target_ids
def UpperCamelCase_ ( self, A=None, A=None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
if targets is not None:
SCREAMING_SNAKE_CASE : Any = self.get_target_ids(A, A )
SCREAMING_SNAKE_CASE : str = target_ids
if top_k is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = top_k
if self.tokenizer.mask_token_id is None:
raise PipelineException(
'fill-mask', self.model.base_model_prefix, 'The tokenizer does not define a `mask_token`.' )
return {}, {}, postprocess_params
def __call__( self, A, *A, **A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = super().__call__(A, **A )
if isinstance(A, A ) and len(A ) == 1:
return outputs[0]
return outputs
| 246 | 1 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_SCREAMING_SNAKE_CASE = 2_9_9_7_9_2_4_5_8
# Symbols
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = symbols("""ct x y z""")
def lowercase( UpperCamelCase_ ) -> float:
'''simple docstring'''
if velocity > c:
raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!""" )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError("""Speed must be greater than or equal to 1!""" )
return velocity / c
def lowercase( UpperCamelCase_ ) -> float:
'''simple docstring'''
return 1 / sqrt(1 - beta(UpperCamelCase_ ) ** 2 )
def lowercase( UpperCamelCase_ ) -> np.ndarray:
'''simple docstring'''
return np.array(
[
[gamma(UpperCamelCase_ ), -gamma(UpperCamelCase_ ) * beta(UpperCamelCase_ ), 0, 0],
[-gamma(UpperCamelCase_ ) * beta(UpperCamelCase_ ), gamma(UpperCamelCase_ ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def lowercase( UpperCamelCase_ , UpperCamelCase_ = None ) -> np.ndarray:
'''simple docstring'''
# Ensure event is not empty
if event is None:
UpperCamelCase = np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(UpperCamelCase_ ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
_SCREAMING_SNAKE_CASE = transform(2_9_9_7_9_2_4_5)
print("""Example of four vector: """)
print(F'''ct\' = {four_vector[0]}''')
print(F'''x\' = {four_vector[1]}''')
print(F'''y\' = {four_vector[2]}''')
print(F'''z\' = {four_vector[3]}''')
# Substitute symbols with numerical values
_SCREAMING_SNAKE_CASE = {ct: c, x: 1, y: 1, z: 1}
_SCREAMING_SNAKE_CASE = [four_vector[i].subs(sub_dict) for i in range(4)]
print(F'''\n{numerical_vector}''')
| 343 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_SCREAMING_SNAKE_CASE = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvNextConfig""", """ConvNextOnnxConfig"""]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = ["""ConvNextFeatureExtractor"""]
_SCREAMING_SNAKE_CASE = ["""ConvNextImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
"""CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ConvNextForImageClassification""",
"""ConvNextModel""",
"""ConvNextPreTrainedModel""",
"""ConvNextBackbone""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
"""TFConvNextForImageClassification""",
"""TFConvNextModel""",
"""TFConvNextPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 343 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
UpperCamelCase_ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCamelCase_ = {
"vocab_file": {
"google/electra-small-generator": (
"https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"
),
"google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt",
"google/electra-large-generator": (
"https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"
),
"google/electra-small-discriminator": (
"https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"
),
"google/electra-base-discriminator": (
"https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"
),
"google/electra-large-discriminator": (
"https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"google/electra-small-generator": (
"https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"
),
"google/electra-base-generator": (
"https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"
),
"google/electra-large-generator": (
"https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"
),
"google/electra-small-discriminator": (
"https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"
),
"google/electra-base-discriminator": (
"https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"
),
"google/electra-large-discriminator": (
"https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"
),
},
}
UpperCamelCase_ = {
"google/electra-small-generator": 512,
"google/electra-base-generator": 512,
"google/electra-large-generator": 512,
"google/electra-small-discriminator": 512,
"google/electra-base-discriminator": 512,
"google/electra-large-discriminator": 512,
}
UpperCamelCase_ = {
"google/electra-small-generator": {"do_lower_case": True},
"google/electra-base-generator": {"do_lower_case": True},
"google/electra-large-generator": {"do_lower_case": True},
"google/electra-small-discriminator": {"do_lower_case": True},
"google/electra-base-discriminator": {"do_lower_case": True},
"google/electra-large-discriminator": {"do_lower_case": True},
}
class a_ ( _snake_case ):
UpperCamelCase__ : List[str] =VOCAB_FILES_NAMES
UpperCamelCase__ : Dict =PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ : List[str] =PRETRAINED_INIT_CONFIGURATION
UpperCamelCase__ : Tuple =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ : Union[str, Any] =ElectraTokenizer
def __init__( self :List[Any] , _lowercase :List[str]=None , _lowercase :Any=None , _lowercase :int=True , _lowercase :Dict="[UNK]" , _lowercase :Dict="[SEP]" , _lowercase :Tuple="[PAD]" , _lowercase :Optional[int]="[CLS]" , _lowercase :List[Any]="[MASK]" , _lowercase :List[Any]=True , _lowercase :Any=None , **_lowercase :Any , ) -> List[str]:
super().__init__(
_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , tokenize_chinese_chars=_lowercase , strip_accents=_lowercase , **_lowercase , )
UpperCAmelCase_ = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get('''lowercase''' , _lowercase) != do_lower_case
or normalizer_state.get('''strip_accents''' , _lowercase) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , _lowercase) != tokenize_chinese_chars
):
UpperCAmelCase_ = getattr(_lowercase , normalizer_state.pop('''type'''))
UpperCAmelCase_ = do_lower_case
UpperCAmelCase_ = strip_accents
UpperCAmelCase_ = tokenize_chinese_chars
UpperCAmelCase_ = normalizer_class(**_lowercase)
UpperCAmelCase_ = do_lower_case
def __a ( self :Union[str, Any] , _lowercase :Dict , _lowercase :Optional[int]=None) -> Union[str, Any]:
UpperCAmelCase_ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __a ( self :List[str] , _lowercase :List[int] , _lowercase :Optional[List[int]] = None) -> List[int]:
UpperCAmelCase_ = [self.sep_token_id]
UpperCAmelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]
def __a ( self :Optional[int] , _lowercase :str , _lowercase :Optional[str] = None) -> Tuple[str]:
UpperCAmelCase_ = self._tokenizer.model.save(_lowercase , name=_lowercase)
return tuple(_lowercase)
| 344 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase_ = logging.get_logger(__name__)
class a_ ( _snake_case , _snake_case ):
UpperCamelCase__ : Union[str, Any] ="maskformer-swin"
UpperCamelCase__ : List[str] ={
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self :Union[str, Any] , _lowercase :Optional[int]=224 , _lowercase :List[str]=4 , _lowercase :Tuple=3 , _lowercase :List[Any]=96 , _lowercase :Any=[2, 2, 6, 2] , _lowercase :int=[3, 6, 12, 24] , _lowercase :List[Any]=7 , _lowercase :Dict=4.0 , _lowercase :Any=True , _lowercase :int=0.0 , _lowercase :List[Any]=0.0 , _lowercase :Tuple=0.1 , _lowercase :str="gelu" , _lowercase :Union[str, Any]=False , _lowercase :Tuple=0.02 , _lowercase :List[str]=1E-5 , _lowercase :List[str]=None , _lowercase :Any=None , **_lowercase :Any , ) -> Union[str, Any]:
super().__init__(**_lowercase)
UpperCAmelCase_ = image_size
UpperCAmelCase_ = patch_size
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = embed_dim
UpperCAmelCase_ = depths
UpperCAmelCase_ = len(_lowercase)
UpperCAmelCase_ = num_heads
UpperCAmelCase_ = window_size
UpperCAmelCase_ = mlp_ratio
UpperCAmelCase_ = qkv_bias
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = drop_path_rate
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = use_absolute_embeddings
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
UpperCAmelCase_ = int(embed_dim * 2 ** (len(_lowercase) - 1))
UpperCAmelCase_ = ['''stem'''] + [f"stage{idx}" for idx in range(1 , len(_lowercase) + 1)]
UpperCAmelCase_ , UpperCAmelCase_ = get_aligned_output_features_output_indices(
out_features=_lowercase , out_indices=_lowercase , stage_names=self.stage_names)
| 344 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_a = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
' Distillation'
)
)
parser.add_argument('--model_type', default='bert', choices=['bert'])
parser.add_argument('--model_name', default='bert-base-uncased', type=str)
parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
parser.add_argument('--vocab_transform', action='store_true')
_a = parser.parse_args()
if args.model_type == "bert":
_a = BertForMaskedLM.from_pretrained(args.model_name)
_a = 'bert'
else:
raise ValueError('args.model_type should be "bert".')
_a = model.state_dict()
_a = {}
for w in ["word_embeddings", "position_embeddings"]:
_a = state_dict[f"""{prefix}.embeddings.{w}.weight"""]
for w in ["weight", "bias"]:
_a = state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]
_a = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
_a = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
]
_a = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
]
_a = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
]
_a = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
]
_a = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
]
_a = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
]
_a = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
]
_a = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
]
std_idx += 1
_a = state_dict['cls.predictions.decoder.weight']
_a = state_dict['cls.predictions.bias']
if args.vocab_transform:
for w in ["weight", "bias"]:
_a = state_dict[f"""cls.predictions.transform.dense.{w}"""]
_a = state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]
print(f"""N layers selected for distillation: {std_idx}""")
print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 61 |
'''simple docstring'''
from __future__ import annotations
def _a( UpperCamelCase__ : list[int] ):
'''simple docstring'''
if not nums:
return 0
SCREAMING_SNAKE_CASE__ : Dict =nums[0]
SCREAMING_SNAKE_CASE__ : Optional[int] =0
for num in nums[1:]:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict =(
max_excluding + num,
max(UpperCamelCase__, UpperCamelCase__ ),
)
return max(UpperCamelCase__, UpperCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 152 | 0 |
'''simple docstring'''
from __future__ import annotations
def A_( A : list[int | float] , A : int , A : int):
if len(A) == 0:
raise ValueError('find_max() arg is an empty sequence')
if (
left >= len(A)
or left < -len(A)
or right >= len(A)
or right < -len(A)
):
raise IndexError('list index out of range')
if left == right:
return nums[left]
UpperCamelCase = (left + right) >> 1 # the middle
UpperCamelCase = find_max(A , A , A) # find max in range[left, mid]
UpperCamelCase = find_max(A , mid + 1 , A) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 363 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
lowerCAmelCase : List[Any] = logging.get_logger(__name__)
lowerCAmelCase : Optional[int] = {
'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
'microsoft/deberta-v2-xlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
),
'microsoft/deberta-v2-xxlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
),
}
class SCREAMING_SNAKE_CASE__ ( snake_case_):
lowerCAmelCase_ = """deberta-v2"""
def __init__( self , A_=128100 , A_=1536 , A_=24 , A_=24 , A_=6144 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=0 , A_=0.02 , A_=1e-7 , A_=False , A_=-1 , A_=0 , A_=True , A_=None , A_=0 , A_="gelu" , **A_ , )-> Any:
'''simple docstring'''
super().__init__(**A_ )
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = initializer_range
UpperCamelCase = relative_attention
UpperCamelCase = max_relative_positions
UpperCamelCase = pad_token_id
UpperCamelCase = position_biased_input
# Backwards compatibility
if type(A_ ) == str:
UpperCamelCase = [x.strip() for x in pos_att_type.lower().split('|' )]
UpperCamelCase = pos_att_type
UpperCamelCase = vocab_size
UpperCamelCase = layer_norm_eps
UpperCamelCase = kwargs.get('pooler_hidden_size' , A_ )
UpperCamelCase = pooler_dropout
UpperCamelCase = pooler_hidden_act
class SCREAMING_SNAKE_CASE__ ( snake_case_):
@property
def UpperCAmelCase_ ( self )-> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
UpperCamelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
UpperCamelCase = {0: 'batch', 1: 'sequence'}
if self._config.type_vocab_size > 0:
return OrderedDict(
[('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)] )
else:
return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)] )
@property
def UpperCAmelCase_ ( self )-> int:
'''simple docstring'''
return 12
def UpperCAmelCase_ ( self , A_ , A_ = -1 , A_ = -1 , A_ = -1 , A_ = False , A_ = None , A_ = 3 , A_ = 40 , A_ = 40 , A_ = None , )-> Mapping[str, Any]:
'''simple docstring'''
UpperCamelCase = super().generate_dummy_inputs(preprocessor=A_ , framework=A_ )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 251 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (self : Optional[int]) ->List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def SCREAMING_SNAKE_CASE_ (self : Any) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: List[Any] =1
lowerCamelCase__: Optional[Any] =3
lowerCamelCase__: str =(32, 32)
lowerCamelCase__: str =floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0)).to(UpperCAmelCase_)
return image
@property
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->Tuple:
'''simple docstring'''
torch.manual_seed(0)
lowerCamelCase__: List[Any] =UNetaDConditionModel(
block_out_channels=(32, 32, 64) , layers_per_block=2 , sample_size=32 , in_channels=7 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , attention_head_dim=8 , use_linear_projection=UpperCAmelCase_ , only_cross_attention=(True, True, False) , num_class_embeds=100 , )
return model
@property
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->int:
'''simple docstring'''
torch.manual_seed(0)
lowerCamelCase__: Optional[int] =AutoencoderKL(
block_out_channels=[32, 32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
return model
@property
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->str:
'''simple docstring'''
torch.manual_seed(0)
lowerCamelCase__: Dict =CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , hidden_act="gelu" , projection_dim=512 , )
return CLIPTextModel(UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Optional[int]) ->int:
'''simple docstring'''
lowerCamelCase__: List[Any] ="cpu" # ensure determinism for the device-dependent torch.Generator
lowerCamelCase__: str =self.dummy_cond_unet_upscale
lowerCamelCase__: int =DDPMScheduler()
lowerCamelCase__: str =DDIMScheduler(prediction_type="v_prediction")
lowerCamelCase__: Any =self.dummy_vae
lowerCamelCase__: List[Any] =self.dummy_text_encoder
lowerCamelCase__: Any =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
lowerCamelCase__: Dict =self.dummy_image.cpu().permute(0 , 2 , 3 , 1)[0]
lowerCamelCase__: List[str] =Image.fromarray(np.uinta(UpperCAmelCase_)).convert("RGB").resize((64, 64))
# make sure here that pndm scheduler skips prk
lowerCamelCase__: int =StableDiffusionUpscalePipeline(
unet=UpperCAmelCase_ , low_res_scheduler=UpperCAmelCase_ , scheduler=UpperCAmelCase_ , vae=UpperCAmelCase_ , text_encoder=UpperCAmelCase_ , tokenizer=UpperCAmelCase_ , max_noise_level=350 , )
lowerCamelCase__: List[Any] =sd_pipe.to(UpperCAmelCase_)
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)
lowerCamelCase__: Optional[Any] ="A painting of a squirrel eating a burger"
lowerCamelCase__: Tuple =torch.Generator(device=UpperCAmelCase_).manual_seed(0)
lowerCamelCase__: Tuple =sd_pipe(
[prompt] , image=UpperCAmelCase_ , generator=UpperCAmelCase_ , guidance_scale=6.0 , noise_level=20 , num_inference_steps=2 , output_type="np" , )
lowerCamelCase__: int =output.images
lowerCamelCase__: List[str] =torch.Generator(device=UpperCAmelCase_).manual_seed(0)
lowerCamelCase__: List[str] =sd_pipe(
[prompt] , image=UpperCAmelCase_ , generator=UpperCAmelCase_ , guidance_scale=6.0 , noise_level=20 , num_inference_steps=2 , output_type="np" , return_dict=UpperCAmelCase_ , )[0]
lowerCamelCase__: Optional[int] =image[0, -3:, -3:, -1]
lowerCamelCase__: int =image_from_tuple[0, -3:, -3:, -1]
lowerCamelCase__: List[Any] =low_res_image.size[0] * 4
assert image.shape == (1, expected_height_width, expected_height_width, 3)
lowerCamelCase__: Dict =np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->str:
'''simple docstring'''
lowerCamelCase__: Dict ="cpu" # ensure determinism for the device-dependent torch.Generator
lowerCamelCase__: Any =self.dummy_cond_unet_upscale
lowerCamelCase__: Optional[Any] =DDPMScheduler()
lowerCamelCase__: Optional[int] =DDIMScheduler(prediction_type="v_prediction")
lowerCamelCase__: List[str] =self.dummy_vae
lowerCamelCase__: List[Any] =self.dummy_text_encoder
lowerCamelCase__: str =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
lowerCamelCase__: int =self.dummy_image.cpu().permute(0 , 2 , 3 , 1)[0]
lowerCamelCase__: Any =Image.fromarray(np.uinta(UpperCAmelCase_)).convert("RGB").resize((64, 64))
# make sure here that pndm scheduler skips prk
lowerCamelCase__: Dict =StableDiffusionUpscalePipeline(
unet=UpperCAmelCase_ , low_res_scheduler=UpperCAmelCase_ , scheduler=UpperCAmelCase_ , vae=UpperCAmelCase_ , text_encoder=UpperCAmelCase_ , tokenizer=UpperCAmelCase_ , max_noise_level=350 , )
lowerCamelCase__: List[Any] =sd_pipe.to(UpperCAmelCase_)
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)
lowerCamelCase__: List[str] ="A painting of a squirrel eating a burger"
lowerCamelCase__: Optional[Any] =sd_pipe(
2 * [prompt] , image=2 * [low_res_image] , guidance_scale=6.0 , noise_level=20 , num_inference_steps=2 , output_type="np" , )
lowerCamelCase__: List[str] =output.images
assert image.shape[0] == 2
lowerCamelCase__: Optional[int] =torch.Generator(device=UpperCAmelCase_).manual_seed(0)
lowerCamelCase__: Optional[int] =sd_pipe(
[prompt] , image=UpperCAmelCase_ , generator=UpperCAmelCase_ , num_images_per_prompt=2 , guidance_scale=6.0 , noise_level=20 , num_inference_steps=2 , output_type="np" , )
lowerCamelCase__: Union[str, Any] =output.images
assert image.shape[0] == 2
@unittest.skipIf(torch_device != "cuda" , "This test requires a GPU")
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->Tuple:
'''simple docstring'''
lowerCamelCase__: Dict =self.dummy_cond_unet_upscale
lowerCamelCase__: str =DDPMScheduler()
lowerCamelCase__: Dict =DDIMScheduler(prediction_type="v_prediction")
lowerCamelCase__: Optional[Any] =self.dummy_vae
lowerCamelCase__: Dict =self.dummy_text_encoder
lowerCamelCase__: str =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
lowerCamelCase__: Union[str, Any] =self.dummy_image.cpu().permute(0 , 2 , 3 , 1)[0]
lowerCamelCase__: List[Any] =Image.fromarray(np.uinta(UpperCAmelCase_)).convert("RGB").resize((64, 64))
# put models in fp16, except vae as it overflows in fp16
lowerCamelCase__: List[str] =unet.half()
lowerCamelCase__: List[Any] =text_encoder.half()
# make sure here that pndm scheduler skips prk
lowerCamelCase__: List[Any] =StableDiffusionUpscalePipeline(
unet=UpperCAmelCase_ , low_res_scheduler=UpperCAmelCase_ , scheduler=UpperCAmelCase_ , vae=UpperCAmelCase_ , text_encoder=UpperCAmelCase_ , tokenizer=UpperCAmelCase_ , max_noise_level=350 , )
lowerCamelCase__: Tuple =sd_pipe.to(UpperCAmelCase_)
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)
lowerCamelCase__: List[str] ="A painting of a squirrel eating a burger"
lowerCamelCase__: Optional[Any] =torch.manual_seed(0)
lowerCamelCase__: Union[str, Any] =sd_pipe(
[prompt] , image=UpperCAmelCase_ , generator=UpperCAmelCase_ , num_inference_steps=2 , output_type="np" , ).images
lowerCamelCase__: int =low_res_image.size[0] * 4
assert image.shape == (1, expected_height_width, expected_height_width, 3)
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (self : str) ->int:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->str:
'''simple docstring'''
lowerCamelCase__: List[Any] =load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png")
lowerCamelCase__: Union[str, Any] =load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
"/upsampled_cat.npy")
lowerCamelCase__: List[Any] ="stabilityai/stable-diffusion-x4-upscaler"
lowerCamelCase__: Optional[Any] =StableDiffusionUpscalePipeline.from_pretrained(UpperCAmelCase_)
pipe.to(UpperCAmelCase_)
pipe.set_progress_bar_config(disable=UpperCAmelCase_)
pipe.enable_attention_slicing()
lowerCamelCase__: str ="a cat sitting on a park bench"
lowerCamelCase__: Any =torch.manual_seed(0)
lowerCamelCase__: Union[str, Any] =pipe(
prompt=UpperCAmelCase_ , image=UpperCAmelCase_ , generator=UpperCAmelCase_ , output_type="np" , )
lowerCamelCase__: Optional[int] =output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image).max() < 1E-3
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__: str =load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png")
lowerCamelCase__: int =load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
"/upsampled_cat_fp16.npy")
lowerCamelCase__: Optional[int] ="stabilityai/stable-diffusion-x4-upscaler"
lowerCamelCase__: Dict =StableDiffusionUpscalePipeline.from_pretrained(
UpperCAmelCase_ , torch_dtype=torch.floataa , )
pipe.to(UpperCAmelCase_)
pipe.set_progress_bar_config(disable=UpperCAmelCase_)
pipe.enable_attention_slicing()
lowerCamelCase__: Optional[Any] ="a cat sitting on a park bench"
lowerCamelCase__: Optional[Any] =torch.manual_seed(0)
lowerCamelCase__: List[Any] =pipe(
prompt=UpperCAmelCase_ , image=UpperCAmelCase_ , generator=UpperCAmelCase_ , output_type="np" , )
lowerCamelCase__: List[str] =output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image).max() < 5E-1
def SCREAMING_SNAKE_CASE_ (self : Any) ->List[Any]:
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowerCamelCase__: Optional[Any] =load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png")
lowerCamelCase__: List[str] ="stabilityai/stable-diffusion-x4-upscaler"
lowerCamelCase__: int =StableDiffusionUpscalePipeline.from_pretrained(
UpperCAmelCase_ , torch_dtype=torch.floataa , )
pipe.to(UpperCAmelCase_)
pipe.set_progress_bar_config(disable=UpperCAmelCase_)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
lowerCamelCase__: Optional[int] ="a cat sitting on a park bench"
lowerCamelCase__: Tuple =torch.manual_seed(0)
lowerCamelCase__: Any =pipe(
prompt=UpperCAmelCase_ , image=UpperCAmelCase_ , generator=UpperCAmelCase_ , num_inference_steps=5 , output_type="np" , )
lowerCamelCase__: Dict =torch.cuda.max_memory_allocated()
# make sure that less than 2.9 GB is allocated
assert mem_bytes < 2.9 * 10**9
| 10 |
import os
def lowerCamelCase__ ():
SCREAMING_SNAKE_CASE = os.path.dirname(os.path.realpath(_UpperCAmelCase))
SCREAMING_SNAKE_CASE = os.path.join(_UpperCAmelCase , 'triangle.txt')
with open(_UpperCAmelCase) as f:
SCREAMING_SNAKE_CASE = f.readlines()
SCREAMING_SNAKE_CASE = []
for line in triangle:
SCREAMING_SNAKE_CASE = []
for number in line.strip().split(' '):
numbers_from_line.append(int(_UpperCAmelCase))
a.append(_UpperCAmelCase)
for i in range(1 , len(_UpperCAmelCase)):
for j in range(len(a[i])):
SCREAMING_SNAKE_CASE = a[i - 1][j] if j != len(a[i - 1]) else 0
SCREAMING_SNAKE_CASE = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(_UpperCAmelCase , _UpperCAmelCase)
return max(a[-1])
if __name__ == "__main__":
print(solution())
| 137 | 0 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
"huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json",
}
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = '''autoformer'''
UpperCamelCase__ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self :int , __magic_name__ :Optional[int] = None , __magic_name__ :Optional[int] = None , __magic_name__ :str = "student_t" , __magic_name__ :str = "nll" , __magic_name__ :int = 1 , __magic_name__ :List[int] = [1, 2, 3, 4, 5, 6, 7] , __magic_name__ :bool = True , __magic_name__ :int = 0 , __magic_name__ :int = 0 , __magic_name__ :int = 0 , __magic_name__ :int = 0 , __magic_name__ :Optional[List[int]] = None , __magic_name__ :Optional[List[int]] = None , __magic_name__ :int = 64 , __magic_name__ :int = 2 , __magic_name__ :int = 2 , __magic_name__ :int = 2 , __magic_name__ :int = 2 , __magic_name__ :int = 32 , __magic_name__ :int = 32 , __magic_name__ :str = "gelu" , __magic_name__ :float = 0.1 , __magic_name__ :float = 0.1 , __magic_name__ :float = 0.1 , __magic_name__ :float = 0.1 , __magic_name__ :float = 0.1 , __magic_name__ :int = 100 , __magic_name__ :float = 0.02 , __magic_name__ :bool = True , __magic_name__ :str=True , __magic_name__ :int = 10 , __magic_name__ :int = 25 , __magic_name__ :int = 3 , **__magic_name__ :Tuple , ):
'''simple docstring'''
a = prediction_length
a = context_length if context_length is not None else prediction_length
a = distribution_output
a = loss
a = input_size
a = num_time_features
a = lags_sequence
a = scaling
a = num_dynamic_real_features
a = num_static_real_features
a = num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(__magic_name__ ) != num_static_categorical_features:
raise ValueError(
"""The cardinality should be a list of the same length as `num_static_categorical_features`""" )
a = cardinality
else:
a = [0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(__magic_name__ ) != num_static_categorical_features:
raise ValueError(
"""The embedding dimension should be a list of the same length as `num_static_categorical_features`""" )
a = embedding_dimension
else:
a = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
a = num_parallel_samples
# Transformer architecture configuration
a = input_size * len(self.lags_sequence ) + self._number_of_features
a = d_model
a = encoder_attention_heads
a = decoder_attention_heads
a = encoder_ffn_dim
a = decoder_ffn_dim
a = encoder_layers
a = decoder_layers
a = dropout
a = attention_dropout
a = activation_dropout
a = encoder_layerdrop
a = decoder_layerdrop
a = activation_function
a = init_std
a = use_cache
# Autoformer
a = label_length
a = moving_average
a = autocorrelation_factor
super().__init__(is_encoder_decoder=__magic_name__ , **__magic_name__ )
@property
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 347 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__UpperCamelCase : Any = datasets.utils.logging.get_logger(__name__)
@dataclass
class __lowerCAmelCase ( datasets.BuilderConfig ):
UpperCamelCase__ = None
UpperCamelCase__ = "utf-8"
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = True # deprecated
UpperCamelCase__ = None # deprecated
UpperCamelCase__ = 10 << 20 # 10MB
UpperCamelCase__ = None
class __lowerCAmelCase ( datasets.ArrowBasedBuilder ):
UpperCamelCase__ = JsonConfig
def lowerCamelCase__ ( self :str ):
'''simple docstring'''
if self.config.block_size is not None:
logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead""" )
a = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
"""The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore.""" )
if self.config.newlines_in_values is not None:
raise ValueError("""The JSON loader parameter `newlines_in_values` is no longer supported""" )
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase__ ( self :Tuple , __magic_name__ :str ):
'''simple docstring'''
if not self.config.data_files:
raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}' )
a = dl_manager.download_and_extract(self.config.data_files )
if isinstance(__magic_name__ , (str, list, tuple) ):
a = data_files
if isinstance(__magic_name__ , __magic_name__ ):
a = [files]
a = [dl_manager.iter_files(__magic_name__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""files""": files} )]
a = []
for split_name, files in data_files.items():
if isinstance(__magic_name__ , __magic_name__ ):
a = [files]
a = [dl_manager.iter_files(__magic_name__ ) for file in files]
splits.append(datasets.SplitGenerator(name=__magic_name__ , gen_kwargs={"""files""": files} ) )
return splits
def lowerCamelCase__ ( self :List[str] , __magic_name__ :pa.Table ):
'''simple docstring'''
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
a = self.config.features.arrow_schema.field(__magic_name__ ).type
a = pa_table.append_column(__magic_name__ , pa.array([None] * len(__magic_name__ ) , type=__magic_name__ ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
a = table_cast(__magic_name__ , self.config.features.arrow_schema )
return pa_table
def lowerCamelCase__ ( self :Optional[int] , __magic_name__ :Union[str, Any] ):
'''simple docstring'''
for file_idx, file in enumerate(itertools.chain.from_iterable(__magic_name__ ) ):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(__magic_name__ , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
a = json.load(__magic_name__ )
# We keep only the field we are interested in
a = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(__magic_name__ , (list, tuple) ):
a = set().union(*[row.keys() for row in dataset] )
a = {col: [row.get(__magic_name__ ) for row in dataset] for col in keys}
else:
a = dataset
a = pa.Table.from_pydict(__magic_name__ )
yield file_idx, self._cast_table(__magic_name__ )
# If the file has one json object per line
else:
with open(__magic_name__ , """rb""" ) as f:
a = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
a = max(self.config.chunksize // 32 , 16 << 10 )
a = (
self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
)
while True:
a = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(__magic_name__ )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
a = batch.decode(self.config.encoding , errors=__magic_name__ ).encode("""utf-8""" )
try:
while True:
try:
a = paj.read_json(
io.BytesIO(__magic_name__ ) , read_options=paj.ReadOptions(block_size=__magic_name__ ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(__magic_name__ , pa.ArrowInvalid )
and "straddling" not in str(__magic_name__ )
or block_size > len(__magic_name__ )
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
F'Batch of {len(__magic_name__ )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.' )
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
__magic_name__ , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
a = json.load(__magic_name__ )
except json.JSONDecodeError:
logger.error(F'Failed to read file \'{file}\' with error {type(__magic_name__ )}: {e}' )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(__magic_name__ , __magic_name__ ): # list is the only sequence type supported in JSON
try:
a = set().union(*[row.keys() for row in dataset] )
a = {col: [row.get(__magic_name__ ) for row in dataset] for col in keys}
a = pa.Table.from_pydict(__magic_name__ )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(F'Failed to read file \'{file}\' with error {type(__magic_name__ )}: {e}' )
raise ValueError(F'Not able to read records in the JSON file at {file}.' ) from None
yield file_idx, self._cast_table(__magic_name__ )
break
else:
logger.error(F'Failed to read file \'{file}\' with error {type(__magic_name__ )}: {e}' )
raise ValueError(
F'Not able to read records in the JSON file at {file}. '
F'You should probably indicate the field of the JSON file containing your records. '
F'This JSON file contain the following fields: {str(list(dataset.keys() ) )}. '
F'Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ' ) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(__magic_name__ )
batch_idx += 1
| 347 | 1 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __snake_case ( nn.Module ):
def __init__( self ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
snake_case__ : Optional[int] = nn.Linear(3 , 4 )
snake_case__ : Optional[int] = nn.BatchNormad(4 )
snake_case__ : List[str] = nn.Linear(4 , 5 )
def __a ( self , __UpperCamelCase ) -> str:
'''simple docstring'''
return self.lineara(self.batchnorm(self.lineara(__UpperCamelCase ) ) )
class __snake_case ( __SCREAMING_SNAKE_CASE ):
def __a ( self , __UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
return (args[0] + 1,) + args[1:], kwargs
class __snake_case ( __SCREAMING_SNAKE_CASE ):
def __a ( self , __UpperCamelCase , __UpperCamelCase ) -> List[Any]:
'''simple docstring'''
return output + 1
class __snake_case ( unittest.TestCase ):
def __a ( self ) -> List[Any]:
'''simple docstring'''
snake_case__ : List[Any] = ModelForTest()
snake_case__ : str = ModelHook()
add_hook_to_module(__UpperCamelCase , __UpperCamelCase )
self.assertEqual(test_model._hf_hook , __UpperCamelCase )
self.assertTrue(hasattr(__UpperCamelCase , '_old_forward' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , 'forward' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['x'] )
remove_hook_from_module(__UpperCamelCase )
self.assertFalse(hasattr(__UpperCamelCase , '_hf_hook' ) )
self.assertFalse(hasattr(__UpperCamelCase , '_old_forward' ) )
def __a ( self ) -> str:
'''simple docstring'''
snake_case__ : Optional[int] = ModelForTest()
snake_case__ : Tuple = ModelHook()
add_hook_to_module(__UpperCamelCase , __UpperCamelCase )
add_hook_to_module(__UpperCamelCase , __UpperCamelCase , append=__UpperCamelCase )
self.assertEqual(isinstance(test_model._hf_hook , __UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(__UpperCamelCase , '_old_forward' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , 'forward' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['x'] )
remove_hook_from_module(__UpperCamelCase )
self.assertFalse(hasattr(__UpperCamelCase , '_hf_hook' ) )
self.assertFalse(hasattr(__UpperCamelCase , '_old_forward' ) )
def __a ( self ) -> Dict:
'''simple docstring'''
snake_case__ : int = ModelForTest()
snake_case__ : Optional[Any] = torch.randn(2 , 3 )
snake_case__ : Any = test_model(x + 1 )
snake_case__ : List[Any] = test_model(x + 2 )
snake_case__ : Tuple = PreForwardHook()
add_hook_to_module(__UpperCamelCase , __UpperCamelCase )
snake_case__ : List[str] = test_model(__UpperCamelCase )
self.assertTrue(torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
snake_case__ : Optional[Any] = PreForwardHook()
add_hook_to_module(__UpperCamelCase , __UpperCamelCase )
snake_case__ : Tuple = test_model(__UpperCamelCase )
self.assertTrue(torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
snake_case__ : Optional[Any] = SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(__UpperCamelCase , __UpperCamelCase )
snake_case__ : Optional[int] = test_model(__UpperCamelCase )
assert torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1E-5 )
def __a ( self ) -> Tuple:
'''simple docstring'''
snake_case__ : Tuple = ModelForTest()
snake_case__ : Optional[Any] = torch.randn(2 , 3 )
snake_case__ : int = test_model(__UpperCamelCase )
snake_case__ : Any = PostForwardHook()
add_hook_to_module(__UpperCamelCase , __UpperCamelCase )
snake_case__ : Tuple = test_model(__UpperCamelCase )
self.assertTrue(torch.allclose(__UpperCamelCase , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
snake_case__ : Optional[Any] = PostForwardHook()
add_hook_to_module(__UpperCamelCase , __UpperCamelCase )
snake_case__ : Dict = test_model(__UpperCamelCase )
self.assertTrue(torch.allclose(__UpperCamelCase , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
snake_case__ : Union[str, Any] = SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(__UpperCamelCase , __UpperCamelCase )
snake_case__ : List[str] = test_model(__UpperCamelCase )
assert torch.allclose(__UpperCamelCase , output + 2 , atol=1E-5 )
def __a ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ : Optional[int] = ModelForTest()
snake_case__ : Optional[Any] = torch.randn(2 , 3 )
snake_case__ : int = test_model(__UpperCamelCase )
snake_case__ : Any = PostForwardHook()
add_hook_to_module(__UpperCamelCase , __UpperCamelCase )
snake_case__ : List[str] = test_model(__UpperCamelCase )
self.assertTrue(torch.allclose(__UpperCamelCase , output + 1 ) )
self.assertTrue(outputa.requires_grad )
snake_case__ : Optional[Any] = True
snake_case__ : Union[str, Any] = test_model(__UpperCamelCase )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def __a ( self ) -> List[Any]:
'''simple docstring'''
snake_case__ : Tuple = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
snake_case__ : Optional[int] = torch.randn(2 , 3 )
snake_case__ : Tuple = model(__UpperCamelCase )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(__UpperCamelCase , AlignDevicesHook(io_same_device=__UpperCamelCase ) )
snake_case__ : Dict = torch.randn(2 , 3 ).to(0 )
snake_case__ : Dict = model(__UpperCamelCase )
self.assertEqual(output.device , torch.device(0 ) )
def __a ( self ) -> Any:
'''simple docstring'''
snake_case__ : Optional[int] = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# This will move each submodule on different devices
snake_case__ : Optional[Any] = {'execution_device': 0 if torch.cuda.is_available() else 'cpu', 'offload': True}
add_hook_to_module(model.lineara , AlignDevicesHook(**__UpperCamelCase ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**__UpperCamelCase ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**__UpperCamelCase ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
# Buffers are not included in the offload by default, so are on the execution device
snake_case__ : Dict = torch.device(hook_kwargs['execution_device'] )
self.assertEqual(model.batchnorm.running_mean.device , __UpperCamelCase )
snake_case__ : List[Any] = torch.randn(2 , 3 )
snake_case__ : Union[str, Any] = model(__UpperCamelCase )
self.assertEqual(output.device , __UpperCamelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# Now test with buffers included in the offload
snake_case__ : Optional[int] = {
'execution_device': 0 if torch.cuda.is_available() else 'cpu',
'offload': True,
'offload_buffers': True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**__UpperCamelCase ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**__UpperCamelCase ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**__UpperCamelCase ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('meta' ) )
snake_case__ : Optional[Any] = torch.randn(2 , 3 )
snake_case__ : str = model(__UpperCamelCase )
self.assertEqual(output.device , __UpperCamelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
def __a ( self ) -> List[Any]:
'''simple docstring'''
snake_case__ : str = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# This will move each submodule on different devices
snake_case__ : Tuple = 0 if torch.cuda.is_available() else 'cpu'
attach_align_device_hook(__UpperCamelCase , execution_device=__UpperCamelCase , offload=__UpperCamelCase )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
# Buffers are not included in the offload by default, so are on the execution device
snake_case__ : str = torch.device(__UpperCamelCase )
self.assertEqual(model.batchnorm.running_mean.device , __UpperCamelCase )
snake_case__ : Union[str, Any] = torch.randn(2 , 3 )
snake_case__ : Optional[int] = model(__UpperCamelCase )
self.assertEqual(output.device , __UpperCamelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__UpperCamelCase )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# Now test with buffers included in the offload
attach_align_device_hook(__UpperCamelCase , execution_device=__UpperCamelCase , offload=__UpperCamelCase , offload_buffers=__UpperCamelCase )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('meta' ) )
snake_case__ : Optional[Any] = torch.randn(2 , 3 )
snake_case__ : List[str] = model(__UpperCamelCase )
self.assertEqual(output.device , __UpperCamelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__UpperCamelCase )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
def __a ( self ) -> Tuple:
'''simple docstring'''
snake_case__ : Optional[int] = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# This will move each submodule on different devices
snake_case__ : Any = 0 if torch.cuda.is_available() else 'cpu'
attach_align_device_hook(
__UpperCamelCase , execution_device=__UpperCamelCase , offload=__UpperCamelCase , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
# Buffers are not included in the offload by default, so are on the execution device
snake_case__ : str = torch.device(__UpperCamelCase )
self.assertEqual(model.batchnorm.running_mean.device , __UpperCamelCase )
snake_case__ : List[str] = torch.randn(2 , 3 )
snake_case__ : Optional[Any] = model(__UpperCamelCase )
self.assertEqual(output.device , __UpperCamelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__UpperCamelCase )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# Now test with buffers included in the offload
attach_align_device_hook(
__UpperCamelCase , execution_device=__UpperCamelCase , offload=__UpperCamelCase , weights_map=model.state_dict() , offload_buffers=__UpperCamelCase , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('meta' ) )
snake_case__ : str = torch.randn(2 , 3 )
snake_case__ : Optional[int] = model(__UpperCamelCase )
self.assertEqual(output.device , __UpperCamelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__UpperCamelCase )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
| 143 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class lowercase_ ( unittest.TestCase ):
def __init__( self , __UpperCamelCase , __UpperCamelCase=7 , __UpperCamelCase=3 , __UpperCamelCase=3_0 , __UpperCamelCase=4_0_0 , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=True , __UpperCamelCase=[0.5, 0.5, 0.5] , __UpperCamelCase=[0.5, 0.5, 0.5] , __UpperCamelCase=True , __UpperCamelCase=1 / 2_5_5 , __UpperCamelCase=True , ):
"""simple docstring"""
UpperCamelCase_ = size if size is not None else {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}
UpperCamelCase_ = parent
UpperCamelCase_ = batch_size
UpperCamelCase_ = num_channels
UpperCamelCase_ = min_resolution
UpperCamelCase_ = max_resolution
UpperCamelCase_ = do_resize
UpperCamelCase_ = size
UpperCamelCase_ = do_normalize
UpperCamelCase_ = image_mean
UpperCamelCase_ = image_std
UpperCamelCase_ = do_rescale
UpperCamelCase_ = rescale_factor
UpperCamelCase_ = do_pad
def lowerCamelCase_ ( self ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase=False ):
"""simple docstring"""
if not batched:
UpperCamelCase_ = image_inputs[0]
if isinstance(__UpperCamelCase , Image.Image ):
UpperCamelCase_ , UpperCamelCase_ = image.size
else:
UpperCamelCase_ , UpperCamelCase_ = image.shape[1], image.shape[2]
if w < h:
UpperCamelCase_ = int(self.size["""shortest_edge"""] * h / w )
UpperCamelCase_ = self.size["""shortest_edge"""]
elif w > h:
UpperCamelCase_ = self.size["""shortest_edge"""]
UpperCamelCase_ = int(self.size["""shortest_edge"""] * w / h )
else:
UpperCamelCase_ = self.size["""shortest_edge"""]
UpperCamelCase_ = self.size["""shortest_edge"""]
else:
UpperCamelCase_ = []
for image in image_inputs:
UpperCamelCase_ , UpperCamelCase_ = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCamelCase_ = max(__UpperCamelCase , key=lambda __UpperCamelCase : item[0] )[0]
UpperCamelCase_ = max(__UpperCamelCase , key=lambda __UpperCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowercase_ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
A__ : str = YolosImageProcessor if is_vision_available() else None
def lowerCamelCase_ ( self ):
"""simple docstring"""
UpperCamelCase_ = YolosImageProcessingTester(self )
@property
def lowerCamelCase_ ( self ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self ):
"""simple docstring"""
UpperCamelCase_ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__UpperCamelCase , """image_mean""" ) )
self.assertTrue(hasattr(__UpperCamelCase , """image_std""" ) )
self.assertTrue(hasattr(__UpperCamelCase , """do_normalize""" ) )
self.assertTrue(hasattr(__UpperCamelCase , """do_resize""" ) )
self.assertTrue(hasattr(__UpperCamelCase , """size""" ) )
def lowerCamelCase_ ( self ):
"""simple docstring"""
UpperCamelCase_ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3} )
self.assertEqual(image_processor.do_pad , __UpperCamelCase )
UpperCamelCase_ = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=__UpperCamelCase )
self.assertEqual(image_processor.size , {"""shortest_edge""": 4_2, """longest_edge""": 8_4} )
self.assertEqual(image_processor.do_pad , __UpperCamelCase )
def lowerCamelCase_ ( self ):
"""simple docstring"""
pass
def lowerCamelCase_ ( self ):
"""simple docstring"""
UpperCamelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCamelCase , Image.Image )
# Test not batched input
UpperCamelCase_ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
UpperCamelCase_ , UpperCamelCase_ = self.image_processor_tester.get_expected_values(__UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase_ , UpperCamelCase_ = self.image_processor_tester.get_expected_values(__UpperCamelCase , batched=__UpperCamelCase )
UpperCamelCase_ = image_processing(__UpperCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCamelCase_ ( self ):
"""simple docstring"""
UpperCamelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase , numpify=__UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCamelCase , np.ndarray )
# Test not batched input
UpperCamelCase_ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
UpperCamelCase_ , UpperCamelCase_ = self.image_processor_tester.get_expected_values(__UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase_ = image_processing(__UpperCamelCase , return_tensors="""pt""" ).pixel_values
UpperCamelCase_ , UpperCamelCase_ = self.image_processor_tester.get_expected_values(__UpperCamelCase , batched=__UpperCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCamelCase_ ( self ):
"""simple docstring"""
UpperCamelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase , torchify=__UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCamelCase , torch.Tensor )
# Test not batched input
UpperCamelCase_ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
UpperCamelCase_ , UpperCamelCase_ = self.image_processor_tester.get_expected_values(__UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase_ = image_processing(__UpperCamelCase , return_tensors="""pt""" ).pixel_values
UpperCamelCase_ , UpperCamelCase_ = self.image_processor_tester.get_expected_values(__UpperCamelCase , batched=__UpperCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCamelCase_ ( self ):
"""simple docstring"""
UpperCamelCase_ = self.image_processing_class(**self.image_processor_dict )
UpperCamelCase_ = self.image_processing_class(do_resize=__UpperCamelCase , do_normalize=__UpperCamelCase , do_rescale=__UpperCamelCase )
# create random PyTorch tensors
UpperCamelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase , torchify=__UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
UpperCamelCase_ = image_processing_a.pad(__UpperCamelCase , return_tensors="""pt""" )
UpperCamelCase_ = image_processing_a(__UpperCamelCase , return_tensors="""pt""" )
self.assertTrue(
torch.allclose(encoded_images_with_method["""pixel_values"""] , encoded_images["""pixel_values"""] , atol=1e-4 ) )
@slow
def lowerCamelCase_ ( self ):
"""simple docstring"""
UpperCamelCase_ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
UpperCamelCase_ = json.loads(f.read() )
UpperCamelCase_ = {"""image_id""": 3_9_7_6_9, """annotations""": target}
# encode them
UpperCamelCase_ = YolosImageProcessor.from_pretrained("""hustvl/yolos-small""" )
UpperCamelCase_ = image_processing(images=__UpperCamelCase , annotations=__UpperCamelCase , return_tensors="""pt""" )
# verify pixel values
UpperCamelCase_ = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding["""pixel_values"""].shape , __UpperCamelCase )
UpperCamelCase_ = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , __UpperCamelCase , atol=1e-4 ) )
# verify area
UpperCamelCase_ = torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , __UpperCamelCase ) )
# verify boxes
UpperCamelCase_ = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , __UpperCamelCase )
UpperCamelCase_ = torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , __UpperCamelCase , atol=1e-3 ) )
# verify image_id
UpperCamelCase_ = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , __UpperCamelCase ) )
# verify is_crowd
UpperCamelCase_ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , __UpperCamelCase ) )
# verify class_labels
UpperCamelCase_ = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , __UpperCamelCase ) )
# verify orig_size
UpperCamelCase_ = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , __UpperCamelCase ) )
# verify size
UpperCamelCase_ = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , __UpperCamelCase ) )
@slow
def lowerCamelCase_ ( self ):
"""simple docstring"""
UpperCamelCase_ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
UpperCamelCase_ = json.loads(f.read() )
UpperCamelCase_ = {"""file_name""": """000000039769.png""", """image_id""": 3_9_7_6_9, """segments_info""": target}
UpperCamelCase_ = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
UpperCamelCase_ = YolosImageProcessor(format="""coco_panoptic""" )
UpperCamelCase_ = image_processing(images=__UpperCamelCase , annotations=__UpperCamelCase , masks_path=__UpperCamelCase , return_tensors="""pt""" )
# verify pixel values
UpperCamelCase_ = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding["""pixel_values"""].shape , __UpperCamelCase )
UpperCamelCase_ = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , __UpperCamelCase , atol=1e-4 ) )
# verify area
UpperCamelCase_ = torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , __UpperCamelCase ) )
# verify boxes
UpperCamelCase_ = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , __UpperCamelCase )
UpperCamelCase_ = torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , __UpperCamelCase , atol=1e-3 ) )
# verify image_id
UpperCamelCase_ = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , __UpperCamelCase ) )
# verify is_crowd
UpperCamelCase_ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , __UpperCamelCase ) )
# verify class_labels
UpperCamelCase_ = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , __UpperCamelCase ) )
# verify masks
UpperCamelCase_ = 8_2_2_8_7_3
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , __UpperCamelCase )
# verify orig_size
UpperCamelCase_ = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , __UpperCamelCase ) )
# verify size
UpperCamelCase_ = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , __UpperCamelCase ) )
| 122 | 0 |
'''simple docstring'''
def __magic_name__ ( A ) -> int:
snake_case = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def __magic_name__ ( A = 1_0_0 ) -> int:
snake_case = 1
snake_case = 2
for i in range(2 , max_n + 1 ):
snake_case = pre_numerator
snake_case = 2 * i // 3 if i % 3 == 0 else 1
snake_case = cur_numerator
snake_case = e_cont * pre_numerator + temp
return sum_digits(A )
if __name__ == "__main__":
print(f"{solution() = }")
| 359 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
lowerCAmelCase_ = Lock()
def __magic_name__ ( A , A , A , A , A , A , A ) -> Any:
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 1_0 ):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(A )
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
snake_case = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
snake_case = min(A , A )
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(A )
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
snake_case = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
snake_case = max(A , A )
# after all swaps are performed, send the values back to main
result_pipe[1].send(A )
def __magic_name__ ( A ) -> str:
snake_case = []
snake_case = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe() )
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
snake_case = Pipe()
snake_case = Pipe()
process_array_.append(
Process(
target=A , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ) )
snake_case = temp_rs
snake_case = temp_rr
for i in range(1 , len(A ) - 1 ):
snake_case = Pipe()
snake_case = Pipe()
process_array_.append(
Process(
target=A , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ) )
snake_case = temp_rs
snake_case = temp_rr
process_array_.append(
Process(
target=A , args=(
len(A ) - 1,
arr[len(A ) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(A ) - 1],
) , ) )
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(A ) ):
snake_case = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def __magic_name__ ( ) -> Tuple:
snake_case = list(range(1_0 , 0 , -1 ) )
print('Initial List' )
print(*A )
snake_case = odd_even_transposition(A )
print('Sorted List\n' )
print(*A )
if __name__ == "__main__":
main()
| 332 | 0 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''' , [None, 4_00 * 2**20, 6_00 * 2**20] )
@pytest.mark.parametrize('''input_in_memory_max_size''' , ['''default''', 0, 1_00 * 2**20, 9_00 * 2**20] )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> Union[str, Any]:
'''simple docstring'''
if input_in_memory_max_size != "default":
monkeypatch.setattr(datasets.config , '''IN_MEMORY_MAX_SIZE''' , _A )
UpperCAmelCase : Any =datasets.config.IN_MEMORY_MAX_SIZE
if input_in_memory_max_size == "default":
assert in_memory_max_size == 0
else:
assert in_memory_max_size == input_in_memory_max_size
if dataset_size and in_memory_max_size:
UpperCAmelCase : Any =dataset_size < in_memory_max_size
else:
UpperCAmelCase : int =False
UpperCAmelCase : Union[str, Any] =is_small_dataset(_A )
assert result == expected
| 348 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import YolosForObjectDetection, YolosModel
from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class a__ :
"""simple docstring"""
def __init__( self : List[Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : List[Any]=1_3 , UpperCAmelCase__ : List[str]=[3_0, 3_0] , UpperCAmelCase__ : Optional[int]=2 , UpperCAmelCase__ : Dict=3 , UpperCAmelCase__ : str=True , UpperCAmelCase__ : Any=True , UpperCAmelCase__ : int=3_2 , UpperCAmelCase__ : List[str]=5 , UpperCAmelCase__ : Dict=4 , UpperCAmelCase__ : int=3_7 , UpperCAmelCase__ : List[str]="gelu" , UpperCAmelCase__ : Optional[Any]=0.1 , UpperCAmelCase__ : List[str]=0.1 , UpperCAmelCase__ : str=1_0 , UpperCAmelCase__ : Dict=0.02 , UpperCAmelCase__ : Any=3 , UpperCAmelCase__ : int=None , UpperCAmelCase__ : List[Any]=8 , UpperCAmelCase__ : Dict=1_0 , ) ->Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = parent
SCREAMING_SNAKE_CASE : int = batch_size
SCREAMING_SNAKE_CASE : str = image_size
SCREAMING_SNAKE_CASE : List[Any] = patch_size
SCREAMING_SNAKE_CASE : Any = num_channels
SCREAMING_SNAKE_CASE : str = is_training
SCREAMING_SNAKE_CASE : Dict = use_labels
SCREAMING_SNAKE_CASE : List[Any] = hidden_size
SCREAMING_SNAKE_CASE : Optional[int] = num_hidden_layers
SCREAMING_SNAKE_CASE : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE : Dict = intermediate_size
SCREAMING_SNAKE_CASE : int = hidden_act
SCREAMING_SNAKE_CASE : str = hidden_dropout_prob
SCREAMING_SNAKE_CASE : List[str] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : List[Any] = type_sequence_label_size
SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE : str = num_labels
SCREAMING_SNAKE_CASE : Dict = scope
SCREAMING_SNAKE_CASE : Optional[Any] = n_targets
SCREAMING_SNAKE_CASE : Dict = num_detection_tokens
# we set the expected sequence length (which is used in several tests)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
SCREAMING_SNAKE_CASE : Tuple = (image_size[1] // patch_size) * (image_size[0] // patch_size)
SCREAMING_SNAKE_CASE : int = num_patches + 1 + self.num_detection_tokens
def _lowercase ( self : Tuple ) ->Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )
SCREAMING_SNAKE_CASE : int = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
SCREAMING_SNAKE_CASE : str = []
for i in range(self.batch_size ):
SCREAMING_SNAKE_CASE : List[Any] = {}
SCREAMING_SNAKE_CASE : Any = torch.randint(
high=self.num_labels , size=(self.n_targets,) , device=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = torch.rand(self.n_targets , 4 , device=UpperCAmelCase__ )
labels.append(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_config()
return config, pixel_values, labels
def _lowercase ( self : Dict ) ->Optional[Any]:
"""simple docstring"""
return YolosConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=UpperCAmelCase__ , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , )
def _lowercase ( self : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Optional[Any] ) ->Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = YolosModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
SCREAMING_SNAKE_CASE : Optional[Any] = model(UpperCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size) )
def _lowercase ( self : str , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Any , UpperCAmelCase__ : Union[str, Any] ) ->int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = YolosForObjectDetection(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
SCREAMING_SNAKE_CASE : List[Any] = model(pixel_values=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = model(UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
SCREAMING_SNAKE_CASE : int = model(pixel_values=UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
def _lowercase ( self : Dict ) ->Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = config_and_inputs
SCREAMING_SNAKE_CASE : List[Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class a__ ( UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Tuple =(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
UpperCAmelCase__ : Any =(
{"""feature-extraction""": YolosModel, """object-detection""": YolosForObjectDetection} if is_torch_available() else {}
)
UpperCAmelCase__ : Tuple =False
UpperCAmelCase__ : int =False
UpperCAmelCase__ : Tuple =False
UpperCAmelCase__ : Optional[Any] =False
def _lowercase ( self : int , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Any=False ) ->int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = super()._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
if return_labels:
if model_class.__name__ == "YolosForObjectDetection":
SCREAMING_SNAKE_CASE : List[str] = []
for i in range(self.model_tester.batch_size ):
SCREAMING_SNAKE_CASE : Tuple = {}
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.ones(
size=(self.model_tester.n_targets,) , device=UpperCAmelCase__ , dtype=torch.long )
SCREAMING_SNAKE_CASE : str = torch.ones(
self.model_tester.n_targets , 4 , device=UpperCAmelCase__ , dtype=torch.float )
labels.append(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : str = labels
return inputs_dict
def _lowercase ( self : Dict ) ->Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = YolosModelTester(self )
SCREAMING_SNAKE_CASE : Optional[int] = ConfigTester(self , config_class=UpperCAmelCase__ , has_text_modality=UpperCAmelCase__ , hidden_size=3_7 )
def _lowercase ( self : Union[str, Any] ) ->List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _lowercase ( self : List[Any] ) ->int:
"""simple docstring"""
pass
def _lowercase ( self : Optional[int] ) ->Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : List[str] = model_class(UpperCAmelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE : List[str] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase__ , nn.Linear ) )
def _lowercase ( self : List[Any] ) ->int:
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : Tuple = model_class(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE : Tuple = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE : str = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCAmelCase__ )
def _lowercase ( self : Union[str, Any] ) ->Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def _lowercase ( self : Optional[Any] ) ->Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE : Optional[Any] = True
# in YOLOS, the seq_len is different
SCREAMING_SNAKE_CASE : Any = self.model_tester.expected_seq_len
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : Union[str, Any] = False
SCREAMING_SNAKE_CASE : int = True
SCREAMING_SNAKE_CASE : Optional[Any] = model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : Union[str, Any] = model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
SCREAMING_SNAKE_CASE : Union[str, Any] = outputs.attentions
self.assertEqual(len(UpperCAmelCase__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : Tuple = model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : Dict = model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
SCREAMING_SNAKE_CASE : Union[str, Any] = outputs.attentions
self.assertEqual(len(UpperCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
SCREAMING_SNAKE_CASE : List[str] = len(UpperCAmelCase__ )
# Check attention is always last and order is fine
SCREAMING_SNAKE_CASE : Optional[int] = True
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : List[str] = model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[Any] = model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
SCREAMING_SNAKE_CASE : Optional[int] = 1
self.assertEqual(out_len + added_hidden_states , len(UpperCAmelCase__ ) )
SCREAMING_SNAKE_CASE : str = outputs.attentions
self.assertEqual(len(UpperCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def _lowercase ( self : Any ) ->str:
"""simple docstring"""
def check_hidden_states_output(UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : str ):
SCREAMING_SNAKE_CASE : List[Any] = model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[str] = model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
SCREAMING_SNAKE_CASE : Dict = outputs.hidden_states
SCREAMING_SNAKE_CASE : str = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(UpperCAmelCase__ ) , UpperCAmelCase__ )
# YOLOS has a different seq_length
SCREAMING_SNAKE_CASE : Tuple = self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : Any = True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE : Any = True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def _lowercase ( self : Any ) ->Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_object_detection(*UpperCAmelCase__ )
@slow
def _lowercase ( self : str ) ->List[Any]:
"""simple docstring"""
for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : str = YolosModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
def __lowercase ( ) -> List[Any]:
SCREAMING_SNAKE_CASE : Tuple = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class a__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _lowercase ( self : int ) ->Union[str, Any]:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("""hustvl/yolos-small""" ) if is_vision_available() else None
@slow
def _lowercase ( self : List[Any] ) ->Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = YolosForObjectDetection.from_pretrained("""hustvl/yolos-small""" ).to(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Tuple = self.default_image_processor
SCREAMING_SNAKE_CASE : Optional[Any] = prepare_img()
SCREAMING_SNAKE_CASE : str = image_processor(images=UpperCAmelCase__ , return_tensors="""pt""" ).to(UpperCAmelCase__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[Any] = model(inputs.pixel_values )
# verify outputs
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.Size((1, 1_0_0, 9_2) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Any = torch.tensor(
[[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]] , device=UpperCAmelCase__ , )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]] , device=UpperCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , UpperCAmelCase__ , atol=1e-4 ) )
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , UpperCAmelCase__ , atol=1e-4 ) )
# verify postprocessing
SCREAMING_SNAKE_CASE : int = image_processor.post_process_object_detection(
UpperCAmelCase__ , threshold=0.3 , target_sizes=[image.size[::-1]] )[0]
SCREAMING_SNAKE_CASE : str = torch.tensor([0.99_94, 0.97_90, 0.99_64, 0.99_72, 0.98_61] ).to(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : str = [7_5, 7_5, 1_7, 6_3, 1_7]
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([3_35.06_09, 79.38_48, 3_75.42_16, 1_87.24_95] ).to(UpperCAmelCase__ )
self.assertEqual(len(results["""scores"""] ) , 5 )
self.assertTrue(torch.allclose(results["""scores"""] , UpperCAmelCase__ , atol=1e-4 ) )
self.assertSequenceEqual(results["""labels"""].tolist() , UpperCAmelCase__ )
self.assertTrue(torch.allclose(results["""boxes"""][0, :] , UpperCAmelCase__ ) )
| 245 | 0 |
"""simple docstring"""
_lowerCAmelCase : dict[tuple[int, int, int], int] = {}
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
_lowerCamelCase : Optional[int] = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
_lowerCamelCase : int = _calculate(days - 1 , _lowerCamelCase , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
_lowerCamelCase : Tuple = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
_lowerCamelCase : str = _calculate(days - 1 , _lowerCamelCase , 0 )
_lowerCamelCase : List[Any] = state_late + state_absent + state_ontime
_lowerCamelCase : int = prizestrings
return prizestrings
def lowerCamelCase_( _lowerCamelCase = 30 ) -> int:
'''simple docstring'''
return _calculate(_lowerCamelCase , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 340 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> str:
'''simple docstring'''
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = (
first_str_length if first_str_length > second_str_length else second_str_length
)
_lowerCamelCase : list = []
for char_count in range(_lowerCamelCase ):
if char_count < first_str_length:
output_list.append(first_str[char_count] )
if char_count < second_str_length:
output_list.append(second_str[char_count] )
return "".join(_lowerCamelCase )
if __name__ == "__main__":
print(alternative_string_arrange('''AB''', '''XYZ'''), end=''' ''')
| 340 | 1 |
"""simple docstring"""
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowercase ( __UpperCAmelCase):
__lowerCAmelCase : Optional[Any] = (DPMSolverSDEScheduler,)
__lowerCAmelCase : Union[str, Any] = 10
def a_ ( self : Tuple , **_lowerCamelCase : Tuple ):
"""simple docstring"""
A_ : Optional[Any] = {
'''num_train_timesteps''': 11_00,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''noise_sampler_seed''': 0,
}
config.update(**_lowerCamelCase )
return config
def a_ ( self : List[Any] ):
"""simple docstring"""
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=_lowerCamelCase )
def a_ ( self : Tuple ):
"""simple docstring"""
for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=_lowerCamelCase , beta_end=_lowerCamelCase )
def a_ ( self : Optional[int] ):
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=_lowerCamelCase )
def a_ ( self : Union[str, Any] ):
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_lowerCamelCase )
def a_ ( self : Tuple ):
"""simple docstring"""
A_ : str = self.scheduler_classes[0]
A_ : Tuple = self.get_scheduler_config()
A_ : Tuple = scheduler_class(**_lowerCamelCase )
scheduler.set_timesteps(self.num_inference_steps )
A_ : Optional[Any] = self.dummy_model()
A_ : Optional[int] = self.dummy_sample_deter * scheduler.init_noise_sigma
A_ : Any = sample.to(_lowerCamelCase )
for i, t in enumerate(scheduler.timesteps ):
A_ : Optional[int] = scheduler.scale_model_input(_lowerCamelCase , _lowerCamelCase )
A_ : List[Any] = model(_lowerCamelCase , _lowerCamelCase )
A_ : Optional[Any] = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
A_ : Union[str, Any] = output.prev_sample
A_ : List[str] = torch.sum(torch.abs(_lowerCamelCase ) )
A_ : Dict = torch.mean(torch.abs(_lowerCamelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47821044921875 ) < 1E-2
assert abs(result_mean.item() - 0.2178705964565277 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59352111816406 ) < 1E-2
assert abs(result_mean.item() - 0.22342906892299652 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1E-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1E-3
def a_ ( self : Dict ):
"""simple docstring"""
A_ : Tuple = self.scheduler_classes[0]
A_ : Tuple = self.get_scheduler_config(prediction_type='''v_prediction''' )
A_ : Optional[Any] = scheduler_class(**_lowerCamelCase )
scheduler.set_timesteps(self.num_inference_steps )
A_ : Dict = self.dummy_model()
A_ : Tuple = self.dummy_sample_deter * scheduler.init_noise_sigma
A_ : List[Any] = sample.to(_lowerCamelCase )
for i, t in enumerate(scheduler.timesteps ):
A_ : Any = scheduler.scale_model_input(_lowerCamelCase , _lowerCamelCase )
A_ : Optional[int] = model(_lowerCamelCase , _lowerCamelCase )
A_ : str = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
A_ : Optional[Any] = output.prev_sample
A_ : str = torch.sum(torch.abs(_lowerCamelCase ) )
A_ : str = torch.mean(torch.abs(_lowerCamelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77149200439453 ) < 1E-2
assert abs(result_mean.item() - 0.16226289014816284 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1663360595703 ) < 1E-2
assert abs(result_mean.item() - 0.16688326001167297 ) < 1E-3
else:
assert abs(result_sum.item() - 119.8487548828125 ) < 1E-2
assert abs(result_mean.item() - 0.1560530662536621 ) < 1E-3
def a_ ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Dict = self.scheduler_classes[0]
A_ : Dict = self.get_scheduler_config()
A_ : Optional[Any] = scheduler_class(**_lowerCamelCase )
scheduler.set_timesteps(self.num_inference_steps , device=_lowerCamelCase )
A_ : Dict = self.dummy_model()
A_ : Dict = self.dummy_sample_deter.to(_lowerCamelCase ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
A_ : Union[str, Any] = scheduler.scale_model_input(_lowerCamelCase , _lowerCamelCase )
A_ : Any = model(_lowerCamelCase , _lowerCamelCase )
A_ : Tuple = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
A_ : List[str] = output.prev_sample
A_ : int = torch.sum(torch.abs(_lowerCamelCase ) )
A_ : List[str] = torch.mean(torch.abs(_lowerCamelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46957397460938 ) < 1E-2
assert abs(result_mean.item() - 0.21805934607982635 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59353637695312 ) < 1E-2
assert abs(result_mean.item() - 0.22342908382415771 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1E-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1E-3
def a_ ( self : Any ):
"""simple docstring"""
A_ : str = self.scheduler_classes[0]
A_ : Dict = self.get_scheduler_config()
A_ : Union[str, Any] = scheduler_class(**_lowerCamelCase , use_karras_sigmas=_lowerCamelCase )
scheduler.set_timesteps(self.num_inference_steps , device=_lowerCamelCase )
A_ : Any = self.dummy_model()
A_ : Union[str, Any] = self.dummy_sample_deter.to(_lowerCamelCase ) * scheduler.init_noise_sigma
A_ : Any = sample.to(_lowerCamelCase )
for t in scheduler.timesteps:
A_ : Union[str, Any] = scheduler.scale_model_input(_lowerCamelCase , _lowerCamelCase )
A_ : Any = model(_lowerCamelCase , _lowerCamelCase )
A_ : str = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
A_ : int = output.prev_sample
A_ : Any = torch.sum(torch.abs(_lowerCamelCase ) )
A_ : Tuple = torch.mean(torch.abs(_lowerCamelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66974135742188 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63653564453125 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
else:
assert abs(result_sum.item() - 170.3135223388672 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
| 167 |
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Dict = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = ['model.decoder.embed_positions.weights']
def lowercase_ ( _UpperCAmelCase ):
"""simple docstring"""
if "emb" in name:
A_ : Tuple = name.replace('''emb''' , '''model.decoder.embed_tokens''' )
if "transformer" in name:
A_ : Optional[int] = name.replace('''transformer''' , '''model.decoder''' )
if "cross_attention" in name:
A_ : Optional[Any] = name.replace('''cross_attention''' , '''encoder_attn''' )
if "linear1" in name:
A_ : int = name.replace('''linear1''' , '''fc1''' )
if "linear2" in name:
A_ : Optional[int] = name.replace('''linear2''' , '''fc2''' )
if "norm1" in name:
A_ : Any = name.replace('''norm1''' , '''self_attn_layer_norm''' )
if "norm_cross" in name:
A_ : Any = name.replace('''norm_cross''' , '''encoder_attn_layer_norm''' )
if "norm2" in name:
A_ : Dict = name.replace('''norm2''' , '''final_layer_norm''' )
if "out_norm" in name:
A_ : Tuple = name.replace('''out_norm''' , '''model.decoder.layer_norm''' )
if "linears" in name:
A_ : Union[str, Any] = name.replace('''linears''' , '''lm_heads''' )
if "condition_provider.conditioners.description.output_proj" in name:
A_ : Tuple = name.replace('''condition_provider.conditioners.description.output_proj''' , '''enc_to_dec_proj''' )
return name
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : List[Any] = list(state_dict.keys() )
A_ : List[Any] = {}
for key in keys:
A_ : List[str] = state_dict.pop(_UpperCAmelCase )
A_ : Tuple = rename_keys(_UpperCAmelCase )
if "in_proj_weight" in key:
# split fused qkv proj
A_ : Any = val[:hidden_size, :]
A_ : Optional[int] = val[hidden_size : 2 * hidden_size, :]
A_ : Union[str, Any] = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
A_ : List[str] = val
else:
A_ : int = val
return state_dict, enc_dec_proj_state_dict
def lowercase_ ( _UpperCAmelCase ):
"""simple docstring"""
if checkpoint == "small":
# default config values
A_ : Optional[Any] = 1024
A_ : Tuple = 24
A_ : int = 16
elif checkpoint == "medium":
A_ : Any = 1536
A_ : Union[str, Any] = 48
A_ : List[Any] = 24
elif checkpoint == "large":
A_ : Optional[int] = 2048
A_ : Optional[int] = 48
A_ : Tuple = 32
else:
raise ValueError(f"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}.""" )
A_ : Tuple = MusicgenDecoderConfig(
hidden_size=_UpperCAmelCase , ffn_dim=hidden_size * 4 , num_hidden_layers=_UpperCAmelCase , num_attention_heads=_UpperCAmelCase , )
return config
@torch.no_grad()
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase="cpu" ):
"""simple docstring"""
A_ : Any = MusicGen.get_pretrained(_UpperCAmelCase , device=_UpperCAmelCase )
A_ : str = decoder_config_from_checkpoint(_UpperCAmelCase )
A_ : Optional[int] = fairseq_model.lm.state_dict()
A_ , A_ : str = rename_state_dict(
_UpperCAmelCase , hidden_size=decoder_config.hidden_size )
A_ : List[str] = TaEncoderModel.from_pretrained('''t5-base''' )
A_ : Tuple = EncodecModel.from_pretrained('''facebook/encodec_32khz''' )
A_ : Union[str, Any] = MusicgenForCausalLM(_UpperCAmelCase ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
A_ , A_ : Tuple = decoder.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
for key in missing_keys.copy():
if key.startswith(('''text_encoder''', '''audio_encoder''') ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0:
raise ValueError(f"""Missing key(s) in state_dict: {missing_keys}""" )
if len(_UpperCAmelCase ) > 0:
raise ValueError(f"""Unexpected key(s) in state_dict: {unexpected_keys}""" )
# init the composite model
A_ : Tuple = MusicgenForConditionalGeneration(text_encoder=_UpperCAmelCase , audio_encoder=_UpperCAmelCase , decoder=_UpperCAmelCase )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(_UpperCAmelCase )
# check we can do a forward pass
A_ : List[str] = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
A_ : Union[str, Any] = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
A_ : Tuple = model(input_ids=_UpperCAmelCase , decoder_input_ids=_UpperCAmelCase ).logits
if logits.shape != (8, 1, 2048):
raise ValueError('''Incorrect shape for logits''' )
# now construct the processor
A_ : str = AutoTokenizer.from_pretrained('''t5-base''' )
A_ : int = AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz''' , padding_side='''left''' )
A_ : Optional[int] = MusicgenProcessor(feature_extractor=_UpperCAmelCase , tokenizer=_UpperCAmelCase )
# set the appropriate bos/pad token ids
A_ : Tuple = 2048
A_ : Union[str, Any] = 2048
# set other default generation config params
A_ : Union[str, Any] = int(30 * audio_encoder.config.frame_rate )
A_ : List[str] = True
A_ : List[str] = 3.0
if pytorch_dump_folder is not None:
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
logger.info(f"""Saving model {checkpoint} to {pytorch_dump_folder}""" )
model.save_pretrained(_UpperCAmelCase )
processor.save_pretrained(_UpperCAmelCase )
if repo_id:
logger.info(f"""Pushing model {checkpoint} to {repo_id}""" )
model.push_to_hub(_UpperCAmelCase )
processor.push_to_hub(_UpperCAmelCase )
if __name__ == "__main__":
_lowerCamelCase : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint',
default='small',
type=str,
help='Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.',
)
parser.add_argument(
'--pytorch_dump_folder',
required=True,
default=None,
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
parser.add_argument(
'--device', default='cpu', type=str, help='Torch device to run the conversion, either cpu or cuda.'
)
_lowerCamelCase : Optional[Any] = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 167 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a :Any = logging.get_logger(__name__)
__a :Optional[Any] = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://huggingface.co/models?filter=cvt
}
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Dict = 'cvt'
def __init__( self : Tuple , UpperCAmelCase : int=3 , UpperCAmelCase : int=[7, 3, 3] , UpperCAmelCase : Optional[int]=[4, 2, 2] , UpperCAmelCase : Dict=[2, 1, 1] , UpperCAmelCase : Dict=[64, 192, 384] , UpperCAmelCase : Union[str, Any]=[1, 3, 6] , UpperCAmelCase : int=[1, 2, 10] , UpperCAmelCase : Union[str, Any]=[4.0, 4.0, 4.0] , UpperCAmelCase : Tuple=[0.0, 0.0, 0.0] , UpperCAmelCase : Any=[0.0, 0.0, 0.0] , UpperCAmelCase : Optional[Any]=[0.0, 0.0, 0.1] , UpperCAmelCase : Tuple=[True, True, True] , UpperCAmelCase : List[str]=[False, False, True] , UpperCAmelCase : str=["dw_bn", "dw_bn", "dw_bn"] , UpperCAmelCase : str=[3, 3, 3] , UpperCAmelCase : Dict=[1, 1, 1] , UpperCAmelCase : Optional[Any]=[2, 2, 2] , UpperCAmelCase : Optional[int]=[1, 1, 1] , UpperCAmelCase : Dict=[1, 1, 1] , UpperCAmelCase : List[Any]=0.02 , UpperCAmelCase : Union[str, Any]=1E-12 , **UpperCAmelCase : Dict , ):
super().__init__(**UpperCAmelCase )
A_ = num_channels
A_ = patch_sizes
A_ = patch_stride
A_ = patch_padding
A_ = embed_dim
A_ = num_heads
A_ = depth
A_ = mlp_ratio
A_ = attention_drop_rate
A_ = drop_rate
A_ = drop_path_rate
A_ = qkv_bias
A_ = cls_token
A_ = qkv_projection_method
A_ = kernel_qkv
A_ = padding_kv
A_ = stride_kv
A_ = padding_q
A_ = stride_q
A_ = initializer_range
A_ = layer_norm_eps
| 360 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp_seed,
temporary_assignment,
zip_dict,
)
from .utils import require_tf, require_torch
def __snake_case ( __UpperCamelCase : Optional[int] ): # picklable for multiprocessing
"""simple docstring"""
return x.sum()
def __snake_case ( __UpperCamelCase : List[str] ): # picklable for multiprocessing
"""simple docstring"""
return i + 1
@dataclass
class _a :
"""simple docstring"""
_lowerCamelCase : int
_lowerCamelCase : str
class _a ( snake_case_ ):
"""simple docstring"""
def __A ( self : Dict ):
A_ = {}
A_ = []
A_ = 1
A_ = [1, 2]
A_ = {"a": 1, "b": 2}
A_ = {"a": [1, 2], "b": [3, 4]}
A_ = {"a": {"1": 1}, "b": 2}
A_ = {"a": 1, "b": 2, "c": 3, "d": 4}
A_ = {}
A_ = []
A_ = 2
A_ = [2, 3]
A_ = {"a": 2, "b": 3}
A_ = {"a": [2, 3], "b": [4, 5]}
A_ = {"a": {"1": 2}, "b": 3}
A_ = {"a": 2, "b": 3, "c": 4, "d": 5}
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
A_ = 2
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
A_ = {"a": np.eye(2 ), "b": np.zeros(3 ), "c": np.ones(2 )}
A_ = {"a": 2, "b": 0, "c": 2}
A_ = {
"a": np.eye(2 ).astype(UpperCAmelCase ),
"b": np.zeros(3 ).astype(UpperCAmelCase ),
"c": np.ones(2 ).astype(UpperCAmelCase ),
}
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , map_numpy=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(UpperCAmelCase , UpperCAmelCase , map_numpy=UpperCAmelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , map_numpy=UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(UpperCAmelCase , UpperCAmelCase , map_numpy=UpperCAmelCase , num_proc=UpperCAmelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
with self.assertRaises(UpperCAmelCase ): # can't pickle a local lambda
map_nested(lambda UpperCAmelCase : x + 1 , UpperCAmelCase , num_proc=UpperCAmelCase )
def __A ( self : List[str] ):
A_ = {"a": 1, "b": 2}
A_ = {"a": 3, "b": 4}
A_ = {"a": 5, "b": 6}
A_ = sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))] )
self.assertEqual(sorted(zip_dict(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ) , UpperCAmelCase )
def __A ( self : Any ):
class _a :
"""simple docstring"""
_lowerCamelCase : int = 'bar'
A_ = Foo()
self.assertEqual(foo.my_attr , "bar" )
with temporary_assignment(UpperCAmelCase , "my_attr" , "BAR" ):
self.assertEqual(foo.my_attr , "BAR" )
self.assertEqual(foo.my_attr , "bar" )
@pytest.mark.parametrize(
"iterable_length, num_proc, expected_num_proc" ,[
(1, None, 1),
(1, 1, 1),
(2, None, 1),
(2, 1, 1),
(2, 2, 1),
(2, 3, 1),
(3, 2, 1),
(16, 16, 16),
(16, 17, 16),
(17, 16, 16),
] ,)
def __snake_case ( __UpperCamelCase : Optional[int] ,__UpperCamelCase : Tuple ,__UpperCamelCase : List[Any] ):
"""simple docstring"""
with patch("datasets.utils.py_utils._single_map_nested" ) as mock_single_map_nested, patch(
"datasets.parallel.parallel.Pool" ) as mock_multiprocessing_pool:
A_ = {f'''{i}''': i for i in range(__UpperCamelCase )}
A_ = map_nested(lambda __UpperCamelCase : x + 10 ,__UpperCamelCase ,num_proc=__UpperCamelCase ,parallel_min_length=16 )
if expected_num_proc == 1:
assert mock_single_map_nested.called
assert not mock_multiprocessing_pool.called
else:
assert not mock_single_map_nested.called
assert mock_multiprocessing_pool.called
assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class _a ( snake_case_ ):
"""simple docstring"""
@require_tf
def __A ( self : Union[str, Any] ):
import tensorflow as tf
from tensorflow.keras import layers
A_ = layers.Dense(2 )
def gen_random_output():
A_ = tf.random.uniform((1, 3) )
return model(UpperCAmelCase ).numpy()
with temp_seed(42 , set_tensorflow=UpperCAmelCase ):
A_ = gen_random_output()
with temp_seed(42 , set_tensorflow=UpperCAmelCase ):
A_ = gen_random_output()
A_ = gen_random_output()
np.testing.assert_equal(UpperCAmelCase , UpperCAmelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@require_torch
def __A ( self : Optional[int] ):
import torch
def gen_random_output():
A_ = torch.nn.Linear(3 , 2 )
A_ = torch.rand(1 , 3 )
return model(UpperCAmelCase ).detach().numpy()
with temp_seed(42 , set_pytorch=UpperCAmelCase ):
A_ = gen_random_output()
with temp_seed(42 , set_pytorch=UpperCAmelCase ):
A_ = gen_random_output()
A_ = gen_random_output()
np.testing.assert_equal(UpperCAmelCase , UpperCAmelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
def __A ( self : Any ):
def gen_random_output():
return np.random.rand(1 , 3 )
with temp_seed(42 ):
A_ = gen_random_output()
with temp_seed(42 ):
A_ = gen_random_output()
A_ = gen_random_output()
np.testing.assert_equal(UpperCAmelCase , UpperCAmelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@pytest.mark.parametrize("input_data" ,[{}] )
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
A_ = NestedDataStructure(__UpperCamelCase ).data
assert output_data == input_data
@pytest.mark.parametrize(
"data, expected_output" ,[
({}, []),
([], []),
("foo", ["foo"]),
(["foo", "bar"], ["foo", "bar"]),
([["foo", "bar"]], ["foo", "bar"]),
([[["foo"], ["bar"]]], ["foo", "bar"]),
([[["foo"], "bar"]], ["foo", "bar"]),
({"a": 1, "b": 2}, [1, 2]),
({"a": [1, 2], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[1, 2]], "b": [[3, 4]]}, [1, 2, 3, 4]),
({"a": [[1, 2]], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [[[3], [4]]]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [[3, 4]]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [3, [4]]}, [1, 2, 3, 4]),
({"a": {"1": 1}, "b": 2}, [1, 2]),
({"a": {"1": [1]}, "b": 2}, [1, 2]),
({"a": {"1": [1]}, "b": [2]}, [1, 2]),
] ,)
def __snake_case ( __UpperCamelCase : Dict ,__UpperCamelCase : Any ):
"""simple docstring"""
A_ = NestedDataStructure(__UpperCamelCase ).flatten()
assert output == expected_output
def __snake_case ( ):
"""simple docstring"""
A_ = A(x=1 ,y="foobar" )
A_ = {"x": 1, "y": "foobar"}
assert asdict(__UpperCamelCase ) == expected_output
A_ = {"a": {"b": A(x=10 ,y="foo" )}, "c": [A(x=20 ,y="bar" )]}
A_ = {"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
assert asdict(__UpperCamelCase ) == expected_output
with pytest.raises(__UpperCamelCase ):
asdict([1, A(x=10 ,y="foo" )] )
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
return text.split()
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
yield (time.time(), content)
time.sleep(2 )
yield (time.time(), content)
def __snake_case ( ):
"""simple docstring"""
with Pool(2 ) as pool:
A_ = list(iflatmap_unordered(__UpperCamelCase ,_split_text ,kwargs_iterable=[{"text": "hello there"}] * 10 ) )
assert out.count("hello" ) == 10
assert out.count("there" ) == 10
assert len(__UpperCamelCase ) == 20
# check multiprocess from pathos (uses dill for pickling)
with multiprocess.Pool(2 ) as pool:
A_ = list(iflatmap_unordered(__UpperCamelCase ,_split_text ,kwargs_iterable=[{"text": "hello there"}] * 10 ) )
assert out.count("hello" ) == 10
assert out.count("there" ) == 10
assert len(__UpperCamelCase ) == 20
# check that we get items as fast as possible
with Pool(2 ) as pool:
A_ = []
for yield_time, content in iflatmap_unordered(
__UpperCamelCase ,_aseconds_generator_of_aitems_with_timing ,kwargs_iterable=[{"content": "a"}, {"content": "b"}] ):
assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
out.append(__UpperCamelCase )
assert out.count("a" ) == 2
assert out.count("b" ) == 2
assert len(__UpperCamelCase ) == 4
| 329 | 0 |
"""simple docstring"""
from __future__ import annotations
SCREAMING_SNAKE_CASE_ = [True] * 1_000_001
SCREAMING_SNAKE_CASE_ = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
SCREAMING_SNAKE_CASE_ = False
i += 1
def lowercase (_lowerCAmelCase ):
return seive[n]
def lowercase (_lowerCAmelCase ):
return any(digit in """02468""" for digit in str(_lowerCAmelCase ) )
def lowercase (_lowerCAmelCase = 100_0000 ):
__lowerCAmelCase = [2] # result already includes the number 2.
for num in range(3 , limit + 1 , 2 ):
if is_prime(_lowerCAmelCase ) and not contains_an_even_digit(_lowerCAmelCase ):
__lowerCAmelCase = str(_lowerCAmelCase )
__lowerCAmelCase = [int(str_num[j:] + str_num[:j] ) for j in range(len(_lowerCAmelCase ) )]
if all(is_prime(_lowerCAmelCase ) for i in list_nums ):
result.append(_lowerCAmelCase )
return result
def lowercase ():
return len(find_circular_primes() )
if __name__ == "__main__":
print(F"{len(find_circular_primes()) = }")
| 301 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
SCREAMING_SNAKE_CASE_ = {
'''vocab_file''': {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'''
),
'''google/realm-cc-news-pretrained-encoder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'''
),
'''google/realm-cc-news-pretrained-scorer''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'''
),
'''google/realm-cc-news-pretrained-openqa''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'''
),
'''google/realm-orqa-nq-openqa''': '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt''',
'''google/realm-orqa-nq-reader''': '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt''',
'''google/realm-orqa-wq-openqa''': '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt''',
'''google/realm-orqa-wq-reader''': '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt''',
},
'''tokenizer_file''': {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'''
),
'''google/realm-cc-news-pretrained-encoder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'''
),
'''google/realm-cc-news-pretrained-scorer''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'''
),
'''google/realm-cc-news-pretrained-openqa''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'''
),
'''google/realm-orqa-nq-openqa''': (
'''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'''
),
'''google/realm-orqa-nq-reader''': (
'''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'''
),
'''google/realm-orqa-wq-openqa''': (
'''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'''
),
'''google/realm-orqa-wq-reader''': (
'''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'''
),
},
}
SCREAMING_SNAKE_CASE_ = {
'''google/realm-cc-news-pretrained-embedder''': 512,
'''google/realm-cc-news-pretrained-encoder''': 512,
'''google/realm-cc-news-pretrained-scorer''': 512,
'''google/realm-cc-news-pretrained-openqa''': 512,
'''google/realm-orqa-nq-openqa''': 512,
'''google/realm-orqa-nq-reader''': 512,
'''google/realm-orqa-wq-openqa''': 512,
'''google/realm-orqa-wq-reader''': 512,
}
SCREAMING_SNAKE_CASE_ = {
'''google/realm-cc-news-pretrained-embedder''': {'''do_lower_case''': True},
'''google/realm-cc-news-pretrained-encoder''': {'''do_lower_case''': True},
'''google/realm-cc-news-pretrained-scorer''': {'''do_lower_case''': True},
'''google/realm-cc-news-pretrained-openqa''': {'''do_lower_case''': True},
'''google/realm-orqa-nq-openqa''': {'''do_lower_case''': True},
'''google/realm-orqa-nq-reader''': {'''do_lower_case''': True},
'''google/realm-orqa-wq-openqa''': {'''do_lower_case''': True},
'''google/realm-orqa-wq-reader''': {'''do_lower_case''': True},
}
class lowerCAmelCase_ ( A__ ):
'''simple docstring'''
_snake_case = VOCAB_FILES_NAMES
_snake_case = PRETRAINED_VOCAB_FILES_MAP
_snake_case = PRETRAINED_INIT_CONFIGURATION
_snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_snake_case = RealmTokenizer
def __init__( self , snake_case_=None , snake_case_=None , snake_case_=True , snake_case_="[UNK]" , snake_case_="[SEP]" , snake_case_="[PAD]" , snake_case_="[CLS]" , snake_case_="[MASK]" , snake_case_=True , snake_case_=None , **snake_case_ , ) -> Optional[int]:
super().__init__(
snake_case_ , tokenizer_file=snake_case_ , do_lower_case=snake_case_ , unk_token=snake_case_ , sep_token=snake_case_ , pad_token=snake_case_ , cls_token=snake_case_ , mask_token=snake_case_ , tokenize_chinese_chars=snake_case_ , strip_accents=snake_case_ , **snake_case_ , )
__lowerCAmelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , snake_case_ ) != do_lower_case
or normalizer_state.get("""strip_accents""" , snake_case_ ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , snake_case_ ) != tokenize_chinese_chars
):
__lowerCAmelCase = getattr(snake_case_ , normalizer_state.pop("""type""" ) )
__lowerCAmelCase = do_lower_case
__lowerCAmelCase = strip_accents
__lowerCAmelCase = tokenize_chinese_chars
__lowerCAmelCase = normalizer_class(**snake_case_ )
__lowerCAmelCase = do_lower_case
def A__ ( self , snake_case_ , **snake_case_ ) -> Tuple:
__lowerCAmelCase = PaddingStrategy.MAX_LENGTH
__lowerCAmelCase = text
__lowerCAmelCase = kwargs.pop("""text_pair""" , snake_case_ )
__lowerCAmelCase = kwargs.pop("""return_tensors""" , snake_case_ )
__lowerCAmelCase = {
"""input_ids""": [],
"""attention_mask""": [],
"""token_type_ids""": [],
}
for idx, candidate_text in enumerate(snake_case_ ):
if batch_text_pair is not None:
__lowerCAmelCase = batch_text_pair[idx]
else:
__lowerCAmelCase = None
__lowerCAmelCase = super().__call__(snake_case_ , snake_case_ , return_tensors=snake_case_ , **snake_case_ )
__lowerCAmelCase = encoded_candidates.get("""input_ids""" )
__lowerCAmelCase = encoded_candidates.get("""attention_mask""" )
__lowerCAmelCase = encoded_candidates.get("""token_type_ids""" )
if encoded_input_ids is not None:
output_data["input_ids"].append(snake_case_ )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(snake_case_ )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(snake_case_ )
__lowerCAmelCase = {key: item for key, item in output_data.items() if len(snake_case_ ) != 0}
return BatchEncoding(snake_case_ , tensor_type=snake_case_ )
def A__ ( self , snake_case_ , snake_case_=None ) -> Optional[int]:
__lowerCAmelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def A__ ( self , snake_case_ , snake_case_ = None ) -> List[int]:
__lowerCAmelCase = [self.sep_token_id]
__lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def A__ ( self , snake_case_ , snake_case_ = None ) -> Tuple[str]:
__lowerCAmelCase = self._tokenizer.model.save(snake_case_ , name=snake_case_ )
return tuple(snake_case_ )
| 301 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
)
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A , __A=3 , __A=7 , __A=True , __A=True , __A=False , __A=True , __A=99 , __A=32 , __A=5 , __A=4 , __A=37 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=16 , __A=2 , __A=0.0_2 , __A=3 , __A=4 , __A=None , ) -> Optional[int]:
lowerCAmelCase_ :Tuple = parent
lowerCAmelCase_ :int = batch_size
lowerCAmelCase_ :List[str] = seq_length
lowerCAmelCase_ :int = is_training
lowerCAmelCase_ :Optional[int] = use_input_mask
lowerCAmelCase_ :Tuple = use_token_type_ids
lowerCAmelCase_ :Optional[Any] = use_labels
lowerCAmelCase_ :List[str] = vocab_size
lowerCAmelCase_ :Optional[Any] = hidden_size
lowerCAmelCase_ :Any = num_hidden_layers
lowerCAmelCase_ :List[str] = num_attention_heads
lowerCAmelCase_ :int = intermediate_size
lowerCAmelCase_ :Any = hidden_act
lowerCAmelCase_ :List[Any] = hidden_dropout_prob
lowerCAmelCase_ :Dict = attention_probs_dropout_prob
lowerCAmelCase_ :List[str] = max_position_embeddings
lowerCAmelCase_ :Union[str, Any] = type_vocab_size
lowerCAmelCase_ :Optional[Any] = type_sequence_label_size
lowerCAmelCase_ :Tuple = initializer_range
lowerCAmelCase_ :Optional[int] = num_labels
lowerCAmelCase_ :Tuple = num_choices
lowerCAmelCase_ :Optional[Any] = scope
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase_ :Optional[Any] = None
if self.use_input_mask:
lowerCAmelCase_ :List[str] = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase_ :Union[str, Any] = None
lowerCAmelCase_ :int = None
lowerCAmelCase_ :List[str] = None
lowerCAmelCase_ :Union[str, Any] = None
if self.use_labels:
lowerCAmelCase_ :Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase_ :Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase_ :str = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase_ :Tuple = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self ) -> Optional[Any]:
return FalconConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__A , initializer_range=self.initializer_range , pad_token_id=1 , new_decoder_architecture=__A , )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A , __A ) -> Union[str, Any]:
lowerCAmelCase_ :List[str] = FalconModel(config=__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :List[Any] = model(__A , attention_mask=__A )
lowerCAmelCase_ :str = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A , __A , __A , __A , ) -> int:
lowerCAmelCase_ :List[str] = True
lowerCAmelCase_ :List[str] = FalconModel(__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :int = model(
__A , attention_mask=__A , encoder_hidden_states=__A , encoder_attention_mask=__A , )
lowerCAmelCase_ :Optional[Any] = model(
__A , attention_mask=__A , encoder_hidden_states=__A , )
lowerCAmelCase_ :List[str] = model(__A , attention_mask=__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A , __A , __A , __A , ) -> Dict:
lowerCAmelCase_ :Optional[int] = FalconForCausalLM(config=__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :Optional[Any] = model(__A , attention_mask=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A , __A , __A , __A , ) -> Union[str, Any]:
lowerCAmelCase_ :Optional[Any] = True
lowerCAmelCase_ :str = True
lowerCAmelCase_ :List[str] = FalconForCausalLM(config=__A )
model.to(__A )
model.eval()
# first forward pass
lowerCAmelCase_ :int = model(
__A , attention_mask=__A , encoder_hidden_states=__A , encoder_attention_mask=__A , use_cache=__A , )
lowerCAmelCase_ :Optional[Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowerCAmelCase_ :Dict = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowerCAmelCase_ :List[str] = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
lowerCAmelCase_ :Tuple = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCAmelCase_ :Optional[int] = torch.cat([input_mask, next_mask] , dim=-1 )
lowerCAmelCase_ :int = model(
__A , attention_mask=__A , encoder_hidden_states=__A , encoder_attention_mask=__A , output_hidden_states=__A , )["""hidden_states"""][0]
lowerCAmelCase_ :Dict = model(
__A , attention_mask=__A , encoder_hidden_states=__A , encoder_attention_mask=__A , past_key_values=__A , output_hidden_states=__A , )["""hidden_states"""][0]
# select random slice
lowerCAmelCase_ :List[Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCAmelCase_ :Optional[Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCAmelCase_ :List[str] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__A , __A , atol=1E-3 ) )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :Optional[int] = self.prepare_config_and_inputs()
(
(
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) ,
) :Optional[Any] = config_and_inputs
lowerCAmelCase_ :str = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class _SCREAMING_SNAKE_CASE ( A__ , A__ , A__ , unittest.TestCase ):
UpperCAmelCase_ :Optional[int] = (
(
FalconModel,
FalconForCausalLM,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCAmelCase_ :str = (FalconForCausalLM,) if is_torch_available() else ()
UpperCAmelCase_ :Union[str, Any] = (
{
"feature-extraction": FalconModel,
"text-classification": FalconForSequenceClassification,
"text-generation": FalconForCausalLM,
"question-answering": FalconForQuestionAnswering,
"token-classification": FalconForTokenClassification,
"zero-shot": FalconForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase_ :int = False
UpperCAmelCase_ :int = False
def __lowerCAmelCase ( self ) -> Optional[int]:
lowerCAmelCase_ :List[Any] = FalconModelTester(self )
lowerCAmelCase_ :str = ConfigTester(self , config_class=__A , hidden_size=37 )
def __lowerCAmelCase ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ) -> Any:
lowerCAmelCase_ :Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ , *lowerCAmelCase_ :Dict = self.model_tester.prepare_config_and_inputs()
for alibi in [True, False]:
lowerCAmelCase_ :int = alibi
self.model_tester.create_and_check_model(__A , *__A )
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase_ :Optional[Any] = 3
lowerCAmelCase_ :Any = input_dict["""input_ids"""]
lowerCAmelCase_ :int = input_ids.ne(1 ).to(__A )
lowerCAmelCase_ :List[Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCAmelCase_ :Dict = FalconForSequenceClassification(__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :Any = model(__A , attention_mask=__A , labels=__A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ , lowerCAmelCase_ :Any = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase_ :List[str] = 3
lowerCAmelCase_ :Union[str, Any] = """single_label_classification"""
lowerCAmelCase_ :int = input_dict["""input_ids"""]
lowerCAmelCase_ :Tuple = input_ids.ne(1 ).to(__A )
lowerCAmelCase_ :int = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCAmelCase_ :Any = FalconForSequenceClassification(__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :int = model(__A , attention_mask=__A , labels=__A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __lowerCAmelCase ( self ) -> Optional[int]:
lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase_ :Optional[Any] = input_dict["""input_ids"""]
lowerCAmelCase_ :Any = FalconForCausalLM(__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :Optional[int] = model(__A , use_cache=__A )
lowerCAmelCase_ :Union[str, Any] = input_ids.shape[0]
lowerCAmelCase_ :Tuple = model._convert_to_rw_cache(result.past_key_values )
lowerCAmelCase_ :Optional[int] = model._convert_cache_to_standard_format(__A , __A )
for layer in range(len(__A ) ):
for tensor_idx in range(2 ):
self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3 )
self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4 )
self.assertTrue(
torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx] ) )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase_ :Optional[Any] = 3
lowerCAmelCase_ :Optional[int] = """multi_label_classification"""
lowerCAmelCase_ :List[str] = input_dict["""input_ids"""]
lowerCAmelCase_ :Optional[int] = input_ids.ne(1 ).to(__A )
lowerCAmelCase_ :Optional[int] = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
lowerCAmelCase_ :Tuple = FalconForSequenceClassification(__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :Optional[Any] = model(__A , attention_mask=__A , labels=__A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __lowerCAmelCase ( self ) -> Tuple:
# Falcon can have different numbers of KV-heads than the number of query heads, so we need
# to override this test to use the right head counts.
for model_class in self.all_generative_model_classes:
lowerCAmelCase_ , lowerCAmelCase_ :Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
# If it doesn't support cache, pass the test
if not hasattr(__A , """use_cache""" ):
return
lowerCAmelCase_ :Optional[Any] = model_class(__A ).to(__A )
if "use_cache" not in inputs:
lowerCAmelCase_ :Any = True
lowerCAmelCase_ :List[str] = model(**__A )
# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
if "past_key_values" not in outputs:
return
lowerCAmelCase_ :int = (
getattr(__A , """decoder_layers""" , __A )
or getattr(__A , """num_decoder_layers""" , __A )
or config.num_hidden_layers
)
lowerCAmelCase_ :Optional[int] = getattr(__A , """num_kv_heads""" , config.num_attention_heads )
lowerCAmelCase_ :int = getattr(__A , """d_model""" , config.hidden_size )
lowerCAmelCase_ :Tuple = embed_dim // num_attention_heads
lowerCAmelCase_ :List[str] = outputs["""past_key_values"""]
self.assertEqual(len(__A ) , __A )
lowerCAmelCase_ , lowerCAmelCase_ :List[str] = inputs["""input_ids"""].shape
for i in range(__A ):
if config.new_decoder_architecture:
lowerCAmelCase_ :Tuple = config.num_attention_heads
elif config.multi_query:
lowerCAmelCase_ :List[Any] = 1
self.assertEqual(len(past_kv[0] ) , 2 ) # K V for the decoder = 2
self.assertEqual(
past_kv[i][0].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim) )
self.assertEqual(
past_kv[i][1].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim) )
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Tuple = AutoTokenizer.from_pretrained("""Rocketknight1/falcon-rw-1b""" )
lowerCAmelCase_ :int = FalconForCausalLM.from_pretrained("""Rocketknight1/falcon-rw-1b""" )
model.eval()
model.to(__A )
lowerCAmelCase_ :List[Any] = tokenizer("""My favorite food is""" , return_tensors="""pt""" ).to(__A )
lowerCAmelCase_ :List[str] = (
"""My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."""
)
lowerCAmelCase_ :int = model.generate(**__A , do_sample=__A , max_new_tokens=19 )
lowerCAmelCase_ :List[str] = tokenizer.batch_decode(__A )[0]
self.assertEqual(__A , __A )
@slow
def __lowerCAmelCase ( self ) -> Optional[Any]:
# The big models are way too big for the CI, so we use tiny random models that resemble their
# architectures but with much smaller and fewer layers
for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
lowerCAmelCase_ :Union[str, Any] = AutoTokenizer.from_pretrained(__A )
lowerCAmelCase_ :str = FalconForCausalLM.from_pretrained(__A )
model.eval()
model.to(__A )
lowerCAmelCase_ :List[str] = tokenizer("""My favorite food is""" , return_tensors="""pt""" ).to(__A )
# We just test that these run without errors - the models are randomly initialized
# and so the actual text outputs will be garbage
model.generate(**__A , do_sample=__A , max_new_tokens=4 )
model.generate(**__A , do_sample=__A , max_new_tokens=4 )
model.generate(**__A , num_beams=2 , max_new_tokens=4 )
@slow
def __lowerCAmelCase ( self ) -> Union[str, Any]:
# The big models are way too big for the CI, so we use tiny random models that resemble their
# architectures but with much smaller and fewer layers
with torch.no_grad():
for repo in [
"Rocketknight1/falcon-rw-1b",
"Rocketknight1/tiny-random-falcon-7b",
"Rocketknight1/tiny-random-falcon-40b",
]:
lowerCAmelCase_ :Optional[int] = AutoTokenizer.from_pretrained(__A )
lowerCAmelCase_ :Dict = FalconForCausalLM.from_pretrained(__A )
model.eval()
model.to(device=__A )
lowerCAmelCase_ :Tuple = tokenizer("""My favorite food is""" , return_tensors="""pt""" ).to(__A )
# Test results are the same with and without cache
lowerCAmelCase_ :List[Any] = model.generate(**__A , do_sample=__A , max_new_tokens=20 , use_cache=__A )
lowerCAmelCase_ :Dict = model.generate(**__A , do_sample=__A , max_new_tokens=20 , use_cache=__A )
self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0 )
| 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : list , lowercase__ : list , lowercase__ : int , lowercase__ : int , lowercase__ : int ) -> int:
'''simple docstring'''
if index == number_of_items:
return 0
lowerCAmelCase_ :Any = 0
lowerCAmelCase_ :str = 0
lowerCAmelCase_ :Dict = knapsack(lowercase__ , lowercase__ , lowercase__ , lowercase__ , index + 1 )
if weights[index] <= max_weight:
lowerCAmelCase_ :str = values[index] + knapsack(
lowercase__ , lowercase__ , lowercase__ , max_weight - weights[index] , index + 1 )
return max(lowercase__ , lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 | 1 |
from __future__ import annotations
import math
def UpperCAmelCase_ ( __snake_case ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
UpperCAmelCase__ = [num for num in range(3, 10_0001, 2) if not is_prime(num)]
def UpperCAmelCase_ ( __snake_case ) -> list[int]:
"""simple docstring"""
if not isinstance(__snake_case , __snake_case ):
raise ValueError('''n must be an integer''' )
if n <= 0:
raise ValueError('''n must be >= 0''' )
_lowercase =[]
for num in range(len(__snake_case ) ):
_lowercase =0
while 2 * i * i <= odd_composites[num]:
_lowercase =odd_composites[num] - 2 * i * i
if is_prime(__snake_case ):
break
i += 1
else:
list_nums.append(odd_composites[num] )
if len(__snake_case ) == n:
return list_nums
return []
def UpperCAmelCase_ ( ) -> int:
"""simple docstring"""
return compute_nums(1 )[0]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 5 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class a_ ( a__ , a__ , a__ , unittest.TestCase ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = StableUnCLIPImgaImgPipeline
__SCREAMING_SNAKE_CASE : List[str] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
__SCREAMING_SNAKE_CASE : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
__SCREAMING_SNAKE_CASE : Any = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
__SCREAMING_SNAKE_CASE : Tuple = frozenset([] )
def __lowerCAmelCase ( self ) ->Union[str, Any]:
SCREAMING_SNAKE_CASE : Optional[Any] = 32
SCREAMING_SNAKE_CASE : Tuple = embedder_hidden_size
# image encoding components
SCREAMING_SNAKE_CASE : int = CLIPImageProcessor(crop_size=32 , size=32 )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[int] = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=_lowerCamelCase , projection_dim=_lowerCamelCase , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ) )
# regular denoising components
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : int = StableUnCLIPImageNormalizer(embedding_dim=_lowerCamelCase )
SCREAMING_SNAKE_CASE : Tuple = DDPMScheduler(beta_schedule='''squaredcos_cap_v2''' )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[str] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_lowerCamelCase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type='''projection''' , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=_lowerCamelCase , layers_per_block=1 , upcast_attention=_lowerCamelCase , use_linear_projection=_lowerCamelCase , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[int] = DDIMScheduler(
beta_schedule='''scaled_linear''' , beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , prediction_type='''v_prediction''' , set_alpha_to_one=_lowerCamelCase , steps_offset=1 , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Dict = AutoencoderKL()
SCREAMING_SNAKE_CASE : Optional[Any] = {
# image encoding components
'''feature_extractor''': feature_extractor,
'''image_encoder''': image_encoder.eval(),
# image noising components
'''image_normalizer''': image_normalizer.eval(),
'''image_noising_scheduler''': image_noising_scheduler,
# regular denoising components
'''tokenizer''': tokenizer,
'''text_encoder''': text_encoder.eval(),
'''unet''': unet.eval(),
'''scheduler''': scheduler,
'''vae''': vae.eval(),
}
return components
def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase=0 , _lowerCamelCase=True ) ->Optional[int]:
if str(_lowerCamelCase ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE : Optional[int] = torch.manual_seed(_lowerCamelCase )
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
SCREAMING_SNAKE_CASE : List[str] = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowerCamelCase ) ).to(_lowerCamelCase )
if pil_image:
SCREAMING_SNAKE_CASE : Any = input_image * 0.5 + 0.5
SCREAMING_SNAKE_CASE : int = input_image.clamp(0 , 1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
SCREAMING_SNAKE_CASE : List[str] = DiffusionPipeline.numpy_to_pil(_lowerCamelCase )[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def __lowerCAmelCase ( self ) ->Tuple:
SCREAMING_SNAKE_CASE : Any = '''cpu''' # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_dummy_components()
SCREAMING_SNAKE_CASE : Tuple = StableUnCLIPImgaImgPipeline(**_lowerCamelCase )
SCREAMING_SNAKE_CASE : str = sd_pipe.to(_lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCamelCase )
SCREAMING_SNAKE_CASE : List[Any] = self.get_dummy_inputs(_lowerCamelCase )
inputs.update({'''image_embeds''': None} )
SCREAMING_SNAKE_CASE : Optional[int] = sd_pipe(**_lowerCamelCase ).images
SCREAMING_SNAKE_CASE : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE : Tuple = np.array([0.3_8_7_2, 0.7_2_2_4, 0.5_6_0_1, 0.4_7_4_1, 0.6_8_7_2, 0.5_8_1_4, 0.4_6_3_6, 0.3_8_6_7, 0.5_0_7_8] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowerCAmelCase ( self ) ->Tuple:
SCREAMING_SNAKE_CASE : str = torch_device in ['''cpu''', '''mps''']
self._test_attention_slicing_forward_pass(test_max_difference=_lowerCamelCase )
def __lowerCAmelCase ( self ) ->List[Any]:
SCREAMING_SNAKE_CASE : Tuple = torch_device in ['''cpu''', '''mps''']
self._test_inference_batch_single_identical(test_max_difference=_lowerCamelCase )
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def __lowerCAmelCase ( self ) ->Optional[int]:
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_lowerCamelCase )
@slow
@require_torch_gpu
class a_ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ) ->int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self ) ->Tuple:
SCREAMING_SNAKE_CASE : List[Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''' )
SCREAMING_SNAKE_CASE : Optional[Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy''' )
SCREAMING_SNAKE_CASE : List[Any] = StableUnCLIPImgaImgPipeline.from_pretrained(
'''fusing/stable-unclip-2-1-l-img2img''' , torch_dtype=torch.floataa )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.Generator(device='''cpu''' ).manual_seed(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(_lowerCamelCase , '''anime turle''' , generator=_lowerCamelCase , output_type='''np''' )
SCREAMING_SNAKE_CASE : List[str] = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_lowerCamelCase , _lowerCamelCase )
def __lowerCAmelCase ( self ) ->Optional[Any]:
SCREAMING_SNAKE_CASE : List[str] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''' )
SCREAMING_SNAKE_CASE : Optional[int] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy''' )
SCREAMING_SNAKE_CASE : List[str] = StableUnCLIPImgaImgPipeline.from_pretrained(
'''fusing/stable-unclip-2-1-h-img2img''' , torch_dtype=torch.floataa )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
SCREAMING_SNAKE_CASE : Any = torch.Generator(device='''cpu''' ).manual_seed(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(_lowerCamelCase , '''anime turle''' , generator=_lowerCamelCase , output_type='''np''' )
SCREAMING_SNAKE_CASE : List[str] = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_lowerCamelCase , _lowerCamelCase )
def __lowerCAmelCase ( self ) ->Any:
SCREAMING_SNAKE_CASE : Union[str, Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''' )
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
SCREAMING_SNAKE_CASE : str = StableUnCLIPImgaImgPipeline.from_pretrained(
'''fusing/stable-unclip-2-1-h-img2img''' , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE : Dict = pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
SCREAMING_SNAKE_CASE : Dict = pipe(
_lowerCamelCase , '''anime turtle''' , num_inference_steps=2 , output_type='''np''' , )
SCREAMING_SNAKE_CASE : Any = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 313 | 0 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
lowerCamelCase__ : List[Any] = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016}\n}\n"
lowerCamelCase__ : Tuple = "\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n"
lowerCamelCase__ : Union[str, Any] = "\nComputes SQuAD scores (F1 and EM).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - 'id': id of the question-answer pair as given in the references (see below)\n - 'prediction_text': the text of the answer\n references: List of question-answers dictionaries with the following key-values:\n - 'id': id of the question-answer pair (see above),\n - 'answers': a Dict in the SQuAD dataset format\n {\n 'text': list of possible texts for the answer, as a list of strings\n 'answer_start': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n 'exact_match': Exact match (the normalized answer exactly match the gold answer)\n 'f1': The F-score of predicted tokens versus the gold answer\nExamples:\n\n >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]\n >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n >>> squad_metric = datasets.load_metric(\"squad\")\n >>> results = squad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 100.0, 'f1': 100.0}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class A__ ( datasets.Metric):
def __lowerCamelCase ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': {'id': datasets.Value('string' ), 'prediction_text': datasets.Value('string' )},
'references': {
'id': datasets.Value('string' ),
'answers': datasets.features.Sequence(
{
'text': datasets.Value('string' ),
'answer_start': datasets.Value('int32' ),
} ),
},
} ) , codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/'] , reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/'] , )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Optional[int] = {prediction['id']: prediction['prediction_text'] for prediction in predictions}
__lowerCAmelCase : str = [
{
'paragraphs': [
{
'qas': [
{
'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
'id': ref['id'],
}
for ref in references
]
}
]
}
]
__lowerCAmelCase : Tuple = evaluate(dataset=a__ , predictions=a__ )
return score
| 354 |
"""simple docstring"""
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 182 | 0 |
"""simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class a__ ( a_, unittest.TestCase ):
__lowerCAmelCase = CpmAntTokenizer
__lowerCAmelCase = False
def __magic_name__ ( self ):
super().setUp()
lowercase : List[str] = [
"<d>",
"</d>",
"<s>",
"</s>",
"</_>",
"<unk>",
"<pad>",
"</n>",
"我",
"是",
"C",
"P",
"M",
"A",
"n",
"t",
]
lowercase : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
@tooslow
def __magic_name__ ( self ):
lowercase : Tuple = CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b" )
lowercase : str = "今天天气真好！"
lowercase : Optional[int] = ["今天", "天气", "真", "好", "！"]
lowercase : int = tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
lowercase : Any = "今天天气真好！"
lowercase : int = [tokenizer.bos_token] + tokens
lowercase : List[str] = [6, 9_802, 14_962, 2_082, 831, 244]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , _a )
lowercase : str = tokenizer.decode(_a )
self.assertEqual(_a , _a )
| 202 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..utils import cached_file
# docstyle-ignore
_A : Optional[int] = """
Human: <<task>>
Assistant: """
_A : List[Any] = """huggingface-tools/default-prompts"""
_A : Optional[int] = {"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}
def __magic_name__ ( __snake_case : int , __snake_case : List[Any] , __snake_case : Dict="run" ) -> Union[str, Any]:
if prompt_or_repo_id is None:
lowercase : List[Any] = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("\\s" , __snake_case ) is not None:
return prompt_or_repo_id
lowercase : Optional[int] = cached_file(
__snake_case , PROMPT_FILES[mode] , repo_type="dataset" , user_agent={"agent": agent_name} )
with open(__snake_case , "r" , encoding="utf-8" ) as f:
return f.read()
| 202 | 1 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self) -> int:
__UpperCamelCase :str = 0
def UpperCamelCase__ ( self) -> Optional[Any]:
__UpperCamelCase :Dict = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''')
self.assertIsInstance(__lowercase , __lowercase)
def UpperCamelCase__ ( self) -> Optional[int]:
with tempfile.TemporaryDirectory() as tmpdirname:
__UpperCamelCase :int = Path(__lowercase) / '''preprocessor_config.json'''
__UpperCamelCase :Dict = Path(__lowercase) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__lowercase , '''w''') , )
json.dump({'''model_type''': '''clip'''} , open(__lowercase , '''w'''))
__UpperCamelCase :Union[str, Any] = AutoImageProcessor.from_pretrained(__lowercase)
self.assertIsInstance(__lowercase , __lowercase)
def UpperCamelCase__ ( self) -> Union[str, Any]:
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__UpperCamelCase :str = Path(__lowercase) / '''preprocessor_config.json'''
__UpperCamelCase :Union[str, Any] = Path(__lowercase) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(__lowercase , '''w''') , )
json.dump({'''model_type''': '''clip'''} , open(__lowercase , '''w'''))
__UpperCamelCase :Dict = AutoImageProcessor.from_pretrained(__lowercase)
self.assertIsInstance(__lowercase , __lowercase)
def UpperCamelCase__ ( self) -> Optional[int]:
with tempfile.TemporaryDirectory() as tmpdirname:
__UpperCamelCase :int = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__UpperCamelCase :Tuple = Path(__lowercase) / '''preprocessor_config.json'''
__UpperCamelCase :Optional[Any] = Path(__lowercase) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__lowercase , '''w''') , )
json.dump({'''model_type''': '''clip'''} , open(__lowercase , '''w'''))
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__UpperCamelCase :Optional[Any] = AutoImageProcessor.from_pretrained(__lowercase).to_dict()
config_dict.pop('''image_processor_type''')
__UpperCamelCase :List[str] = CLIPImageProcessor(**__lowercase)
# save in new folder
model_config.save_pretrained(__lowercase)
config.save_pretrained(__lowercase)
__UpperCamelCase :Dict = AutoImageProcessor.from_pretrained(__lowercase)
# make sure private variable is not incorrectly saved
__UpperCamelCase :Union[str, Any] = json.loads(config.to_json_string())
self.assertTrue('''_processor_class''' not in dict_as_saved)
self.assertIsInstance(__lowercase , __lowercase)
def UpperCamelCase__ ( self) -> List[str]:
with tempfile.TemporaryDirectory() as tmpdirname:
__UpperCamelCase :Tuple = Path(__lowercase) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__lowercase , '''w''') , )
__UpperCamelCase :List[str] = AutoImageProcessor.from_pretrained(__lowercase)
self.assertIsInstance(__lowercase , __lowercase)
def UpperCamelCase__ ( self) -> Optional[int]:
with self.assertRaisesRegex(
__lowercase , '''clip-base is not a local folder and is not a valid model identifier'''):
__UpperCamelCase :Optional[Any] = AutoImageProcessor.from_pretrained('''clip-base''')
def UpperCamelCase__ ( self) -> List[Any]:
with self.assertRaisesRegex(
__lowercase , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''):
__UpperCamelCase :str = AutoImageProcessor.from_pretrained(__lowercase , revision='''aaaaaa''')
def UpperCamelCase__ ( self) -> List[str]:
with self.assertRaisesRegex(
__lowercase , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__UpperCamelCase :Optional[Any] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''')
def UpperCamelCase__ ( self) -> str:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(__lowercase):
__UpperCamelCase :Dict = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''')
# If remote code is disabled, we can't load this config.
with self.assertRaises(__lowercase):
__UpperCamelCase :List[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__lowercase)
__UpperCamelCase :Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__lowercase)
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''')
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__lowercase)
__UpperCamelCase :List[Any] = AutoImageProcessor.from_pretrained(__lowercase , trust_remote_code=__lowercase)
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''')
def UpperCamelCase__ ( self) -> Optional[Any]:
try:
AutoConfig.register('''custom''' , __lowercase)
AutoImageProcessor.register(__lowercase , __lowercase)
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__lowercase):
AutoImageProcessor.register(__lowercase , __lowercase)
with tempfile.TemporaryDirectory() as tmpdirname:
__UpperCamelCase :int = Path(__lowercase) / '''preprocessor_config.json'''
__UpperCamelCase :List[str] = Path(__lowercase) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(__lowercase , '''w''') , )
json.dump({'''model_type''': '''clip'''} , open(__lowercase , '''w'''))
__UpperCamelCase :int = CustomImageProcessor.from_pretrained(__lowercase)
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__lowercase)
__UpperCamelCase :int = AutoImageProcessor.from_pretrained(__lowercase)
self.assertIsInstance(__lowercase , __lowercase)
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def UpperCamelCase__ ( self) -> List[Any]:
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : List[str] = True
try:
AutoConfig.register('''custom''' , __lowercase)
AutoImageProcessor.register(__lowercase , __lowercase)
# If remote code is not set, the default is to use local
__UpperCamelCase :str = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''')
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''')
self.assertTrue(image_processor.is_local)
# If remote code is disabled, we load the local one.
__UpperCamelCase :Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__lowercase)
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''')
self.assertTrue(image_processor.is_local)
# If remote is enabled, we load from the Hub
__UpperCamelCase :List[str] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__lowercase)
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''')
self.assertTrue(not hasattr(__lowercase , '''is_local'''))
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 105 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
__lowercase = logging.get_logger(__name__)
logging.set_verbosity_info()
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if "xprophetnet" in prophetnet_checkpoint_path:
__UpperCamelCase :Dict = XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
__UpperCamelCase , __UpperCamelCase :int = XLMProphetNetForConditionalGeneration.from_pretrained(
SCREAMING_SNAKE_CASE , output_loading_info=SCREAMING_SNAKE_CASE )
else:
__UpperCamelCase :Union[str, Any] = ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
__UpperCamelCase , __UpperCamelCase :Union[str, Any] = ProphetNetForConditionalGeneration.from_pretrained(
SCREAMING_SNAKE_CASE , output_loading_info=SCREAMING_SNAKE_CASE )
__UpperCamelCase :Dict = ['''key_proj''', '''value_proj''', '''query_proj''']
__UpperCamelCase :Optional[Any] = {
'''self_attn''': '''ngram_self_attn''',
'''cross_attn''': '''encoder_attn''',
'''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
'''feed_forward_layer_norm''': '''final_layer_norm''',
'''feed_forward''': '''''',
'''intermediate''': '''fc1''',
'''output''': '''fc2''',
'''key_proj''': '''k_proj''',
'''query_proj''': '''q_proj''',
'''value_proj''': '''v_proj''',
'''word_embeddings''': '''embed_tokens''',
'''embeddings_layer_norm''': '''emb_layer_norm''',
'''relative_pos_embeddings''': '''relative_linear''',
'''ngram_embeddings''': '''ngram_input_embed''',
'''position_embeddings''': '''embed_positions''',
}
for key in loading_info["missing_keys"]:
__UpperCamelCase :Tuple = key.split('''.''' )
if attributes[0] == "lm_head":
__UpperCamelCase :Union[str, Any] = prophet
__UpperCamelCase :Any = prophet_old
else:
__UpperCamelCase :Any = prophet.prophetnet
__UpperCamelCase :int = prophet_old.model
__UpperCamelCase :Optional[Any] = False
for attribute in attributes:
if attribute in mapping:
__UpperCamelCase :str = mapping[attribute]
if not hasattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and len(SCREAMING_SNAKE_CASE ) > 0:
__UpperCamelCase :Optional[int] = attribute
elif hasattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__UpperCamelCase :List[Any] = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
__UpperCamelCase :Tuple = old_model.weight
logger.info(f"""{attribute} is initialized.""" )
__UpperCamelCase :Union[str, Any] = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
__UpperCamelCase :Union[str, Any] = old_model.bias
logger.info(f"""{attribute} is initialized""" )
__UpperCamelCase :List[Any] = True
break
elif attribute in special_keys and hasattr(SCREAMING_SNAKE_CASE , '''in_proj_weight''' ):
__UpperCamelCase :str = old_model.in_proj_weight.shape[0] // 3
__UpperCamelCase :Optional[Any] = getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
__UpperCamelCase :Optional[int] = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
__UpperCamelCase :Tuple = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
__UpperCamelCase :List[Any] = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
__UpperCamelCase :Dict = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
__UpperCamelCase :Tuple = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
__UpperCamelCase :Dict = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
__UpperCamelCase :Optional[int] = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
__UpperCamelCase :Optional[int] = nn.Parameter(old_model.embed_positions.weight[:512, :] )
__UpperCamelCase :List[Any] = True
break
if attribute.isdigit():
__UpperCamelCase :List[Any] = model[int(SCREAMING_SNAKE_CASE )]
__UpperCamelCase :Optional[int] = old_model[int(SCREAMING_SNAKE_CASE )]
else:
__UpperCamelCase :Optional[Any] = getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if old_attribute == "":
__UpperCamelCase :Any = old_model
else:
if not hasattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise ValueError(f"""{old_model} does not have {old_attribute}""" )
__UpperCamelCase :Any = getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if not is_key_init:
raise ValueError(f"""{key} was not correctly initialized!""" )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
prophet.save_pretrained(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__lowercase = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 105 | 1 |
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : Tuple = {
"nvidia/segformer-b0-finetuned-ade-512-512": (
"https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase : List[str] = "segformer"
def __init__( self , A_=3 , A_=4 , A_=[2, 2, 2, 2] , A_=[8, 4, 2, 1] , A_=[32, 64, 160, 256] , A_=[7, 3, 3, 3] , A_=[4, 2, 2, 2] , A_=[1, 2, 5, 8] , A_=[4, 4, 4, 4] , A_="gelu" , A_=0.0 , A_=0.0 , A_=0.1 , A_=0.02 , A_=0.1 , A_=1e-6 , A_=256 , A_=255 , **A_ , ) -> Any:
"""simple docstring"""
super().__init__(**A_ )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
' removed, as the behaviour will default to that of reshape_last_stage = True.' , A_ , )
UpperCamelCase = num_channels
UpperCamelCase = num_encoder_blocks
UpperCamelCase = depths
UpperCamelCase = sr_ratios
UpperCamelCase = hidden_sizes
UpperCamelCase = patch_sizes
UpperCamelCase = strides
UpperCamelCase = mlp_ratios
UpperCamelCase = num_attention_heads
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = classifier_dropout_prob
UpperCamelCase = initializer_range
UpperCamelCase = drop_path_rate
UpperCamelCase = layer_norm_eps
UpperCamelCase = decoder_hidden_size
UpperCamelCase = kwargs.get('reshape_last_stage' , A_ )
UpperCamelCase = semantic_loss_ignore_index
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase : List[str] = version.parse("1.11" )
@property
def __UpperCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def __UpperCamelCase ( self ) -> float:
"""simple docstring"""
return 1e-4
@property
def __UpperCamelCase ( self ) -> int:
"""simple docstring"""
return 12
| 222 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class lowercase ( _SCREAMING_SNAKE_CASE ):
def __init__( self , A_ , A_ , A_ , A_ , ) -> Optional[int]:
"""simple docstring"""
super().__init__()
UpperCamelCase = value_function
UpperCamelCase = unet
UpperCamelCase = scheduler
UpperCamelCase = env
UpperCamelCase = env.get_dataset()
UpperCamelCase = {}
for key in self.data.keys():
try:
UpperCamelCase = self.data[key].mean()
except: # noqa: E722
pass
UpperCamelCase = {}
for key in self.data.keys():
try:
UpperCamelCase = self.data[key].std()
except: # noqa: E722
pass
UpperCamelCase = env.observation_space.shape[0]
UpperCamelCase = env.action_space.shape[0]
def __UpperCamelCase ( self , A_ , A_ ) -> Optional[Any]:
"""simple docstring"""
return (x_in - self.means[key]) / self.stds[key]
def __UpperCamelCase ( self , A_ , A_ ) -> Union[str, Any]:
"""simple docstring"""
return x_in * self.stds[key] + self.means[key]
def __UpperCamelCase ( self , A_ ) -> Any:
"""simple docstring"""
if type(A_ ) is dict:
return {k: self.to_torch(A_ ) for k, v in x_in.items()}
elif torch.is_tensor(A_ ):
return x_in.to(self.unet.device )
return torch.tensor(A_ , device=self.unet.device )
def __UpperCamelCase ( self , A_ , A_ , A_ ) -> Optional[int]:
"""simple docstring"""
for key, val in cond.items():
UpperCamelCase = val.clone()
return x_in
def __UpperCamelCase ( self , A_ , A_ , A_ , A_ ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = x.shape[0]
UpperCamelCase = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
UpperCamelCase = torch.full((batch_size,) , A_ , device=self.unet.device , dtype=torch.long )
for _ in range(A_ ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
UpperCamelCase = self.value_function(x.permute(0 , 2 , 1 ) , A_ ).sample
UpperCamelCase = torch.autograd.grad([y.sum()] , [x] )[0]
UpperCamelCase = self.scheduler._get_variance(A_ )
UpperCamelCase = torch.exp(0.5 * posterior_variance )
UpperCamelCase = model_std * grad
UpperCamelCase = 0
UpperCamelCase = x.detach()
UpperCamelCase = x + scale * grad
UpperCamelCase = self.reset_xa(A_ , A_ , self.action_dim )
UpperCamelCase = self.unet(x.permute(0 , 2 , 1 ) , A_ ).sample.permute(0 , 2 , 1 )
# TODO: verify deprecation of this kwarg
UpperCamelCase = self.scheduler.step(A_ , A_ , A_ , predict_epsilon=A_ )['prev_sample']
# apply conditions to the trajectory (set the initial state)
UpperCamelCase = self.reset_xa(A_ , A_ , self.action_dim )
UpperCamelCase = self.to_torch(A_ )
return x, y
def __call__( self , A_ , A_=64 , A_=32 , A_=2 , A_=0.1 ) -> List[str]:
"""simple docstring"""
# normalize the observations and create batch dimension
UpperCamelCase = self.normalize(A_ , 'observations' )
UpperCamelCase = obs[None].repeat(A_ , axis=0 )
UpperCamelCase = {0: self.to_torch(A_ )}
UpperCamelCase = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
UpperCamelCase = randn_tensor(A_ , device=self.unet.device )
UpperCamelCase = self.reset_xa(A_ , A_ , self.action_dim )
UpperCamelCase = self.to_torch(A_ )
# run the diffusion process
UpperCamelCase , UpperCamelCase = self.run_diffusion(A_ , A_ , A_ , A_ )
# sort output trajectories by value
UpperCamelCase = y.argsort(0 , descending=A_ ).squeeze()
UpperCamelCase = x[sorted_idx]
UpperCamelCase = sorted_values[:, :, : self.action_dim]
UpperCamelCase = actions.detach().cpu().numpy()
UpperCamelCase = self.de_normalize(A_ , key='actions' )
# select the action with the highest value
if y is not None:
UpperCamelCase = 0
else:
# if we didn't run value guiding, select a random action
UpperCamelCase = np.random.randint(0 , A_ )
UpperCamelCase = denorm_actions[selected_index, 0]
return denorm_actions
| 222 | 1 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
_SCREAMING_SNAKE_CASE = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('', '|', '|'),
datarow=DataRow('', '|', '|'),
padding=1,
with_header_hide=None,
)
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = {'type': 'section', 'text': {'type': 'plain_text', 'text': 'No failed tests! 🤗', 'emoji': True}}
_SCREAMING_SNAKE_CASE = [
{
'type': 'header',
'text': {
'type': 'plain_text',
'text': F'''🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results''',
'emoji': True,
},
}
]
_SCREAMING_SNAKE_CASE = 0
for log in Path().glob('*.log'):
_SCREAMING_SNAKE_CASE = 0
with open(log, 'r') as f:
for line in f:
_SCREAMING_SNAKE_CASE = json.loads(line)
if line.get('nodeid', '') != "":
_SCREAMING_SNAKE_CASE = line['nodeid']
if line.get('duration', None) is not None:
_SCREAMING_SNAKE_CASE = F'''{line["duration"]:.4f}'''
if line.get('outcome', '') == "failed":
section_num_failed += 1
failed.append([test, duration, log.name.split('_')[0]])
total_num_failed += 1
group_info.append([str(log), section_num_failed, failed])
_SCREAMING_SNAKE_CASE = []
log.unlink()
_SCREAMING_SNAKE_CASE = ''
_SCREAMING_SNAKE_CASE = []
if total_num_failed > 0:
for name, num_failed, failed_tests in group_info:
if num_failed > 0:
if num_failed == 1:
message += F"*{name[1:]}: {num_failed} failed test*\n"
else:
message += F"*{name[1:]}: {num_failed} failed tests*\n"
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = {}
for test in failed_tests:
_SCREAMING_SNAKE_CASE = test[0].split('::')
_SCREAMING_SNAKE_CASE = data[0].split('/')[-1]
if data[0] not in filesafailed:
_SCREAMING_SNAKE_CASE = [data[1:]]
else:
filesafailed[data[0]] += [data[1:]]
failed_table.append(data)
_SCREAMING_SNAKE_CASE = [test[0] for test in failed_table]
_SCREAMING_SNAKE_CASE = list(set(files))
# Count number of instances in failed_tests
_SCREAMING_SNAKE_CASE = []
for file in individual_files:
table.append([file, len(filesafailed[file])])
_SCREAMING_SNAKE_CASE = tabulate(
table,
headers=['Test Location', 'Num Failed'],
tablefmt=hf_table_format,
stralign='right',
)
message += F"\n```\n{failed_table}\n```"
all_filesafailed.append(filesafailed)
if len(message) > 3_000:
_SCREAMING_SNAKE_CASE = 'Too many failed tests, please see the full report in the Action results.'
_SCREAMING_SNAKE_CASE = len(err) + 10
_SCREAMING_SNAKE_CASE = message[: 3_000 - offset] + F'''\n...\n```\n{err}'''
print(F'''### {message}''')
else:
_SCREAMING_SNAKE_CASE = 'No failed tests! 🤗'
print(F'''## {message}''')
payload.append(no_error_payload)
if os.environ.get('TEST_TYPE', '') != "":
from slack_sdk import WebClient
_SCREAMING_SNAKE_CASE = WebClient(token=os.environ['SLACK_API_TOKEN'])
if message != "No failed tests! 🤗":
_SCREAMING_SNAKE_CASE = {
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': message,
},
}
payload.append(md_report)
_SCREAMING_SNAKE_CASE = {
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': '*For more details:*',
},
'accessory': {
'type': 'button',
'text': {
'type': 'plain_text',
'text': 'Check Action results',
'emoji': True,
},
'url': F'''https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}''',
},
}
payload.append(action_button)
_SCREAMING_SNAKE_CASE = {
'type': 'context',
'elements': [
{
'type': 'plain_text',
'text': F'''Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}''',
}
],
}
payload.append(date_report)
_SCREAMING_SNAKE_CASE = client.chat_postMessage(channel='#accelerate-ci-daily', text=message, blocks=payload)
_SCREAMING_SNAKE_CASE = response.data['ts']
for failed_file in all_filesafailed:
for test_location, test_failures in failed_file.items():
# Keep only the first instance of the test name
_SCREAMING_SNAKE_CASE = ''
for i, row in enumerate(test_failures):
if row[0] != test_class:
_SCREAMING_SNAKE_CASE = row[0]
else:
_SCREAMING_SNAKE_CASE = ''
_SCREAMING_SNAKE_CASE = {
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': F'''Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```''',
},
}
client.chat_postMessage(
channel='#accelerate-ci-daily',
thread_ts=ts,
blocks=[payload],
)
| 366 |
def snake_case ( snake_case__ :int , snake_case__ :int) -> str:
return "\n".join(
F'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1))
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 81 | 0 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
lowerCAmelCase_ = logging.getLogger(__name__)
def __SCREAMING_SNAKE_CASE ():
snake_case_ = argparse.ArgumentParser(
description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.''' )
parser.add_argument(
'''--dataset_name''' , type=SCREAMING_SNAKE_CASE__ , default='''wikitext''' , help='''Name of the training. Explore datasets at: hf.co/datasets.''' , )
parser.add_argument(
'''--dataset_config''' , type=SCREAMING_SNAKE_CASE__ , default='''wikitext-103-raw-v1''' , help='''Configuration name of the dataset.''' )
parser.add_argument(
'''--tokenizer_name_or_path''' , type=SCREAMING_SNAKE_CASE__ , default='''sayakpaul/unigram-tokenizer-wikitext''' , help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.''' , )
parser.add_argument(
'''--shard_size''' , type=SCREAMING_SNAKE_CASE__ , default=1000 , help='''Number of entries to go in a single shard.''' , )
parser.add_argument('''--split''' , type=SCREAMING_SNAKE_CASE__ , default='''train''' , choices=['''train''', '''test''', '''validation'''] )
parser.add_argument(
'''--limit''' , default=SCREAMING_SNAKE_CASE__ , type=SCREAMING_SNAKE_CASE__ , help='''Limit the number of shards (used for debugging).''' , )
parser.add_argument(
'''--max_length''' , type=SCREAMING_SNAKE_CASE__ , default=512 , help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
''' sequence length that is a multiple of 8.''' , )
parser.add_argument(
'''--output_dir''' , default='''tf-tpu''' , type=SCREAMING_SNAKE_CASE__ , help='''Output directory where the TFRecord shards will be saved. If the'''
''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
''' shards will be directly saved to a Google Cloud Storage bucket.''' , )
snake_case_ = parser.parse_args()
return args
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
def fn(SCREAMING_SNAKE_CASE__ ):
return tokenizer(examples['''text'''] )
return fn
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = []
for i in range(len(tokenized_data['''input_ids'''] ) ):
snake_case_ = {
'''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i] ) ),
'''attention_mask''': tf.train.Feature(
intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i] ) ),
}
snake_case_ = tf.train.Features(feature=SCREAMING_SNAKE_CASE__ )
snake_case_ = tf.train.Example(features=SCREAMING_SNAKE_CASE__ )
snake_case_ = example.SerializeToString()
records.append(SCREAMING_SNAKE_CASE__ )
return records
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = datasets.load_dataset(args.dataset_name , args.dataset_config , split=args.split )
if args.limit is not None:
snake_case_ = min(len(SCREAMING_SNAKE_CASE__ ) , args.limit )
snake_case_ = dataset.select(range(SCREAMING_SNAKE_CASE__ ) )
print(F'''Limiting the dataset to {args.limit} entries.''' )
snake_case_ = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path )
# Handle output directory creation.
# For serializing into a Google Cloud Storage Bucket, one needs to first
# create a bucket.
if "gs" not in args.output_dir:
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
snake_case_ = os.path.join(args.output_dir , args.split )
if not os.path.exists(SCREAMING_SNAKE_CASE__ ):
os.makedirs(SCREAMING_SNAKE_CASE__ )
else:
snake_case_ = os.path.join(args.output_dir , args.split )
# Tokenize the whole dataset at once.
snake_case_ = tokenize_function(SCREAMING_SNAKE_CASE__ )
snake_case_ = dataset.map(SCREAMING_SNAKE_CASE__ , batched=SCREAMING_SNAKE_CASE__ , num_proc=4 , remove_columns=['''text'''] )
# We need to concatenate all our texts together, and then split the result
# into chunks of a fixed size, which we will call block_size. To do this, we
# will use the map method again, with the option batched=True. When we use batched=True,
# the function we pass to map() will be passed multiple inputs at once, allowing us
# to group them into more or fewer examples than we had in the input.
# This allows us to create our new fixed-length samples. The advantage of this
# method is that we don't lose a whole lot of content from the dataset compared to the
# case where we simply tokenize with a pre-defined max_length.
def group_texts(SCREAMING_SNAKE_CASE__ ):
# Concatenate all texts.
snake_case_ = {k: sum(examples[k] , [] ) for k in examples.keys()}
snake_case_ = len(concatenated_examples[list(examples.keys() )[0]] )
# We drop the small remainder, though you could add padding instead if the model supports it
# In this, as in all things, we advise you to follow your heart 🫀
snake_case_ = (total_length // args.max_length) * args.max_length
# Split by chunks of max_len.
snake_case_ = {
k: [t[i : i + args.max_length] for i in range(0 , SCREAMING_SNAKE_CASE__ , args.max_length )]
for k, t in concatenated_examples.items()
}
return result
snake_case_ = dataset_tokenized.map(SCREAMING_SNAKE_CASE__ , batched=SCREAMING_SNAKE_CASE__ , batch_size=1000 , num_proc=4 )
snake_case_ = 0
snake_case_ = 0
for shard in range(0 , len(SCREAMING_SNAKE_CASE__ ) , args.shard_size ):
snake_case_ = grouped_dataset[shard : shard + args.shard_size]
snake_case_ = len(dataset_snapshot['''input_ids'''] )
snake_case_ = os.path.join(SCREAMING_SNAKE_CASE__ , F'''dataset-{shard_count}-{records_containing}.tfrecord''' )
snake_case_ = get_serialized_examples(SCREAMING_SNAKE_CASE__ )
with tf.io.TFRecordWriter(SCREAMING_SNAKE_CASE__ ) as out_file:
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
snake_case_ = serialized_examples[i]
out_file.write(SCREAMING_SNAKE_CASE__ )
print('''Wrote file {} containing {} records'''.format(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
shard_count += 1
total_records += records_containing
with open(F'''split-{args.split}-records-count.txt''' , '''w''' ) as f:
print(F'''Total {args.split} records: {total_records}''' , file=SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
lowerCAmelCase_ = parse_args()
main(args)
| 8 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class snake_case_ ( __A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = "vit_msn"
def __init__( self : Dict , _UpperCamelCase : Optional[int]=7_6_8 , _UpperCamelCase : Optional[Any]=1_2 , _UpperCamelCase : Union[str, Any]=1_2 , _UpperCamelCase : str=3_0_7_2 , _UpperCamelCase : Tuple="gelu" , _UpperCamelCase : Dict=0.0 , _UpperCamelCase : Dict=0.0 , _UpperCamelCase : List[str]=0.02 , _UpperCamelCase : List[Any]=1e-06 , _UpperCamelCase : Any=2_2_4 , _UpperCamelCase : Optional[Any]=1_6 , _UpperCamelCase : Any=3 , _UpperCamelCase : str=True , **_UpperCamelCase : Any , ) ->int:
super().__init__(**_UpperCamelCase )
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = initializer_range
snake_case_ = layer_norm_eps
snake_case_ = image_size
snake_case_ = patch_size
snake_case_ = num_channels
snake_case_ = qkv_bias
| 8 | 1 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__lowerCamelCase : Optional[Any] = 16
__lowerCamelCase : Optional[Any] = 32
def SCREAMING_SNAKE_CASE ( snake_case_ : Accelerator , snake_case_ : int = 16 ):
snake_case__ : Optional[Any] = AutoTokenizer.from_pretrained("bert-base-cased" )
snake_case__ : List[Any] = load_dataset("glue" , "mrpc" )
def tokenize_function(snake_case_ : Union[str, Any] ):
# max_length=None => use the model max length (it's actually the default)
snake_case__ : Any = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=snake_case_ , max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
snake_case__ : Dict = datasets.map(
snake_case_ , batched=snake_case_ , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case__ : str = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(snake_case_ : Optional[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
snake_case__ : int = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
snake_case__ : Optional[Any] = 16
elif accelerator.mixed_precision != "no":
snake_case__ : Optional[int] = 8
else:
snake_case__ : Tuple = None
return tokenizer.pad(
snake_case_ , padding="longest" , max_length=snake_case_ , pad_to_multiple_of=snake_case_ , return_tensors="pt" , )
# Instantiate dataloaders.
snake_case__ : Any = DataLoader(
tokenized_datasets["train"] , shuffle=snake_case_ , collate_fn=snake_case_ , batch_size=snake_case_ )
snake_case__ : Tuple = DataLoader(
tokenized_datasets["validation"] , shuffle=snake_case_ , collate_fn=snake_case_ , batch_size=snake_case_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__lowerCamelCase : Optional[int] = mocked_dataloaders # noqa: F811
def SCREAMING_SNAKE_CASE ( snake_case_ : str , snake_case_ : str ):
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS" , snake_case_ ) == "1":
snake_case__ : Union[str, Any] = 2
# Initialize accelerator
snake_case__ : int = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case__ : Optional[int] = config["lr"]
snake_case__ : Tuple = int(config["num_epochs"] )
snake_case__ : Optional[int] = int(config["seed"] )
snake_case__ : Union[str, Any] = int(config["batch_size"] )
snake_case__ : List[Any] = evaluate.load("glue" , "mrpc" )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=snake_case_ )
def inner_training_loop(snake_case_ : List[str] ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(snake_case_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case__ : Tuple = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=snake_case_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
snake_case__ : Optional[int] = model.to(accelerator.device )
# Instantiate optimizer
snake_case__ : Tuple = AdamW(params=model.parameters() , lr=snake_case_ )
snake_case__, snake_case__ : Dict = get_dataloaders(snake_case_ , snake_case_ )
# Instantiate scheduler
snake_case__ : str = get_linear_schedule_with_warmup(
optimizer=snake_case_ , num_warmup_steps=100 , num_training_steps=(len(snake_case_ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
snake_case__, snake_case__, snake_case__, snake_case__, snake_case__ : str = accelerator.prepare(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Now we train the model
for epoch in range(snake_case_ ):
model.train()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
snake_case__ : Any = model(**snake_case_ )
snake_case__ : int = outputs.loss
accelerator.backward(snake_case_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case__ : Tuple = model(**snake_case_ )
snake_case__ : Any = outputs.logits.argmax(dim=-1 )
snake_case__, snake_case__ : Any = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=snake_case_ , references=snake_case_ , )
snake_case__ : Optional[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , snake_case_ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def SCREAMING_SNAKE_CASE ( ):
snake_case__ : str = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=snake_case_ , default=snake_case_ , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
snake_case__ : List[Any] = parser.parse_args()
snake_case__ : Any = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(snake_case_ , snake_case_ )
if __name__ == "__main__":
main()
| 286 |
def SCREAMING_SNAKE_CASE ( snake_case_ : int , snake_case_ : float , snake_case_ : float ):
return round(float(moles / volume ) * nfactor )
def SCREAMING_SNAKE_CASE ( snake_case_ : float , snake_case_ : float , snake_case_ : float ):
return round(float((moles * 0.08_21 * temperature) / (volume) ) )
def SCREAMING_SNAKE_CASE ( snake_case_ : float , snake_case_ : float , snake_case_ : float ):
return round(float((moles * 0.08_21 * temperature) / (pressure) ) )
def SCREAMING_SNAKE_CASE ( snake_case_ : float , snake_case_ : float , snake_case_ : float ):
return round(float((pressure * volume) / (0.08_21 * moles) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 286 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
_snake_case = [
'''openmmlab/upernet-convnext-tiny''',
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
_snake_case = '''UperNetConfig'''
class _snake_case ( nn.Module ):
def __init__( self: str , __lowerCamelCase: int , __lowerCamelCase: int , __lowerCamelCase: Union[int, Tuple[int, int]] , __lowerCamelCase: Union[int, Tuple[int, int], str] = 0 , __lowerCamelCase: bool = False , __lowerCamelCase: Union[int, Tuple[int, int]] = 1 , ) -> None:
super().__init__()
__UpperCAmelCase : List[str] = nn.Convad(
in_channels=_SCREAMING_SNAKE_CASE , out_channels=_SCREAMING_SNAKE_CASE , kernel_size=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , bias=_SCREAMING_SNAKE_CASE , dilation=_SCREAMING_SNAKE_CASE , )
__UpperCAmelCase : List[str] = nn.BatchNormad(_SCREAMING_SNAKE_CASE )
__UpperCAmelCase : Union[str, Any] = nn.ReLU()
def _lowerCamelCase ( self: int , __lowerCamelCase: torch.Tensor ) -> torch.Tensor:
__UpperCAmelCase : Optional[int] = self.conv(_SCREAMING_SNAKE_CASE )
__UpperCAmelCase : Optional[int] = self.batch_norm(_SCREAMING_SNAKE_CASE )
__UpperCAmelCase : Optional[int] = self.activation(_SCREAMING_SNAKE_CASE )
return output
class _snake_case ( nn.Module ):
def __init__( self: List[Any] , __lowerCamelCase: int , __lowerCamelCase: int , __lowerCamelCase: int ) -> None:
super().__init__()
__UpperCAmelCase : Dict = [
nn.AdaptiveAvgPoolad(_SCREAMING_SNAKE_CASE ),
UperNetConvModule(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def _lowerCamelCase ( self: Any , __lowerCamelCase: torch.Tensor ) -> torch.Tensor:
__UpperCAmelCase : str = input
for layer in self.layers:
__UpperCAmelCase : Union[str, Any] = layer(_SCREAMING_SNAKE_CASE )
return hidden_state
class _snake_case ( nn.Module ):
def __init__( self: Optional[Any] , __lowerCamelCase: Tuple[int, ...] , __lowerCamelCase: int , __lowerCamelCase: int , __lowerCamelCase: bool ) -> None:
super().__init__()
__UpperCAmelCase : Optional[Any] = pool_scales
__UpperCAmelCase : int = align_corners
__UpperCAmelCase : Any = in_channels
__UpperCAmelCase : str = channels
__UpperCAmelCase : List[Any] = []
for i, pool_scale in enumerate(_SCREAMING_SNAKE_CASE ):
__UpperCAmelCase : Tuple = UperNetPyramidPoolingBlock(pool_scale=_SCREAMING_SNAKE_CASE , in_channels=_SCREAMING_SNAKE_CASE , channels=_SCREAMING_SNAKE_CASE )
self.blocks.append(_SCREAMING_SNAKE_CASE )
self.add_module(str(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def _lowerCamelCase ( self: Optional[int] , __lowerCamelCase: torch.Tensor ) -> List[torch.Tensor]:
__UpperCAmelCase : Dict = []
for ppm in self.blocks:
__UpperCAmelCase : List[Any] = ppm(_SCREAMING_SNAKE_CASE )
__UpperCAmelCase : Optional[int] = nn.functional.interpolate(
_SCREAMING_SNAKE_CASE , size=x.size()[2:] , mode="bilinear" , align_corners=self.align_corners )
ppm_outs.append(_SCREAMING_SNAKE_CASE )
return ppm_outs
class _snake_case ( nn.Module ):
def __init__( self: Optional[Any] , __lowerCamelCase: Optional[int] , __lowerCamelCase: Dict ) -> Union[str, Any]:
super().__init__()
__UpperCAmelCase : int = config
__UpperCAmelCase : List[str] = config.pool_scales # e.g. (1, 2, 3, 6)
__UpperCAmelCase : Union[str, Any] = in_channels
__UpperCAmelCase : List[str] = config.hidden_size
__UpperCAmelCase : Optional[Any] = False
__UpperCAmelCase : Dict = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
__UpperCAmelCase : int = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
__UpperCAmelCase : List[Any] = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
__UpperCAmelCase : Optional[int] = nn.ModuleList()
__UpperCAmelCase : str = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
__UpperCAmelCase : Tuple = UperNetConvModule(_SCREAMING_SNAKE_CASE , self.channels , kernel_size=1 )
__UpperCAmelCase : int = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(_SCREAMING_SNAKE_CASE )
self.fpn_convs.append(_SCREAMING_SNAKE_CASE )
__UpperCAmelCase : List[Any] = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def _lowerCamelCase ( self: Union[str, Any] ) -> str:
self.apply(self._init_weights )
def _lowerCamelCase ( self: Union[str, Any] , __lowerCamelCase: Optional[int] ) -> Tuple:
if isinstance(_SCREAMING_SNAKE_CASE , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def _lowerCamelCase ( self: str , __lowerCamelCase: List[Any] ) -> Dict:
__UpperCAmelCase : Optional[int] = inputs[-1]
__UpperCAmelCase : int = [x]
psp_outs.extend(self.psp_modules(_SCREAMING_SNAKE_CASE ) )
__UpperCAmelCase : str = torch.cat(_SCREAMING_SNAKE_CASE , dim=1 )
__UpperCAmelCase : str = self.bottleneck(_SCREAMING_SNAKE_CASE )
return output
def _lowerCamelCase ( self: Dict , __lowerCamelCase: torch.Tensor ) -> torch.Tensor:
# build laterals
__UpperCAmelCase : Tuple = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(_SCREAMING_SNAKE_CASE ) )
# build top-down path
__UpperCAmelCase : List[Any] = len(_SCREAMING_SNAKE_CASE )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
__UpperCAmelCase : Dict = laterals[i - 1].shape[2:]
__UpperCAmelCase : Optional[Any] = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=_SCREAMING_SNAKE_CASE , mode="bilinear" , align_corners=self.align_corners )
# build outputs
__UpperCAmelCase : Dict = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
__UpperCAmelCase : List[Any] = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode="bilinear" , align_corners=self.align_corners )
__UpperCAmelCase : int = torch.cat(_SCREAMING_SNAKE_CASE , dim=1 )
__UpperCAmelCase : int = self.fpn_bottleneck(_SCREAMING_SNAKE_CASE )
__UpperCAmelCase : List[Any] = self.classifier(_SCREAMING_SNAKE_CASE )
return output
class _snake_case ( nn.Module ):
def __init__( self: List[str] , __lowerCamelCase: Any , __lowerCamelCase: int = 2 , __lowerCamelCase: int = 3 , __lowerCamelCase: Union[int, Tuple[int, int]] = 1 ) -> None:
super().__init__()
__UpperCAmelCase : Optional[int] = config
__UpperCAmelCase : str = config.auxiliary_in_channels
__UpperCAmelCase : str = config.auxiliary_channels
__UpperCAmelCase : Union[str, Any] = config.auxiliary_num_convs
__UpperCAmelCase : str = config.auxiliary_concat_input
__UpperCAmelCase : Union[str, Any] = in_index
__UpperCAmelCase : Tuple = (kernel_size // 2) * dilation
__UpperCAmelCase : int = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , dilation=_SCREAMING_SNAKE_CASE ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , dilation=_SCREAMING_SNAKE_CASE ) )
if self.num_convs == 0:
__UpperCAmelCase : Dict = nn.Identity()
else:
__UpperCAmelCase : List[str] = nn.Sequential(*_SCREAMING_SNAKE_CASE )
if self.concat_input:
__UpperCAmelCase : Union[str, Any] = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=_SCREAMING_SNAKE_CASE , padding=kernel_size // 2 )
__UpperCAmelCase : Any = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def _lowerCamelCase ( self: str ) -> List[Any]:
self.apply(self._init_weights )
def _lowerCamelCase ( self: Any , __lowerCamelCase: Tuple ) -> Tuple:
if isinstance(_SCREAMING_SNAKE_CASE , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def _lowerCamelCase ( self: List[Any] , __lowerCamelCase: torch.Tensor ) -> torch.Tensor:
# just take the relevant feature maps
__UpperCAmelCase : List[Any] = encoder_hidden_states[self.in_index]
__UpperCAmelCase : Optional[int] = self.convs(_SCREAMING_SNAKE_CASE )
if self.concat_input:
__UpperCAmelCase : Optional[Any] = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
__UpperCAmelCase : Optional[int] = self.classifier(_SCREAMING_SNAKE_CASE )
return output
class _snake_case ( _lowercase ):
lowerCamelCase__: Tuple = UperNetConfig
lowerCamelCase__: Optional[int] = '''pixel_values'''
lowerCamelCase__: Union[str, Any] = True
def _lowerCamelCase ( self: Optional[int] , __lowerCamelCase: Union[str, Any] ) -> Any:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def _lowerCamelCase ( self: Dict ) -> Optional[int]:
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def _lowerCamelCase ( self: List[str] , __lowerCamelCase: Optional[int] , __lowerCamelCase: List[str]=False ) -> Optional[int]:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__UpperCAmelCase : Any = value
_snake_case = r'''
Parameters:
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
_snake_case = r'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
`attentions` under returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under
returned tensors for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes." , _lowercase , )
class _snake_case ( _lowercase ):
def __init__( self: Optional[int] , __lowerCamelCase: Dict ) -> List[str]:
super().__init__(_SCREAMING_SNAKE_CASE )
__UpperCAmelCase : Tuple = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
__UpperCAmelCase : Any = UperNetHead(_SCREAMING_SNAKE_CASE , in_channels=self.backbone.channels )
__UpperCAmelCase : Any = UperNetFCNHead(_SCREAMING_SNAKE_CASE ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format("batch_size, sequence_length" ) )
@replace_return_docstrings(output_type=_SCREAMING_SNAKE_CASE , config_class=_CONFIG_FOR_DOC )
def _lowerCamelCase ( self: int , __lowerCamelCase: Optional[torch.Tensor] = None , __lowerCamelCase: Optional[bool] = None , __lowerCamelCase: Optional[bool] = None , __lowerCamelCase: Optional[torch.Tensor] = None , __lowerCamelCase: Optional[bool] = None , ) -> Union[tuple, SemanticSegmenterOutput]:
__UpperCAmelCase : List[Any] = return_dict if return_dict is not None else self.config.use_return_dict
__UpperCAmelCase : List[str] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__UpperCAmelCase : Dict = output_attentions if output_attentions is not None else self.config.output_attentions
__UpperCAmelCase : List[str] = self.backbone.forward_with_filtered_kwargs(
_SCREAMING_SNAKE_CASE , output_hidden_states=_SCREAMING_SNAKE_CASE , output_attentions=_SCREAMING_SNAKE_CASE )
__UpperCAmelCase : Union[str, Any] = outputs.feature_maps
__UpperCAmelCase : Tuple = self.decode_head(_SCREAMING_SNAKE_CASE )
__UpperCAmelCase : List[str] = nn.functional.interpolate(_SCREAMING_SNAKE_CASE , size=pixel_values.shape[2:] , mode="bilinear" , align_corners=_SCREAMING_SNAKE_CASE )
__UpperCAmelCase : Dict = None
if self.auxiliary_head is not None:
__UpperCAmelCase : Optional[Any] = self.auxiliary_head(_SCREAMING_SNAKE_CASE )
__UpperCAmelCase : Optional[int] = nn.functional.interpolate(
_SCREAMING_SNAKE_CASE , size=pixel_values.shape[2:] , mode="bilinear" , align_corners=_SCREAMING_SNAKE_CASE )
__UpperCAmelCase : Any = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError("The number of labels should be greater than one" )
else:
# compute weighted loss
__UpperCAmelCase : str = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
__UpperCAmelCase : str = loss_fct(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__UpperCAmelCase : List[str] = loss_fct(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__UpperCAmelCase : Optional[Any] = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
__UpperCAmelCase : str = (logits,) + outputs[1:]
else:
__UpperCAmelCase : int = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=_SCREAMING_SNAKE_CASE , logits=_SCREAMING_SNAKE_CASE , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 157 |
from __future__ import annotations
lowerCamelCase = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
class _a :
def __init__( self : Tuple , _SCREAMING_SNAKE_CASE : dict[str, list[str]] , _SCREAMING_SNAKE_CASE : str )-> None:
lowerCAmelCase__ : List[Any] = graph
# mapping node to its parent in resulting breadth first tree
lowerCAmelCase__ : dict[str, str | None] = {}
lowerCAmelCase__ : str = source_vertex
def UpperCAmelCase__( self : str )-> None:
lowerCAmelCase__ : Dict = {self.source_vertex}
lowerCAmelCase__ : Union[str, Any] = None
lowerCAmelCase__ : List[str] = [self.source_vertex] # first in first out queue
while queue:
lowerCAmelCase__ : int = queue.pop(0 )
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : str = vertex
queue.append(_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__( self : List[Any] , _SCREAMING_SNAKE_CASE : str )-> str:
if target_vertex == self.source_vertex:
return self.source_vertex
lowerCAmelCase__ : str = self.parent.get(_SCREAMING_SNAKE_CASE )
if target_vertex_parent is None:
lowerCAmelCase__ : Optional[Any] = (
F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
)
raise ValueError(_SCREAMING_SNAKE_CASE )
return self.shortest_path(_SCREAMING_SNAKE_CASE ) + F'->{target_vertex}'
if __name__ == "__main__":
lowerCamelCase = Graph(graph, '''G''')
g.breath_first_search()
print(g.shortest_path('''D'''))
print(g.shortest_path('''G'''))
print(g.shortest_path('''Foo'''))
| 131 | 0 |
from collections import deque
from math import floor
from random import random
from time import time
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = {}
def _A ( self , _A , _A , _A=1 ):
'''simple docstring'''
if self.graph.get(_A ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
__SCREAMING_SNAKE_CASE = [[w, v]]
if not self.graph.get(_A ):
__SCREAMING_SNAKE_CASE = []
def _A ( self ):
'''simple docstring'''
return list(self.graph )
def _A ( self , _A , _A ):
'''simple docstring'''
if self.graph.get(_A ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(_A )
def _A ( self , _A=-2 , _A=-1 ):
'''simple docstring'''
if s == d:
return []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
if s == -2:
__SCREAMING_SNAKE_CASE = list(self.graph )[0]
stack.append(_A )
visited.append(_A )
__SCREAMING_SNAKE_CASE = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__SCREAMING_SNAKE_CASE = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(_A )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
__SCREAMING_SNAKE_CASE = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(_A ) != 0:
__SCREAMING_SNAKE_CASE = stack[len(_A ) - 1]
else:
__SCREAMING_SNAKE_CASE = ss
# check if se have reached the starting point
if len(_A ) == 0:
return visited
def _A ( self , _A=-1 ):
'''simple docstring'''
if c == -1:
__SCREAMING_SNAKE_CASE = floor(random() * 10_000 ) + 10
for i in range(_A ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
__SCREAMING_SNAKE_CASE = floor(random() * c ) + 1
if n != i:
self.add_pair(_A , _A , 1 )
def _A ( self , _A=-2 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = deque()
__SCREAMING_SNAKE_CASE = []
if s == -2:
__SCREAMING_SNAKE_CASE = list(self.graph )[0]
d.append(_A )
visited.append(_A )
while d:
__SCREAMING_SNAKE_CASE = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def _A ( self , _A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def _A ( self , _A ):
'''simple docstring'''
return len(self.graph[u] )
def _A ( self , _A=-2 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
if s == -2:
__SCREAMING_SNAKE_CASE = list(self.graph )[0]
stack.append(_A )
visited.append(_A )
__SCREAMING_SNAKE_CASE = s
__SCREAMING_SNAKE_CASE = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__SCREAMING_SNAKE_CASE = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__SCREAMING_SNAKE_CASE = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(_A ) != 0:
__SCREAMING_SNAKE_CASE = stack[len(_A ) - 1]
else:
__SCREAMING_SNAKE_CASE = ss
# check if se have reached the starting point
if len(_A ) == 0:
return sorted_nodes
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = list(self.graph )[0]
stack.append(_A )
visited.append(_A )
__SCREAMING_SNAKE_CASE = -2
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = s
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__SCREAMING_SNAKE_CASE = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
__SCREAMING_SNAKE_CASE = len(_A ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__SCREAMING_SNAKE_CASE = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
__SCREAMING_SNAKE_CASE = True
if len(_A ) != 0:
__SCREAMING_SNAKE_CASE = stack[len(_A ) - 1]
else:
__SCREAMING_SNAKE_CASE = False
indirect_parents.append(_A )
__SCREAMING_SNAKE_CASE = s
__SCREAMING_SNAKE_CASE = ss
# check if se have reached the starting point
if len(_A ) == 0:
return list(_A )
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = list(self.graph )[0]
stack.append(_A )
visited.append(_A )
__SCREAMING_SNAKE_CASE = -2
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = s
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__SCREAMING_SNAKE_CASE = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
__SCREAMING_SNAKE_CASE = len(_A ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__SCREAMING_SNAKE_CASE = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
__SCREAMING_SNAKE_CASE = True
if len(_A ) != 0:
__SCREAMING_SNAKE_CASE = stack[len(_A ) - 1]
else:
__SCREAMING_SNAKE_CASE = False
indirect_parents.append(_A )
__SCREAMING_SNAKE_CASE = s
__SCREAMING_SNAKE_CASE = ss
# check if se have reached the starting point
if len(_A ) == 0:
return False
def _A ( self , _A=-2 , _A=-1 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = time()
self.dfs(_A , _A )
__SCREAMING_SNAKE_CASE = time()
return end - begin
def _A ( self , _A=-2 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = time()
self.bfs(_A )
__SCREAMING_SNAKE_CASE = time()
return end - begin
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = {}
def _A ( self , _A , _A , _A=1 ):
'''simple docstring'''
if self.graph.get(_A ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
__SCREAMING_SNAKE_CASE = [[w, v]]
# add the other way
if self.graph.get(_A ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
__SCREAMING_SNAKE_CASE = [[w, u]]
def _A ( self , _A , _A ):
'''simple docstring'''
if self.graph.get(_A ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(_A )
# the other way round
if self.graph.get(_A ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(_A )
def _A ( self , _A=-2 , _A=-1 ):
'''simple docstring'''
if s == d:
return []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
if s == -2:
__SCREAMING_SNAKE_CASE = list(self.graph )[0]
stack.append(_A )
visited.append(_A )
__SCREAMING_SNAKE_CASE = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__SCREAMING_SNAKE_CASE = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(_A )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
__SCREAMING_SNAKE_CASE = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(_A ) != 0:
__SCREAMING_SNAKE_CASE = stack[len(_A ) - 1]
else:
__SCREAMING_SNAKE_CASE = ss
# check if se have reached the starting point
if len(_A ) == 0:
return visited
def _A ( self , _A=-1 ):
'''simple docstring'''
if c == -1:
__SCREAMING_SNAKE_CASE = floor(random() * 10_000 ) + 10
for i in range(_A ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
__SCREAMING_SNAKE_CASE = floor(random() * c ) + 1
if n != i:
self.add_pair(_A , _A , 1 )
def _A ( self , _A=-2 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = deque()
__SCREAMING_SNAKE_CASE = []
if s == -2:
__SCREAMING_SNAKE_CASE = list(self.graph )[0]
d.append(_A )
visited.append(_A )
while d:
__SCREAMING_SNAKE_CASE = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def _A ( self , _A ):
'''simple docstring'''
return len(self.graph[u] )
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = list(self.graph )[0]
stack.append(_A )
visited.append(_A )
__SCREAMING_SNAKE_CASE = -2
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = s
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__SCREAMING_SNAKE_CASE = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
__SCREAMING_SNAKE_CASE = len(_A ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__SCREAMING_SNAKE_CASE = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
__SCREAMING_SNAKE_CASE = True
if len(_A ) != 0:
__SCREAMING_SNAKE_CASE = stack[len(_A ) - 1]
else:
__SCREAMING_SNAKE_CASE = False
indirect_parents.append(_A )
__SCREAMING_SNAKE_CASE = s
__SCREAMING_SNAKE_CASE = ss
# check if se have reached the starting point
if len(_A ) == 0:
return list(_A )
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = list(self.graph )[0]
stack.append(_A )
visited.append(_A )
__SCREAMING_SNAKE_CASE = -2
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = s
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__SCREAMING_SNAKE_CASE = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
__SCREAMING_SNAKE_CASE = len(_A ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__SCREAMING_SNAKE_CASE = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
__SCREAMING_SNAKE_CASE = True
if len(_A ) != 0:
__SCREAMING_SNAKE_CASE = stack[len(_A ) - 1]
else:
__SCREAMING_SNAKE_CASE = False
indirect_parents.append(_A )
__SCREAMING_SNAKE_CASE = s
__SCREAMING_SNAKE_CASE = ss
# check if se have reached the starting point
if len(_A ) == 0:
return False
def _A ( self ):
'''simple docstring'''
return list(self.graph )
def _A ( self , _A=-2 , _A=-1 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = time()
self.dfs(_A , _A )
__SCREAMING_SNAKE_CASE = time()
return end - begin
def _A ( self , _A=-2 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = time()
self.bfs(_A )
__SCREAMING_SNAKE_CASE = time()
return end - begin
| 118 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def __lowercase ( a__ ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = [2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
__SCREAMING_SNAKE_CASE = True if 'large' in model_name or 'huge' in model_name else False
__SCREAMING_SNAKE_CASE = True if 'large' in model_name or 'huge' in model_name else False
__SCREAMING_SNAKE_CASE = True if 'large' in model_name or 'huge' in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
__SCREAMING_SNAKE_CASE = [3, 3, 3, 3]
__SCREAMING_SNAKE_CASE = [5, 5, 5, 5]
elif "fl4" in model_name:
__SCREAMING_SNAKE_CASE = [4, 4, 4, 4]
__SCREAMING_SNAKE_CASE = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
__SCREAMING_SNAKE_CASE = [3, 3, 3, 3]
if "lrf" in model_name:
__SCREAMING_SNAKE_CASE = [3, 3, 3, 3]
else:
__SCREAMING_SNAKE_CASE = [2, 2, 2, 2]
if "tiny" in model_name:
__SCREAMING_SNAKE_CASE = 96
elif "small" in model_name:
__SCREAMING_SNAKE_CASE = 96
elif "base" in model_name:
__SCREAMING_SNAKE_CASE = 1_28
elif "large" in model_name:
__SCREAMING_SNAKE_CASE = 1_92
elif "xlarge" in model_name:
__SCREAMING_SNAKE_CASE = 2_56
elif "huge" in model_name:
__SCREAMING_SNAKE_CASE = 3_52
# set label information
__SCREAMING_SNAKE_CASE = 'huggingface/label-files'
if "large" in model_name or "huge" in model_name:
__SCREAMING_SNAKE_CASE = 'imagenet-22k-id2label.json'
else:
__SCREAMING_SNAKE_CASE = 'imagenet-1k-id2label.json'
__SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(a__ , a__ , repo_type='dataset' ) , 'r' ) )
__SCREAMING_SNAKE_CASE = {int(a__ ): v for k, v in idalabel.items()}
__SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
__SCREAMING_SNAKE_CASE = FocalNetConfig(
embed_dim=a__ , depths=a__ , focal_levels=a__ , focal_windows=a__ , use_conv_embed=a__ , idalabel=a__ , labelaid=a__ , use_post_layernorm=a__ , use_layerscale=a__ , )
return config
def __lowercase ( a__ ) -> Any:
if "patch_embed.proj" in name:
__SCREAMING_SNAKE_CASE = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
__SCREAMING_SNAKE_CASE = name.replace('patch_embed.norm' , 'embeddings.norm' )
if "layers" in name:
__SCREAMING_SNAKE_CASE = 'encoder.' + name
if "encoder.layers" in name:
__SCREAMING_SNAKE_CASE = name.replace('encoder.layers' , 'encoder.stages' )
if "downsample.proj" in name:
__SCREAMING_SNAKE_CASE = name.replace('downsample.proj' , 'downsample.projection' )
if "blocks" in name:
__SCREAMING_SNAKE_CASE = name.replace('blocks' , 'layers' )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
__SCREAMING_SNAKE_CASE = name.replace('modulation.f' , 'modulation.projection_in' )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
__SCREAMING_SNAKE_CASE = name.replace('modulation.h' , 'modulation.projection_context' )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
__SCREAMING_SNAKE_CASE = name.replace('modulation.proj' , 'modulation.projection_out' )
if name == "norm.weight":
__SCREAMING_SNAKE_CASE = 'layernorm.weight'
if name == "norm.bias":
__SCREAMING_SNAKE_CASE = 'layernorm.bias'
if "head" in name:
__SCREAMING_SNAKE_CASE = name.replace('head' , 'classifier' )
else:
__SCREAMING_SNAKE_CASE = 'focalnet.' + name
return name
def __lowercase ( a__ , a__ , a__=False ) -> Dict:
# fmt: off
__SCREAMING_SNAKE_CASE = {
'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
}
# fmt: on
__SCREAMING_SNAKE_CASE = model_name_to_url[model_name]
print('Checkpoint URL: ' , a__ )
__SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(a__ , map_location='cpu' )['model']
# rename keys
for key in state_dict.copy().keys():
__SCREAMING_SNAKE_CASE = state_dict.pop(a__ )
__SCREAMING_SNAKE_CASE = val
__SCREAMING_SNAKE_CASE = get_focalnet_config(a__ )
__SCREAMING_SNAKE_CASE = FocalNetForImageClassification(a__ )
model.eval()
# load state dict
model.load_state_dict(a__ )
# verify conversion
__SCREAMING_SNAKE_CASE = 'http://images.cocodataset.org/val2017/000000039769.jpg'
__SCREAMING_SNAKE_CASE = BitImageProcessor(
do_resize=a__ , size={'shortest_edge': 2_56} , resample=PILImageResampling.BILINEAR , do_center_crop=a__ , crop_size=2_24 , do_normalize=a__ , image_mean=a__ , image_std=a__ , )
__SCREAMING_SNAKE_CASE = Image.open(requests.get(a__ , stream=a__ ).raw )
__SCREAMING_SNAKE_CASE = processor(images=a__ , return_tensors='pt' )
__SCREAMING_SNAKE_CASE = transforms.Compose(
[
transforms.Resize(2_56 ),
transforms.CenterCrop(2_24 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] ),
] )
__SCREAMING_SNAKE_CASE = image_transforms(a__ ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values , a__ , atol=1E-4 )
__SCREAMING_SNAKE_CASE = model(**a__ )
__SCREAMING_SNAKE_CASE = outputs.logits.argmax(-1 ).item()
print('Predicted class:' , model.config.idalabel[predicted_class_idx] )
print('First values of logits:' , outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
__SCREAMING_SNAKE_CASE = torch.tensor([0.2166, -0.4368, 0.2191] )
elif model_name == "focalnet-tiny-lrf":
__SCREAMING_SNAKE_CASE = torch.tensor([1.1669, 0.0125, -0.1695] )
elif model_name == "focalnet-small":
__SCREAMING_SNAKE_CASE = torch.tensor([0.4917, -0.0430, 0.1341] )
elif model_name == "focalnet-small-lrf":
__SCREAMING_SNAKE_CASE = torch.tensor([-0.2588, -0.5342, -0.2331] )
elif model_name == "focalnet-base":
__SCREAMING_SNAKE_CASE = torch.tensor([-0.1655, -0.4090, -0.1730] )
elif model_name == "focalnet-base-lrf":
__SCREAMING_SNAKE_CASE = torch.tensor([0.5306, -0.0483, -0.3928] )
assert torch.allclose(outputs.logits[0, :3] , a__ , atol=1E-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(a__ )
processor.save_pretrained(a__ )
if push_to_hub:
print(f"""Pushing model and processor of {model_name} to the hub...""" )
model.push_to_hub(f"""{model_name}""" )
processor.push_to_hub(f"""{model_name}""" )
if __name__ == "__main__":
lowerCAmelCase__ : int =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''focalnet-tiny''',
type=str,
help='''Name of the FocalNet model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model and processor to the hub.''',
)
lowerCAmelCase__ : List[Any] =parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 118 | 1 |
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(lowerCAmelCase_ ) )
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> bool:
# Base Case
if index == len(lowerCAmelCase_ ):
return True
# Recursive Step
for i in range(lowerCAmelCase_ ):
if valid_coloring(graph[index] , lowerCAmelCase_ , lowerCAmelCase_ ):
# Color current vertex
lowerCAmelCase_ : List[str] = i
# Validate coloring
if util_color(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , index + 1 ):
return True
# Backtrack
lowerCAmelCase_ : Union[str, Any] = -1
return False
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> list[int]:
lowerCAmelCase_ : List[Any] = [-1] * len(lowerCAmelCase_ )
if util_color(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , 0 ):
return colored_vertices
return []
| 262 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ = "cpu" , lowerCAmelCase_ = None )-> None:
lowerCAmelCase_ : str = torch.load(lowerCAmelCase_ , map_location=lowerCAmelCase_ )
for k, v in tqdm(state_dict.items() ):
if not isinstance(lowerCAmelCase_ , torch.Tensor ):
raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin''' )
lowerCAmelCase_ : int = v.half()
if save_path is None: # overwrite src_path
lowerCAmelCase_ : Tuple = src_path
torch.save(lowerCAmelCase_ , lowerCAmelCase_ )
if __name__ == "__main__":
fire.Fire(convert)
| 262 | 1 |
'''simple docstring'''
import math
import sys
def UpperCAmelCase_ ( __lowercase : int ) -> int:
'''simple docstring'''
if number != int(__lowercase ):
raise ValueError("the value of input must be a natural number" )
if number < 0:
raise ValueError("the value of input must not be a negative number" )
if number == 0:
return 1
_UpperCAmelCase = [-1] * (number + 1)
_UpperCAmelCase = 0
for i in range(1 , number + 1 ):
_UpperCAmelCase = sys.maxsize
_UpperCAmelCase = int(math.sqrt(__lowercase ) )
for j in range(1 , root + 1 ):
_UpperCAmelCase = 1 + answers[i - (j**2)]
_UpperCAmelCase = min(__lowercase , __lowercase )
_UpperCAmelCase = answer
return answers[number]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 156 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE :Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :Dict = '''▁'''
__SCREAMING_SNAKE_CASE :List[str] = {
'''vocab_file''': '''vocab.json''',
'''spm_file''': '''sentencepiece.bpe.model''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__SCREAMING_SNAKE_CASE :Tuple = {
'''vocab_file''': {
'''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
'''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
},
'''spm_file''': {
'''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
'''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
},
'''tokenizer_config_file''': {
'''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
'''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
},
}
__SCREAMING_SNAKE_CASE :Optional[int] = {
'''facebook/m2m100_418M''': 1024,
}
# fmt: off
__SCREAMING_SNAKE_CASE :Dict = {
'''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
'''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : List[str] = VOCAB_FILES_NAMES
_lowerCamelCase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : List[str] = ["""input_ids""", """attention_mask"""]
_lowerCamelCase : List[int] = []
_lowerCamelCase : List[int] = []
def __init__( self : List[str] , snake_case_ : Any , snake_case_ : List[Any] , snake_case_ : str=None , snake_case_ : int=None , snake_case_ : str="<s>" , snake_case_ : int="</s>" , snake_case_ : Any="</s>" , snake_case_ : List[str]="<pad>" , snake_case_ : Optional[int]="<unk>" , snake_case_ : Union[str, Any]="m2m100" , snake_case_ : Optional[Dict[str, Any]] = None , snake_case_ : List[str]=8 , **snake_case_ : str , ):
_UpperCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
_UpperCAmelCase = language_codes
_UpperCAmelCase = FAIRSEQ_LANGUAGE_CODES[language_codes]
_UpperCAmelCase = {lang_code: f'__{lang_code}__' for lang_code in fairseq_language_code}
_UpperCAmelCase = kwargs.get("additional_special_tokens" , [] )
kwargs["additional_special_tokens"] += [
self.get_lang_token(snake_case_ )
for lang_code in fairseq_language_code
if self.get_lang_token(snake_case_ ) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=snake_case_ , tgt_lang=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , sep_token=snake_case_ , unk_token=snake_case_ , pad_token=snake_case_ , language_codes=snake_case_ , sp_model_kwargs=self.sp_model_kwargs , num_madeup_words=snake_case_ , **snake_case_ , )
_UpperCAmelCase = vocab_file
_UpperCAmelCase = load_json(snake_case_ )
_UpperCAmelCase = {v: k for k, v in self.encoder.items()}
_UpperCAmelCase = spm_file
_UpperCAmelCase = load_spm(snake_case_ , self.sp_model_kwargs )
_UpperCAmelCase = len(self.encoder )
_UpperCAmelCase = {
self.get_lang_token(snake_case_ ): self.encoder_size + i for i, lang_code in enumerate(snake_case_ )
}
_UpperCAmelCase = {lang_code: self.encoder_size + i for i, lang_code in enumerate(snake_case_ )}
_UpperCAmelCase = {v: k for k, v in self.lang_token_to_id.items()}
_UpperCAmelCase = src_lang if src_lang is not None else "en"
_UpperCAmelCase = tgt_lang
_UpperCAmelCase = self.get_lang_id(self._src_lang )
self.set_src_lang_special_tokens(self._src_lang )
_UpperCAmelCase = num_madeup_words
@property
def lowercase ( self : int ):
return len(self.encoder ) + len(self.lang_token_to_id )
@property
def lowercase ( self : List[Any] ):
return self._src_lang
@src_lang.setter
def lowercase ( self : str , snake_case_ : str ):
_UpperCAmelCase = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def lowercase ( self : str , snake_case_ : str ):
return self.sp_model.encode(snake_case_ , out_type=snake_case_ )
def lowercase ( self : Optional[Any] , snake_case_ : int ):
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(snake_case_ , self.encoder[self.unk_token] )
def lowercase ( self : Any , snake_case_ : int ):
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(snake_case_ , self.unk_token )
def lowercase ( self : List[str] , snake_case_ : List[str] ):
_UpperCAmelCase = []
_UpperCAmelCase = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(snake_case_ ) + token
_UpperCAmelCase = []
else:
current_sub_tokens.append(snake_case_ )
out_string += self.sp_model.decode(snake_case_ )
return out_string.strip()
def lowercase ( self : str , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None , snake_case_ : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case_ , token_ids_a=snake_case_ , already_has_special_tokens=snake_case_ )
_UpperCAmelCase = [1] * len(self.prefix_tokens )
_UpperCAmelCase = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(snake_case_ )) + suffix_ones
return prefix_ones + ([0] * len(snake_case_ )) + ([0] * len(snake_case_ )) + suffix_ones
def lowercase ( self : Optional[int] , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ):
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def lowercase ( self : Dict ):
_UpperCAmelCase = {self.convert_ids_to_tokens(snake_case_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Any ):
_UpperCAmelCase = self.__dict__.copy()
_UpperCAmelCase = None
return state
def __setstate__( self : List[str] , snake_case_ : Dict ):
_UpperCAmelCase = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
_UpperCAmelCase = {}
_UpperCAmelCase = load_spm(self.spm_file , self.sp_model_kwargs )
def lowercase ( self : int , snake_case_ : str , snake_case_ : Optional[str] = None ):
_UpperCAmelCase = Path(snake_case_ )
if not save_dir.is_dir():
raise OSError(f'{save_directory} should be a directory' )
_UpperCAmelCase = save_dir / (
(filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
)
_UpperCAmelCase = save_dir / (
(filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
)
save_json(self.encoder , snake_case_ )
if os.path.abspath(self.spm_file ) != os.path.abspath(snake_case_ ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , snake_case_ )
elif not os.path.isfile(self.spm_file ):
with open(snake_case_ , "wb" ) as fi:
_UpperCAmelCase = self.sp_model.serialized_model_proto()
fi.write(snake_case_ )
return (str(snake_case_ ), str(snake_case_ ))
def lowercase ( self : Dict , snake_case_ : List[str] , snake_case_ : str = "en" , snake_case_ : Optional[List[str]] = None , snake_case_ : str = "ro" , **snake_case_ : Any , ):
_UpperCAmelCase = src_lang
_UpperCAmelCase = tgt_lang
self.set_src_lang_special_tokens(self.src_lang )
return super().prepare_seqaseq_batch(snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : Tuple , snake_case_ : Optional[Any] , snake_case_ : Optional[str] , snake_case_ : Optional[str] , **snake_case_ : Any ):
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
_UpperCAmelCase = src_lang
_UpperCAmelCase = self(snake_case_ , add_special_tokens=snake_case_ , **snake_case_ )
_UpperCAmelCase = self.get_lang_id(snake_case_ )
_UpperCAmelCase = tgt_lang_id
return inputs
def lowercase ( self : List[str] ):
self.set_src_lang_special_tokens(self.src_lang )
def lowercase ( self : Optional[Any] ):
self.set_tgt_lang_special_tokens(self.tgt_lang )
def lowercase ( self : Any , snake_case_ : str ):
_UpperCAmelCase = self.get_lang_token(snake_case_ )
_UpperCAmelCase = self.lang_token_to_id[lang_token]
_UpperCAmelCase = [self.cur_lang_id]
_UpperCAmelCase = [self.eos_token_id]
def lowercase ( self : List[Any] , snake_case_ : str ):
_UpperCAmelCase = self.get_lang_token(snake_case_ )
_UpperCAmelCase = self.lang_token_to_id[lang_token]
_UpperCAmelCase = [self.cur_lang_id]
_UpperCAmelCase = [self.eos_token_id]
def lowercase ( self : Tuple , snake_case_ : str ):
return self.lang_code_to_token[lang]
def lowercase ( self : List[str] , snake_case_ : str ):
_UpperCAmelCase = self.get_lang_token(snake_case_ )
return self.lang_token_to_id[lang_token]
def UpperCAmelCase_ ( __lowercase : str , __lowercase : Dict[str, Any] ) -> sentencepiece.SentencePieceProcessor:
'''simple docstring'''
_UpperCAmelCase = sentencepiece.SentencePieceProcessor(**__lowercase )
spm.Load(str(__lowercase ) )
return spm
def UpperCAmelCase_ ( __lowercase : str ) -> Union[Dict, List]:
'''simple docstring'''
with open(__lowercase , "r" ) as f:
return json.load(__lowercase )
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> None:
'''simple docstring'''
with open(__lowercase , "w" ) as f:
json.dump(__lowercase , __lowercase , indent=2 )
| 156 | 1 |
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