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"""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 A( unittest.TestCase ):
"""simple docstring"""
def _UpperCamelCase( self ) -> Any:
"""simple docstring"""
_UpperCamelCase :Tuple = [[1, 2, 4], [1, 2, 3, 4]]
_UpperCamelCase :Tuple = DisjunctiveConstraint(__lowerCamelCase )
self.assertTrue(isinstance(dc.token_ids , __lowerCamelCase ) )
with self.assertRaises(__lowerCamelCase ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(__lowerCamelCase ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def _UpperCamelCase( self ) -> List[str]:
"""simple docstring"""
_UpperCamelCase :Dict = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(__lowerCamelCase ):
DisjunctiveConstraint(__lowerCamelCase ) # fails here
def _UpperCamelCase( self ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase :Union[str, Any] = [[1, 2, 3], [1, 2, 4]]
_UpperCamelCase :Union[str, Any] = DisjunctiveConstraint(__lowerCamelCase )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase :Tuple = dc.update(1 )
_UpperCamelCase :int = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCamelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase :List[str] = dc.update(2 )
_UpperCamelCase :str = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCamelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase :str = dc.update(3 )
_UpperCamelCase :Tuple = stepped is True and completed is True and reset is False
self.assertTrue(__lowerCamelCase )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def _UpperCamelCase( self ) -> List[str]:
"""simple docstring"""
_UpperCamelCase :List[Any] = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
_UpperCamelCase :str = DisjunctiveConstraint(__lowerCamelCase )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase :List[str] = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase :int = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase :Any = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase :Optional[Any] = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase :Dict = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase :Tuple = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase :Dict = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 355 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
snake_case = random.Random()
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_=1.0 , lowerCAmelCase_=None , lowerCAmelCase_=None ) -> List[str]:
if rng is None:
_snake_case = global_rng
_snake_case = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCAmelCase ( unittest.TestCase ):
def __init__( self : Tuple , __lowerCamelCase : List[str] , __lowerCamelCase : Optional[Any]=7 , __lowerCamelCase : List[Any]=4_0_0 , __lowerCamelCase : Any=2_0_0_0 , __lowerCamelCase : Any=2_0_4_8 , __lowerCamelCase : Any=1_2_8 , __lowerCamelCase : Any=1 , __lowerCamelCase : Optional[int]=5_1_2 , __lowerCamelCase : Tuple=3_0 , __lowerCamelCase : List[Any]=4_4_1_0_0 , ):
"""simple docstring"""
_snake_case = parent
_snake_case = batch_size
_snake_case = min_seq_length
_snake_case = max_seq_length
_snake_case = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_snake_case = spectrogram_length
_snake_case = feature_size
_snake_case = num_audio_channels
_snake_case = hop_length
_snake_case = chunk_length
_snake_case = sampling_rate
def __UpperCAmelCase ( self : List[Any] ):
"""simple docstring"""
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def __UpperCAmelCase ( self : Union[str, Any] , __lowerCamelCase : Any=False , __lowerCamelCase : int=False ):
"""simple docstring"""
def _flatten(__lowerCamelCase : List[str] ):
return list(itertools.chain(*__lowerCamelCase ) )
if equal_length:
_snake_case = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
_snake_case = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_snake_case = [np.asarray(__lowerCamelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE,unittest.TestCase ):
A__ : Tuple = TvltFeatureExtractor
def __UpperCAmelCase ( self : int ):
"""simple docstring"""
_snake_case = TvltFeatureExtractionTester(self )
def __UpperCAmelCase ( self : Any ):
"""simple docstring"""
_snake_case = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(__lowerCamelCase , '''spectrogram_length''' ) )
self.assertTrue(hasattr(__lowerCamelCase , '''feature_size''' ) )
self.assertTrue(hasattr(__lowerCamelCase , '''num_audio_channels''' ) )
self.assertTrue(hasattr(__lowerCamelCase , '''hop_length''' ) )
self.assertTrue(hasattr(__lowerCamelCase , '''chunk_length''' ) )
self.assertTrue(hasattr(__lowerCamelCase , '''sampling_rate''' ) )
def __UpperCAmelCase ( self : int ):
"""simple docstring"""
_snake_case = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_snake_case = feat_extract_first.save_pretrained(__lowerCamelCase )[0]
check_json_file_has_correct_format(__lowerCamelCase )
_snake_case = self.feature_extraction_class.from_pretrained(__lowerCamelCase )
_snake_case = feat_extract_first.to_dict()
_snake_case = feat_extract_second.to_dict()
_snake_case = dict_first.pop('''mel_filters''' )
_snake_case = dict_second.pop('''mel_filters''' )
self.assertTrue(np.allclose(__lowerCamelCase , __lowerCamelCase ) )
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( self : int ):
"""simple docstring"""
_snake_case = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_snake_case = os.path.join(__lowerCamelCase , '''feat_extract.json''' )
feat_extract_first.to_json_file(__lowerCamelCase )
_snake_case = self.feature_extraction_class.from_json_file(__lowerCamelCase )
_snake_case = feat_extract_first.to_dict()
_snake_case = feat_extract_second.to_dict()
_snake_case = dict_first.pop('''mel_filters''' )
_snake_case = dict_second.pop('''mel_filters''' )
self.assertTrue(np.allclose(__lowerCamelCase , __lowerCamelCase ) )
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( self : Dict ):
"""simple docstring"""
# Initialize feature_extractor
_snake_case = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
_snake_case = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
_snake_case = [np.asarray(__lowerCamelCase ) for speech_input in speech_inputs]
# Test not batched input
_snake_case = feature_extractor(np_speech_inputs[0] , return_tensors='''np''' , sampling_rate=4_4_1_0_0 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
_snake_case = feature_extractor(__lowerCamelCase , return_tensors='''np''' , sampling_rate=4_4_1_0_0 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
_snake_case = feature_extractor(
__lowerCamelCase , return_tensors='''np''' , sampling_rate=4_4_1_0_0 , mask_audio=__lowerCamelCase ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
_snake_case = [floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
_snake_case = np.asarray(__lowerCamelCase )
_snake_case = feature_extractor(__lowerCamelCase , return_tensors='''np''' , sampling_rate=4_4_1_0_0 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def __UpperCAmelCase ( self : Union[str, Any] , __lowerCamelCase : Optional[int] ):
"""simple docstring"""
_snake_case = load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation''' )
# automatic decoding with librispeech
_snake_case = ds.sort('''id''' ).select(range(__lowerCamelCase ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def __UpperCAmelCase ( self : Dict ):
"""simple docstring"""
_snake_case = self._load_datasamples(1 )
_snake_case = TvltFeatureExtractor()
_snake_case = feature_extractor(__lowerCamelCase , return_tensors='''pt''' ).audio_values
self.assertEquals(audio_values.shape , (1, 1, 1_9_2, 1_2_8) )
_snake_case = torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] , __lowerCamelCase , atol=1E-4 ) )
| 103 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __A ( A_ ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : Any = CLIPTokenizer
lowerCAmelCase : List[str] = CLIPTokenizerFast
lowerCAmelCase : str = True
lowerCAmelCase : Tuple = {}
lowerCAmelCase : List[Any] = False
def UpperCAmelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
super().setUp()
# fmt: off
lowercase__ : Optional[Any] = ['''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
lowercase__ : int = dict(zip(_snake_case ,range(len(_snake_case ) ) ) )
lowercase__ : Any = ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''']
lowercase__ : Optional[Any] = {'''unk_token''': '''<unk>'''}
lowercase__ : Optional[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['''vocab_file'''] )
lowercase__ : Tuple = 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(_snake_case ) + '''\n''' )
with open(self.merges_file ,'''w''' ,encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(_snake_case ) )
def UpperCAmelCase ( self : Union[str, Any] ,**_snake_case : List[Any] ) -> int:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CLIPTokenizer.from_pretrained(self.tmpdirname ,**_snake_case )
def UpperCAmelCase ( self : Optional[int] ,**_snake_case : Dict ) -> Any:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,**_snake_case )
def UpperCAmelCase ( self : Optional[int] ,_snake_case : int ) -> Optional[int]:
"""simple docstring"""
lowercase__ : str = '''lower newer'''
lowercase__ : Dict = '''lower newer'''
return input_text, output_text
def UpperCAmelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
lowercase__ : int = CLIPTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map )
lowercase__ : List[Any] = '''lower newer'''
lowercase__ : Tuple = ['''lo''', '''w''', '''er</w>''', '''n''', '''e''', '''w''', '''er</w>''']
lowercase__ : Tuple = tokenizer.tokenize(_snake_case )
self.assertListEqual(_snake_case ,_snake_case )
lowercase__ : List[Any] = tokens + [tokenizer.unk_token]
lowercase__ : str = [10, 2, 16, 9, 3, 2, 16, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case ) ,_snake_case )
@require_ftfy
def UpperCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
lowercase__ : Any = self.tokenizer_class.from_pretrained(_snake_case ,**_snake_case )
lowercase__ : Dict = self.rust_tokenizer_class.from_pretrained(_snake_case ,**_snake_case )
lowercase__ : Dict = '''A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'''
lowercase__ : int = tokenizer_s.tokenize(_snake_case )
lowercase__ : Any = tokenizer_r.tokenize(_snake_case )
self.assertListEqual(_snake_case ,_snake_case )
# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
# with Tilde) encoded in 2 different ways
lowercase__ : Any = '''xa\u0303y''' + ''' ''' + '''x\xe3y'''
lowercase__ : List[str] = tokenizer_s.tokenize(_snake_case )
lowercase__ : Union[str, Any] = tokenizer_r.tokenize(_snake_case )
self.assertListEqual(_snake_case ,_snake_case )
# Test that the tokenization is identical on unicode of space type
lowercase__ : str = [
'''\u0009''', # (horizontal tab, '\t')
'''\u000B''', # (vertical tab)
'''\u000C''', # (form feed)
'''\u0020''', # (space, ' ')
'''\u200E''', # (left-to-right mark):w
'''\u200F''', # (right-to-left mark)
]
for unicode_seq in spaces_unicodes:
lowercase__ : str = tokenizer_s.tokenize(_snake_case )
lowercase__ : List[str] = tokenizer_r.tokenize(_snake_case )
self.assertListEqual(_snake_case ,_snake_case )
# Test that the tokenization is identical on unicode of line break type
lowercase__ : Optional[Any] = [
'''\u000A''', # (line feed, '\n')
'''\r\n''', # (carriage return and line feed, '\r\n')
'''\u000D''', # (carriage return, '\r')
'''\r''', # (carriage return, '\r')
'''\u000D''', # (carriage return, '\r')
'''\u2028''', # (line separator)
'''\u2029''', # (paragraph separator)
# "\u0085", # (next line)
]
# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
# space (and thus into an empty list).
for unicode_seq in line_break_unicodes:
lowercase__ : Dict = tokenizer_s.tokenize(_snake_case )
lowercase__ : Optional[int] = tokenizer_r.tokenize(_snake_case )
self.assertListEqual(_snake_case ,_snake_case )
def UpperCAmelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
lowercase__ : Any = '''hello''' # `hello` is a token in the vocabulary of `pretrained_name`
lowercase__ : Union[str, Any] = f"""{text_of_1_token} {text_of_1_token}"""
lowercase__ : Dict = self.rust_tokenizer_class.from_pretrained(
_snake_case ,use_fast=_snake_case ,)
lowercase__ : List[Any] = tokenizer_r(_snake_case ,return_offsets_mapping=_snake_case ,add_special_tokens=_snake_case )
self.assertEqual(encoding.offset_mapping[0] ,(0, len(_snake_case )) )
self.assertEqual(
encoding.offset_mapping[1] ,(len(_snake_case ) + 1, len(_snake_case ) + 1 + len(_snake_case )) ,)
lowercase__ : List[str] = f""" {text}"""
lowercase__ : Optional[int] = self.rust_tokenizer_class.from_pretrained(
_snake_case ,use_fast=_snake_case ,)
lowercase__ : Tuple = tokenizer_r(_snake_case ,return_offsets_mapping=_snake_case ,add_special_tokens=_snake_case )
self.assertEqual(encoding.offset_mapping[0] ,(1, 1 + len(_snake_case )) )
self.assertEqual(
encoding.offset_mapping[1] ,(1 + len(_snake_case ) + 1, 1 + len(_snake_case ) + 1 + len(_snake_case )) ,)
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
with self.assertRaises(_snake_case ) as context:
self.rust_tokenizer_class.from_pretrained('''robot-test/old-clip-tokenizer''' )
self.assertTrue(
context.exception.args[0].startswith(
'''The `backend_tokenizer` provided does not match the expected format.''' ) )
@require_ftfy
def UpperCAmelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
super().test_tokenization_python_rust_equals()
def UpperCAmelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
pass
| 122 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, 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 OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class __A :
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : Tuple ,_snake_case : Dict=13 ,_snake_case : Optional[int]=7 ,_snake_case : List[str]=True ,_snake_case : Optional[Any]=True ,_snake_case : str=False ,_snake_case : Optional[int]=True ,_snake_case : int=99 ,_snake_case : int=32 ,_snake_case : str=5 ,_snake_case : Any=4 ,_snake_case : str=37 ,_snake_case : str="gelu" ,_snake_case : Optional[Any]=0.1 ,_snake_case : Union[str, Any]=0.1 ,_snake_case : str=512 ,_snake_case : Dict=16 ,_snake_case : Dict=2 ,_snake_case : Tuple=0.02 ,_snake_case : int=3 ,_snake_case : Optional[int]=4 ,_snake_case : int=None ,) -> Tuple:
"""simple docstring"""
lowercase__ : Optional[Any] = parent
lowercase__ : List[str] = batch_size
lowercase__ : str = seq_length
lowercase__ : Tuple = is_training
lowercase__ : List[str] = use_input_mask
lowercase__ : Optional[Any] = use_token_type_ids
lowercase__ : str = use_labels
lowercase__ : Any = vocab_size
lowercase__ : str = hidden_size
lowercase__ : int = num_hidden_layers
lowercase__ : int = num_attention_heads
lowercase__ : Optional[int] = intermediate_size
lowercase__ : Dict = hidden_act
lowercase__ : Optional[int] = hidden_dropout_prob
lowercase__ : Optional[int] = attention_probs_dropout_prob
lowercase__ : List[str] = max_position_embeddings
lowercase__ : Union[str, Any] = type_vocab_size
lowercase__ : Any = type_sequence_label_size
lowercase__ : List[str] = initializer_range
lowercase__ : Tuple = num_labels
lowercase__ : int = num_choices
lowercase__ : Optional[int] = scope
def UpperCAmelCase ( self : str ) -> Tuple:
"""simple docstring"""
lowercase__ : int = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
lowercase__ : Optional[int] = None
if self.use_input_mask:
lowercase__ : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ : Tuple = None
if self.use_token_type_ids:
lowercase__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
lowercase__ : Optional[Any] = None
lowercase__ : Any = None
lowercase__ : List[Any] = None
if self.use_labels:
lowercase__ : List[str] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
lowercase__ : str = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
lowercase__ : List[Any] = ids_tensor([self.batch_size] ,self.num_choices )
lowercase__ : str = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase ( self : int ) -> List[str]:
"""simple docstring"""
return OpenLlamaConfig(
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=_snake_case ,initializer_range=self.initializer_range ,use_stable_embedding=_snake_case ,)
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : List[Any] ,_snake_case : Tuple ,_snake_case : int ,_snake_case : Optional[Any] ,_snake_case : Optional[int] ,_snake_case : Tuple ,_snake_case : Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase__ : Optional[int] = OpenLlamaModel(config=_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : Optional[Any] = model(_snake_case ,attention_mask=_snake_case )
lowercase__ : Dict = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self : List[str] ,_snake_case : Optional[int] ,_snake_case : Tuple ,_snake_case : int ,_snake_case : Tuple ,_snake_case : Union[str, Any] ,_snake_case : str ,_snake_case : List[str] ,_snake_case : List[str] ,_snake_case : Optional[Any] ,) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[str] = True
lowercase__ : Tuple = OpenLlamaModel(_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : Dict = model(
_snake_case ,attention_mask=_snake_case ,encoder_hidden_states=_snake_case ,encoder_attention_mask=_snake_case ,)
lowercase__ : str = model(
_snake_case ,attention_mask=_snake_case ,encoder_hidden_states=_snake_case ,)
lowercase__ : Optional[Any] = model(_snake_case ,attention_mask=_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self : List[Any] ,_snake_case : Optional[Any] ,_snake_case : Union[str, Any] ,_snake_case : List[str] ,_snake_case : List[Any] ,_snake_case : Any ,_snake_case : List[Any] ,_snake_case : List[str] ,_snake_case : List[Any] ,_snake_case : Union[str, Any] ,) -> Dict:
"""simple docstring"""
lowercase__ : int = OpenLlamaForCausalLM(config=_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : List[str] = model(_snake_case ,attention_mask=_snake_case ,labels=_snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Optional[int] ,_snake_case : Dict ,_snake_case : Union[str, Any] ,_snake_case : Tuple ,_snake_case : str ,_snake_case : int ,_snake_case : List[Any] ,_snake_case : Optional[Any] ,_snake_case : Union[str, Any] ,) -> int:
"""simple docstring"""
lowercase__ : List[Any] = True
lowercase__ : Tuple = True
lowercase__ : Optional[int] = OpenLlamaForCausalLM(config=_snake_case )
model.to(_snake_case )
model.eval()
# first forward pass
lowercase__ : List[str] = model(
_snake_case ,attention_mask=_snake_case ,encoder_hidden_states=_snake_case ,encoder_attention_mask=_snake_case ,use_cache=_snake_case ,)
lowercase__ : Union[str, Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowercase__ : List[Any] = ids_tensor((self.batch_size, 3) ,config.vocab_size )
lowercase__ : Tuple = ids_tensor((self.batch_size, 3) ,vocab_size=2 )
# append to next input_ids and
lowercase__ : str = torch.cat([input_ids, next_tokens] ,dim=-1 )
lowercase__ : Tuple = torch.cat([input_mask, next_mask] ,dim=-1 )
lowercase__ : Tuple = model(
_snake_case ,attention_mask=_snake_case ,encoder_hidden_states=_snake_case ,encoder_attention_mask=_snake_case ,output_hidden_states=_snake_case ,)['''hidden_states'''][0]
lowercase__ : Union[str, Any] = model(
_snake_case ,attention_mask=_snake_case ,encoder_hidden_states=_snake_case ,encoder_attention_mask=_snake_case ,past_key_values=_snake_case ,output_hidden_states=_snake_case ,)['''hidden_states'''][0]
# select random slice
lowercase__ : Optional[int] = ids_tensor((1,) ,output_from_past.shape[-1] ).item()
lowercase__ : List[str] = output_from_no_past[:, -3:, random_slice_idx].detach()
lowercase__ : int = 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(_snake_case ,_snake_case ,atol=1e-3 ) )
def UpperCAmelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.prepare_config_and_inputs()
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) : str = config_and_inputs
lowercase__ : Dict = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __A ( A_ ,A_ ,A_ ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : Optional[Any] = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
lowerCAmelCase : List[str] = (OpenLlamaForCausalLM,) if is_torch_available() else ()
lowerCAmelCase : int = (
{
"feature-extraction": OpenLlamaModel,
"text-classification": OpenLlamaForSequenceClassification,
"text-generation": OpenLlamaForCausalLM,
"zero-shot": OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase : Union[str, Any] = False
lowerCAmelCase : Any = False
def UpperCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
lowercase__ : Tuple = OpenLlamaModelTester(self )
lowercase__ : Tuple = ConfigTester(self ,config_class=_snake_case ,hidden_size=37 )
def UpperCAmelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def UpperCAmelCase ( self : Any ) -> Dict:
"""simple docstring"""
lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase__ : int = type
self.model_tester.create_and_check_model(*_snake_case )
def UpperCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ , lowercase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Optional[Any] = 3
lowercase__ : List[str] = input_dict['''input_ids''']
lowercase__ : Optional[Any] = input_ids.ne(1 ).to(_snake_case )
lowercase__ : Optional[int] = ids_tensor([self.model_tester.batch_size] ,self.model_tester.type_sequence_label_size )
lowercase__ : Dict = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : Any = model(_snake_case ,attention_mask=_snake_case ,labels=_snake_case )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
def UpperCAmelCase ( self : str ) -> Optional[int]:
"""simple docstring"""
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Optional[int] = 3
lowercase__ : str = '''single_label_classification'''
lowercase__ : Optional[Any] = input_dict['''input_ids''']
lowercase__ : Any = input_ids.ne(1 ).to(_snake_case )
lowercase__ : List[Any] = ids_tensor([self.model_tester.batch_size] ,self.model_tester.type_sequence_label_size )
lowercase__ : Any = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : Union[str, Any] = model(_snake_case ,attention_mask=_snake_case ,labels=_snake_case )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
def UpperCAmelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
lowercase__ , lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Optional[int] = 3
lowercase__ : Tuple = '''multi_label_classification'''
lowercase__ : str = input_dict['''input_ids''']
lowercase__ : Union[str, Any] = input_ids.ne(1 ).to(_snake_case )
lowercase__ : List[Any] = ids_tensor(
[self.model_tester.batch_size, config.num_labels] ,self.model_tester.type_sequence_label_size ).to(torch.float )
lowercase__ : Optional[Any] = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : List[str] = model(_snake_case ,attention_mask=_snake_case ,labels=_snake_case )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test''' )
def UpperCAmelCase ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
pass
@parameterized.expand([('''linear''',), ('''dynamic''',)] )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Optional[int] = ids_tensor([1, 10] ,config.vocab_size )
lowercase__ : int = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] ,config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase__ : Optional[int] = OpenLlamaModel(_snake_case )
original_model.to(_snake_case )
original_model.eval()
lowercase__ : List[Any] = original_model(_snake_case ).last_hidden_state
lowercase__ : Optional[Any] = original_model(_snake_case ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase__ : Optional[int] = {'''type''': scaling_type, '''factor''': 10.0}
lowercase__ : Optional[int] = OpenLlamaModel(_snake_case )
scaled_model.to(_snake_case )
scaled_model.eval()
lowercase__ : Optional[int] = scaled_model(_snake_case ).last_hidden_state
lowercase__ : Optional[Any] = scaled_model(_snake_case ).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(_snake_case ,_snake_case ,atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(_snake_case ,_snake_case ,atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(_snake_case ,_snake_case ,atol=1e-5 ) )
| 122 | 1 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : List[Any] , lowerCamelCase_ : List[Any] ):
'''simple docstring'''
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 664 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"google/pix2struct-textcaps-base": (
"https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"
),
}
class lowerCAmelCase__ ( __lowercase ):
UpperCamelCase_ : Union[str, Any] = "pix2struct_text_model"
UpperCamelCase_ : str = ["past_key_values"]
UpperCamelCase_ : str = {
"hidden_size": "hidden_size",
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self , a=5_02_44 , a=7_68 , a=64 , a=20_48 , a=12 , a=12 , a=32 , a=1_28 , a=0.1 , a=1e-6 , a=1.0 , a="gelu_new" , a=0 , a=False , a=0 , a=1 , a=False , a=True , **a , ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = d_kv
_UpperCamelCase = d_ff
_UpperCamelCase = num_layers
_UpperCamelCase = num_heads
_UpperCamelCase = relative_attention_num_buckets
_UpperCamelCase = relative_attention_max_distance
_UpperCamelCase = dropout_rate
_UpperCamelCase = layer_norm_epsilon
_UpperCamelCase = initializer_factor
_UpperCamelCase = use_cache
_UpperCamelCase = eos_token_id
_UpperCamelCase = decoder_start_token_id
# for backwards compatibility
_UpperCamelCase = dense_act_fn
super().__init__(
pad_token_id=a , eos_token_id=a , decoder_start_token_id=a , tie_word_embeddings=a , is_decoder=a , **a , )
@classmethod
def A_ ( cls , a , **a ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(a )
_UpperCamelCase , _UpperCamelCase = cls.get_config_dict(a , **a )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get("""model_type""" ) == "pix2struct":
_UpperCamelCase = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(a , **a )
class lowerCAmelCase__ ( __lowercase ):
UpperCamelCase_ : int = "pix2struct_vision_model"
def __init__( self , a=7_68 , a=7_68 , a=20_48 , a=64 , a=12 , a=12 , a="gelu_new" , a=1e-6 , a=0.0 , a=0.0 , a=1e-10 , a=1.0 , a=40_96 , a=32 , a=1_28 , **a , ) -> Tuple:
'''simple docstring'''
super().__init__(**a )
_UpperCamelCase = hidden_size
_UpperCamelCase = patch_embed_hidden_size
_UpperCamelCase = d_ff
_UpperCamelCase = dropout_rate
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = initializer_range
_UpperCamelCase = initializer_factor
_UpperCamelCase = attention_dropout
_UpperCamelCase = layer_norm_eps
_UpperCamelCase = dense_act_fn
_UpperCamelCase = seq_len
_UpperCamelCase = relative_attention_num_buckets
_UpperCamelCase = relative_attention_max_distance
_UpperCamelCase = d_kv
@classmethod
def A_ ( cls , a , **a ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(a )
_UpperCamelCase , _UpperCamelCase = cls.get_config_dict(a , **a )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get("""model_type""" ) == "pix2struct":
_UpperCamelCase = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(a , **a )
class lowerCAmelCase__ ( __lowercase ):
UpperCamelCase_ : Dict = "pix2struct"
UpperCamelCase_ : int = True
def __init__( self , a=None , a=None , a=1.0 , a=0.02 , a=False , a=False , a=True , **a , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(tie_word_embeddings=a , is_encoder_decoder=a , **a )
if text_config is None:
_UpperCamelCase = {}
logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values.""" )
if vision_config is None:
_UpperCamelCase = {}
logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values.""" )
_UpperCamelCase = PixaStructTextConfig(**a )
_UpperCamelCase = PixaStructVisionConfig(**a )
_UpperCamelCase = self.text_config.decoder_start_token_id
_UpperCamelCase = self.text_config.pad_token_id
_UpperCamelCase = self.text_config.eos_token_id
_UpperCamelCase = initializer_factor
_UpperCamelCase = initializer_range
_UpperCamelCase = self.initializer_range
_UpperCamelCase = self.initializer_range
_UpperCamelCase = is_vqa
@classmethod
def A_ ( cls , a , a , **a ) -> str:
'''simple docstring'''
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **a )
def A_ ( self ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = copy.deepcopy(self.__dict__ )
_UpperCamelCase = self.text_config.to_dict()
_UpperCamelCase = self.vision_config.to_dict()
_UpperCamelCase = self.__class__.model_type
return output
| 612 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case_ : str = logging.get_logger(__name__)
snake_case_ : str = {
'''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class A__ ( UpperCamelCase__ ):
UpperCAmelCase = "data2vec-text"
def __init__( self : Optional[int] , _a : Union[str, Any]=3_0522 , _a : Any=768 , _a : List[Any]=12 , _a : Any=12 , _a : List[str]=3072 , _a : Optional[Any]="gelu" , _a : List[str]=0.1 , _a : Optional[Any]=0.1 , _a : Optional[Any]=512 , _a : Any=2 , _a : Optional[Any]=0.02 , _a : Dict=1E-12 , _a : int=1 , _a : Any=0 , _a : List[Any]=2 , _a : Dict="absolute" , _a : Optional[Any]=True , _a : Dict=None , **_a : Optional[Any] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a )
_SCREAMING_SNAKE_CASE =vocab_size
_SCREAMING_SNAKE_CASE =hidden_size
_SCREAMING_SNAKE_CASE =num_hidden_layers
_SCREAMING_SNAKE_CASE =num_attention_heads
_SCREAMING_SNAKE_CASE =hidden_act
_SCREAMING_SNAKE_CASE =intermediate_size
_SCREAMING_SNAKE_CASE =hidden_dropout_prob
_SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE =max_position_embeddings
_SCREAMING_SNAKE_CASE =type_vocab_size
_SCREAMING_SNAKE_CASE =initializer_range
_SCREAMING_SNAKE_CASE =layer_norm_eps
_SCREAMING_SNAKE_CASE =position_embedding_type
_SCREAMING_SNAKE_CASE =use_cache
_SCREAMING_SNAKE_CASE =classifier_dropout
class A__ ( UpperCamelCase__ ):
@property
def __UpperCamelCase ( self : List[str] ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
_SCREAMING_SNAKE_CASE ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_SCREAMING_SNAKE_CASE ={0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] ) | 191 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
snake_case_ : Optional[Any] = '''scheduler_config.json'''
class A__ ( UpperCamelCase__ ):
UpperCAmelCase = 1
UpperCAmelCase = 2
UpperCAmelCase = 3
UpperCAmelCase = 4
UpperCAmelCase = 5
UpperCAmelCase = 6
UpperCAmelCase = 7
UpperCAmelCase = 8
UpperCAmelCase = 9
UpperCAmelCase = 10
UpperCAmelCase = 11
UpperCAmelCase = 12
UpperCAmelCase = 13
UpperCAmelCase = 14
@dataclass
class A__ ( UpperCamelCase__ ):
UpperCAmelCase = 42
class A__ :
UpperCAmelCase = SCHEDULER_CONFIG_NAME
UpperCAmelCase = []
UpperCAmelCase = True
@classmethod
def __UpperCamelCase ( cls : List[str] , _a : Dict[str, Any] = None , _a : Optional[str] = None , _a : Optional[Any]=False , **_a : Dict , ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =cls.load_config(
pretrained_model_name_or_path=_a , subfolder=_a , return_unused_kwargs=_a , return_commit_hash=_a , **_a , )
return cls.from_config(_a , return_unused_kwargs=_a , **_a )
def __UpperCamelCase ( self : Dict , _a : Union[str, os.PathLike] , _a : bool = False , **_a : int ) -> Dict:
"""simple docstring"""
self.save_config(save_directory=_a , push_to_hub=_a , **_a )
@property
def __UpperCamelCase ( self : List[str] ) -> List[Any]:
"""simple docstring"""
return self._get_compatibles()
@classmethod
def __UpperCamelCase ( cls : List[Any] ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =list(set([cls.__name__] + cls._compatibles ) )
_SCREAMING_SNAKE_CASE =importlib.import_module(__name__.split('''.''' )[0] )
_SCREAMING_SNAKE_CASE =[
getattr(_a , _a ) for c in compatible_classes_str if hasattr(_a , _a )
]
return compatible_classes | 191 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class lowercase__ ( snake_case_ ):
'''simple docstring'''
_snake_case = '''SpeechT5FeatureExtractor'''
_snake_case = '''SpeechT5Tokenizer'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
super().__init__(lowerCamelCase__ , lowerCamelCase__ )
def __call__( self , *lowerCamelCase__ , **lowerCamelCase__ ):
'''simple docstring'''
UpperCamelCase = kwargs.pop('''audio''' , lowerCamelCase__ )
UpperCamelCase = kwargs.pop('''text''' , lowerCamelCase__ )
UpperCamelCase = kwargs.pop('''text_target''' , lowerCamelCase__ )
UpperCamelCase = kwargs.pop('''audio_target''' , lowerCamelCase__ )
UpperCamelCase = kwargs.pop('''sampling_rate''' , lowerCamelCase__ )
if audio is not None and text is not None:
raise ValueError(
'''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?''' )
if audio_target is not None and text_target is not None:
raise ValueError(
'''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?''' )
if audio is None and audio_target is None and text is None and text_target is None:
raise ValueError(
'''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.''' )
if audio is not None:
UpperCamelCase = self.feature_extractor(lowerCamelCase__ , *lowerCamelCase__ , sampling_rate=lowerCamelCase__ , **lowerCamelCase__ )
elif text is not None:
UpperCamelCase = self.tokenizer(lowerCamelCase__ , **lowerCamelCase__ )
else:
UpperCamelCase = None
if audio_target is not None:
UpperCamelCase = self.feature_extractor(audio_target=lowerCamelCase__ , *lowerCamelCase__ , sampling_rate=lowerCamelCase__ , **lowerCamelCase__ )
UpperCamelCase = targets['''input_values''']
elif text_target is not None:
UpperCamelCase = self.tokenizer(lowerCamelCase__ , **lowerCamelCase__ )
UpperCamelCase = targets['''input_ids''']
else:
UpperCamelCase = None
if inputs is None:
return targets
if targets is not None:
UpperCamelCase = labels
UpperCamelCase = targets.get('''attention_mask''' )
if decoder_attention_mask is not None:
UpperCamelCase = decoder_attention_mask
return inputs
def UpperCAmelCase ( self , *lowerCamelCase__ , **lowerCamelCase__ ):
'''simple docstring'''
UpperCamelCase = kwargs.pop('''input_values''' , lowerCamelCase__ )
UpperCamelCase = kwargs.pop('''input_ids''' , lowerCamelCase__ )
UpperCamelCase = kwargs.pop('''labels''' , lowerCamelCase__ )
if input_values is not None and input_ids is not None:
raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.''' )
if input_values is None and input_ids is None and labels is None:
raise ValueError(
'''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.''' )
if input_values is not None:
UpperCamelCase = self.feature_extractor.pad(lowerCamelCase__ , *lowerCamelCase__ , **lowerCamelCase__ )
elif input_ids is not None:
UpperCamelCase = self.tokenizer.pad(lowerCamelCase__ , **lowerCamelCase__ )
else:
UpperCamelCase = None
if labels is not None:
if "input_ids" in labels or (isinstance(lowerCamelCase__ , lowerCamelCase__ ) and "input_ids" in labels[0]):
UpperCamelCase = self.tokenizer.pad(lowerCamelCase__ , **lowerCamelCase__ )
UpperCamelCase = targets['''input_ids''']
else:
UpperCamelCase = self.feature_extractor.feature_size
UpperCamelCase = self.feature_extractor.num_mel_bins
UpperCamelCase = self.feature_extractor.pad(lowerCamelCase__ , *lowerCamelCase__ , **lowerCamelCase__ )
UpperCamelCase = feature_size_hack
UpperCamelCase = targets['''input_values''']
else:
UpperCamelCase = None
if inputs is None:
return targets
if targets is not None:
UpperCamelCase = labels
UpperCamelCase = targets.get('''attention_mask''' )
if decoder_attention_mask is not None:
UpperCamelCase = decoder_attention_mask
return inputs
def UpperCAmelCase ( self , *lowerCamelCase__ , **lowerCamelCase__ ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCamelCase__ , **lowerCamelCase__ )
def UpperCAmelCase ( self , *lowerCamelCase__ , **lowerCamelCase__ ):
'''simple docstring'''
return self.tokenizer.decode(*lowerCamelCase__ , **lowerCamelCase__ )
| 212 |
'''simple docstring'''
from math import factorial
def __snake_case ( _UpperCAmelCase : int = 100):
return sum(map(_UpperCAmelCase, str(factorial(_UpperCAmelCase))))
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 212 | 1 |
from ... import PretrainedConfig
snake_case = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class __A ( snake_case__ ):
'''simple docstring'''
a_ = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
a_ = '''nezha'''
def __init__( self , _snake_case=2_1128 , _snake_case=768 , _snake_case=12 , _snake_case=12 , _snake_case=3072 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=64 , _snake_case=2 , _snake_case=0.02 , _snake_case=1E-1_2 , _snake_case=0.1 , _snake_case=0 , _snake_case=2 , _snake_case=3 , _snake_case=True , **_snake_case , ):
super().__init__(pad_token_id=_snake_case , bos_token_id=_snake_case , eos_token_id=_snake_case , **_snake_case )
_lowerCAmelCase : Optional[int] = vocab_size
_lowerCAmelCase : int = hidden_size
_lowerCAmelCase : Any = num_hidden_layers
_lowerCAmelCase : Dict = num_attention_heads
_lowerCAmelCase : str = hidden_act
_lowerCAmelCase : List[str] = intermediate_size
_lowerCAmelCase : Optional[Any] = hidden_dropout_prob
_lowerCAmelCase : Dict = attention_probs_dropout_prob
_lowerCAmelCase : str = max_position_embeddings
_lowerCAmelCase : List[Any] = max_relative_position
_lowerCAmelCase : Optional[Any] = type_vocab_size
_lowerCAmelCase : Tuple = initializer_range
_lowerCAmelCase : Optional[Any] = layer_norm_eps
_lowerCAmelCase : List[Any] = classifier_dropout
_lowerCAmelCase : Optional[Any] = use_cache
| 703 | from __future__ import annotations
from typing import Generic, TypeVar
snake_case = TypeVar("T")
class __A ( Generic[T] ):
'''simple docstring'''
def __init__( self , _snake_case ):
_lowerCAmelCase : List[Any] = data
_lowerCAmelCase : Dict = self
_lowerCAmelCase : Tuple = 0
class __A ( Generic[T] ):
'''simple docstring'''
def __init__( self ):
# map from node name to the node object
_lowerCAmelCase : dict[T, DisjointSetTreeNode[T]] = {}
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ):
# create a new set with x as its member
_lowerCAmelCase : List[str] = DisjointSetTreeNode(_snake_case )
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ):
# find the set x belongs to (with path-compression)
_lowerCAmelCase : Dict = self.map[data]
if elem_ref != elem_ref.parent:
_lowerCAmelCase : Tuple = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case ):
# helper function for union operation
if nodea.rank > nodea.rank:
_lowerCAmelCase : int = nodea
else:
_lowerCAmelCase : Optional[Any] = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case ):
# merge 2 disjoint sets
self.link(self.find_set(_snake_case ) , self.find_set(_snake_case ) )
class __A ( Generic[T] ):
'''simple docstring'''
def __init__( self ):
# connections: map from the node to the neighbouring nodes (with weights)
_lowerCAmelCase : dict[T, dict[T, int]] = {}
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ):
# add a node ONLY if its not present in the graph
if node not in self.connections:
_lowerCAmelCase : Any = {}
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case , _snake_case ):
# add an edge with the given weight
self.add_node(_snake_case )
self.add_node(_snake_case )
_lowerCAmelCase : int = weight
_lowerCAmelCase : Optional[int] = weight
def SCREAMING_SNAKE_CASE__ ( self ):
_lowerCAmelCase : Tuple = []
_lowerCAmelCase : Optional[Any] = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda _snake_case : x[2] )
# creating the disjoint set
_lowerCAmelCase : Tuple = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(_snake_case )
# MST generation
_lowerCAmelCase : Optional[Any] = 0
_lowerCAmelCase : Dict = 0
_lowerCAmelCase : int = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Union[str, Any] = edges[index]
index += 1
_lowerCAmelCase : Dict = disjoint_set.find_set(_snake_case )
_lowerCAmelCase : List[str] = disjoint_set.find_set(_snake_case )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(_snake_case , _snake_case , _snake_case )
disjoint_set.union(_snake_case , _snake_case )
return graph
| 587 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['PLBartTokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'PLBartForCausalLM',
'PLBartForConditionalGeneration',
'PLBartForSequenceClassification',
'PLBartModel',
'PLBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 245 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _A ( snake_case , snake_case , snake_case , snake_case , ) -> list[float]:
_lowercase , _lowercase : Union[str, Any] = coefficient_matrix.shape
_lowercase , _lowercase : Optional[Any] = constant_matrix.shape
if rowsa != colsa:
_lowercase : Any = F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
raise ValueError(snake_case )
if colsa != 1:
_lowercase : Dict = F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
raise ValueError(snake_case )
if rowsa != rowsa:
_lowercase : int = (
"Coefficient and constant matrices dimensions must be nxn and nx1 but "
F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
)
raise ValueError(snake_case )
if len(snake_case ) != rowsa:
_lowercase : Tuple = (
"Number of initial values must be equal to number of rows in coefficient "
F'''matrix but received {len(snake_case )} and {rowsa}'''
)
raise ValueError(snake_case )
if iterations <= 0:
raise ValueError("Iterations must be at least 1" )
_lowercase : NDArray[floataa] = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
_lowercase , _lowercase : Dict = table.shape
strictly_diagonally_dominant(snake_case )
# Iterates the whole matrix for given number of times
for _ in range(snake_case ):
_lowercase : int = []
for row in range(snake_case ):
_lowercase : Tuple = 0
for col in range(snake_case ):
if col == row:
_lowercase : str = table[row][col]
elif col == cols - 1:
_lowercase : List[str] = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
_lowercase : List[str] = (temp + val) / denom
new_val.append(snake_case )
_lowercase : str = new_val
return [float(snake_case ) for i in new_val]
def _A ( snake_case ) -> bool:
_lowercase , _lowercase : Optional[int] = table.shape
_lowercase : Optional[Any] = True
for i in range(0 , snake_case ):
_lowercase : Dict = 0
for j in range(0 , cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError("Coefficient matrix is not strictly diagonally dominant" )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 245 | 1 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
_snake_case = logging.get_logger(__name__)
@add_end_docstrings(__lowerCamelCase )
class lowerCAmelCase__ ( __lowerCamelCase ):
"""simple docstring"""
def __init__( self , *a_ , **a_ ):
super().__init__(*a_ , **a_ )
requires_backends(self , "decord" )
self.check_model_type(a_ )
def _UpperCamelCase ( self , a_=None , a_=None , a_=None ):
lowerCamelCase_ : Dict = {}
if frame_sampling_rate is not None:
lowerCamelCase_ : Dict = frame_sampling_rate
if num_frames is not None:
lowerCamelCase_ : Any = num_frames
lowerCamelCase_ : Optional[int] = {}
if top_k is not None:
lowerCamelCase_ : Optional[Any] = top_k
return preprocess_params, {}, postprocess_params
def __call__( self , a_ , **a_ ):
return super().__call__(a_ , **a_ )
def _UpperCamelCase ( self , a_ , a_=None , a_=1 ):
if num_frames is None:
lowerCamelCase_ : str = self.model.config.num_frames
if video.startswith("http://" ) or video.startswith("https://" ):
lowerCamelCase_ : int = BytesIO(requests.get(a_ ).content )
lowerCamelCase_ : Optional[int] = VideoReader(a_ )
videoreader.seek(0 )
lowerCamelCase_ : List[str] = 0
lowerCamelCase_ : str = num_frames * frame_sampling_rate - 1
lowerCamelCase_ : str = np.linspace(a_ , a_ , num=a_ , dtype=np.intaa )
lowerCamelCase_ : List[str] = videoreader.get_batch(a_ ).asnumpy()
lowerCamelCase_ : int = list(a_ )
lowerCamelCase_ : Union[str, Any] = self.image_processor(a_ , return_tensors=self.framework )
return model_inputs
def _UpperCamelCase ( self , a_ ):
lowerCamelCase_ : int = self.model(**a_ )
return model_outputs
def _UpperCamelCase ( self , a_ , a_=5 ):
if top_k > self.model.config.num_labels:
lowerCamelCase_ : int = self.model.config.num_labels
if self.framework == "pt":
lowerCamelCase_ : Tuple = model_outputs.logits.softmax(-1 )[0]
lowerCamelCase_ : Tuple = probs.topk(a_ )
else:
raise ValueError(F"""Unsupported framework: {self.framework}""" )
lowerCamelCase_ : Tuple = scores.tolist()
lowerCamelCase_ : Union[str, Any] = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(a_ , a_ )]
| 714 |
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_):
'''simple docstring'''
if digit_amount > 0:
return round(number - int(lowerCAmelCase_) , lowerCAmelCase_)
return number - int(lowerCAmelCase_)
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.3_45, 1))
print(decimal_isolate(35.3_45, 2))
print(decimal_isolate(35.3_45, 3))
print(decimal_isolate(-14.7_89, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.1_23, 1))
print(decimal_isolate(-14.1_23, 2))
print(decimal_isolate(-14.1_23, 3))
| 73 | 0 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def _a ( ) -> List[Any]:
"""simple docstring"""
lowerCamelCase__ : Any = {
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''path''': ['''test_1.py''', '''test_2.py''', '''unit_test.py'''],
'''content''': ['''a ''' * 20, '''a ''' * 30, '''b ''' * 7],
}
lowerCamelCase__ : List[str] = Dataset.from_dict(UpperCAmelCase )
return dataset
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
def __lowerCamelCase ( self : Optional[int] ) ->Tuple:
lowerCamelCase__ : Optional[Any] = get_dataset()
lowerCamelCase__ : int = make_duplicate_clusters(A , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def __lowerCamelCase ( self : List[str] ) ->Any:
lowerCamelCase__ : str = get_dataset()
lowerCamelCase__ , lowerCamelCase__ : Any = deduplicate_dataset(A )
self.assertEqual(len(A ) , 2 )
print(A )
self.assertEqual(duplicate_clusters[0][0]['''copies'''] , 2 )
self.assertEqual(duplicate_clusters[0][0]['''is_extreme'''] , A )
| 315 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _a ( UpperCAmelCase ) -> Tuple:
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] = SwinConfig(image_size=192 )
if "base" in model_name:
lowerCamelCase__ : List[str] = 6
lowerCamelCase__ : Any = 128
lowerCamelCase__ : Tuple = (2, 2, 18, 2)
lowerCamelCase__ : int = (4, 8, 16, 32)
elif "large" in model_name:
lowerCamelCase__ : Any = 12
lowerCamelCase__ : List[Any] = 192
lowerCamelCase__ : Any = (2, 2, 18, 2)
lowerCamelCase__ : Optional[int] = (6, 12, 24, 48)
else:
raise ValueError('''Model not supported, only supports base and large variants''' )
lowerCamelCase__ : List[str] = window_size
lowerCamelCase__ : Optional[int] = embed_dim
lowerCamelCase__ : Optional[int] = depths
lowerCamelCase__ : int = num_heads
return config
def _a ( UpperCAmelCase ) -> Any:
"""simple docstring"""
if "encoder.mask_token" in name:
lowerCamelCase__ : str = name.replace('''encoder.mask_token''' , '''embeddings.mask_token''' )
if "encoder.patch_embed.proj" in name:
lowerCamelCase__ : int = name.replace('''encoder.patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "encoder.patch_embed.norm" in name:
lowerCamelCase__ : Optional[int] = name.replace('''encoder.patch_embed.norm''' , '''embeddings.norm''' )
if "attn.proj" in name:
lowerCamelCase__ : int = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
lowerCamelCase__ : Optional[Any] = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
lowerCamelCase__ : int = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
lowerCamelCase__ : Optional[Any] = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
lowerCamelCase__ : Tuple = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
lowerCamelCase__ : Union[str, Any] = name.replace('''mlp.fc2''' , '''output.dense''' )
if name == "encoder.norm.weight":
lowerCamelCase__ : Union[str, Any] = '''layernorm.weight'''
if name == "encoder.norm.bias":
lowerCamelCase__ : List[Any] = '''layernorm.bias'''
if "decoder" in name:
pass
else:
lowerCamelCase__ : List[str] = '''swin.''' + name
return name
def _a ( UpperCAmelCase , UpperCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
lowerCamelCase__ : Optional[Any] = orig_state_dict.pop(UpperCAmelCase )
if "attn_mask" in key:
pass
elif "qkv" in key:
lowerCamelCase__ : str = key.split('''.''' )
lowerCamelCase__ : Tuple = int(key_split[2] )
lowerCamelCase__ : Any = int(key_split[4] )
lowerCamelCase__ : int = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
lowerCamelCase__ : Optional[Any] = val[:dim, :]
lowerCamelCase__ : Union[str, Any] = val[
dim : dim * 2, :
]
lowerCamelCase__ : List[str] = val[-dim:, :]
else:
lowerCamelCase__ : Tuple = val[
:dim
]
lowerCamelCase__ : List[Any] = val[
dim : dim * 2
]
lowerCamelCase__ : Tuple = val[
-dim:
]
else:
lowerCamelCase__ : Any = val
return orig_state_dict
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Dict:
"""simple docstring"""
lowerCamelCase__ : int = torch.load(UpperCAmelCase , map_location='''cpu''' )['''model''']
lowerCamelCase__ : Dict = get_swin_config(UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = SwinForMaskedImageModeling(UpperCAmelCase )
model.eval()
lowerCamelCase__ : Optional[int] = convert_state_dict(UpperCAmelCase , UpperCAmelCase )
model.load_state_dict(UpperCAmelCase )
lowerCamelCase__ : str = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowerCamelCase__ : Any = ViTImageProcessor(size={'''height''': 192, '''width''': 192} )
lowerCamelCase__ : str = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
lowerCamelCase__ : Any = image_processor(images=UpperCAmelCase , return_tensors='''pt''' )
with torch.no_grad():
lowerCamelCase__ : List[Any] = model(**UpperCAmelCase ).logits
print(outputs.keys() )
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(UpperCAmelCase )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(UpperCAmelCase )
if push_to_hub:
print(f"Pushing model and image processor for {model_name} to hub" )
model.push_to_hub(f"microsoft/{model_name}" )
image_processor.push_to_hub(f"microsoft/{model_name}" )
if __name__ == "__main__":
_A : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='swin-base-simmim-window6-192',
type=str,
choices=['swin-base-simmim-window6-192', 'swin-large-simmim-window12-192'],
help='Name of the Swin SimMIM model you\'d like to convert.',
)
parser.add_argument(
'--checkpoint_path',
default='/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth',
type=str,
help='Path to the original PyTorch checkpoint (.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 : List[str] = parser.parse_args()
convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 315 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : Dict = logging.get_logger(__name__)
lowercase : List[Any] = {
'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/config.json',
'distilbert-base-uncased-distilled-squad': (
'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'
),
'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/config.json',
'distilbert-base-cased-distilled-squad': (
'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'
),
'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json',
'distilbert-base-multilingual-cased': (
'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'
),
'distilbert-base-uncased-finetuned-sst-2-english': (
'https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'
),
}
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
_A = 'distilbert'
_A = {
'hidden_size': 'dim',
'num_attention_heads': 'n_heads',
'num_hidden_layers': 'n_layers',
}
def __init__( self :Tuple , a :Any=3_0_5_2_2 , a :List[str]=5_1_2 , a :Optional[Any]=False , a :int=6 , a :Any=1_2 , a :Optional[Any]=7_6_8 , a :Optional[int]=4 * 7_6_8 , a :Tuple=0.1 , a :int=0.1 , a :Union[str, Any]="gelu" , a :List[Any]=0.02 , a :Any=0.1 , a :List[str]=0.2 , a :List[Any]=0 , **a :List[Any] , ) -> Tuple:
__UpperCamelCase : Any = vocab_size
__UpperCamelCase : List[str] = max_position_embeddings
__UpperCamelCase : Tuple = sinusoidal_pos_embds
__UpperCamelCase : Union[str, Any] = n_layers
__UpperCamelCase : Tuple = n_heads
__UpperCamelCase : int = dim
__UpperCamelCase : Union[str, Any] = hidden_dim
__UpperCamelCase : List[str] = dropout
__UpperCamelCase : Dict = attention_dropout
__UpperCamelCase : Any = activation
__UpperCamelCase : Union[str, Any] = initializer_range
__UpperCamelCase : Optional[Any] = qa_dropout
__UpperCamelCase : Tuple = seq_classif_dropout
super().__init__(**a , pad_token_id=a )
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
@property
def _lowerCamelCase ( self :Optional[int] ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
__UpperCamelCase : Tuple = {0: "batch", 1: "choice", 2: "sequence"}
else:
__UpperCamelCase : Dict = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] ) | 715 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase : Optional[Any] = logging.get_logger(__name__)
lowercase : List[str] = {
'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class lowerCamelCase__ ( __lowercase , __lowercase):
'''simple docstring'''
_A = 'nat'
_A = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self :Optional[Any] , a :Any=4 , a :Any=3 , a :int=6_4 , a :Dict=[3, 4, 6, 5] , a :Dict=[2, 4, 8, 1_6] , a :Optional[Any]=7 , a :Any=3.0 , a :Optional[int]=True , a :int=0.0 , a :Union[str, Any]=0.0 , a :List[Any]=0.1 , a :str="gelu" , a :Union[str, Any]=0.02 , a :Tuple=1E-5 , a :str=0.0 , a :Optional[int]=None , a :Dict=None , **a :Optional[Any] , ) -> int:
super().__init__(**a )
__UpperCamelCase : Any = patch_size
__UpperCamelCase : str = num_channels
__UpperCamelCase : List[Any] = embed_dim
__UpperCamelCase : str = depths
__UpperCamelCase : str = len(a )
__UpperCamelCase : Optional[Any] = num_heads
__UpperCamelCase : Dict = kernel_size
__UpperCamelCase : Union[str, Any] = mlp_ratio
__UpperCamelCase : Union[str, Any] = qkv_bias
__UpperCamelCase : List[str] = hidden_dropout_prob
__UpperCamelCase : Any = attention_probs_dropout_prob
__UpperCamelCase : Any = drop_path_rate
__UpperCamelCase : Any = hidden_act
__UpperCamelCase : Tuple = layer_norm_eps
__UpperCamelCase : Dict = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__UpperCamelCase : int = int(embed_dim * 2 ** (len(a ) - 1) )
__UpperCamelCase : List[Any] = layer_scale_init_value
__UpperCamelCase : Optional[Any] = ["stem"] + [f'stage{idx}' for idx in range(1 , len(a ) + 1 )]
__UpperCamelCase , __UpperCamelCase : Any = get_aligned_output_features_output_indices(
out_features=a , out_indices=a , stage_names=self.stage_names ) | 94 | 0 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def __snake_case ( SCREAMING_SNAKE_CASE: Features ):
"""simple docstring"""
_lowerCAmelCase = np.inf
def set_batch_size(SCREAMING_SNAKE_CASE: FeatureType ) -> None:
nonlocal batch_size
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = min(SCREAMING_SNAKE_CASE , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = min(SCREAMING_SNAKE_CASE , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and feature.dtype == "binary":
_lowerCAmelCase = min(SCREAMING_SNAKE_CASE , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return None if batch_size is np.inf else batch_size
class _SCREAMING_SNAKE_CASE ( UpperCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[int] , UpperCAmelCase_ : NestedDataStructureLike[PathLike] , UpperCAmelCase_ : Optional[NamedSplit] = None , UpperCAmelCase_ : Optional[Features] = None , UpperCAmelCase_ : str = None , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : Optional[int] = None , **UpperCAmelCase_ : List[Any] , ) -> Tuple:
"""simple docstring"""
super().__init__(
UpperCAmelCase_ , split=UpperCAmelCase_ , features=UpperCAmelCase_ , cache_dir=UpperCAmelCase_ , keep_in_memory=UpperCAmelCase_ , streaming=UpperCAmelCase_ , num_proc=UpperCAmelCase_ , **UpperCAmelCase_ , )
_lowerCAmelCase = path_or_paths if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else {self.split: path_or_paths}
_lowerCAmelCase = _PACKAGED_DATASETS_MODULES['parquet'][1]
_lowerCAmelCase = Parquet(
cache_dir=UpperCAmelCase_ , data_files=UpperCAmelCase_ , features=UpperCAmelCase_ , hash=UpperCAmelCase_ , **UpperCAmelCase_ , )
def __lowerCamelCase ( self : int ) -> List[str]:
"""simple docstring"""
if self.streaming:
_lowerCAmelCase = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
self.builder.download_and_prepare(
download_config=UpperCAmelCase_ , download_mode=UpperCAmelCase_ , verification_mode=UpperCAmelCase_ , base_path=UpperCAmelCase_ , num_proc=self.num_proc , )
_lowerCAmelCase = self.builder.as_dataset(
split=self.split , verification_mode=UpperCAmelCase_ , in_memory=self.keep_in_memory )
return dataset
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Dict , UpperCAmelCase_ : Dataset , UpperCAmelCase_ : Union[PathLike, BinaryIO] , UpperCAmelCase_ : Optional[int] = None , **UpperCAmelCase_ : List[str] , ) -> Optional[int]:
"""simple docstring"""
_lowerCAmelCase = dataset
_lowerCAmelCase = path_or_buf
_lowerCAmelCase = batch_size or get_writer_batch_size(dataset.features )
_lowerCAmelCase = parquet_writer_kwargs
def __lowerCamelCase ( self : List[Any] ) -> int:
"""simple docstring"""
_lowerCAmelCase = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with open(self.path_or_buf , 'wb+' ) as buffer:
_lowerCAmelCase = self._write(file_obj=UpperCAmelCase_ , batch_size=UpperCAmelCase_ , **self.parquet_writer_kwargs )
else:
_lowerCAmelCase = self._write(file_obj=self.path_or_buf , batch_size=UpperCAmelCase_ , **self.parquet_writer_kwargs )
return written
def __lowerCamelCase ( self : List[Any] , UpperCAmelCase_ : BinaryIO , UpperCAmelCase_ : int , **UpperCAmelCase_ : Dict ) -> int:
"""simple docstring"""
_lowerCAmelCase = 0
_lowerCAmelCase = parquet_writer_kwargs.pop('path_or_buf' , UpperCAmelCase_ )
_lowerCAmelCase = self.dataset.features.arrow_schema
_lowerCAmelCase = pq.ParquetWriter(UpperCAmelCase_ , schema=UpperCAmelCase_ , **UpperCAmelCase_ )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , UpperCAmelCase_ ) , unit='ba' , disable=not logging.is_progress_bar_enabled() , desc='Creating parquet from Arrow format' , ):
_lowerCAmelCase = query_table(
table=self.dataset._data , key=slice(UpperCAmelCase_ , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(UpperCAmelCase_ )
written += batch.nbytes
writer.close()
return written
| 580 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_snake_case = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
journal={arXiv preprint arXiv:1905.00537},
year={2019}
}
'''
_snake_case = '''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''
_snake_case = '''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
predictions: list of predictions to score. Depending on the SuperGlUE subset:
- for \'record\': list of question-answer dictionaries with the following keys:
- \'idx\': index of the question as specified by the dataset
- \'prediction_text\': the predicted answer text
- for \'multirc\': list of question-answer dictionaries with the following keys:
- \'idx\': index of the question-answer pair as specified by the dataset
- \'prediction\': the predicted answer label
- otherwise: list of predicted labels
references: list of reference labels. Depending on the SuperGLUE subset:
- for \'record\': list of question-answers dictionaries with the following keys:
- \'idx\': index of the question as specified by the dataset
- \'answers\': list of possible answers
- otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
- for \'record\':
- \'exact_match\': Exact match between answer and gold answer
- \'f1\': F1 score
- for \'multirc\':
- \'exact_match\': Exact match between answer and gold answer
- \'f1_m\': Per-question macro-F1 score
- \'f1_a\': Average F1 score over all answers
- for \'axb\':
\'matthews_correlation\': Matthew Correlation
- for \'cb\':
- \'accuracy\': Accuracy
- \'f1\': F1 score
- for all others:
- \'accuracy\': Accuracy
Examples:
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\') # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0, \'f1\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
>>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
>>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 1.0, \'f1\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
>>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'matthews_correlation\': 1.0}
'''
def __snake_case ( SCREAMING_SNAKE_CASE: List[str] , SCREAMING_SNAKE_CASE: Dict ):
"""simple docstring"""
return float((preds == labels).mean() )
def __snake_case ( SCREAMING_SNAKE_CASE: Dict , SCREAMING_SNAKE_CASE: str , SCREAMING_SNAKE_CASE: Union[str, Any]="binary" ):
"""simple docstring"""
_lowerCAmelCase = simple_accuracy(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
_lowerCAmelCase = float(fa_score(y_true=SCREAMING_SNAKE_CASE , y_pred=SCREAMING_SNAKE_CASE , average=SCREAMING_SNAKE_CASE ) )
return {
"accuracy": acc,
"f1": fa,
}
def __snake_case ( SCREAMING_SNAKE_CASE: Any , SCREAMING_SNAKE_CASE: List[Any] ):
"""simple docstring"""
_lowerCAmelCase = {}
for id_pred, label in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = f"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
_lowerCAmelCase = id_pred['prediction']
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
_lowerCAmelCase = [(pred, label)]
_lowerCAmelCase , _lowerCAmelCase = [], []
for question, preds_labels in question_map.items():
_lowerCAmelCase , _lowerCAmelCase = zip(*SCREAMING_SNAKE_CASE )
_lowerCAmelCase = fa_score(y_true=SCREAMING_SNAKE_CASE , y_pred=SCREAMING_SNAKE_CASE , average='macro' )
fas.append(SCREAMING_SNAKE_CASE )
_lowerCAmelCase = int(sum(pred == label for pred, label in preds_labels ) == len(SCREAMING_SNAKE_CASE ) )
ems.append(SCREAMING_SNAKE_CASE )
_lowerCAmelCase = float(sum(SCREAMING_SNAKE_CASE ) / len(SCREAMING_SNAKE_CASE ) )
_lowerCAmelCase = sum(SCREAMING_SNAKE_CASE ) / len(SCREAMING_SNAKE_CASE )
_lowerCAmelCase = float(fa_score(y_true=SCREAMING_SNAKE_CASE , y_pred=[id_pred['prediction'] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _SCREAMING_SNAKE_CASE ( datasets.Metric ):
'''simple docstring'''
def __lowerCamelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
'You should supply a configuration name selected in '
'["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format='numpy' if not self.config_name == 'record' and not self.config_name == 'multirc' else None , )
def __lowerCamelCase ( self : int ) -> Union[str, Any]:
"""simple docstring"""
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value('int64' ),
"query": datasets.Value('int64' ),
},
"prediction_text": datasets.Value('string' ),
},
"references": {
"idx": {
"passage": datasets.Value('int64' ),
"query": datasets.Value('int64' ),
},
"answers": datasets.Sequence(datasets.Value('string' ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value('int64' ),
"paragraph": datasets.Value('int64' ),
"question": datasets.Value('int64' ),
},
"prediction": datasets.Value('int64' ),
},
"references": datasets.Value('int64' ),
}
else:
return {
"predictions": datasets.Value('int64' ),
"references": datasets.Value('int64' ),
}
def __lowerCamelCase ( self : Dict , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Union[str, Any] ) -> Dict:
"""simple docstring"""
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(UpperCAmelCase_ , UpperCAmelCase_ )}
elif self.config_name == "cb":
return acc_and_fa(UpperCAmelCase_ , UpperCAmelCase_ , fa_avg='macro' )
elif self.config_name == "record":
_lowerCAmelCase = [
{
'qas': [
{'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
for ref in references
]
}
]
_lowerCAmelCase = {pred['idx']['query']: pred['prediction_text'] for pred in predictions}
return evaluate_record(UpperCAmelCase_ , UpperCAmelCase_ )[0]
elif self.config_name == "multirc":
return evaluate_multirc(UpperCAmelCase_ , UpperCAmelCase_ )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(UpperCAmelCase_ , UpperCAmelCase_ )}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]' )
| 580 | 1 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __snake_case ( snake_case__ ):
"""simple docstring"""
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : float ) -> float:
'''simple docstring'''
return 0.0
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Tuple = min([-20, np.min(fft_results[1 : samplerate // 2 - 1])])
lowerCAmelCase_ : List[str] = max([20, np.max(fft_results[1 : samplerate // 2 - 1])])
return lowest, highest
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Tuple = 5_12
lowerCAmelCase_ : Union[str, Any] = [1] + [0] * (size - 1)
lowerCAmelCase_ : Any = [filter_type.process(snake_case__) for item in inputs]
lowerCAmelCase_ : Optional[int] = [0] * (samplerate - size) # zero-padding
outputs += filler
lowerCAmelCase_ : Optional[Any] = np.abs(np.fft.fft(snake_case__))
lowerCAmelCase_ : Any = 20 * np.logaa(snake_case__)
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1)
plt.xlabel("Frequency (Hz)")
plt.xscale("log")
# Display within reasonable bounds
lowerCAmelCase_ : Optional[Any] = get_bounds(snake_case__ , snake_case__)
plt.ylim(max([-80, bounds[0]]) , min([80, bounds[1]]))
plt.ylabel("Gain (dB)")
plt.plot(snake_case__)
plt.show()
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : List[Any] = 5_12
lowerCAmelCase_ : Tuple = [1] + [0] * (size - 1)
lowerCAmelCase_ : Optional[Any] = [filter_type.process(snake_case__) for item in inputs]
lowerCAmelCase_ : str = [0] * (samplerate - size) # zero-padding
outputs += filler
lowerCAmelCase_ : str = np.angle(np.fft.fft(snake_case__))
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1)
plt.xlabel("Frequency (Hz)")
plt.xscale("log")
plt.ylim(-2 * pi , 2 * pi)
plt.ylabel("Phase shift (Radians)")
plt.plot(np.unwrap(snake_case__ , -2 * pi))
plt.show()
| 683 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
'''processing_git''': ['''GitProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GitForCausalLM''',
'''GitModel''',
'''GitPreTrainedModel''',
'''GitVisionModel''',
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 683 | 1 |
"""simple docstring"""
A_ = """Alexander Joslin"""
import operator as op
from .stack import Stack
def lowercase ( lowerCAmelCase__ ):
lowerCamelCase_ = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
lowerCamelCase_ = Stack()
lowerCamelCase_ = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(lowerCAmelCase__ ) )
elif i in operators:
# RULE 2
operator_stack.push(lowerCAmelCase__ )
elif i == ")":
# RULE 4
lowerCamelCase_ = operator_stack.peek()
operator_stack.pop()
lowerCamelCase_ = operand_stack.peek()
operand_stack.pop()
lowerCamelCase_ = operand_stack.peek()
operand_stack.pop()
lowerCamelCase_ = operators[opr](lowerCAmelCase__ ,lowerCAmelCase__ )
operand_stack.push(lowerCAmelCase__ )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
A_ = """(5 + ((4 * 2) * (2 + 3)))"""
# answer = 45
print(f"{equation} = {dijkstras_two_stack_algorithm(equation)}")
| 29 |
"""simple docstring"""
from jiwer import compute_measures
import datasets
A_ = """\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
"""
A_ = """\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.
The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.
This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.
Word error rate can then be computed as:
WER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).
This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
"""
A_ = """
Compute WER score of transcribed segments against references.
Args:
references: List of references for each speech input.
predictions: List of transcriptions to score.
concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.
Returns:
(float): the word error rate
Examples:
>>> predictions = [\"this is the prediction\", \"there is an other sample\"]
>>> references = [\"this is the reference\", \"there is another one\"]
>>> wer = datasets.load_metric(\"wer\")
>>> wer_score = wer.compute(predictions=predictions, references=references)
>>> print(wer_score)
0.5
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCamelCase ( datasets.Metric ):
def UpperCAmelCase__ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/jitsi/jiwer/'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/Word_error_rate''',
] , )
def UpperCAmelCase__ ( self , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=False ):
if concatenate_texts:
return compute_measures(UpperCAmelCase , UpperCAmelCase )["wer"]
else:
lowerCamelCase_ = 0
lowerCamelCase_ = 0
for prediction, reference in zip(UpperCAmelCase , UpperCAmelCase ):
lowerCamelCase_ = compute_measures(UpperCAmelCase , UpperCAmelCase )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 29 | 1 |
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = len(_a)
SCREAMING_SNAKE_CASE : Optional[Any] = [[False] * (required_sum + 1) for _ in range(arr_len + 1)]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1):
SCREAMING_SNAKE_CASE : Dict = True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1):
SCREAMING_SNAKE_CASE : Union[str, Any] = False
for i in range(1 , arr_len + 1):
for j in range(1 , required_sum + 1):
if arr[i - 1] > j:
SCREAMING_SNAKE_CASE : Optional[int] = subset[i - 1][j]
if arr[i - 1] <= j:
SCREAMING_SNAKE_CASE : List[Any] = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod() | 710 |
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 ):
'''simple docstring'''
lowerCamelCase__ =10000
lowerCamelCase__ =None
lowerCamelCase__ =None
class _UpperCamelCase ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
lowerCamelCase__ =ParquetConfig
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def __UpperCamelCase ( self : Dict , a : List[Any] ) -> Tuple:
"""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}" )
SCREAMING_SNAKE_CASE : Tuple = dl_manager.download_and_extract(self.config.data_files )
if isinstance(a , (str, list, tuple) ):
SCREAMING_SNAKE_CASE : Dict = data_files
if isinstance(a , a ):
SCREAMING_SNAKE_CASE : Union[str, Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
SCREAMING_SNAKE_CASE : Optional[Any] = [dl_manager.iter_files(a ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
SCREAMING_SNAKE_CASE : str = []
for split_name, files in data_files.items():
if isinstance(a , a ):
SCREAMING_SNAKE_CASE : Optional[int] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
SCREAMING_SNAKE_CASE : Tuple = [dl_manager.iter_files(a ) 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(a ):
with open(a , "rb" ) as f:
SCREAMING_SNAKE_CASE : Dict = datasets.Features.from_arrow_schema(pq.read_schema(a ) )
break
splits.append(datasets.SplitGenerator(name=a , gen_kwargs={"files": files} ) )
return splits
def __UpperCamelCase ( self : Dict , a : pa.Table ) -> pa.Table:
"""simple docstring"""
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
SCREAMING_SNAKE_CASE : str = table_cast(a , self.info.features.arrow_schema )
return pa_table
def __UpperCamelCase ( self : List[str] , a : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : 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(a ) ):
with open(a , "rb" ) as f:
SCREAMING_SNAKE_CASE : Optional[int] = pq.ParquetFile(a )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
SCREAMING_SNAKE_CASE : int = 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(a )
except ValueError as e:
logger.error(F"Failed to read file '{file}' with error {type(a )}: {e}" )
raise | 193 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GPTBigCodeForSequenceClassification""",
"""GPTBigCodeForTokenClassification""",
"""GPTBigCodeForCausalLM""",
"""GPTBigCodeModel""",
"""GPTBigCodePreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 67 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Accelerator
def snake_case_ (__A : Tuple ) -> str:
__lowerCAmelCase : List[str] = fname.split(os.path.sep )[-1]
return re.search(r"""^(.*)_\d+\.jpg$""" , __A ).groups()[0]
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def __init__( self : Optional[int] , lowerCAmelCase : str , lowerCAmelCase : List[str]=None , lowerCAmelCase : Tuple=None ) -> int:
"""simple docstring"""
__lowerCAmelCase : int = file_names
__lowerCAmelCase : Dict = image_transform
__lowerCAmelCase : int = label_to_id
def __len__( self : Any ) -> Union[str, Any]:
"""simple docstring"""
return len(self.file_names )
def __getitem__( self : Dict , lowerCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : List[str] = self.file_names[idx]
__lowerCAmelCase : Any = PIL.Image.open(lowerCAmelCase )
__lowerCAmelCase : List[Any] = raw_image.convert("""RGB""" )
if self.image_transform is not None:
__lowerCAmelCase : Union[str, Any] = self.image_transform(lowerCAmelCase )
__lowerCAmelCase : int = extract_label(lowerCAmelCase )
if self.label_to_id is not None:
__lowerCAmelCase : Union[str, Any] = self.label_to_id[label]
return {"image": image, "label": label}
def snake_case_ (__A : Optional[Any] , __A : Union[str, Any] ) -> Tuple:
# Initialize accelerator
if args.with_tracking:
__lowerCAmelCase : Tuple = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="""all""" , project_dir=args.project_dir )
else:
__lowerCAmelCase : Optional[int] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowerCAmelCase : Union[str, Any] = config["""lr"""]
__lowerCAmelCase : List[str] = int(config["""num_epochs"""] )
__lowerCAmelCase : List[Any] = int(config["""seed"""] )
__lowerCAmelCase : Optional[int] = int(config["""batch_size"""] )
__lowerCAmelCase : int = config["""image_size"""]
if not isinstance(__A , (list, tuple) ):
__lowerCAmelCase : Optional[Any] = (image_size, image_size)
# Parse out whether we are saving every epoch or after a certain number of batches
if hasattr(args.checkpointing_steps , """isdigit""" ):
if args.checkpointing_steps == "epoch":
__lowerCAmelCase : int = args.checkpointing_steps
elif args.checkpointing_steps.isdigit():
__lowerCAmelCase : List[str] = int(args.checkpointing_steps )
else:
raise ValueError(
f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''' )
else:
__lowerCAmelCase : List[str] = None
# We need to initialize the trackers we use, and also store our configuration
if args.with_tracking:
__lowerCAmelCase : Any = os.path.split(__A )[-1].split(""".""" )[0]
accelerator.init_trackers(__A , __A )
# Grab all the image filenames
__lowerCAmelCase : Optional[int] = [os.path.join(args.data_dir , __A ) for fname in os.listdir(args.data_dir ) if fname.endswith(""".jpg""" )]
# Build the label correspondences
__lowerCAmelCase : Tuple = [extract_label(__A ) for fname in file_names]
__lowerCAmelCase : Union[str, Any] = list(set(__A ) )
id_to_label.sort()
__lowerCAmelCase : Optional[Any] = {lbl: i for i, lbl in enumerate(__A )}
# Set the seed before splitting the data.
np.random.seed(__A )
torch.manual_seed(__A )
torch.cuda.manual_seed_all(__A )
# Split our filenames between train and validation
__lowerCAmelCase : str = np.random.permutation(len(__A ) )
__lowerCAmelCase : Any = int(0.8 * len(__A ) )
__lowerCAmelCase : List[str] = random_perm[:cut]
__lowerCAmelCase : Tuple = random_perm[cut:]
# For training we use a simple RandomResizedCrop
__lowerCAmelCase : Dict = Compose([RandomResizedCrop(__A , scale=(0.5, 1.0) ), ToTensor()] )
__lowerCAmelCase : List[str] = PetsDataset(
[file_names[i] for i in train_split] , image_transform=__A , label_to_id=__A )
# For evaluation, we use a deterministic Resize
__lowerCAmelCase : Optional[Any] = Compose([Resize(__A ), ToTensor()] )
__lowerCAmelCase : List[Any] = PetsDataset([file_names[i] for i in eval_split] , image_transform=__A , label_to_id=__A )
# Instantiate dataloaders.
__lowerCAmelCase : Union[str, Any] = DataLoader(__A , shuffle=__A , batch_size=__A , num_workers=4 )
__lowerCAmelCase : Union[str, Any] = DataLoader(__A , shuffle=__A , batch_size=__A , num_workers=4 )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowerCAmelCase : List[Any] = create_model("""resnet50d""" , pretrained=__A , num_classes=len(__A ) )
# 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).
__lowerCAmelCase : Optional[int] = model.to(accelerator.device )
# Freezing the base model
for param in model.parameters():
__lowerCAmelCase : Dict = False
for param in model.get_classifier().parameters():
__lowerCAmelCase : Union[str, Any] = True
# We normalize the batches of images to be a bit faster.
__lowerCAmelCase : Tuple = torch.tensor(model.default_cfg["""mean"""] )[None, :, None, None].to(accelerator.device )
__lowerCAmelCase : Union[str, Any] = torch.tensor(model.default_cfg["""std"""] )[None, :, None, None].to(accelerator.device )
# Instantiate optimizer
__lowerCAmelCase : int = torch.optim.Adam(params=model.parameters() , lr=lr / 2_5 )
# Instantiate learning rate scheduler
__lowerCAmelCase : List[Any] = OneCycleLR(optimizer=__A , max_lr=__A , epochs=__A , steps_per_epoch=len(__A ) )
# 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.
__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase : Optional[Any] = accelerator.prepare(
__A , __A , __A , __A , __A )
# We need to keep track of how many total steps we have iterated over
__lowerCAmelCase : Tuple = 0
# We also need to keep track of the starting epoch so files are named properly
__lowerCAmelCase : Union[str, Any] = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}''' )
accelerator.load_state(args.resume_from_checkpoint )
__lowerCAmelCase : Dict = os.path.basename(args.resume_from_checkpoint )
else:
# Get the most recent checkpoint
__lowerCAmelCase : Tuple = [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
dirs.sort(key=os.path.getctime )
__lowerCAmelCase : Union[str, Any] = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
__lowerCAmelCase : Any = os.path.splitext(__A )[0]
if "epoch" in training_difference:
__lowerCAmelCase : List[str] = int(training_difference.replace("""epoch_""" , """""" ) ) + 1
__lowerCAmelCase : Any = None
else:
__lowerCAmelCase : Optional[int] = int(training_difference.replace("""step_""" , """""" ) )
__lowerCAmelCase : int = resume_step // len(__A )
resume_step -= starting_epoch * len(__A )
# Now we train the model
for epoch in range(__A , __A ):
model.train()
if args.with_tracking:
__lowerCAmelCase : Dict = 0
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We need to skip steps until we reach the resumed step
__lowerCAmelCase : str = accelerator.skip_first_batches(__A , __A )
overall_step += resume_step
else:
# After the first iteration though, we need to go back to the original dataloader
__lowerCAmelCase : List[str] = train_dataloader
for batch in active_dataloader:
# We could avoid this line since we set the accelerator with `device_placement=True`.
__lowerCAmelCase : List[str] = {k: v.to(accelerator.device ) for k, v in batch.items()}
__lowerCAmelCase : Tuple = (batch["""image"""] - mean) / std
__lowerCAmelCase : List[Any] = model(__A )
__lowerCAmelCase : str = torch.nn.functional.cross_entropy(__A , batch["""label"""] )
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(__A )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
if isinstance(__A , __A ):
__lowerCAmelCase : Union[str, Any] = f'''step_{overall_step}'''
if overall_step % checkpointing_steps == 0:
if args.output_dir is not None:
__lowerCAmelCase : Optional[Any] = os.path.join(args.output_dir , __A )
accelerator.save_state(__A )
model.eval()
__lowerCAmelCase : str = 0
__lowerCAmelCase : Union[str, Any] = 0
for step, batch in enumerate(__A ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
__lowerCAmelCase : Union[str, Any] = {k: v.to(accelerator.device ) for k, v in batch.items()}
__lowerCAmelCase : Optional[int] = (batch["""image"""] - mean) / std
with torch.no_grad():
__lowerCAmelCase : List[Any] = model(__A )
__lowerCAmelCase : Optional[Any] = outputs.argmax(dim=-1 )
__lowerCAmelCase ,__lowerCAmelCase : Any = accelerator.gather_for_metrics((predictions, batch["""label"""]) )
__lowerCAmelCase : int = predictions == references
num_elems += accurate_preds.shape[0]
accurate += accurate_preds.long().sum()
__lowerCAmelCase : Optional[int] = accurate.item() / num_elems
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}: {1_0_0 * eval_metric:.2f}''' )
if args.with_tracking:
accelerator.log(
{
"""accuracy""": 1_0_0 * eval_metric,
"""train_loss""": total_loss.item() / len(__A ),
"""epoch""": epoch,
} , step=__A , )
if checkpointing_steps == "epoch":
__lowerCAmelCase : Union[str, Any] = f'''epoch_{epoch}'''
if args.output_dir is not None:
__lowerCAmelCase : Any = os.path.join(args.output_dir , __A )
accelerator.save_state(__A )
if args.with_tracking:
accelerator.end_training()
def snake_case_ () -> Any:
__lowerCAmelCase : Tuple = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument("""--data_dir""" , required=__A , help="""The data folder on disk.""" )
parser.add_argument("""--fp16""" , action="""store_true""" , help="""If passed, will use FP16 training.""" )
parser.add_argument(
"""--mixed_precision""" , type=__A , default=__A , 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.""" )
parser.add_argument(
"""--checkpointing_steps""" , type=__A , default=__A , help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.""" , )
parser.add_argument(
"""--output_dir""" , type=__A , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--resume_from_checkpoint""" , type=__A , default=__A , help="""If the training should continue from a checkpoint folder.""" , )
parser.add_argument(
"""--with_tracking""" , action="""store_true""" , help="""Whether to load in all available experiment trackers from the environment and use them for logging.""" , )
parser.add_argument(
"""--project_dir""" , type=__A , default="""logs""" , help="""Location on where to store experiment tracking logs` and relevent project information""" , )
__lowerCAmelCase : int = parser.parse_args()
__lowerCAmelCase : Tuple = {"""lr""": 3e-2, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 6_4, """image_size""": 2_2_4}
training_function(__A , __A )
if __name__ == "__main__":
main()
| 651 | 0 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
A_ : Optional[Any] ="""\
@misc{chen2021evaluating,
title={Evaluating Large Language Models Trained on Code},
author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \
and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \
and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \
and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \
and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \
and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \
and Mohammad Bavarian and Clemens Winter and Philippe Tillet \
and Felipe Petroski Such and Dave Cummings and Matthias Plappert \
and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \
and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \
and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \
and William Saunders and Christopher Hesse and Andrew N. Carr \
and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \
and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \
and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \
and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
year={2021},
eprint={2107.03374},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
"""
A_ : int ="""\
This metric implements the evaluation harness for the HumanEval problem solving dataset
described in the paper \"Evaluating Large Language Models Trained on Code\"
(https://arxiv.org/abs/2107.03374).
"""
A_ : List[str] ="""
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of candidates to evaluate. Each candidates should be a list
of strings with several code candidates to solve the problem.
references: a list with a test for each prediction. Each test should evaluate the
correctness of a code candidate.
k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])
num_workers: number of workers used to evaluate the canidate programs (Default: 4).
timeout:
Returns:
pass_at_k: dict with pass rates for each k
results: dict with granular results of each unittest
Examples:
>>> code_eval = datasets.load_metric(\"code_eval\")
>>> test_cases = [\"assert add(2,3)==5\"]
>>> candidates = [[\"def add(a,b): return a*b\", \"def add(a, b): return a+b\"]]
>>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])
>>> print(pass_at_k)
{'pass@1': 0.5, 'pass@2': 1.0}
"""
A_ : Union[str, Any] ="""
################################################################################
!!!WARNING!!!
################################################################################
The \"code_eval\" metric executes untrusted model-generated code in Python.
Although it is highly unlikely that model-generated code will do something
overtly malicious in response to this test suite, model-generated code may act
destructively due to a lack of model capability or alignment.
Users are strongly encouraged to sandbox this evaluation suite so that it
does not perform destructive actions on their host or network. For more
information on how OpenAI sandboxes its code, see the paper \"Evaluating Large
Language Models Trained on Code\" (https://arxiv.org/abs/2107.03374).
Once you have read this disclaimer and taken appropriate precautions,
set the environment variable HF_ALLOW_CODE_EVAL=\"1\". Within Python you can to this
with:
>>> import os
>>> os.environ[\"HF_ALLOW_CODE_EVAL\"] = \"1\"
################################################################################\
"""
A_ : Optional[int] ="""The MIT License
Copyright (c) OpenAI (https://openai.com)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the \"Software\"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE."""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __a ( datasets.Metric ):
def snake_case_ ( self ):
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('string' ) ),
'references': datasets.Value('string' ),
} ) , homepage='https://github.com/openai/human-eval' , codebase_urls=['https://github.com/openai/human-eval'] , reference_urls=['https://github.com/openai/human-eval'] , license=_LICENSE , )
def snake_case_ ( self , a__ , a__ , a__=[1, 10, 1_00] , a__=4 , a__=3.0 ):
if os.getenv('HF_ALLOW_CODE_EVAL' , 0 ) != "1":
raise ValueError(_WARNING )
if os.name == "nt":
raise NotImplementedError('This metric is currently not supported on Windows.' )
with ThreadPoolExecutor(max_workers=__a ) as executor:
_lowerCamelCase = []
_lowerCamelCase = Counter()
_lowerCamelCase = 0
_lowerCamelCase = defaultdict(__a )
for task_id, (candidates, test_case) in enumerate(zip(__a , __a ) ):
for candidate in candidates:
_lowerCamelCase = candidate + '\n' + test_case
_lowerCamelCase = (test_program, timeout, task_id, completion_id[task_id])
_lowerCamelCase = executor.submit(__a , *__a )
futures.append(__a )
completion_id[task_id] += 1
n_samples += 1
for future in as_completed(__a ):
_lowerCamelCase = future.result()
results[result["task_id"]].append((result['completion_id'], result) )
_lowerCamelCase , _lowerCamelCase = [], []
for result in results.values():
result.sort()
_lowerCamelCase = [r[1]['passed'] for r in result]
total.append(len(__a ) )
correct.append(sum(__a ) )
_lowerCamelCase = np.array(__a )
_lowerCamelCase = np.array(__a )
_lowerCamelCase = k
_lowerCamelCase = {F'pass@{k}': estimate_pass_at_k(__a , __a , __a ).mean() for k in ks if (total >= k).all()}
return pass_at_k, results
def SCREAMING_SNAKE_CASE_ ( snake_case : Union[str, Any] , snake_case : str , snake_case : Optional[int] )-> Dict:
def estimator(snake_case : List[Any] , snake_case : int , snake_case : List[Any] ) -> float:
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) )
if isinstance(__snake_case , __snake_case ):
_lowerCamelCase = itertools.repeat(__snake_case , len(__snake_case ) )
else:
assert len(__snake_case ) == len(__snake_case )
_lowerCamelCase = iter(__snake_case )
return np.array([estimator(int(__snake_case ) , int(__snake_case ) , __snake_case ) for n, c in zip(__snake_case , __snake_case )] )
| 712 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Optional[Any] =logging.get_logger(__name__)
A_ : Optional[Any] ={
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class __a ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE__ : int = "speech_to_text_2"
SCREAMING_SNAKE_CASE__ : int = ["past_key_values"]
SCREAMING_SNAKE_CASE__ : Any = {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}
def __init__( self , a__=1_00_00 , a__=6 , a__=20_48 , a__=4 , a__=0.0 , a__=True , a__="relu" , a__=2_56 , a__=0.1 , a__=0.0 , a__=0.0 , a__=0.02 , a__=2 , a__=True , a__=1 , a__=0 , a__=2 , a__=10_24 , **a__ , ):
_lowerCamelCase = vocab_size
_lowerCamelCase = d_model
_lowerCamelCase = decoder_ffn_dim
_lowerCamelCase = decoder_layers
_lowerCamelCase = decoder_attention_heads
_lowerCamelCase = dropout
_lowerCamelCase = attention_dropout
_lowerCamelCase = activation_dropout
_lowerCamelCase = activation_function
_lowerCamelCase = init_std
_lowerCamelCase = decoder_layerdrop
_lowerCamelCase = use_cache
_lowerCamelCase = decoder_layers
_lowerCamelCase = scale_embedding # scale factor will be sqrt(d_model) if True
_lowerCamelCase = max_target_positions
super().__init__(
pad_token_id=a__ , bos_token_id=a__ , eos_token_id=a__ , decoder_start_token_id=a__ , **a__ , )
| 222 | 0 |
"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
_lowercase = '''__DUMMY_TRANSFORMERS_USER__'''
_lowercase = '''Dummy User'''
_lowercase = '''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'''
_lowercase = '''https://hub-ci.huggingface.co'''
_lowercase = CI_HUB_ENDPOINT + '''/datasets/{repo_id}/resolve/{revision}/{path}'''
_lowercase = CI_HUB_ENDPOINT + '''/{repo_id}/resolve/{revision}/{filename}'''
_lowercase = Path('''~/.huggingface/hub_ci_token''').expanduser()
@pytest.fixture
def _snake_case ( snake_case__ : List[Any] ):
monkeypatch.setattr(
'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE' , snake_case__ )
@pytest.fixture
def _snake_case ( snake_case__ : int ):
monkeypatch.setattr('datasets.config.HF_ENDPOINT' , snake_case__ )
monkeypatch.setattr('datasets.config.HUB_DATASETS_URL' , snake_case__ )
@pytest.fixture
def _snake_case ( snake_case__ : List[Any] ):
monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token' , snake_case__ )
@pytest.fixture
def _snake_case ( snake_case__ : Tuple , snake_case__ : Any ):
HfFolder.save_token(snake_case__ )
yield
HfFolder.delete_token()
@pytest.fixture(scope='session' )
def _snake_case ( ):
return HfApi(endpoint=snake_case__ )
@pytest.fixture(scope='session' )
def _snake_case ( snake_case__ : HfApi ):
A = HfFolder.get_token()
HfFolder.save_token(snake_case__ )
yield CI_HUB_USER_TOKEN
if previous_token is not None:
HfFolder.save_token(snake_case__ )
@pytest.fixture
def _snake_case ( snake_case__ : Optional[int] ):
def _cleanup_repo(snake_case__ : Dict ):
hf_api.delete_repo(snake_case__ , token=snake_case__ , repo_type='dataset' )
return _cleanup_repo
@pytest.fixture
def _snake_case ( snake_case__ : Optional[Any] ):
@contextmanager
def _temporary_repo(snake_case__ : Dict ):
try:
yield repo_id
finally:
cleanup_repo(snake_case__ )
return _temporary_repo
@pytest.fixture(scope='session' )
def _snake_case ( snake_case__ : HfApi , snake_case__ : Union[str, Any] , snake_case__ : List[Any] ):
A = F'repo_txt_data-{int(time.time() * 10e3 )}'
A = F'{CI_HUB_USER}/{repo_name}'
hf_api.create_repo(snake_case__ , token=snake_case__ , repo_type='dataset' , private=snake_case__ )
hf_api.upload_file(
token=snake_case__ , path_or_fileobj=str(snake_case__ ) , path_in_repo='data/text_data.txt' , repo_id=snake_case__ , repo_type='dataset' , )
yield repo_id
try:
hf_api.delete_repo(snake_case__ , token=snake_case__ , repo_type='dataset' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def _snake_case ( snake_case__ : str , snake_case__ : Dict , snake_case__ : Dict ):
return hf_private_dataset_repo_txt_data_
@pytest.fixture(scope='session' )
def _snake_case ( snake_case__ : HfApi , snake_case__ : Any , snake_case__ : str ):
A = F'repo_zipped_txt_data-{int(time.time() * 10e3 )}'
A = F'{CI_HUB_USER}/{repo_name}'
hf_api.create_repo(snake_case__ , token=snake_case__ , repo_type='dataset' , private=snake_case__ )
hf_api.upload_file(
token=snake_case__ , path_or_fileobj=str(snake_case__ ) , path_in_repo='data.zip' , repo_id=snake_case__ , repo_type='dataset' , )
yield repo_id
try:
hf_api.delete_repo(snake_case__ , token=snake_case__ , repo_type='dataset' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def _snake_case ( snake_case__ : Union[str, Any] , snake_case__ : str , snake_case__ : Dict ):
return hf_private_dataset_repo_zipped_txt_data_
@pytest.fixture(scope='session' )
def _snake_case ( snake_case__ : HfApi , snake_case__ : Union[str, Any] , snake_case__ : Optional[int] ):
A = F'repo_zipped_img_data-{int(time.time() * 10e3 )}'
A = F'{CI_HUB_USER}/{repo_name}'
hf_api.create_repo(snake_case__ , token=snake_case__ , repo_type='dataset' , private=snake_case__ )
hf_api.upload_file(
token=snake_case__ , path_or_fileobj=str(snake_case__ ) , path_in_repo='data.zip' , repo_id=snake_case__ , repo_type='dataset' , )
yield repo_id
try:
hf_api.delete_repo(snake_case__ , token=snake_case__ , repo_type='dataset' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def _snake_case ( snake_case__ : Optional[int] , snake_case__ : int , snake_case__ : Union[str, Any] ):
return hf_private_dataset_repo_zipped_img_data_ | 91 |
"""simple docstring"""
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
# ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
# --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
# 1. --tf32 0 --fp16 0
# 2. --tf32 0 --fp16 1
# 3. --tf32 0 --bf16 1
# 4. --tf32 1 --fp16 0
# 5. --tf32 1 --fp16 1
# 6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
# --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
# 1. --tf32 0
# 2. --tf32 0 --fp16
# 3. --tf32 0 --bf16
# 4. --tf32 1
# 5. --tf32 1 --fp16
# 6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
# `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation | Train | Diff | Train |
# | | samples | % | loss |
# | | per | | |
# | | second | | |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0 | 285.11 | 0 | 2.51 |
# | --tf32 1 | 342.09 | 20 | 2.51 |
# | --fp16 --tf32 0 | 423.49 | 49 | 2.51 |
# | --fp16 --tf32 1 | 423.13 | 48 | 2.51 |
# | --bf16 --tf32 0 | 416.80 | 46 | 2.52 |
# | --bf16 --tf32 1 | 415.87 | 46 | 2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
# --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#
import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean
import pandas as pd
import torch
from tqdm import tqdm
import transformers
_lowercase = float('''nan''')
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : List[str] ,A_ : Tuple ) -> Any:
A = sys.stdout
A = open(A_ ,'a' )
def __getattr__( self : int ,A_ : Optional[Any] ) -> Tuple:
return getattr(self.stdout ,A_ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,A_ : Optional[int] ) -> str:
self.stdout.write(A_ )
# strip tqdm codes
self.file.write(re.sub(R'^.*\r' ,'' ,A_ ,0 ,re.M ) )
def _snake_case ( snake_case__ : Optional[Any]=80 , snake_case__ : List[str]=False ):
A = []
# deal with critical env vars
A = ['CUDA_VISIBLE_DEVICES']
for key in env_keys:
A = os.environ.get(snake_case__ , snake_case__ )
if val is not None:
cmd.append(F'{key}={val}' )
# python executable (not always needed if the script is executable)
A = sys.executable if full_python_path else sys.executable.split('/' )[-1]
cmd.append(snake_case__ )
# now the normal args
cmd += list(map(shlex.quote , sys.argv ) )
# split up into up to MAX_WIDTH lines with shell multi-line escapes
A = []
A = ''
while len(snake_case__ ) > 0:
current_line += F'{cmd.pop(0 )} '
if len(snake_case__ ) == 0 or len(snake_case__ ) + len(cmd[0] ) + 1 > max_width - 1:
lines.append(snake_case__ )
A = ''
return "\\\n".join(snake_case__ )
def _snake_case ( snake_case__ : str , snake_case__ : str ):
# unwrap multi-line input
A = re.sub(r'[\\\n]+' , ' ' , args.base_cmd )
# remove --output_dir if any and set our own
A = re.sub('--output_dir\s+[^\s]+' , '' , args.base_cmd )
args.base_cmd += F' --output_dir {output_dir}'
# ensure we have --overwrite_output_dir
A = re.sub('--overwrite_output_dir\s+' , '' , args.base_cmd )
args.base_cmd += " --overwrite_output_dir"
return [sys.executable] + shlex.split(args.base_cmd )
def _snake_case ( snake_case__ : List[str] , snake_case__ : Union[str, Any] , snake_case__ : str , snake_case__ : int , snake_case__ : Optional[Any] , snake_case__ : Any , snake_case__ : List[Any] ):
# Enable to debug everything but the run itself, to do it fast and see the progress.
# This is useful for debugging the output formatting quickly - we can remove it later once
# everybody is happy with the output
if 0:
import random
from time import sleep
sleep(0 )
return dict(
{k: random.uniform(0 , 100 ) for k in metric_keys} , **{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6666, 222.22222222] )} , )
A = subprocess.run(snake_case__ , capture_output=snake_case__ , text=snake_case__ )
if verbose:
print('STDOUT' , result.stdout )
print('STDERR' , result.stderr )
# save the streams
A = variation.replace(' ' , '-' )
with open(Path(snake_case__ ) / F'log.{prefix}.stdout.txt' , 'w' ) as f:
f.write(result.stdout )
with open(Path(snake_case__ ) / F'log.{prefix}.stderr.txt' , 'w' ) as f:
f.write(result.stderr )
if result.returncode != 0:
if verbose:
print('failed' )
return {target_metric_key: nan}
with io.open(F'{output_dir}/all_results.json' , 'r' , encoding='utf-8' ) as f:
A = json.load(snake_case__ )
# filter out just the keys we want
return {k: v for k, v in metrics.items() if k in metric_keys}
def _snake_case ( snake_case__ : str , snake_case__ : str , snake_case__ : str , snake_case__ : List[str] , snake_case__ : Optional[int] , snake_case__ : Tuple , snake_case__ : List[Any] , snake_case__ : Tuple , snake_case__ : Dict , snake_case__ : Optional[Any] , ):
A = []
A = []
A = F'{id}: {variation:<{longest_variation_len}}'
A = F'{preamble}: '
A = set(report_metric_keys + [target_metric_key] )
for i in tqdm(range(snake_case__ ) , desc=snake_case__ , leave=snake_case__ ):
A = process_run_single(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
A = single_run_metrics[target_metric_key]
if not math.isnan(snake_case__ ):
metrics.append(snake_case__ )
results.append(snake_case__ )
outcome += "✓"
else:
outcome += "✘"
A = F'\33[2K\r{outcome}'
if len(snake_case__ ) > 0:
A = {k: fmean([x[k] for x in metrics] ) for k in metrics[0].keys()}
A = round(mean_metrics[target_metric_key] , 2 )
A = F'{outcome} {mean_target}'
if len(snake_case__ ) > 1:
results_str += F' {tuple(round(snake_case__ , 2 ) for x in results )}'
print(snake_case__ )
A = variation
return mean_metrics
else:
print(snake_case__ )
return {variation_key: variation, target_metric_key: nan}
def _snake_case ( ):
A = torch.cuda.get_device_properties(torch.device('cuda' ) )
return F'\nDatetime : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S" )}\n\nSoftware:\ntransformers: {transformers.__version__}\ntorch : {torch.__version__}\ncuda : {torch.version.cuda}\npython : {platform.python_version()}\n\nHardware:\n{torch.cuda.device_count()} GPUs : {properties.name}, {properties.total_memory/2**30:0.2f}GB\n'
def _snake_case ( snake_case__ : List[str] , snake_case__ : Tuple , snake_case__ : Tuple , snake_case__ : Tuple , snake_case__ : Union[str, Any] ):
A = pd.DataFrame(snake_case__ )
A = 'variation'
A = 'diff_%'
A = nan
if base_variation is not None and len(df[df[variation_key] == base_variation] ):
# this may still return nan
A = df.loc[df[variation_key] == base_variation][target_metric_key].item()
if math.isnan(snake_case__ ):
# as a fallback, use the minimal value as the sentinel
A = df.loc[df[target_metric_key] != nan][target_metric_key].min()
# create diff column if possible
if not math.isnan(snake_case__ ):
A = df.apply(
lambda snake_case__ : round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value )
if not math.isnan(r[target_metric_key] )
else 0 , axis='columns' , )
# re-order columns
A = [variation_key, target_metric_key, diff_key, *report_metric_keys]
A = df.reindex(snake_case__ , axis='columns' ) # reorder cols
# capitalize
A = df.rename(str.capitalize , axis='columns' )
# make the cols as narrow as possible
A = df.rename(lambda snake_case__ : c.replace('_' , '<br>' ) , axis='columns' )
A = df.rename(lambda snake_case__ : c.replace('_' , '\n' ) , axis='columns' )
A = ['', 'Copy between the cut-here-lines and paste as is to github or a forum']
report += ["----------8<-----------------8<--------"]
report += ["*** Results:", df_github.to_markdown(index=snake_case__ , floatfmt='.2f' )]
report += ["```"]
report += ["*** Setup:", get_versions()]
report += ["*** The benchmark command line was:", get_original_command()]
report += ["```"]
report += ["----------8<-----------------8<--------"]
report += ["*** Results (console):", df_console.to_markdown(index=snake_case__ , floatfmt='.2f' )]
print('\n\n'.join(snake_case__ ) )
def _snake_case ( ):
A = argparse.ArgumentParser()
parser.add_argument(
'--base-cmd' , default=snake_case__ , type=snake_case__ , required=snake_case__ , help='Base cmd' , )
parser.add_argument(
'--variations' , default=snake_case__ , type=snake_case__ , nargs='+' , required=snake_case__ , help='Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\'' , )
parser.add_argument(
'--base-variation' , default=snake_case__ , type=snake_case__ , help='Baseline variation to compare to. if None the minimal target value will be used to compare against' , )
parser.add_argument(
'--target-metric-key' , default=snake_case__ , type=snake_case__ , required=snake_case__ , help='Target metric key in output_dir/all_results.json, e.g., train_samples_per_second' , )
parser.add_argument(
'--report-metric-keys' , default='' , type=snake_case__ , help='Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., \'train_loss train_samples' , )
parser.add_argument(
'--repeat-times' , default=1 , type=snake_case__ , help='How many times to re-run each variation - an average will be reported' , )
parser.add_argument(
'--output_dir' , default='output_benchmark' , type=snake_case__ , help='The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked' , )
parser.add_argument(
'--verbose' , default=snake_case__ , action='store_true' , help='Whether to show the outputs of each run or just the benchmark progress' , )
A = parser.parse_args()
A = args.output_dir
Path(snake_case__ ).mkdir(exist_ok=snake_case__ )
A = get_base_command(snake_case__ , snake_case__ )
# split each dimension into its --foo variations
A = [list(map(str.strip , re.split(r'\|' , snake_case__ ) ) ) for x in args.variations]
# build a cartesian product of dimensions and convert those back into cmd-line arg strings,
# while stripping white space for inputs that were empty
A = list(map(str.strip , map(' '.join , itertools.product(*snake_case__ ) ) ) )
A = max(len(snake_case__ ) for x in variations )
# split wanted keys
A = args.report_metric_keys.split()
# capture prints into a log file for convenience
A = F'benchmark-report-{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S" )}.txt'
print(F'\nNote: each run\'s output is also logged under {output_dir}/log.*.std*.txt' )
print(F'and this script\'s output is also piped into {report_fn}' )
A = Tee(snake_case__ )
print(F'\n*** Running {len(snake_case__ )} benchmarks:' )
print(F'Base command: {" ".join(snake_case__ )}' )
A = 'variation'
A = []
for id, variation in enumerate(tqdm(snake_case__ , desc='Total completion: ' , leave=snake_case__ ) ):
A = base_cmd + variation.split()
results.append(
process_run(
id + 1 , snake_case__ , snake_case__ , snake_case__ , snake_case__ , args.target_metric_key , snake_case__ , args.repeat_times , snake_case__ , args.verbose , ) )
process_results(snake_case__ , args.target_metric_key , snake_case__ , args.base_variation , snake_case__ )
if __name__ == "__main__":
main() | 91 | 1 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / """utils"""))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class _UpperCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self ):
# A mock response for an HTTP head request to emulate server down
A_ : Tuple = mock.Mock()
A_ : List[str] = 500
A_ : Any = {}
A_ : Union[str, Any] = HTTPError
A_ : Tuple = {}
# Download this model to make sure it's in the cache.
A_ : Any = BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("""requests.Session.request""" , return_value=a__ ) as mock_head:
A_ : Dict = BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def _lowerCamelCase ( self ):
# A mock response for an HTTP head request to emulate server down
A_ : Union[str, Any] = mock.Mock()
A_ : int = 500
A_ : Any = {}
A_ : Dict = HTTPError
A_ : List[Any] = {}
# Download this model to make sure it's in the cache.
A_ : Optional[int] = GPTaTokenizerFast.from_pretrained("""gpt2""" )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("""requests.Session.request""" , return_value=a__ ) as mock_head:
A_ : Any = GPTaTokenizerFast.from_pretrained("""gpt2""" )
# This check we did call the fake head request
mock_head.assert_called()
def _lowerCamelCase ( self ):
# This test is for deprecated behavior and can be removed in v5
try:
A_ : Union[str, Any] = tempfile.mktemp()
with open(a__ , """wb""" ) as f:
http_get("""https://huggingface.co/albert-base-v1/resolve/main/spiece.model""" , a__ )
A_ : Tuple = AlbertTokenizer.from_pretrained(a__ )
finally:
os.remove(a__ )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile("""tokenizer.json""" ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open("""tokenizer.json""" , """wb""" ) as f:
http_get("""https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json""" , a__ )
A_ : Dict = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size , 1000 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove("""tokenizer.json""" )
def _lowerCamelCase ( self ):
# This test is for deprecated behavior and can be removed in v5
A_ : Union[str, Any] = AlbertTokenizer.from_pretrained("""https://huggingface.co/albert-base-v1/resolve/main/spiece.model""" )
@is_staging_test
class _UpperCAmelCase ( unittest.TestCase ):
a = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']
@classmethod
def _lowerCamelCase ( cls ):
A_ : List[str] = TOKEN
HfFolder.save_token(a__ )
@classmethod
def _lowerCamelCase ( cls ):
try:
delete_repo(token=cls._token , repo_id="""test-tokenizer""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-tokenizer-org""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""test-dynamic-tokenizer""" )
except HTTPError:
pass
def _lowerCamelCase ( self ):
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : List[str] = os.path.join(a__ , """vocab.txt""" )
with open(a__ , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
A_ : str = BertTokenizer(a__ )
tokenizer.push_to_hub("""test-tokenizer""" , use_auth_token=self._token )
A_ : Dict = BertTokenizer.from_pretrained(F"""{USER}/test-tokenizer""" )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
# Reset repo
delete_repo(token=self._token , repo_id="""test-tokenizer""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(a__ , repo_id="""test-tokenizer""" , push_to_hub=a__ , use_auth_token=self._token )
A_ : List[str] = BertTokenizer.from_pretrained(F"""{USER}/test-tokenizer""" )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
def _lowerCamelCase ( self ):
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : List[Any] = os.path.join(a__ , """vocab.txt""" )
with open(a__ , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
A_ : Any = BertTokenizer(a__ )
tokenizer.push_to_hub("""valid_org/test-tokenizer-org""" , use_auth_token=self._token )
A_ : Any = BertTokenizer.from_pretrained("""valid_org/test-tokenizer-org""" )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-tokenizer-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
a__ , repo_id="""valid_org/test-tokenizer-org""" , push_to_hub=a__ , use_auth_token=self._token )
A_ : Union[str, Any] = BertTokenizer.from_pretrained("""valid_org/test-tokenizer-org""" )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
@require_tokenizers
def _lowerCamelCase ( self ):
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : str = os.path.join(a__ , """vocab.txt""" )
with open(a__ , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
A_ : Optional[int] = CustomTokenizer(a__ )
# No fast custom tokenizer
tokenizer.push_to_hub("""test-dynamic-tokenizer""" , use_auth_token=self._token )
A_ : Union[str, Any] = AutoTokenizer.from_pretrained(F"""{USER}/test-dynamic-tokenizer""" , trust_remote_code=a__ )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , """CustomTokenizer""" )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : int = os.path.join(a__ , """vocab.txt""" )
with open(a__ , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
A_ : Tuple = BertTokenizerFast.from_pretrained(a__ )
bert_tokenizer.save_pretrained(a__ )
A_ : List[Any] = CustomTokenizerFast.from_pretrained(a__ )
tokenizer.push_to_hub("""test-dynamic-tokenizer""" , use_auth_token=self._token )
A_ : List[Any] = AutoTokenizer.from_pretrained(F"""{USER}/test-dynamic-tokenizer""" , trust_remote_code=a__ )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , """CustomTokenizerFast""" )
A_ : List[str] = AutoTokenizer.from_pretrained(
F"""{USER}/test-dynamic-tokenizer""" , use_fast=a__ , trust_remote_code=a__ )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , """CustomTokenizer""" )
class _UpperCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self ):
A_ : int = Trie()
trie.add("""Hello 友達""" )
self.assertEqual(trie.data , {"""H""": {"""e""": {"""l""": {"""l""": {"""o""": {""" """: {"""友""": {"""達""": {"""""": 1}}}}}}}}} )
trie.add("""Hello""" )
trie.data
self.assertEqual(trie.data , {"""H""": {"""e""": {"""l""": {"""l""": {"""o""": {"""""": 1, """ """: {"""友""": {"""達""": {"""""": 1}}}}}}}}} )
def _lowerCamelCase ( self ):
A_ : List[str] = Trie()
self.assertEqual(trie.split("""[CLS] This is a extra_id_100""" ) , ["""[CLS] This is a extra_id_100"""] )
trie.add("""[CLS]""" )
trie.add("""extra_id_1""" )
trie.add("""extra_id_100""" )
self.assertEqual(trie.split("""[CLS] This is a extra_id_100""" ) , ["""[CLS]""", """ This is a """, """extra_id_100"""] )
def _lowerCamelCase ( self ):
A_ : Optional[Any] = Trie()
trie.add("""A""" )
self.assertEqual(trie.split("""ABC""" ) , ["""A""", """BC"""] )
self.assertEqual(trie.split("""BCA""" ) , ["""BC""", """A"""] )
def _lowerCamelCase ( self ):
A_ : Any = Trie()
trie.add("""TOKEN]""" )
trie.add("""[SPECIAL_TOKEN]""" )
self.assertEqual(trie.split("""This is something [SPECIAL_TOKEN]""" ) , ["""This is something """, """[SPECIAL_TOKEN]"""] )
def _lowerCamelCase ( self ):
A_ : List[Any] = Trie()
trie.add("""A""" )
trie.add("""P""" )
trie.add("""[SPECIAL_TOKEN]""" )
self.assertEqual(trie.split("""This is something [SPECIAL_TOKEN]""" ) , ["""This is something """, """[SPECIAL_TOKEN]"""] )
def _lowerCamelCase ( self ):
A_ : str = Trie()
trie.add("""AB""" )
trie.add("""B""" )
trie.add("""C""" )
self.assertEqual(trie.split("""ABC""" ) , ["""AB""", """C"""] )
def _lowerCamelCase ( self ):
A_ : Dict = Trie()
trie.add("""ABC""" )
trie.add("""B""" )
trie.add("""CD""" )
self.assertEqual(trie.split("""ABCD""" ) , ["""ABC""", """D"""] )
def _lowerCamelCase ( self ):
# Even if the offsets are wrong, we necessarily output correct string
# parts.
A_ : Optional[int] = Trie()
A_ : Union[str, Any] = trie.cut_text("""ABC""" , [0, 0, 2, 1, 2, 3] )
self.assertEqual(a__ , ["""AB""", """C"""] )
| 713 |
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
A_ : Union[str, Any] = [0] * len(_lowerCAmelCase )
A_ : Optional[int] = []
A_ : str = []
A_ : Dict = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_lowerCAmelCase ) ):
if indegree[i] == 0:
queue.append(_lowerCAmelCase )
while queue:
A_ : List[str] = queue.pop(0 )
cnt += 1
topo.append(_lowerCAmelCase )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(_lowerCAmelCase )
if cnt != len(_lowerCAmelCase ):
print("""Cycle exists""" )
else:
print(_lowerCAmelCase )
# Adjacency List of Graph
_lowerCAmelCase = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 481 | 0 |
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE="attention" ) -> List[str]:
"""simple docstring"""
__snake_case = __snake_case = np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :] )
__snake_case = k_tmp.reshape(k_tmp.shape[0] , k_tmp.shape[1] * k_tmp.shape[2] )
__snake_case = np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :] )
__snake_case = o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] , o_tmp.shape[2] )
__snake_case = np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :] )
__snake_case = q_tmp.reshape(q_tmp.shape[0] , q_tmp.shape[1] * q_tmp.shape[2] )
__snake_case = np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :] )
__snake_case = v_tmp.reshape(v_tmp.shape[0] , v_tmp.shape[1] * v_tmp.shape[2] )
return k, o, q, v
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False ) -> Optional[int]:
"""simple docstring"""
if split_mlp_wi:
__snake_case = params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
__snake_case = params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
__snake_case = (wi_a, wi_a)
else:
__snake_case = params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]
__snake_case = params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
return wi, wo
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , *, SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False ) -> int:
"""simple docstring"""
__snake_case = traverse_util.flatten_dict(variables["target"] )
__snake_case = {"/".join(SCREAMING_SNAKE_CASE ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
__snake_case = "encoder/encoder/mlp/wi_0/kernel" in old
print("Split MLP:" , SCREAMING_SNAKE_CASE )
__snake_case = collections.OrderedDict()
# Shared embeddings.
__snake_case = old["token_embedder/embedding"]
# Encoder.
for i in range(SCREAMING_SNAKE_CASE ):
# Block i, layer 0 (Self Attention).
__snake_case = tax_layer_norm_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , "encoder" , "pre_attention_layer_norm" )
__snake_case , __snake_case , __snake_case , __snake_case = tax_attention_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , "encoder" , "attention" )
__snake_case = layer_norm
__snake_case = k.T
__snake_case = o.T
__snake_case = q.T
__snake_case = v.T
# Block i, layer 1 (MLP).
__snake_case = tax_layer_norm_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , "encoder" , "pre_mlp_layer_norm" )
__snake_case , __snake_case = tax_mlp_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , "encoder" , SCREAMING_SNAKE_CASE )
__snake_case = layer_norm
if split_mlp_wi:
__snake_case = wi[0].T
__snake_case = wi[1].T
else:
__snake_case = wi.T
__snake_case = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
__snake_case = tax_relpos_bias_lookup(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , "encoder" ).T
__snake_case = old["encoder/encoder_norm/scale"]
if not scalable_attention:
__snake_case = tax_relpos_bias_lookup(
SCREAMING_SNAKE_CASE , 0 , "encoder" ).T
__snake_case = tax_relpos_bias_lookup(
SCREAMING_SNAKE_CASE , 0 , "decoder" ).T
if not is_encoder_only:
# Decoder.
for i in range(SCREAMING_SNAKE_CASE ):
# Block i, layer 0 (Self Attention).
__snake_case = tax_layer_norm_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , "decoder" , "pre_self_attention_layer_norm" )
__snake_case , __snake_case , __snake_case , __snake_case = tax_attention_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , "decoder" , "self_attention" )
__snake_case = layer_norm
__snake_case = k.T
__snake_case = o.T
__snake_case = q.T
__snake_case = v.T
# Block i, layer 1 (Cross Attention).
__snake_case = tax_layer_norm_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , "decoder" , "pre_cross_attention_layer_norm" )
__snake_case , __snake_case , __snake_case , __snake_case = tax_attention_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , "decoder" , "encoder_decoder_attention" )
__snake_case = layer_norm
__snake_case = k.T
__snake_case = o.T
__snake_case = q.T
__snake_case = v.T
# Block i, layer 2 (MLP).
__snake_case = tax_layer_norm_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , "decoder" , "pre_mlp_layer_norm" )
__snake_case , __snake_case = tax_mlp_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , "decoder" , SCREAMING_SNAKE_CASE )
__snake_case = layer_norm
if split_mlp_wi:
__snake_case = wi[0].T
__snake_case = wi[1].T
else:
__snake_case = wi.T
__snake_case = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
__snake_case = tax_relpos_bias_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , "decoder" ).T
__snake_case = old["decoder/decoder_norm/scale"]
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
__snake_case = old["decoder/logits_dense/kernel"].T
return new
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
__snake_case = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
__snake_case = state_dict["shared.weight"]
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
__snake_case = state_dict["shared.weight"]
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print("Using shared word embeddings as lm_head." )
__snake_case = state_dict["shared.weight"]
return state_dict
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
__snake_case = checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE )
__snake_case = convert_tax_to_pytorch(
SCREAMING_SNAKE_CASE , num_layers=config.num_layers , is_encoder_only=SCREAMING_SNAKE_CASE , scalable_attention=SCREAMING_SNAKE_CASE )
__snake_case = make_state_dict(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
model.load_state_dict(SCREAMING_SNAKE_CASE , strict=SCREAMING_SNAKE_CASE )
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = False , ) -> Optional[Any]:
"""simple docstring"""
__snake_case = MTaConfig.from_json_file(SCREAMING_SNAKE_CASE )
print(F'''Building PyTorch model from configuration: {config}''' )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
__snake_case = UMTaEncoderModel(SCREAMING_SNAKE_CASE )
else:
__snake_case = UMTaForConditionalGeneration(SCREAMING_SNAKE_CASE )
# Load weights from tf checkpoint
load_tax_weights_in_ta(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
model.save_pretrained(SCREAMING_SNAKE_CASE )
# Verify that we can load the checkpoint.
model.from_pretrained(SCREAMING_SNAKE_CASE )
print("Done" )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
# Required parameters
parser.add_argument(
"""--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
)
parser.add_argument(
"""--scalable_attention""",
action="""store_true""",
help="""Whether the model uses scaled attention (umt5 model)""",
default=False,
)
_SCREAMING_SNAKE_CASE = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path,
args.config_file,
args.pytorch_dump_path,
args.is_encoder_only,
args.scalable_attention,
)
| 163 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class __magic_name__ ( lowercase__ ):
def __init__( self : str , *snake_case_ : Optional[Any] , **snake_case_ : int ):
warnings.warn(
"The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use SegformerImageProcessor instead." , snake_case_ , )
super().__init__(*snake_case_ , **snake_case_ )
| 163 | 1 |
def snake_case_ ( snake_case , snake_case , snake_case , snake_case ) -> List[str]:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowercase__: Tuple = mf_knapsack(i - 1 , snake_case , snake_case , snake_case )
else:
lowercase__: Optional[Any] = max(
mf_knapsack(i - 1 , snake_case , snake_case , snake_case ) , mf_knapsack(i - 1 , snake_case , snake_case , j - wt[i - 1] ) + val[i - 1] , )
lowercase__: Optional[Any] = val
return f[i][j]
def snake_case_ ( snake_case , snake_case , snake_case , snake_case ) -> Dict:
lowercase__: Dict = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
lowercase__: Tuple = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
lowercase__: Optional[Any] = dp[i - 1][w_]
return dp[n][w_], dp
def snake_case_ ( snake_case , snake_case , snake_case ) -> str:
if not (isinstance(snake_case , (list, tuple) ) and isinstance(snake_case , (list, tuple) )):
raise ValueError(
'Both the weights and values vectors must be either lists or tuples' )
lowercase__: str = len(snake_case )
if num_items != len(snake_case ):
lowercase__: List[str] = (
'The number of weights must be the same as the number of values.\n'
f'But got {num_items} weights and {len(snake_case )} values'
)
raise ValueError(snake_case )
for i in range(snake_case ):
if not isinstance(wt[i] , snake_case ):
lowercase__: List[Any] = (
'All weights must be integers but got weight of '
f'type {type(wt[i] )} at index {i}'
)
raise TypeError(snake_case )
lowercase__ , lowercase__: Union[str, Any] = knapsack(snake_case , snake_case , snake_case , snake_case )
lowercase__: set = set()
_construct_solution(snake_case , snake_case , snake_case , snake_case , snake_case )
return optimal_val, example_optional_set
def snake_case_ ( snake_case , snake_case , snake_case , snake_case , snake_case ) -> str:
# for the current item i at a maximum weight j to be part of an optimal subset,
# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
# where i - 1 means considering only the previous items at the given maximum weight
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(snake_case , snake_case , i - 1 , snake_case , snake_case )
else:
optimal_set.add(snake_case )
_construct_solution(snake_case , snake_case , i - 1 , j - wt[i - 1] , snake_case )
if __name__ == "__main__":
__lowerCAmelCase = [3, 2, 4, 4]
__lowerCAmelCase = [4, 3, 2, 3]
__lowerCAmelCase = 4
__lowerCAmelCase = 6
__lowerCAmelCase = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
__lowerCAmelCase ,__lowerCAmelCase = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
__lowerCAmelCase ,__lowerCAmelCase = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print('''optimal_value = ''', optimal_solution)
print('''An optimal subset corresponding to the optimal value''', optimal_subset)
| 335 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib
__lowerCAmelCase = threading.Lock()
__lowerCAmelCase = None
__lowerCAmelCase = {
'''debug''': logging.DEBUG,
'''info''': logging.INFO,
'''warning''': logging.WARNING,
'''error''': logging.ERROR,
'''critical''': logging.CRITICAL,
}
__lowerCAmelCase = logging.WARNING
__lowerCAmelCase = True
def snake_case_ ( ) -> Optional[Any]:
lowercase__: Optional[int] = os.getenv('TRANSFORMERS_VERBOSITY' , snake_case )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
f'Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, '
f'has to be one of: { ", ".join(log_levels.keys() ) }' )
return _default_log_level
def snake_case_ ( ) -> str:
return __name__.split('.' )[0]
def snake_case_ ( ) -> logging.Logger:
return logging.getLogger(_get_library_name() )
def snake_case_ ( ) -> None:
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
lowercase__: str = logging.StreamHandler() # Set sys.stderr as stream.
lowercase__: Optional[Any] = sys.stderr.flush
# Apply our default configuration to the library root logger.
lowercase__: Any = _get_library_root_logger()
library_root_logger.addHandler(_default_handler )
library_root_logger.setLevel(_get_default_logging_level() )
lowercase__: Union[str, Any] = False
def snake_case_ ( ) -> None:
global _default_handler
with _lock:
if not _default_handler:
return
lowercase__: Optional[Any] = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler )
library_root_logger.setLevel(logging.NOTSET )
lowercase__: List[str] = None
def snake_case_ ( ) -> Union[str, Any]:
return log_levels
def snake_case_ ( snake_case = None ) -> logging.Logger:
if name is None:
lowercase__: Optional[Any] = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(snake_case )
def snake_case_ ( ) -> int:
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def snake_case_ ( snake_case ) -> None:
_configure_library_root_logger()
_get_library_root_logger().setLevel(snake_case )
def snake_case_ ( ) -> List[str]:
return set_verbosity(snake_case )
def snake_case_ ( ) -> List[Any]:
return set_verbosity(snake_case )
def snake_case_ ( ) -> Union[str, Any]:
return set_verbosity(snake_case )
def snake_case_ ( ) -> Any:
return set_verbosity(snake_case )
def snake_case_ ( ) -> None:
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler )
def snake_case_ ( ) -> None:
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler )
def snake_case_ ( snake_case ) -> None:
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(snake_case )
def snake_case_ ( snake_case ) -> None:
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(snake_case )
def snake_case_ ( ) -> None:
_configure_library_root_logger()
lowercase__: Optional[Any] = False
def snake_case_ ( ) -> None:
_configure_library_root_logger()
lowercase__: Optional[int] = True
def snake_case_ ( ) -> None:
lowercase__: str = _get_library_root_logger().handlers
for handler in handlers:
lowercase__: Any = logging.Formatter('[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s' )
handler.setFormatter(snake_case )
def snake_case_ ( ) -> None:
lowercase__: Optional[int] = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(snake_case )
def snake_case_ ( self , *snake_case , **snake_case ) -> Union[str, Any]:
lowercase__: Any = os.getenv('TRANSFORMERS_NO_ADVISORY_WARNINGS' , snake_case )
if no_advisory_warnings:
return
self.warning(*snake_case , **snake_case )
__lowerCAmelCase = warning_advice
@functools.lru_cache(snake_case )
def snake_case_ ( self , *snake_case , **snake_case ) -> Any:
self.warning(*snake_case , **snake_case )
__lowerCAmelCase = warning_once
class __a :
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> List[str]: # pylint: disable=unused-argument
'''simple docstring'''
lowercase__: Union[str, Any] = args[0] if args else None
def __iter__( self ) -> List[Any]:
'''simple docstring'''
return iter(self._iterator )
def __getattr__( self , lowerCAmelCase__ ) -> Tuple:
'''simple docstring'''
def empty_fn(*lowerCAmelCase__ , **lowerCAmelCase__ ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self ) -> List[str]:
'''simple docstring'''
return self
def __exit__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
'''simple docstring'''
return
class __a :
def __call__( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if _tqdm_active:
return tqdm_lib.tqdm(*lowerCAmelCase__ , **lowerCAmelCase__ )
else:
return EmptyTqdm(*lowerCAmelCase__ , **lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> List[str]:
'''simple docstring'''
lowercase__: str = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*lowerCAmelCase__ , **lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
'''simple docstring'''
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
__lowerCAmelCase = _tqdm_cls()
def snake_case_ ( ) -> bool:
global _tqdm_active
return bool(_tqdm_active )
def snake_case_ ( ) -> Union[str, Any]:
global _tqdm_active
lowercase__: List[str] = True
hf_hub_utils.enable_progress_bars()
def snake_case_ ( ) -> int:
global _tqdm_active
lowercase__: List[Any] = False
hf_hub_utils.disable_progress_bars()
| 335 | 1 |
'''simple docstring'''
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase : int = logging.get_logger(__name__)
lowerCamelCase : str = 'https://openaipublic.azureedge.net/jukebox/models/'
lowerCamelCase : Optional[Any] = {
'jukebox-1b-lyrics': [
'5b/vqvae.pth.tar',
'5b/prior_level_0.pth.tar',
'5b/prior_level_1.pth.tar',
'1b_lyrics/prior_level_2.pth.tar',
],
'jukebox-5b-lyrics': [
'5b/vqvae.pth.tar',
'5b/prior_level_0.pth.tar',
'5b/prior_level_1.pth.tar',
'5b_lyrics/prior_level_2.pth.tar',
],
}
def _SCREAMING_SNAKE_CASE (A ) -> Optional[int]:
"""simple docstring"""
if key.endswith('''.model.1.bias''' ) and len(key.split('''.''' ) ) > 10:
lowercase__ = key.replace('''.model.1.bias''' , '''.conv1d_1.bias''' )
elif key.endswith('''.model.1.weight''' ) and len(key.split('''.''' ) ) > 10:
lowercase__ = key.replace('''.model.1.weight''' , '''.conv1d_1.weight''' )
elif key.endswith('''.model.3.bias''' ) and len(key.split('''.''' ) ) > 10:
lowercase__ = key.replace('''.model.3.bias''' , '''.conv1d_2.bias''' )
elif key.endswith('''.model.3.weight''' ) and len(key.split('''.''' ) ) > 10:
lowercase__ = key.replace('''.model.3.weight''' , '''.conv1d_2.weight''' )
if "conditioner_blocks.0." in key:
lowercase__ = key.replace('''conditioner_blocks.0''' , '''conditioner_blocks''' )
if "prime_prior" in key:
lowercase__ = key.replace('''prime_prior''' , '''encoder''' )
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
lowercase__ = key.replace('''.emb.''' , '''.''' )
if key.endswith('''k''' ): # replace vqvae.X.k with vqvae.X.codebook
return key.replace('''.k''' , '''.codebook''' )
if "y_emb." in key:
return key.replace('''y_emb.''' , '''metadata_embedding.''' )
if "x_emb.emb." in key:
lowercase__ = key.replace('''0.x_emb.emb''' , '''embed_tokens''' )
if "prime_state_ln" in key:
return key.replace('''prime_state_ln''' , '''encoder.final_layer_norm''' )
if ".ln" in key:
return key.replace('''.ln''' , '''.layer_norm''' )
if "_ln" in key:
return key.replace('''_ln''' , '''_layer_norm''' )
if "prime_state_proj" in key:
return key.replace('''prime_state_proj''' , '''encoder.proj_in''' )
if "prime_x_out" in key:
return key.replace('''prime_x_out''' , '''encoder.lm_head''' )
if "prior.x_out" in key:
return key.replace('''x_out''' , '''fc_proj_out''' )
if "x_emb" in key:
return key.replace('''x_emb''' , '''embed_tokens''' )
return key
def _SCREAMING_SNAKE_CASE (A , A , A , A ) -> Tuple:
"""simple docstring"""
lowercase__ = {}
import re
lowercase__ = re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)''' )
lowercase__ = re.compile(
R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
lowercase__ = re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)''' )
lowercase__ = re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)''' )
lowercase__ = re.compile(
R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
lowercase__ = re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)''' )
lowercase__ = re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)''' )
lowercase__ = re.compile(
R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
lowercase__ = re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)''' )
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(A ):
lowercase__ = re_encoder_block_conv_in.match(A )
lowercase__ = regex_match.groups()
lowercase__ = int(groups[2] ) * 2 + int(groups[3] )
lowercase__ = f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
lowercase__ = re_encoder_block_conv_in.sub(A , A )
elif re_encoder_block_resnet.fullmatch(A ):
lowercase__ = re_encoder_block_resnet.match(A )
lowercase__ = regex_match.groups()
lowercase__ = int(groups[2] ) * 2 + int(groups[3] )
lowercase__ = {'''1''': 1, '''3''': 2}[groups[-2]]
lowercase__ = f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
lowercase__ = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
lowercase__ = prefix + resnet_block
lowercase__ = re_encoder_block_resnet.sub(A , A )
elif re_encoder_block_proj_out.fullmatch(A ):
lowercase__ = re_encoder_block_proj_out.match(A )
lowercase__ = regex_match.groups()
lowercase__ = f"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
lowercase__ = re_encoder_block_proj_out.sub(A , A )
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(A ):
lowercase__ = re_decoder_block_conv_out.match(A )
lowercase__ = regex_match.groups()
lowercase__ = int(groups[2] ) * 2 + int(groups[3] ) - 2
lowercase__ = f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
lowercase__ = re_decoder_block_conv_out.sub(A , A )
elif re_decoder_block_resnet.fullmatch(A ):
lowercase__ = re_decoder_block_resnet.match(A )
lowercase__ = regex_match.groups()
lowercase__ = int(groups[2] ) * 2 + int(groups[3] ) - 2
lowercase__ = {'''1''': 1, '''3''': 2}[groups[-2]]
lowercase__ = f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
lowercase__ = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
lowercase__ = prefix + resnet_block
lowercase__ = re_decoder_block_resnet.sub(A , A )
elif re_decoder_block_proj_in.fullmatch(A ):
lowercase__ = re_decoder_block_proj_in.match(A )
lowercase__ = regex_match.groups()
lowercase__ = f"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
lowercase__ = re_decoder_block_proj_in.sub(A , A )
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(A ):
lowercase__ = re_prior_cond_conv_out.match(A )
lowercase__ = regex_match.groups()
lowercase__ = int(groups[1] ) * 2 + int(groups[2] ) - 2
lowercase__ = f"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
lowercase__ = re_prior_cond_conv_out.sub(A , A )
elif re_prior_cond_resnet.fullmatch(A ):
lowercase__ = re_prior_cond_resnet.match(A )
lowercase__ = regex_match.groups()
lowercase__ = int(groups[1] ) * 2 + int(groups[2] ) - 2
lowercase__ = {'''1''': 1, '''3''': 2}[groups[-2]]
lowercase__ = f"conditioner_blocks.upsampler.upsample_block.{block_index}."
lowercase__ = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
lowercase__ = prefix + resnet_block
lowercase__ = re_prior_cond_resnet.sub(A , A )
elif re_prior_cond_proj_in.fullmatch(A ):
lowercase__ = re_prior_cond_proj_in.match(A )
lowercase__ = regex_match.groups()
lowercase__ = f"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
lowercase__ = re_prior_cond_proj_in.sub(A , A )
# keep original key
else:
lowercase__ = original_key
lowercase__ = replace_key(A )
if f"{key_prefix}.{key}" not in model_state_dict or key is None:
print(f"failed converting {original_key} to {key}, does not match" )
# handle missmatched shape
elif value.shape != model_state_dict[f"{key_prefix}.{key}"].shape:
lowercase__ = model_state_dict[f"{key_prefix}.{key}"]
print(f"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match" )
lowercase__ = original_key
lowercase__ = original_key
lowercase__ = value
return new_dict
@torch.no_grad()
def _SCREAMING_SNAKE_CASE (A=None , A=None ) -> Tuple:
"""simple docstring"""
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(f"{pytorch_dump_folder_path}/{file.split('/' )[-1]}" ):
lowercase__ = requests.get(f"{PREFIX}{file}" , allow_redirects=A )
os.makedirs(f"{pytorch_dump_folder_path}/" , exist_ok=A )
open(f"{pytorch_dump_folder_path}/{file.split('/' )[-1]}" , '''wb''' ).write(r.content )
lowercase__ = MODEL_MAPPING[model_name.split('''/''' )[-1]]
lowercase__ = JukeboxConfig.from_pretrained(A )
lowercase__ = JukeboxModel(A )
lowercase__ = []
lowercase__ = {}
for i, dict_name in enumerate(A ):
lowercase__ = torch.load(f"{pytorch_dump_folder_path}/{dict_name.split('/' )[-1]}" )['''model''']
lowercase__ = {}
for k in old_dic.keys():
if k.endswith('''.b''' ):
lowercase__ = old_dic[k]
elif k.endswith('''.w''' ):
lowercase__ = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
lowercase__ = old_dic[k]
else:
lowercase__ = old_dic[k]
lowercase__ = '''vqvae''' if i == 0 else f"priors.{3 - i}"
lowercase__ = fix_jukebox_keys(A , model.state_dict() , A , A )
weight_dict.append(A )
lowercase__ = weight_dict.pop(0 )
model.vqvae.load_state_dict(A )
for i in range(len(A ) ):
model.priors[i].load_state_dict(weight_dict[2 - i] )
Path(A ).mkdir(exist_ok=A )
with open(f"{pytorch_dump_folder_path}/mapping.json" , '''w''' ) as txtfile:
json.dump(A , A )
print(f"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(A )
return weight_dict
if __name__ == "__main__":
lowerCamelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='jukebox-5b-lyrics',
type=str,
help='Name of the model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='jukebox-5b-lyrics-converted',
type=str,
help='Path to the output PyTorch model directory.',
)
lowerCamelCase : str = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 460 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _SCREAMING_SNAKE_CASE (A ) -> Dict:
"""simple docstring"""
lowercase__ = os.path.join(args.tf_model_dir , '''parameters.json''' )
lowercase__ = json.loads(open(A ).read() )
if not params:
raise ValueError(
f"It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file." )
if not args.output.endswith('''.pt''' ):
lowercase__ = args.output + '''.pt'''
lowercase__ = OrderedDict()
with tf.device('''/CPU:0''' ):
lowercase__ = tf.train.load_checkpoint(args.tf_model_dir )
lowercase__ = reader.get_variable_to_shape_map()
for key_name in shapes.keys():
lowercase__ = reader.get_tensor(A ).astype(np.floataa )
if key_name.endswith('''/adam_m''' ) or key_name.endswith('''/adam_v''' ):
continue
if key_name.startswith('''pasts/''' ):
if key_name.startswith('''pasts/mlp''' ):
lowercase__ = int(key_name[9] )
elif key_name.startswith('''pasts/out''' ):
lowercase__ = 8
lowercase__ = '''model.sqout.%d.weight''' % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
lowercase__ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase__ = torch.tensor(A )
elif key_name.startswith('''model/moe''' ):
lowercase__ = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/switch_gating/kernel''' ):
lowercase__ = '''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
lowercase__ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase__ = torch.tensor(A )
elif key_name.endswith('''/softmlp/kernel''' ):
lowercase__ = '''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
lowercase__ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase__ = torch.tensor(A )
elif key_name.endswith('''/wo/kernel''' ) or key_name.endswith('''/wi/kernel''' ):
lowercase__ = key_name[-9:-7]
for i in range(16 ):
lowercase__ = '''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
lowercase__ = (
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
lowercase__ = torch.tensor(A )
elif key_name.startswith('''model/mlp''' ):
lowercase__ = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/p1/kernel''' ):
lowercase__ = '''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
lowercase__ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase__ = torch.tensor(A )
elif key_name.endswith('''/p1/bias''' ):
lowercase__ = '''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
lowercase__ = vnp.copy() # same because it is one dimensional
lowercase__ = torch.tensor(A )
elif key_name.endswith('''/p2/kernel''' ):
lowercase__ = '''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
lowercase__ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase__ = torch.tensor(A )
elif key_name.endswith('''/p2/bias''' ):
lowercase__ = '''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
lowercase__ = vnp.copy() # same because it is one dimensional
lowercase__ = torch.tensor(A )
elif key_name.startswith('''model/ln''' ):
lowercase__ = int(key_name[8:].split('''/''' )[0] )
if key_name.endswith('''/b''' ):
lowercase__ = '''model.blocks.%d.feed_forward.norm.bias''' % player
lowercase__ = vnp.copy() # same because it is one dimensional
lowercase__ = torch.tensor(A )
elif key_name.endswith('''/g''' ):
lowercase__ = '''model.blocks.%d.feed_forward.norm.weight''' % player
lowercase__ = vnp.copy() # same because it is one dimensional
lowercase__ = torch.tensor(A )
elif key_name.startswith('''model/att''' ):
lowercase__ = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/qkv/kernel''' ):
lowercase__ = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
lowercase__ = state[:, 0, :, :]
lowercase__ = state[:, 1, :, :]
lowercase__ = state[:, 2, :, :]
lowercase__ = (
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
lowercase__ = (
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
lowercase__ = (
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
lowercase__ = '''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
lowercase__ = torch.tensor(A )
lowercase__ = '''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
lowercase__ = torch.tensor(A )
lowercase__ = '''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
lowercase__ = torch.tensor(A )
elif key_name.endswith('''/o/kernel''' ):
lowercase__ = '''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
lowercase__ = (
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
lowercase__ = torch.tensor(A )
elif key_name.startswith('''model/an''' ):
lowercase__ = int(key_name[8:].split('''/''' )[0] )
if key_name.endswith('''/b''' ):
lowercase__ = '''model.blocks.%d.self_attn.norm.bias''' % player
lowercase__ = vnp.copy() # same because it is one dimensional
lowercase__ = torch.tensor(A )
elif key_name.endswith('''/g''' ):
lowercase__ = '''model.blocks.%d.self_attn.norm.weight''' % player
lowercase__ = vnp.copy() # same because it is one dimensional
lowercase__ = torch.tensor(A )
elif (
key_name.startswith('''model/wte''' )
or key_name.startswith('''model/wpe''' )
or key_name.startswith('''model/ete''' )
):
lowercase__ = {'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
key_name[-3:]
]
lowercase__ = '''model.%s.weight''' % nlayer
lowercase__ = vnp.copy() # same in embedded
lowercase__ = torch.tensor(A )
if key_name.startswith('''model/wte''' ):
lowercase__ = '''lm_head.weight'''
lowercase__ = vnp.copy() # same in embedded
lowercase__ = torch.tensor(A )
elif key_name.startswith('''model/wob''' ):
lowercase__ = '''final_logits_bias'''
lowercase__ = vnp.copy() # same in embedded
lowercase__ = state.reshape((1, -1) )
lowercase__ = torch.tensor(A )
elif key_name == "model/dense/kernel":
lowercase__ = '''model.last_project.weight'''
lowercase__ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase__ = torch.tensor(A )
elif key_name == "model/dense_1/bias":
lowercase__ = '''model.last_project.bias'''
lowercase__ = vnp.copy() # same because it is one dimensional
lowercase__ = torch.tensor(A )
torch.save(A , args.output )
if __name__ == "__main__":
lowerCamelCase : Optional[int] = argparse.ArgumentParser(
description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
lowerCamelCase : Dict = parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 460 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def lowerCAmelCase ( UpperCAmelCase ) ->Dict:
"""simple docstring"""
__magic_name__ : int = SwinvaConfig()
__magic_name__ : List[Any] = swinva_name.split('''_''' )
__magic_name__ : Union[str, Any] = name_split[1]
if "to" in name_split[3]:
__magic_name__ : Union[str, Any] = int(name_split[3][-3:] )
else:
__magic_name__ : Dict = int(name_split[3] )
if "to" in name_split[2]:
__magic_name__ : Union[str, Any] = int(name_split[2][-2:] )
else:
__magic_name__ : Optional[int] = int(name_split[2][6:] )
if model_size == "tiny":
__magic_name__ : List[str] = 96
__magic_name__ : int = (2, 2, 6, 2)
__magic_name__ : Optional[int] = (3, 6, 12, 24)
elif model_size == "small":
__magic_name__ : Dict = 96
__magic_name__ : Optional[Any] = (2, 2, 18, 2)
__magic_name__ : int = (3, 6, 12, 24)
elif model_size == "base":
__magic_name__ : Tuple = 128
__magic_name__ : Any = (2, 2, 18, 2)
__magic_name__ : Union[str, Any] = (4, 8, 16, 32)
else:
__magic_name__ : List[str] = 192
__magic_name__ : str = (2, 2, 18, 2)
__magic_name__ : Any = (6, 12, 24, 48)
if "to" in swinva_name:
__magic_name__ : Optional[int] = (12, 12, 12, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
__magic_name__ : Tuple = 2_1841
__magic_name__ : List[Any] = '''huggingface/label-files'''
__magic_name__ : List[str] = '''imagenet-22k-id2label.json'''
__magic_name__ : Optional[int] = json.load(open(hf_hub_download(UpperCAmelCase, UpperCAmelCase, repo_type='''dataset''' ), '''r''' ) )
__magic_name__ : Optional[int] = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
__magic_name__ : List[str] = idalabel
__magic_name__ : Any = {v: k for k, v in idalabel.items()}
else:
__magic_name__ : Optional[Any] = 1000
__magic_name__ : Union[str, Any] = '''huggingface/label-files'''
__magic_name__ : List[str] = '''imagenet-1k-id2label.json'''
__magic_name__ : List[str] = json.load(open(hf_hub_download(UpperCAmelCase, UpperCAmelCase, repo_type='''dataset''' ), '''r''' ) )
__magic_name__ : Optional[Any] = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
__magic_name__ : List[str] = idalabel
__magic_name__ : Any = {v: k for k, v in idalabel.items()}
__magic_name__ : Optional[int] = img_size
__magic_name__ : int = num_classes
__magic_name__ : List[str] = embed_dim
__magic_name__ : List[Any] = depths
__magic_name__ : Any = num_heads
__magic_name__ : Optional[int] = window_size
return config
def lowerCAmelCase ( UpperCAmelCase ) ->Dict:
"""simple docstring"""
if "patch_embed.proj" in name:
__magic_name__ : List[str] = name.replace('''patch_embed.proj''', '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
__magic_name__ : Union[str, Any] = name.replace('''patch_embed.norm''', '''embeddings.norm''' )
if "layers" in name:
__magic_name__ : List[str] = '''encoder.''' + name
if "attn.proj" in name:
__magic_name__ : List[str] = name.replace('''attn.proj''', '''attention.output.dense''' )
if "attn" in name:
__magic_name__ : List[Any] = name.replace('''attn''', '''attention.self''' )
if "norm1" in name:
__magic_name__ : int = name.replace('''norm1''', '''layernorm_before''' )
if "norm2" in name:
__magic_name__ : Optional[Any] = name.replace('''norm2''', '''layernorm_after''' )
if "mlp.fc1" in name:
__magic_name__ : Tuple = name.replace('''mlp.fc1''', '''intermediate.dense''' )
if "mlp.fc2" in name:
__magic_name__ : List[Any] = name.replace('''mlp.fc2''', '''output.dense''' )
if "q_bias" in name:
__magic_name__ : str = name.replace('''q_bias''', '''query.bias''' )
if "k_bias" in name:
__magic_name__ : str = name.replace('''k_bias''', '''key.bias''' )
if "v_bias" in name:
__magic_name__ : Tuple = name.replace('''v_bias''', '''value.bias''' )
if "cpb_mlp" in name:
__magic_name__ : List[Any] = name.replace('''cpb_mlp''', '''continuous_position_bias_mlp''' )
if name == "norm.weight":
__magic_name__ : Dict = '''layernorm.weight'''
if name == "norm.bias":
__magic_name__ : int = '''layernorm.bias'''
if "head" in name:
__magic_name__ : Optional[Any] = name.replace('''head''', '''classifier''' )
else:
__magic_name__ : Optional[int] = '''swinv2.''' + name
return name
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase ) ->int:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
__magic_name__ : int = orig_state_dict.pop(UpperCAmelCase )
if "mask" in key:
continue
elif "qkv" in key:
__magic_name__ : Union[str, Any] = key.split('''.''' )
__magic_name__ : Any = int(key_split[1] )
__magic_name__ : Optional[Any] = int(key_split[3] )
__magic_name__ : Optional[int] = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
__magic_name__ : str = val[:dim, :]
__magic_name__ : Optional[Any] = val[dim : dim * 2, :]
__magic_name__ : Any = val[-dim:, :]
else:
__magic_name__ : Any = val[:dim]
__magic_name__ : List[Any] = val[
dim : dim * 2
]
__magic_name__ : int = val[-dim:]
else:
__magic_name__ : str = val
return orig_state_dict
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase ) ->Any:
"""simple docstring"""
__magic_name__ : Tuple = timm.create_model(UpperCAmelCase, pretrained=UpperCAmelCase )
timm_model.eval()
__magic_name__ : Dict = get_swinva_config(UpperCAmelCase )
__magic_name__ : int = SwinvaForImageClassification(UpperCAmelCase )
model.eval()
__magic_name__ : Optional[Any] = convert_state_dict(timm_model.state_dict(), UpperCAmelCase )
model.load_state_dict(UpperCAmelCase )
__magic_name__ : Tuple = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
__magic_name__ : Optional[Any] = AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_''', '''-''' ) ) )
__magic_name__ : List[str] = Image.open(requests.get(UpperCAmelCase, stream=UpperCAmelCase ).raw )
__magic_name__ : Optional[Any] = image_processor(images=UpperCAmelCase, return_tensors='''pt''' )
__magic_name__ : Optional[Any] = timm_model(inputs['''pixel_values'''] )
__magic_name__ : Tuple = model(**UpperCAmelCase ).logits
assert torch.allclose(UpperCAmelCase, UpperCAmelCase, atol=1E-3 )
print(F'''Saving model {swinva_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase )
model.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase, UpperCAmelCase ), organization='''nandwalritik''', commit_message='''Add model''', )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--swinv2_name''',
default='''swinv2_tiny_patch4_window8_256''',
type=str,
help='''Name of the Swinv2 timm 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.'''
)
lowercase_ = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
| 336 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class A__ ( unittest.TestCase ):
def lowercase ( self ) -> Dict:
"""simple docstring"""
__magic_name__ : List[Any] = {
'''task_specific_params''': {
'''summarization''': {'''length_penalty''': 1.0, '''max_length''': 128, '''min_length''': 12, '''num_beams''': 4},
'''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 142, '''min_length''': 56, '''num_beams''': 4},
'''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6},
}
}
__magic_name__ : int = {
'''task_specific_params.summarization.length_penalty''': 1.0,
'''task_specific_params.summarization.max_length''': 128,
'''task_specific_params.summarization.min_length''': 12,
'''task_specific_params.summarization.num_beams''': 4,
'''task_specific_params.summarization_cnn.length_penalty''': 2.0,
'''task_specific_params.summarization_cnn.max_length''': 142,
'''task_specific_params.summarization_cnn.min_length''': 56,
'''task_specific_params.summarization_cnn.num_beams''': 4,
'''task_specific_params.summarization_xsum.length_penalty''': 1.0,
'''task_specific_params.summarization_xsum.max_length''': 62,
'''task_specific_params.summarization_xsum.min_length''': 11,
'''task_specific_params.summarization_xsum.num_beams''': 6,
}
self.assertEqual(flatten_dict(lowerCamelCase ) , lowerCamelCase )
def lowercase ( self ) -> Tuple:
"""simple docstring"""
__magic_name__ : Optional[Any] = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(lowerCamelCase ) , x.transpose() ) )
__magic_name__ : Union[str, Any] = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(lowerCamelCase , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def lowercase ( self ) -> Optional[int]:
"""simple docstring"""
__magic_name__ : Union[str, Any] = np.random.randn(3 , 4 )
__magic_name__ : List[str] = torch.tensor(lowerCamelCase )
self.assertTrue(np.allclose(transpose(lowerCamelCase ) , transpose(lowerCamelCase ).numpy() ) )
__magic_name__ : int = np.random.randn(3 , 4 , 5 )
__magic_name__ : Union[str, Any] = torch.tensor(lowerCamelCase )
self.assertTrue(np.allclose(transpose(lowerCamelCase , axes=(1, 2, 0) ) , transpose(lowerCamelCase , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def lowercase ( self ) -> Tuple:
"""simple docstring"""
__magic_name__ : Dict = np.random.randn(3 , 4 )
__magic_name__ : Any = tf.constant(lowerCamelCase )
self.assertTrue(np.allclose(transpose(lowerCamelCase ) , transpose(lowerCamelCase ).numpy() ) )
__magic_name__ : str = np.random.randn(3 , 4 , 5 )
__magic_name__ : Optional[int] = tf.constant(lowerCamelCase )
self.assertTrue(np.allclose(transpose(lowerCamelCase , axes=(1, 2, 0) ) , transpose(lowerCamelCase , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def lowercase ( self ) -> int:
"""simple docstring"""
__magic_name__ : Union[str, Any] = np.random.randn(3 , 4 )
__magic_name__ : Optional[Any] = jnp.array(lowerCamelCase )
self.assertTrue(np.allclose(transpose(lowerCamelCase ) , np.asarray(transpose(lowerCamelCase ) ) ) )
__magic_name__ : int = np.random.randn(3 , 4 , 5 )
__magic_name__ : Tuple = jnp.array(lowerCamelCase )
self.assertTrue(np.allclose(transpose(lowerCamelCase , axes=(1, 2, 0) ) , np.asarray(transpose(lowerCamelCase , axes=(1, 2, 0) ) ) ) )
def lowercase ( self ) -> Optional[Any]:
"""simple docstring"""
__magic_name__ : Dict = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(lowerCamelCase , (4, 3) ) , np.reshape(lowerCamelCase , (4, 3) ) ) )
__magic_name__ : Optional[int] = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(lowerCamelCase , (12, 5) ) , np.reshape(lowerCamelCase , (12, 5) ) ) )
@require_torch
def lowercase ( self ) -> int:
"""simple docstring"""
__magic_name__ : Tuple = np.random.randn(3 , 4 )
__magic_name__ : List[Any] = torch.tensor(lowerCamelCase )
self.assertTrue(np.allclose(reshape(lowerCamelCase , (4, 3) ) , reshape(lowerCamelCase , (4, 3) ).numpy() ) )
__magic_name__ : List[str] = np.random.randn(3 , 4 , 5 )
__magic_name__ : Tuple = torch.tensor(lowerCamelCase )
self.assertTrue(np.allclose(reshape(lowerCamelCase , (12, 5) ) , reshape(lowerCamelCase , (12, 5) ).numpy() ) )
@require_tf
def lowercase ( self ) -> Union[str, Any]:
"""simple docstring"""
__magic_name__ : Union[str, Any] = np.random.randn(3 , 4 )
__magic_name__ : List[str] = tf.constant(lowerCamelCase )
self.assertTrue(np.allclose(reshape(lowerCamelCase , (4, 3) ) , reshape(lowerCamelCase , (4, 3) ).numpy() ) )
__magic_name__ : Optional[Any] = np.random.randn(3 , 4 , 5 )
__magic_name__ : str = tf.constant(lowerCamelCase )
self.assertTrue(np.allclose(reshape(lowerCamelCase , (12, 5) ) , reshape(lowerCamelCase , (12, 5) ).numpy() ) )
@require_flax
def lowercase ( self ) -> Tuple:
"""simple docstring"""
__magic_name__ : Dict = np.random.randn(3 , 4 )
__magic_name__ : Optional[Any] = jnp.array(lowerCamelCase )
self.assertTrue(np.allclose(reshape(lowerCamelCase , (4, 3) ) , np.asarray(reshape(lowerCamelCase , (4, 3) ) ) ) )
__magic_name__ : Union[str, Any] = np.random.randn(3 , 4 , 5 )
__magic_name__ : List[Any] = jnp.array(lowerCamelCase )
self.assertTrue(np.allclose(reshape(lowerCamelCase , (12, 5) ) , np.asarray(reshape(lowerCamelCase , (12, 5) ) ) ) )
def lowercase ( self ) -> Dict:
"""simple docstring"""
__magic_name__ : Optional[Any] = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(lowerCamelCase ) , np.squeeze(lowerCamelCase ) ) )
__magic_name__ : int = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(lowerCamelCase , axis=2 ) , np.squeeze(lowerCamelCase , axis=2 ) ) )
@require_torch
def lowercase ( self ) -> List[Any]:
"""simple docstring"""
__magic_name__ : Any = np.random.randn(1 , 3 , 4 )
__magic_name__ : List[str] = torch.tensor(lowerCamelCase )
self.assertTrue(np.allclose(squeeze(lowerCamelCase ) , squeeze(lowerCamelCase ).numpy() ) )
__magic_name__ : Union[str, Any] = np.random.randn(1 , 4 , 1 , 5 )
__magic_name__ : Tuple = torch.tensor(lowerCamelCase )
self.assertTrue(np.allclose(squeeze(lowerCamelCase , axis=2 ) , squeeze(lowerCamelCase , axis=2 ).numpy() ) )
@require_tf
def lowercase ( self ) -> str:
"""simple docstring"""
__magic_name__ : Optional[int] = np.random.randn(1 , 3 , 4 )
__magic_name__ : Any = tf.constant(lowerCamelCase )
self.assertTrue(np.allclose(squeeze(lowerCamelCase ) , squeeze(lowerCamelCase ).numpy() ) )
__magic_name__ : int = np.random.randn(1 , 4 , 1 , 5 )
__magic_name__ : str = tf.constant(lowerCamelCase )
self.assertTrue(np.allclose(squeeze(lowerCamelCase , axis=2 ) , squeeze(lowerCamelCase , axis=2 ).numpy() ) )
@require_flax
def lowercase ( self ) -> List[Any]:
"""simple docstring"""
__magic_name__ : str = np.random.randn(1 , 3 , 4 )
__magic_name__ : List[str] = jnp.array(lowerCamelCase )
self.assertTrue(np.allclose(squeeze(lowerCamelCase ) , np.asarray(squeeze(lowerCamelCase ) ) ) )
__magic_name__ : Optional[int] = np.random.randn(1 , 4 , 1 , 5 )
__magic_name__ : Optional[int] = jnp.array(lowerCamelCase )
self.assertTrue(np.allclose(squeeze(lowerCamelCase , axis=2 ) , np.asarray(squeeze(lowerCamelCase , axis=2 ) ) ) )
def lowercase ( self ) -> Optional[int]:
"""simple docstring"""
__magic_name__ : Tuple = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase , axis=1 ) , np.expand_dims(lowerCamelCase , axis=1 ) ) )
@require_torch
def lowercase ( self ) -> List[Any]:
"""simple docstring"""
__magic_name__ : Union[str, Any] = np.random.randn(3 , 4 )
__magic_name__ : str = torch.tensor(lowerCamelCase )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase , axis=1 ) , expand_dims(lowerCamelCase , axis=1 ).numpy() ) )
@require_tf
def lowercase ( self ) -> Any:
"""simple docstring"""
__magic_name__ : List[str] = np.random.randn(3 , 4 )
__magic_name__ : Union[str, Any] = tf.constant(lowerCamelCase )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase , axis=1 ) , expand_dims(lowerCamelCase , axis=1 ).numpy() ) )
@require_flax
def lowercase ( self ) -> Optional[Any]:
"""simple docstring"""
__magic_name__ : List[Any] = np.random.randn(3 , 4 )
__magic_name__ : int = jnp.array(lowerCamelCase )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase , axis=1 ) , np.asarray(expand_dims(lowerCamelCase , axis=1 ) ) ) )
| 336 | 1 |
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.modeling_flax_big_bird import (
FlaxBigBirdForCausalLM,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForPreTraining,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
FlaxBigBirdModel,
)
class lowercase ( unittest.TestCase ):
def __init__( self : Dict , _lowercase : Tuple , _lowercase : Optional[Any]=2 , _lowercase : int=56 , _lowercase : List[str]=True , _lowercase : Tuple=True , _lowercase : Optional[Any]=True , _lowercase : Optional[Any]=True , _lowercase : List[str]=99 , _lowercase : Dict=32 , _lowercase : Dict=2 , _lowercase : str=2 , _lowercase : Union[str, Any]=7 , _lowercase : Union[str, Any]="gelu_new" , _lowercase : List[Any]=0.1 , _lowercase : str=0.1 , _lowercase : Dict=5_12 , _lowercase : List[Any]=16 , _lowercase : Optional[int]=2 , _lowercase : Dict=0.02 , _lowercase : Optional[int]=4 , _lowercase : str="block_sparse" , _lowercase : str=True , _lowercase : List[str]=False , _lowercase : Any=2 , _lowercase : Tuple=3 , ):
SCREAMING_SNAKE_CASE__ : Any = parent
SCREAMING_SNAKE_CASE__ : int = batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_length
SCREAMING_SNAKE_CASE__ : Tuple = is_training
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_attention_mask
SCREAMING_SNAKE_CASE__ : int = use_token_type_ids
SCREAMING_SNAKE_CASE__ : Dict = use_labels
SCREAMING_SNAKE_CASE__ : List[str] = vocab_size
SCREAMING_SNAKE_CASE__ : Any = hidden_size
SCREAMING_SNAKE_CASE__ : Tuple = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Any = intermediate_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[Any] = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_choices
SCREAMING_SNAKE_CASE__ : Union[str, Any] = rescale_embeddings
SCREAMING_SNAKE_CASE__ : Tuple = attention_type
SCREAMING_SNAKE_CASE__ : List[Any] = use_bias
SCREAMING_SNAKE_CASE__ : int = block_size
SCREAMING_SNAKE_CASE__ : Dict = num_random_blocks
def lowercase__ ( self : List[str] ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : List[Any] = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = BigBirdConfig(
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=_lowercase , initializer_range=self.initializer_range , attention_type=self.attention_type , block_size=self.block_size , num_random_blocks=self.num_random_blocks , use_bias=self.use_bias , rescale_embeddings=self.rescale_embeddings , )
return config, input_ids, token_type_ids, attention_mask
def lowercase__ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = config_and_inputs
SCREAMING_SNAKE_CASE__ : Dict = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_flax
class lowercase ( _UpperCAmelCase , unittest.TestCase ):
lowerCamelCase : str = (
(
FlaxBigBirdForCausalLM,
FlaxBigBirdModel,
FlaxBigBirdForPreTraining,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
)
if is_flax_available()
else ()
)
lowerCamelCase : int = False
lowerCamelCase : Optional[int] = False
def lowercase__ ( self : Tuple ):
SCREAMING_SNAKE_CASE__ : Tuple = FlaxBigBirdModelTester(self )
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def lowercase__ ( self : Optional[int] ):
super().test_from_pretrained_save_pretrained()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def lowercase__ ( self : Tuple ):
super().test_from_pretrained_with_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def lowercase__ ( self : Optional[int] ):
super().test_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def lowercase__ ( self : Tuple ):
super().test_hidden_states_output()
@slow
def lowercase__ ( self : Tuple ):
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Tuple = model_class_name.from_pretrained('''google/bigbird-roberta-base''' )
self.assertIsNotNone(_lowercase )
def lowercase__ ( self : Union[str, Any] ):
if self.test_attn_probs:
super().test_attention_outputs()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def lowercase__ ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
SCREAMING_SNAKE_CASE__ : str = self._prepare_for_class(_lowercase , _lowercase )
SCREAMING_SNAKE_CASE__ : int = model_class(_lowercase )
@jax.jit
def model_jitted(_lowercase : Optional[int] , _lowercase : List[Any]=None , **_lowercase : Tuple ):
return model(input_ids=_lowercase , attention_mask=_lowercase , **_lowercase )
with self.subTest('''JIT Enabled''' ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model_jitted(**_lowercase ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
SCREAMING_SNAKE_CASE__ : Optional[Any] = model_jitted(**_lowercase ).to_tuple()
self.assertEqual(len(_lowercase ) , len(_lowercase ) )
for jitted_output, output in zip(_lowercase , _lowercase ):
self.assertEqual(jitted_output.shape , output.shape )
def lowercase__ ( self : List[Any] , _lowercase : Dict , _lowercase : Optional[Any] , _lowercase : str , _lowercase : Tuple=1E-5 , _lowercase : Dict="outputs" , _lowercase : Optional[Any]=None ):
# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
# an effort was done to return `attention_probs` (yet to be verified).
if name.startswith('''outputs.attentions''' ):
return
else:
super().check_pt_flax_outputs(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
| 35 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ : List[Any] = logging.get_logger(__name__)
UpperCAmelCase__ : List[str] = {"vocab_file": "vocab.json"}
UpperCAmelCase__ : Optional[Any] = {
"vocab_file": {
"mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
}
}
UpperCAmelCase__ : Union[str, Any] = {"mgp-str": 27}
class A ( SCREAMING_SNAKE_CASE__ ):
snake_case__ :Any = VOCAB_FILES_NAMES
snake_case__ :Dict = PRETRAINED_VOCAB_FILES_MAP
snake_case__ :Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Union[str, Any] , __magic_name__ : List[str] , __magic_name__ : int="[GO]" , __magic_name__ : Optional[Any]="[GO]" , __magic_name__ : List[str]="[s]" , __magic_name__ : str="[GO]" , **__magic_name__ : List[Any] ):
"""simple docstring"""
super().__init__(
unk_token=__magic_name__ , bos_token=__magic_name__ , eos_token=__magic_name__ , pad_token=__magic_name__ , **__magic_name__ , )
with open(__magic_name__ , encoding="utf-8" ) as vocab_handle:
lowerCAmelCase__ = json.load(__magic_name__ )
lowerCAmelCase__ = {v: k for k, v in self.vocab.items()}
@property
def __SCREAMING_SNAKE_CASE ( self : Optional[int] ):
"""simple docstring"""
return len(self.vocab )
def __SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
return dict(self.vocab , **self.added_tokens_encoder )
def __SCREAMING_SNAKE_CASE ( self : Any , __magic_name__ : Dict ):
"""simple docstring"""
lowerCAmelCase__ = []
for s in text:
char_tokens.extend(__magic_name__ )
return char_tokens
def __SCREAMING_SNAKE_CASE ( self : Dict , __magic_name__ : str ):
"""simple docstring"""
return self.vocab.get(__magic_name__ , self.vocab.get(self.unk_token ) )
def __SCREAMING_SNAKE_CASE ( self : int , __magic_name__ : Tuple ):
"""simple docstring"""
return self.decoder.get(__magic_name__ )
def __SCREAMING_SNAKE_CASE ( self : str , __magic_name__ : str , __magic_name__ : Optional[str] = None ):
"""simple docstring"""
if not os.path.isdir(__magic_name__ ):
logger.error("Vocabulary path ({}) should be a directory".format(__magic_name__ ) )
return
lowerCAmelCase__ = os.path.join(
__magic_name__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
with open(__magic_name__ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.vocab , indent=2 , sort_keys=__magic_name__ , ensure_ascii=__magic_name__ ) + "\n" )
return (vocab_file,)
| 48 | 0 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
A_ : Optional[Any] = yaml.safe_load(
"\\nname: \"\"\nallow_empty: false\nallow_empty_text: true\nsubsections:\n - name: \"Dataset Card for X\" # First-level markdown heading\n allow_empty: false\n allow_empty_text: true\n subsections:\n - name: \"Table of Contents\"\n allow_empty: false\n allow_empty_text: false\n subsections: null\n - name: \"Dataset Description\"\n allow_empty: false\n allow_empty_text: false\n subsections:\n - name: \"Dataset Summary\"\n allow_empty: false\n allow_empty_text: false\n subsections: null\n - name: \"Supported Tasks and Leaderboards\"\n allow_empty: true\n allow_empty_text: true\n subsections: null\n - name: Languages\n allow_empty: false\n allow_empty_text: true\n subsections: null\n"
)
A_ : Optional[int] = {
'''name''': '''root''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [
{
'''name''': '''Dataset Card for My Dataset''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [
{'''name''': '''Table of Contents''', '''text''': '''Some text here.''', '''is_empty_text''': False, '''subsections''': []},
{
'''name''': '''Dataset Description''',
'''text''': '''Some text here.''',
'''is_empty_text''': False,
'''subsections''': [
{
'''name''': '''Dataset Summary''',
'''text''': '''Some text here.''',
'''is_empty_text''': False,
'''subsections''': [],
},
{
'''name''': '''Supported Tasks and Leaderboards''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [],
},
{'''name''': '''Languages''', '''text''': '''Language Text''', '''is_empty_text''': False, '''subsections''': []},
],
},
],
}
],
}
A_ : List[Any] = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : Optional[int] = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
#### Extra Ignored Subsection
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : Any = {
'''name''': '''root''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [
{
'''name''': '''Dataset Card for My Dataset''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [
{'''name''': '''Table of Contents''', '''text''': '''Some text here.''', '''is_empty_text''': False, '''subsections''': []},
{
'''name''': '''Dataset Description''',
'''text''': '''Some text here.''',
'''is_empty_text''': False,
'''subsections''': [
{
'''name''': '''Dataset Summary''',
'''text''': '''Some text here.''',
'''is_empty_text''': False,
'''subsections''': [
{
'''name''': '''Extra Ignored Subsection''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [],
}
],
},
{
'''name''': '''Supported Tasks and Leaderboards''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [],
},
{'''name''': '''Languages''', '''text''': '''Language Text''', '''is_empty_text''': False, '''subsections''': []},
],
},
],
}
],
}
A_ : Optional[Any] = '''\
---
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : Dict = (
'''The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README.'''
)
A_ : List[str] = '''\
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : List[str] = (
'''The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README.'''
)
A_ : Optional[Any] = '''\
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : str = '''The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README.'''
A_ : List[str] = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : Optional[Any] = '''The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored).'''
A_ : Dict = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
'''
A_ : Union[str, Any] = '''The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found \'None\'.'''
A_ : Any = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Languages
Language Text
'''
A_ : List[str] = '''The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`.'''
A_ : Tuple = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
'''
A_ : str = '''The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty.'''
A_ : Any = '''\
---
language:
- zh
- en
---
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : str = '''The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.'''
A_ : Optional[Any] = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
# Dataset Card My Dataset
'''
A_ : List[Any] = '''The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README.'''
A_ : Tuple = '''\
---
language:
- zh
- en
---
# Dataset Card My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : Union[str, Any] = '''The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README.'''
A_ : Union[str, Any] = ''''''
A_ : Any = '''The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README.'''
A_ : int = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : Optional[Any] = '''The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections.'''
@pytest.mark.parametrize(
"""readme_md, expected_dict""" , [
(README_CORRECT, CORRECT_DICT),
(README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
] , )
def UpperCamelCase__ ( __magic_name__ : str , __magic_name__ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
assert ReadMe.from_string(_lowercase , _lowercase ).to_dict() == expected_dict
@pytest.mark.parametrize(
"""readme_md, expected_error""" , [
(README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
(README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
(README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
(README_EMPTY, EXPECTED_ERROR_README_EMPTY),
(README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
(README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
(README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
(README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
(README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
(README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
(README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
] , )
def UpperCamelCase__ ( __magic_name__ : Tuple , __magic_name__ : List[Any] ) -> Tuple:
'''simple docstring'''
with pytest.raises(_lowercase , match=re.escape(expected_error.format(path="""root""" ) ) ):
snake_case__ : Optional[Any] = ReadMe.from_string(_lowercase , _lowercase )
readme.validate()
@pytest.mark.parametrize(
"""readme_md, expected_error""" , [
(README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
] , )
def UpperCamelCase__ ( __magic_name__ : List[str] , __magic_name__ : List[Any] ) -> Any:
'''simple docstring'''
with pytest.raises(_lowercase , match=re.escape(expected_error.format(path="""root""" ) ) ):
ReadMe.from_string(_lowercase , _lowercase )
@pytest.mark.parametrize(
"""readme_md,""" , [
(README_MULTIPLE_SAME_HEADING_1),
] , )
def UpperCamelCase__ ( __magic_name__ : Optional[Any] ) -> str:
'''simple docstring'''
ReadMe.from_string(_lowercase , _lowercase , suppress_parsing_errors=_lowercase )
@pytest.mark.parametrize(
"""readme_md, expected_dict""" , [
(README_CORRECT, CORRECT_DICT),
(README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
] , )
def UpperCamelCase__ ( __magic_name__ : str , __magic_name__ : List[str] ) -> List[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ : List[Any] = Path(_lowercase ) / 'README.md'
with open(_lowercase , """w+""" ) as readme_file:
readme_file.write(_lowercase )
snake_case__ : List[Any] = ReadMe.from_readme(_lowercase , _lowercase ).to_dict()
assert out["name"] == path
assert out["text"] == ""
assert out["is_empty_text"]
assert out["subsections"] == expected_dict["subsections"]
@pytest.mark.parametrize(
"""readme_md, expected_error""" , [
(README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
(README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
(README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
(README_EMPTY, EXPECTED_ERROR_README_EMPTY),
(README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
(README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
(README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
(README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
(README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
(README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
(README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
] , )
def UpperCamelCase__ ( __magic_name__ : Optional[int] , __magic_name__ : str ) -> Optional[int]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ : str = Path(_lowercase ) / 'README.md'
with open(_lowercase , """w+""" ) as readme_file:
readme_file.write(_lowercase )
snake_case__ : Optional[Any] = expected_error.format(path=_lowercase )
with pytest.raises(_lowercase , match=re.escape(_lowercase ) ):
snake_case__ : int = ReadMe.from_readme(_lowercase , _lowercase )
readme.validate()
@pytest.mark.parametrize(
"""readme_md, expected_error""" , [
(README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
] , )
def UpperCamelCase__ ( __magic_name__ : int , __magic_name__ : Any ) -> str:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ : Optional[Any] = Path(_lowercase ) / 'README.md'
with open(_lowercase , """w+""" ) as readme_file:
readme_file.write(_lowercase )
snake_case__ : Optional[Any] = expected_error.format(path=_lowercase )
with pytest.raises(_lowercase , match=re.escape(_lowercase ) ):
ReadMe.from_readme(_lowercase , _lowercase )
@pytest.mark.parametrize(
"""readme_md,""" , [
(README_MULTIPLE_SAME_HEADING_1),
] , )
def UpperCamelCase__ ( __magic_name__ : int ) -> str:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ : Optional[Any] = Path(_lowercase ) / 'README.md'
with open(_lowercase , """w+""" ) as readme_file:
readme_file.write(_lowercase )
ReadMe.from_readme(_lowercase , _lowercase , suppress_parsing_errors=_lowercase )
| 704 |
'''simple docstring'''
class __snake_case :
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE ):
snake_case__ : Dict = val
snake_case__ : List[str] = None
snake_case__ : Tuple = None
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE ):
if self.val:
if val < self.val:
if self.left is None:
snake_case__ : Any = Node(__SCREAMING_SNAKE_CASE )
else:
self.left.insert(__SCREAMING_SNAKE_CASE )
elif val > self.val:
if self.right is None:
snake_case__ : List[Any] = Node(__SCREAMING_SNAKE_CASE )
else:
self.right.insert(__SCREAMING_SNAKE_CASE )
else:
snake_case__ : Tuple = val
def UpperCamelCase__ ( __magic_name__ : Optional[int] , __magic_name__ : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
if root:
inorder(root.left , __magic_name__ )
res.append(root.val )
inorder(root.right , __magic_name__ )
def UpperCamelCase__ ( __magic_name__ : Union[str, Any] ) -> str:
'''simple docstring'''
if len(__magic_name__ ) == 0:
return arr
snake_case__ : int = Node(arr[0] )
for i in range(1 , len(__magic_name__ ) ):
root.insert(arr[i] )
# Traverse BST in order.
snake_case__ : str = []
inorder(__magic_name__ , __magic_name__ )
return res
if __name__ == "__main__":
print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
| 419 | 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, is_vision_available, logging
if is_vision_available():
import PIL
lowerCAmelCase__ : Union[str, Any] =logging.get_logger(__name__)
class __lowercase (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCAmelCase = ["""pixel_values"""]
def __init__( self , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = PILImageResampling.BILINEAR , lowerCAmelCase__ = True , lowerCAmelCase__ = 1 / 2_5_5 , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = None , **lowerCAmelCase__ , ):
"""simple docstring"""
super().__init__(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Dict = size if size is not None else {'shortest_edge': 3_8_4}
SCREAMING_SNAKE_CASE_ : List[str] = get_size_dict(lowerCAmelCase__ , default_to_square=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : List[Any] = do_resize
SCREAMING_SNAKE_CASE_ : Union[str, Any] = size
# Default value set here for backwards compatibility where the value in config is None
SCREAMING_SNAKE_CASE_ : str = crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
SCREAMING_SNAKE_CASE_ : Dict = resample
SCREAMING_SNAKE_CASE_ : int = do_rescale
SCREAMING_SNAKE_CASE_ : Union[str, Any] = rescale_factor
SCREAMING_SNAKE_CASE_ : List[Any] = do_normalize
SCREAMING_SNAKE_CASE_ : Union[str, Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
SCREAMING_SNAKE_CASE_ : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCamelCase__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = PILImageResampling.BICUBIC , lowerCAmelCase__ = None , **lowerCAmelCase__ , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = get_size_dict(lowerCAmelCase__ , default_to_square=lowerCAmelCase__ )
if "shortest_edge" not in size:
raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}''' )
SCREAMING_SNAKE_CASE_ : List[Any] = size['shortest_edge']
if shortest_edge < 3_8_4:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
SCREAMING_SNAKE_CASE_ : Tuple = int(shortest_edge / crop_pct )
SCREAMING_SNAKE_CASE_ : List[str] = get_resize_output_image_size(lowerCAmelCase__ , size=lowerCAmelCase__ , default_to_square=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : List[Any] = resize(image=lowerCAmelCase__ , size=lowerCAmelCase__ , resample=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=lowerCAmelCase__ , size=(shortest_edge, shortest_edge) , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
lowerCAmelCase__ , size=(shortest_edge, shortest_edge) , resample=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def UpperCamelCase__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = None , **lowerCAmelCase__ , ):
"""simple docstring"""
return rescale(lowerCAmelCase__ , scale=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def UpperCamelCase__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = None , **lowerCAmelCase__ , ):
"""simple docstring"""
return normalize(lowerCAmelCase__ , mean=lowerCAmelCase__ , std=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def UpperCamelCase__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = ChannelDimension.FIRST , **lowerCAmelCase__ , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE_ : Any = crop_pct if crop_pct is not None else self.crop_pct
SCREAMING_SNAKE_CASE_ : str = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE_ : Optional[Any] = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE_ : str = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE_ : List[str] = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE_ : Optional[int] = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE_ : List[Any] = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE_ : Union[str, Any] = size if size is not None else self.size
SCREAMING_SNAKE_CASE_ : Union[str, Any] = get_size_dict(lowerCAmelCase__ , default_to_square=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : List[Any] = 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 or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
raise ValueError('crop_pct must be specified if size < 384.' )
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.
SCREAMING_SNAKE_CASE_ : Optional[int] = [to_numpy_array(lowerCAmelCase__ ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE_ : List[Any] = [self.resize(image=lowerCAmelCase__ , size=lowerCAmelCase__ , crop_pct=lowerCAmelCase__ , resample=lowerCAmelCase__ ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE_ : int = [self.rescale(image=lowerCAmelCase__ , scale=lowerCAmelCase__ ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE_ : Optional[int] = [self.normalize(image=lowerCAmelCase__ , mean=lowerCAmelCase__ , std=lowerCAmelCase__ ) for image in images]
SCREAMING_SNAKE_CASE_ : str = [to_channel_dimension_format(lowerCAmelCase__ , lowerCAmelCase__ ) for image in images]
SCREAMING_SNAKE_CASE_ : int = {'pixel_values': images}
return BatchFeature(data=lowerCAmelCase__ , tensor_type=lowerCAmelCase__ )
| 101 | '''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__ = DebertaTokenizer
UpperCAmelCase__ = True
UpperCAmelCase__ = DebertaTokenizerFast
def snake_case__ ( self : List[str] ) ->List[Any]:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_UpperCamelCase : Optional[Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"[UNK]",
]
_UpperCamelCase : str = dict(zip(lowercase__ , range(len(lowercase__ ) ) ) )
_UpperCamelCase : Any = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
_UpperCamelCase : Union[str, Any] = {"unk_token": "[UNK]"}
_UpperCamelCase : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
_UpperCamelCase : Dict = 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(lowercase__ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(lowercase__ ) )
def snake_case__ ( self : Dict , **lowercase__ : str ) ->List[str]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowercase__ )
def snake_case__ ( self : Tuple , lowercase__ : Tuple ) ->Any:
'''simple docstring'''
_UpperCamelCase : List[Any] = "lower newer"
_UpperCamelCase : Optional[Any] = "lower newer"
return input_text, output_text
def snake_case__ ( self : Optional[int] ) ->Dict:
'''simple docstring'''
_UpperCamelCase : Dict = self.get_tokenizer()
_UpperCamelCase : List[str] = "lower newer"
_UpperCamelCase : List[Any] = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
_UpperCamelCase : Dict = tokenizer.tokenize(lowercase__ )
self.assertListEqual(lowercase__ , lowercase__ )
_UpperCamelCase : Optional[Any] = tokens + [tokenizer.unk_token]
_UpperCamelCase : List[str] = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__ ) , lowercase__ )
def snake_case__ ( self : Dict ) ->Tuple:
'''simple docstring'''
_UpperCamelCase : Optional[int] = self.get_tokenizer()
_UpperCamelCase : Optional[int] = tokenizer("Hello" , "World" )
_UpperCamelCase : Optional[int] = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
self.assertListEqual(tokd["token_type_ids"] , lowercase__ )
@slow
def snake_case__ ( self : Any ) ->Union[str, Any]:
'''simple docstring'''
_UpperCamelCase : Optional[int] = self.tokenizer_class.from_pretrained("microsoft/deberta-base" )
_UpperCamelCase : int = tokenizer.encode("sequence builders" , add_special_tokens=lowercase__ )
_UpperCamelCase : Any = tokenizer.encode("multi-sequence build" , add_special_tokens=lowercase__ )
_UpperCamelCase : Any = tokenizer.encode(
"sequence builders" , add_special_tokens=lowercase__ , add_prefix_space=lowercase__ )
_UpperCamelCase : Dict = tokenizer.encode(
"sequence builders" , "multi-sequence build" , add_special_tokens=lowercase__ , add_prefix_space=lowercase__ )
_UpperCamelCase : Dict = tokenizer.build_inputs_with_special_tokens(lowercase__ )
_UpperCamelCase : Dict = tokenizer.build_inputs_with_special_tokens(lowercase__ , lowercase__ )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
@slow
def snake_case__ ( self : Dict ) ->Optional[Any]:
'''simple docstring'''
_UpperCamelCase : str = [self.tokenizer_class]
if self.test_rust_tokenizer:
tokenizer_classes.append(self.rust_tokenizer_class )
for tokenizer_class in tokenizer_classes:
_UpperCamelCase : int = tokenizer_class.from_pretrained("microsoft/deberta-base" )
_UpperCamelCase : List[str] = [
"ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
"ALBERT incorporates two parameter reduction techniques",
"The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
" embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
" vocabulary embedding.",
]
_UpperCamelCase : Optional[int] = tokenizer(lowercase__ , padding=lowercase__ )
_UpperCamelCase : Union[str, Any] = [tokenizer.decode(lowercase__ , skip_special_tokens=lowercase__ ) for seq in encoding["input_ids"]]
# fmt: off
_UpperCamelCase : List[Any] = {
"input_ids": [
[1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 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],
[1, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 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],
[1, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
],
"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]
],
"attention_mask": [
[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],
[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],
[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]
]
}
# fmt: on
_UpperCamelCase : Any = [
"ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
"ALBERT incorporates two parameter reduction techniques",
"The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
" embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
" vocabulary embedding.",
]
self.assertDictEqual(encoding.data , lowercase__ )
for expected, decoded in zip(lowercase__ , lowercase__ ):
self.assertEqual(lowercase__ , lowercase__ )
| 435 | 0 |
'''simple docstring'''
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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = tempfile.mkdtemp()
UpperCamelCase = BlipImageProcessor()
UpperCamelCase = BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel''' )
UpperCamelCase = BlipProcessor(_lowerCAmelCase , _lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
def UpperCAmelCase ( self , **lowerCamelCase__ ):
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **_lowerCAmelCase ).tokenizer
def UpperCAmelCase ( self , **lowerCamelCase__ ):
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **_lowerCAmelCase ).image_processor
def UpperCAmelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
UpperCamelCase = [Image.fromarray(np.moveaxis(_lowerCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
UpperCamelCase = self.get_image_processor(do_normalize=_lowerCAmelCase , padding_value=1.0 )
UpperCamelCase = BlipProcessor.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 )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = BlipProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = image_processor(_lowerCAmelCase , return_tensors='''np''' )
UpperCamelCase = 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 UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = BlipProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
UpperCamelCase = '''lower newer'''
UpperCamelCase = processor(text=_lowerCAmelCase )
UpperCamelCase = tokenizer(_lowerCAmelCase , return_token_type_ids=_lowerCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = BlipProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
UpperCamelCase = '''lower newer'''
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = processor(text=_lowerCAmelCase , images=_lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ['''pixel_values''', '''input_ids''', '''attention_mask'''] )
# test if it raises when no input is passed
with pytest.raises(_lowerCAmelCase ):
processor()
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = BlipProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
UpperCamelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCamelCase = processor.batch_decode(_lowerCAmelCase )
UpperCamelCase = tokenizer.batch_decode(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = BlipProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
UpperCamelCase = '''lower newer'''
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = processor(text=_lowerCAmelCase , images=_lowerCAmelCase )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ['''pixel_values''', '''input_ids''', '''attention_mask'''] )
| 709 |
'''simple docstring'''
def __snake_case ( _UpperCAmelCase : Optional[int]):
UpperCamelCase = []
UpperCamelCase = []
UpperCamelCase = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
'''+''': 1,
'''-''': 1,
} # Priority of each operator
UpperCamelCase = len(_UpperCAmelCase) if (len(_UpperCAmelCase) > 7) else 7
# Print table header for output
print(
'''Symbol'''.center(8), '''Stack'''.center(_UpperCAmelCase), '''Postfix'''.center(_UpperCAmelCase), sep=''' | ''', )
print('''-''' * (print_width * 3 + 7))
for x in infix:
if x.isalpha() or x.isdigit():
post_fix.append(_UpperCAmelCase) # if x is Alphabet / Digit, add it to Postfix
elif x == "(":
stack.append(_UpperCAmelCase) # if x is "(" push to Stack
elif x == ")": # if x is ")" pop stack until "(" is encountered
while stack[-1] != "(":
post_fix.append(stack.pop()) # Pop stack & add the content to Postfix
stack.pop()
else:
if len(_UpperCAmelCase) == 0:
stack.append(_UpperCAmelCase) # If stack is empty, push x to stack
else: # while priority of x is not > priority of element in the stack
while len(_UpperCAmelCase) > 0 and priority[x] <= priority[stack[-1]]:
post_fix.append(stack.pop()) # pop stack & add to Postfix
stack.append(_UpperCAmelCase) # push x to stack
print(
x.center(8), (''''''.join(_UpperCAmelCase)).ljust(_UpperCAmelCase), (''''''.join(_UpperCAmelCase)).ljust(_UpperCAmelCase), sep=''' | ''', ) # Output in tabular format
while len(_UpperCAmelCase) > 0: # while stack is not empty
post_fix.append(stack.pop()) # pop stack & add to Postfix
print(
''' '''.center(8), (''''''.join(_UpperCAmelCase)).ljust(_UpperCAmelCase), (''''''.join(_UpperCAmelCase)).ljust(_UpperCAmelCase), sep=''' | ''', ) # Output in tabular format
return "".join(_UpperCAmelCase) # return Postfix as str
def __snake_case ( _UpperCAmelCase : str):
UpperCamelCase = list(infix[::-1]) # reverse the infix equation
for i in range(len(_UpperCAmelCase)):
if infix[i] == "(":
UpperCamelCase = ''')''' # change "(" to ")"
elif infix[i] == ")":
UpperCamelCase = '''(''' # change ")" to "("
return (infix_2_postfix(''''''.join(_UpperCAmelCase)))[
::-1
] # call infix_2_postfix on Infix, return reverse of Postfix
if __name__ == "__main__":
snake_case_ : str = input('\nEnter an Infix Equation = ') # Input an Infix equation
snake_case_ : Union[str, Any] = ''.join(Infix.split()) # Remove spaces from the input
print('\n\t', Infix, '(Infix) -> ', infix_2_prefix(Infix), '(Prefix)')
| 350 | 0 |
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
lowerCamelCase_ : List[str] = (
"""This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate """
"""library. You can have a look at this example script for pointers: """
"""https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""
)
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase ):
warnings.warn(__lowerCamelCase , __lowerCamelCase )
requires_backends(__lowerCamelCase , 'sklearn' )
return (preds == labels).mean()
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase ):
warnings.warn(__lowerCamelCase , __lowerCamelCase )
requires_backends(__lowerCamelCase , 'sklearn' )
__a = simple_accuracy(__lowerCamelCase , __lowerCamelCase )
__a = fa_score(y_true=__lowerCamelCase , y_pred=__lowerCamelCase )
return {
"acc": acc,
"f1": fa,
"acc_and_f1": (acc + fa) / 2,
}
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase ):
warnings.warn(__lowerCamelCase , __lowerCamelCase )
requires_backends(__lowerCamelCase , 'sklearn' )
__a = pearsonr(__lowerCamelCase , __lowerCamelCase )[0]
__a = spearmanr(__lowerCamelCase , __lowerCamelCase )[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
warnings.warn(__lowerCamelCase , __lowerCamelCase )
requires_backends(__lowerCamelCase , 'sklearn' )
assert len(__lowerCamelCase ) == len(__lowerCamelCase ), f'''Predictions and labels have mismatched lengths {len(__lowerCamelCase )} and {len(__lowerCamelCase )}'''
if task_name == "cola":
return {"mcc": matthews_corrcoef(__lowerCamelCase , __lowerCamelCase )}
elif task_name == "sst-2":
return {"acc": simple_accuracy(__lowerCamelCase , __lowerCamelCase )}
elif task_name == "mrpc":
return acc_and_fa(__lowerCamelCase , __lowerCamelCase )
elif task_name == "sts-b":
return pearson_and_spearman(__lowerCamelCase , __lowerCamelCase )
elif task_name == "qqp":
return acc_and_fa(__lowerCamelCase , __lowerCamelCase )
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(__lowerCamelCase , __lowerCamelCase )}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(__lowerCamelCase , __lowerCamelCase )}
elif task_name == "qnli":
return {"acc": simple_accuracy(__lowerCamelCase , __lowerCamelCase )}
elif task_name == "rte":
return {"acc": simple_accuracy(__lowerCamelCase , __lowerCamelCase )}
elif task_name == "wnli":
return {"acc": simple_accuracy(__lowerCamelCase , __lowerCamelCase )}
elif task_name == "hans":
return {"acc": simple_accuracy(__lowerCamelCase , __lowerCamelCase )}
else:
raise KeyError(__lowerCamelCase )
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
warnings.warn(__lowerCamelCase , __lowerCamelCase )
requires_backends(__lowerCamelCase , 'sklearn' )
if len(__lowerCamelCase ) != len(__lowerCamelCase ):
raise ValueError(f'''Predictions and labels have mismatched lengths {len(__lowerCamelCase )} and {len(__lowerCamelCase )}''' )
if task_name == "xnli":
return {"acc": simple_accuracy(__lowerCamelCase , __lowerCamelCase )}
else:
raise KeyError(__lowerCamelCase )
| 559 | def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase ):
# Check if the input is valid
if not len(__lowerCamelCase ) == len(__lowerCamelCase ) == 3:
raise ValueError('Please enter a valid equation.' )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError('Both a & b of two equations can\'t be zero.' )
# Extract the coefficients
__a , __a , __a = equationa
__a , __a , __a = equationa
# Calculate the determinants of the matrices
__a = aa * ba - aa * ba
__a = ca * ba - ca * ba
__a = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError('Infinite solutions. (Consistent system)' )
else:
raise ValueError('No solution. (Inconsistent system)' )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
__a = determinant_x / determinant
__a = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 559 | 1 |
import numpy
class UpperCAmelCase__ :
def __init__( self , A__ , A__ ):
"""simple docstring"""
UpperCAmelCase_: Optional[Any] = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
UpperCAmelCase_: str = numpy.random.rand(
self.input_array.shape[1] , 4 )
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
UpperCAmelCase_: Tuple = numpy.random.rand(
4 , 3 )
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
UpperCAmelCase_: Dict = numpy.random.rand(3 , 1 )
# Real output values provided.
UpperCAmelCase_: Any = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
UpperCAmelCase_: Dict = numpy.zeros(output_array.shape )
def snake_case_ ( self ):
"""simple docstring"""
UpperCAmelCase_: Dict = sigmoid(
numpy.dot(self.input_array , self.input_layer_and_first_hidden_layer_weights ) )
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
UpperCAmelCase_: List[str] = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
UpperCAmelCase_: Dict = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return self.layer_between_second_hidden_layer_and_output
def snake_case_ ( self ):
"""simple docstring"""
UpperCAmelCase_: Any = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T , 2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , )
UpperCAmelCase_: List[str] = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T , numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , )
UpperCAmelCase_: Optional[int] = numpy.dot(
self.input_array.T , numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , self.first_hidden_layer_and_second_hidden_layer_weights.T , )
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer ) , )
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def snake_case_ ( self , A__ , A__ , A__ ):
"""simple docstring"""
for iteration in range(1 , iterations + 1 ):
UpperCAmelCase_: Any = self.feedforward()
self.back_propagation()
if give_loss:
UpperCAmelCase_: List[str] = numpy.mean(numpy.square(output - self.feedforward() ) )
print(F"Iteration {iteration} Loss: {loss}" )
def snake_case_ ( self , A__ ):
"""simple docstring"""
UpperCAmelCase_: Dict = input_arr
UpperCAmelCase_: Dict = sigmoid(
numpy.dot(self.array , self.input_layer_and_first_hidden_layer_weights ) )
UpperCAmelCase_: Union[str, Any] = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
UpperCAmelCase_: Dict = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return int(self.layer_between_second_hidden_layer_and_output > 0.6 )
def lowercase ( _a ) -> numpy.ndarray:
return 1 / (1 + numpy.exp(-value ))
def lowercase ( _a ) -> numpy.ndarray:
return (value) * (1 - (value))
def lowercase ( ) -> int:
UpperCAmelCase_: Tuple = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
) ,dtype=numpy.floataa ,)
# True output values for the given input values.
UpperCAmelCase_: Optional[int] = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) ,dtype=numpy.floataa )
# Calling neural network class.
UpperCAmelCase_: Tuple = TwoHiddenLayerNeuralNetwork(
input_array=_a ,output_array=_a )
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=_a ,iterations=10 ,give_loss=_a )
return neural_network.predict(numpy.array(([1, 1, 1]) ,dtype=numpy.floataa ) )
if __name__ == "__main__":
example() | 306 |
_lowerCAmelCase = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_lowerCAmelCase = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def lowercase ( _a ,_a ,_a ) -> list[int]:
UpperCAmelCase_: Tuple = True
UpperCAmelCase_: Optional[int] = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(_a ,_a ,_a )
order.append(_a )
return order
def lowercase ( _a ,_a ,_a ) -> list[int]:
UpperCAmelCase_: Optional[int] = True
UpperCAmelCase_: str = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(_a ,_a ,_a )
return component
def lowercase ( _a ) -> list[list[int]]:
UpperCAmelCase_: Union[str, Any] = len(_a ) * [False]
UpperCAmelCase_: dict[int, list[int]] = {vert: [] for vert in range(len(_a ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(_a )
UpperCAmelCase_: Optional[int] = []
for i, was_visited in enumerate(_a ):
if not was_visited:
order += topology_sort(_a ,_a ,_a )
UpperCAmelCase_: Optional[Any] = []
UpperCAmelCase_: Union[str, Any] = len(_a ) * [False]
for i in range(len(_a ) ):
UpperCAmelCase_: str = order[len(_a ) - i - 1]
if not visited[vert]:
UpperCAmelCase_: List[str] = find_components(_a ,_a ,_a )
components_list.append(_a )
return components_list | 306 | 1 |
from collections.abc import Sequence
def lowerCAmelCase_ ( __a , __a = False ) -> float:
"""simple docstring"""
if not arr:
return 0
lowerCamelCase__: Tuple =0 if allow_empty_subarrays else float("-inf" )
lowerCamelCase__: Optional[int] =0.0
for num in arr:
lowerCamelCase__: Union[str, Any] =max(0 if allow_empty_subarrays else num , curr_sum + num )
lowerCamelCase__: List[str] =max(__a , __a )
return max_sum
if __name__ == "__main__":
from doctest import testmod
testmod()
__A = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(f'{max_subarray_sum(nums) = }')
| 59 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase_ ( __a , __a ) -> List[Any]:
"""simple docstring"""
assert isinstance(__a , __a )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def lowerCAmelCase_ ( __a , __a , __a ) -> Any:
"""simple docstring"""
lowerCamelCase__: Any =tmp_path / "cache"
lowerCamelCase__: Optional[int] ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowerCamelCase__: Tuple =ParquetDatasetReader(__a , cache_dir=__a , keep_in_memory=__a ).read()
_check_parquet_dataset(__a , __a )
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def lowerCAmelCase_ ( __a , __a , __a ) -> List[str]:
"""simple docstring"""
lowerCamelCase__: int =tmp_path / "cache"
lowerCamelCase__: Tuple ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
lowerCamelCase__: Union[str, Any] =features.copy() if features else default_expected_features
lowerCamelCase__: Optional[int] =(
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
lowerCamelCase__: int =ParquetDatasetReader(__a , features=__a , cache_dir=__a ).read()
_check_parquet_dataset(__a , __a )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def lowerCAmelCase_ ( __a , __a , __a ) -> Any:
"""simple docstring"""
lowerCamelCase__: Any =tmp_path / "cache"
lowerCamelCase__: Optional[Any] ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
lowerCamelCase__: Optional[Any] =ParquetDatasetReader(__a , cache_dir=__a , split=__a ).read()
_check_parquet_dataset(__a , __a )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type" , [str, list] )
def lowerCAmelCase_ ( __a , __a , __a ) -> int:
"""simple docstring"""
if issubclass(__a , __a ):
lowerCamelCase__: List[Any] =parquet_path
elif issubclass(__a , __a ):
lowerCamelCase__: str =[parquet_path]
lowerCamelCase__: Tuple =tmp_path / "cache"
lowerCamelCase__: Optional[Any] ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
lowerCamelCase__: int =ParquetDatasetReader(__a , cache_dir=__a ).read()
_check_parquet_dataset(__a , __a )
def lowerCAmelCase_ ( __a , __a , __a=("train",) ) -> Dict:
"""simple docstring"""
assert isinstance(__a , __a )
for split in splits:
lowerCamelCase__: Tuple =dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def lowerCAmelCase_ ( __a , __a , __a ) -> Any:
"""simple docstring"""
lowerCamelCase__: List[Any] =tmp_path / "cache"
lowerCamelCase__: Optional[Any] ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowerCamelCase__: Tuple =ParquetDatasetReader(
{"train": parquet_path} , cache_dir=__a , keep_in_memory=__a ).read()
_check_parquet_datasetdict(__a , __a )
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def lowerCAmelCase_ ( __a , __a , __a ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase__: Tuple =tmp_path / "cache"
lowerCamelCase__: Optional[int] ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
lowerCamelCase__: List[Any] =features.copy() if features else default_expected_features
lowerCamelCase__: int =(
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
lowerCamelCase__: Optional[Any] =ParquetDatasetReader({"train": parquet_path} , features=__a , cache_dir=__a ).read()
_check_parquet_datasetdict(__a , __a )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def lowerCAmelCase_ ( __a , __a , __a ) -> Union[str, Any]:
"""simple docstring"""
if split:
lowerCamelCase__: Any ={split: parquet_path}
else:
lowerCamelCase__: int ="train"
lowerCamelCase__: Any ={"train": parquet_path, "test": parquet_path}
lowerCamelCase__: str =tmp_path / "cache"
lowerCamelCase__: Any ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
lowerCamelCase__: int =ParquetDatasetReader(__a , cache_dir=__a ).read()
_check_parquet_datasetdict(__a , __a , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCAmelCase_ ( __a , __a ) -> int:
"""simple docstring"""
lowerCamelCase__: List[str] =ParquetDatasetWriter(__a , tmp_path / "foo.parquet" )
assert writer.write() > 0
lowerCamelCase__: List[str] =pq.ParquetFile(tmp_path / "foo.parquet" )
lowerCamelCase__: List[str] =pf.read()
assert dataset.data.table == output_table
def lowerCAmelCase_ ( __a , __a ) -> List[str]:
"""simple docstring"""
lowerCamelCase__: List[str] =str(shared_datadir / "test_image_rgb.jpg" )
lowerCamelCase__: Union[str, Any] ={"image": [image_path]}
lowerCamelCase__: Optional[Any] =Features({"image": Image()} )
lowerCamelCase__: Optional[int] =Dataset.from_dict(__a , features=__a )
lowerCamelCase__: Optional[int] =ParquetDatasetWriter(__a , tmp_path / "foo.parquet" )
assert writer.write() > 0
lowerCamelCase__: Dict =Dataset.from_parquet(str(tmp_path / "foo.parquet" ) )
assert dataset.features == reloaded_dataset.features
lowerCamelCase__: Optional[Any] =ParquetDatasetReader(str(tmp_path / "foo.parquet" ) , streaming=__a ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"feature, expected" , [
(Features({"foo": Value("int32" )} ), None),
(Features({"image": Image(), "foo": Value("int32" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"nested": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def lowerCAmelCase_ ( __a , __a ) -> Optional[Any]:
"""simple docstring"""
assert get_writer_batch_size(__a ) == expected
| 59 | 1 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''', [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''', num_bytes=1337, num_examples=42, dataset_name='''my_dataset''')}),
SplitDict({'''train''': SplitInfo(name='''train''', num_bytes=1337, num_examples=42)}),
SplitDict({'''train''': SplitInfo()}),
], )
def __snake_case ( _UpperCAmelCase : SplitDict):
UpperCamelCase = split_dict._to_yaml_list()
assert len(_UpperCAmelCase) == len(_UpperCAmelCase)
UpperCamelCase = SplitDict._from_yaml_list(_UpperCAmelCase)
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
UpperCamelCase = None
# the split name of split_dict takes over the name of the split info object
UpperCamelCase = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
'''split_info''', [SplitInfo(), SplitInfo(dataset_name=_UpperCAmelCase), SplitInfo(dataset_name='''my_dataset''')])
def __snake_case ( _UpperCAmelCase : Optional[Any]):
# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
UpperCamelCase = asdict(SplitDict({'''train''': split_info}))
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 712 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
snake_case_ : Tuple = get_tests_dir('fixtures/test_sentencepiece_no_bos.model')
@require_sentencepiece
@require_tokenizers
class lowercase__ ( snake_case_, unittest.TestCase ):
'''simple docstring'''
_snake_case = PegasusTokenizer
_snake_case = PegasusTokenizerFast
_snake_case = True
_snake_case = True
def UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
UpperCamelCase = PegasusTokenizer(lowerCamelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def UpperCAmelCase ( self ):
'''simple docstring'''
return PegasusTokenizer.from_pretrained('''google/pegasus-large''' )
def UpperCAmelCase ( self , **lowerCamelCase__ ):
'''simple docstring'''
return PegasusTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase__ )
def UpperCAmelCase ( self , lowerCamelCase__ ):
'''simple docstring'''
return ("This is a test", "This is a test")
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = '''</s>'''
UpperCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__ ) , lowerCamelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__ ) , lowerCamelCase__ )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''</s>''' )
self.assertEqual(vocab_keys[-1] , '''v''' )
self.assertEqual(len(lowerCamelCase__ ) , 1_1_0_3 )
def UpperCAmelCase ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1_1_0_3 )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
UpperCamelCase = self.tokenizer_class.from_pretrained(self.tmpdirname )
UpperCamelCase = (
'''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
''' </s> <pad> <pad> <pad>'''
)
UpperCamelCase = rust_tokenizer([raw_input_str] , return_tensors=lowerCamelCase__ , add_special_tokens=lowerCamelCase__ ).input_ids[0]
UpperCamelCase = py_tokenizer([raw_input_str] , return_tensors=lowerCamelCase__ , add_special_tokens=lowerCamelCase__ ).input_ids[0]
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
UpperCamelCase = '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
UpperCamelCase = [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
UpperCamelCase = tokenizer([raw_input_str] , return_tensors=lowerCamelCase__ ).input_ids[0]
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6_1_0_3
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 1_0_3
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1_0_2_4
UpperCamelCase = '''To ensure a smooth flow of bank resolutions.'''
UpperCamelCase = [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
UpperCamelCase = tokenizer([raw_input_str] , return_tensors=lowerCamelCase__ ).input_ids[0]
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = ['''This is going to be way too long.''' * 1_5_0, '''short example''']
UpperCamelCase = ['''not super long but more than 5 tokens''', '''tiny''']
UpperCamelCase = self._large_tokenizer(lowerCamelCase__ , padding=lowerCamelCase__ , truncation=lowerCamelCase__ , return_tensors='''pt''' )
UpperCamelCase = self._large_tokenizer(
text_target=lowerCamelCase__ , max_length=5 , padding=lowerCamelCase__ , truncation=lowerCamelCase__ , return_tensors='''pt''' )
assert batch.input_ids.shape == (2, 1_0_2_4)
assert batch.attention_mask.shape == (2, 1_0_2_4)
assert targets["input_ids"].shape == (2, 5)
assert len(lowerCamelCase__ ) == 2 # input_ids, attention_mask.
@slow
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = {'''input_ids''': [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 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], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 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]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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='''google/bigbird-pegasus-large-arxiv''' , revision='''ba85d0851d708441f91440d509690f1ab6353415''' , )
@require_sentencepiece
@require_tokenizers
class lowercase__ ( snake_case_, unittest.TestCase ):
'''simple docstring'''
_snake_case = PegasusTokenizer
_snake_case = PegasusTokenizerFast
_snake_case = True
_snake_case = True
def UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
UpperCamelCase = PegasusTokenizer(lowerCamelCase__ , offset=0 , mask_token_sent=lowerCamelCase__ , mask_token='''[MASK]''' )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def UpperCAmelCase ( self ):
'''simple docstring'''
return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv''' )
def UpperCAmelCase ( self , **lowerCamelCase__ ):
'''simple docstring'''
return PegasusTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase__ )
def UpperCAmelCase ( self , lowerCamelCase__ ):
'''simple docstring'''
return ("This is a test", "This is a test")
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
UpperCamelCase = self.tokenizer_class.from_pretrained(self.tmpdirname )
UpperCamelCase = (
'''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
''' <pad> <pad> <pad>'''
)
UpperCamelCase = rust_tokenizer([raw_input_str] , return_tensors=lowerCamelCase__ , add_special_tokens=lowerCamelCase__ ).input_ids[0]
UpperCamelCase = py_tokenizer([raw_input_str] , return_tensors=lowerCamelCase__ , add_special_tokens=lowerCamelCase__ ).input_ids[0]
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@require_torch
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = ['''This is going to be way too long.''' * 1_0_0_0, '''short example''']
UpperCamelCase = ['''not super long but more than 5 tokens''', '''tiny''']
UpperCamelCase = self._large_tokenizer(lowerCamelCase__ , padding=lowerCamelCase__ , truncation=lowerCamelCase__ , return_tensors='''pt''' )
UpperCamelCase = self._large_tokenizer(
text_target=lowerCamelCase__ , max_length=5 , padding=lowerCamelCase__ , truncation=lowerCamelCase__ , return_tensors='''pt''' )
assert batch.input_ids.shape == (2, 4_0_9_6)
assert batch.attention_mask.shape == (2, 4_0_9_6)
assert targets["input_ids"].shape == (2, 5)
assert len(lowerCamelCase__ ) == 2 # input_ids, attention_mask.
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = (
'''This is an example string that is used to test the original TF implementation against the HF'''
''' implementation'''
)
UpperCamelCase = self._large_tokenizer(lowerCamelCase__ ).input_ids
self.assertListEqual(
lowerCamelCase__ , [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1] , )
| 350 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger(__name__)
def __UpperCAmelCase ( lowerCamelCase_ , lowerCamelCase_=False) -> List[Any]:
UpperCamelCase__ : Any = []
# fmt: off
# stem:
rename_keys.append(('cls_token', 'vit.embeddings.cls_token'))
rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings'))
rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'))
rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'))
# backbone
rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight'))
rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight'))
rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias'))
for stage_idx in range(len(config.backbone_config.depths)):
for layer_idx in range(config.backbone_config.depths[stage_idx]):
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight'))
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight'))
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias'))
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight'))
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight'))
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias'))
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight'))
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight'))
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias'))
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight'))
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight'))
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias'))
# transformer encoder
for i in range(config.num_hidden_layers):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'blocks.{i}.norm1.weight', f'vit.encoder.layer.{i}.layernorm_before.weight'))
rename_keys.append((f'blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias'))
rename_keys.append((f'blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight'))
rename_keys.append((f'blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias'))
rename_keys.append((f'blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight'))
rename_keys.append((f'blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias'))
rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight'))
rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias'))
rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight'))
rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias'))
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('norm.weight', 'layernorm.weight'),
('norm.bias', 'layernorm.bias'),
('pre_logits.fc.weight', 'pooler.dense.weight'),
('pre_logits.fc.bias', 'pooler.dense.bias'),
])
# if just the base model, we should remove "vit" from all keys that start with "vit"
UpperCamelCase__ : Union[str, Any] = [(pair[0], pair[1][4:]) if pair[1].startswith('vit') else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('norm.weight', 'vit.layernorm.weight'),
('norm.bias', 'vit.layernorm.bias'),
('head.weight', 'classifier.weight'),
('head.bias', 'classifier.bias'),
])
# fmt: on
return rename_keys
def __UpperCAmelCase ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=False) -> Tuple:
for i in range(config.num_hidden_layers):
if base_model:
UpperCamelCase__ : Union[str, Any] = ''
else:
UpperCamelCase__ : Optional[int] = 'vit.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
UpperCamelCase__ : List[Any] = state_dict.pop(f'blocks.{i}.attn.qkv.weight')
UpperCamelCase__ : Optional[Any] = state_dict.pop(f'blocks.{i}.attn.qkv.bias')
# next, add query, keys and values (in that order) to the state dict
UpperCamelCase__ : List[str] = in_proj_weight[
: config.hidden_size, :
]
UpperCamelCase__ : str = in_proj_bias[: config.hidden_size]
UpperCamelCase__ : Optional[int] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCamelCase__ : Tuple = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
UpperCamelCase__ : Union[str, Any] = in_proj_weight[
-config.hidden_size :, :
]
UpperCamelCase__ : Dict = in_proj_bias[-config.hidden_size :]
def __UpperCAmelCase ( lowerCamelCase_) -> str:
UpperCamelCase__ : Tuple = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(UpperCamelCase__ , UpperCamelCase__)
def __UpperCAmelCase ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_) -> List[Any]:
UpperCamelCase__ : int = dct.pop(UpperCamelCase__)
UpperCamelCase__ : Union[str, Any] = val
def __UpperCAmelCase ( ) -> Optional[Any]:
UpperCamelCase__ : Any = 'http://images.cocodataset.org/val2017/000000039769.jpg'
UpperCamelCase__ : Optional[int] = Image.open(requests.get(UpperCamelCase__ , stream=UpperCamelCase__).raw)
return im
@torch.no_grad()
def __UpperCAmelCase ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=False) -> Any:
UpperCamelCase__ : Dict = BitConfig(
global_padding='same' , layer_type='bottleneck' , depths=(3, 4, 9) , out_features=['stage3'] , embedding_dynamic_padding=UpperCamelCase__ , )
UpperCamelCase__ : int = ViTHybridConfig(backbone_config=UpperCamelCase__ , image_size=384 , num_labels=1_000)
UpperCamelCase__ : List[str] = False
# load original model from timm
UpperCamelCase__ : List[str] = timm.create_model(UpperCamelCase__ , pretrained=UpperCamelCase__)
timm_model.eval()
# load state_dict of original model, remove and rename some keys
UpperCamelCase__ : Any = timm_model.state_dict()
if base_model:
remove_classification_head_(UpperCamelCase__)
UpperCamelCase__ : Optional[Any] = create_rename_keys(UpperCamelCase__ , UpperCamelCase__)
for src, dest in rename_keys:
rename_key(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
read_in_q_k_v(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
UpperCamelCase__ : Union[str, Any] = 'huggingface/label-files'
UpperCamelCase__ : Tuple = 'imagenet-1k-id2label.json'
UpperCamelCase__ : Optional[int] = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type='dataset') , 'r'))
UpperCamelCase__ : List[Any] = {int(UpperCamelCase__): v for k, v in idalabel.items()}
UpperCamelCase__ : Any = idalabel
UpperCamelCase__ : Tuple = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
UpperCamelCase__ : Union[str, Any] = ViTHybridModel(UpperCamelCase__).eval()
else:
UpperCamelCase__ : int = ViTHybridForImageClassification(UpperCamelCase__).eval()
model.load_state_dict(UpperCamelCase__)
# create image processor
UpperCamelCase__ : Any = create_transform(**resolve_data_config({} , model=UpperCamelCase__))
UpperCamelCase__ : Any = transform.transforms
UpperCamelCase__ : Any = {
'bilinear': PILImageResampling.BILINEAR,
'bicubic': PILImageResampling.BICUBIC,
'nearest': PILImageResampling.NEAREST,
}
UpperCamelCase__ : Any = ViTHybridImageProcessor(
do_resize=UpperCamelCase__ , size={'shortest_edge': timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=UpperCamelCase__ , crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]} , do_normalize=UpperCamelCase__ , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
UpperCamelCase__ : Optional[Any] = prepare_img()
UpperCamelCase__ : Tuple = transform(UpperCamelCase__).unsqueeze(0)
UpperCamelCase__ : int = processor(UpperCamelCase__ , return_tensors='pt').pixel_values
# verify pixel values
assert torch.allclose(UpperCamelCase__ , UpperCamelCase__)
# verify logits
with torch.no_grad():
UpperCamelCase__ : Dict = model(UpperCamelCase__)
UpperCamelCase__ : str = outputs.logits
print('Predicted class:' , logits.argmax(-1).item())
if base_model:
UpperCamelCase__ : Dict = timm_model.forward_features(UpperCamelCase__)
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(UpperCamelCase__ , outputs.pooler_output , atol=1e-3)
else:
UpperCamelCase__ : Tuple = timm_model(UpperCamelCase__)
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(UpperCamelCase__ , outputs.logits , atol=1e-3)
print('Looks ok!')
if pytorch_dump_folder_path is not None:
Path(UpperCamelCase__).mkdir(exist_ok=UpperCamelCase__)
print(f'Saving model {vit_name} to {pytorch_dump_folder_path}')
model.save_pretrained(UpperCamelCase__)
print(f'Saving processor to {pytorch_dump_folder_path}')
processor.save_pretrained(UpperCamelCase__)
if push_to_hub:
print(f'Pushing model and processor to the hub {vit_name}')
model.push_to_hub(f'ybelkada/{vit_name}')
processor.push_to_hub(f'ybelkada/{vit_name}')
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--vit_name',
default='vit_base_r50_s16_384',
type=str,
help='Name of the hybrid ViT timm 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 upload the model to the HuggingFace hub.'
)
lowerCAmelCase__ = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 596 |
def lowerCamelCase_ ( ) -> List[str]:
"""simple docstring"""
__lowerCamelCase = 0
for i in range(1 , 1001 ):
total += i**i
return str(UpperCamelCase__ )[-10:]
if __name__ == "__main__":
print(solution())
| 469 | 0 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class SCREAMING_SNAKE_CASE_ :
__magic_name__: str = field(
metadata={"help": "The output directory where the model will be written."} , )
__magic_name__: str = field(
metadata={
"help": (
"The encoder model checkpoint for weights initialization."
"Don't set if you want to train an encoder model from scratch."
)
} , )
__magic_name__: str = field(
metadata={
"help": (
"The decoder model checkpoint for weights initialization."
"Don't set if you want to train a decoder model from scratch."
)
} , )
__magic_name__: Optional[str] = field(
default=snake_case_ , metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"} )
__magic_name__: Optional[str] = field(
default=snake_case_ , metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"} )
def SCREAMING_SNAKE_CASE__ ( ):
snake_case_ : str = HfArgumentParser((ModelArguments,) )
(snake_case_) : Union[str, Any] = parser.parse_args_into_dataclasses()
# Load pretrained model and tokenizer
# Use explicit specified encoder config
if model_args.encoder_config_name:
snake_case_ : Optional[Any] = AutoConfig.from_pretrained(model_args.encoder_config_name )
# Use pretrained encoder model's config
else:
snake_case_ : Optional[Any] = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path )
# Use explicit specified decoder config
if model_args.decoder_config_name:
snake_case_ : int = AutoConfig.from_pretrained(model_args.decoder_config_name )
# Use pretrained decoder model's config
else:
snake_case_ : int = AutoConfig.from_pretrained(model_args.decoder_model_name_or_path )
# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
snake_case_ : List[str] = True
snake_case_ : Dict = True
snake_case_ : List[Any] = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path , decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path , encoder_config=__a , decoder_config=__a , )
# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
snake_case_ : Tuple = decoder_config.decoder_start_token_id
snake_case_ : List[Any] = decoder_config.pad_token_id
if decoder_start_token_id is None:
snake_case_ : Optional[int] = decoder_config.bos_token_id
if pad_token_id is None:
snake_case_ : List[str] = decoder_config.eos_token_id
# This is necessary to make Flax's generate() work
snake_case_ : str = decoder_config.eos_token_id
snake_case_ : Optional[int] = decoder_start_token_id
snake_case_ : List[str] = pad_token_id
snake_case_ : List[Any] = AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )
snake_case_ : Optional[int] = AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
snake_case_ : Tuple = tokenizer.convert_ids_to_tokens(model.config.pad_token_id )
model.save_pretrained(model_args.output_dir )
image_processor.save_pretrained(model_args.output_dir )
tokenizer.save_pretrained(model_args.output_dir )
if __name__ == "__main__":
main()
| 717 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
require_torch,
require_torch_gpu,
slow,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
__magic_name__: Union[str, Any] = MODEL_FOR_MASKED_LM_MAPPING
__magic_name__: Optional[int] = TF_MODEL_FOR_MASKED_LM_MAPPING
def UpperCAmelCase_ ( self : str ) -> str:
"""simple docstring"""
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
if is_torch_available():
import torch
torch.cuda.empty_cache()
@require_tf
def UpperCAmelCase_ ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : int = pipeline(task='fill-mask' , model='sshleifer/tiny-distilroberta-base' , top_k=2 , framework='tf' )
snake_case_ : Optional[Any] = unmasker('My name is <mask>' )
self.assertEqual(
nested_simplify(_A , decimals=6 ) , [
{'sequence': 'My name is grouped', 'score': 2.1E-05, 'token': 38015, 'token_str': ' grouped'},
{'sequence': 'My name is accuser', 'score': 2.1E-05, 'token': 25506, 'token_str': ' accuser'},
] , )
snake_case_ : int = unmasker('The largest city in France is <mask>' )
self.assertEqual(
nested_simplify(_A , decimals=6 ) , [
{
'sequence': 'The largest city in France is grouped',
'score': 2.1E-05,
'token': 38015,
'token_str': ' grouped',
},
{
'sequence': 'The largest city in France is accuser',
'score': 2.1E-05,
'token': 25506,
'token_str': ' accuser',
},
] , )
snake_case_ : Any = unmasker('My name is <mask>' , targets=[' Patrick', ' Clara', ' Teven'] , top_k=3 )
self.assertEqual(
nested_simplify(_A , decimals=6 ) , [
{'sequence': 'My name is Clara', 'score': 2E-05, 'token': 13606, 'token_str': ' Clara'},
{'sequence': 'My name is Patrick', 'score': 2E-05, 'token': 3499, 'token_str': ' Patrick'},
{'sequence': 'My name is Te', 'score': 1.9E-05, 'token': 2941, 'token_str': ' Te'},
] , )
@require_torch
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : Tuple = pipeline(task='fill-mask' , model='sshleifer/tiny-distilroberta-base' , top_k=2 , framework='pt' )
snake_case_ : Tuple = unmasker('My name is <mask>' )
self.assertEqual(
nested_simplify(_A , decimals=6 ) , [
{'sequence': 'My name is Maul', 'score': 2.2E-05, 'token': 35676, 'token_str': ' Maul'},
{'sequence': 'My name isELS', 'score': 2.2E-05, 'token': 16416, 'token_str': 'ELS'},
] , )
snake_case_ : int = unmasker('The largest city in France is <mask>' )
self.assertEqual(
nested_simplify(_A , decimals=6 ) , [
{
'sequence': 'The largest city in France is Maul',
'score': 2.2E-05,
'token': 35676,
'token_str': ' Maul',
},
{'sequence': 'The largest city in France isELS', 'score': 2.2E-05, 'token': 16416, 'token_str': 'ELS'},
] , )
snake_case_ : Any = unmasker('My name is <mask>' , targets=[' Patrick', ' Clara', ' Teven'] , top_k=3 )
self.assertEqual(
nested_simplify(_A , decimals=6 ) , [
{'sequence': 'My name is Patrick', 'score': 2.1E-05, 'token': 3499, 'token_str': ' Patrick'},
{'sequence': 'My name is Te', 'score': 2E-05, 'token': 2941, 'token_str': ' Te'},
{'sequence': 'My name is Clara', 'score': 2E-05, 'token': 13606, 'token_str': ' Clara'},
] , )
snake_case_ : List[Any] = unmasker('My name is <mask> <mask>' , top_k=2 )
self.assertEqual(
nested_simplify(_A , decimals=6 ) , [
[
{
'score': 2.2E-05,
'token': 35676,
'token_str': ' Maul',
'sequence': '<s>My name is Maul<mask></s>',
},
{'score': 2.2E-05, 'token': 16416, 'token_str': 'ELS', 'sequence': '<s>My name isELS<mask></s>'},
],
[
{
'score': 2.2E-05,
'token': 35676,
'token_str': ' Maul',
'sequence': '<s>My name is<mask> Maul</s>',
},
{'score': 2.2E-05, 'token': 16416, 'token_str': 'ELS', 'sequence': '<s>My name is<mask>ELS</s>'},
],
] , )
@require_torch_gpu
def UpperCAmelCase_ ( self : str ) -> Any:
"""simple docstring"""
snake_case_ : Union[str, Any] = pipeline('fill-mask' , model='hf-internal-testing/tiny-random-distilbert' , device=0 , framework='pt' )
# convert model to fp16
pipe.model.half()
snake_case_ : Tuple = pipe('Paris is the [MASK] of France.' )
# We actually don't care about the result, we just want to make sure
# it works, meaning the float16 tensor got casted back to float32
# for postprocessing.
self.assertIsInstance(_A , _A )
@slow
@require_torch
def UpperCAmelCase_ ( self : str ) -> List[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = pipeline(task='fill-mask' , model='distilroberta-base' , top_k=2 , framework='pt' )
self.run_large_test(_A )
@slow
@require_tf
def UpperCAmelCase_ ( self : Optional[int] ) -> int:
"""simple docstring"""
snake_case_ : Dict = pipeline(task='fill-mask' , model='distilroberta-base' , top_k=2 , framework='tf' )
self.run_large_test(_A )
def UpperCAmelCase_ ( self : Dict , _A : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : List[Any] = unmasker('My name is <mask>' )
self.assertEqual(
nested_simplify(_A ) , [
{'sequence': 'My name is John', 'score': 0.0_0_8, 'token': 610, 'token_str': ' John'},
{'sequence': 'My name is Chris', 'score': 0.0_0_7, 'token': 1573, 'token_str': ' Chris'},
] , )
snake_case_ : List[Any] = unmasker('The largest city in France is <mask>' )
self.assertEqual(
nested_simplify(_A ) , [
{
'sequence': 'The largest city in France is Paris',
'score': 0.2_5_1,
'token': 2201,
'token_str': ' Paris',
},
{
'sequence': 'The largest city in France is Lyon',
'score': 0.2_1_4,
'token': 12790,
'token_str': ' Lyon',
},
] , )
snake_case_ : Tuple = unmasker('My name is <mask>' , targets=[' Patrick', ' Clara', ' Teven'] , top_k=3 )
self.assertEqual(
nested_simplify(_A ) , [
{'sequence': 'My name is Patrick', 'score': 0.0_0_5, 'token': 3499, 'token_str': ' Patrick'},
{'sequence': 'My name is Clara', 'score': 0.0_0_0, 'token': 13606, 'token_str': ' Clara'},
{'sequence': 'My name is Te', 'score': 0.0_0_0, 'token': 2941, 'token_str': ' Te'},
] , )
@require_torch
def UpperCAmelCase_ ( self : Optional[int] ) -> Any:
"""simple docstring"""
snake_case_ : Optional[Any] = pipeline(task='fill-mask' , model='sshleifer/tiny-distilroberta-base' , framework='pt' )
snake_case_ : Tuple = None
snake_case_ : str = None
self.run_pipeline_test(_A , [] )
@require_tf
def UpperCAmelCase_ ( self : Tuple ) -> Dict:
"""simple docstring"""
snake_case_ : List[Any] = pipeline(task='fill-mask' , model='sshleifer/tiny-distilroberta-base' , framework='tf' )
snake_case_ : List[str] = None
snake_case_ : List[str] = None
self.run_pipeline_test(_A , [] )
def UpperCAmelCase_ ( self : List[str] , _A : List[Any] , _A : Tuple , _A : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
if tokenizer is None or tokenizer.mask_token_id is None:
self.skipTest('The provided tokenizer has no mask token, (probably reformer or wav2vec2)' )
snake_case_ : Dict = FillMaskPipeline(model=_A , tokenizer=_A )
snake_case_ : Optional[Any] = [
F"""This is another {tokenizer.mask_token} test""",
]
return fill_masker, examples
def UpperCAmelCase_ ( self : Optional[Any] , _A : str , _A : List[Any] ) -> int:
"""simple docstring"""
snake_case_ : Optional[int] = fill_masker.tokenizer
snake_case_ : List[Any] = fill_masker.model
snake_case_ : int = fill_masker(
F"""This is a {tokenizer.mask_token}""" , )
self.assertEqual(
_A , [
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
] , )
snake_case_ : Dict = fill_masker([F"""This is a {tokenizer.mask_token}"""] )
self.assertEqual(
_A , [
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
] , )
snake_case_ : Optional[int] = fill_masker([F"""This is a {tokenizer.mask_token}""", F"""Another {tokenizer.mask_token} great test."""] )
self.assertEqual(
_A , [
[
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
],
[
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
],
] , )
with self.assertRaises(_A ):
fill_masker([None] )
# No mask_token is not supported
with self.assertRaises(_A ):
fill_masker('This is' )
self.run_test_top_k(_A , _A )
self.run_test_targets(_A , _A )
self.run_test_top_k_targets(_A , _A )
self.fill_mask_with_duplicate_targets_and_top_k(_A , _A )
self.fill_mask_with_multiple_masks(_A , _A )
def UpperCAmelCase_ ( self : Optional[Any] , _A : Any , _A : Optional[int] ) -> Any:
"""simple docstring"""
snake_case_ : Dict = tokenizer.get_vocab()
snake_case_ : List[Any] = sorted(vocab.keys() )[:2]
# Pipeline argument
snake_case_ : Dict = FillMaskPipeline(model=_A , tokenizer=_A , targets=_A )
snake_case_ : Optional[int] = fill_masker(F"""This is a {tokenizer.mask_token}""" )
self.assertEqual(
_A , [
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
] , )
snake_case_ : List[str] = {vocab[el] for el in targets}
self.assertEqual({el['token'] for el in outputs} , _A )
snake_case_ : int = [tokenizer.decode([x] ) for x in target_ids]
self.assertEqual({el['token_str'] for el in outputs} , set(_A ) )
# Call argument
snake_case_ : Dict = FillMaskPipeline(model=_A , tokenizer=_A )
snake_case_ : Optional[int] = fill_masker(F"""This is a {tokenizer.mask_token}""" , targets=_A )
self.assertEqual(
_A , [
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
] , )
snake_case_ : Any = {vocab[el] for el in targets}
self.assertEqual({el['token'] for el in outputs} , _A )
snake_case_ : Tuple = [tokenizer.decode([x] ) for x in target_ids]
self.assertEqual({el['token_str'] for el in outputs} , set(_A ) )
# Score equivalence
snake_case_ : Union[str, Any] = fill_masker(F"""This is a {tokenizer.mask_token}""" , targets=_A )
snake_case_ : Any = [top_mask['token_str'] for top_mask in outputs]
snake_case_ : Optional[Any] = [top_mask['score'] for top_mask in outputs]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(_A ) == set(_A ):
snake_case_ : int = fill_masker(F"""This is a {tokenizer.mask_token}""" , targets=_A )
snake_case_ : Union[str, Any] = [top_mask['score'] for top_mask in unmasked_targets]
self.assertEqual(nested_simplify(_A ) , nested_simplify(_A ) )
# Raises with invalid
with self.assertRaises(_A ):
snake_case_ : Dict = fill_masker(F"""This is a {tokenizer.mask_token}""" , targets=[] )
# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
if "" not in tokenizer.get_vocab():
with self.assertRaises(_A ):
snake_case_ : Tuple = fill_masker(F"""This is a {tokenizer.mask_token}""" , targets=[''] )
with self.assertRaises(_A ):
snake_case_ : Union[str, Any] = fill_masker(F"""This is a {tokenizer.mask_token}""" , targets='' )
def UpperCAmelCase_ ( self : Tuple , _A : Any , _A : Optional[Any] ) -> Any:
"""simple docstring"""
snake_case_ : str = FillMaskPipeline(model=_A , tokenizer=_A , top_k=2 )
snake_case_ : List[str] = fill_masker(F"""This is a {tokenizer.mask_token}""" )
self.assertEqual(
_A , [
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
] , )
snake_case_ : Any = FillMaskPipeline(model=_A , tokenizer=_A )
snake_case_ : int = fill_masker(F"""This is a {tokenizer.mask_token}""" , top_k=2 )
self.assertEqual(
_A , [
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
] , )
self.assertEqual(nested_simplify(_A ) , nested_simplify(_A ) )
def UpperCAmelCase_ ( self : Tuple , _A : Any , _A : Dict ) -> str:
"""simple docstring"""
snake_case_ : str = tokenizer.get_vocab()
snake_case_ : Tuple = FillMaskPipeline(model=_A , tokenizer=_A )
# top_k=2, ntargets=3
snake_case_ : str = sorted(vocab.keys() )[:3]
snake_case_ : Union[str, Any] = fill_masker(F"""This is a {tokenizer.mask_token}""" , top_k=2 , targets=_A )
# If we use the most probably targets, and filter differently, we should still
# have the same results
snake_case_ : Any = [el['token_str'] for el in sorted(_A , key=lambda _A : x["score"] , reverse=_A )]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(_A ).issubset(_A ):
snake_case_ : str = fill_masker(F"""This is a {tokenizer.mask_token}""" , top_k=3 , targets=_A )
# They should yield exactly the same result
self.assertEqual(nested_simplify(_A ) , nested_simplify(_A ) )
def UpperCAmelCase_ ( self : str , _A : Dict , _A : Tuple ) -> Dict:
"""simple docstring"""
snake_case_ : Tuple = FillMaskPipeline(model=_A , tokenizer=_A )
snake_case_ : List[Any] = tokenizer.get_vocab()
# String duplicates + id duplicates
snake_case_ : str = sorted(vocab.keys() )[:3]
snake_case_ : Tuple = [targets[0], targets[1], targets[0], targets[2], targets[1]]
snake_case_ : str = fill_masker(F"""My name is {tokenizer.mask_token}""" , targets=_A , top_k=10 )
# The target list contains duplicates, so we can't output more
# than them
self.assertEqual(len(_A ) , 3 )
def UpperCAmelCase_ ( self : List[str] , _A : str , _A : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : Union[str, Any] = FillMaskPipeline(model=_A , tokenizer=_A )
snake_case_ : List[str] = fill_masker(
F"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}""" , top_k=2 )
self.assertEqual(
_A , [
[
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
],
[
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
],
[
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
{'sequence': ANY(_A ), 'score': ANY(_A ), 'token': ANY(_A ), 'token_str': ANY(_A )},
],
] , )
| 534 | 0 |
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = len(SCREAMING_SNAKE_CASE )
for i in range(length - 1 ):
lowercase__ = i
for k in range(i + 1 , SCREAMING_SNAKE_CASE ):
if collection[k] < collection[least]:
lowercase__ = k
if least != i:
lowercase__ , lowercase__ = (collection[i], collection[least])
return collection
if __name__ == "__main__":
lowerCAmelCase = input('Enter numbers separated by a comma:\n').strip()
lowerCAmelCase = [int(item) for item in user_input.split(',')]
print(selection_sort(unsorted))
| 43 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = os.path.join(args.tf_model_dir , '''parameters.json''' )
lowercase__ = json.loads(open(SCREAMING_SNAKE_CASE ).read() )
if not params:
raise ValueError(
f'It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.' )
if not args.output.endswith('''.pt''' ):
lowercase__ = args.output + '''.pt'''
lowercase__ = OrderedDict()
with tf.device('''/CPU:0''' ):
lowercase__ = tf.train.load_checkpoint(args.tf_model_dir )
lowercase__ = reader.get_variable_to_shape_map()
for key_name in shapes.keys():
lowercase__ = reader.get_tensor(SCREAMING_SNAKE_CASE ).astype(np.floataa )
if key_name.endswith('''/adam_m''' ) or key_name.endswith('''/adam_v''' ):
continue
if key_name.startswith('''pasts/''' ):
if key_name.startswith('''pasts/mlp''' ):
lowercase__ = int(key_name[9] )
elif key_name.startswith('''pasts/out''' ):
lowercase__ = 8
lowercase__ = '''model.sqout.%d.weight''' % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
lowercase__ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
elif key_name.startswith('''model/moe''' ):
lowercase__ = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/switch_gating/kernel''' ):
lowercase__ = '''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
lowercase__ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
elif key_name.endswith('''/softmlp/kernel''' ):
lowercase__ = '''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
lowercase__ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
elif key_name.endswith('''/wo/kernel''' ) or key_name.endswith('''/wi/kernel''' ):
lowercase__ = key_name[-9:-7]
for i in range(16 ):
lowercase__ = '''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
lowercase__ = (
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
elif key_name.startswith('''model/mlp''' ):
lowercase__ = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/p1/kernel''' ):
lowercase__ = '''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
lowercase__ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
elif key_name.endswith('''/p1/bias''' ):
lowercase__ = '''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
lowercase__ = vnp.copy() # same because it is one dimensional
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
elif key_name.endswith('''/p2/kernel''' ):
lowercase__ = '''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
lowercase__ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
elif key_name.endswith('''/p2/bias''' ):
lowercase__ = '''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
lowercase__ = vnp.copy() # same because it is one dimensional
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
elif key_name.startswith('''model/ln''' ):
lowercase__ = int(key_name[8:].split('''/''' )[0] )
if key_name.endswith('''/b''' ):
lowercase__ = '''model.blocks.%d.feed_forward.norm.bias''' % player
lowercase__ = vnp.copy() # same because it is one dimensional
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
elif key_name.endswith('''/g''' ):
lowercase__ = '''model.blocks.%d.feed_forward.norm.weight''' % player
lowercase__ = vnp.copy() # same because it is one dimensional
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
elif key_name.startswith('''model/att''' ):
lowercase__ = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/qkv/kernel''' ):
lowercase__ = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
lowercase__ = state[:, 0, :, :]
lowercase__ = state[:, 1, :, :]
lowercase__ = state[:, 2, :, :]
lowercase__ = (
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
lowercase__ = (
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
lowercase__ = (
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
lowercase__ = '''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
lowercase__ = '''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
lowercase__ = '''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
elif key_name.endswith('''/o/kernel''' ):
lowercase__ = '''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
lowercase__ = (
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
elif key_name.startswith('''model/an''' ):
lowercase__ = int(key_name[8:].split('''/''' )[0] )
if key_name.endswith('''/b''' ):
lowercase__ = '''model.blocks.%d.self_attn.norm.bias''' % player
lowercase__ = vnp.copy() # same because it is one dimensional
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
elif key_name.endswith('''/g''' ):
lowercase__ = '''model.blocks.%d.self_attn.norm.weight''' % player
lowercase__ = vnp.copy() # same because it is one dimensional
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
elif (
key_name.startswith('''model/wte''' )
or key_name.startswith('''model/wpe''' )
or key_name.startswith('''model/ete''' )
):
lowercase__ = {'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
key_name[-3:]
]
lowercase__ = '''model.%s.weight''' % nlayer
lowercase__ = vnp.copy() # same in embedded
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
if key_name.startswith('''model/wte''' ):
lowercase__ = '''lm_head.weight'''
lowercase__ = vnp.copy() # same in embedded
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
elif key_name.startswith('''model/wob''' ):
lowercase__ = '''final_logits_bias'''
lowercase__ = vnp.copy() # same in embedded
lowercase__ = state.reshape((1, -1) )
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
elif key_name == "model/dense/kernel":
lowercase__ = '''model.last_project.weight'''
lowercase__ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
elif key_name == "model/dense_1/bias":
lowercase__ = '''model.last_project.bias'''
lowercase__ = vnp.copy() # same because it is one dimensional
lowercase__ = torch.tensor(SCREAMING_SNAKE_CASE )
torch.save(SCREAMING_SNAKE_CASE , args.output )
if __name__ == "__main__":
lowerCAmelCase = argparse.ArgumentParser(
description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
lowerCAmelCase = parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 43 | 1 |
"""simple docstring"""
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
__A = logging.get_logger(__name__)
__A = {
"""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 _lowerCAmelCase :
"""simple docstring"""
def __init__( self , __UpperCAmelCase=None , **__UpperCAmelCase ):
'''simple docstring'''
logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.' )
lowerCAmelCase__ :Tuple = model
lowerCAmelCase__ :Optional[Any] = kwargs.get('model_save_dir' , __UpperCAmelCase )
lowerCAmelCase__ :Tuple = kwargs.get('latest_model_name' , __UpperCAmelCase )
def __call__( self , **__UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = {k: np.array(__UpperCAmelCase ) for k, v in kwargs.items()}
return self.model.run(__UpperCAmelCase , __UpperCAmelCase )
@staticmethod
def snake_case ( __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=None ):
'''simple docstring'''
if provider is None:
logger.info('No onnxruntime provider specified, using CPUExecutionProvider' )
lowerCAmelCase__ :Optional[Any] = 'CPUExecutionProvider'
return ort.InferenceSession(__UpperCAmelCase , providers=[provider] , sess_options=__UpperCAmelCase )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :str = file_name if file_name is not None else ONNX_WEIGHTS_NAME
lowerCAmelCase__ :List[Any] = self.model_save_dir.joinpath(self.latest_model_name )
lowerCAmelCase__ :List[str] = Path(__UpperCAmelCase ).joinpath(__UpperCAmelCase )
try:
shutil.copyfile(__UpperCAmelCase , __UpperCAmelCase )
except shutil.SameFileError:
pass
# copy external weights (for models >2GB)
lowerCAmelCase__ :Union[str, Any] = self.model_save_dir.joinpath(__UpperCAmelCase )
if src_path.exists():
lowerCAmelCase__ :Optional[Any] = Path(__UpperCAmelCase ).joinpath(__UpperCAmelCase )
try:
shutil.copyfile(__UpperCAmelCase , __UpperCAmelCase )
except shutil.SameFileError:
pass
def snake_case ( self , __UpperCAmelCase , **__UpperCAmelCase , ):
'''simple docstring'''
if os.path.isfile(__UpperCAmelCase ):
logger.error(F"Provided path ({save_directory}) should be a directory, not a file" )
return
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
# saving model weights/files
self._save_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
@classmethod
def snake_case ( cls , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = False , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
lowerCAmelCase__ :Optional[int] = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(__UpperCAmelCase ):
lowerCAmelCase__ :Union[str, Any] = OnnxRuntimeModel.load_model(
os.path.join(__UpperCAmelCase , __UpperCAmelCase ) , provider=__UpperCAmelCase , sess_options=__UpperCAmelCase )
lowerCAmelCase__ :str = Path(__UpperCAmelCase )
# load model from hub
else:
# download model
lowerCAmelCase__ :Union[str, Any] = hf_hub_download(
repo_id=__UpperCAmelCase , filename=__UpperCAmelCase , use_auth_token=__UpperCAmelCase , revision=__UpperCAmelCase , cache_dir=__UpperCAmelCase , force_download=__UpperCAmelCase , )
lowerCAmelCase__ :List[str] = Path(__UpperCAmelCase ).parent
lowerCAmelCase__ :Union[str, Any] = Path(__UpperCAmelCase ).name
lowerCAmelCase__ :str = OnnxRuntimeModel.load_model(__UpperCAmelCase , provider=__UpperCAmelCase , sess_options=__UpperCAmelCase )
return cls(model=__UpperCAmelCase , **__UpperCAmelCase )
@classmethod
def snake_case ( cls , __UpperCAmelCase , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = None
if len(str(__UpperCAmelCase ).split('@' ) ) == 2:
lowerCAmelCase__ :Tuple = model_id.split('@' )
return cls._from_pretrained(
model_id=__UpperCAmelCase , revision=__UpperCAmelCase , cache_dir=__UpperCAmelCase , force_download=__UpperCAmelCase , use_auth_token=__UpperCAmelCase , **__UpperCAmelCase , )
| 717 |
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class _lowerCAmelCase ( a ):
"""simple docstring"""
def snake_case ( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase ):
'''simple docstring'''
if tokenize_kwargs is None:
lowerCAmelCase__ :List[Any] = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)' )
lowerCAmelCase__ :List[str] = truncation
lowerCAmelCase__ :Union[str, Any] = tokenize_kwargs
lowerCAmelCase__ :List[str] = {}
if return_tensors is not None:
lowerCAmelCase__ :List[str] = return_tensors
return preprocess_params, {}, postprocess_params
def snake_case ( self , __UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Dict = self.framework
lowerCAmelCase__ :Optional[Any] = self.tokenizer(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
return model_inputs
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Tuple = self.model(**__UpperCAmelCase )
return model_outputs
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase=False ):
'''simple docstring'''
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
return super().__call__(*__UpperCAmelCase , **__UpperCAmelCase )
| 560 | 0 |
'''simple docstring'''
import argparse
import json
import os
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.deepspeed import DummyOptim, DummyScheduler
SCREAMING_SNAKE_CASE_: Tuple =16
SCREAMING_SNAKE_CASE_: Tuple =32
def lowerCAmelCase_ ( snake_case_ : Accelerator , snake_case_ : int = 16 , snake_case_ : str = "bert-base-cased" ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase_ = AutoTokenizer.from_pretrained(snake_case_ )
UpperCAmelCase_ = load_dataset("glue" , "mrpc" )
def tokenize_function(snake_case_ : Optional[Any] ):
# max_length=None => use the model max length (it's actually the default)
UpperCAmelCase_ = 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
UpperCAmelCase_ = datasets.map(
snake_case_ , batched=snake_case_ , remove_columns=["idx", "sentence1", "sentence2"] , load_from_cache_file=snake_case_ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCAmelCase_ = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(snake_case_ : Union[str, Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(snake_case_ , padding="max_length" , max_length=1_28 , return_tensors="pt" )
return tokenizer.pad(snake_case_ , padding="longest" , return_tensors="pt" )
# Instantiate dataloaders.
UpperCAmelCase_ = DataLoader(
tokenized_datasets["train"] , shuffle=snake_case_ , collate_fn=snake_case_ , batch_size=snake_case_ )
UpperCAmelCase_ = DataLoader(
tokenized_datasets["validation"] , shuffle=snake_case_ , collate_fn=snake_case_ , batch_size=snake_case_ )
return train_dataloader, eval_dataloader
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : str ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase_ = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCAmelCase_ = config["lr"]
UpperCAmelCase_ = int(config["num_epochs"] )
UpperCAmelCase_ = int(config["seed"] )
UpperCAmelCase_ = int(config["batch_size"] )
UpperCAmelCase_ = args.model_name_or_path
set_seed(snake_case_ )
UpperCAmelCase_ , UpperCAmelCase_ = get_dataloaders(snake_case_ , snake_case_ , snake_case_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCAmelCase_ = AutoModelForSequenceClassification.from_pretrained(snake_case_ , return_dict=snake_case_ )
# Instantiate optimizer
UpperCAmelCase_ = (
AdamW
if accelerator.state.deepspeed_plugin is None
or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
UpperCAmelCase_ = optimizer_cls(params=model.parameters() , lr=snake_case_ )
if accelerator.state.deepspeed_plugin is not None:
UpperCAmelCase_ = accelerator.state.deepspeed_plugin.deepspeed_config[
"gradient_accumulation_steps"
]
else:
UpperCAmelCase_ = 1
UpperCAmelCase_ = (len(snake_case_ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
UpperCAmelCase_ = get_linear_schedule_with_warmup(
optimizer=snake_case_ , num_warmup_steps=0 , num_training_steps=snake_case_ , )
else:
UpperCAmelCase_ = DummyScheduler(snake_case_ , total_num_steps=snake_case_ , warmup_num_steps=0 )
# 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.
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = accelerator.prepare(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# We need to keep track of how many total steps we have iterated over
UpperCAmelCase_ = 0
# We also need to keep track of the stating epoch so files are named properly
UpperCAmelCase_ = 0
# Now we train the model
UpperCAmelCase_ = evaluate.load("glue" , "mrpc" )
UpperCAmelCase_ = 0
UpperCAmelCase_ = {}
for epoch in range(snake_case_ , snake_case_ ):
model.train()
for step, batch in enumerate(snake_case_ ):
UpperCAmelCase_ = model(**snake_case_ )
UpperCAmelCase_ = outputs.loss
UpperCAmelCase_ = loss / gradient_accumulation_steps
accelerator.backward(snake_case_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
UpperCAmelCase_ = 0
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():
UpperCAmelCase_ = model(**snake_case_ )
UpperCAmelCase_ = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
UpperCAmelCase_ , UpperCAmelCase_ = accelerator.gather(
(predictions, batch["labels"]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(snake_case_ ) - 1:
UpperCAmelCase_ = predictions[: len(eval_dataloader.dataset ) - samples_seen]
UpperCAmelCase_ = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=snake_case_ , references=snake_case_ , )
UpperCAmelCase_ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , snake_case_ )
UpperCAmelCase_ = eval_metric["accuracy"]
if best_performance < eval_metric["accuracy"]:
UpperCAmelCase_ = eval_metric["accuracy"]
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , "all_results.json" ) , "w" ) as f:
json.dump(snake_case_ , snake_case_ )
def lowerCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase_ = argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage." )
parser.add_argument(
"--model_name_or_path" , type=snake_case_ , default="bert-base-cased" , help="Path to pretrained model or model identifier from huggingface.co/models." , required=snake_case_ , )
parser.add_argument(
"--output_dir" , type=snake_case_ , default="." , help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory." , )
parser.add_argument(
"--performance_lower_bound" , type=snake_case_ , default=snake_case_ , help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value." , )
parser.add_argument(
"--num_epochs" , type=snake_case_ , default=3 , help="Number of train epochs." , )
UpperCAmelCase_ = parser.parse_args()
UpperCAmelCase_ = {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
training_function(snake_case_ , snake_case_ )
if __name__ == "__main__":
main()
| 78 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class a ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def UpperCAmelCase_ ( self : Tuple ) -> int:
"""simple docstring"""
__lowercase = tempfile.mkdtemp()
__lowercase = 5
# Realm tok
__lowercase = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''test''',
'''question''',
'''this''',
'''is''',
'''the''',
'''first''',
'''second''',
'''third''',
'''fourth''',
'''fifth''',
'''record''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
__lowercase = os.path.join(self.tmpdirname , '''realm_tokenizer''' )
os.makedirs(lowerCamelCase__ , exist_ok=lowerCamelCase__ )
__lowercase = os.path.join(lowerCamelCase__ , 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] ) )
__lowercase = os.path.join(self.tmpdirname , '''realm_block_records''' )
os.makedirs(lowerCamelCase__ , exist_ok=lowerCamelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> RealmTokenizer:
"""simple docstring"""
return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''realm_tokenizer''' ) )
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = RealmConfig(num_block_records=self.num_block_records )
return config
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
"""simple docstring"""
__lowercase = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''question''': ['''foo''', '''bar'''],
'''answers''': [['''Foo''', '''Bar'''], ['''Bar''']],
} )
return dataset
def UpperCAmelCase_ ( self : int ) -> List[Any]:
"""simple docstring"""
__lowercase = np.array(
[
b'''This is the first record''',
b'''This is the second record''',
b'''This is the third record''',
b'''This is the fourth record''',
b'''This is the fifth record''',
b'''This is a longer longer longer record''',
] , dtype=lowerCamelCase__ , )
return block_records
def UpperCAmelCase_ ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = RealmRetriever(
block_records=self.get_dummy_block_records() , tokenizer=self.get_tokenizer() , )
return retriever
def UpperCAmelCase_ ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowercase = self.get_config()
__lowercase = self.get_dummy_retriever()
__lowercase = retriever.tokenizer
__lowercase = np.array([0, 3] , dtype='''long''' )
__lowercase = tokenizer(['''Test question'''] ).input_ids
__lowercase = tokenizer(
['''the fourth'''] , add_special_tokens=lowerCamelCase__ , return_token_type_ids=lowerCamelCase__ , return_attention_mask=lowerCamelCase__ , ).input_ids
__lowercase = config.reader_seq_len
__lowercase , __lowercase , __lowercase , __lowercase = retriever(
lowerCamelCase__ , lowerCamelCase__ , answer_ids=lowerCamelCase__ , max_length=lowerCamelCase__ , return_tensors='''np''' )
self.assertEqual(len(lowerCamelCase__ ) , 2 )
self.assertEqual(len(lowerCamelCase__ ) , 2 )
self.assertEqual(len(lowerCamelCase__ ) , 2 )
self.assertEqual(concat_inputs.input_ids.shape , (2, 10) )
self.assertEqual(concat_inputs.attention_mask.shape , (2, 10) )
self.assertEqual(concat_inputs.token_type_ids.shape , (2, 10) )
self.assertEqual(concat_inputs.special_tokens_mask.shape , (2, 10) )
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] ) , ['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''first''', '''record''', '''[SEP]'''] , )
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] ) , ['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''fourth''', '''record''', '''[SEP]'''] , )
def UpperCAmelCase_ ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.get_config()
__lowercase = self.get_dummy_retriever()
__lowercase = retriever.tokenizer
__lowercase = np.array([0, 3, 5] , dtype='''long''' )
__lowercase = tokenizer(['''Test question'''] ).input_ids
__lowercase = tokenizer(
['''the fourth''', '''longer longer'''] , add_special_tokens=lowerCamelCase__ , return_token_type_ids=lowerCamelCase__ , return_attention_mask=lowerCamelCase__ , ).input_ids
__lowercase = config.reader_seq_len
__lowercase , __lowercase , __lowercase , __lowercase = retriever(
lowerCamelCase__ , lowerCamelCase__ , answer_ids=lowerCamelCase__ , max_length=lowerCamelCase__ , return_tensors='''np''' )
self.assertEqual([False, True, True] , lowerCamelCase__ )
self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]] , lowerCamelCase__ )
self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]] , lowerCamelCase__ )
def UpperCAmelCase_ ( self : int ) -> str:
"""simple docstring"""
__lowercase = self.get_dummy_retriever()
retriever.save_pretrained(os.path.join(self.tmpdirname , '''realm_block_records''' ) )
# Test local path
__lowercase = retriever.from_pretrained(os.path.join(self.tmpdirname , '''realm_block_records''' ) )
self.assertEqual(retriever.block_records[0] , b'''This is the first record''' )
# Test mocked remote path
with patch('''transformers.models.realm.retrieval_realm.hf_hub_download''' ) as mock_hf_hub_download:
__lowercase = os.path.join(
os.path.join(self.tmpdirname , '''realm_block_records''' ) , _REALM_BLOCK_RECORDS_FILENAME )
__lowercase = RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa''' )
self.assertEqual(retriever.block_records[0] , b'''This is the first record''' )
| 332 | 0 |
'''simple docstring'''
from typing import Dict
from .base import GenericTensor, Pipeline
class lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def lowerCAmelCase__ ( self , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_=None , **UpperCamelCase_ ):
'''simple docstring'''
if tokenize_kwargs is None:
UpperCamelCase__ :Any = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
'''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)''' )
UpperCamelCase__ :Union[str, Any] = truncation
UpperCamelCase__ :Union[str, Any] = tokenize_kwargs
UpperCamelCase__ :Tuple = {}
if return_tensors is not None:
UpperCamelCase__ :Union[str, Any] = return_tensors
return preprocess_params, {}, postprocess_params
def lowerCAmelCase__ ( self , UpperCamelCase_ , **UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :Dict = self.framework
UpperCamelCase__ :Optional[Any] = self.tokenizer(_snake_case , return_tensors=_snake_case , **_snake_case )
return model_inputs
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :Dict = self.model(**_snake_case )
return model_outputs
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_=False ):
'''simple docstring'''
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self , *UpperCamelCase_ , **UpperCamelCase_ ):
'''simple docstring'''
return super().__call__(*_snake_case , **_snake_case ) | 708 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def a ( __a ) -> Optional[int]:
'''simple docstring'''
random.seed(__a )
np.random.seed(__a )
torch.manual_seed(__a )
torch.cuda.manual_seed_all(__a )
# ^^ safe to call this function even if cuda is not available
class lowercase :
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_ = 0.9999 , UpperCamelCase_ = 0.0 , UpperCamelCase_ = 0 , UpperCamelCase_ = False , UpperCamelCase_ = 1.0 , UpperCamelCase_ = 2 / 3 , UpperCamelCase_ = None , UpperCamelCase_ = None , **UpperCamelCase_ , ):
'''simple docstring'''
if isinstance(UpperCamelCase_ , torch.nn.Module ):
UpperCamelCase__ :Optional[Any] = (
'''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
'''Please pass the parameters of the module instead.'''
)
deprecate(
'''passing a `torch.nn.Module` to `ExponentialMovingAverage`''' , '''1.0.0''' , UpperCamelCase_ , standard_warn=UpperCamelCase_ , )
UpperCamelCase__ :Optional[int] = parameters.parameters()
# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
UpperCamelCase__ :str = True
if kwargs.get('''max_value''' , UpperCamelCase_ ) is not None:
UpperCamelCase__ :List[str] = '''The `max_value` argument is deprecated. Please use `decay` instead.'''
deprecate('''max_value''' , '''1.0.0''' , UpperCamelCase_ , standard_warn=UpperCamelCase_ )
UpperCamelCase__ :int = kwargs['''max_value''']
if kwargs.get('''min_value''' , UpperCamelCase_ ) is not None:
UpperCamelCase__ :Union[str, Any] = '''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
deprecate('''min_value''' , '''1.0.0''' , UpperCamelCase_ , standard_warn=UpperCamelCase_ )
UpperCamelCase__ :Any = kwargs['''min_value''']
UpperCamelCase__ :Optional[int] = list(UpperCamelCase_ )
UpperCamelCase__ :Tuple = [p.clone().detach() for p in parameters]
if kwargs.get('''device''' , UpperCamelCase_ ) is not None:
UpperCamelCase__ :str = '''The `device` argument is deprecated. Please use `to` instead.'''
deprecate('''device''' , '''1.0.0''' , UpperCamelCase_ , standard_warn=UpperCamelCase_ )
self.to(device=kwargs['''device'''] )
UpperCamelCase__ :Union[str, Any] = None
UpperCamelCase__ :List[Any] = decay
UpperCamelCase__ :List[str] = min_decay
UpperCamelCase__ :Optional[int] = update_after_step
UpperCamelCase__ :int = use_ema_warmup
UpperCamelCase__ :Any = inv_gamma
UpperCamelCase__ :Union[str, Any] = power
UpperCamelCase__ :Union[str, Any] = 0
UpperCamelCase__ :Dict = None # set in `step()`
UpperCamelCase__ :List[Any] = model_cls
UpperCamelCase__ :Optional[Any] = model_config
@classmethod
def lowerCAmelCase__ ( cls , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ :Tuple = model_cls.load_config(UpperCamelCase_ , return_unused_kwargs=UpperCamelCase_ )
UpperCamelCase__ :List[str] = model_cls.from_pretrained(UpperCamelCase_ )
UpperCamelCase__ :str = cls(model.parameters() , model_cls=UpperCamelCase_ , model_config=model.config )
ema_model.load_state_dict(UpperCamelCase_ )
return ema_model
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
if self.model_cls is None:
raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.''' )
if self.model_config is None:
raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.''' )
UpperCamelCase__ :Optional[Any] = self.model_cls.from_config(self.model_config )
UpperCamelCase__ :Optional[int] = self.state_dict()
state_dict.pop('''shadow_params''' , UpperCamelCase_ )
model.register_to_config(**UpperCamelCase_ )
self.copy_to(model.parameters() )
model.save_pretrained(UpperCamelCase_ )
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :str = max(0 , optimization_step - self.update_after_step - 1 )
if step <= 0:
return 0.0
if self.use_ema_warmup:
UpperCamelCase__ :Dict = 1 - (1 + step / self.inv_gamma) ** -self.power
else:
UpperCamelCase__ :int = (1 + step) / (10 + step)
UpperCamelCase__ :Any = min(UpperCamelCase_ , self.decay )
# make sure decay is not smaller than min_decay
UpperCamelCase__ :Dict = max(UpperCamelCase_ , self.min_decay )
return cur_decay_value
@torch.no_grad()
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
if isinstance(UpperCamelCase_ , torch.nn.Module ):
UpperCamelCase__ :Optional[Any] = (
'''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
'''Please pass the parameters of the module instead.'''
)
deprecate(
'''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`''' , '''1.0.0''' , UpperCamelCase_ , standard_warn=UpperCamelCase_ , )
UpperCamelCase__ :str = parameters.parameters()
UpperCamelCase__ :Dict = list(UpperCamelCase_ )
self.optimization_step += 1
# Compute the decay factor for the exponential moving average.
UpperCamelCase__ :Any = self.get_decay(self.optimization_step )
UpperCamelCase__ :Tuple = decay
UpperCamelCase__ :List[Any] = 1 - decay
UpperCamelCase__ :Optional[Any] = contextlib.nullcontext
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
import deepspeed
for s_param, param in zip(self.shadow_params , UpperCamelCase_ ):
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
UpperCamelCase__ :Dict = deepspeed.zero.GatheredParameters(UpperCamelCase_ , modifier_rank=UpperCamelCase_ )
with context_manager():
if param.requires_grad:
s_param.sub_(one_minus_decay * (s_param - param) )
else:
s_param.copy_(UpperCamelCase_ )
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :Tuple = list(UpperCamelCase_ )
for s_param, param in zip(self.shadow_params , UpperCamelCase_ ):
param.data.copy_(s_param.to(param.device ).data )
def lowerCAmelCase__ ( self , UpperCamelCase_=None , UpperCamelCase_=None ):
'''simple docstring'''
UpperCamelCase__ :Tuple = [
p.to(device=UpperCamelCase_ , dtype=UpperCamelCase_ ) if p.is_floating_point() else p.to(device=UpperCamelCase_ )
for p in self.shadow_params
]
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return {
"decay": self.decay,
"min_decay": self.min_decay,
"optimization_step": self.optimization_step,
"update_after_step": self.update_after_step,
"use_ema_warmup": self.use_ema_warmup,
"inv_gamma": self.inv_gamma,
"power": self.power,
"shadow_params": self.shadow_params,
}
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :List[str] = [param.detach().cpu().clone() for param in parameters]
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
if self.temp_stored_params is None:
raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`''' )
for c_param, param in zip(self.temp_stored_params , UpperCamelCase_ ):
param.data.copy_(c_param.data )
# Better memory-wise.
UpperCamelCase__ :Optional[Any] = None
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :int = copy.deepcopy(UpperCamelCase_ )
UpperCamelCase__ :Dict = state_dict.get('''decay''' , self.decay )
if self.decay < 0.0 or self.decay > 1.0:
raise ValueError('''Decay must be between 0 and 1''' )
UpperCamelCase__ :Union[str, Any] = state_dict.get('''min_decay''' , self.min_decay )
if not isinstance(self.min_decay , UpperCamelCase_ ):
raise ValueError('''Invalid min_decay''' )
UpperCamelCase__ :Union[str, Any] = state_dict.get('''optimization_step''' , self.optimization_step )
if not isinstance(self.optimization_step , UpperCamelCase_ ):
raise ValueError('''Invalid optimization_step''' )
UpperCamelCase__ :List[Any] = state_dict.get('''update_after_step''' , self.update_after_step )
if not isinstance(self.update_after_step , UpperCamelCase_ ):
raise ValueError('''Invalid update_after_step''' )
UpperCamelCase__ :List[str] = state_dict.get('''use_ema_warmup''' , self.use_ema_warmup )
if not isinstance(self.use_ema_warmup , UpperCamelCase_ ):
raise ValueError('''Invalid use_ema_warmup''' )
UpperCamelCase__ :Optional[int] = state_dict.get('''inv_gamma''' , self.inv_gamma )
if not isinstance(self.inv_gamma , (float, int) ):
raise ValueError('''Invalid inv_gamma''' )
UpperCamelCase__ :str = state_dict.get('''power''' , self.power )
if not isinstance(self.power , (float, int) ):
raise ValueError('''Invalid power''' )
UpperCamelCase__ :Tuple = state_dict.get('''shadow_params''' , UpperCamelCase_ )
if shadow_params is not None:
UpperCamelCase__ :Dict = shadow_params
if not isinstance(self.shadow_params , UpperCamelCase_ ):
raise ValueError('''shadow_params must be a list''' )
if not all(isinstance(UpperCamelCase_ , torch.Tensor ) for p in self.shadow_params ):
raise ValueError('''shadow_params must all be Tensors''' ) | 280 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_A : Union[str, Any] = logging.get_logger(__name__)
class __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , A_ , A_ , A_ , **A_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = feature_size
SCREAMING_SNAKE_CASE__ = sampling_rate
SCREAMING_SNAKE_CASE__ = padding_value
SCREAMING_SNAKE_CASE__ = kwargs.pop('''padding_side''' , '''right''' )
SCREAMING_SNAKE_CASE__ = kwargs.pop('''return_attention_mask''' , A_ )
super().__init__(**A_ )
def lowercase_ ( self , A_ , A_ = True , A_ = None , A_ = False , A_ = None , A_ = None , A_ = None , ):
'''simple docstring'''
if isinstance(A_ , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
SCREAMING_SNAKE_CASE__ = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
'''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
f''' to this method that includes {self.model_input_names[0]}, but you provided'''
f''' {list(processed_features.keys() )}''' )
SCREAMING_SNAKE_CASE__ = processed_features[self.model_input_names[0]]
SCREAMING_SNAKE_CASE__ = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(A_ ) == 0:
if return_attention_mask:
SCREAMING_SNAKE_CASE__ = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
SCREAMING_SNAKE_CASE__ = required_input[0]
if isinstance(A_ , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
SCREAMING_SNAKE_CASE__ = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(A_ ):
SCREAMING_SNAKE_CASE__ = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(A_ ):
SCREAMING_SNAKE_CASE__ = '''tf'''
elif is_torch_tensor(A_ ):
SCREAMING_SNAKE_CASE__ = '''pt'''
elif isinstance(A_ , (int, float, list, tuple, np.ndarray) ):
SCREAMING_SNAKE_CASE__ = '''np'''
else:
raise ValueError(
f'''type of {first_element} unknown: {type(A_ )}. '''
'''Should be one of a python, numpy, pytorch or tensorflow object.''' )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
SCREAMING_SNAKE_CASE__ = to_numpy(A_ )
else:
SCREAMING_SNAKE_CASE__ = [to_numpy(A_ ) for v in value]
# Convert padding_strategy in PaddingStrategy
SCREAMING_SNAKE_CASE__ = self._get_padding_strategies(padding=A_ , max_length=A_ )
SCREAMING_SNAKE_CASE__ = processed_features[self.model_input_names[0]]
SCREAMING_SNAKE_CASE__ = len(A_ )
if not all(len(A_ ) == batch_size for v in processed_features.values() ):
raise ValueError('''Some items in the output dictionary have a different batch size than others.''' )
SCREAMING_SNAKE_CASE__ = []
for i in range(A_ ):
SCREAMING_SNAKE_CASE__ = {k: v[i] for k, v in processed_features.items()}
# truncation
SCREAMING_SNAKE_CASE__ = self._truncate(
A_ , max_length=A_ , pad_to_multiple_of=A_ , truncation=A_ , )
truncated_inputs.append(A_ )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
SCREAMING_SNAKE_CASE__ = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
SCREAMING_SNAKE_CASE__ = PaddingStrategy.MAX_LENGTH
SCREAMING_SNAKE_CASE__ = {}
for i in range(A_ ):
# padding
SCREAMING_SNAKE_CASE__ = self._pad(
truncated_inputs[i] , max_length=A_ , padding_strategy=A_ , pad_to_multiple_of=A_ , return_attention_mask=A_ , )
for key, value in outputs.items():
if key not in batch_outputs:
SCREAMING_SNAKE_CASE__ = []
if value.dtype is np.dtype(np.floataa ):
SCREAMING_SNAKE_CASE__ = value.astype(np.floataa )
batch_outputs[key].append(A_ )
return BatchFeature(A_ , tensor_type=A_ )
def lowercase_ ( self , A_ , A_ = None , A_ = PaddingStrategy.DO_NOT_PAD , A_ = None , A_ = None , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
SCREAMING_SNAKE_CASE__ = len(A_ )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
SCREAMING_SNAKE_CASE__ = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
SCREAMING_SNAKE_CASE__ = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A_ ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
SCREAMING_SNAKE_CASE__ = np.ones(len(A_ ) , dtype=np.intaa )
if needs_to_be_padded:
SCREAMING_SNAKE_CASE__ = max_length - len(A_ )
if self.padding_side == "right":
if return_attention_mask:
SCREAMING_SNAKE_CASE__ = np.pad(
processed_features['''attention_mask'''] , (0, difference) )
SCREAMING_SNAKE_CASE__ = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
SCREAMING_SNAKE_CASE__ = np.pad(
A_ , A_ , '''constant''' , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
SCREAMING_SNAKE_CASE__ = np.pad(
processed_features['''attention_mask'''] , (difference, 0) )
SCREAMING_SNAKE_CASE__ = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
SCREAMING_SNAKE_CASE__ = np.pad(
A_ , A_ , '''constant''' , constant_values=self.padding_value )
else:
raise ValueError('''Invalid padding strategy:''' + str(self.padding_side ) )
return processed_features
def lowercase_ ( self , A_ , A_ = None , A_ = None , A_ = None , ):
'''simple docstring'''
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.''' )
SCREAMING_SNAKE_CASE__ = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
SCREAMING_SNAKE_CASE__ = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
SCREAMING_SNAKE_CASE__ = len(A_ ) > max_length
if needs_to_be_truncated:
SCREAMING_SNAKE_CASE__ = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
SCREAMING_SNAKE_CASE__ = processed_features['''attention_mask'''][:max_length]
return processed_features
def lowercase_ ( self , A_=False , A_=None ):
'''simple docstring'''
if padding is not False:
if padding is True:
SCREAMING_SNAKE_CASE__ = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(A_ , A_ ):
SCREAMING_SNAKE_CASE__ = PaddingStrategy(A_ )
elif isinstance(A_ , A_ ):
SCREAMING_SNAKE_CASE__ = padding
else:
SCREAMING_SNAKE_CASE__ = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined''' )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
'''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.''' )
return padding_strategy
| 100 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
a :str = {
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :str = [
"ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
"ErnieForCausalLM",
"ErnieForMaskedLM",
"ErnieForMultipleChoice",
"ErnieForNextSentencePrediction",
"ErnieForPreTraining",
"ErnieForQuestionAnswering",
"ErnieForSequenceClassification",
"ErnieForTokenClassification",
"ErnieModel",
"ErniePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
a :Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 680 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__magic_name__ = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['''SpeechEncoderDecoderModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['''FlaxSpeechEncoderDecoderModel''']
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 314 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
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
__magic_name__ = get_tests_dir('''fixtures''')
__magic_name__ = get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
__magic_name__ = get_tests_dir('''fixtures/dummy-config.json''')
class a__ ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self :str ):
lowercase = 0
def __UpperCAmelCase ( self :Tuple ):
lowercase = AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h' )
self.assertIsInstance(lowercase__ , lowercase__ )
def __UpperCAmelCase ( self :Any ):
lowercase = AutoFeatureExtractor.from_pretrained(lowercase__ )
self.assertIsInstance(lowercase__ , lowercase__ )
def __UpperCAmelCase ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase = WavaVecaConfig()
# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
lowercase = AutoFeatureExtractor.from_pretrained(lowercase__ ).to_dict()
config_dict.pop('feature_extractor_type' )
lowercase = WavaVecaFeatureExtractor(**lowercase__ )
# save in new folder
model_config.save_pretrained(lowercase__ )
config.save_pretrained(lowercase__ )
lowercase = AutoFeatureExtractor.from_pretrained(lowercase__ )
# make sure private variable is not incorrectly saved
lowercase = json.loads(config.to_json_string() )
self.assertTrue('_processor_class' not in dict_as_saved )
self.assertIsInstance(lowercase__ , lowercase__ )
def __UpperCAmelCase ( self :List[Any] ):
lowercase = AutoFeatureExtractor.from_pretrained(lowercase__ )
self.assertIsInstance(lowercase__ , lowercase__ )
def __UpperCAmelCase ( self :List[Any] ):
with self.assertRaisesRegex(
lowercase__ , 'bert-base is not a local folder and is not a valid model identifier' ):
lowercase = AutoFeatureExtractor.from_pretrained('bert-base' )
def __UpperCAmelCase ( self :List[str] ):
with self.assertRaisesRegex(
lowercase__ , R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
lowercase = AutoFeatureExtractor.from_pretrained(lowercase__ , revision='aaaaaa' )
def __UpperCAmelCase ( self :Any ):
with self.assertRaisesRegex(
lowercase__ , 'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.' , ):
lowercase = AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model' )
def __UpperCAmelCase ( self :Tuple ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(lowercase__ ):
lowercase = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowercase__ ):
lowercase = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' , trust_remote_code=lowercase__ )
lowercase = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' , trust_remote_code=lowercase__ )
self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
# Test feature extractor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(lowercase__ )
lowercase = AutoFeatureExtractor.from_pretrained(lowercase__ , trust_remote_code=lowercase__ )
self.assertEqual(reloaded_feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
def __UpperCAmelCase ( self :Optional[int] ):
try:
AutoConfig.register('custom' , lowercase__ )
AutoFeatureExtractor.register(lowercase__ , lowercase__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowercase__ ):
AutoFeatureExtractor.register(lowercase__ , lowercase__ )
# Now that the config is registered, it can be used as any other config with the auto-API
lowercase = CustomFeatureExtractor.from_pretrained(lowercase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(lowercase__ )
lowercase = AutoFeatureExtractor.from_pretrained(lowercase__ )
self.assertIsInstance(lowercase__ , lowercase__ )
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]
def __UpperCAmelCase ( self :Any ):
class a__ ( _snake_case ):
"""simple docstring"""
A__ : Union[str, Any] = True
try:
AutoConfig.register('custom' , lowercase__ )
AutoFeatureExtractor.register(lowercase__ , lowercase__ )
# If remote code is not set, the default is to use local
lowercase = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' )
self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
self.assertTrue(feature_extractor.is_local )
# If remote code is disabled, we load the local one.
lowercase = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' , trust_remote_code=lowercase__ )
self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
self.assertTrue(feature_extractor.is_local )
# If remote is enabled, we load from the Hub
lowercase = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' , trust_remote_code=lowercase__ )
self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
self.assertTrue(not hasattr(lowercase__ , 'is_local' ) )
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]
| 314 | 1 |
'''simple docstring'''
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 ConditionalDetrImageProcessor
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 , ) -> str:
"""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 ) -> Optional[int]:
"""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 ) -> str:
"""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_ ( _A , unittest.TestCase ):
a_ = ConditionalDetrImageProcessor if is_vision_available() else None
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = ConditionalDetrImageProcessingTester(self )
@property
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self ) -> Any:
"""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 ) -> int:
"""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 ) -> str:
"""simple docstring"""
pass
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""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 ) -> Tuple:
"""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 ) -> Dict:
"""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,
) , )
@slow
def lowerCamelCase_ ( self ) -> List[str]:
"""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_ = ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50" )
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([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , UpperCamelCase__ , atol=1e-4 ) )
# verify area
UpperCAmelCase_ = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
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([0.5503, 0.2765, 0.0604, 0.2215] )
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 ) -> Dict:
"""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_ = ConditionalDetrImageProcessor(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([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , UpperCamelCase__ , atol=1e-4 ) )
# verify area
UpperCAmelCase_ = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
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([0.2625, 0.5437, 0.4688, 0.8625] )
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__ ) )
| 660 | '''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase__ ( A_ ):
def decorator(A_ ):
UpperCAmelCase_ = getattr(A_ , "handle_key" , [] )
handle += [key]
setattr(A_ , "handle_key" , A_ )
return func
return decorator
def lowerCamelCase__ ( *A_ ):
def decorator(A_ ):
UpperCAmelCase_ = getattr(A_ , "handle_key" , [] )
handle += keys
setattr(A_ , "handle_key" , A_ )
return func
return decorator
class lowercase_ ( _A ):
def __new__( cls , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = super().__new__(cls , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if not hasattr(UpperCamelCase__ , "key_handler" ):
setattr(UpperCamelCase__ , "key_handler" , {} )
setattr(UpperCamelCase__ , "handle_input" , KeyHandler.handle_input )
for value in attrs.values():
UpperCAmelCase_ = getattr(UpperCamelCase__ , "handle_key" , [] )
for key in handled_keys:
UpperCAmelCase_ = value
return new_cls
@staticmethod
def lowerCamelCase_ ( cls ) -> str:
"""simple docstring"""
UpperCAmelCase_ = get_character()
if char != KEYMAP["undefined"]:
UpperCAmelCase_ = ord(UpperCamelCase__ )
UpperCAmelCase_ = cls.key_handler.get(UpperCamelCase__ )
if handler:
UpperCAmelCase_ = char
return handler(cls )
else:
return None
def lowerCamelCase__ ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 660 | 1 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class UpperCAmelCase__ ( snake_case__ ):
snake_case_ = 42
snake_case_ = None
def lowercase ( _a ,_a=0.999 ,_a="cosine" ,) -> str:
if alpha_transform_type == "cosine":
def alpha_bar_fn(_a ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(_a ):
return math.exp(t * -12.0 )
else:
raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}" )
UpperCAmelCase_: List[str] = []
for i in range(_a ):
UpperCAmelCase_: Optional[Any] = i / num_diffusion_timesteps
UpperCAmelCase_: Optional[int] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(_a ) / alpha_bar_fn(_a ) ,_a ) )
return torch.tensor(_a ,dtype=torch.floataa )
class UpperCAmelCase__ ( snake_case__ , snake_case__ ):
@register_to_config
def __init__( self , A__ = 1000 , A__ = "fixed_small_log" , A__ = True , A__ = 1.0 , A__ = "epsilon" , A__ = "squaredcos_cap_v2" , ):
"""simple docstring"""
if beta_schedule != "squaredcos_cap_v2":
raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'" )
UpperCAmelCase_: List[Any] = betas_for_alpha_bar(A__ )
UpperCAmelCase_: str = 1.0 - self.betas
UpperCAmelCase_: Any = torch.cumprod(self.alphas , dim=0 )
UpperCAmelCase_: int = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
UpperCAmelCase_: str = 1.0
# setable values
UpperCAmelCase_: Tuple = None
UpperCAmelCase_: List[str] = torch.from_numpy(np.arange(0 , A__ )[::-1].copy() )
UpperCAmelCase_: Any = variance_type
def snake_case_ ( self , A__ , A__ = None ):
"""simple docstring"""
return sample
def snake_case_ ( self , A__ , A__ = None ):
"""simple docstring"""
UpperCAmelCase_: Tuple = num_inference_steps
UpperCAmelCase_: Optional[int] = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
UpperCAmelCase_: Optional[int] = (np.arange(0 , A__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
UpperCAmelCase_: int = torch.from_numpy(A__ ).to(A__ )
def snake_case_ ( self , A__ , A__=None , A__=None , A__=None ):
"""simple docstring"""
if prev_timestep is None:
UpperCAmelCase_: Dict = t - 1
UpperCAmelCase_: Any = self.alphas_cumprod[t]
UpperCAmelCase_: Tuple = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
UpperCAmelCase_: Optional[int] = 1 - alpha_prod_t
UpperCAmelCase_: Dict = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
UpperCAmelCase_: List[str] = self.betas[t]
else:
UpperCAmelCase_: Optional[Any] = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
UpperCAmelCase_: List[Any] = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
UpperCAmelCase_: int = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
UpperCAmelCase_: str = torch.log(torch.clamp(A__ , min=1E-20 ) )
UpperCAmelCase_: List[Any] = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
UpperCAmelCase_: Tuple = variance.log()
UpperCAmelCase_: List[str] = beta.log()
UpperCAmelCase_: Optional[int] = (predicted_variance + 1) / 2
UpperCAmelCase_: Any = frac * max_log + (1 - frac) * min_log
return variance
def snake_case_ ( self , A__ , A__ , A__ , A__ = None , A__=None , A__ = True , ):
"""simple docstring"""
UpperCAmelCase_: str = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
UpperCAmelCase_: Optional[int] = torch.split(A__ , sample.shape[1] , dim=1 )
else:
UpperCAmelCase_: Dict = None
# 1. compute alphas, betas
if prev_timestep is None:
UpperCAmelCase_: Any = t - 1
UpperCAmelCase_: Union[str, Any] = self.alphas_cumprod[t]
UpperCAmelCase_: Any = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
UpperCAmelCase_: Any = 1 - alpha_prod_t
UpperCAmelCase_: List[Any] = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
UpperCAmelCase_: Union[str, Any] = self.betas[t]
UpperCAmelCase_: Any = self.alphas[t]
else:
UpperCAmelCase_: List[Any] = 1 - alpha_prod_t / alpha_prod_t_prev
UpperCAmelCase_: int = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
UpperCAmelCase_: Optional[int] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
UpperCAmelCase_: Dict = model_output
else:
raise ValueError(
F"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
" for the UnCLIPScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
UpperCAmelCase_: Any = torch.clamp(
A__ , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase_: Any = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
UpperCAmelCase_: Tuple = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase_: Tuple = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
UpperCAmelCase_: Optional[int] = 0
if t > 0:
UpperCAmelCase_: List[Any] = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=A__ , device=model_output.device )
UpperCAmelCase_: Any = self._get_variance(
A__ , predicted_variance=A__ , prev_timestep=A__ , )
if self.variance_type == "fixed_small_log":
UpperCAmelCase_: Tuple = variance
elif self.variance_type == "learned_range":
UpperCAmelCase_: Any = (0.5 * variance).exp()
else:
raise ValueError(
F"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
" for the UnCLIPScheduler." )
UpperCAmelCase_: Union[str, Any] = variance * variance_noise
UpperCAmelCase_: Tuple = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=A__ , pred_original_sample=A__ )
def snake_case_ ( self , A__ , A__ , A__ , ):
"""simple docstring"""
UpperCAmelCase_: List[str] = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
UpperCAmelCase_: List[str] = timesteps.to(original_samples.device )
UpperCAmelCase_: Optional[Any] = alphas_cumprod[timesteps] ** 0.5
UpperCAmelCase_: Optional[Any] = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
UpperCAmelCase_: Any = sqrt_alpha_prod.unsqueeze(-1 )
UpperCAmelCase_: Any = (1 - alphas_cumprod[timesteps]) ** 0.5
UpperCAmelCase_: List[Any] = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
UpperCAmelCase_: Tuple = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
UpperCAmelCase_: int = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples | 718 |
_lowerCAmelCase = 9.8_06_65
def lowercase ( _a ,_a ,_a = g ) -> float:
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volume" )
if gravity <= 0:
raise ValueError("Impossible Gravity" )
return fluid_density * gravity * volume
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod() | 306 | 0 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
A__ : List[Any] = True
except ImportError:
A__ : List[Any] = False
A__ : Tuple = logging.get_logger(__name__) # pylint: disable=invalid-name
def UpperCamelCase( __UpperCamelCase : Namespace ):
return AddNewModelCommand(args.testing ,args.testing_file ,path=args.path )
class __snake_case ( UpperCamelCase_ ):
@staticmethod
def UpperCAmelCase__ ( A_ : ArgumentParser):
lowerCAmelCase_ : Optional[Any] = parser.add_parser('''add-new-model''')
add_new_model_parser.add_argument('''--testing''' , action='''store_true''' , help='''If in testing mode.''')
add_new_model_parser.add_argument('''--testing_file''' , type=A_ , help='''Configuration file on which to run.''')
add_new_model_parser.add_argument(
'''--path''' , type=A_ , help='''Path to cookiecutter. Should only be used for testing purposes.''')
add_new_model_parser.set_defaults(func=A_)
def __init__( self : List[str] , A_ : bool , A_ : str , A_ : Any=None , *A_ : str):
lowerCAmelCase_ : str = testing
lowerCAmelCase_ : Optional[Any] = testing_file
lowerCAmelCase_ : str = path
def UpperCAmelCase__ ( self : List[str]):
warnings.warn(
'''The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '''
'''It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '''
'''checks, you should use `transformers-cli add-new-model-like` instead.''')
if not _has_cookiecutter:
raise ImportError(
'''Model creation dependencies are required to use the `add_new_model` command. Install them by running '''
'''the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n''')
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
lowerCAmelCase_ : Dict = [directory for directory in os.listdir() if '''cookiecutter-template-''' == directory[:2_2]]
if len(A_) > 0:
raise ValueError(
'''Several directories starting with `cookiecutter-template-` in current working directory. '''
'''Please clean your directory by removing all folders starting with `cookiecutter-template-` or '''
'''change your working directory.''')
lowerCAmelCase_ : Optional[int] = (
Path(A_).parent.parent.parent.parent if self._path is None else Path(self._path).parent.parent
)
lowerCAmelCase_ : List[Any] = path_to_transformer_root / '''templates''' / '''adding_a_new_model'''
# Execute cookiecutter
if not self._testing:
cookiecutter(str(A_))
else:
with open(self._testing_file , '''r''') as configuration_file:
lowerCAmelCase_ : int = json.load(A_)
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path) , no_input=A_ , extra_context=A_ , )
lowerCAmelCase_ : List[str] = [directory for directory in os.listdir() if '''cookiecutter-template-''' in directory[:2_2]][0]
# Retrieve configuration
with open(directory + '''/configuration.json''' , '''r''') as configuration_file:
lowerCAmelCase_ : List[str] = json.load(A_)
lowerCAmelCase_ : List[Any] = configuration['''lowercase_modelname''']
lowerCAmelCase_ : List[Any] = configuration['''generate_tensorflow_pytorch_and_flax''']
os.remove(F"""{directory}/configuration.json""")
lowerCAmelCase_ : int = '''PyTorch''' in generate_tensorflow_pytorch_and_flax
lowerCAmelCase_ : List[Any] = '''TensorFlow''' in generate_tensorflow_pytorch_and_flax
lowerCAmelCase_ : Union[str, Any] = '''Flax''' in generate_tensorflow_pytorch_and_flax
lowerCAmelCase_ : List[Any] = F"""{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"""
os.makedirs(A_ , exist_ok=A_)
os.makedirs(F"""{path_to_transformer_root}/tests/models/{lowercase_model_name}""" , exist_ok=A_)
# Tests require submodules as they have parent imports
with open(F"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py""" , '''w'''):
pass
shutil.move(
F"""{directory}/__init__.py""" , F"""{model_dir}/__init__.py""" , )
shutil.move(
F"""{directory}/configuration_{lowercase_model_name}.py""" , F"""{model_dir}/configuration_{lowercase_model_name}.py""" , )
def remove_copy_lines(A_ : Union[str, Any]):
with open(A_ , '''r''') as f:
lowerCAmelCase_ : Optional[int] = f.readlines()
with open(A_ , '''w''') as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(A_)
if output_pytorch:
if not self._testing:
remove_copy_lines(F"""{directory}/modeling_{lowercase_model_name}.py""")
shutil.move(
F"""{directory}/modeling_{lowercase_model_name}.py""" , F"""{model_dir}/modeling_{lowercase_model_name}.py""" , )
shutil.move(
F"""{directory}/test_modeling_{lowercase_model_name}.py""" , F"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py""" , )
else:
os.remove(F"""{directory}/modeling_{lowercase_model_name}.py""")
os.remove(F"""{directory}/test_modeling_{lowercase_model_name}.py""")
if output_tensorflow:
if not self._testing:
remove_copy_lines(F"""{directory}/modeling_tf_{lowercase_model_name}.py""")
shutil.move(
F"""{directory}/modeling_tf_{lowercase_model_name}.py""" , F"""{model_dir}/modeling_tf_{lowercase_model_name}.py""" , )
shutil.move(
F"""{directory}/test_modeling_tf_{lowercase_model_name}.py""" , F"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py""" , )
else:
os.remove(F"""{directory}/modeling_tf_{lowercase_model_name}.py""")
os.remove(F"""{directory}/test_modeling_tf_{lowercase_model_name}.py""")
if output_flax:
if not self._testing:
remove_copy_lines(F"""{directory}/modeling_flax_{lowercase_model_name}.py""")
shutil.move(
F"""{directory}/modeling_flax_{lowercase_model_name}.py""" , F"""{model_dir}/modeling_flax_{lowercase_model_name}.py""" , )
shutil.move(
F"""{directory}/test_modeling_flax_{lowercase_model_name}.py""" , F"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py""" , )
else:
os.remove(F"""{directory}/modeling_flax_{lowercase_model_name}.py""")
os.remove(F"""{directory}/test_modeling_flax_{lowercase_model_name}.py""")
shutil.move(
F"""{directory}/{lowercase_model_name}.md""" , F"""{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md""" , )
shutil.move(
F"""{directory}/tokenization_{lowercase_model_name}.py""" , F"""{model_dir}/tokenization_{lowercase_model_name}.py""" , )
shutil.move(
F"""{directory}/tokenization_fast_{lowercase_model_name}.py""" , F"""{model_dir}/tokenization_{lowercase_model_name}_fast.py""" , )
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(A_ : str , A_ : str , A_ : List[str]):
# Create temp file
lowerCAmelCase_ , lowerCAmelCase_ : str = mkstemp()
lowerCAmelCase_ : List[str] = False
with fdopen(A_ , '''w''') as new_file:
with open(A_) as old_file:
for line in old_file:
new_file.write(A_)
if line_to_copy_below in line:
lowerCAmelCase_ : Optional[Any] = True
for line_to_copy in lines_to_copy:
new_file.write(A_)
if not line_found:
raise ValueError(F"""Line {line_to_copy_below} was not found in file.""")
# Copy the file permissions from the old file to the new file
copymode(A_ , A_)
# Remove original file
remove(A_)
# Move new file
move(A_ , A_)
def skip_units(A_ : Optional[int]):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(A_ : Dict):
with open(A_) as datafile:
lowerCAmelCase_ : Dict = []
lowerCAmelCase_ : Any = False
lowerCAmelCase_ : Dict = False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
lowerCAmelCase_ : Tuple = line.split('''"''')[1]
lowerCAmelCase_ : Union[str, Any] = skip_units(A_)
elif "# Below: " in line and "##" not in line:
lowerCAmelCase_ : List[str] = line.split('''"''')[1]
lowerCAmelCase_ : List[str] = skip_units(A_)
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(A_ , A_ , A_)
lowerCAmelCase_ : Any = []
elif "# Replace with" in line and "##" not in line:
lowerCAmelCase_ : str = []
elif "##" not in line:
lines_to_copy.append(A_)
remove(A_)
replace_in_files(F"""{directory}/to_replace_{lowercase_model_name}.py""")
os.rmdir(A_)
| 171 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
A__ : str = subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
A__ : Union[str, Any] = subprocess.check_output(F'''git diff --name-only {fork_point_sha}'''.split()).decode('''utf-8''').split()
A__ : Any = '''|'''.join(sys.argv[1:])
A__ : Tuple = re.compile(RF'''^({joined_dirs}).*?\.py$''')
A__ : List[str] = [x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')
| 171 | 1 |
from collections import namedtuple
lowerCAmelCase_ = namedtuple('''from_to''', '''from_ to''')
lowerCAmelCase_ = {
'''cubicmeter''': from_to(1, 1),
'''litre''': from_to(0.0_01, 1000),
'''kilolitre''': from_to(1, 1),
'''gallon''': from_to(0.0_04_54, 264.172),
'''cubicyard''': from_to(0.7_64_55, 1.3_07_95),
'''cubicfoot''': from_to(0.0_28, 35.31_47),
'''cup''': from_to(0.0_00_23_65_88, 4226.75),
}
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> float:
if from_type not in METRIC_CONVERSION:
raise ValueError(
F"""Invalid 'from_type' value: {from_type!r} Supported values are:\n"""
+ """, """.join(__SCREAMING_SNAKE_CASE ) )
if to_type not in METRIC_CONVERSION:
raise ValueError(
F"""Invalid 'to_type' value: {to_type!r}. Supported values are:\n"""
+ """, """.join(__SCREAMING_SNAKE_CASE ) )
return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to
if __name__ == "__main__":
import doctest
doctest.testmod()
| 703 | """simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
# See all SEW models at https://huggingface.co/models?filter=sew
}
class _snake_case ( __snake_case ):
"""simple docstring"""
a = "sew"
def __init__( self : List[Any] , _A : Tuple=3_2 , _A : str=7_6_8 , _A : Dict=1_2 , _A : Tuple=1_2 , _A : Optional[Any]=3_0_7_2 , _A : List[str]=2 , _A : Dict="gelu" , _A : Union[str, Any]=0.1 , _A : Optional[int]=0.1 , _A : Optional[int]=0.1 , _A : Optional[int]=0.0 , _A : str=0.1 , _A : Tuple=0.1 , _A : Optional[int]=0.02 , _A : Dict=1e-5 , _A : str="group" , _A : Tuple="gelu" , _A : Union[str, Any]=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2) , _A : Optional[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , _A : Any=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , _A : Tuple=False , _A : Tuple=1_2_8 , _A : int=1_6 , _A : Union[str, Any]=True , _A : Optional[Any]=0.05 , _A : List[Any]=1_0 , _A : Union[str, Any]=2 , _A : Tuple=0.0 , _A : Union[str, Any]=1_0 , _A : Optional[int]=0 , _A : Union[str, Any]="mean" , _A : Optional[int]=False , _A : List[Any]=False , _A : int=2_5_6 , _A : str=0 , _A : Optional[int]=1 , _A : List[Any]=2 , **_A : Dict , ):
"""simple docstring"""
super().__init__(**_A , pad_token_id=_A , bos_token_id=_A , eos_token_id=_A)
_SCREAMING_SNAKE_CASE : str = hidden_size
_SCREAMING_SNAKE_CASE : Optional[int] = feat_extract_norm
_SCREAMING_SNAKE_CASE : Optional[int] = feat_extract_activation
_SCREAMING_SNAKE_CASE : Dict = list(_A)
_SCREAMING_SNAKE_CASE : int = list(_A)
_SCREAMING_SNAKE_CASE : int = list(_A)
_SCREAMING_SNAKE_CASE : str = conv_bias
_SCREAMING_SNAKE_CASE : Tuple = num_conv_pos_embeddings
_SCREAMING_SNAKE_CASE : List[str] = num_conv_pos_embedding_groups
_SCREAMING_SNAKE_CASE : Tuple = len(self.conv_dim)
_SCREAMING_SNAKE_CASE : Tuple = num_hidden_layers
_SCREAMING_SNAKE_CASE : List[str] = intermediate_size
_SCREAMING_SNAKE_CASE : str = squeeze_factor
_SCREAMING_SNAKE_CASE : Dict = hidden_act
_SCREAMING_SNAKE_CASE : str = num_attention_heads
_SCREAMING_SNAKE_CASE : Dict = hidden_dropout
_SCREAMING_SNAKE_CASE : Tuple = attention_dropout
_SCREAMING_SNAKE_CASE : int = activation_dropout
_SCREAMING_SNAKE_CASE : Any = feat_proj_dropout
_SCREAMING_SNAKE_CASE : str = final_dropout
_SCREAMING_SNAKE_CASE : Union[str, Any] = layerdrop
_SCREAMING_SNAKE_CASE : Any = layer_norm_eps
_SCREAMING_SNAKE_CASE : int = initializer_range
_SCREAMING_SNAKE_CASE : List[Any] = 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
_SCREAMING_SNAKE_CASE : List[Any] = apply_spec_augment
_SCREAMING_SNAKE_CASE : List[Any] = mask_time_prob
_SCREAMING_SNAKE_CASE : List[str] = mask_time_length
_SCREAMING_SNAKE_CASE : List[Any] = mask_time_min_masks
_SCREAMING_SNAKE_CASE : List[Any] = mask_feature_prob
_SCREAMING_SNAKE_CASE : int = mask_feature_length
_SCREAMING_SNAKE_CASE : List[Any] = mask_feature_min_masks
# ctc loss
_SCREAMING_SNAKE_CASE : int = ctc_loss_reduction
_SCREAMING_SNAKE_CASE : Optional[int] = ctc_zero_infinity
# sequence classification
_SCREAMING_SNAKE_CASE : Dict = use_weighted_layer_sum
_SCREAMING_SNAKE_CASE : List[str] = classifier_proj_size
@property
def _lowerCAmelCase ( self : Any):
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1)
| 635 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=a_ ):
"""simple docstring"""
lowercase__ = ["torch", "torchsde"]
def __init__( self : str ,*lowercase_ : Optional[int] ,**lowercase_ : Union[str, Any] ):
requires_backends(self ,['''torch''', '''torchsde'''] )
@classmethod
def __lowerCAmelCase ( cls : str ,*lowercase_ : int ,**lowercase_ : str ):
requires_backends(cls ,['''torch''', '''torchsde'''] )
@classmethod
def __lowerCAmelCase ( cls : Dict ,*lowercase_ : Union[str, Any] ,**lowercase_ : Optional[int] ):
requires_backends(cls ,['''torch''', '''torchsde'''] )
| 450 |
"""simple docstring"""
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
__UpperCamelCase : int = logging.get_logger(__name__)
__UpperCamelCase : Optional[Any] = {
'''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 SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Optional[Any] ,lowercase_ : Union[str, Any]=None ,**lowercase_ : List[str] ):
logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.''' )
lowerCAmelCase__ : List[str] = model
lowerCAmelCase__ : Optional[Any] = kwargs.get('''model_save_dir''' ,lowercase_ )
lowerCAmelCase__ : str = kwargs.get('''latest_model_name''' ,lowercase_ )
def __call__( self : Any ,**lowercase_ : Dict ):
lowerCAmelCase__ : List[Any] = {k: np.array(lowercase_ ) for k, v in kwargs.items()}
return self.model.run(lowercase_ ,lowercase_ )
@staticmethod
def __lowerCAmelCase ( lowercase_ : Union[str, Path] ,lowercase_ : str=None ,lowercase_ : str=None ):
if provider is None:
logger.info('''No onnxruntime provider specified, using CPUExecutionProvider''' )
lowerCAmelCase__ : Optional[Any] = '''CPUExecutionProvider'''
return ort.InferenceSession(lowercase_ ,providers=[provider] ,sess_options=lowercase_ )
def __lowerCAmelCase ( self : Dict ,lowercase_ : Union[str, Path] ,lowercase_ : Optional[str] = None ,**lowercase_ : str ):
lowerCAmelCase__ : Any = file_name if file_name is not None else ONNX_WEIGHTS_NAME
lowerCAmelCase__ : Tuple = self.model_save_dir.joinpath(self.latest_model_name )
lowerCAmelCase__ : List[Any] = Path(lowercase_ ).joinpath(lowercase_ )
try:
shutil.copyfile(lowercase_ ,lowercase_ )
except shutil.SameFileError:
pass
# copy external weights (for models >2GB)
lowerCAmelCase__ : Dict = self.model_save_dir.joinpath(lowercase_ )
if src_path.exists():
lowerCAmelCase__ : Union[str, Any] = Path(lowercase_ ).joinpath(lowercase_ )
try:
shutil.copyfile(lowercase_ ,lowercase_ )
except shutil.SameFileError:
pass
def __lowerCAmelCase ( self : Optional[int] ,lowercase_ : Union[str, os.PathLike] ,**lowercase_ : Union[str, Any] ,):
if os.path.isfile(lowercase_ ):
logger.error(F'Provided path ({save_directory}) should be a directory, not a file' )
return
os.makedirs(lowercase_ ,exist_ok=lowercase_ )
# saving model weights/files
self._save_pretrained(lowercase_ ,**lowercase_ )
@classmethod
def __lowerCAmelCase ( cls : Dict ,lowercase_ : Union[str, Path] ,lowercase_ : Optional[Union[bool, str, None]] = None ,lowercase_ : Optional[Union[str, None]] = None ,lowercase_ : bool = False ,lowercase_ : Optional[str] = None ,lowercase_ : Optional[str] = None ,lowercase_ : Optional[str] = None ,lowercase_ : Optional["ort.SessionOptions"] = None ,**lowercase_ : Optional[int] ,):
lowerCAmelCase__ : Optional[Any] = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(lowercase_ ):
lowerCAmelCase__ : str = OnnxRuntimeModel.load_model(
os.path.join(lowercase_ ,lowercase_ ) ,provider=lowercase_ ,sess_options=lowercase_ )
lowerCAmelCase__ : Any = Path(lowercase_ )
# load model from hub
else:
# download model
lowerCAmelCase__ : Optional[Any] = hf_hub_download(
repo_id=lowercase_ ,filename=lowercase_ ,use_auth_token=lowercase_ ,revision=lowercase_ ,cache_dir=lowercase_ ,force_download=lowercase_ ,)
lowerCAmelCase__ : str = Path(lowercase_ ).parent
lowerCAmelCase__ : Union[str, Any] = Path(lowercase_ ).name
lowerCAmelCase__ : Optional[Any] = OnnxRuntimeModel.load_model(lowercase_ ,provider=lowercase_ ,sess_options=lowercase_ )
return cls(model=lowercase_ ,**lowercase_ )
@classmethod
def __lowerCAmelCase ( cls : Any ,lowercase_ : Union[str, Path] ,lowercase_ : bool = True ,lowercase_ : Optional[str] = None ,lowercase_ : Optional[str] = None ,**lowercase_ : Any ,):
lowerCAmelCase__ : Union[str, Any] = None
if len(str(lowercase_ ).split('''@''' ) ) == 2:
lowerCAmelCase__ ,lowerCAmelCase__ : str = model_id.split('''@''' )
return cls._from_pretrained(
model_id=lowercase_ ,revision=lowercase_ ,cache_dir=lowercase_ ,force_download=lowercase_ ,use_auth_token=lowercase_ ,**lowercase_ ,)
| 450 | 1 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowerCAmelCase ( ):
"""simple docstring"""
__A = {
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''path''': ['''test_1.py''', '''test_2.py''', '''unit_test.py'''],
'''content''': ['''a ''' * 2_0, '''a ''' * 3_0, '''b ''' * 7],
}
__A = Dataset.from_dict(__UpperCamelCase )
return dataset
class snake_case ( _lowerCAmelCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
'''simple docstring'''
__A = get_dataset()
__A = make_duplicate_clusters(_lowerCamelCase, 0.85 )
self.assertEqual(len(duplicate_clusters[0] ), 2 )
def _SCREAMING_SNAKE_CASE ( self : Any ):
'''simple docstring'''
__A = get_dataset()
__A , __A = deduplicate_dataset(_lowerCamelCase )
self.assertEqual(len(_lowerCamelCase ), 2 )
print(_lowerCamelCase )
self.assertEqual(duplicate_clusters[0][0]['''copies'''], 2 )
self.assertEqual(duplicate_clusters[0][0]['''is_extreme'''], _lowerCamelCase )
| 215 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class snake_case ( _lowerCAmelCase ):
'''simple docstring'''
A_ : Optional[torch.FloatTensor] = None
A_ : torch.FloatTensor = None
A_ : Optional[Tuple[torch.FloatTensor]] = None
A_ : Optional[Tuple[torch.FloatTensor]] = None
class snake_case ( _lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Tuple, _lowerCamelCase : List[str]=1, _lowerCamelCase : Union[str, Any]=0, _lowerCamelCase : List[str]=2, _lowerCamelCase : Optional[int]=5_12, _lowerCamelCase : Optional[Any]="cls", _lowerCamelCase : List[str]=False, _lowerCamelCase : Optional[Any]=True, **_lowerCamelCase : Any, ):
'''simple docstring'''
super().__init__(pad_token_id=_lowerCamelCase, bos_token_id=_lowerCamelCase, eos_token_id=_lowerCamelCase, **_lowerCamelCase )
__A = project_dim
__A = pooler_fn
__A = learn_encoder
__A = use_attention_mask
class snake_case ( _lowerCAmelCase ):
'''simple docstring'''
A_ : int = [R"pooler", R"logit_scale"]
A_ : List[Any] = [R"position_ids", R"predictions.decoder.bias"]
A_ : Union[str, Any] = "roberta"
A_ : Dict = RobertaSeriesConfig
def __init__( self : Optional[Any], _lowerCamelCase : Tuple ):
'''simple docstring'''
super().__init__(_lowerCamelCase )
__A = XLMRobertaModel(_lowerCamelCase )
__A = nn.Linear(config.hidden_size, config.project_dim )
__A = getattr(_lowerCamelCase, '''has_pre_transformation''', _lowerCamelCase )
if self.has_pre_transformation:
__A = nn.Linear(config.hidden_size, config.project_dim )
__A = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps )
self.post_init()
def _SCREAMING_SNAKE_CASE ( self : List[str], _lowerCamelCase : Optional[torch.Tensor] = None, _lowerCamelCase : Optional[torch.Tensor] = None, _lowerCamelCase : Optional[torch.Tensor] = None, _lowerCamelCase : Optional[torch.Tensor] = None, _lowerCamelCase : Optional[torch.Tensor] = None, _lowerCamelCase : Optional[torch.Tensor] = None, _lowerCamelCase : Optional[torch.Tensor] = None, _lowerCamelCase : Optional[torch.Tensor] = None, _lowerCamelCase : Optional[bool] = None, _lowerCamelCase : Optional[bool] = None, _lowerCamelCase : Optional[bool] = None, ):
'''simple docstring'''
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.base_model(
input_ids=_lowerCamelCase, attention_mask=_lowerCamelCase, token_type_ids=_lowerCamelCase, position_ids=_lowerCamelCase, head_mask=_lowerCamelCase, inputs_embeds=_lowerCamelCase, encoder_hidden_states=_lowerCamelCase, encoder_attention_mask=_lowerCamelCase, output_attentions=_lowerCamelCase, output_hidden_states=True if self.has_pre_transformation else output_hidden_states, return_dict=_lowerCamelCase, )
if self.has_pre_transformation:
__A = outputs['''hidden_states'''][-2]
__A = self.pre_LN(_lowerCamelCase )
__A = self.transformation_pre(_lowerCamelCase )
return TransformationModelOutput(
projection_state=_lowerCamelCase, last_hidden_state=outputs.last_hidden_state, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
else:
__A = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=_lowerCamelCase, last_hidden_state=outputs.last_hidden_state, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
| 215 | 1 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPTaConfig,
GPTaLMHeadModel,
GPTaTokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
lowercase__ = {
"""distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
"""roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
"""bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
"""gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def __lowerCamelCase ( __UpperCamelCase ) -> int:
"""simple docstring"""
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts )
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config )
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights )
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase ) -> Any:
"""simple docstring"""
if args.student_type == "roberta":
lowerCAmelCase_ : Optional[int] = False
elif args.student_type == "gpt2":
lowerCAmelCase_ : List[str] = False
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
if args.student_type == "roberta":
lowerCAmelCase_ : Optional[int] = False
def __lowerCamelCase ( ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase_ : int = argparse.ArgumentParser(description="Training" )
parser.add_argument("--force" , action="store_true" , help="Overwrite dump_path if it already exists." )
parser.add_argument(
"--dump_path" , type=__UpperCamelCase , required=__UpperCamelCase , help="The output directory (log, checkpoints, parameters, etc.)" )
parser.add_argument(
"--data_file" , type=__UpperCamelCase , required=__UpperCamelCase , help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence." , )
parser.add_argument(
"--student_type" , type=__UpperCamelCase , choices=["distilbert", "roberta", "gpt2"] , required=__UpperCamelCase , help="The student type (DistilBERT, RoBERTa)." , )
parser.add_argument("--student_config" , type=__UpperCamelCase , required=__UpperCamelCase , help="Path to the student configuration." )
parser.add_argument(
"--student_pretrained_weights" , default=__UpperCamelCase , type=__UpperCamelCase , help="Load student initialization checkpoint." )
parser.add_argument(
"--teacher_type" , choices=["bert", "roberta", "gpt2"] , required=__UpperCamelCase , help="Teacher type (BERT, RoBERTa)." )
parser.add_argument("--teacher_name" , type=__UpperCamelCase , required=__UpperCamelCase , help="The teacher model." )
parser.add_argument("--temperature" , default=2.0 , type=__UpperCamelCase , help="Temperature for the softmax temperature." )
parser.add_argument(
"--alpha_ce" , default=0.5 , type=__UpperCamelCase , help="Linear weight for the distillation loss. Must be >=0." )
parser.add_argument(
"--alpha_mlm" , default=0.0 , type=__UpperCamelCase , help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag." , )
parser.add_argument("--alpha_clm" , default=0.5 , type=__UpperCamelCase , help="Linear weight for the CLM loss. Must be >=0." )
parser.add_argument("--alpha_mse" , default=0.0 , type=__UpperCamelCase , help="Linear weight of the MSE loss. Must be >=0." )
parser.add_argument(
"--alpha_cos" , default=0.0 , type=__UpperCamelCase , help="Linear weight of the cosine embedding loss. Must be >=0." )
parser.add_argument(
"--mlm" , action="store_true" , help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM." )
parser.add_argument(
"--mlm_mask_prop" , default=0.15 , type=__UpperCamelCase , help="Proportion of tokens for which we need to make a prediction." , )
parser.add_argument("--word_mask" , default=0.8 , type=__UpperCamelCase , help="Proportion of tokens to mask out." )
parser.add_argument("--word_keep" , default=0.1 , type=__UpperCamelCase , help="Proportion of tokens to keep." )
parser.add_argument("--word_rand" , default=0.1 , type=__UpperCamelCase , help="Proportion of tokens to randomly replace." )
parser.add_argument(
"--mlm_smoothing" , default=0.7 , type=__UpperCamelCase , help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)." , )
parser.add_argument("--token_counts" , type=__UpperCamelCase , help="The token counts in the data_file for MLM." )
parser.add_argument(
"--restrict_ce_to_mask" , action="store_true" , help="If true, compute the distillation loss only the [MLM] prediction distribution." , )
parser.add_argument(
"--freeze_pos_embs" , action="store_true" , help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only." , )
parser.add_argument(
"--freeze_token_type_embds" , action="store_true" , help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only." , )
parser.add_argument("--n_epoch" , type=__UpperCamelCase , default=3 , help="Number of pass on the whole dataset." )
parser.add_argument("--batch_size" , type=__UpperCamelCase , default=5 , help="Batch size (for each process)." )
parser.add_argument(
"--group_by_size" , action="store_false" , help="If true, group sequences that have similar length into the same batch. Default is true." , )
parser.add_argument(
"--gradient_accumulation_steps" , type=__UpperCamelCase , default=50 , help="Gradient accumulation for larger training batches." , )
parser.add_argument("--warmup_prop" , default=0.05 , type=__UpperCamelCase , help="Linear warmup proportion." )
parser.add_argument("--weight_decay" , default=0.0 , type=__UpperCamelCase , help="Weight decay if we apply some." )
parser.add_argument("--learning_rate" , default=5e-4 , type=__UpperCamelCase , help="The initial learning rate for Adam." )
parser.add_argument("--adam_epsilon" , default=1e-6 , type=__UpperCamelCase , help="Epsilon for Adam optimizer." )
parser.add_argument("--max_grad_norm" , default=5.0 , type=__UpperCamelCase , help="Max gradient norm." )
parser.add_argument("--initializer_range" , default=0.02 , type=__UpperCamelCase , help="Random initialization range." )
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=__UpperCamelCase , default="O1" , 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_gpu" , type=__UpperCamelCase , default=1 , help="Number of GPUs in the node." )
parser.add_argument("--local_rank" , type=__UpperCamelCase , default=-1 , help="Distributed training - Local rank" )
parser.add_argument("--seed" , type=__UpperCamelCase , default=56 , help="Random seed" )
parser.add_argument("--log_interval" , type=__UpperCamelCase , default=500 , help="Tensorboard logging interval." )
parser.add_argument("--checkpoint_interval" , type=__UpperCamelCase , default=4000 , help="Checkpoint interval." )
lowerCAmelCase_ : List[str] = parser.parse_args()
sanity_checks(__UpperCamelCase )
# ARGS #
init_gpu_params(__UpperCamelCase )
set_seed(__UpperCamelCase )
if args.is_master:
if os.path.exists(args.dump_path ):
if not args.force:
raise ValueError(
f'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
" itUse `--force` if you want to overwrite it" )
else:
shutil.rmtree(args.dump_path )
if not os.path.exists(args.dump_path ):
os.makedirs(args.dump_path )
logger.info(f'''Experiment will be dumped and logged in {args.dump_path}''' )
# SAVE PARAMS #
logger.info(f'''Param: {args}''' )
with open(os.path.join(args.dump_path , "parameters.json" ) , "w" ) as f:
json.dump(vars(__UpperCamelCase ) , __UpperCamelCase , indent=4 )
git_log(args.dump_path )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = MODEL_CLASSES[args.student_type]
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : str = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
lowerCAmelCase_ : Union[str, Any] = teacher_tokenizer_class.from_pretrained(args.teacher_name )
lowerCAmelCase_ : Any = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
lowerCAmelCase_ : str = tokenizer.all_special_tokens.index(__UpperCamelCase )
lowerCAmelCase_ : Optional[Any] = tokenizer.all_special_ids[idx]
logger.info(f'''Special tokens {special_tok_ids}''' )
lowerCAmelCase_ : Union[str, Any] = special_tok_ids
lowerCAmelCase_ : List[Any] = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(f'''Loading data from {args.data_file}''' )
with open(args.data_file , "rb" ) as fp:
lowerCAmelCase_ : Optional[int] = pickle.load(__UpperCamelCase )
if args.mlm:
logger.info(f'''Loading token counts from {args.token_counts} (already pre-computed)''' )
with open(args.token_counts , "rb" ) as fp:
lowerCAmelCase_ : List[str] = pickle.load(__UpperCamelCase )
lowerCAmelCase_ : Union[str, Any] = np.maximum(__UpperCamelCase , 1 ) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
lowerCAmelCase_ : str = 0.0 # do not predict special tokens
lowerCAmelCase_ : Tuple = torch.from_numpy(__UpperCamelCase )
else:
lowerCAmelCase_ : List[str] = None
lowerCAmelCase_ : Union[str, Any] = LmSeqsDataset(params=__UpperCamelCase , data=__UpperCamelCase )
logger.info("Data loader created." )
# STUDENT #
logger.info(f'''Loading student config from {args.student_config}''' )
lowerCAmelCase_ : str = student_config_class.from_pretrained(args.student_config )
lowerCAmelCase_ : Dict = True
if args.student_pretrained_weights is not None:
logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}''' )
lowerCAmelCase_ : Tuple = student_model_class.from_pretrained(args.student_pretrained_weights , config=__UpperCamelCase )
else:
lowerCAmelCase_ : Dict = student_model_class(__UpperCamelCase )
if args.n_gpu > 0:
student.to(f'''cuda:{args.local_rank}''' )
logger.info("Student loaded." )
# TEACHER #
lowerCAmelCase_ : Optional[Any] = teacher_model_class.from_pretrained(args.teacher_name , output_hidden_states=__UpperCamelCase )
if args.n_gpu > 0:
teacher.to(f'''cuda:{args.local_rank}''' )
logger.info(f'''Teacher loaded from {args.teacher_name}.''' )
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(__UpperCamelCase , __UpperCamelCase )
if args.freeze_token_type_embds:
freeze_token_type_embeddings(__UpperCamelCase , __UpperCamelCase )
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0 ) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
lowerCAmelCase_ : Any = Distiller(
params=__UpperCamelCase , dataset=__UpperCamelCase , token_probs=__UpperCamelCase , student=__UpperCamelCase , teacher=__UpperCamelCase )
distiller.train()
logger.info("Let's go get some drinks." )
if __name__ == "__main__":
main()
| 610 |
"""simple docstring"""
lowercase__ = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
lowercase__ = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowercase__ = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 610 | 1 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowercase__ ( _UpperCamelCase) -> List[Any]:
"""simple docstring"""
return x + 2
class A__ ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = 'x = 3'
UpperCamelCase = {}
UpperCamelCase = evaluate(_SCREAMING_SNAKE_CASE , {} , state=_SCREAMING_SNAKE_CASE )
assert result == 3
self.assertDictEqual(_SCREAMING_SNAKE_CASE , {'x': 3} )
UpperCamelCase = 'x = y'
UpperCamelCase = {'y': 5}
UpperCamelCase = evaluate(_SCREAMING_SNAKE_CASE , {} , state=_SCREAMING_SNAKE_CASE )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_SCREAMING_SNAKE_CASE , {'x': 5, 'y': 5} )
def _SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
UpperCamelCase = 'y = add_two(x)'
UpperCamelCase = {'x': 3}
UpperCamelCase = evaluate(_SCREAMING_SNAKE_CASE , {'add_two': add_two} , state=_SCREAMING_SNAKE_CASE )
assert result == 5
self.assertDictEqual(_SCREAMING_SNAKE_CASE , {'x': 3, 'y': 5} )
# Won't work without the tool
with CaptureStdout() as out:
UpperCamelCase = evaluate(_SCREAMING_SNAKE_CASE , {} , state=_SCREAMING_SNAKE_CASE )
assert result is None
assert "tried to execute add_two" in out.out
def _SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
UpperCamelCase = 'x = 3'
UpperCamelCase = {}
UpperCamelCase = evaluate(_SCREAMING_SNAKE_CASE , {} , state=_SCREAMING_SNAKE_CASE )
assert result == 3
self.assertDictEqual(_SCREAMING_SNAKE_CASE , {'x': 3} )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
"""simple docstring"""
UpperCamelCase = 'test_dict = {\'x\': x, \'y\': add_two(x)}'
UpperCamelCase = {'x': 3}
UpperCamelCase = evaluate(_SCREAMING_SNAKE_CASE , {'add_two': add_two} , state=_SCREAMING_SNAKE_CASE )
self.assertDictEqual(_SCREAMING_SNAKE_CASE , {'x': 3, 'y': 5} )
self.assertDictEqual(_SCREAMING_SNAKE_CASE , {'x': 3, 'test_dict': {'x': 3, 'y': 5}} )
def _SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
UpperCamelCase = 'x = 3\ny = 5'
UpperCamelCase = {}
UpperCamelCase = evaluate(_SCREAMING_SNAKE_CASE , {} , state=_SCREAMING_SNAKE_CASE )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_SCREAMING_SNAKE_CASE , {'x': 3, 'y': 5} )
def _SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
UpperCamelCase = 'text = f\'This is x: {x}.\''
UpperCamelCase = {'x': 3}
UpperCamelCase = evaluate(_SCREAMING_SNAKE_CASE , {} , state=_SCREAMING_SNAKE_CASE )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(_SCREAMING_SNAKE_CASE , {'x': 3, 'text': 'This is x: 3.'} )
def _SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
UpperCamelCase = 'if x <= 3:\n y = 2\nelse:\n y = 5'
UpperCamelCase = {'x': 3}
UpperCamelCase = evaluate(_SCREAMING_SNAKE_CASE , {} , state=_SCREAMING_SNAKE_CASE )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(_SCREAMING_SNAKE_CASE , {'x': 3, 'y': 2} )
UpperCamelCase = {'x': 8}
UpperCamelCase = evaluate(_SCREAMING_SNAKE_CASE , {} , state=_SCREAMING_SNAKE_CASE )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_SCREAMING_SNAKE_CASE , {'x': 8, 'y': 5} )
def _SCREAMING_SNAKE_CASE ( self : int ):
"""simple docstring"""
UpperCamelCase = 'test_list = [x, add_two(x)]'
UpperCamelCase = {'x': 3}
UpperCamelCase = evaluate(_SCREAMING_SNAKE_CASE , {'add_two': add_two} , state=_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , [3, 5] )
self.assertDictEqual(_SCREAMING_SNAKE_CASE , {'x': 3, 'test_list': [3, 5]} )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = 'y = x'
UpperCamelCase = {'x': 3}
UpperCamelCase = evaluate(_SCREAMING_SNAKE_CASE , {} , state=_SCREAMING_SNAKE_CASE )
assert result == 3
self.assertDictEqual(_SCREAMING_SNAKE_CASE , {'x': 3, 'y': 3} )
def _SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
UpperCamelCase = 'test_list = [x, add_two(x)]\ntest_list[1]'
UpperCamelCase = {'x': 3}
UpperCamelCase = evaluate(_SCREAMING_SNAKE_CASE , {'add_two': add_two} , state=_SCREAMING_SNAKE_CASE )
assert result == 5
self.assertDictEqual(_SCREAMING_SNAKE_CASE , {'x': 3, 'test_list': [3, 5]} )
UpperCamelCase = 'test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'
UpperCamelCase = {'x': 3}
UpperCamelCase = evaluate(_SCREAMING_SNAKE_CASE , {'add_two': add_two} , state=_SCREAMING_SNAKE_CASE )
assert result == 5
self.assertDictEqual(_SCREAMING_SNAKE_CASE , {'x': 3, 'test_dict': {'x': 3, 'y': 5}} )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = 'x = 0\nfor i in range(3):\n x = i'
UpperCamelCase = {}
UpperCamelCase = evaluate(_SCREAMING_SNAKE_CASE , {'range': range} , state=_SCREAMING_SNAKE_CASE )
assert result == 2
self.assertDictEqual(_SCREAMING_SNAKE_CASE , {'x': 2, 'i': 2} )
| 410 |
from __future__ import annotations
import queue
class A__ :
'''simple docstring'''
def __init__( self : str , _SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
UpperCamelCase = data
UpperCamelCase = None
UpperCamelCase = None
def lowercase__ ( ) -> TreeNode:
"""simple docstring"""
print('\n********Press N to stop entering at any point of time********\n')
UpperCamelCase = input('Enter the value of the root node: ').strip().lower()
UpperCamelCase = queue.Queue()
UpperCamelCase = TreeNode(int(_UpperCamelCase))
q.put(_UpperCamelCase)
while not q.empty():
UpperCamelCase = q.get()
UpperCamelCase = F'Enter the left node of {node_found.data}: '
UpperCamelCase = input(_UpperCamelCase).strip().lower() or 'n'
if check == "n":
return tree_node
UpperCamelCase = TreeNode(int(_UpperCamelCase))
UpperCamelCase = left_node
q.put(_UpperCamelCase)
UpperCamelCase = F'Enter the right node of {node_found.data}: '
UpperCamelCase = input(_UpperCamelCase).strip().lower() or 'n'
if check == "n":
return tree_node
UpperCamelCase = TreeNode(int(_UpperCamelCase))
UpperCamelCase = right_node
q.put(_UpperCamelCase)
raise
def lowercase__ ( _UpperCamelCase) -> None:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase) or not node:
return
print(node.data , end=',')
pre_order(node.left)
pre_order(node.right)
def lowercase__ ( _UpperCamelCase) -> None:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase) or not node:
return
in_order(node.left)
print(node.data , end=',')
in_order(node.right)
def lowercase__ ( _UpperCamelCase) -> None:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase) or not node:
return
post_order(node.left)
post_order(node.right)
print(node.data , end=',')
def lowercase__ ( _UpperCamelCase) -> None:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase) or not node:
return
UpperCamelCase = queue.Queue()
q.put(_UpperCamelCase)
while not q.empty():
UpperCamelCase = q.get()
print(node_dequeued.data , end=',')
if node_dequeued.left:
q.put(node_dequeued.left)
if node_dequeued.right:
q.put(node_dequeued.right)
def lowercase__ ( _UpperCamelCase) -> None:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase) or not node:
return
UpperCamelCase = queue.Queue()
q.put(_UpperCamelCase)
while not q.empty():
UpperCamelCase = []
while not q.empty():
UpperCamelCase = q.get()
print(node_dequeued.data , end=',')
if node_dequeued.left:
list_.append(node_dequeued.left)
if node_dequeued.right:
list_.append(node_dequeued.right)
print()
for node in list_:
q.put(_UpperCamelCase)
def lowercase__ ( _UpperCamelCase) -> None:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase) or not node:
return
UpperCamelCase = []
UpperCamelCase = node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end=',')
stack.append(_UpperCamelCase)
UpperCamelCase = n.left
# end of while means current node doesn't have left child
UpperCamelCase = stack.pop()
# start to traverse its right child
UpperCamelCase = n.right
def lowercase__ ( _UpperCamelCase) -> None:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase) or not node:
return
UpperCamelCase = []
UpperCamelCase = node
while n or stack:
while n:
stack.append(_UpperCamelCase)
UpperCamelCase = n.left
UpperCamelCase = stack.pop()
print(n.data , end=',')
UpperCamelCase = n.right
def lowercase__ ( _UpperCamelCase) -> None:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase) or not node:
return
UpperCamelCase , UpperCamelCase = [], []
UpperCamelCase = node
stacka.append(_UpperCamelCase)
while stacka: # to find the reversed order of post order, store it in stack2
UpperCamelCase = stacka.pop()
if n.left:
stacka.append(n.left)
if n.right:
stacka.append(n.right)
stacka.append(_UpperCamelCase)
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end=',')
def lowercase__ ( _UpperCamelCase = "" , _UpperCamelCase=50 , _UpperCamelCase="*") -> str:
"""simple docstring"""
if not s:
return "\n" + width * char
UpperCamelCase , UpperCamelCase = divmod(width - len(_UpperCamelCase) - 2 , 2)
return F'{left * char} {s} {(left + extra) * char}'
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt('''Binary Tree Traversals'''))
__magic_name__ : TreeNode = build_tree()
print(prompt('''Pre Order Traversal'''))
pre_order(node)
print(prompt() + '''\n''')
print(prompt('''In Order Traversal'''))
in_order(node)
print(prompt() + '''\n''')
print(prompt('''Post Order Traversal'''))
post_order(node)
print(prompt() + '''\n''')
print(prompt('''Level Order Traversal'''))
level_order(node)
print(prompt() + '''\n''')
print(prompt('''Actual Level Order Traversal'''))
level_order_actual(node)
print('''*''' * 50 + '''\n''')
print(prompt('''Pre Order Traversal - Iteration Version'''))
pre_order_iter(node)
print(prompt() + '''\n''')
print(prompt('''In Order Traversal - Iteration Version'''))
in_order_iter(node)
print(prompt() + '''\n''')
print(prompt('''Post Order Traversal - Iteration Version'''))
post_order_iter(node)
print(prompt())
| 410 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
A_ : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
def UpperCamelCase__ ( __magic_name__ : str ) -> Union[str, Any]:
'''simple docstring'''
if not path:
return "pipe"
for ext in PipelineDataFormat.SUPPORTED_FORMATS:
if path.endswith(__magic_name__ ):
return ext
raise Exception(
f"Unable to determine file format from file extension {path}. "
f"Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}" )
def UpperCamelCase__ ( __magic_name__ : Optional[Any] ) -> int:
'''simple docstring'''
snake_case__ : Optional[Any] = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
snake_case__ : List[Any] = try_infer_format_from_ext(args.input ) if args.format == """infer""" else args.format
snake_case__ : Dict = PipelineDataFormat.from_str(
format=__magic_name__ , output_path=args.output , input_path=args.input , column=args.column if args.column else nlp.default_input_names , overwrite=args.overwrite , )
return RunCommand(__magic_name__ , __magic_name__ )
class __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
snake_case__ : Optional[int] = nlp
snake_case__ : Union[str, Any] = reader
@staticmethod
def __UpperCamelCase ( __SCREAMING_SNAKE_CASE ):
snake_case__ : Optional[Any] = parser.add_parser("""run""" , help="""Run a pipeline through the CLI""" )
run_parser.add_argument("""--task""" , choices=get_supported_tasks() , help="""Task to run""" )
run_parser.add_argument("""--input""" , type=__SCREAMING_SNAKE_CASE , help="""Path to the file to use for inference""" )
run_parser.add_argument("""--output""" , type=__SCREAMING_SNAKE_CASE , help="""Path to the file that will be used post to write results.""" )
run_parser.add_argument("""--model""" , type=__SCREAMING_SNAKE_CASE , help="""Name or path to the model to instantiate.""" )
run_parser.add_argument("""--config""" , type=__SCREAMING_SNAKE_CASE , help="""Name or path to the model's config to instantiate.""" )
run_parser.add_argument(
"""--tokenizer""" , type=__SCREAMING_SNAKE_CASE , help="""Name of the tokenizer to use. (default: same as the model name)""" )
run_parser.add_argument(
"""--column""" , type=__SCREAMING_SNAKE_CASE , help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)""" , )
run_parser.add_argument(
"""--format""" , type=__SCREAMING_SNAKE_CASE , default="""infer""" , choices=PipelineDataFormat.SUPPORTED_FORMATS , help="""Input format to read from""" , )
run_parser.add_argument(
"""--device""" , type=__SCREAMING_SNAKE_CASE , default=-1 , help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)""" , )
run_parser.add_argument("""--overwrite""" , action="""store_true""" , help="""Allow overwriting the output file.""" )
run_parser.set_defaults(func=__SCREAMING_SNAKE_CASE )
def __UpperCamelCase ( self ):
snake_case__ , snake_case__ : List[Any] = self._nlp, []
for entry in self._reader:
snake_case__ : Tuple = nlp(**__SCREAMING_SNAKE_CASE ) if self._reader.is_multi_columns else nlp(__SCREAMING_SNAKE_CASE )
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
outputs.append(__SCREAMING_SNAKE_CASE )
else:
outputs += output
# Saving data
if self._nlp.binary_output:
snake_case__ : int = self._reader.save_binary(__SCREAMING_SNAKE_CASE )
logger.warning(f"Current pipeline requires output to be in binary format, saving at {binary_path}" )
else:
self._reader.save(__SCREAMING_SNAKE_CASE )
| 38 |
'''simple docstring'''
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
A_ : Optional[int] = logging.get_logger(__name__)
A_ : 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 __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCamelCase__ = '''segformer'''
def __init__( self , __SCREAMING_SNAKE_CASE=3 , __SCREAMING_SNAKE_CASE=4 , __SCREAMING_SNAKE_CASE=[2, 2, 2, 2] , __SCREAMING_SNAKE_CASE=[8, 4, 2, 1] , __SCREAMING_SNAKE_CASE=[3_2, 6_4, 1_6_0, 2_5_6] , __SCREAMING_SNAKE_CASE=[7, 3, 3, 3] , __SCREAMING_SNAKE_CASE=[4, 2, 2, 2] , __SCREAMING_SNAKE_CASE=[1, 2, 5, 8] , __SCREAMING_SNAKE_CASE=[4, 4, 4, 4] , __SCREAMING_SNAKE_CASE="gelu" , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=0.02 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=1e-6 , __SCREAMING_SNAKE_CASE=2_5_6 , __SCREAMING_SNAKE_CASE=2_5_5 , **__SCREAMING_SNAKE_CASE , ):
super().__init__(**__SCREAMING_SNAKE_CASE )
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.""" , __SCREAMING_SNAKE_CASE , )
snake_case__ : Dict = num_channels
snake_case__ : Optional[Any] = num_encoder_blocks
snake_case__ : Any = depths
snake_case__ : Optional[int] = sr_ratios
snake_case__ : Tuple = hidden_sizes
snake_case__ : List[str] = patch_sizes
snake_case__ : str = strides
snake_case__ : Optional[int] = mlp_ratios
snake_case__ : Optional[Any] = num_attention_heads
snake_case__ : Dict = hidden_act
snake_case__ : Optional[int] = hidden_dropout_prob
snake_case__ : List[str] = attention_probs_dropout_prob
snake_case__ : List[Any] = classifier_dropout_prob
snake_case__ : int = initializer_range
snake_case__ : List[str] = drop_path_rate
snake_case__ : int = layer_norm_eps
snake_case__ : List[Any] = decoder_hidden_size
snake_case__ : List[Any] = kwargs.get("""reshape_last_stage""" , __SCREAMING_SNAKE_CASE )
snake_case__ : Dict = semantic_loss_ignore_index
class __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCamelCase__ = version.parse('''1.11''' )
@property
def __UpperCamelCase ( self ):
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def __UpperCamelCase ( self ):
return 1e-4
@property
def __UpperCamelCase ( self ):
return 1_2
| 38 | 1 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={"vocab_file": "spiece.model"}
_lowerCamelCase ={
"vocab_file": {
"t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
"t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
"t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
}
}
# TODO(PVP) - this should be removed in Transformers v5
_lowerCamelCase ={
"t5-small": 5_12,
"t5-base": 5_12,
"t5-large": 5_12,
"t5-3b": 5_12,
"t5-11b": 5_12,
}
_lowerCamelCase ="▁"
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = ['input_ids', 'attention_mask']
def __init__( self : str ,snake_case : int ,snake_case : Any="</s>" ,snake_case : Dict="<unk>" ,snake_case : Optional[int]="<pad>" ,snake_case : int=100 ,snake_case : Any=None ,snake_case : Optional[Dict[str, Any]] = None ,snake_case : Optional[int]=True ,**snake_case : str ,):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
SCREAMING_SNAKE_CASE =[f'<extra_id_{i}>' for i in range(snake_case )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
SCREAMING_SNAKE_CASE =len(set(filter(lambda snake_case : bool('extra_id' in str(snake_case ) ) ,snake_case ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
' tokens' )
if legacy:
logger.warning_once(
f'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
' read the related pull request available at https://github.com/huggingface/transformers/pull/24565' )
SCREAMING_SNAKE_CASE =legacy
SCREAMING_SNAKE_CASE ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=snake_case ,unk_token=snake_case ,pad_token=snake_case ,extra_ids=snake_case ,additional_special_tokens=snake_case ,sp_model_kwargs=self.sp_model_kwargs ,legacy=snake_case ,**snake_case ,)
SCREAMING_SNAKE_CASE =vocab_file
SCREAMING_SNAKE_CASE =extra_ids
SCREAMING_SNAKE_CASE =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case )
@staticmethod
def _lowerCAmelCase ( snake_case : str ,snake_case : List[Any] ,snake_case : Optional[int] ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
SCREAMING_SNAKE_CASE =TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'This tokenizer was incorrectly instantiated with a model max length of'
f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
f' {pretrained_model_name_or_path} automatically truncating your input to'
f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
' instantiate this tokenizer with `model_max_length` set to your preferred value.' ,snake_case ,)
return max_model_length
@property
def _lowerCAmelCase ( self : Union[str, Any] ):
return self.sp_model.get_piece_size() + self._extra_ids
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE ={self.convert_ids_to_tokens(snake_case ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _lowerCAmelCase ( 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 )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(snake_case )) + [1]
return ([0] * len(snake_case )) + [1] + ([0] * len(snake_case )) + [1]
def _lowerCAmelCase ( self : Optional[int] ):
return list(
set(filter(lambda snake_case : bool(re.search(r'<extra_id_\d+>' ,snake_case ) ) is not None ,self.additional_special_tokens ) ) )
def _lowerCAmelCase ( self : Any ):
return [self._convert_token_to_id(snake_case ) for token in self.get_sentinel_tokens()]
def _lowerCAmelCase ( self : Tuple ,snake_case : List[int] ):
if len(snake_case ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
' eos tokens being added.' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def _lowerCAmelCase ( self : List[str] ,snake_case : List[int] ,snake_case : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE =[self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def _lowerCAmelCase ( self : Dict ,snake_case : List[int] ,snake_case : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE =self._add_eos_if_not_present(snake_case )
if token_ids_a is None:
return token_ids_a
else:
SCREAMING_SNAKE_CASE =self._add_eos_if_not_present(snake_case )
return token_ids_a + token_ids_a
def __getstate__( self : List[str] ):
SCREAMING_SNAKE_CASE =self.__dict__.copy()
SCREAMING_SNAKE_CASE =None
return state
def __setstate__( self : Optional[Any] ,snake_case : int ):
SCREAMING_SNAKE_CASE =d
# for backward compatibility
if not hasattr(self ,'sp_model_kwargs' ):
SCREAMING_SNAKE_CASE ={}
SCREAMING_SNAKE_CASE =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : "TextInput" ,**snake_case : Optional[Any] ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
SCREAMING_SNAKE_CASE =SPIECE_UNDERLINE + text.replace(snake_case ,' ' )
return super().tokenize(snake_case ,**snake_case )
def _lowerCAmelCase ( self : int ,snake_case : Union[str, Any] ,**snake_case : Tuple ):
if not self.legacy:
SCREAMING_SNAKE_CASE =text.startswith(snake_case )
if is_first:
SCREAMING_SNAKE_CASE =text[1:]
SCREAMING_SNAKE_CASE =self.sp_model.encode(snake_case ,out_type=snake_case )
if not self.legacy and not is_first and not text.startswith(' ' ) and tokens[0].startswith(snake_case ):
SCREAMING_SNAKE_CASE =([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : Dict ):
if token.startswith('<extra_id_' ):
SCREAMING_SNAKE_CASE =re.match(r'<extra_id_(\d+)>' ,snake_case )
SCREAMING_SNAKE_CASE =int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(snake_case )
def _lowerCAmelCase ( self : Optional[int] ,snake_case : int ):
if index < self.sp_model.get_piece_size():
SCREAMING_SNAKE_CASE =self.sp_model.IdToPiece(snake_case )
else:
SCREAMING_SNAKE_CASE =f'<extra_id_{self.vocab_size - 1 - index}>'
return token
def _lowerCAmelCase ( self : str ,snake_case : str ):
SCREAMING_SNAKE_CASE =[]
SCREAMING_SNAKE_CASE =''
SCREAMING_SNAKE_CASE =False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(snake_case ) + token
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =[]
else:
current_sub_tokens.append(snake_case )
SCREAMING_SNAKE_CASE =False
out_string += self.sp_model.decode(snake_case )
return out_string.strip()
def _lowerCAmelCase ( self : List[Any] ,snake_case : str ,snake_case : Optional[str] = None ):
if not os.path.isdir(snake_case ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
SCREAMING_SNAKE_CASE =os.path.join(
snake_case ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,snake_case )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case ,'wb' ) as fi:
SCREAMING_SNAKE_CASE =self.sp_model.serialized_model_proto()
fi.write(snake_case )
return (out_vocab_file,)
| 252 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase ={
"configuration_perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PerceiverConfig", "PerceiverOnnxConfig"],
"tokenization_perceiver": ["PerceiverTokenizer"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =["PerceiverFeatureExtractor"]
_lowerCamelCase =["PerceiverImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST",
"PerceiverForImageClassificationConvProcessing",
"PerceiverForImageClassificationFourier",
"PerceiverForImageClassificationLearned",
"PerceiverForMaskedLM",
"PerceiverForMultimodalAutoencoding",
"PerceiverForOpticalFlow",
"PerceiverForSequenceClassification",
"PerceiverLayer",
"PerceiverModel",
"PerceiverPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
from .tokenization_perceiver import PerceiverTokenizer
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_perceiver import PerceiverFeatureExtractor
from .image_processing_perceiver import PerceiverImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_perceiver import (
PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForImageClassificationLearned,
PerceiverForMaskedLM,
PerceiverForMultimodalAutoencoding,
PerceiverForOpticalFlow,
PerceiverForSequenceClassification,
PerceiverLayer,
PerceiverModel,
PerceiverPreTrainedModel,
)
else:
import sys
_lowerCamelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 252 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE_ = {
"""configuration_clip""": [
"""CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPConfig""",
"""CLIPOnnxConfig""",
"""CLIPTextConfig""",
"""CLIPVisionConfig""",
],
"""processing_clip""": ["""CLIPProcessor"""],
"""tokenization_clip""": ["""CLIPTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""CLIPTokenizerFast"""]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""CLIPFeatureExtractor"""]
SCREAMING_SNAKE_CASE_ = ["""CLIPImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CLIPModel""",
"""CLIPPreTrainedModel""",
"""CLIPTextModel""",
"""CLIPTextModelWithProjection""",
"""CLIPVisionModel""",
"""CLIPVisionModelWithProjection""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFCLIPModel""",
"""TFCLIPPreTrainedModel""",
"""TFCLIPTextModel""",
"""TFCLIPVisionModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""FlaxCLIPModel""",
"""FlaxCLIPPreTrainedModel""",
"""FlaxCLIPTextModel""",
"""FlaxCLIPTextPreTrainedModel""",
"""FlaxCLIPVisionModel""",
"""FlaxCLIPVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_clip import (
CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPConfig,
CLIPOnnxConfig,
CLIPTextConfig,
CLIPVisionConfig,
)
from .processing_clip import CLIPProcessor
from .tokenization_clip import CLIPTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_clip_fast import CLIPTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clip import CLIPFeatureExtractor
from .image_processing_clip import CLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clip import (
CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPModel,
CLIPPreTrainedModel,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPVisionModel,
CLIPVisionModelWithProjection,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_clip import (
TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCLIPModel,
TFCLIPPreTrainedModel,
TFCLIPTextModel,
TFCLIPVisionModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_clip import (
FlaxCLIPModel,
FlaxCLIPPreTrainedModel,
FlaxCLIPTextModel,
FlaxCLIPTextPreTrainedModel,
FlaxCLIPVisionModel,
FlaxCLIPVisionPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 237 |
"""simple docstring"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ) -> List[str]:
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=0 ) -> Tuple:
return sorted(SCREAMING_SNAKE_CASE__, key=lambda SCREAMING_SNAKE_CASE__ : x[column] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=float("inf" ) ) -> Union[str, Any]:
for i in range(points_counts - 1 ):
for j in range(i + 1, SCREAMING_SNAKE_CASE__ ):
a_ : Union[str, Any] = euclidean_distance_sqr(points[i], points[j] )
if current_dis < min_dis:
a_ : Optional[Any] = current_dis
return min_dis
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=float("inf" ) ) -> Any:
for i in range(min(6, points_counts - 1 ), SCREAMING_SNAKE_CASE__ ):
for j in range(max(0, i - 6 ), SCREAMING_SNAKE_CASE__ ):
a_ : Tuple = euclidean_distance_sqr(points[i], points[j] )
if current_dis < min_dis:
a_ : Any = current_dis
return min_dis
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ) -> Any:
# base case
if points_counts <= 3:
return dis_between_closest_pair(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
# recursion
a_ : Optional[int] = points_counts // 2
a_ : Union[str, Any] = closest_pair_of_points_sqr(
SCREAMING_SNAKE_CASE__, points_sorted_on_y[:mid], SCREAMING_SNAKE_CASE__ )
a_ : Optional[Any] = closest_pair_of_points_sqr(
SCREAMING_SNAKE_CASE__, points_sorted_on_y[mid:], points_counts - mid )
a_ : Dict = min(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
a_ : Tuple = []
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
cross_strip.append(SCREAMING_SNAKE_CASE__ )
a_ : List[str] = dis_between_closest_in_strip(
SCREAMING_SNAKE_CASE__, len(SCREAMING_SNAKE_CASE__ ), SCREAMING_SNAKE_CASE__ )
return min(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ) -> Optional[Any]:
a_ : List[str] = column_based_sort(SCREAMING_SNAKE_CASE__, column=0 )
a_ : Union[str, Any] = column_based_sort(SCREAMING_SNAKE_CASE__, column=1 )
return (
closest_pair_of_points_sqr(
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
) ** 0.5
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
print("""Distance:""", closest_pair_of_points(points, len(points))) | 237 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
def UpperCAmelCase_ ( A ):
_a : Any = SwinConfig(
embed_dim=1_9_2 , depths=(2, 2, 1_8, 2) , num_heads=(6, 1_2, 2_4, 4_8) , window_size=1_2 , out_features=['stage2', 'stage3', 'stage4'] , )
_a : Dict = DetaConfig(
backbone_config=UpperCamelCase__ , num_queries=9_0_0 , encoder_ffn_dim=2_0_4_8 , decoder_ffn_dim=2_0_4_8 , num_feature_levels=5 , assign_first_stage=UpperCamelCase__ , with_box_refine=UpperCamelCase__ , two_stage=UpperCamelCase__ , )
# set labels
_a : int = 'huggingface/label-files'
if "o365" in model_name:
_a : List[str] = 3_6_6
_a : Any = 'object365-id2label.json'
else:
_a : Tuple = 9_1
_a : Tuple = 'coco-detection-id2label.json'
_a : Dict = num_labels
_a : Union[str, Any] = json.load(open(cached_download(hf_hub_url(UpperCamelCase__ , UpperCamelCase__ , repo_type='dataset' ) ) , 'r' ) )
_a : Optional[int] = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
_a : str = idalabel
_a : Union[str, Any] = {v: k for k, v in idalabel.items()}
return config
def UpperCAmelCase_ ( A ):
_a : Any = []
# stem
# fmt: off
rename_keys.append(('backbone.0.body.patch_embed.proj.weight', 'model.backbone.model.embeddings.patch_embeddings.projection.weight') )
rename_keys.append(('backbone.0.body.patch_embed.proj.bias', 'model.backbone.model.embeddings.patch_embeddings.projection.bias') )
rename_keys.append(('backbone.0.body.patch_embed.norm.weight', 'model.backbone.model.embeddings.norm.weight') )
rename_keys.append(('backbone.0.body.patch_embed.norm.bias', 'model.backbone.model.embeddings.norm.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.0.body.layers.{i}.blocks.{j}.norm1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''') )
rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''') )
rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''') )
rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''') )
rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''') )
rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''') )
rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''') )
rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''') )
rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''') )
rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''') )
rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''') )
rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''') )
if i < 3:
rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.reduction.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''') )
rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''') )
rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.bias''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''') )
rename_keys.append(('backbone.0.body.norm1.weight', 'model.backbone.model.hidden_states_norms.stage2.weight') )
rename_keys.append(('backbone.0.body.norm1.bias', 'model.backbone.model.hidden_states_norms.stage2.bias') )
rename_keys.append(('backbone.0.body.norm2.weight', 'model.backbone.model.hidden_states_norms.stage3.weight') )
rename_keys.append(('backbone.0.body.norm2.bias', 'model.backbone.model.hidden_states_norms.stage3.bias') )
rename_keys.append(('backbone.0.body.norm3.weight', 'model.backbone.model.hidden_states_norms.stage4.weight') )
rename_keys.append(('backbone.0.body.norm3.bias', 'model.backbone.model.hidden_states_norms.stage4.bias') )
# transformer encoder
for i in range(config.encoder_layers ):
rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''') )
# transformer decoder
for i in range(config.decoder_layers ):
rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''') )
# fmt: on
return rename_keys
def UpperCAmelCase_ ( A , A , A ):
_a : Any = dct.pop(UpperCamelCase__ )
_a : Tuple = val
def UpperCAmelCase_ ( A , A ):
_a : Tuple = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_a : List[str] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_a : str = state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight''' )
_a : List[Any] = state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
_a : Tuple = in_proj_weight[:dim, :]
_a : List[Any] = in_proj_bias[: dim]
_a : Dict = in_proj_weight[
dim : dim * 2, :
]
_a : List[Any] = in_proj_bias[
dim : dim * 2
]
_a : Tuple = in_proj_weight[
-dim :, :
]
_a : List[Any] = in_proj_bias[-dim :]
# fmt: on
def UpperCAmelCase_ ( A , A ):
_a : str = config.d_model
for i in range(config.decoder_layers ):
# read in weights + bias of input projection layer of self-attention
_a : str = state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
_a : Optional[int] = state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
_a : int = in_proj_weight[:hidden_size, :]
_a : Tuple = in_proj_bias[:hidden_size]
_a : Dict = in_proj_weight[
hidden_size : hidden_size * 2, :
]
_a : Optional[Any] = in_proj_bias[hidden_size : hidden_size * 2]
_a : int = in_proj_weight[-hidden_size:, :]
_a : Any = in_proj_bias[-hidden_size:]
def UpperCAmelCase_ ( ):
_a : Dict = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_a : List[str] = Image.open(requests.get(UpperCamelCase__ , stream=UpperCamelCase__ ).raw )
return im
@torch.no_grad()
def UpperCAmelCase_ ( A , A , A ):
_a : Optional[int] = get_deta_config(UpperCamelCase__ )
# load original state dict
if model_name == "deta-swin-large":
_a : Optional[Any] = hf_hub_download(repo_id='nielsr/deta-checkpoints' , filename='adet_swin_ft.pth' )
elif model_name == "deta-swin-large-o365":
_a : Optional[int] = hf_hub_download(repo_id='jozhang97/deta-swin-l-o365' , filename='deta_swin_pt_o365.pth' )
else:
raise ValueError(f'''Model name {model_name} not supported''' )
_a : Any = torch.load(UpperCamelCase__ , map_location='cpu' )['model']
# original state dict
for name, param in state_dict.items():
print(UpperCamelCase__ , param.shape )
# rename keys
_a : Any = create_rename_keys(UpperCamelCase__ )
for src, dest in rename_keys:
rename_key(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
read_in_swin_q_k_v(UpperCamelCase__ , config.backbone_config )
read_in_decoder_q_k_v(UpperCamelCase__ , UpperCamelCase__ )
# fix some prefixes
for key in state_dict.copy().keys():
if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
_a : List[str] = state_dict.pop(UpperCamelCase__ )
_a : int = val
if "input_proj" in key:
_a : Dict = state_dict.pop(UpperCamelCase__ )
_a : List[Any] = val
if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
_a : Any = state_dict.pop(UpperCamelCase__ )
_a : Tuple = val
# finally, create HuggingFace model and load state dict
_a : Dict = DetaForObjectDetection(UpperCamelCase__ )
model.load_state_dict(UpperCamelCase__ )
model.eval()
_a : Tuple = 'cuda' if torch.cuda.is_available() else 'cpu'
model.to(UpperCamelCase__ )
# load image processor
_a : Optional[Any] = DetaImageProcessor(format='coco_detection' )
# verify our conversion on image
_a : str = prepare_img()
_a : str = processor(images=UpperCamelCase__ , return_tensors='pt' )
_a : List[Any] = encoding['pixel_values']
_a : str = model(pixel_values.to(UpperCamelCase__ ) )
# verify logits
print('Logits:' , outputs.logits[0, :3, :3] )
print('Boxes:' , outputs.pred_boxes[0, :3, :3] )
if model_name == "deta-swin-large":
_a : Optional[Any] = torch.tensor(
[[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]] )
_a : List[Any] = torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]] )
elif model_name == "deta-swin-large-o365":
_a : Any = torch.tensor(
[[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]] )
_a : Union[str, Any] = torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]] )
assert torch.allclose(outputs.logits[0, :3, :3] , expected_logits.to(UpperCamelCase__ ) , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , expected_boxes.to(UpperCamelCase__ ) , atol=1E-4 )
print('Everything ok!' )
if pytorch_dump_folder_path:
# Save model and processor
logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...''' )
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
model.save_pretrained(UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
# Push to hub
if push_to_hub:
print('Pushing model and processor to hub...' )
model.push_to_hub(f'''jozhang97/{model_name}''' )
processor.push_to_hub(f'''jozhang97/{model_name}''' )
if __name__ == "__main__":
UpperCAmelCase_ : List[str] = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
type=str,
default="deta-swin-large",
choices=["deta-swin-large", "deta-swin-large-o365"],
help="Name of the model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
help="Path to the folder to output PyTorch model.",
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
UpperCAmelCase_ : Tuple = parser.parse_args()
convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 703 |
'''simple docstring'''
import math
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
if initial_intensity < 0:
raise ValueError('The value of intensity cannot be negative' )
# handling of negative values of initial intensity
if angle < 0 or angle > 3_6_0:
raise ValueError('In Malus Law, the angle is in the range 0-360 degrees' )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(A ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name="malus_law")
| 424 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
__magic_name__ : str = tuple[int, int]
class __snake_case :
def __init__( self: Tuple , A_: set[int] , A_: Mapping[EdgeT, int] ):
__lowerCamelCase = vertices
__lowerCamelCase = {
(min(A_ ), max(A_ )): weight for edge, weight in edges.items()
}
def __a ( self: Optional[int] , A_: EdgeT , A_: int ):
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
__lowerCamelCase = weight
def __a ( self: Dict ):
__lowerCamelCase = Graph({min(self.vertices )} , {} )
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
while len(subgraph.vertices ) < len(self.vertices ):
__lowerCamelCase = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
__lowerCamelCase = edge
__lowerCamelCase = weight
subgraph.add_edge(A_ , A_ )
return subgraph
def a_ ( lowercase__ :str = "p107_network.txt" ):
__lowerCamelCase = os.path.abspath(os.path.dirname(lowercase__ ) )
__lowerCamelCase = os.path.join(lowercase__, lowercase__ )
__lowerCamelCase = {}
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
with open(lowercase__ ) as f:
__lowerCamelCase = f.read().strip().split("""\n""" )
__lowerCamelCase = [line.split(""",""" ) for line in data]
for edgea in range(1, len(lowercase__ ) ):
for edgea in range(lowercase__ ):
if adjaceny_matrix[edgea][edgea] != "-":
__lowerCamelCase = int(adjaceny_matrix[edgea][edgea] )
__lowerCamelCase = Graph(set(range(len(lowercase__ ) ) ), lowercase__ )
__lowerCamelCase = graph.prims_algorithm()
__lowerCamelCase = sum(graph.edges.values() )
__lowerCamelCase = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(f"""{solution() = }""")
| 281 |
"""simple docstring"""
from __future__ import annotations
def a_ ( lowercase__ :list[float] ):
if len(lowercase__ ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
if any(i <= 0 for i in nums ):
raise ValueError("""All values must be greater than 0""" )
__lowerCamelCase = nums.copy()
copy_nums.sort()
return copy_nums[-1] < sum(copy_nums[:-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 281 | 1 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase ( _a ):
_SCREAMING_SNAKE_CASE : Dict =(DDPMScheduler,)
def a__ ( self , **lowerCAmelCase__ ):
_A= {
'num_train_timesteps': 1000,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'variance_type': 'fixed_small',
'clip_sample': True,
}
config.update(**lowerCAmelCase__ )
return config
def a__ ( self ):
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )
def a__ ( self ):
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=lowerCAmelCase__ , beta_end=lowerCAmelCase__ )
def a__ ( self ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=lowerCAmelCase__ )
def a__ ( self ):
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=lowerCAmelCase__ )
def a__ ( self ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=lowerCAmelCase__ )
def a__ ( self ):
self.check_over_configs(thresholding=lowerCAmelCase__ )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=lowerCAmelCase__ , prediction_type=lowerCAmelCase__ , sample_max_value=lowerCAmelCase__ , )
def a__ ( self ):
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase__ )
def a__ ( self ):
for t in [0, 500, 999]:
self.check_over_forward(time_step=lowerCAmelCase__ )
def a__ ( self ):
_A= self.scheduler_classes[0]
_A= self.get_scheduler_config()
_A= scheduler_class(**lowerCAmelCase__ )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def a__ ( self ):
_A= self.scheduler_classes[0]
_A= self.get_scheduler_config()
_A= scheduler_class(**lowerCAmelCase__ )
_A= len(lowerCAmelCase__ )
_A= self.dummy_model()
_A= self.dummy_sample_deter
_A= torch.manual_seed(0 )
for t in reversed(range(lowerCAmelCase__ ) ):
# 1. predict noise residual
_A= model(lowerCAmelCase__ , lowerCAmelCase__ )
# 2. predict previous mean of sample x_t-1
_A= scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_A= pred_prev_sample
_A= torch.sum(torch.abs(lowerCAmelCase__ ) )
_A= torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 258.9606 ) < 1E-2
assert abs(result_mean.item() - 0.3372 ) < 1E-3
def a__ ( self ):
_A= self.scheduler_classes[0]
_A= self.get_scheduler_config(prediction_type='v_prediction' )
_A= scheduler_class(**lowerCAmelCase__ )
_A= len(lowerCAmelCase__ )
_A= self.dummy_model()
_A= self.dummy_sample_deter
_A= torch.manual_seed(0 )
for t in reversed(range(lowerCAmelCase__ ) ):
# 1. predict noise residual
_A= model(lowerCAmelCase__ , lowerCAmelCase__ )
# 2. predict previous mean of sample x_t-1
_A= scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_A= pred_prev_sample
_A= torch.sum(torch.abs(lowerCAmelCase__ ) )
_A= torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 202.0296 ) < 1E-2
assert abs(result_mean.item() - 0.2631 ) < 1E-3
def a__ ( self ):
_A= self.scheduler_classes[0]
_A= self.get_scheduler_config()
_A= scheduler_class(**lowerCAmelCase__ )
_A= [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=lowerCAmelCase__ )
_A= scheduler.timesteps
for i, timestep in enumerate(lowerCAmelCase__ ):
if i == len(lowerCAmelCase__ ) - 1:
_A= -1
else:
_A= timesteps[i + 1]
_A= scheduler.previous_timestep(lowerCAmelCase__ )
_A= prev_t.item()
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def a__ ( self ):
_A= self.scheduler_classes[0]
_A= self.get_scheduler_config()
_A= scheduler_class(**lowerCAmelCase__ )
_A= [100, 87, 50, 51, 0]
with self.assertRaises(lowerCAmelCase__ , msg='`custom_timesteps` must be in descending order.' ):
scheduler.set_timesteps(timesteps=lowerCAmelCase__ )
def a__ ( self ):
_A= self.scheduler_classes[0]
_A= self.get_scheduler_config()
_A= scheduler_class(**lowerCAmelCase__ )
_A= [100, 87, 50, 1, 0]
_A= len(lowerCAmelCase__ )
with self.assertRaises(lowerCAmelCase__ , msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
scheduler.set_timesteps(num_inference_steps=lowerCAmelCase__ , timesteps=lowerCAmelCase__ )
def a__ ( self ):
_A= self.scheduler_classes[0]
_A= self.get_scheduler_config()
_A= scheduler_class(**lowerCAmelCase__ )
_A= [scheduler.config.num_train_timesteps]
with self.assertRaises(
lowerCAmelCase__ , msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}' , ):
scheduler.set_timesteps(timesteps=lowerCAmelCase__ ) | 476 | UpperCAmelCase_ = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter''': '''cookiecutter==1.7.3''',
'''dataclasses''': '''dataclasses''',
'''datasets''': '''datasets!=2.5.0''',
'''decord''': '''decord==0.6.0''',
'''deepspeed''': '''deepspeed>=0.9.3''',
'''diffusers''': '''diffusers''',
'''dill''': '''dill<0.3.5''',
'''evaluate''': '''evaluate>=0.2.0''',
'''fairscale''': '''fairscale>0.3''',
'''faiss-cpu''': '''faiss-cpu''',
'''fastapi''': '''fastapi''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1,<=0.7.0''',
'''ftfy''': '''ftfy''',
'''fugashi''': '''fugashi>=1.0''',
'''GitPython''': '''GitPython<3.1.19''',
'''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
'''huggingface-hub''': '''huggingface-hub>=0.14.1,<1.0''',
'''importlib_metadata''': '''importlib_metadata''',
'''ipadic''': '''ipadic>=1.0.0,<2.0''',
'''isort''': '''isort>=5.5.4''',
'''jax''': '''jax>=0.2.8,!=0.3.2,<=0.4.13''',
'''jaxlib''': '''jaxlib>=0.1.65,<=0.4.13''',
'''jieba''': '''jieba''',
'''kenlm''': '''kenlm''',
'''keras-nlp''': '''keras-nlp>=0.3.1''',
'''librosa''': '''librosa''',
'''nltk''': '''nltk''',
'''natten''': '''natten>=0.14.6''',
'''numpy''': '''numpy>=1.17''',
'''onnxconverter-common''': '''onnxconverter-common''',
'''onnxruntime-tools''': '''onnxruntime-tools>=1.4.2''',
'''onnxruntime''': '''onnxruntime>=1.4.0''',
'''opencv-python''': '''opencv-python''',
'''optuna''': '''optuna''',
'''optax''': '''optax>=0.0.8,<=0.1.4''',
'''packaging''': '''packaging>=20.0''',
'''parameterized''': '''parameterized''',
'''phonemizer''': '''phonemizer''',
'''protobuf''': '''protobuf''',
'''psutil''': '''psutil''',
'''pyyaml''': '''pyyaml>=5.1''',
'''pydantic''': '''pydantic<2''',
'''pytest''': '''pytest>=7.2.0''',
'''pytest-timeout''': '''pytest-timeout''',
'''pytest-xdist''': '''pytest-xdist''',
'''python''': '''python>=3.8.0''',
'''ray[tune]''': '''ray[tune]''',
'''regex''': '''regex!=2019.12.17''',
'''requests''': '''requests''',
'''rhoknp''': '''rhoknp>=1.1.0,<1.3.1''',
'''rjieba''': '''rjieba''',
'''rouge-score''': '''rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1''',
'''ruff''': '''ruff>=0.0.241,<=0.0.259''',
'''sacrebleu''': '''sacrebleu>=1.4.12,<2.0.0''',
'''sacremoses''': '''sacremoses''',
'''safetensors''': '''safetensors>=0.3.1''',
'''sagemaker''': '''sagemaker>=2.31.0''',
'''scikit-learn''': '''scikit-learn''',
'''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
'''sigopt''': '''sigopt''',
'''starlette''': '''starlette''',
'''sudachipy''': '''sudachipy>=0.6.6''',
'''sudachidict_core''': '''sudachidict_core>=20220729''',
'''tensorflow-cpu''': '''tensorflow-cpu>=2.6,<2.14''',
'''tensorflow''': '''tensorflow>=2.6,<2.14''',
'''tensorflow-text''': '''tensorflow-text<2.14''',
'''tf2onnx''': '''tf2onnx''',
'''timeout-decorator''': '''timeout-decorator''',
'''timm''': '''timm''',
'''tokenizers''': '''tokenizers>=0.11.1,!=0.11.3,<0.14''',
'''torch''': '''torch>=1.9,!=1.12.0''',
'''torchaudio''': '''torchaudio''',
'''torchvision''': '''torchvision''',
'''pyctcdecode''': '''pyctcdecode>=0.4.0''',
'''tqdm''': '''tqdm>=4.27''',
'''unidic''': '''unidic>=1.0.2''',
'''unidic_lite''': '''unidic_lite>=1.0.7''',
'''urllib3''': '''urllib3<2.0.0''',
'''uvicorn''': '''uvicorn''',
} | 476 | 1 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class snake_case__ ( __snake_case ):
'''simple docstring'''
def UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
UpperCAmelCase_ = tempfile.mkdtemp()
UpperCAmelCase_ = 8
# DPR tok
UpperCAmelCase_ = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
UpperCAmelCase_ = os.path.join(self.tmpdirname , '''dpr_tokenizer''' )
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
UpperCAmelCase_ = os.path.join(lowerCAmelCase_ , DPR_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] ) )
# BART tok
UpperCAmelCase_ = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
UpperCAmelCase_ = dict(zip(lowerCAmelCase_ , range(len(lowerCAmelCase_ ) ) ) )
UpperCAmelCase_ = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
UpperCAmelCase_ = {'''unk_token''': '''<unk>'''}
UpperCAmelCase_ = os.path.join(self.tmpdirname , '''bart_tokenizer''' )
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
UpperCAmelCase_ = os.path.join(lowerCAmelCase_ , BART_VOCAB_FILES_NAMES['''vocab_file'''] )
UpperCAmelCase_ = os.path.join(lowerCAmelCase_ , BART_VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(lowerCAmelCase_ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(lowerCAmelCase_ ) )
def UpperCamelCase ( self : Union[str, Any] ) -> DPRQuestionEncoderTokenizer:
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def UpperCamelCase ( self : str ) -> DPRContextEncoderTokenizer:
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def UpperCamelCase ( self : str ) -> BartTokenizer:
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''bart_tokenizer''' ) )
def UpperCamelCase ( self : Dict ) -> List[str]:
shutil.rmtree(self.tmpdirname )
def UpperCamelCase ( self : Optional[Any] ) -> Union[str, Any]:
UpperCAmelCase_ = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def UpperCamelCase ( self : Dict ) -> List[str]:
UpperCAmelCase_ = self.get_dummy_dataset()
UpperCAmelCase_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
UpperCAmelCase_ = dataset
UpperCAmelCase_ = RagRetriever(
lowerCAmelCase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def UpperCamelCase ( self : List[str] , lowerCAmelCase_ : bool ) -> Tuple:
UpperCAmelCase_ = self.get_dummy_dataset()
UpperCAmelCase_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''custom''' , )
if from_disk:
UpperCAmelCase_ = os.path.join(self.tmpdirname , '''dataset''' )
UpperCAmelCase_ = os.path.join(self.tmpdirname , '''index.faiss''' )
dataset.get_index('''embeddings''' ).save(os.path.join(self.tmpdirname , '''index.faiss''' ) )
dataset.drop_index('''embeddings''' )
dataset.save_to_disk(os.path.join(self.tmpdirname , '''dataset''' ) )
del dataset
UpperCAmelCase_ = RagRetriever(
lowerCAmelCase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
UpperCAmelCase_ = RagRetriever(
lowerCAmelCase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , lowerCAmelCase_ ) , )
return retriever
def UpperCamelCase ( self : List[Any] ) -> str:
UpperCAmelCase_ = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
UpperCAmelCase_ = os.path.join(self.tmpdirname , '''hf_bert_base.hnswSQ8_correct_phi_128.c_index''' )
dataset.save_faiss_index('''embeddings''' , index_file_name + '''.index.dpr''' )
pickle.dump(dataset['''id'''] , open(index_file_name + '''.index_meta.dpr''' , '''wb''' ) )
UpperCAmelCase_ = os.path.join(self.tmpdirname , '''psgs_w100.tsv.pkl''' )
UpperCAmelCase_ = {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
pickle.dump(lowerCAmelCase_ , open(lowerCAmelCase_ , '''wb''' ) )
UpperCAmelCase_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''legacy''' , index_path=self.tmpdirname , )
UpperCAmelCase_ = RagRetriever(
lowerCAmelCase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def UpperCamelCase ( self : Optional[Any] ) -> Optional[int]:
UpperCAmelCase_ = 1
UpperCAmelCase_ = self.get_dummy_canonical_hf_index_retriever()
UpperCAmelCase_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ = retriever.retrieve(lowerCAmelCase_ , n_docs=lowerCAmelCase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowerCAmelCase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , lowerCAmelCase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def UpperCamelCase ( self : Union[str, Any] ) -> List[Any]:
UpperCAmelCase_ = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
UpperCAmelCase_ = self.get_dummy_dataset()
retriever.save_pretrained(lowerCAmelCase_ )
UpperCAmelCase_ = RagRetriever.from_pretrained(lowerCAmelCase_ )
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_ )
UpperCAmelCase_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase_ = retriever.retrieve(lowerCAmelCase_ , n_docs=1 )
self.assertTrue(out is not None )
def UpperCamelCase ( self : Union[str, Any] ) -> Optional[Any]:
UpperCAmelCase_ = 1
UpperCAmelCase_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_ )
UpperCAmelCase_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ = retriever.retrieve(lowerCAmelCase_ , n_docs=lowerCAmelCase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowerCAmelCase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , lowerCAmelCase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def UpperCamelCase ( self : int ) -> List[Any]:
UpperCAmelCase_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowerCAmelCase_ )
UpperCAmelCase_ = RagRetriever.from_pretrained(lowerCAmelCase_ )
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_ )
UpperCAmelCase_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase_ = retriever.retrieve(lowerCAmelCase_ , n_docs=1 )
self.assertTrue(out is not None )
def UpperCamelCase ( self : List[str] ) -> int:
UpperCAmelCase_ = 1
UpperCAmelCase_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_ )
UpperCAmelCase_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ = retriever.retrieve(lowerCAmelCase_ , n_docs=lowerCAmelCase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowerCAmelCase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , lowerCAmelCase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def UpperCamelCase ( self : List[Any] ) -> Dict:
UpperCAmelCase_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowerCAmelCase_ )
UpperCAmelCase_ = RagRetriever.from_pretrained(lowerCAmelCase_ )
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_ )
UpperCAmelCase_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase_ = retriever.retrieve(lowerCAmelCase_ , n_docs=1 )
self.assertTrue(out is not None )
def UpperCamelCase ( self : List[str] ) -> str:
UpperCAmelCase_ = 1
UpperCAmelCase_ = self.get_dummy_legacy_index_retriever()
UpperCAmelCase_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ = retriever.retrieve(lowerCAmelCase_ , n_docs=lowerCAmelCase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowerCAmelCase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''text'''] ) , lowerCAmelCase_ )
self.assertEqual(doc_dicts[0]['''text'''][0] , '''bar''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''text'''][0] , '''foo''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def UpperCamelCase ( self : Dict ) -> str:
UpperCAmelCase_ = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowerCAmelCase_ )
UpperCAmelCase_ = RagRetriever.from_pretrained(lowerCAmelCase_ )
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_ )
UpperCAmelCase_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase_ = retriever.retrieve(lowerCAmelCase_ , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def UpperCamelCase ( self : List[str] ) -> int:
import torch
UpperCAmelCase_ = 1
UpperCAmelCase_ = self.get_dummy_canonical_hf_index_retriever()
UpperCAmelCase_ = [[5, 7], [10, 11]]
UpperCAmelCase_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase_ = retriever(lowerCAmelCase_ , lowerCAmelCase_ , prefix=retriever.config.generator.prefix , n_docs=lowerCAmelCase_ )
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ = (
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_ )
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_ )
self.assertIsInstance(lowerCAmelCase_ , np.ndarray )
UpperCAmelCase_ = retriever(
lowerCAmelCase_ , lowerCAmelCase_ , prefix=retriever.config.generator.prefix , n_docs=lowerCAmelCase_ , return_tensors='''pt''' , )
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ = ( # noqa: F841
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
out['''doc_ids'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(lowerCAmelCase_ , torch.Tensor )
self.assertIsInstance(lowerCAmelCase_ , torch.Tensor )
self.assertIsInstance(lowerCAmelCase_ , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def UpperCamelCase ( self : Tuple ) -> int:
UpperCAmelCase_ = self.get_dpr_ctx_encoder_tokenizer()
UpperCAmelCase_ = 1
UpperCAmelCase_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_ )
retriever.set_ctx_encoder_tokenizer(lowerCAmelCase_ )
UpperCAmelCase_ = [[5, 7], [10, 11]]
UpperCAmelCase_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase_ = retriever(lowerCAmelCase_ , lowerCAmelCase_ , prefix=retriever.config.generator.prefix , n_docs=lowerCAmelCase_ )
self.assertEqual(
len(lowerCAmelCase_ ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''') ) , lowerCAmelCase_ ) # check for doc token related keys in dictionary.
| 121 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_lowerCamelCase : List[Any] = logging.getLogger(__name__)
class snake_case__ ( __snake_case ):
'''simple docstring'''
def __init__( self : Tuple , lowerCAmelCase_ : Tuple=-1 ) -> List[Any]:
# in NER datasets, the last column is usually reserved for NER label
UpperCAmelCase_ = label_idx
def UpperCamelCase ( self : int , lowerCAmelCase_ : Any , lowerCAmelCase_ : Union[Split, str] ) -> List[InputExample]:
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
UpperCAmelCase_ = mode.value
UpperCAmelCase_ = os.path.join(lowerCAmelCase_ , F'''{mode}.txt''' )
UpperCAmelCase_ = 1
UpperCAmelCase_ = []
with open(lowerCAmelCase_ , encoding='''utf-8''' ) as f:
UpperCAmelCase_ = []
UpperCAmelCase_ = []
for line in f:
if line.startswith('''-DOCSTART-''' ) or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=F'''{mode}-{guid_index}''' , words=lowerCAmelCase_ , labels=lowerCAmelCase_ ) )
guid_index += 1
UpperCAmelCase_ = []
UpperCAmelCase_ = []
else:
UpperCAmelCase_ = line.split(''' ''' )
words.append(splits[0] )
if len(lowerCAmelCase_ ) > 1:
labels.append(splits[self.label_idx].replace('''\n''' , '''''' ) )
else:
# Examples could have no label for mode = "test"
labels.append('''O''' )
if words:
examples.append(InputExample(guid=F'''{mode}-{guid_index}''' , words=lowerCAmelCase_ , labels=lowerCAmelCase_ ) )
return examples
def UpperCamelCase ( self : Tuple , lowerCAmelCase_ : TextIO , lowerCAmelCase_ : TextIO , lowerCAmelCase_ : List ) -> str:
UpperCAmelCase_ = 0
for line in test_input_reader:
if line.startswith('''-DOCSTART-''' ) or line == "" or line == "\n":
writer.write(lowerCAmelCase_ )
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
UpperCAmelCase_ = line.split()[0] + ''' ''' + preds_list[example_id].pop(0 ) + '''\n'''
writer.write(lowerCAmelCase_ )
else:
logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.''' , line.split()[0] )
def UpperCamelCase ( self : Union[str, Any] , lowerCAmelCase_ : str ) -> List[str]:
if path:
with open(lowerCAmelCase_ , '''r''' ) as f:
UpperCAmelCase_ = f.read().splitlines()
if "O" not in labels:
UpperCAmelCase_ = ['''O'''] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class snake_case__ ( __snake_case ):
'''simple docstring'''
def __init__( self : str ) -> Dict:
# in CONLL2003 dataset chunk column is second-to-last
super().__init__(label_idx=-2 )
def UpperCamelCase ( self : Union[str, Any] , lowerCAmelCase_ : str ) -> List[str]:
if path:
with open(lowerCAmelCase_ , '''r''' ) as f:
UpperCAmelCase_ = f.read().splitlines()
if "O" not in labels:
UpperCAmelCase_ = ['''O'''] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class snake_case__ ( __snake_case ):
'''simple docstring'''
def UpperCamelCase ( self : Union[str, Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : Union[Split, str] ) -> List[InputExample]:
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
UpperCAmelCase_ = mode.value
UpperCAmelCase_ = os.path.join(lowerCAmelCase_ , F'''{mode}.txt''' )
UpperCAmelCase_ = 1
UpperCAmelCase_ = []
with open(lowerCAmelCase_ , encoding='''utf-8''' ) as f:
for sentence in parse_incr(lowerCAmelCase_ ):
UpperCAmelCase_ = []
UpperCAmelCase_ = []
for token in sentence:
words.append(token['''form'''] )
labels.append(token['''upos'''] )
assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ )
if words:
examples.append(InputExample(guid=F'''{mode}-{guid_index}''' , words=lowerCAmelCase_ , labels=lowerCAmelCase_ ) )
guid_index += 1
return examples
def UpperCamelCase ( self : int , lowerCAmelCase_ : TextIO , lowerCAmelCase_ : TextIO , lowerCAmelCase_ : List ) -> List[str]:
UpperCAmelCase_ = 0
for sentence in parse_incr(lowerCAmelCase_ ):
UpperCAmelCase_ = preds_list[example_id]
UpperCAmelCase_ = ''''''
for token in sentence:
out += F'''{token['form']} ({token['upos']}|{s_p.pop(0 )}) '''
out += "\n"
writer.write(lowerCAmelCase_ )
example_id += 1
def UpperCamelCase ( self : Dict , lowerCAmelCase_ : str ) -> List[str]:
if path:
with open(lowerCAmelCase_ , '''r''' ) as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
| 121 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'
),
}
class _A ( UpperCAmelCase_ ):
lowercase_ : str = '''gptsan-japanese'''
lowercase_ : int = [
'''past_key_values''',
]
lowercase_ : str = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self : Dict , lowerCamelCase__ : Any=3_60_00 , lowerCamelCase__ : List[Any]=12_80 , lowerCamelCase__ : Tuple=10_24 , lowerCamelCase__ : Tuple=81_92 , lowerCamelCase__ : Optional[Any]=40_96 , lowerCamelCase__ : List[str]=1_28 , lowerCamelCase__ : int=10 , lowerCamelCase__ : int=0 , lowerCamelCase__ : List[str]=16 , lowerCamelCase__ : Any=16 , lowerCamelCase__ : Optional[Any]=1_28 , lowerCamelCase__ : Optional[Any]=0.0 , lowerCamelCase__ : Optional[Any]=1e-5 , lowerCamelCase__ : Optional[int]=False , lowerCamelCase__ : Any=0.0 , lowerCamelCase__ : Tuple="float32" , lowerCamelCase__ : int=False , lowerCamelCase__ : List[Any]=False , lowerCamelCase__ : int=False , lowerCamelCase__ : Optional[Any]=0.002 , lowerCamelCase__ : Optional[Any]=False , lowerCamelCase__ : Dict=True , lowerCamelCase__ : Optional[int]=3_59_98 , lowerCamelCase__ : Optional[Any]=3_59_95 , lowerCamelCase__ : List[Any]=3_59_99 , **lowerCamelCase__ : Tuple , ):
"""simple docstring"""
__UpperCamelCase : str = vocab_size
__UpperCamelCase : Optional[int] = max_position_embeddings
__UpperCamelCase : List[Any] = d_model
__UpperCamelCase : Tuple = d_ff
__UpperCamelCase : Tuple = d_ext
__UpperCamelCase : Tuple = d_spout
__UpperCamelCase : Union[str, Any] = num_switch_layers
__UpperCamelCase : str = num_ext_layers
__UpperCamelCase : Dict = num_switch_layers + num_ext_layers
__UpperCamelCase : Dict = num_heads
__UpperCamelCase : List[Any] = num_experts
__UpperCamelCase : Optional[int] = expert_capacity
__UpperCamelCase : Dict = dropout_rate
__UpperCamelCase : Any = layer_norm_epsilon
__UpperCamelCase : int = router_bias
__UpperCamelCase : List[str] = router_jitter_noise
__UpperCamelCase : Optional[int] = router_dtype
__UpperCamelCase : Any = router_ignore_padding_tokens
__UpperCamelCase : Optional[Any] = output_hidden_states
__UpperCamelCase : int = output_attentions
__UpperCamelCase : Dict = initializer_factor
__UpperCamelCase : List[str] = output_router_logits
__UpperCamelCase : str = use_cache
super().__init__(
separator_token_id=lowerCamelCase__ , pad_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ , **lowerCamelCase__ , )
| 515 |
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 _A ( UpperCAmelCase_ ):
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__ : Any , ):
"""simple docstring"""
super().__init__(
lowerCamelCase__ , split=lowerCamelCase__ , features=lowerCamelCase__ , cache_dir=lowerCamelCase__ , keep_in_memory=lowerCamelCase__ , streaming=lowerCamelCase__ , num_proc=lowerCamelCase__ , **lowerCamelCase__ , )
__UpperCamelCase : Dict = 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 a ( self : Optional[int] ):
"""simple docstring"""
if self.streaming:
__UpperCamelCase : Union[str, Any] = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
__UpperCamelCase : Any = None
__UpperCamelCase : int = None
__UpperCamelCase : int = None
__UpperCamelCase : Optional[Any] = None
self.builder.download_and_prepare(
download_config=lowerCamelCase__ , download_mode=lowerCamelCase__ , verification_mode=lowerCamelCase__ , base_path=lowerCamelCase__ , num_proc=self.num_proc , )
__UpperCamelCase : Tuple = self.builder.as_dataset(
split=self.split , verification_mode=lowerCamelCase__ , in_memory=self.keep_in_memory )
return dataset
| 515 | 1 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCAmelCase ( __UpperCamelCase , unittest.TestCase ):
__A : Tuple = CodeGenTokenizer
__A : Dict = CodeGenTokenizerFast
__A : List[Any] = True
__A : Any = {'add_prefix_space': True}
__A : Optional[Any] = False
def UpperCAmelCase_ ( self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCAmelCase_ = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
'<|endoftext|>',
]
lowerCAmelCase_ = dict(zip(_lowerCamelCase , range(len(_lowerCamelCase ) ) ) )
lowerCAmelCase_ = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
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(_lowerCamelCase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(_lowerCamelCase ) )
def UpperCAmelCase_ ( self , **_lowerCamelCase ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def UpperCAmelCase_ ( self , **_lowerCamelCase ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def UpperCAmelCase_ ( self , _lowerCamelCase ):
lowerCAmelCase_ = 'lower newer'
lowerCAmelCase_ = 'lower newer'
return input_text, output_text
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
lowerCAmelCase_ = 'lower newer'
lowerCAmelCase_ = ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
lowerCAmelCase_ = tokenizer.tokenize(_lowerCamelCase , add_prefix_space=_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
lowerCAmelCase_ = tokens + [tokenizer.unk_token]
lowerCAmelCase_ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , _lowerCamelCase )
def UpperCAmelCase_ ( self ):
if not self.test_rust_tokenizer:
return
lowerCAmelCase_ = self.get_tokenizer()
lowerCAmelCase_ = self.get_rust_tokenizer(add_prefix_space=_lowerCamelCase )
lowerCAmelCase_ = 'lower newer'
# Testing tokenization
lowerCAmelCase_ = tokenizer.tokenize(_lowerCamelCase , add_prefix_space=_lowerCamelCase )
lowerCAmelCase_ = rust_tokenizer.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
# Testing conversion to ids without special tokens
lowerCAmelCase_ = tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase , add_prefix_space=_lowerCamelCase )
lowerCAmelCase_ = rust_tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
# Testing conversion to ids with special tokens
lowerCAmelCase_ = self.get_rust_tokenizer(add_prefix_space=_lowerCamelCase )
lowerCAmelCase_ = tokenizer.encode(_lowerCamelCase , add_prefix_space=_lowerCamelCase )
lowerCAmelCase_ = rust_tokenizer.encode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
# Testing the unknown token
lowerCAmelCase_ = tokens + [rust_tokenizer.unk_token]
lowerCAmelCase_ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , _lowerCamelCase )
def UpperCAmelCase_ ( self , *_lowerCamelCase , **_lowerCamelCase ):
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def UpperCAmelCase_ ( self , _lowerCamelCase=15 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCAmelCase_ = self.rust_tokenizer_class.from_pretrained(_lowerCamelCase , **_lowerCamelCase )
# Simple input
lowerCAmelCase_ = 'This is a simple input'
lowerCAmelCase_ = ['This is a simple input 1', 'This is a simple input 2']
lowerCAmelCase_ = ('This is a simple input', 'This is a pair')
lowerCAmelCase_ = [
('This is a simple input 1', 'This is a simple input 2'),
('This is a simple pair 1', 'This is a simple pair 2'),
]
# Simple input tests
self.assertRaises(_lowerCamelCase , tokenizer_r.encode , _lowerCamelCase , max_length=_lowerCamelCase , padding='''max_length''' )
# Simple input
self.assertRaises(_lowerCamelCase , tokenizer_r.encode_plus , _lowerCamelCase , max_length=_lowerCamelCase , padding='''max_length''' )
# Simple input
self.assertRaises(
_lowerCamelCase , tokenizer_r.batch_encode_plus , _lowerCamelCase , max_length=_lowerCamelCase , padding='''max_length''' , )
# Pair input
self.assertRaises(_lowerCamelCase , tokenizer_r.encode , _lowerCamelCase , max_length=_lowerCamelCase , padding='''max_length''' )
# Pair input
self.assertRaises(_lowerCamelCase , tokenizer_r.encode_plus , _lowerCamelCase , max_length=_lowerCamelCase , padding='''max_length''' )
# Pair input
self.assertRaises(
_lowerCamelCase , tokenizer_r.batch_encode_plus , _lowerCamelCase , max_length=_lowerCamelCase , padding='''max_length''' , )
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token='''<pad>''' )
# Simple input
lowerCAmelCase_ = 'This is a simple input'
lowerCAmelCase_ = ['This is a simple input looooooooong', 'This is a simple input']
lowerCAmelCase_ = ('This is a simple input', 'This is a pair')
lowerCAmelCase_ = [
('This is a simple input loooooong', 'This is a simple input'),
('This is a simple pair loooooong', 'This is a simple pair'),
]
lowerCAmelCase_ = tokenizer.pad_token_id
lowerCAmelCase_ = tokenizer(_lowerCamelCase , padding='''max_length''' , max_length=30 , return_tensors='''np''' )
lowerCAmelCase_ = tokenizer(_lowerCamelCase , padding=_lowerCamelCase , truncate=_lowerCamelCase , return_tensors='''np''' )
lowerCAmelCase_ = tokenizer(*_lowerCamelCase , padding='''max_length''' , max_length=60 , return_tensors='''np''' )
lowerCAmelCase_ = tokenizer(_lowerCamelCase , padding=_lowerCamelCase , truncate=_lowerCamelCase , return_tensors='''np''' )
# s
# test single string max_length padding
self.assertEqual(out_s['''input_ids'''].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['''input_ids'''] )
self.assertTrue(0 in out_s['''attention_mask'''] )
# s2
# test automatic padding
self.assertEqual(out_sa['''input_ids'''].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['''input_ids'''][0] )
self.assertFalse(0 in out_sa['''attention_mask'''][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['''input_ids'''][1] )
self.assertTrue(0 in out_sa['''attention_mask'''][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['''input_ids'''].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['''input_ids'''] )
self.assertTrue(0 in out_p['''attention_mask'''] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['''input_ids'''].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['''input_ids'''][0] )
self.assertFalse(0 in out_pa['''attention_mask'''][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['''input_ids'''][1] )
self.assertTrue(0 in out_pa['''attention_mask'''][1] )
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = '$$$'
lowerCAmelCase_ = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=_lowerCamelCase , add_bos_token=_lowerCamelCase )
lowerCAmelCase_ = 'This is a simple input'
lowerCAmelCase_ = ['This is a simple input 1', 'This is a simple input 2']
lowerCAmelCase_ = tokenizer.bos_token_id
lowerCAmelCase_ = tokenizer(_lowerCamelCase )
lowerCAmelCase_ = tokenizer(_lowerCamelCase )
self.assertEqual(out_s.input_ids[0] , _lowerCamelCase )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
lowerCAmelCase_ = tokenizer.decode(out_s.input_ids )
lowerCAmelCase_ = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , _lowerCamelCase )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = CodeGenTokenizer.from_pretrained('''Salesforce/codegen-350M-mono''' )
lowerCAmelCase_ = '\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#'
lowerCAmelCase_ = '\nif len_a > len_b: result = a\nelse: result = b'
lowerCAmelCase_ = tokenizer.encode(_lowerCamelCase )
lowerCAmelCase_ = ['^#', re.escape('''<|endoftext|>''' ), '^\'\'\'', '^"""', '\n\n\n']
lowerCAmelCase_ = tokenizer.decode(_lowerCamelCase , truncate_before_pattern=_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def UpperCAmelCase_ ( self ):
pass
| 274 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class A_ :
'''simple docstring'''
def __init__( self: Optional[int] , a: Union[str, Any] , a: Union[str, Any]=2 , a: str=3 , a: Any=4 , a: Union[str, Any]=2 , a: Tuple=7 , a: int=True , a: Tuple=True , a: List[str]=True , a: Union[str, Any]=True , a: str=99 , a: Tuple=36 , a: int=2 , a: Dict=4 , a: Union[str, Any]=37 , a: List[str]="gelu" , a: List[Any]=0.1 , a: Optional[int]=0.1 , a: Dict=512 , a: Union[str, Any]=16 , a: str=2 , a: int=0.0_2 , a: Optional[Any]=6 , a: Optional[int]=6 , a: Dict=3 , a: Optional[Any]=4 , a: Optional[Any]=None , a: Dict=1000 , ):
__lowerCamelCase : List[str] = parent
__lowerCamelCase : Optional[Any] = batch_size
__lowerCamelCase : Optional[int] = num_channels
__lowerCamelCase : str = image_size
__lowerCamelCase : int = patch_size
__lowerCamelCase : List[str] = is_training
__lowerCamelCase : Dict = use_input_mask
__lowerCamelCase : Any = use_token_type_ids
__lowerCamelCase : List[str] = use_labels
__lowerCamelCase : str = vocab_size
__lowerCamelCase : List[Any] = hidden_size
__lowerCamelCase : List[Any] = num_hidden_layers
__lowerCamelCase : Any = num_attention_heads
__lowerCamelCase : List[Any] = intermediate_size
__lowerCamelCase : List[Any] = hidden_act
__lowerCamelCase : Any = hidden_dropout_prob
__lowerCamelCase : Optional[int] = attention_probs_dropout_prob
__lowerCamelCase : Dict = max_position_embeddings
__lowerCamelCase : Tuple = type_vocab_size
__lowerCamelCase : int = type_sequence_label_size
__lowerCamelCase : List[str] = initializer_range
__lowerCamelCase : List[str] = coordinate_size
__lowerCamelCase : int = shape_size
__lowerCamelCase : Union[str, Any] = num_labels
__lowerCamelCase : int = num_choices
__lowerCamelCase : int = scope
__lowerCamelCase : Any = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
__lowerCamelCase : Any = text_seq_length
__lowerCamelCase : Optional[Any] = (image_size // patch_size) ** 2 + 1
__lowerCamelCase : Any = self.text_seq_length + self.image_seq_length
def _snake_case ( self: List[str] ):
__lowerCamelCase : Any = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
__lowerCamelCase : Tuple = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
__lowerCamelCase : int = bbox.numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
__lowerCamelCase : List[str] = bbox[i, j, 3]
__lowerCamelCase : str = bbox[i, j, 1]
__lowerCamelCase : Dict = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
__lowerCamelCase : Tuple = bbox[i, j, 2]
__lowerCamelCase : Any = bbox[i, j, 0]
__lowerCamelCase : List[str] = tmp_coordinate
__lowerCamelCase : str = tf.constant(a )
__lowerCamelCase : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCamelCase : Any = None
if self.use_input_mask:
__lowerCamelCase : int = random_attention_mask([self.batch_size, self.text_seq_length] )
__lowerCamelCase : Tuple = None
if self.use_token_type_ids:
__lowerCamelCase : List[Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
__lowerCamelCase : Dict = None
__lowerCamelCase : Union[str, Any] = None
if self.use_labels:
__lowerCamelCase : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase : Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
__lowerCamelCase : Dict = LayoutLMvaConfig(
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 , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def _snake_case ( self: Tuple , a: List[Any] , a: Any , a: List[str] , a: Dict , a: Optional[Any] , a: Dict ):
__lowerCamelCase : Optional[Any] = TFLayoutLMvaModel(config=a )
# text + image
__lowerCamelCase : Optional[Any] = model(a , pixel_values=a , training=a )
__lowerCamelCase : int = model(
a , bbox=a , pixel_values=a , attention_mask=a , token_type_ids=a , training=a , )
__lowerCamelCase : List[Any] = model(a , bbox=a , pixel_values=a , training=a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
__lowerCamelCase : List[Any] = model(a , training=a )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
__lowerCamelCase : Optional[Any] = model({'pixel_values': pixel_values} , training=a )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def _snake_case ( self: Dict , a: Dict , a: Optional[Any] , a: int , a: Optional[int] , a: List[str] , a: List[str] , a: List[str] ):
__lowerCamelCase : List[str] = self.num_labels
__lowerCamelCase : str = TFLayoutLMvaForSequenceClassification(config=a )
__lowerCamelCase : int = model(
a , bbox=a , pixel_values=a , attention_mask=a , token_type_ids=a , labels=a , training=a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self: Optional[int] , a: Union[str, Any] , a: Union[str, Any] , a: Dict , a: Optional[Any] , a: Tuple , a: Optional[Any] , a: List[Any] ):
__lowerCamelCase : Union[str, Any] = self.num_labels
__lowerCamelCase : Any = TFLayoutLMvaForTokenClassification(config=a )
__lowerCamelCase : Optional[Any] = model(
a , bbox=a , pixel_values=a , attention_mask=a , token_type_ids=a , labels=a , training=a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def _snake_case ( self: Dict , a: Optional[Any] , a: str , a: Dict , a: Union[str, Any] , a: List[Any] , a: Optional[int] , a: List[str] ):
__lowerCamelCase : List[Any] = 2
__lowerCamelCase : Any = TFLayoutLMvaForQuestionAnswering(config=a )
__lowerCamelCase : Any = model(
a , bbox=a , pixel_values=a , attention_mask=a , token_type_ids=a , start_positions=a , end_positions=a , training=a , )
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 _snake_case ( self: List[Any] ):
__lowerCamelCase : str = self.prepare_config_and_inputs()
((__lowerCamelCase) , (__lowerCamelCase) , (__lowerCamelCase) , (__lowerCamelCase) , (__lowerCamelCase) , (__lowerCamelCase) , (__lowerCamelCase) , (__lowerCamelCase)) : List[Any] = config_and_inputs
__lowerCamelCase : Tuple = {
'input_ids': input_ids,
'bbox': bbox,
'pixel_values': pixel_values,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_tf
class A_ ( __UpperCamelCase , __UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
__snake_case = (
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
__snake_case = (
{"""document-question-answering""": TFLayoutLMvaForQuestionAnswering, """feature-extraction""": TFLayoutLMvaModel}
if is_tf_available()
else {}
)
__snake_case = False
__snake_case = False
__snake_case = False
def _snake_case ( self: int , a: List[str] , a: Any , a: Optional[Any] , a: Tuple , a: Tuple ):
return True
def _snake_case ( self: str , a: Any , a: Any , a: Optional[int]=False ):
__lowerCamelCase : List[str] = copy.deepcopy(a )
if model_class in get_values(a ):
__lowerCamelCase : Tuple = {
k: tf.tile(tf.expand_dims(a , 1 ) , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(a , tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(a ):
__lowerCamelCase : Any = tf.ones(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(a ):
__lowerCamelCase : Optional[Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
__lowerCamelCase : Optional[Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(a ):
__lowerCamelCase : str = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(a ):
__lowerCamelCase : Dict = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=tf.intaa )
return inputs_dict
def _snake_case ( self: Tuple ):
__lowerCamelCase : int = TFLayoutLMvaModelTester(self )
__lowerCamelCase : str = ConfigTester(self , config_class=a , hidden_size=37 )
def _snake_case ( self: Union[str, Any] ):
self.config_tester.run_common_tests()
def _snake_case ( self: Union[str, Any] ):
__lowerCamelCase , __lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase : int = model_class(a )
if getattr(a , 'hf_compute_loss' , a ):
# The number of elements in the loss should be the same as the number of elements in the label
__lowerCamelCase : Union[str, Any] = self._prepare_for_class(inputs_dict.copy() , a , return_labels=a )
__lowerCamelCase : int = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=a )[0]
]
__lowerCamelCase : Dict = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
__lowerCamelCase : Optional[int] = self._prepare_for_class(inputs_dict.copy() , a , return_labels=a )
__lowerCamelCase : Dict = prepared_for_class.pop('input_ids' )
__lowerCamelCase : str = model(a , **a )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
__lowerCamelCase : List[Any] = self._prepare_for_class(inputs_dict.copy() , a , return_labels=a )
__lowerCamelCase : List[str] = prepared_for_class.pop('input_ids' )
if "labels" in prepared_for_class:
__lowerCamelCase : int = prepared_for_class['labels'].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
__lowerCamelCase : Tuple = -100
__lowerCamelCase : Tuple = tf.convert_to_tensor(a )
__lowerCamelCase : Tuple = model(a , **a )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
__lowerCamelCase : int = self._prepare_for_class(inputs_dict.copy() , a , return_labels=a )
__lowerCamelCase : str = model(a )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
__lowerCamelCase : str = self._prepare_for_class(inputs_dict.copy() , a , return_labels=a )
# Get keys that were added with the _prepare_for_class function
__lowerCamelCase : Optional[Any] = prepared_for_class.keys() - inputs_dict.keys()
__lowerCamelCase : List[Any] = inspect.signature(model.call ).parameters
__lowerCamelCase : List[str] = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
__lowerCamelCase : Optional[int] = {0: 'input_ids'}
for label_key in label_keys:
__lowerCamelCase : Dict = signature_names.index(a )
__lowerCamelCase : str = label_key
__lowerCamelCase : List[str] = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
__lowerCamelCase : Optional[int] = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
__lowerCamelCase : Optional[int] = prepared_for_class[value]
__lowerCamelCase : Any = tuple(a )
# Send to model
__lowerCamelCase : int = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def _snake_case ( self: List[str] ):
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
) : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(a , a , a , a , a , a )
def _snake_case ( self: int ):
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
) : str = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCamelCase : Union[str, Any] = type
self.model_tester.create_and_check_model(a , a , a , a , a , a )
def _snake_case ( self: Dict ):
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
) : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
a , a , a , a , a , a , a )
def _snake_case ( self: str ):
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
) : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
a , a , a , a , a , a , a )
def _snake_case ( self: str ):
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
) : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
a , a , a , a , a , a , a )
@slow
def _snake_case ( self: int ):
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase : Dict = TFLayoutLMvaModel.from_pretrained(a )
self.assertIsNotNone(a )
def UpperCamelCase__ ( ):
__lowerCamelCase : List[str] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
class A_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _snake_case ( self: Optional[int] ):
return LayoutLMvaImageProcessor(apply_ocr=a ) if is_vision_available() else None
@slow
def _snake_case ( self: Optional[Any] ):
__lowerCamelCase : Tuple = TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base' )
__lowerCamelCase : Union[str, Any] = self.default_image_processor
__lowerCamelCase : List[Any] = prepare_img()
__lowerCamelCase : str = image_processor(images=a , return_tensors='tf' ).pixel_values
__lowerCamelCase : Union[str, Any] = tf.constant([[1, 2]] )
__lowerCamelCase : str = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) , axis=0 )
# forward pass
__lowerCamelCase : int = model(input_ids=a , bbox=a , pixel_values=a , training=a )
# verify the logits
__lowerCamelCase : Optional[int] = (1, 199, 768)
self.assertEqual(outputs.last_hidden_state.shape , a )
__lowerCamelCase : Any = tf.constant(
[[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , a , atol=1e-4 ) )
| 669 | 0 |
"""simple docstring"""
def __magic_name__ ( _lowerCamelCase : float , _lowerCamelCase : list[float] ):
if discount_rate < 0:
raise ValueError("""Discount rate cannot be negative""" )
if not cash_flows:
raise ValueError("""Cash flows list cannot be empty""" )
__a : Tuple = sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_lowerCamelCase ) )
return round(_lowerCamelCase , ndigits=2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 63 |
"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class SCREAMING_SNAKE_CASE__ ( __snake_case ):
def __init__(self , _lowercase , _lowercase=None , _lowercase=None , _lowercase=0 ):
'''simple docstring'''
__a : Any = 1.0 if scale is None else scale
__a : str = 0.0 if loc is None else loc
super().__init__(_lowercase , [AffineTransform(loc=self.loc , scale=self.scale , event_dim=_lowercase )] )
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return self.base_dist.mean * self.scale + self.loc
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return self.base_dist.variance * self.scale**2
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return self.variance.sqrt()
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__(self , _lowercase , _lowercase , _lowercase , **_lowercase ):
'''simple docstring'''
super().__init__(**_lowercase )
__a : str = args_dim
__a : List[Any] = nn.ModuleList([nn.Linear(_lowercase , _lowercase ) for dim in args_dim.values()] )
__a : Dict = domain_map
def lowerCAmelCase__(self , _lowercase ):
'''simple docstring'''
__a : List[Any] = [proj(_lowercase ) for proj in self.proj]
return self.domain_map(*_lowercase )
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__(self , _lowercase ):
'''simple docstring'''
super().__init__()
__a : Optional[int] = function
def lowerCAmelCase__(self , _lowercase , *_lowercase ):
'''simple docstring'''
return self.function(_lowercase , *_lowercase )
class SCREAMING_SNAKE_CASE__ :
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
def __init__(self , _lowercase = 1 ):
'''simple docstring'''
__a : Optional[int] = dim
__a : str = {k: dim * self.args_dim[k] for k in self.args_dim}
def lowerCAmelCase__(self , _lowercase ):
'''simple docstring'''
if self.dim == 1:
return self.distribution_class(*_lowercase )
else:
return Independent(self.distribution_class(*_lowercase ) , 1 )
def lowerCAmelCase__(self , _lowercase , _lowercase = None , _lowercase = None , ):
'''simple docstring'''
__a : Tuple = self._base_distribution(_lowercase )
if loc is None and scale is None:
return distr
else:
return AffineTransformed(_lowercase , loc=_lowercase , scale=_lowercase , event_dim=self.event_dim )
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return () if self.dim == 1 else (self.dim,)
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return len(self.event_shape )
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return 0.0
def lowerCAmelCase__(self , _lowercase ):
'''simple docstring'''
return ParameterProjection(
in_features=_lowercase , args_dim=self.args_dim , domain_map=LambdaLayer(self.domain_map ) , )
def lowerCAmelCase__(self , *_lowercase ):
'''simple docstring'''
raise NotImplementedError()
@staticmethod
def lowerCAmelCase__(_lowercase ):
'''simple docstring'''
return (x + torch.sqrt(torch.square(_lowercase ) + 4.0 )) / 2.0
class SCREAMING_SNAKE_CASE__ ( __snake_case ):
_lowerCAmelCase = {"df": 1, "loc": 1, "scale": 1}
_lowerCAmelCase = StudentT
@classmethod
def lowerCAmelCase__(cls , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
__a : int = cls.squareplus(_lowercase ).clamp_min(torch.finfo(scale.dtype ).eps )
__a : Optional[Any] = 2.0 + cls.squareplus(_lowercase )
return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )
class SCREAMING_SNAKE_CASE__ ( __snake_case ):
_lowerCAmelCase = {"loc": 1, "scale": 1}
_lowerCAmelCase = Normal
@classmethod
def lowerCAmelCase__(cls , _lowercase , _lowercase ):
'''simple docstring'''
__a : str = cls.squareplus(_lowercase ).clamp_min(torch.finfo(scale.dtype ).eps )
return loc.squeeze(-1 ), scale.squeeze(-1 )
class SCREAMING_SNAKE_CASE__ ( __snake_case ):
_lowerCAmelCase = {"total_count": 1, "logits": 1}
_lowerCAmelCase = NegativeBinomial
@classmethod
def lowerCAmelCase__(cls , _lowercase , _lowercase ):
'''simple docstring'''
__a : Union[str, Any] = cls.squareplus(_lowercase )
return total_count.squeeze(-1 ), logits.squeeze(-1 )
def lowerCAmelCase__(self , _lowercase ):
'''simple docstring'''
__a , __a : Optional[Any] = distr_args
if self.dim == 1:
return self.distribution_class(total_count=_lowercase , logits=_lowercase )
else:
return Independent(self.distribution_class(total_count=_lowercase , logits=_lowercase ) , 1 )
def lowerCAmelCase__(self , _lowercase , _lowercase = None , _lowercase = None ):
'''simple docstring'''
__a , __a : List[Any] = distr_args
if scale is not None:
# See scaling property of Gamma.
logits += scale.log()
return self._base_distribution((total_count, logits) )
| 63 | 1 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
A__ : Dict = """src/transformers"""
# Matches is_xxx_available()
A__ : Union[str, Any] = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
A__ : Tuple = re.compile(R"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
A__ : str = re.compile(R"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
A__ : Union[str, Any] = re.compile(R"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
A__ : int = re.compile(R"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
A__ : List[Any] = re.compile(R"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma: "MyModel",
A__ : Tuple = re.compile("""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only: ["foo", "bar"],
A__ : List[Any] = re.compile("""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
A__ : Any = re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
A__ : str = re.compile(R"""^\s*try:""")
# Catches a line with else:
A__ : List[str] = re.compile(R"""^\s*else:""")
def UpperCAmelCase__ ( UpperCAmelCase_ : List[Any] ) -> List[str]:
if _re_test_backend.search(UpperCAmelCase_ ) is None:
return None
__lowerCamelCase : str = [b[0] for b in _re_backend.findall(UpperCAmelCase_ )]
backends.sort()
return "_and_".join(UpperCAmelCase_ )
def UpperCAmelCase__ ( UpperCAmelCase_ : Tuple ) -> Union[str, Any]:
with open(UpperCAmelCase_ , 'r' , encoding='utf-8' , newline='\n' ) as f:
__lowerCamelCase : Union[str, Any] = f.readlines()
__lowerCamelCase : Any = 0
while line_index < len(UpperCAmelCase_ ) and not lines[line_index].startswith('_import_structure = {' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(UpperCAmelCase_ ):
return None
# First grab the objects without a specific backend in _import_structure
__lowerCamelCase : Dict = []
while not lines[line_index].startswith('if TYPE_CHECKING' ) and find_backend(lines[line_index] ) is None:
__lowerCamelCase : Optional[Any] = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(UpperCAmelCase_ ):
__lowerCamelCase : Optional[int] = _re_one_line_import_struct.search(UpperCAmelCase_ ).groups()[0]
__lowerCamelCase : Union[str, Any] = re.findall('\[([^\]]+)\]' , UpperCAmelCase_ )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ' )] )
line_index += 1
continue
__lowerCamelCase : Tuple = _re_import_struct_key_value.search(UpperCAmelCase_ )
if single_line_import_search is not None:
__lowerCamelCase : Dict = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(', ' ) if len(UpperCAmelCase_ ) > 0]
objects.extend(UpperCAmelCase_ )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
line_index += 1
__lowerCamelCase : Tuple = {'none': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('if TYPE_CHECKING' ):
# If the line is an if not is_backend_available, we grab all objects associated.
__lowerCamelCase : Dict = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
__lowerCamelCase : int = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
__lowerCamelCase : Tuple = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 4 ):
__lowerCamelCase : int = lines[line_index]
if _re_import_struct_add_one.search(UpperCAmelCase_ ) is not None:
objects.append(_re_import_struct_add_one.search(UpperCAmelCase_ ).groups()[0] )
elif _re_import_struct_add_many.search(UpperCAmelCase_ ) is not None:
__lowerCamelCase : int = _re_import_struct_add_many.search(UpperCAmelCase_ ).groups()[0].split(', ' )
__lowerCamelCase : Union[str, Any] = [obj[1:-1] for obj in imports if len(UpperCAmelCase_ ) > 0]
objects.extend(UpperCAmelCase_ )
elif _re_between_brackets.search(UpperCAmelCase_ ) is not None:
__lowerCamelCase : Dict = _re_between_brackets.search(UpperCAmelCase_ ).groups()[0].split(', ' )
__lowerCamelCase : Union[str, Any] = [obj[1:-1] for obj in imports if len(UpperCAmelCase_ ) > 0]
objects.extend(UpperCAmelCase_ )
elif _re_quote_object.search(UpperCAmelCase_ ) is not None:
objects.append(_re_quote_object.search(UpperCAmelCase_ ).groups()[0] )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
elif line.startswith(' ' * 12 + '"' ):
objects.append(line[13:-3] )
line_index += 1
__lowerCamelCase : Optional[Any] = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
__lowerCamelCase : Union[str, Any] = []
while (
line_index < len(UpperCAmelCase_ )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('else' )
):
__lowerCamelCase : List[str] = lines[line_index]
__lowerCamelCase : Dict = _re_import.search(UpperCAmelCase_ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 8 ):
objects.append(line[8:-2] )
line_index += 1
__lowerCamelCase : str = {'none': objects}
# Let's continue with backend-specific objects
while line_index < len(UpperCAmelCase_ ):
# If the line is an if is_backend_available, we grab all objects associated.
__lowerCamelCase : Tuple = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
__lowerCamelCase : Optional[Any] = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
__lowerCamelCase : Dict = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
__lowerCamelCase : Optional[Any] = lines[line_index]
__lowerCamelCase : Optional[Any] = _re_import.search(UpperCAmelCase_ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 12 ):
objects.append(line[12:-2] )
line_index += 1
__lowerCamelCase : Any = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def UpperCAmelCase__ ( UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : List[Any] ) -> List[Any]:
def find_duplicates(UpperCAmelCase_ : List[str] ):
return [k for k, v in collections.Counter(UpperCAmelCase_ ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
__lowerCamelCase : Dict = []
for key in import_dict_objects.keys():
__lowerCamelCase : Any = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'Duplicate _import_structure definitions for: {duplicate_imports}' )
__lowerCamelCase : Optional[Any] = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F'Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
__lowerCamelCase : Tuple = 'base imports' if key == 'none' else F'{key} backend'
errors.append(F'Differences for {name}:' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F' {a} in TYPE_HINT but not in _import_structure.' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F' {a} in _import_structure but not in TYPE_HINT.' )
return errors
def UpperCAmelCase__ ( ) -> str:
__lowerCamelCase : str = []
for root, _, files in os.walk(UpperCAmelCase_ ):
if "__init__.py" in files:
__lowerCamelCase : Dict = os.path.join(UpperCAmelCase_ , '__init__.py' )
__lowerCamelCase : int = parse_init(UpperCAmelCase_ )
if objects is not None:
__lowerCamelCase : Optional[Any] = analyze_results(*UpperCAmelCase_ )
if len(UpperCAmelCase_ ) > 0:
__lowerCamelCase : Optional[int] = F'Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'
failures.append('\n'.join(UpperCAmelCase_ ) )
if len(UpperCAmelCase_ ) > 0:
raise ValueError('\n\n'.join(UpperCAmelCase_ ) )
def UpperCAmelCase__ ( ) -> Union[str, Any]:
__lowerCamelCase : List[Any] = []
for path, directories, files in os.walk(UpperCAmelCase_ ):
for folder in directories:
# Ignore private modules
if folder.startswith('_' ):
directories.remove(UpperCAmelCase_ )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(UpperCAmelCase_ ) / folder).glob('*.py' ) ) ) == 0:
continue
__lowerCamelCase : List[str] = str((Path(UpperCAmelCase_ ) / folder).relative_to(UpperCAmelCase_ ) )
__lowerCamelCase : int = short_path.replace(os.path.sep , '.' )
submodules.append(UpperCAmelCase_ )
for fname in files:
if fname == "__init__.py":
continue
__lowerCamelCase : int = str((Path(UpperCAmelCase_ ) / fname).relative_to(UpperCAmelCase_ ) )
__lowerCamelCase : int = short_path.replace('.py' , '' ).replace(os.path.sep , '.' )
if len(submodule.split('.' ) ) == 1:
submodules.append(UpperCAmelCase_ )
return submodules
A__ : Optional[Any] = [
"""convert_pytorch_checkpoint_to_tf2""",
"""modeling_flax_pytorch_utils""",
]
def UpperCAmelCase__ ( ) -> List[Any]:
# This is to make sure the transformers module imported is the one in the repo.
__lowerCamelCase : List[str] = importlib.util.spec_from_file_location(
'transformers' , os.path.join(UpperCAmelCase_ , '__init__.py' ) , submodule_search_locations=[PATH_TO_TRANSFORMERS] , )
__lowerCamelCase : Dict = spec.loader.load_module()
__lowerCamelCase : str = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(UpperCAmelCase_ ) > 0:
__lowerCamelCase : List[Any] = '\n'.join(F'- {module}' for module in module_not_registered )
raise ValueError(
'The following submodules are not properly registered in the main init of Transformers:\n'
F'{list_of_modules}\n'
'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 13 |
def lowerCAmelCase_ ( A_):
if not all(char in "01" for char in bin_string):
raise ValueError("Non-binary value was passed to the function")
if not bin_string:
raise ValueError("Empty string was passed to the function")
UpperCamelCase__: List[Any] = ""
while len(A_) % 3 != 0:
UpperCamelCase__: int = "0" + bin_string
UpperCamelCase__: Optional[int] = [
bin_string[index : index + 3]
for index in range(len(A_))
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
UpperCamelCase__: Union[str, Any] = 0
for index, val in enumerate(A_):
oct_val += int(2 ** (2 - index) * int(A_))
oct_string += str(A_)
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 380 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_A : Tuple = logging.get_logger(__name__)
_A : Optional[Any] = {
'''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class __lowercase ( UpperCAmelCase__ , UpperCAmelCase__ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : List[Any] = 'nat'
_SCREAMING_SNAKE_CASE : Optional[Any] = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Optional[int]=4 , SCREAMING_SNAKE_CASE__ : int=3 , SCREAMING_SNAKE_CASE__ : Any=64 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=[3, 4, 6, 5] , SCREAMING_SNAKE_CASE__ : Optional[Any]=[2, 4, 8, 16] , SCREAMING_SNAKE_CASE__ : Any=7 , SCREAMING_SNAKE_CASE__ : int=3.0 , SCREAMING_SNAKE_CASE__ : Optional[int]=True , SCREAMING_SNAKE_CASE__ : Optional[int]=0.0 , SCREAMING_SNAKE_CASE__ : int=0.0 , SCREAMING_SNAKE_CASE__ : List[Any]=0.1 , SCREAMING_SNAKE_CASE__ : Dict="gelu" , SCREAMING_SNAKE_CASE__ : Dict=0.0_2 , SCREAMING_SNAKE_CASE__ : Optional[Any]=1e-5 , SCREAMING_SNAKE_CASE__ : str=0.0 , SCREAMING_SNAKE_CASE__ : Dict=None , SCREAMING_SNAKE_CASE__ : Dict=None , **SCREAMING_SNAKE_CASE__ : str , ) -> Any:
super().__init__(**SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = patch_size
__lowerCAmelCase = num_channels
__lowerCAmelCase = embed_dim
__lowerCAmelCase = depths
__lowerCAmelCase = len(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = num_heads
__lowerCAmelCase = kernel_size
__lowerCAmelCase = mlp_ratio
__lowerCAmelCase = qkv_bias
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = drop_path_rate
__lowerCAmelCase = hidden_act
__lowerCAmelCase = layer_norm_eps
__lowerCAmelCase = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__lowerCAmelCase = int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__ ) - 1) )
__lowerCAmelCase = layer_scale_init_value
__lowerCAmelCase = ["""stem"""] + [f"""stage{idx}""" for idx in range(1 , len(SCREAMING_SNAKE_CASE__ ) + 1 )]
__lowerCAmelCase = get_aligned_output_features_output_indices(
out_features=SCREAMING_SNAKE_CASE__ , out_indices=SCREAMING_SNAKE_CASE__ , stage_names=self.stage_names )
| 714 | '''simple docstring'''
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def a ( self : List[str] ) -> Optional[int]:
__lowerCAmelCase = logging.get_logger()
# the current default level is logging.WARNING
__lowerCAmelCase = logging.get_verbosity()
logging.set_verbosity_error()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_warning()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_info()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_debug()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
# restore to the original level
logging.set_verbosity(SCREAMING_SNAKE_CASE__ )
def a ( self : int ) -> str:
__lowerCAmelCase = logging.get_verbosity()
__lowerCAmelCase = logging.get_logger("""transformers.models.bart.tokenization_bart""" )
__lowerCAmelCase = """Testing 1, 2, 3"""
# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
if level_origin <= logging.WARNING:
with CaptureLogger(SCREAMING_SNAKE_CASE__ ) as cl:
logger.warning(SCREAMING_SNAKE_CASE__ )
self.assertEqual(cl.out , msg + """\n""" )
# this is setting the level for all of `transformers.*` loggers
logging.set_verbosity_error()
# should not be able to log warnings
with CaptureLogger(SCREAMING_SNAKE_CASE__ ) as cl:
logger.warning(SCREAMING_SNAKE_CASE__ )
self.assertEqual(cl.out , """""" )
# should be able to log warnings again
logging.set_verbosity_warning()
with CaptureLogger(SCREAMING_SNAKE_CASE__ ) as cl:
logger.warning(SCREAMING_SNAKE_CASE__ )
self.assertEqual(cl.out , msg + """\n""" )
# restore to the original level
logging.set_verbosity(SCREAMING_SNAKE_CASE__ )
@mockenv(TRANSFORMERS_VERBOSITY="""error""" )
def a ( self : Optional[Any] ) -> List[Any]:
# reset for the env var to take effect, next time some logger call is made
transformers.utils.logging._reset_library_root_logger()
# this action activates the env var
__lowerCAmelCase = logging.get_logger("""transformers.models.bart.tokenization_bart""" )
__lowerCAmelCase = os.getenv("""TRANSFORMERS_VERBOSITY""" , SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = logging.log_levels[env_level_str]
__lowerCAmelCase = logging.get_verbosity()
self.assertEqual(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , f"""TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}""" , )
# restore to the original level
__lowerCAmelCase = """"""
transformers.utils.logging._reset_library_root_logger()
@mockenv(TRANSFORMERS_VERBOSITY="""super-error""" )
def a ( self : int ) -> List[Any]:
# reset for the env var to take effect, next time some logger call is made
transformers.utils.logging._reset_library_root_logger()
__lowerCAmelCase = logging.logging.getLogger()
with CaptureLogger(SCREAMING_SNAKE_CASE__ ) as cl:
# this action activates the env var
logging.get_logger("""transformers.models.bart.tokenization_bart""" )
self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error""" , cl.out )
# no need to restore as nothing was changed
def a ( self : str ) -> Optional[Any]:
# testing `logger.warning_advice()`
transformers.utils.logging._reset_library_root_logger()
__lowerCAmelCase = logging.get_logger("""transformers.models.bart.tokenization_bart""" )
__lowerCAmelCase = """Testing 1, 2, 3"""
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1""" ):
# nothing should be logged as env var disables this method
with CaptureLogger(SCREAMING_SNAKE_CASE__ ) as cl:
logger.warning_advice(SCREAMING_SNAKE_CASE__ )
self.assertEqual(cl.out , """""" )
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""""" ):
# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
with CaptureLogger(SCREAMING_SNAKE_CASE__ ) as cl:
logger.warning_advice(SCREAMING_SNAKE_CASE__ )
self.assertEqual(cl.out , msg + """\n""" )
def UpperCamelCase_ ( ) -> List[str]:
'''simple docstring'''
disable_progress_bar()
assert are_progress_bars_disabled()
enable_progress_bar()
assert not are_progress_bars_disabled()
| 330 | 0 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
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 (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
)
from transformers.models.efficientformer.modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class _snake_case :
def __init__( self : List[str], __lowercase : Optional[int], __lowercase : int = 13, __lowercase : int = 64, __lowercase : int = 2, __lowercase : int = 3, __lowercase : int = 3, __lowercase : bool = True, __lowercase : bool = True, __lowercase : int = 128, __lowercase : str=[16, 32, 64, 128], __lowercase : int = 7, __lowercase : int = 4, __lowercase : int = 37, __lowercase : str = "gelu", __lowercase : float = 0.1, __lowercase : float = 0.1, __lowercase : int = 10, __lowercase : float = 0.02, __lowercase : int = 2, __lowercase : int = 1, __lowercase : int = 128, __lowercase : List[int] = [2, 2, 2, 2], __lowercase : int = 2, __lowercase : int = 2, ):
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = image_size
lowercase__ = patch_size
lowercase__ = num_channels
lowercase__ = is_training
lowercase__ = use_labels
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__ = type_sequence_label_size
lowercase__ = initializer_range
lowercase__ = encoder_stride
lowercase__ = num_attention_outputs
lowercase__ = embed_dim
lowercase__ = embed_dim + 1
lowercase__ = resolution
lowercase__ = depths
lowercase__ = hidden_sizes
lowercase__ = dim
lowercase__ = mlp_expansion_ratio
def A__ ( self : List[str] ):
lowercase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ = None
if self.use_labels:
lowercase__ = ids_tensor([self.batch_size], self.type_sequence_label_size )
lowercase__ = self.get_config()
return config, pixel_values, labels
def A__ ( self : List[Any] ):
return EfficientFormerConfig(
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=__lowercase, initializer_range=self.initializer_range, encoder_stride=self.encoder_stride, resolution=self.resolution, depths=self.depths, hidden_sizes=self.hidden_sizes, dim=self.dim, mlp_expansion_ratio=self.mlp_expansion_ratio, )
def A__ ( self : Union[str, Any], __lowercase : Optional[Any], __lowercase : List[str], __lowercase : Optional[Any] ):
lowercase__ = TFEfficientFormerModel(config=__lowercase )
lowercase__ = model(__lowercase, training=__lowercase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def A__ ( self : Union[str, Any], __lowercase : List[str], __lowercase : str, __lowercase : List[str] ):
lowercase__ = self.type_sequence_label_size
lowercase__ = TFEfficientFormerForImageClassification(__lowercase )
lowercase__ = model(__lowercase, labels=__lowercase, training=__lowercase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowercase__ = 1
lowercase__ = TFEfficientFormerForImageClassification(__lowercase )
lowercase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase__ = model(__lowercase, labels=__lowercase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
def A__ ( self : Dict ):
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class _snake_case ( lowercase__ , lowercase__ , unittest.TestCase):
UpperCamelCase__ : Optional[Any] =(
(
TFEfficientFormerModel,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerForImageClassification,
)
if is_tf_available()
else ()
)
UpperCamelCase__ : Dict =(
{
"""feature-extraction""": TFEfficientFormerModel,
"""image-classification""": (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
),
}
if is_tf_available()
else {}
)
UpperCamelCase__ : List[str] =False
UpperCamelCase__ : int =False
UpperCamelCase__ : List[str] =False
UpperCamelCase__ : Tuple =False
UpperCamelCase__ : List[str] =False
def A__ ( self : int ):
lowercase__ = TFEfficientFormerModelTester(self )
lowercase__ = ConfigTester(
self, config_class=__lowercase, has_text_modality=__lowercase, hidden_size=37 )
def A__ ( self : Tuple ):
self.config_tester.run_common_tests()
@unittest.skip(reason="EfficientFormer does not use inputs_embeds" )
def A__ ( self : Optional[Any] ):
pass
@unittest.skip(reason="EfficientFormer does not support input and output embeddings" )
def A__ ( self : Optional[int] ):
pass
def A__ ( self : Optional[Any] ):
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(__lowercase )
lowercase__ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ = [*signature.parameters.keys()]
lowercase__ = ["pixel_values"]
self.assertListEqual(arg_names[:1], __lowercase )
def A__ ( self : int ):
def check_hidden_states_output(__lowercase : Union[str, Any], __lowercase : Optional[int], __lowercase : Optional[Any] ):
lowercase__ = model_class(__lowercase )
lowercase__ = model(**self._prepare_for_class(__lowercase, __lowercase ), training=__lowercase )
lowercase__ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase__ = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(__lowercase ), __lowercase )
if hasattr(self.model_tester, "encoder_seq_length" ):
lowercase__ = self.model_tester.encoder_seq_length
if hasattr(self.model_tester, "chunk_length" ) and self.model_tester.chunk_length > 1:
lowercase__ = seq_length * self.model_tester.chunk_length
else:
lowercase__ = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ), [seq_length, self.model_tester.hidden_size], )
if config.is_encoder_decoder:
lowercase__ = outputs.decoder_hidden_states
self.asseretIsInstance(__lowercase, (list, tuple) )
self.assertEqual(len(__lowercase ), __lowercase )
lowercase__ = getattr(self.model_tester, "seq_length", __lowercase )
lowercase__ = getattr(self.model_tester, "decoder_seq_length", __lowercase )
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ), [decoder_seq_length, self.model_tester.hidden_size], )
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = True
check_hidden_states_output(__lowercase, __lowercase, __lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ = True
check_hidden_states_output(__lowercase, __lowercase, __lowercase )
def A__ ( self : Optional[Any], __lowercase : Any, __lowercase : List[str], __lowercase : Dict=False ):
lowercase__ = super()._prepare_for_class(__lowercase, __lowercase, return_labels=__lowercase )
if return_labels:
if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def A__ ( self : int ):
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase )
@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet" )
def A__ ( self : Union[str, Any] ):
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__lowercase )
def A__ ( self : Any ):
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowercase )
@slow
def A__ ( self : int ):
for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = TFEfficientFormerModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
def A__ ( self : Optional[int] ):
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ = True
lowercase__ = getattr(self.model_tester, "seq_length", __lowercase )
lowercase__ = getattr(self.model_tester, "encoder_seq_length", __lowercase )
lowercase__ = getattr(self.model_tester, "key_length", __lowercase )
lowercase__ = getattr(self.model_tester, "chunk_length", __lowercase )
if chunk_length is not None and hasattr(self.model_tester, "num_hashes" ):
lowercase__ = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
lowercase__ = True
lowercase__ = False
lowercase__ = True
lowercase__ = model_class(__lowercase )
lowercase__ = model(**self._prepare_for_class(__lowercase, __lowercase ), training=__lowercase )
lowercase__ = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(__lowercase ), self.model_tester.num_attention_outputs )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ = True
lowercase__ = model_class(__lowercase )
lowercase__ = model(**self._prepare_for_class(__lowercase, __lowercase ), training=__lowercase )
lowercase__ = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(__lowercase ), self.model_tester.num_attention_outputs )
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:] ), [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length], )
else:
self.assertListEqual(
list(attentions[0].shape[-3:] ), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], )
def A__ ( self : Optional[int] ):
# We use a simplified version of this test for EfficientFormer because it requires training=False
# and Keras refuses to let us force that during functional construction
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Prepare our model
lowercase__ = model_class(__lowercase )
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
lowercase__ = {
key: tf.keras.Input(shape=val.shape[1:], dtype=val.dtype, name=__lowercase )
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
lowercase__ = model(__lowercase )
self.assertTrue(outputs_dict is not None )
def __lowerCAmelCase ( ):
lowercase__ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class _snake_case ( unittest.TestCase):
@cached_property
def A__ ( self : Dict ):
return (
EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300" )
if is_vision_available()
else None
)
@slow
def A__ ( self : List[str] ):
lowercase__ = TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300" )
lowercase__ = self.default_image_processor
lowercase__ = prepare_img()
lowercase__ = image_processor(images=__lowercase, return_tensors="tf" )
# forward pass
lowercase__ = model(**__lowercase, training=__lowercase )
# verify the logits
lowercase__ = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape, __lowercase )
lowercase__ = tf.constant([-0.0555, 0.4825, -0.0852] )
self.assertTrue(np.allclose(outputs.logits[0, :3], __lowercase, atol=1e-4 ) )
@slow
def A__ ( self : Optional[int] ):
lowercase__ = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
"snap-research/efficientformer-l1-300" )
lowercase__ = self.default_image_processor
lowercase__ = prepare_img()
lowercase__ = image_processor(images=__lowercase, return_tensors="tf" )
# forward pass
lowercase__ = model(**__lowercase, training=__lowercase )
# verify the logits
lowercase__ = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape, __lowercase )
lowercase__ = tf.constant([-0.1312, 0.4353, -1.0499] )
self.assertTrue(np.allclose(outputs.logits[0, :3], __lowercase, atol=1e-4 ) )
| 413 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, 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
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
lowercase_ = logging.get_logger(__name__)
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
def constraint_to_multiple_of(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=None ):
lowercase__ = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
lowercase__ = math.floor(val / multiple ) * multiple
if x < min_val:
lowercase__ = math.ceil(val / multiple ) * multiple
return x
lowercase__ = (output_size, output_size) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else output_size
lowercase__ , lowercase__ = get_image_size(SCREAMING_SNAKE_CASE_ )
lowercase__ , lowercase__ = output_size
# determine new height and width
lowercase__ = output_height / input_height
lowercase__ = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
lowercase__ = scale_width
else:
# fit height
lowercase__ = scale_height
lowercase__ = constraint_to_multiple_of(scale_height * input_height , multiple=SCREAMING_SNAKE_CASE_ )
lowercase__ = constraint_to_multiple_of(scale_width * input_width , multiple=SCREAMING_SNAKE_CASE_ )
return (new_height, new_width)
class _snake_case ( lowercase__):
UpperCamelCase__ : Tuple =["""pixel_values"""]
def __init__( self : Any, __lowercase : bool = True, __lowercase : Dict[str, int] = None, __lowercase : PILImageResampling = PILImageResampling.BILINEAR, __lowercase : bool = False, __lowercase : int = 1, __lowercase : bool = True, __lowercase : Union[int, float] = 1 / 255, __lowercase : bool = True, __lowercase : Optional[Union[float, List[float]]] = None, __lowercase : Optional[Union[float, List[float]]] = None, **__lowercase : List[Any], ):
super().__init__(**__lowercase )
lowercase__ = size if size is not None else {"height": 384, "width": 384}
lowercase__ = get_size_dict(__lowercase )
lowercase__ = do_resize
lowercase__ = size
lowercase__ = keep_aspect_ratio
lowercase__ = ensure_multiple_of
lowercase__ = resample
lowercase__ = do_rescale
lowercase__ = rescale_factor
lowercase__ = do_normalize
lowercase__ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase__ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def A__ ( self : List[Any], __lowercase : np.ndarray, __lowercase : Dict[str, int], __lowercase : bool = False, __lowercase : int = 1, __lowercase : PILImageResampling = PILImageResampling.BICUBIC, __lowercase : Optional[Union[str, ChannelDimension]] = None, **__lowercase : Union[str, Any], ):
lowercase__ = get_size_dict(__lowercase )
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()}''' )
lowercase__ = get_resize_output_image_size(
__lowercase, output_size=(size["height"], size["width"]), keep_aspect_ratio=__lowercase, multiple=__lowercase, )
return resize(__lowercase, size=__lowercase, resample=__lowercase, data_format=__lowercase, **__lowercase )
def A__ ( self : str, __lowercase : np.ndarray, __lowercase : Union[int, float], __lowercase : Optional[Union[str, ChannelDimension]] = None, **__lowercase : List[Any], ):
return rescale(__lowercase, scale=__lowercase, data_format=__lowercase, **__lowercase )
def A__ ( self : Any, __lowercase : np.ndarray, __lowercase : Union[float, List[float]], __lowercase : Union[float, List[float]], __lowercase : Optional[Union[str, ChannelDimension]] = None, **__lowercase : Optional[Any], ):
return normalize(__lowercase, mean=__lowercase, std=__lowercase, data_format=__lowercase, **__lowercase )
def A__ ( self : List[str], __lowercase : ImageInput, __lowercase : bool = None, __lowercase : int = None, __lowercase : bool = None, __lowercase : int = None, __lowercase : PILImageResampling = 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 : Optional[Union[str, TensorType]] = None, __lowercase : ChannelDimension = ChannelDimension.FIRST, **__lowercase : Tuple, ):
lowercase__ = do_resize if do_resize is not None else self.do_resize
lowercase__ = size if size is not None else self.size
lowercase__ = get_size_dict(__lowercase )
lowercase__ = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
lowercase__ = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
lowercase__ = resample if resample is not None else self.resample
lowercase__ = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ = image_mean if image_mean is not None else self.image_mean
lowercase__ = image_std if image_std is not None else self.image_std
lowercase__ = 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 or resample is None:
raise ValueError("Size and resample 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("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
lowercase__ = [to_numpy_array(__lowercase ) for image in images]
if do_resize:
lowercase__ = [self.resize(image=__lowercase, size=__lowercase, resample=__lowercase ) for image in images]
if do_rescale:
lowercase__ = [self.rescale(image=__lowercase, scale=__lowercase ) for image in images]
if do_normalize:
lowercase__ = [self.normalize(image=__lowercase, mean=__lowercase, std=__lowercase ) for image in images]
lowercase__ = [to_channel_dimension_format(__lowercase, __lowercase ) for image in images]
lowercase__ = {"pixel_values": images}
return BatchFeature(data=__lowercase, tensor_type=__lowercase )
def A__ ( self : int, __lowercase : Optional[Any], __lowercase : List[Tuple] = None ):
lowercase__ = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(__lowercase ) != len(__lowercase ):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits" )
if is_torch_tensor(__lowercase ):
lowercase__ = target_sizes.numpy()
lowercase__ = []
for idx in range(len(__lowercase ) ):
lowercase__ = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ), size=target_sizes[idx], mode="bilinear", align_corners=__lowercase )
lowercase__ = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(__lowercase )
else:
lowercase__ = logits.argmax(dim=1 )
lowercase__ = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 413 | 1 |
import math
import qiskit
def UpperCamelCase__( UpperCamelCase__ : int = 1 , UpperCamelCase__ : int = 1 , UpperCamelCase__ : int = 1 )->qiskit.result.counts.Counts:
if (
isinstance(UpperCamelCase__ , UpperCamelCase__ )
or isinstance(UpperCamelCase__ , UpperCamelCase__ )
or isinstance(UpperCamelCase__ , UpperCamelCase__ )
):
raise TypeError('''inputs must be integers.''' )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError('''inputs must be positive.''' )
if (
(math.floor(UpperCamelCase__ ) != input_a)
or (math.floor(UpperCamelCase__ ) != input_a)
or (math.floor(UpperCamelCase__ ) != carry_in)
):
raise ValueError('''inputs must be exact integers.''' )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError('''inputs must be less or equal to 2.''' )
# build registers
A__ = qiskit.QuantumRegister(4 , '''qr''' )
A__ = qiskit.ClassicalRegister(2 , '''cr''' )
# list the entries
A__ = [input_a, input_a, carry_in]
A__ = qiskit.QuantumCircuit(UpperCamelCase__ , UpperCamelCase__ )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(UpperCamelCase__ ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(UpperCamelCase__ ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(UpperCamelCase__ ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , UpperCamelCase__ ) # measure the last two qbits
A__ = qiskit.Aer.get_backend('''aer_simulator''' )
A__ = qiskit.execute(UpperCamelCase__ , UpperCamelCase__ , shots=10_00 )
return job.result().get_counts(UpperCamelCase__ )
if __name__ == "__main__":
print(F"Total sum count for state is: {quantum_full_adder(1, 1, 1)}")
| 212 |
def UpperCamelCase__( UpperCamelCase__ : int )->list:
A__ = int(UpperCamelCase__ )
if n_element < 1:
A__ = ValueError('''a should be a positive number''' )
raise my_error
A__ = [1]
A__ , A__ , A__ = (0, 0, 0)
A__ = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
a__: str = input('Enter the last number (nth term) of the Hamming Number Series: ')
print('Formula of Hamming Number Series => 2^i * 3^j * 5^k')
a__: Union[str, Any] = hamming(int(n))
print('-----------------------------------------------------')
print(F"The list with nth numbers is: {hamming_numbers}")
print('-----------------------------------------------------')
| 212 | 1 |
"""simple docstring"""
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def UpperCAmelCase ( a__ , a__ , a__ , a__=None , a__=None , a__=None , a__=None , a__=None , ):
'''simple docstring'''
if attention_mask is None:
lowerCAmelCase :Dict = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
lowerCAmelCase :Any = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
lowerCAmelCase :int = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=a__ )
if decoder_head_mask is None:
lowerCAmelCase :Any = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=a__ )
if cross_attn_head_mask is None:
lowerCAmelCase :Optional[int] = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=a__ )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class __UpperCamelCase :
def __init__( self : int , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any]=13 , UpperCAmelCase : Any=7 , UpperCAmelCase : Union[str, Any]=True , UpperCAmelCase : List[str]=False , UpperCAmelCase : Optional[Any]=99 , UpperCAmelCase : List[str]=16 , UpperCAmelCase : Union[str, Any]=2 , UpperCAmelCase : List[str]=4 , UpperCAmelCase : Dict=4 , UpperCAmelCase : Dict="relu" , UpperCAmelCase : Optional[Any]=0.1 , UpperCAmelCase : List[Any]=0.1 , UpperCAmelCase : List[Any]=0.0 , UpperCAmelCase : Optional[int]=0.0 , UpperCAmelCase : Tuple=20 , UpperCAmelCase : Dict=2 , UpperCAmelCase : List[Any]=1 , UpperCAmelCase : int=0 , ) -> Any:
lowerCAmelCase :str = parent
lowerCAmelCase :List[str] = batch_size
lowerCAmelCase :Optional[Any] = seq_length
lowerCAmelCase :Dict = is_training
lowerCAmelCase :Any = use_labels
lowerCAmelCase :Dict = vocab_size
lowerCAmelCase :Union[str, Any] = hidden_size
lowerCAmelCase :List[Any] = num_hidden_layers
lowerCAmelCase :Dict = num_attention_heads
lowerCAmelCase :Any = intermediate_size
lowerCAmelCase :Union[str, Any] = hidden_act
lowerCAmelCase :str = hidden_dropout_prob
lowerCAmelCase :Optional[Any] = attention_probs_dropout_prob
lowerCAmelCase :int = encoder_layerdrop
lowerCAmelCase :List[str] = decoder_layerdrop
lowerCAmelCase :List[str] = max_position_embeddings
lowerCAmelCase :Dict = eos_token_id
lowerCAmelCase :List[str] = pad_token_id
lowerCAmelCase :Any = bos_token_id
def UpperCAmelCase__ ( self : List[Any] ) -> Optional[int]:
lowerCAmelCase :Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase :Dict = self.eos_token_id # Eos Token
lowerCAmelCase :Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
lowerCAmelCase :int = input_ids.clamp(self.pad_token_id + 1 )
lowerCAmelCase :Optional[Any] = decoder_input_ids.clamp(self.pad_token_id + 1 )
lowerCAmelCase :str = self.get_config()
lowerCAmelCase :Tuple = prepare_mam_aaa_inputs_dict(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
return config, inputs_dict
def UpperCAmelCase__ ( self : str ) -> Tuple:
return MaMaaaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , 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 , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def UpperCAmelCase__ ( self : Optional[int] ) -> List[str]:
lowerCAmelCase , lowerCAmelCase :Union[str, Any] = self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCAmelCase__ ( self : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[int] ) -> Optional[int]:
lowerCAmelCase :Optional[int] = MaMaaaModel(config=UpperCAmelCase ).get_decoder().to(UpperCAmelCase ).eval()
lowerCAmelCase :Optional[Any] = inputs_dict['input_ids']
lowerCAmelCase :Any = inputs_dict['attention_mask']
lowerCAmelCase :Any = inputs_dict['head_mask']
# first forward pass
lowerCAmelCase :Any = model(UpperCAmelCase , attention_mask=UpperCAmelCase , head_mask=UpperCAmelCase , use_cache=UpperCAmelCase )
lowerCAmelCase , lowerCAmelCase :str = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
lowerCAmelCase :Optional[int] = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowerCAmelCase :int = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
lowerCAmelCase :int = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCAmelCase :str = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
lowerCAmelCase :List[str] = model(UpperCAmelCase , attention_mask=UpperCAmelCase )['last_hidden_state']
lowerCAmelCase :Optional[int] = model(UpperCAmelCase , attention_mask=UpperCAmelCase , past_key_values=UpperCAmelCase )[
'last_hidden_state'
]
# select random slice
lowerCAmelCase :List[Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCAmelCase :int = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCAmelCase :int = 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(UpperCAmelCase , UpperCAmelCase , atol=1e-2 ) )
def UpperCAmelCase__ ( self : List[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Tuple ) -> Tuple:
lowerCAmelCase :Optional[Any] = MaMaaaModel(config=UpperCAmelCase ).to(UpperCAmelCase ).eval()
lowerCAmelCase :str = model(**UpperCAmelCase )
lowerCAmelCase :Optional[int] = outputs.encoder_last_hidden_state
lowerCAmelCase :Dict = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase :List[str] = model.get_encoder()
encoder.save_pretrained(UpperCAmelCase )
lowerCAmelCase :Optional[int] = MaMaaaEncoder.from_pretrained(UpperCAmelCase ).to(UpperCAmelCase )
lowerCAmelCase :List[Any] = encoder(inputs_dict['input_ids'] , attention_mask=inputs_dict['attention_mask'] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase :List[str] = model.get_decoder()
decoder.save_pretrained(UpperCAmelCase )
lowerCAmelCase :Optional[int] = MaMaaaDecoder.from_pretrained(UpperCAmelCase ).to(UpperCAmelCase )
lowerCAmelCase :Dict = decoder(
input_ids=inputs_dict['decoder_input_ids'] , attention_mask=inputs_dict['decoder_attention_mask'] , encoder_hidden_states=UpperCAmelCase , encoder_attention_mask=inputs_dict['attention_mask'] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class __UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , unittest.TestCase ):
lowercase_ : str = (
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
lowercase_ : Tuple = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
lowercase_ : Dict = (
{
"""conversational""": MaMaaaForConditionalGeneration,
"""feature-extraction""": MaMaaaModel,
"""summarization""": MaMaaaForConditionalGeneration,
"""text2text-generation""": MaMaaaForConditionalGeneration,
"""translation""": MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
lowercase_ : Optional[int] = True
lowercase_ : Union[str, Any] = True
lowercase_ : str = False
lowercase_ : Tuple = False
def UpperCAmelCase__ ( self : Optional[Any] , UpperCAmelCase : Any , UpperCAmelCase : List[str] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Any , UpperCAmelCase : Union[str, Any] ) -> List[Any]:
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def UpperCAmelCase__ ( self : List[str] ) -> Dict:
lowerCAmelCase :Any = MaMaaaModelTester(self )
lowerCAmelCase :str = ConfigTester(self , config_class=UpperCAmelCase )
def UpperCAmelCase__ ( self : int ) -> int:
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self : Any ) -> Tuple:
lowerCAmelCase , lowerCAmelCase :List[str] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
lowerCAmelCase :Optional[int] = model_class(UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCAmelCase )
lowerCAmelCase , lowerCAmelCase :Tuple = model_class.from_pretrained(UpperCAmelCase , output_loading_info=UpperCAmelCase )
self.assertEqual(info['missing_keys'] , [] )
def UpperCAmelCase__ ( self : Tuple ) -> Optional[Any]:
lowerCAmelCase :Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCAmelCase )
def UpperCAmelCase__ ( self : Dict ) -> Optional[int]:
lowerCAmelCase :Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*UpperCAmelCase )
def UpperCAmelCase__ ( self : Dict ) -> int:
lowerCAmelCase , lowerCAmelCase :str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
lowerCAmelCase :Tuple = model_class(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
lowerCAmelCase :List[str] = copy.deepcopy(self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )
if not self.is_encoder_decoder:
lowerCAmelCase :int = inputs['input_ids']
del inputs["input_ids"]
else:
lowerCAmelCase :List[Any] = inputs['input_ids']
lowerCAmelCase :Optional[Any] = inputs.get('decoder_input_ids' , UpperCAmelCase )
del inputs["input_ids"]
inputs.pop('decoder_input_ids' , UpperCAmelCase )
lowerCAmelCase :Any = model.get_input_embeddings()
if not self.is_encoder_decoder:
lowerCAmelCase :Union[str, Any] = wte(UpperCAmelCase )
else:
lowerCAmelCase :Tuple = wte(UpperCAmelCase )
lowerCAmelCase :Union[str, Any] = wte(UpperCAmelCase )
with torch.no_grad():
model(**UpperCAmelCase )[0]
def UpperCAmelCase__ ( self : Union[str, Any] ) -> List[str]:
lowerCAmelCase , lowerCAmelCase :int = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase :Union[str, Any] = input_dict['input_ids']
lowerCAmelCase :int = input_ids.ne(1 ).to(UpperCAmelCase )
lowerCAmelCase :Dict = MaMaaaForConditionalGeneration(UpperCAmelCase ).eval().to(UpperCAmelCase )
if torch_device == "cuda":
model.half()
model.generate(UpperCAmelCase , attention_mask=UpperCAmelCase )
model.generate(num_beams=4 , do_sample=UpperCAmelCase , early_stopping=UpperCAmelCase , num_return_sequences=3 )
def UpperCAmelCase ( a__ ):
'''simple docstring'''
return torch.tensor(a__ , dtype=torch.long , device=a__ )
__SCREAMING_SNAKE_CASE = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def UpperCAmelCase__ ( self : Dict ) -> Dict:
return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' )
def UpperCAmelCase__ ( self : List[str] ) -> List[str]:
lowerCAmelCase :Dict = MaMaaaModel.from_pretrained('facebook/m2m100_418M' ).to(UpperCAmelCase )
lowerCAmelCase :Tuple = _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]] )
lowerCAmelCase :Union[str, Any] = _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]] )
lowerCAmelCase :Tuple = prepare_mam_aaa_inputs_dict(model.config , UpperCAmelCase , UpperCAmelCase )
with torch.no_grad():
lowerCAmelCase :Optional[int] = model(**UpperCAmelCase )[0]
lowerCAmelCase :List[Any] = torch.Size((1, 11, 1024) )
self.assertEqual(output.shape , UpperCAmelCase )
# change to expected output here
lowerCAmelCase :List[str] = torch.tensor(
[[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]] , device=UpperCAmelCase )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCAmelCase , atol=UpperCAmelCase ) )
def UpperCAmelCase__ ( self : List[Any] ) -> Union[str, Any]:
lowerCAmelCase :Optional[Any] = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(UpperCAmelCase )
# change to intended input
lowerCAmelCase :List[str] = _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]] )
lowerCAmelCase :List[Any] = _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]] )
lowerCAmelCase :Dict = prepare_mam_aaa_inputs_dict(model.config , UpperCAmelCase , UpperCAmelCase )
with torch.no_grad():
lowerCAmelCase :Optional[Any] = model(**UpperCAmelCase )[0]
lowerCAmelCase :str = torch.Size((1, 11, model.config.vocab_size) )
self.assertEqual(output.shape , UpperCAmelCase )
# change to expected output here
lowerCAmelCase :List[Any] = torch.tensor(
[[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]] , device=UpperCAmelCase )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCAmelCase , atol=UpperCAmelCase ) )
def UpperCAmelCase__ ( self : List[Any] ) -> List[Any]:
lowerCAmelCase :List[Any] = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(UpperCAmelCase )
lowerCAmelCase :Any = MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' , src_lang='fr' , tgt_lang='en' )
lowerCAmelCase :Union[str, Any] = [
'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
lowerCAmelCase :int = tokenizer(UpperCAmelCase , padding=UpperCAmelCase , return_tensors='pt' )
lowerCAmelCase :Dict = model.generate(
input_ids=dct['input_ids'].to(UpperCAmelCase ) , attention_mask=dct['attention_mask'].to(UpperCAmelCase ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id('en' ) , )
lowerCAmelCase :Optional[Any] = [
'The NSA case highlights the total absence of intelligence debate',
'I think there are two levels of response from the French government.',
'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
' communications in France.',
]
lowerCAmelCase :int = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=UpperCAmelCase , skip_special_tokens=UpperCAmelCase )
assert generated == expected_en | 553 |
"""simple docstring"""
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
__SCREAMING_SNAKE_CASE = {
'n_samples': 64,
'horizon': 32,
'num_inference_steps': 20,
'n_guide_steps': 2, # can set to 0 for faster sampling, does not use value network
'scale_grad_by_std': True,
'scale': 0.1,
'eta': 0.0,
't_grad_cutoff': 2,
'device': 'cpu',
}
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE = 'hopper-medium-v2'
__SCREAMING_SNAKE_CASE = gym.make(env_name)
__SCREAMING_SNAKE_CASE = ValueGuidedRLPipeline.from_pretrained(
'bglick13/hopper-medium-v2-value-function-hor32',
env=env,
)
env.seed(0)
__SCREAMING_SNAKE_CASE = env.reset()
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 10_00
__SCREAMING_SNAKE_CASE = [obs.copy()]
try:
for t in tqdm.tqdm(range(T)):
# call the policy
__SCREAMING_SNAKE_CASE = pipeline(obs, planning_horizon=32)
# execute action in environment
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE = env.step(denorm_actions)
__SCREAMING_SNAKE_CASE = env.get_normalized_score(total_reward)
# update return
total_reward += reward
total_score += score
print(
F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
F""" {total_score}"""
)
# save observations for rendering
rollout.append(next_observation.copy())
__SCREAMING_SNAKE_CASE = next_observation
except KeyboardInterrupt:
pass
print(F"""Total reward: {total_reward}""") | 553 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ : Optional[int] = {
"configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
"tokenization_roc_bert": ["RoCBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Dict = [
"ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"RoCBertForCausalLM",
"RoCBertForMaskedLM",
"RoCBertForMultipleChoice",
"RoCBertForPreTraining",
"RoCBertForQuestionAnswering",
"RoCBertForSequenceClassification",
"RoCBertForTokenClassification",
"RoCBertLayer",
"RoCBertModel",
"RoCBertPreTrainedModel",
"load_tf_weights_in_roc_bert",
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
A_ : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 701 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class lowerCamelCase (unittest.TestCase ):
@property
def SCREAMING_SNAKE_CASE ( self : Dict ) -> str:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ = ort.SessionOptions()
SCREAMING_SNAKE_CASE__ = False
return options
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Any:
SCREAMING_SNAKE_CASE__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
SCREAMING_SNAKE_CASE__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
SCREAMING_SNAKE_CASE__ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy""" )
# using the PNDM scheduler by default
SCREAMING_SNAKE_CASE__ = OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""onnx""" , safety_checker=__UpperCAmelCase , feature_extractor=__UpperCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = """A red cat sitting on a park bench"""
SCREAMING_SNAKE_CASE__ = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE__ = pipe(
prompt=__UpperCAmelCase , image=__UpperCAmelCase , mask_image=__UpperCAmelCase , strength=0.75 , guidance_scale=7.5 , num_inference_steps=1_5 , generator=__UpperCAmelCase , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert np.abs(expected_image - image ).max() < 1e-2
| 616 | 0 |
# Copyright 2023 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.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : List[Any] = {'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ : str = ['''TimmBackbone''']
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
SCREAMING_SNAKE_CASE_ : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 375 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ : Any = False, False, False
@dataclass
class snake_case_ :
'''simple docstring'''
__UpperCamelCase = None
__UpperCamelCase = True
__UpperCamelCase = True
__UpperCamelCase = None
# Automatically constructed
__UpperCamelCase = "dict"
__UpperCamelCase = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()} )
__UpperCamelCase = field(default='''Audio''' , init=UpperCAmelCase_ , repr=UpperCAmelCase_ )
def __call__( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
return self.pa_type
def UpperCAmelCase ( self : List[Any] , __lowerCamelCase : Union[str, bytes, dict] ) -> dict:
'''simple docstring'''
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError('To support encoding audio data, please install \'soundfile\'.' ) from err
if isinstance(__lowerCamelCase , __lowerCamelCase ):
return {"bytes": None, "path": value}
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
__lowercase = BytesIO()
sf.write(__lowerCamelCase , value['array'] , value['sampling_rate'] , format='wav' )
return {"bytes": buffer.getvalue(), "path": None}
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
if value["path"].endswith('pcm' ):
# "PCM" only has raw audio bytes
if value.get('sampling_rate' ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError('To use PCM files, please specify a \'sampling_rate\' in Audio object' )
if value.get('bytes' ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
__lowercase = np.frombuffer(value['bytes'] , dtype=np.intaa ).astype(np.floataa ) / 32_767
else:
__lowercase = np.memmap(value['path'] , dtype='h' , mode='r' ).astype(np.floataa ) / 32_767
__lowercase = BytesIO(bytes() )
sf.write(__lowerCamelCase , __lowerCamelCase , value['sampling_rate'] , format='wav' )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get('path' )}
elif value.get('bytes' ) is not None or value.get('path' ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get('bytes' ), "path": value.get('path' )}
else:
raise ValueError(
F"An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}." )
def UpperCAmelCase ( self : Any , __lowerCamelCase : dict , __lowerCamelCase : Optional[Dict[str, Union[str, bool, None]]] = None ) -> dict:
'''simple docstring'''
if not self.decode:
raise RuntimeError('Decoding is disabled for this feature. Please use Audio(decode=True) instead.' )
__lowercase , __lowercase = (value['path'], BytesIO(value['bytes'] )) if value['bytes'] is not None else (value['path'], None)
if path is None and file is None:
raise ValueError(F"An audio sample should have one of 'path' or 'bytes' but both are None in {value}." )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError('To support decoding audio files, please install \'librosa\' and \'soundfile\'.' ) from err
__lowercase = xsplitext(__lowerCamelCase )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
'Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '
'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ' )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
'Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '
'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ' )
if file is None:
__lowercase = token_per_repo_id or {}
__lowercase = path.split('::' )[-1]
try:
__lowercase = string_to_dict(__lowerCamelCase , config.HUB_DATASETS_URL )['repo_id']
__lowercase = token_per_repo_id[repo_id]
except (ValueError, KeyError):
__lowercase = None
with xopen(__lowerCamelCase , 'rb' , use_auth_token=__lowerCamelCase ) as f:
__lowercase , __lowercase = sf.read(__lowerCamelCase )
else:
__lowercase , __lowercase = sf.read(__lowerCamelCase )
__lowercase = array.T
if self.mono:
__lowercase = librosa.to_mono(__lowerCamelCase )
if self.sampling_rate and self.sampling_rate != sampling_rate:
__lowercase = librosa.resample(__lowerCamelCase , orig_sr=__lowerCamelCase , target_sr=self.sampling_rate )
__lowercase = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def UpperCAmelCase ( self : int ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
'''simple docstring'''
from .features import Value
if self.decode:
raise ValueError('Cannot flatten a decoded Audio feature.' )
return {
"bytes": Value('binary' ),
"path": Value('string' ),
}
def UpperCAmelCase ( self : List[Any] , __lowerCamelCase : Union[pa.StringArray, pa.StructArray] ) -> pa.StructArray:
'''simple docstring'''
if pa.types.is_string(storage.type ):
__lowercase = pa.array([None] * len(__lowerCamelCase ) , type=pa.binary() )
__lowercase = pa.StructArray.from_arrays([bytes_array, storage] , ['bytes', 'path'] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
__lowercase = pa.array([None] * len(__lowerCamelCase ) , type=pa.string() )
__lowercase = pa.StructArray.from_arrays([storage, path_array] , ['bytes', 'path'] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices('array' ):
__lowercase = pa.array([Audio().encode_example(__lowerCamelCase ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index('bytes' ) >= 0:
__lowercase = storage.field('bytes' )
else:
__lowercase = pa.array([None] * len(__lowerCamelCase ) , type=pa.binary() )
if storage.type.get_field_index('path' ) >= 0:
__lowercase = storage.field('path' )
else:
__lowercase = pa.array([None] * len(__lowerCamelCase ) , type=pa.string() )
__lowercase = pa.StructArray.from_arrays([bytes_array, path_array] , ['bytes', 'path'] , mask=storage.is_null() )
return array_cast(__lowerCamelCase , self.pa_type )
def UpperCAmelCase ( self : Union[str, Any] , __lowerCamelCase : pa.StructArray ) -> pa.StructArray:
'''simple docstring'''
@no_op_if_value_is_null
def path_to_bytes(__lowerCamelCase : Any ):
with xopen(__lowerCamelCase , 'rb' ) as f:
__lowercase = f.read()
return bytes_
__lowercase = 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() , )
__lowercase = pa.array(
[os.path.basename(__lowerCamelCase ) if path is not None else None for path in storage.field('path' ).to_pylist()] , type=pa.string() , )
__lowercase = pa.StructArray.from_arrays([bytes_array, path_array] , ['bytes', 'path'] , mask=bytes_array.is_null() )
return array_cast(__lowerCamelCase , self.pa_type )
| 375 | 1 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def lowercase_ ( self , __lowercase ) -> float:
return 0.0
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> tuple[int | float, int | float]:
lowerCAmelCase_ : int = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
lowerCAmelCase_ : str = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> None:
lowerCAmelCase_ : List[Any] = 512
lowerCAmelCase_ : Any = [1] + [0] * (size - 1)
lowerCAmelCase_ : Optional[int] = [filter_type.process(lowerCAmelCase_ ) for item in inputs]
lowerCAmelCase_ : int = [0] * (samplerate - size) # zero-padding
outputs += filler
lowerCAmelCase_ : Tuple = np.abs(np.fft.fft(lowerCAmelCase_ ) )
lowerCAmelCase_ : Any = 20 * np.logaa(lowerCAmelCase_ )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
# Display within reasonable bounds
lowerCAmelCase_ : Dict = get_bounds(lowerCAmelCase_ , lowerCAmelCase_ )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel('''Gain (dB)''' )
plt.plot(lowerCAmelCase_ )
plt.show()
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> None:
lowerCAmelCase_ : Union[str, Any] = 512
lowerCAmelCase_ : Optional[Any] = [1] + [0] * (size - 1)
lowerCAmelCase_ : Optional[Any] = [filter_type.process(lowerCAmelCase_ ) for item in inputs]
lowerCAmelCase_ : Optional[Any] = [0] * (samplerate - size) # zero-padding
outputs += filler
lowerCAmelCase_ : Dict = np.angle(np.fft.fft(lowerCAmelCase_ ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel('''Phase shift (Radians)''' )
plt.plot(np.unwrap(lowerCAmelCase_ , -2 * pi ) )
plt.show()
| 708 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = field(default="""automatic-speech-recognition""", metadata={"""include_in_asdict_even_if_is_default""": True} )
SCREAMING_SNAKE_CASE__ : ClassVar[Features] = Features({"""audio""": Audio()} )
SCREAMING_SNAKE_CASE__ : ClassVar[Features] = Features({"""transcription""": Value("""string""" )} )
SCREAMING_SNAKE_CASE__ : str = "audio"
SCREAMING_SNAKE_CASE__ : str = "transcription"
def lowercase_ ( self , __lowercase ) -> int:
if self.audio_column not in features:
raise ValueError(f"""Column {self.audio_column} is not present in features.""" )
if not isinstance(features[self.audio_column] , __lowercase ):
raise ValueError(f"""Column {self.audio_column} is not an Audio type.""" )
lowerCAmelCase_ : List[str] = copy.deepcopy(self )
lowerCAmelCase_ : Optional[Any] = self.input_schema.copy()
lowerCAmelCase_ : Optional[Any] = features[self.audio_column]
lowerCAmelCase_ : List[str] = input_schema
return task_template
@property
def lowercase_ ( self ) -> Dict[str, str]:
return {self.audio_column: "audio", self.transcription_column: "transcription"} | 619 | 0 |
'''simple docstring'''
from manim import *
class snake_case ( lowercase_ ):
"""simple docstring"""
def a__ ( self ) -> str:
SCREAMING_SNAKE_CASE_ = Rectangle(height=0.5, width=0.5 )
SCREAMING_SNAKE_CASE_ = Rectangle(height=0.25, width=0.25 )
SCREAMING_SNAKE_CASE_ = Rectangle(height=0.46, width=0.46 ).set_stroke(width=0 )
SCREAMING_SNAKE_CASE_ = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE_ = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE_ = VGroup(*_lowercase ).arrange(_lowercase, buff=0 )
SCREAMING_SNAKE_CASE_ = VGroup(*_lowercase ).arrange(_lowercase, buff=0 )
SCREAMING_SNAKE_CASE_ = VGroup(_lowercase, _lowercase ).arrange(_lowercase, buff=0 )
SCREAMING_SNAKE_CASE_ = Text('CPU', font_size=24 )
SCREAMING_SNAKE_CASE_ = Group(_lowercase, _lowercase ).arrange(_lowercase, buff=0.5, aligned_edge=_lowercase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_lowercase )
SCREAMING_SNAKE_CASE_ = [mem.copy() for i in range(4 )]
SCREAMING_SNAKE_CASE_ = VGroup(*_lowercase ).arrange(_lowercase, buff=0 )
SCREAMING_SNAKE_CASE_ = Text('GPU', font_size=24 )
SCREAMING_SNAKE_CASE_ = Group(_lowercase, _lowercase ).arrange(_lowercase, buff=0.5, aligned_edge=_lowercase )
gpu.move_to([-1, -1, 0] )
self.add(_lowercase )
SCREAMING_SNAKE_CASE_ = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE_ = VGroup(*_lowercase ).arrange(_lowercase, buff=0 )
SCREAMING_SNAKE_CASE_ = Text('Model', font_size=24 )
SCREAMING_SNAKE_CASE_ = Group(_lowercase, _lowercase ).arrange(_lowercase, buff=0.5, aligned_edge=_lowercase )
model.move_to([3, -1.0, 0] )
self.add(_lowercase )
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = []
for i, rect in enumerate(_lowercase ):
rect.set_stroke(_lowercase )
SCREAMING_SNAKE_CASE_ = Rectangle(height=0.46 / 4, width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(_lowercase, opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ), buff=0.02, direction=_lowercase )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0], direction=_lowercase, buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1], direction=_lowercase, buff=0.0 )
self.add(_lowercase )
model_cpu_arr.append(_lowercase )
self.add(*_lowercase, *_lowercase, *_lowercase )
SCREAMING_SNAKE_CASE_ = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE_ = VGroup(*_lowercase ).arrange(_lowercase, buff=0 )
SCREAMING_SNAKE_CASE_ = Text('Loaded Checkpoint', font_size=24 )
SCREAMING_SNAKE_CASE_ = Group(_lowercase, _lowercase ).arrange(_lowercase, buff=0.5, aligned_edge=_lowercase )
checkpoint.move_to([3, 0.5, 0] )
self.add(_lowercase )
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = []
for i, rect in enumerate(_lowercase ):
SCREAMING_SNAKE_CASE_ = fill.copy().set_fill(_lowercase, opacity=0.7 )
target.move_to(_lowercase )
ckpt_arr.append(_lowercase )
SCREAMING_SNAKE_CASE_ = target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.move_to(cpu_right_col_base[i - 5] )
ckpt_cpu_arr.append(_lowercase )
self.add(*_lowercase, *_lowercase )
SCREAMING_SNAKE_CASE_ = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
SCREAMING_SNAKE_CASE_ = MarkupText(
f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""", font_size=18, )
key_text.move_to([-5, 2.4, 0] )
self.add(_lowercase, _lowercase )
SCREAMING_SNAKE_CASE_ = MarkupText(
f"""<span fgcolor='{BLUE}'>●</span> Checkpoint""", font_size=18, )
blue_text.next_to(_lowercase, DOWN * 2.4, aligned_edge=key_text.get_left() )
self.add(_lowercase )
SCREAMING_SNAKE_CASE_ = MarkupText(
f"""Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.""", font_size=24, )
step_a.move_to([2, 2, 0] )
SCREAMING_SNAKE_CASE_ = [meta_mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE_ = [meta_mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE_ = VGroup(*_lowercase ).arrange(_lowercase, buff=0 )
SCREAMING_SNAKE_CASE_ = VGroup(*_lowercase ).arrange(_lowercase, buff=0 )
SCREAMING_SNAKE_CASE_ = VGroup(_lowercase, _lowercase ).arrange(_lowercase, buff=0 )
SCREAMING_SNAKE_CASE_ = Text('Disk', font_size=24 )
SCREAMING_SNAKE_CASE_ = Group(_lowercase, _lowercase ).arrange(_lowercase, buff=0.5, aligned_edge=_lowercase )
disk.move_to([-4.0, -1.25, 0] )
self.play(Write(_lowercase, run_time=3 ), Write(_lowercase, run_time=1 ), Create(_lowercase, run_time=1 ) )
SCREAMING_SNAKE_CASE_ = []
for i, rect in enumerate(_lowercase ):
SCREAMING_SNAKE_CASE_ = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(_lowercase, run_time=1.5 ) )
self.play(*_lowercase )
self.play(FadeOut(_lowercase ) )
SCREAMING_SNAKE_CASE_ = MarkupText(f"""Then, the checkpoint is removed from memory\nthrough garbage collection.""", font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(_lowercase, run_time=3 ) )
self.play(
FadeOut(_lowercase, _lowercase, *_lowercase, *_lowercase ), )
self.wait()
| 294 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
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
SCREAMING_SNAKE_CASE : int = get_tests_dir("fixtures")
SCREAMING_SNAKE_CASE : str = get_tests_dir("fixtures/dummy_feature_extractor_config.json")
SCREAMING_SNAKE_CASE : str = get_tests_dir("fixtures/dummy-config.json")
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def a__ ( self ) -> str:
SCREAMING_SNAKE_CASE_ = 0
def a__ ( self ) -> Tuple:
SCREAMING_SNAKE_CASE_ = AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h' )
self.assertIsInstance(_lowercase, _lowercase )
def a__ ( self ) -> List[str]:
SCREAMING_SNAKE_CASE_ = AutoFeatureExtractor.from_pretrained(_lowercase )
self.assertIsInstance(_lowercase, _lowercase )
def a__ ( self ) -> str:
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE_ = WavaVecaConfig()
# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
SCREAMING_SNAKE_CASE_ = AutoFeatureExtractor.from_pretrained(_lowercase ).to_dict()
config_dict.pop('feature_extractor_type' )
SCREAMING_SNAKE_CASE_ = WavaVecaFeatureExtractor(**_lowercase )
# save in new folder
model_config.save_pretrained(_lowercase )
config.save_pretrained(_lowercase )
SCREAMING_SNAKE_CASE_ = AutoFeatureExtractor.from_pretrained(_lowercase )
# make sure private variable is not incorrectly saved
SCREAMING_SNAKE_CASE_ = json.loads(config.to_json_string() )
self.assertTrue('_processor_class' not in dict_as_saved )
self.assertIsInstance(_lowercase, _lowercase )
def a__ ( self ) -> int:
SCREAMING_SNAKE_CASE_ = AutoFeatureExtractor.from_pretrained(_lowercase )
self.assertIsInstance(_lowercase, _lowercase )
def a__ ( self ) -> Any:
with self.assertRaisesRegex(
_lowercase, 'bert-base is not a local folder and is not a valid model identifier' ):
SCREAMING_SNAKE_CASE_ = AutoFeatureExtractor.from_pretrained('bert-base' )
def a__ ( self ) -> List[Any]:
with self.assertRaisesRegex(
_lowercase, R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
SCREAMING_SNAKE_CASE_ = AutoFeatureExtractor.from_pretrained(_lowercase, revision='aaaaaa' )
def a__ ( self ) -> List[Any]:
with self.assertRaisesRegex(
_lowercase, 'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.', ):
SCREAMING_SNAKE_CASE_ = AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model' )
def a__ ( self ) -> List[str]:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowercase ):
SCREAMING_SNAKE_CASE_ = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowercase ):
SCREAMING_SNAKE_CASE_ = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor', trust_remote_code=_lowercase )
SCREAMING_SNAKE_CASE_ = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor', trust_remote_code=_lowercase )
self.assertEqual(feature_extractor.__class__.__name__, 'NewFeatureExtractor' )
# Test feature extractor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(_lowercase )
SCREAMING_SNAKE_CASE_ = AutoFeatureExtractor.from_pretrained(_lowercase, trust_remote_code=_lowercase )
self.assertEqual(reloaded_feature_extractor.__class__.__name__, 'NewFeatureExtractor' )
def a__ ( self ) -> int:
try:
AutoConfig.register('custom', _lowercase )
AutoFeatureExtractor.register(_lowercase, _lowercase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowercase ):
AutoFeatureExtractor.register(_lowercase, _lowercase )
# Now that the config is registered, it can be used as any other config with the auto-API
SCREAMING_SNAKE_CASE_ = CustomFeatureExtractor.from_pretrained(_lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(_lowercase )
SCREAMING_SNAKE_CASE_ = AutoFeatureExtractor.from_pretrained(_lowercase )
self.assertIsInstance(_lowercase, _lowercase )
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]
def a__ ( self ) -> str:
class snake_case ( lowercase_ ):
"""simple docstring"""
_a = True
try:
AutoConfig.register('custom', _lowercase )
AutoFeatureExtractor.register(_lowercase, _lowercase )
# If remote code is not set, the default is to use local
SCREAMING_SNAKE_CASE_ = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' )
self.assertEqual(feature_extractor.__class__.__name__, 'NewFeatureExtractor' )
self.assertTrue(feature_extractor.is_local )
# If remote code is disabled, we load the local one.
SCREAMING_SNAKE_CASE_ = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor', trust_remote_code=_lowercase )
self.assertEqual(feature_extractor.__class__.__name__, 'NewFeatureExtractor' )
self.assertTrue(feature_extractor.is_local )
# If remote is enabled, we load from the Hub
SCREAMING_SNAKE_CASE_ = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor', trust_remote_code=_lowercase )
self.assertEqual(feature_extractor.__class__.__name__, 'NewFeatureExtractor' )
self.assertTrue(not hasattr(_lowercase, 'is_local' ) )
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]
| 294 | 1 |
'''simple docstring'''
from __future__ import annotations
def a ( UpperCamelCase_ : list , UpperCamelCase_ : int ) -> Optional[int]:
# Checks if the entire collection has been sorted
if len(UpperCamelCase_ ) <= 1 or n <= 1:
return
insert_next(UpperCamelCase_ , n - 1 )
rec_insertion_sort(UpperCamelCase_ , n - 1 )
def a ( UpperCamelCase_ : list , UpperCamelCase_ : int ) -> Optional[int]:
# Checks order between adjacent elements
if index >= len(UpperCamelCase_ ) or collection[index - 1] <= collection[index]:
return
# Swaps adjacent elements since they are not in ascending order
snake_case__ , snake_case__ =(
collection[index],
collection[index - 1],
)
insert_next(UpperCamelCase_ , index + 1 )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : List[str] = input('''Enter integers separated by spaces: ''')
SCREAMING_SNAKE_CASE__ : list[int] = [int(num) for num in numbers.split()]
rec_insertion_sort(number_list, len(number_list))
print(number_list)
| 581 |
'''simple docstring'''
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 a ( UpperCamelCase_ : str , UpperCamelCase_ : str , UpperCamelCase_ : str ) -> List[str]:
def get_masked_lm_array(UpperCamelCase_ : str ):
snake_case__ =f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
snake_case__ =tf.train.load_variable(UpperCamelCase_ , UpperCamelCase_ )
if "kernel" in name:
snake_case__ =array.transpose()
return torch.from_numpy(UpperCamelCase_ )
def get_encoder_array(UpperCamelCase_ : str ):
snake_case__ =f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
snake_case__ =tf.train.load_variable(UpperCamelCase_ , UpperCamelCase_ )
if "kernel" in name:
snake_case__ =array.transpose()
return torch.from_numpy(UpperCamelCase_ )
def get_encoder_layer_array(UpperCamelCase_ : int , UpperCamelCase_ : str ):
snake_case__ =f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
snake_case__ =tf.train.load_variable(UpperCamelCase_ , UpperCamelCase_ )
if "kernel" in name:
snake_case__ =array.transpose()
return torch.from_numpy(UpperCamelCase_ )
def get_encoder_attention_layer_array(UpperCamelCase_ : int , UpperCamelCase_ : str , UpperCamelCase_ : int ):
snake_case__ =f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
snake_case__ =tf.train.load_variable(UpperCamelCase_ , UpperCamelCase_ )
snake_case__ =array.reshape(UpperCamelCase_ )
if "kernel" in name:
snake_case__ =array.transpose()
return torch.from_numpy(UpperCamelCase_ )
print(f"""Loading model based on config from {config_path}...""" )
snake_case__ =BertConfig.from_json_file(UpperCamelCase_ )
snake_case__ =BertForMaskedLM(UpperCamelCase_ )
# Layers
for layer_index in range(0 , config.num_hidden_layers ):
snake_case__ =model.bert.encoder.layer[layer_index]
# Self-attention
snake_case__ =layer.attention.self
snake_case__ =get_encoder_attention_layer_array(
UpperCamelCase_ , '_query_dense/kernel' , self_attn.query.weight.data.shape )
snake_case__ =get_encoder_attention_layer_array(
UpperCamelCase_ , '_query_dense/bias' , self_attn.query.bias.data.shape )
snake_case__ =get_encoder_attention_layer_array(
UpperCamelCase_ , '_key_dense/kernel' , self_attn.key.weight.data.shape )
snake_case__ =get_encoder_attention_layer_array(
UpperCamelCase_ , '_key_dense/bias' , self_attn.key.bias.data.shape )
snake_case__ =get_encoder_attention_layer_array(
UpperCamelCase_ , '_value_dense/kernel' , self_attn.value.weight.data.shape )
snake_case__ =get_encoder_attention_layer_array(
UpperCamelCase_ , '_value_dense/bias' , self_attn.value.bias.data.shape )
# Self-attention Output
snake_case__ =layer.attention.output
snake_case__ =get_encoder_attention_layer_array(
UpperCamelCase_ , '_output_dense/kernel' , self_output.dense.weight.data.shape )
snake_case__ =get_encoder_attention_layer_array(
UpperCamelCase_ , '_output_dense/bias' , self_output.dense.bias.data.shape )
snake_case__ =get_encoder_layer_array(UpperCamelCase_ , '_attention_layer_norm/gamma' )
snake_case__ =get_encoder_layer_array(UpperCamelCase_ , '_attention_layer_norm/beta' )
# Intermediate
snake_case__ =layer.intermediate
snake_case__ =get_encoder_layer_array(UpperCamelCase_ , '_intermediate_dense/kernel' )
snake_case__ =get_encoder_layer_array(UpperCamelCase_ , '_intermediate_dense/bias' )
# Output
snake_case__ =layer.output
snake_case__ =get_encoder_layer_array(UpperCamelCase_ , '_output_dense/kernel' )
snake_case__ =get_encoder_layer_array(UpperCamelCase_ , '_output_dense/bias' )
snake_case__ =get_encoder_layer_array(UpperCamelCase_ , '_output_layer_norm/gamma' )
snake_case__ =get_encoder_layer_array(UpperCamelCase_ , '_output_layer_norm/beta' )
# Embeddings
snake_case__ =get_encoder_array('_position_embedding_layer/embeddings' )
snake_case__ =get_encoder_array('_type_embedding_layer/embeddings' )
snake_case__ =get_encoder_array('_embedding_norm_layer/gamma' )
snake_case__ =get_encoder_array('_embedding_norm_layer/beta' )
# LM Head
snake_case__ =model.cls.predictions.transform
snake_case__ =get_masked_lm_array('dense/kernel' )
snake_case__ =get_masked_lm_array('dense/bias' )
snake_case__ =get_masked_lm_array('layer_norm/gamma' )
snake_case__ =get_masked_lm_array('layer_norm/beta' )
snake_case__ =get_masked_lm_array('embedding_table' )
# Pooling
snake_case__ =BertPooler(config=UpperCamelCase_ )
snake_case__ =get_encoder_array('_pooler_layer/kernel' )
snake_case__ =get_encoder_array('_pooler_layer/bias' )
# Export final model
model.save_pretrained(UpperCamelCase_ )
# Integration test - should load without any errors ;)
snake_case__ =BertForMaskedLM.from_pretrained(UpperCamelCase_ )
print(new_model.eval() )
print('Model conversion was done sucessfully!' )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : str = 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.''',
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = parser.parse_args()
convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 581 | 1 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCamelCase (lowercase_: Optional[int] , lowercase_: Tuple=7 ) -> Dict:
A__ : Optional[int] = None
if token is not None:
A__ : Any = {"""Accept""": """application/vnd.github+json""", """Authorization""": f"""Bearer {token}"""}
# The id of a workflow (not of a workflow run)
A__ : List[str] = """636036"""
A__ : Optional[int] = f"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += f"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""
A__ : List[str] = requests.get(SCREAMING_SNAKE_CASE_ , headers=SCREAMING_SNAKE_CASE_ ).json()
return result["workflow_runs"]
def UpperCamelCase (lowercase_: Optional[Any] ) -> List[Any]:
A__ : str = get_daily_ci_runs(SCREAMING_SNAKE_CASE_ )
A__ : Optional[Any] = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
A__ : List[str] = workflow_run["""id"""]
break
return workflow_run_id
def UpperCamelCase (lowercase_: Union[str, Any] , lowercase_: List[Any] , lowercase_: Dict ) -> Dict:
A__ : Optional[Any] = get_last_daily_ci_runs(SCREAMING_SNAKE_CASE_ )
if workflow_run_id is not None:
A__ : Any = get_artifacts_links(worflow_run_id=SCREAMING_SNAKE_CASE_ , token=SCREAMING_SNAKE_CASE_ )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
A__ : List[str] = artifacts_links[artifact_name]
download_artifact(
artifact_name=SCREAMING_SNAKE_CASE_ , artifact_url=SCREAMING_SNAKE_CASE_ , output_dir=SCREAMING_SNAKE_CASE_ , token=SCREAMING_SNAKE_CASE_ )
def UpperCamelCase (lowercase_: str , lowercase_: int , lowercase_: Tuple ) -> List[str]:
get_last_daily_ci_artifacts(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A__ : Any = {}
for artifact_name in artifact_names:
A__ : int = os.path.join(SCREAMING_SNAKE_CASE_ , f"""{artifact_name}.zip""" )
if os.path.isfile(SCREAMING_SNAKE_CASE_ ):
A__ : Any = {}
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ ) as z:
for filename in z.namelist():
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
# read the file
with z.open(SCREAMING_SNAKE_CASE_ ) as f:
A__ : Any = f.read().decode("""UTF-8""" )
return results
| 456 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperCamelCase__ : str = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See all SEW models at https://huggingface.co/models?filter=sew
}
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'sew'
def __init__( self , A__=32 , A__=7_68 , A__=12 , A__=12 , A__=30_72 , A__=2 , A__="gelu" , A__=0.1 , A__=0.1 , A__=0.1 , A__=0.0 , A__=0.1 , A__=0.1 , A__=0.02 , A__=1E-5 , A__="group" , A__="gelu" , A__=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12) , A__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , A__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , A__=False , A__=1_28 , A__=16 , A__=True , A__=0.05 , A__=10 , A__=2 , A__=0.0 , A__=10 , A__=0 , A__="mean" , A__=False , A__=False , A__=2_56 , A__=0 , A__=1 , A__=2 , **A__ , ) -> List[str]:
super().__init__(**A__ , pad_token_id=A__ , bos_token_id=A__ , eos_token_id=A__ )
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = feat_extract_norm
_SCREAMING_SNAKE_CASE = feat_extract_activation
_SCREAMING_SNAKE_CASE = list(A__ )
_SCREAMING_SNAKE_CASE = list(A__ )
_SCREAMING_SNAKE_CASE = list(A__ )
_SCREAMING_SNAKE_CASE = conv_bias
_SCREAMING_SNAKE_CASE = num_conv_pos_embeddings
_SCREAMING_SNAKE_CASE = num_conv_pos_embedding_groups
_SCREAMING_SNAKE_CASE = len(self.conv_dim )
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = squeeze_factor
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = hidden_dropout
_SCREAMING_SNAKE_CASE = attention_dropout
_SCREAMING_SNAKE_CASE = activation_dropout
_SCREAMING_SNAKE_CASE = feat_proj_dropout
_SCREAMING_SNAKE_CASE = final_dropout
_SCREAMING_SNAKE_CASE = layerdrop
_SCREAMING_SNAKE_CASE = layer_norm_eps
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = 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
_SCREAMING_SNAKE_CASE = apply_spec_augment
_SCREAMING_SNAKE_CASE = mask_time_prob
_SCREAMING_SNAKE_CASE = mask_time_length
_SCREAMING_SNAKE_CASE = mask_time_min_masks
_SCREAMING_SNAKE_CASE = mask_feature_prob
_SCREAMING_SNAKE_CASE = mask_feature_length
_SCREAMING_SNAKE_CASE = mask_feature_min_masks
# ctc loss
_SCREAMING_SNAKE_CASE = ctc_loss_reduction
_SCREAMING_SNAKE_CASE = ctc_zero_infinity
# sequence classification
_SCREAMING_SNAKE_CASE = use_weighted_layer_sum
_SCREAMING_SNAKE_CASE = classifier_proj_size
@property
def UpperCamelCase ( self ) -> Any:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 591 | 0 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class UpperCamelCase ( __lowerCAmelCase , __lowerCAmelCase ):
@register_to_config
def __init__( self , snake_case__ = 768 , ):
"""simple docstring"""
super().__init__()
_SCREAMING_SNAKE_CASE : Optional[int] = nn.Parameter(torch.zeros(1 , _UpperCamelCase ) )
_SCREAMING_SNAKE_CASE : int = nn.Parameter(torch.ones(1 , _UpperCamelCase ) )
def __SCREAMING_SNAKE_CASE ( self , snake_case__ = None , snake_case__ = None , ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : int = nn.Parameter(self.mean.to(_UpperCamelCase ).to(_UpperCamelCase ) )
_SCREAMING_SNAKE_CASE : Optional[Any] = nn.Parameter(self.std.to(_UpperCamelCase ).to(_UpperCamelCase ) )
return self
def __SCREAMING_SNAKE_CASE ( self , snake_case__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Optional[Any] = (embeds - self.mean) * 1.0 / self.std
return embeds
def __SCREAMING_SNAKE_CASE ( self , snake_case__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Any = (embeds * self.std) + self.mean
return embeds
| 715 |
"""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
lowercase_ : List[str] = datasets.utils.logging.get_logger(__name__)
@dataclass
class UpperCamelCase ( datasets.BuilderConfig ):
A__ = 10000
A__ = None
A__ = None
class UpperCamelCase ( datasets.ArrowBasedBuilder ):
A__ = ParquetConfig
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def __SCREAMING_SNAKE_CASE ( self , snake_case__ ):
"""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}''' )
_SCREAMING_SNAKE_CASE : List[Any] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(snake_case__ , (str, list, tuple) ):
_SCREAMING_SNAKE_CASE : int = data_files
if isinstance(snake_case__ , snake_case__ ):
_SCREAMING_SNAKE_CASE : List[Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
_SCREAMING_SNAKE_CASE : Optional[Any] = [dl_manager.iter_files(snake_case__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
_SCREAMING_SNAKE_CASE : Tuple = []
for split_name, files in data_files.items():
if isinstance(snake_case__ , snake_case__ ):
_SCREAMING_SNAKE_CASE : Optional[int] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
_SCREAMING_SNAKE_CASE : Tuple = [dl_manager.iter_files(snake_case__ ) 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(snake_case__ ):
with open(snake_case__ , "rb" ) as f:
_SCREAMING_SNAKE_CASE : Optional[int] = datasets.Features.from_arrow_schema(pq.read_schema(snake_case__ ) )
break
splits.append(datasets.SplitGenerator(name=snake_case__ , gen_kwargs={"files": files} ) )
return splits
def __SCREAMING_SNAKE_CASE ( self , snake_case__ ):
"""simple docstring"""
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
_SCREAMING_SNAKE_CASE : Union[str, Any] = table_cast(snake_case__ , self.info.features.arrow_schema )
return pa_table
def __SCREAMING_SNAKE_CASE ( self , snake_case__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : 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(snake_case__ ) ):
with open(snake_case__ , "rb" ) as f:
_SCREAMING_SNAKE_CASE : List[Any] = pq.ParquetFile(snake_case__ )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
_SCREAMING_SNAKE_CASE : int = 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(snake_case__ )
except ValueError as e:
logger.error(F'''Failed to read file \'{file}\' with error {type(snake_case__ )}: {e}''' )
raise
| 295 | 0 |
def __snake_case ( __UpperCamelCase : list ,__UpperCamelCase : list ,__UpperCamelCase : int ):
"""simple docstring"""
A_ = len(__UpperCamelCase )
A_ = [[0] * n for i in range(__UpperCamelCase )]
for i in range(__UpperCamelCase ):
A_ = y_points[i]
for i in range(2 ,__UpperCamelCase ):
for j in range(__UpperCamelCase ,__UpperCamelCase ):
A_ = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod() | 86 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = (DDPMParallelScheduler,)
def __A ( self : List[Any] , **UpperCAmelCase : Optional[int] ):
A_ = {
"num_train_timesteps": 1000,
"beta_start": 0.0_001,
"beta_end": 0.02,
"beta_schedule": "linear",
"variance_type": "fixed_small",
"clip_sample": True,
}
config.update(**UpperCAmelCase )
return config
def __A ( self : Optional[Any] ):
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase )
def __A ( self : Dict ):
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=UpperCAmelCase , beta_end=UpperCAmelCase )
def __A ( self : int ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=UpperCAmelCase )
def __A ( self : Tuple ):
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=UpperCAmelCase )
def __A ( self : int ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=UpperCAmelCase )
def __A ( self : Union[str, Any] ):
self.check_over_configs(thresholding=UpperCAmelCase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=UpperCAmelCase , prediction_type=UpperCAmelCase , sample_max_value=UpperCAmelCase , )
def __A ( self : Optional[int] ):
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCAmelCase )
def __A ( self : Tuple ):
for t in [0, 500, 999]:
self.check_over_forward(time_step=UpperCAmelCase )
def __A ( self : Tuple ):
A_ = self.scheduler_classes[0]
A_ = self.get_scheduler_config()
A_ = scheduler_class(**UpperCAmelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def __A ( self : List[Any] ):
A_ = self.scheduler_classes[0]
A_ = self.get_scheduler_config()
A_ = scheduler_class(**UpperCAmelCase )
A_ = len(UpperCAmelCase )
A_ = self.dummy_model()
A_ = self.dummy_sample_deter
A_ = self.dummy_sample_deter + 0.1
A_ = self.dummy_sample_deter - 0.1
A_ = samplea.shape[0]
A_ = torch.stack([samplea, samplea, samplea] , dim=0 )
A_ = torch.arange(UpperCAmelCase )[0:3, None].repeat(1 , UpperCAmelCase )
A_ = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
A_ = scheduler.batch_step_no_noise(UpperCAmelCase , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
A_ = torch.sum(torch.abs(UpperCAmelCase ) )
A_ = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_sum.item() - 1_153.1_833 ) < 1E-2
assert abs(result_mean.item() - 0.5_005 ) < 1E-3
def __A ( self : Tuple ):
A_ = self.scheduler_classes[0]
A_ = self.get_scheduler_config()
A_ = scheduler_class(**UpperCAmelCase )
A_ = len(UpperCAmelCase )
A_ = self.dummy_model()
A_ = self.dummy_sample_deter
A_ = torch.manual_seed(0 )
for t in reversed(range(UpperCAmelCase ) ):
# 1. predict noise residual
A_ = model(UpperCAmelCase , UpperCAmelCase )
# 2. predict previous mean of sample x_t-1
A_ = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , generator=UpperCAmelCase ).prev_sample
A_ = pred_prev_sample
A_ = torch.sum(torch.abs(UpperCAmelCase ) )
A_ = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_sum.item() - 258.9_606 ) < 1E-2
assert abs(result_mean.item() - 0.3_372 ) < 1E-3
def __A ( self : Tuple ):
A_ = self.scheduler_classes[0]
A_ = self.get_scheduler_config(prediction_type="v_prediction" )
A_ = scheduler_class(**UpperCAmelCase )
A_ = len(UpperCAmelCase )
A_ = self.dummy_model()
A_ = self.dummy_sample_deter
A_ = torch.manual_seed(0 )
for t in reversed(range(UpperCAmelCase ) ):
# 1. predict noise residual
A_ = model(UpperCAmelCase , UpperCAmelCase )
# 2. predict previous mean of sample x_t-1
A_ = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , generator=UpperCAmelCase ).prev_sample
A_ = pred_prev_sample
A_ = torch.sum(torch.abs(UpperCAmelCase ) )
A_ = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_sum.item() - 202.0_296 ) < 1E-2
assert abs(result_mean.item() - 0.2_631 ) < 1E-3
def __A ( self : Union[str, Any] ):
A_ = self.scheduler_classes[0]
A_ = self.get_scheduler_config()
A_ = scheduler_class(**UpperCAmelCase )
A_ = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=UpperCAmelCase )
A_ = scheduler.timesteps
for i, timestep in enumerate(UpperCAmelCase ):
if i == len(UpperCAmelCase ) - 1:
A_ = -1
else:
A_ = timesteps[i + 1]
A_ = scheduler.previous_timestep(UpperCAmelCase )
A_ = prev_t.item()
self.assertEqual(UpperCAmelCase , UpperCAmelCase )
def __A ( self : List[Any] ):
A_ = self.scheduler_classes[0]
A_ = self.get_scheduler_config()
A_ = scheduler_class(**UpperCAmelCase )
A_ = [100, 87, 50, 51, 0]
with self.assertRaises(UpperCAmelCase , msg="`custom_timesteps` must be in descending order." ):
scheduler.set_timesteps(timesteps=UpperCAmelCase )
def __A ( self : List[Any] ):
A_ = self.scheduler_classes[0]
A_ = self.get_scheduler_config()
A_ = scheduler_class(**UpperCAmelCase )
A_ = [100, 87, 50, 1, 0]
A_ = len(UpperCAmelCase )
with self.assertRaises(UpperCAmelCase , msg="Can only pass one of `num_inference_steps` or `custom_timesteps`." ):
scheduler.set_timesteps(num_inference_steps=UpperCAmelCase , timesteps=UpperCAmelCase )
def __A ( self : Optional[Any] ):
A_ = self.scheduler_classes[0]
A_ = self.get_scheduler_config()
A_ = scheduler_class(**UpperCAmelCase )
A_ = [scheduler.config.num_train_timesteps]
with self.assertRaises(
UpperCAmelCase , msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" , ):
scheduler.set_timesteps(timesteps=UpperCAmelCase ) | 86 | 1 |
'''simple docstring'''
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 _lowerCAmelCase ( __magic_name__ : Tuple ) -> Tuple:
lowercase : List[str] =384
if "tiny" in model_name:
lowercase : List[str] =[3, 3, 9, 3]
lowercase : Dict =[96, 192, 384, 768]
if "small" in model_name:
lowercase : Optional[int] =[3, 3, 27, 3]
lowercase : Dict =[96, 192, 384, 768]
if "base" in model_name:
lowercase : Optional[Any] =[3, 3, 27, 3]
lowercase : Optional[int] =[128, 256, 512, 1024]
lowercase : Tuple =512
if "large" in model_name:
lowercase : Dict =[3, 3, 27, 3]
lowercase : str =[192, 384, 768, 1536]
lowercase : Optional[Any] =768
if "xlarge" in model_name:
lowercase : str =[3, 3, 27, 3]
lowercase : List[Any] =[256, 512, 1024, 2048]
lowercase : Any =1024
# set label information
lowercase : str =150
lowercase : Any ='''huggingface/label-files'''
lowercase : Tuple ='''ade20k-id2label.json'''
lowercase : Union[str, Any] =json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type='''dataset''' ) , '''r''' ) )
lowercase : Dict ={int(UpperCamelCase__ ): v for k, v in idalabel.items()}
lowercase : Any ={v: k for k, v in idalabel.items()}
lowercase : Tuple =ConvNextConfig(
depths=UpperCamelCase__ , hidden_sizes=UpperCamelCase__ , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
lowercase : Optional[int] =UperNetConfig(
backbone_config=UpperCamelCase__ , auxiliary_in_channels=UpperCamelCase__ , num_labels=UpperCamelCase__ , idalabel=UpperCamelCase__ , labelaid=UpperCamelCase__ , )
return config
def _lowerCAmelCase ( __magic_name__ : Optional[Any] ) -> Tuple:
lowercase : List[str] =[]
# 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 _lowerCAmelCase ( __magic_name__ : List[Any] , __magic_name__ : Optional[int] , __magic_name__ : Tuple ) -> str:
lowercase : Union[str, Any] =dct.pop(UpperCamelCase__ )
lowercase : Optional[int] =val
def _lowerCAmelCase ( __magic_name__ : Any , __magic_name__ : List[str] , __magic_name__ : List[Any] ) -> List[Any]:
lowercase : str ={
'''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''',
}
lowercase : Optional[Any] =model_name_to_url[model_name]
lowercase : Tuple =torch.hub.load_state_dict_from_url(UpperCamelCase__ , map_location='''cpu''' )['''state_dict''']
lowercase : Optional[Any] =get_upernet_config(UpperCamelCase__ )
lowercase : int =UperNetForSemanticSegmentation(UpperCamelCase__ )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
lowercase : Dict =state_dict.pop(UpperCamelCase__ )
if "bn" in key:
lowercase : Any =key.replace('''bn''' , '''batch_norm''' )
lowercase : Optional[int] =val
# rename keys
lowercase : List[str] =create_rename_keys(UpperCamelCase__ )
for src, dest in rename_keys:
rename_key(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
model.load_state_dict(UpperCamelCase__ )
# verify on image
lowercase : Optional[int] ='''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
lowercase : Tuple =Image.open(requests.get(UpperCamelCase__ , stream=UpperCamelCase__ ).raw ).convert('''RGB''' )
lowercase : List[Any] =SegformerImageProcessor()
lowercase : Dict =processor(UpperCamelCase__ , return_tensors='''pt''' ).pixel_values
with torch.no_grad():
lowercase : List[str] =model(UpperCamelCase__ )
if model_name == "upernet-convnext-tiny":
lowercase : int =torch.tensor(
[[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]] )
elif model_name == "upernet-convnext-small":
lowercase : List[Any] =torch.tensor(
[[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]] )
elif model_name == "upernet-convnext-base":
lowercase : Union[str, Any] =torch.tensor(
[[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]] )
elif model_name == "upernet-convnext-large":
lowercase : Union[str, Any] =torch.tensor(
[[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]] )
elif model_name == "upernet-convnext-xlarge":
lowercase : Optional[int] =torch.tensor(
[[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]] )
print('''Logits:''' , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , UpperCamelCase__ , 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(UpperCamelCase__ )
print(f'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(UpperCamelCase__ )
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__":
UpperCamelCase_ = 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."""
)
UpperCamelCase_ = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 702 |
'''simple docstring'''
from collections import defaultdict
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : str ) -> bool:
lowercase : Optional[int] =first_str.lower().strip()
lowercase : Union[str, Any] =second_str.lower().strip()
# Remove whitespace
lowercase : Optional[int] =first_str.replace(''' ''' , '''''' )
lowercase : Optional[Any] =second_str.replace(''' ''' , '''''' )
# Strings of different lengths are not anagrams
if len(__magic_name__ ) != len(__magic_name__ ):
return False
# Default values for count should be 0
lowercase : defaultdict[str, int] =defaultdict(__magic_name__ )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(__magic_name__ ) ):
count[first_str[i]] += 1
count[second_str[i]] -= 1
return all(_count == 0 for _count in count.values() )
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCamelCase_ = input("""Enter the first string """).strip()
UpperCamelCase_ = input("""Enter the second string """).strip()
UpperCamelCase_ = check_anagrams(input_a, input_b)
print(f'''{input_a} and {input_b} are {'' if status else 'not '}anagrams.''')
| 88 | 0 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase( __snake_case ):
'''simple docstring'''
__magic_name__ = ['image_processor', 'tokenizer']
__magic_name__ = 'ViltImageProcessor'
__magic_name__ = ('BertTokenizer', 'BertTokenizerFast')
def __init__( self , snake_case_=None , snake_case_=None , **snake_case_ ):
_A = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , snake_case_ , )
_A = kwargs.pop('feature_extractor' )
_A = 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`.' )
super().__init__(snake_case_ , snake_case_ )
_A = self.image_processor
def __call__( self , snake_case_ , snake_case_ = None , snake_case_ = True , snake_case_ = False , snake_case_ = None , snake_case_ = None , snake_case_ = 0 , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = True , snake_case_ = None , **snake_case_ , ):
_A = self.tokenizer(
text=snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , stride=snake_case_ , pad_to_multiple_of=snake_case_ , return_token_type_ids=snake_case_ , return_attention_mask=snake_case_ , return_overflowing_tokens=snake_case_ , return_special_tokens_mask=snake_case_ , return_offsets_mapping=snake_case_ , return_length=snake_case_ , verbose=snake_case_ , return_tensors=snake_case_ , **snake_case_ , )
# add pixel_values + pixel_mask
_A = self.image_processor(snake_case_ , return_tensors=snake_case_ )
encoding.update(snake_case_ )
return encoding
def lowerCAmelCase__ ( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def lowerCAmelCase__ ( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
@property
def lowerCAmelCase__ ( self ):
_A = self.tokenizer.model_input_names
_A = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def lowerCAmelCase__ ( self ):
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , snake_case_ , )
return self.image_processor_class
@property
def lowerCAmelCase__ ( self ):
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , snake_case_ , )
return self.image_processor
| 27 | import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
lowerCamelCase_ : List[Any] = pd.read_csv("""sample_data.csv""", header=None)
lowerCamelCase_ : Optional[Any] = df.shape[:1][0]
# If you're using some other dataset input the target column
lowerCamelCase_ : Union[str, Any] = df.iloc[:, 1:2]
lowerCamelCase_ : str = actual_data.values.reshape(len_data, 1)
lowerCamelCase_ : List[Any] = MinMaxScaler().fit_transform(actual_data)
lowerCamelCase_ : List[str] = 10
lowerCamelCase_ : Tuple = 5
lowerCamelCase_ : Optional[Any] = 20
lowerCamelCase_ : List[Any] = len_data - periods * look_back
lowerCamelCase_ : Union[str, Any] = actual_data[:division]
lowerCamelCase_ : Dict = actual_data[division - look_back :]
lowerCamelCase_ , lowerCamelCase_ : int = [], []
lowerCamelCase_ , lowerCamelCase_ : List[Any] = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
lowerCamelCase_ : Dict = np.array(train_x)
lowerCamelCase_ : Union[str, Any] = np.array(test_x)
lowerCamelCase_ : Optional[Any] = np.array([list(i.ravel()) for i in train_y])
lowerCamelCase_ : Any = np.array([list(i.ravel()) for i in test_y])
lowerCamelCase_ : int = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss="""mean_squared_error""", optimizer="""adam""")
lowerCamelCase_ : Optional[int] = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
lowerCamelCase_ : int = model.predict(x_test)
| 559 | 0 |
"""simple docstring"""
def lowercase_ ( _snake_case = 3 ,_snake_case = 7 ,_snake_case = 1_000_000 ):
SCREAMING_SNAKE_CASE__ : List[Any] = 0
SCREAMING_SNAKE_CASE__ : Tuple = 1
for current_denominator in range(1 ,limit + 1 ):
SCREAMING_SNAKE_CASE__ : Dict = current_denominator * numerator // denominator
if current_denominator % denominator == 0:
current_numerator -= 1
if current_numerator * max_denominator > current_denominator * max_numerator:
SCREAMING_SNAKE_CASE__ : Dict = current_numerator
SCREAMING_SNAKE_CASE__ : int = current_denominator
return max_numerator
if __name__ == "__main__":
print(solution(numerator=3, denominator=7, limit=1_0_0_0_0_0_0))
| 711 |
"""simple docstring"""
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
UpperCAmelCase__ : Optional[int] = logging.getLogger(__name__)
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
__UpperCamelCase : Optional[int] = '''summarization'''
__UpperCamelCase : int = ['''loss''']
__UpperCamelCase : Dict = ROUGE_KEYS
__UpperCamelCase : Any = '''rouge2'''
def __init__(self , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
if hparams.sortish_sampler and hparams.gpus > 1:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 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__(SCREAMING_SNAKE_CASE__ , num_labels=SCREAMING_SNAKE_CASE__ , mode=self.mode , **SCREAMING_SNAKE_CASE__ )
use_task_specific_params(self.model , """summarization""" )
save_git_info(self.hparams.output_dir )
SCREAMING_SNAKE_CASE__ : str = Path(self.output_dir ) / """metrics.json"""
SCREAMING_SNAKE_CASE__ : Any = Path(self.output_dir ) / """hparams.pkl"""
pickle_save(self.hparams , self.hparams_save_path )
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Union[str, Any] = defaultdict(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Any = self.config.model_type
SCREAMING_SNAKE_CASE__ : Dict = self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size
SCREAMING_SNAKE_CASE__ : dict = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""train""": self.hparams.n_train,
"""val""": self.hparams.n_val,
"""test""": self.hparams.n_test,
}
SCREAMING_SNAKE_CASE__ : Optional[Any] = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
SCREAMING_SNAKE_CASE__ : Any = {
"""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__ : Tuple = get_git_info()["""repo_sha"""]
SCREAMING_SNAKE_CASE__ : List[str] = hparams.num_workers
SCREAMING_SNAKE_CASE__ : Optional[int] = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE__ : List[Any] = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
SCREAMING_SNAKE_CASE__ : List[Any] = self.decoder_start_token_id
SCREAMING_SNAKE_CASE__ : List[str] = (
SeqaSeqDataset if hasattr(self.tokenizer , """prepare_seq2seq_batch""" ) else LegacySeqaSeqDataset
)
SCREAMING_SNAKE_CASE__ : Optional[int] = False
SCREAMING_SNAKE_CASE__ : int = 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__ : Optional[int] = self.hparams.eval_max_gen_length
else:
SCREAMING_SNAKE_CASE__ : List[str] = self.model.config.max_length
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> Dict[str, List[str]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
k: self.tokenizer.batch_decode(v.tolist() ) if """mask""" not in k else v.shape for k, v in batch.items()
}
save_json(SCREAMING_SNAKE_CASE__ , 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__ : str = True
return readable_batch
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
return self.model(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.tokenizer.batch_decode(
SCREAMING_SNAKE_CASE__ , skip_special_tokens=SCREAMING_SNAKE_CASE__ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
return lmap(str.strip , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.tokenizer.pad_token_id
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch["""input_ids"""], batch["""attention_mask"""]
SCREAMING_SNAKE_CASE__ : Dict = batch["""labels"""]
if isinstance(self.model , SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE__ : int = self.model._shift_right(SCREAMING_SNAKE_CASE__ )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = shift_tokens_right(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
SCREAMING_SNAKE_CASE__ : List[Any] = decoder_input_ids
self.save_readable_batch(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Dict = self(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , decoder_input_ids=SCREAMING_SNAKE_CASE__ , use_cache=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = outputs["""logits"""]
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
SCREAMING_SNAKE_CASE__ : Dict = nn.CrossEntropyLoss(ignore_index=SCREAMING_SNAKE_CASE__ )
assert lm_logits.shape[-1] == self.vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = ce_loss_fct(lm_logits.view(-1 , lm_logits.shape[-1] ) , tgt_ids.view(-1 ) )
else:
SCREAMING_SNAKE_CASE__ : int = nn.functional.log_softmax(SCREAMING_SNAKE_CASE__ , dim=-1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = label_smoothed_nll_loss(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , self.hparams.label_smoothing , ignore_index=SCREAMING_SNAKE_CASE__ )
return (loss,)
@property
def __magic_name__ (self ) -> int:
"""simple docstring"""
return self.tokenizer.pad_token_id
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self._step(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Dict = dict(zip(self.loss_names , SCREAMING_SNAKE_CASE__ ) )
# tokens per batch
SCREAMING_SNAKE_CASE__ : List[Any] = batch["""input_ids"""].ne(self.pad ).sum() + batch["""labels"""].ne(self.pad ).sum()
SCREAMING_SNAKE_CASE__ : int = batch["""input_ids"""].shape[0]
SCREAMING_SNAKE_CASE__ : int = batch["""input_ids"""].eq(self.pad ).sum()
SCREAMING_SNAKE_CASE__ : int = 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 __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Dict:
"""simple docstring"""
return self._generative_step(SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="val" ) -> Dict:
"""simple docstring"""
self.step_count += 1
SCREAMING_SNAKE_CASE__ : Optional[int] = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
SCREAMING_SNAKE_CASE__ : Any = losses["""loss"""]
SCREAMING_SNAKE_CASE__ : Dict = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
}
SCREAMING_SNAKE_CASE__ : str = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
SCREAMING_SNAKE_CASE__ : torch.FloatTensor = torch.tensor(SCREAMING_SNAKE_CASE__ ).type_as(SCREAMING_SNAKE_CASE__ )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = {F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
SCREAMING_SNAKE_CASE__ : List[Any] = self.step_count
self.metrics[prefix].append(SCREAMING_SNAKE_CASE__ ) # callback writes this to self.metrics_save_path
SCREAMING_SNAKE_CASE__ : Optional[Any] = 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 __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Dict:
"""simple docstring"""
return calculate_rouge(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
SCREAMING_SNAKE_CASE__ : int = self.model.generate(
batch["""input_ids"""] , attention_mask=batch["""attention_mask"""] , use_cache=SCREAMING_SNAKE_CASE__ , decoder_start_token_id=self.decoder_start_token_id , num_beams=self.eval_beams , max_length=self.eval_max_length , )
SCREAMING_SNAKE_CASE__ : Optional[int] = (time.time() - ta) / batch["""input_ids"""].shape[0]
SCREAMING_SNAKE_CASE__ : List[str] = self.ids_to_clean_text(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[str] = self.ids_to_clean_text(batch["""labels"""] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self._step(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = dict(zip(self.loss_names , SCREAMING_SNAKE_CASE__ ) )
SCREAMING_SNAKE_CASE__ : Dict = self.calc_generative_metrics(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = np.mean(lmap(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
base_metrics.update(gen_time=SCREAMING_SNAKE_CASE__ , gen_len=SCREAMING_SNAKE_CASE__ , preds=SCREAMING_SNAKE_CASE__ , target=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
return base_metrics
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
"""simple docstring"""
return self._generative_step(SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> List[str]:
"""simple docstring"""
return self.validation_epoch_end(SCREAMING_SNAKE_CASE__ , prefix="""test""" )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> SeqaSeqDataset:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.n_obs[type_path]
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.target_lens[type_path]
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.dataset_class(
self.tokenizer , type_path=SCREAMING_SNAKE_CASE__ , n_obs=SCREAMING_SNAKE_CASE__ , max_target_length=SCREAMING_SNAKE_CASE__ , **self.dataset_kwargs , )
return dataset
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = False ) -> DataLoader:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_dataset(SCREAMING_SNAKE_CASE__ )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
SCREAMING_SNAKE_CASE__ : Tuple = dataset.make_sortish_sampler(SCREAMING_SNAKE_CASE__ , distributed=self.hparams.gpus > 1 )
return DataLoader(
SCREAMING_SNAKE_CASE__ , batch_size=SCREAMING_SNAKE_CASE__ , collate_fn=dataset.collate_fn , shuffle=SCREAMING_SNAKE_CASE__ , num_workers=self.num_workers , sampler=SCREAMING_SNAKE_CASE__ , )
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
SCREAMING_SNAKE_CASE__ : int = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch , distributed=self.hparams.gpus > 1 )
return DataLoader(
SCREAMING_SNAKE_CASE__ , batch_sampler=SCREAMING_SNAKE_CASE__ , collate_fn=dataset.collate_fn , num_workers=self.num_workers , )
else:
return DataLoader(
SCREAMING_SNAKE_CASE__ , batch_size=SCREAMING_SNAKE_CASE__ , collate_fn=dataset.collate_fn , shuffle=SCREAMING_SNAKE_CASE__ , num_workers=self.num_workers , sampler=SCREAMING_SNAKE_CASE__ , )
def __magic_name__ (self ) -> DataLoader:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.get_dataloader("""train""" , batch_size=self.hparams.train_batch_size , shuffle=SCREAMING_SNAKE_CASE__ )
return dataloader
def __magic_name__ (self ) -> DataLoader:
"""simple docstring"""
return self.get_dataloader("""val""" , batch_size=self.hparams.eval_batch_size )
def __magic_name__ (self ) -> DataLoader:
"""simple docstring"""
return self.get_dataloader("""test""" , batch_size=self.hparams.eval_batch_size )
@staticmethod
def __magic_name__ (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
BaseTransformer.add_model_specific_args(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
add_generic_args(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
parser.add_argument(
"""--max_source_length""" , default=10_24 , type=SCREAMING_SNAKE_CASE__ , 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=SCREAMING_SNAKE_CASE__ , 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=1_42 , type=SCREAMING_SNAKE_CASE__ , 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=1_42 , type=SCREAMING_SNAKE_CASE__ , 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=SCREAMING_SNAKE_CASE__ )
parser.add_argument("""--overwrite_output_dir""" , action="""store_true""" , default=SCREAMING_SNAKE_CASE__ )
parser.add_argument("""--max_tokens_per_batch""" , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ )
parser.add_argument("""--logger_name""" , type=SCREAMING_SNAKE_CASE__ , choices=["""default""", """wandb""", """wandb_shared"""] , default="""default""" )
parser.add_argument("""--n_train""" , type=SCREAMING_SNAKE_CASE__ , default=-1 , required=SCREAMING_SNAKE_CASE__ , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_val""" , type=SCREAMING_SNAKE_CASE__ , default=5_00 , required=SCREAMING_SNAKE_CASE__ , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_test""" , type=SCREAMING_SNAKE_CASE__ , default=-1 , required=SCREAMING_SNAKE_CASE__ , help="""# examples. -1 means use all.""" )
parser.add_argument(
"""--task""" , type=SCREAMING_SNAKE_CASE__ , default="""summarization""" , required=SCREAMING_SNAKE_CASE__ , help="""# examples. -1 means use all.""" )
parser.add_argument("""--label_smoothing""" , type=SCREAMING_SNAKE_CASE__ , default=0.0 , required=SCREAMING_SNAKE_CASE__ )
parser.add_argument("""--src_lang""" , type=SCREAMING_SNAKE_CASE__ , default="""""" , required=SCREAMING_SNAKE_CASE__ )
parser.add_argument("""--tgt_lang""" , type=SCREAMING_SNAKE_CASE__ , default="""""" , required=SCREAMING_SNAKE_CASE__ )
parser.add_argument("""--eval_beams""" , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ , required=SCREAMING_SNAKE_CASE__ )
parser.add_argument(
"""--val_metric""" , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ , required=SCREAMING_SNAKE_CASE__ , choices=["""bleu""", """rouge2""", """loss""", None] )
parser.add_argument("""--eval_max_gen_length""" , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ , help="""never generate more than n tokens""" )
parser.add_argument("""--save_top_k""" , type=SCREAMING_SNAKE_CASE__ , default=1 , required=SCREAMING_SNAKE_CASE__ , help="""How many checkpoints to save""" )
parser.add_argument(
"""--early_stopping_patience""" , type=SCREAMING_SNAKE_CASE__ , default=-1 , required=SCREAMING_SNAKE_CASE__ , 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 lowerCAmelCase_ (a__ ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] = '''translation'''
__UpperCamelCase : Optional[Any] = ['''loss''']
__UpperCamelCase : Optional[int] = ['''bleu''']
__UpperCamelCase : Tuple = '''bleu'''
def __init__(self , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) -> List[str]:
"""simple docstring"""
super().__init__(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = hparams.src_lang
SCREAMING_SNAKE_CASE__ : int = hparams.tgt_lang
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> dict:
"""simple docstring"""
return calculate_bleu(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def lowercase_ ( _snake_case ,_snake_case=None ):
Path(args.output_dir ).mkdir(exist_ok=_snake_case )
check_output_dir(_snake_case ,expected_items=3 )
if model is None:
if "summarization" in args.task:
SCREAMING_SNAKE_CASE__ : SummarizationModule = SummarizationModule(_snake_case )
else:
SCREAMING_SNAKE_CASE__ : SummarizationModule = TranslationModule(_snake_case )
SCREAMING_SNAKE_CASE__ : List[str] = 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__ : List[Any] = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
SCREAMING_SNAKE_CASE__ : Optional[int] = os.environ.get("""WANDB_PROJECT""" ,_snake_case )
SCREAMING_SNAKE_CASE__ : Optional[Any] = WandbLogger(name=model.output_dir.name ,project=_snake_case )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
SCREAMING_SNAKE_CASE__ : Tuple = WandbLogger(name=model.output_dir.name ,project=f'''hf_{dataset}''' )
if args.early_stopping_patience >= 0:
SCREAMING_SNAKE_CASE__ : List[str] = get_early_stopping_callback(model.val_metric ,args.early_stopping_patience )
else:
SCREAMING_SNAKE_CASE__ : Tuple = False
SCREAMING_SNAKE_CASE__ : Any = args.val_metric == """loss"""
SCREAMING_SNAKE_CASE__ : pl.Trainer = generic_train(
_snake_case ,_snake_case ,logging_callback=SeqaSeqLoggingCallback() ,checkpoint_callback=get_checkpoint_callback(
args.output_dir ,model.val_metric ,args.save_top_k ,_snake_case ) ,early_stopping_callback=_snake_case ,logger=_snake_case ,)
pickle_save(model.hparams ,model.output_dir / """hparams.pkl""" )
if not args.do_predict:
return model
SCREAMING_SNAKE_CASE__ : List[str] = """"""
SCREAMING_SNAKE_CASE__ : List[Any] = sorted(glob.glob(os.path.join(args.output_dir ,"""*.ckpt""" ) ,recursive=_snake_case ) )
if checkpoints:
SCREAMING_SNAKE_CASE__ : Optional[int] = checkpoints[-1]
SCREAMING_SNAKE_CASE__ : Any = 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__":
UpperCAmelCase__ : Optional[int] = argparse.ArgumentParser()
UpperCAmelCase__ : Optional[int] = pl.Trainer.add_argparse_args(parser)
UpperCAmelCase__ : List[Any] = SummarizationModule.add_model_specific_args(parser, os.getcwd())
UpperCAmelCase__ : Any = parser.parse_args()
main(args)
| 545 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
"SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
"SEWForCTC",
"SEWForSequenceClassification",
"SEWModel",
"SEWPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_sew import (
SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
SEWForCTC,
SEWForSequenceClassification,
SEWModel,
SEWPreTrainedModel,
)
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 498 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __lowerCamelCase ( a_ : Dict ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE :Optional[int] = os.path.join(args.tf_model_dir , '''parameters.json''' )
__SCREAMING_SNAKE_CASE :Dict = json.loads(open(a_ ).read() )
if not params:
raise ValueError(
f'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.''' )
if not args.output.endswith('''.pt''' ):
__SCREAMING_SNAKE_CASE :Tuple = args.output + '''.pt'''
__SCREAMING_SNAKE_CASE :Union[str, Any] = OrderedDict()
with tf.device('''/CPU:0''' ):
__SCREAMING_SNAKE_CASE :Optional[int] = tf.train.load_checkpoint(args.tf_model_dir )
__SCREAMING_SNAKE_CASE :Tuple = reader.get_variable_to_shape_map()
for key_name in shapes.keys():
__SCREAMING_SNAKE_CASE :str = reader.get_tensor(a_ ).astype(np.floataa )
if key_name.endswith('''/adam_m''' ) or key_name.endswith('''/adam_v''' ):
continue
if key_name.startswith('''pasts/''' ):
if key_name.startswith('''pasts/mlp''' ):
__SCREAMING_SNAKE_CASE :Optional[Any] = int(key_name[9] )
elif key_name.startswith('''pasts/out''' ):
__SCREAMING_SNAKE_CASE :List[str] = 8
__SCREAMING_SNAKE_CASE :List[str] = '''model.sqout.%d.weight''' % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
__SCREAMING_SNAKE_CASE :Optional[int] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
__SCREAMING_SNAKE_CASE :Any = torch.tensor(a_ )
elif key_name.startswith('''model/moe''' ):
__SCREAMING_SNAKE_CASE :List[Any] = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/switch_gating/kernel''' ):
__SCREAMING_SNAKE_CASE :Union[str, Any] = '''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
__SCREAMING_SNAKE_CASE :str = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
__SCREAMING_SNAKE_CASE :Optional[int] = torch.tensor(a_ )
elif key_name.endswith('''/softmlp/kernel''' ):
__SCREAMING_SNAKE_CASE :Tuple = '''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
__SCREAMING_SNAKE_CASE :Union[str, Any] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
__SCREAMING_SNAKE_CASE :List[Any] = torch.tensor(a_ )
elif key_name.endswith('''/wo/kernel''' ) or key_name.endswith('''/wi/kernel''' ):
__SCREAMING_SNAKE_CASE :Optional[Any] = key_name[-9:-7]
for i in range(16 ):
__SCREAMING_SNAKE_CASE :int = '''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
__SCREAMING_SNAKE_CASE :List[Any] = (
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
__SCREAMING_SNAKE_CASE :Optional[int] = torch.tensor(a_ )
elif key_name.startswith('''model/mlp''' ):
__SCREAMING_SNAKE_CASE :Optional[int] = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/p1/kernel''' ):
__SCREAMING_SNAKE_CASE :Optional[Any] = '''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
__SCREAMING_SNAKE_CASE :Optional[int] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
__SCREAMING_SNAKE_CASE :str = torch.tensor(a_ )
elif key_name.endswith('''/p1/bias''' ):
__SCREAMING_SNAKE_CASE :List[Any] = '''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
__SCREAMING_SNAKE_CASE :List[str] = vnp.copy() # same because it is one dimensional
__SCREAMING_SNAKE_CASE :int = torch.tensor(a_ )
elif key_name.endswith('''/p2/kernel''' ):
__SCREAMING_SNAKE_CASE :Optional[Any] = '''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
__SCREAMING_SNAKE_CASE :Tuple = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
__SCREAMING_SNAKE_CASE :Dict = torch.tensor(a_ )
elif key_name.endswith('''/p2/bias''' ):
__SCREAMING_SNAKE_CASE :Dict = '''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
__SCREAMING_SNAKE_CASE :Optional[int] = vnp.copy() # same because it is one dimensional
__SCREAMING_SNAKE_CASE :int = torch.tensor(a_ )
elif key_name.startswith('''model/ln''' ):
__SCREAMING_SNAKE_CASE :Tuple = int(key_name[8:].split('''/''' )[0] )
if key_name.endswith('''/b''' ):
__SCREAMING_SNAKE_CASE :Optional[Any] = '''model.blocks.%d.feed_forward.norm.bias''' % player
__SCREAMING_SNAKE_CASE :Dict = vnp.copy() # same because it is one dimensional
__SCREAMING_SNAKE_CASE :List[str] = torch.tensor(a_ )
elif key_name.endswith('''/g''' ):
__SCREAMING_SNAKE_CASE :Any = '''model.blocks.%d.feed_forward.norm.weight''' % player
__SCREAMING_SNAKE_CASE :List[Any] = vnp.copy() # same because it is one dimensional
__SCREAMING_SNAKE_CASE :Tuple = torch.tensor(a_ )
elif key_name.startswith('''model/att''' ):
__SCREAMING_SNAKE_CASE :Tuple = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/qkv/kernel''' ):
__SCREAMING_SNAKE_CASE :Any = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
__SCREAMING_SNAKE_CASE :Union[str, Any] = state[:, 0, :, :]
__SCREAMING_SNAKE_CASE :Dict = state[:, 1, :, :]
__SCREAMING_SNAKE_CASE :Union[str, Any] = state[:, 2, :, :]
__SCREAMING_SNAKE_CASE :Optional[Any] = (
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
__SCREAMING_SNAKE_CASE :Union[str, Any] = (
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
__SCREAMING_SNAKE_CASE :Tuple = (
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
__SCREAMING_SNAKE_CASE :Any = '''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
__SCREAMING_SNAKE_CASE :List[Any] = torch.tensor(a_ )
__SCREAMING_SNAKE_CASE :int = '''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
__SCREAMING_SNAKE_CASE :str = torch.tensor(a_ )
__SCREAMING_SNAKE_CASE :int = '''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
__SCREAMING_SNAKE_CASE :str = torch.tensor(a_ )
elif key_name.endswith('''/o/kernel''' ):
__SCREAMING_SNAKE_CASE :Union[str, Any] = '''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
__SCREAMING_SNAKE_CASE :Any = (
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
__SCREAMING_SNAKE_CASE :Union[str, Any] = torch.tensor(a_ )
elif key_name.startswith('''model/an''' ):
__SCREAMING_SNAKE_CASE :Optional[int] = int(key_name[8:].split('''/''' )[0] )
if key_name.endswith('''/b''' ):
__SCREAMING_SNAKE_CASE :List[Any] = '''model.blocks.%d.self_attn.norm.bias''' % player
__SCREAMING_SNAKE_CASE :Tuple = vnp.copy() # same because it is one dimensional
__SCREAMING_SNAKE_CASE :Union[str, Any] = torch.tensor(a_ )
elif key_name.endswith('''/g''' ):
__SCREAMING_SNAKE_CASE :Optional[int] = '''model.blocks.%d.self_attn.norm.weight''' % player
__SCREAMING_SNAKE_CASE :List[str] = vnp.copy() # same because it is one dimensional
__SCREAMING_SNAKE_CASE :Tuple = torch.tensor(a_ )
elif (
key_name.startswith('''model/wte''' )
or key_name.startswith('''model/wpe''' )
or key_name.startswith('''model/ete''' )
):
__SCREAMING_SNAKE_CASE :str = {'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
key_name[-3:]
]
__SCREAMING_SNAKE_CASE :Optional[int] = '''model.%s.weight''' % nlayer
__SCREAMING_SNAKE_CASE :int = vnp.copy() # same in embedded
__SCREAMING_SNAKE_CASE :str = torch.tensor(a_ )
if key_name.startswith('''model/wte''' ):
__SCREAMING_SNAKE_CASE :Union[str, Any] = '''lm_head.weight'''
__SCREAMING_SNAKE_CASE :Optional[Any] = vnp.copy() # same in embedded
__SCREAMING_SNAKE_CASE :List[str] = torch.tensor(a_ )
elif key_name.startswith('''model/wob''' ):
__SCREAMING_SNAKE_CASE :Any = '''final_logits_bias'''
__SCREAMING_SNAKE_CASE :int = vnp.copy() # same in embedded
__SCREAMING_SNAKE_CASE :List[Any] = state.reshape((1, -1) )
__SCREAMING_SNAKE_CASE :str = torch.tensor(a_ )
elif key_name == "model/dense/kernel":
__SCREAMING_SNAKE_CASE :int = '''model.last_project.weight'''
__SCREAMING_SNAKE_CASE :Any = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
__SCREAMING_SNAKE_CASE :Dict = torch.tensor(a_ )
elif key_name == "model/dense_1/bias":
__SCREAMING_SNAKE_CASE :List[str] = '''model.last_project.bias'''
__SCREAMING_SNAKE_CASE :Any = vnp.copy() # same because it is one dimensional
__SCREAMING_SNAKE_CASE :Optional[Any] = torch.tensor(a_ )
torch.save(a_ , args.output )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser(
description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
lowerCamelCase_ = parser.parse_args()
convert_tf_gptsan_to_pt(args) | 498 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
a = random.Random()
def UpperCamelCase_( __magic_name__ : Tuple , __magic_name__ : str=1.0 , __magic_name__ : Dict=None , __magic_name__ : int=None ):
"""simple docstring"""
if rng is None:
_lowerCAmelCase :str = global_rng
_lowerCAmelCase :Tuple = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
def __init__( self: List[Any] , _UpperCAmelCase: int , _UpperCAmelCase: List[str]=7 , _UpperCAmelCase: Optional[Any]=400 , _UpperCAmelCase: Optional[int]=2000 , _UpperCAmelCase: Optional[int]=2048 , _UpperCAmelCase: List[Any]=128 , _UpperCAmelCase: Optional[Any]=1 , _UpperCAmelCase: List[Any]=512 , _UpperCAmelCase: List[Any]=30 , _UpperCAmelCase: Optional[Any]=4_4100 , ):
_lowerCAmelCase :List[str] = parent
_lowerCAmelCase :Union[str, Any] = batch_size
_lowerCAmelCase :Optional[int] = min_seq_length
_lowerCAmelCase :Union[str, Any] = max_seq_length
_lowerCAmelCase :Any = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_lowerCAmelCase :List[str] = spectrogram_length
_lowerCAmelCase :Any = feature_size
_lowerCAmelCase :int = num_audio_channels
_lowerCAmelCase :Dict = hop_length
_lowerCAmelCase :Optional[Any] = chunk_length
_lowerCAmelCase :List[Any] = sampling_rate
def SCREAMING_SNAKE_CASE__ ( self: Optional[Any] ):
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def SCREAMING_SNAKE_CASE__ ( self: str , _UpperCAmelCase: Optional[Any]=False , _UpperCAmelCase: List[Any]=False ):
def _flatten(_UpperCAmelCase: str ):
return list(itertools.chain(*_UpperCAmelCase ) )
if equal_length:
_lowerCAmelCase :Tuple = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
_lowerCAmelCase :Optional[int] = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_lowerCAmelCase :Union[str, Any] = [np.asarray(_UpperCAmelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCAmelCase_ (snake_case__ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = TvltFeatureExtractor
def SCREAMING_SNAKE_CASE__ ( self: Dict ):
_lowerCAmelCase :Any = TvltFeatureExtractionTester(self )
def SCREAMING_SNAKE_CASE__ ( self: Optional[Any] ):
_lowerCAmelCase :Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(_UpperCAmelCase , 'spectrogram_length' ) )
self.assertTrue(hasattr(_UpperCAmelCase , 'feature_size' ) )
self.assertTrue(hasattr(_UpperCAmelCase , 'num_audio_channels' ) )
self.assertTrue(hasattr(_UpperCAmelCase , 'hop_length' ) )
self.assertTrue(hasattr(_UpperCAmelCase , 'chunk_length' ) )
self.assertTrue(hasattr(_UpperCAmelCase , 'sampling_rate' ) )
def SCREAMING_SNAKE_CASE__ ( self: Optional[int] ):
_lowerCAmelCase :Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCAmelCase :Dict = feat_extract_first.save_pretrained(_UpperCAmelCase )[0]
check_json_file_has_correct_format(_UpperCAmelCase )
_lowerCAmelCase :Dict = self.feature_extraction_class.from_pretrained(_UpperCAmelCase )
_lowerCAmelCase :Optional[int] = feat_extract_first.to_dict()
_lowerCAmelCase :Optional[Any] = feat_extract_second.to_dict()
_lowerCAmelCase :List[str] = dict_first.pop('mel_filters' )
_lowerCAmelCase :int = dict_second.pop('mel_filters' )
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase ) )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self: str ):
_lowerCAmelCase :Any = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCAmelCase :List[str] = os.path.join(_UpperCAmelCase , 'feat_extract.json' )
feat_extract_first.to_json_file(_UpperCAmelCase )
_lowerCAmelCase :Any = self.feature_extraction_class.from_json_file(_UpperCAmelCase )
_lowerCAmelCase :List[Any] = feat_extract_first.to_dict()
_lowerCAmelCase :Tuple = feat_extract_second.to_dict()
_lowerCAmelCase :str = dict_first.pop('mel_filters' )
_lowerCAmelCase :Any = dict_second.pop('mel_filters' )
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase ) )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self: List[Any] ):
# Initialize feature_extractor
_lowerCAmelCase :Dict = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
_lowerCAmelCase :str = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
_lowerCAmelCase :List[str] = [np.asarray(_UpperCAmelCase ) for speech_input in speech_inputs]
# Test not batched input
_lowerCAmelCase :List[Any] = feature_extractor(np_speech_inputs[0] , return_tensors='np' , sampling_rate=4_4100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
_lowerCAmelCase :Optional[int] = feature_extractor(_UpperCAmelCase , return_tensors='np' , sampling_rate=4_4100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
_lowerCAmelCase :Optional[int] = feature_extractor(
_UpperCAmelCase , return_tensors='np' , sampling_rate=4_4100 , mask_audio=_UpperCAmelCase ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
_lowerCAmelCase :Dict = [floats_list((1, x) )[0] for x in (800, 800, 800)]
_lowerCAmelCase :Union[str, Any] = np.asarray(_UpperCAmelCase )
_lowerCAmelCase :Optional[Any] = feature_extractor(_UpperCAmelCase , return_tensors='np' , sampling_rate=4_4100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def SCREAMING_SNAKE_CASE__ ( self: List[str] , _UpperCAmelCase: List[str] ):
_lowerCAmelCase :Dict = load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' )
# automatic decoding with librispeech
_lowerCAmelCase :Tuple = ds.sort('id' ).select(range(_UpperCAmelCase ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def SCREAMING_SNAKE_CASE__ ( self: Optional[Any] ):
_lowerCAmelCase :Union[str, Any] = self._load_datasamples(1 )
_lowerCAmelCase :Optional[int] = TvltFeatureExtractor()
_lowerCAmelCase :Union[str, Any] = feature_extractor(_UpperCAmelCase , return_tensors='pt' ).audio_values
self.assertEquals(audio_values.shape , (1, 1, 192, 128) )
_lowerCAmelCase :Optional[int] = torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] , _UpperCAmelCase , atol=1e-4 ) ) | 704 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class UpperCAmelCase_ (snake_case__ ):
"""simple docstring"""
lowerCamelCase : Union[List[np.ndarray], torch.FloatTensor]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline | 382 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class _UpperCamelCase :
'''simple docstring'''
a_ : int
a_ : Node | None = None
a_ : Node | None = None
def _UpperCAmelCase ( ):
"""simple docstring"""
__lowerCamelCase : str = Node(1 )
__lowerCamelCase : Union[str, Any] = Node(2 )
__lowerCamelCase : Tuple = Node(3 )
__lowerCamelCase : Tuple = Node(4 )
__lowerCamelCase : Tuple = Node(5 )
return tree
def _UpperCAmelCase ( UpperCAmelCase : Node | None ):
"""simple docstring"""
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def _UpperCAmelCase ( UpperCAmelCase : Node | None ):
"""simple docstring"""
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def _UpperCAmelCase ( UpperCAmelCase : Node | None ):
"""simple docstring"""
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def _UpperCAmelCase ( UpperCAmelCase : Node | None ):
"""simple docstring"""
return (max(height(root.left ) , height(root.right ) ) + 1) if root else 0
def _UpperCAmelCase ( UpperCAmelCase : Node | None ):
"""simple docstring"""
__lowerCamelCase : list[Any] = []
if root is None:
return output
__lowerCamelCase : str = deque([root] )
while process_queue:
__lowerCamelCase : Union[str, Any] = 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 ( UpperCAmelCase : Node | None , UpperCAmelCase : int ):
"""simple docstring"""
__lowerCamelCase : list[Any] = []
def populate_output(UpperCAmelCase : Node | None , UpperCAmelCase : int ) -> 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(UpperCAmelCase , UpperCAmelCase )
return output
def _UpperCAmelCase ( UpperCAmelCase : Node | None , UpperCAmelCase : int ):
"""simple docstring"""
__lowerCamelCase : list[Any] = []
def populate_output(UpperCAmelCase : Node | None , UpperCAmelCase : int ) -> 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(UpperCAmelCase , UpperCAmelCase )
return output
def _UpperCAmelCase ( UpperCAmelCase : Node | None ):
"""simple docstring"""
if root is None:
return []
__lowerCamelCase : list[Sequence[Node | None]] = []
__lowerCamelCase : List[str] = 0
__lowerCamelCase : int = height(UpperCAmelCase )
for h in range(1 , height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(UpperCAmelCase , UpperCAmelCase ) )
__lowerCamelCase : Optional[Any] = 1
else:
output.append(get_nodes_from_right_to_left(UpperCAmelCase , UpperCAmelCase ) )
__lowerCamelCase : Any = 0
return output
def _UpperCAmelCase ( ): # Main function for testing.
"""simple docstring"""
__lowerCamelCase : Tuple = make_tree()
print(f"""In-order Traversal: {inorder(UpperCAmelCase )}""" )
print(f"""Pre-order Traversal: {preorder(UpperCAmelCase )}""" )
print(f"""Post-order Traversal: {postorder(UpperCAmelCase )}""" , """\n""" )
print(f"""Height of Tree: {height(UpperCAmelCase )}""" , """\n""" )
print("""Complete Level Order Traversal: """ )
print(level_order(UpperCAmelCase ) , """\n""" )
print("""Level-wise order Traversal: """ )
for level in range(1 , height(UpperCAmelCase ) + 1 ):
print(f"""Level {level}:""" , get_nodes_from_left_to_right(UpperCAmelCase , level=UpperCAmelCase ) )
print("""\nZigZag order Traversal: """ )
print(zigzag(UpperCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 519 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = [
['attention', 'attn'],
['encoder_attention', 'encoder_attn'],
['q_lin', 'q_proj'],
['k_lin', 'k_proj'],
['v_lin', 'v_proj'],
['out_lin', 'out_proj'],
['norm_embeddings', 'layernorm_embedding'],
['position_embeddings', 'embed_positions'],
['embeddings', 'embed_tokens'],
['ffn.lin', 'fc'],
]
def _UpperCAmelCase ( UpperCAmelCase : int ):
"""simple docstring"""
if k == "embeddings.weight":
return "shared.weight"
for parlai_name, hf_name in PATTERNS:
__lowerCamelCase : Union[str, Any] = k.replace(UpperCAmelCase , UpperCAmelCase )
if k.startswith("""encoder""" ):
__lowerCamelCase : Optional[int] = k.replace(""".attn""" , """.self_attn""" )
__lowerCamelCase : List[str] = k.replace("""norm1""" , """self_attn_layer_norm""" )
__lowerCamelCase : Dict = k.replace("""norm2""" , """final_layer_norm""" )
elif k.startswith("""decoder""" ):
__lowerCamelCase : Optional[Any] = k.replace("""norm1""" , """self_attn_layer_norm""" )
__lowerCamelCase : int = k.replace("""norm2""" , """encoder_attn_layer_norm""" )
__lowerCamelCase : Union[str, Any] = k.replace("""norm3""" , """final_layer_norm""" )
return k
def _UpperCAmelCase ( UpperCAmelCase : Dict ):
"""simple docstring"""
__lowerCamelCase : Optional[int] = [
"""model.encoder.layernorm_embedding.weight""",
"""model.encoder.layernorm_embedding.bias""",
"""model.decoder.layernorm_embedding.weight""",
"""model.decoder.layernorm_embedding.bias""",
]
for k in keys:
__lowerCamelCase : str = sd.pop(UpperCAmelCase )
__lowerCamelCase : int = k.replace("""layernorm_embedding""" , """layer_norm""" )
assert new_k not in sd
__lowerCamelCase : str = v
__UpperCamelCase : Optional[Any] = ['START']
@torch.no_grad()
def _UpperCAmelCase ( UpperCAmelCase : Dict , UpperCAmelCase : Any , UpperCAmelCase : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase : Dict = torch.load(UpperCAmelCase , map_location="""cpu""" )
__lowerCamelCase : List[str] = model["""model"""]
__lowerCamelCase : List[str] = BlenderbotConfig.from_json_file(UpperCAmelCase )
__lowerCamelCase : List[Any] = BlenderbotForConditionalGeneration(UpperCAmelCase )
__lowerCamelCase : str = m.model.state_dict().keys()
__lowerCamelCase : Tuple = []
__lowerCamelCase : Dict = {}
for k, v in sd.items():
if k in IGNORE_KEYS:
continue
__lowerCamelCase : Optional[Any] = rename_state_dict_key(UpperCAmelCase )
if new_k not in valid_keys:
failures.append([k, new_k] )
else:
__lowerCamelCase : Dict = v
if cfg.normalize_before: # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
rename_layernorm_keys(UpperCAmelCase )
m.model.load_state_dict(UpperCAmelCase , strict=UpperCAmelCase )
m.half()
m.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
__UpperCamelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--src_path', type=str, help='like blenderbot-model.bin')
parser.add_argument('--save_dir', default='hf_blenderbot', type=str, help='Where to save converted model.')
parser.add_argument(
'--hf_config_json', default='blenderbot-3b-config.json', type=str, help='Path to config to use'
)
__UpperCamelCase : Union[str, Any] = parser.parse_args()
convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)
| 519 | 1 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
class lowerCAmelCase_ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase : str = """sequence-classification"""
def __init__( self , lowerCAmelCase ):
"""simple docstring"""
if type(lowerCAmelCase ) == dict:
snake_case = Namespace(**lowerCAmelCase )
snake_case = glue_output_modes[hparams.task]
snake_case = glue_tasks_num_labels[hparams.task]
super().__init__(lowerCAmelCase , lowerCAmelCase , self.mode )
def snake_case ( self , **lowerCAmelCase ):
"""simple docstring"""
return self.model(**lowerCAmelCase )
def snake_case ( self , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
snake_case = {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
snake_case = batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
snake_case = self(**lowerCAmelCase )
snake_case = outputs[0]
snake_case = self.trainer.lr_schedulers[0]['scheduler']
snake_case = {'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def snake_case ( self ):
"""simple docstring"""
snake_case = self.hparams
snake_case = processors[args.task]()
snake_case = processor.get_labels()
for mode in ["train", "dev"]:
snake_case = self._feature_file(lowerCAmelCase )
if os.path.exists(lowerCAmelCase ) and not args.overwrite_cache:
logger.info('Loading features from cached file %s' , lowerCAmelCase )
else:
logger.info('Creating features from dataset file at %s' , args.data_dir )
snake_case = (
processor.get_dev_examples(args.data_dir )
if mode == 'dev'
else processor.get_train_examples(args.data_dir )
)
snake_case = convert_examples_to_features(
lowerCAmelCase , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('Saving features into cached file %s' , lowerCAmelCase )
torch.save(lowerCAmelCase , lowerCAmelCase )
def snake_case ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = False ):
"""simple docstring"""
snake_case = 'dev' if mode == 'test' else mode
snake_case = self._feature_file(lowerCAmelCase )
logger.info('Loading features from cached file %s' , lowerCAmelCase )
snake_case = torch.load(lowerCAmelCase )
snake_case = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
snake_case = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
snake_case = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
snake_case = torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
snake_case = torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) , batch_size=lowerCAmelCase , shuffle=lowerCAmelCase , )
def snake_case ( self , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
snake_case = {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
snake_case = batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
snake_case = self(**lowerCAmelCase )
snake_case ,snake_case = outputs[:2]
snake_case = logits.detach().cpu().numpy()
snake_case = inputs['labels'].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def snake_case ( self , lowerCAmelCase ):
"""simple docstring"""
snake_case = torch.stack([x['val_loss'] for x in outputs] ).mean().detach().cpu().item()
snake_case = np.concatenate([x['pred'] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
snake_case = np.argmax(lowerCAmelCase , axis=1 )
elif self.hparams.glue_output_mode == "regression":
snake_case = np.squeeze(lowerCAmelCase )
snake_case = np.concatenate([x['target'] for x in outputs] , axis=0 )
snake_case = [[] for _ in range(out_label_ids.shape[0] )]
snake_case = [[] for _ in range(out_label_ids.shape[0] )]
snake_case = {**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task , lowerCAmelCase , lowerCAmelCase )}
snake_case = dict(results.items() )
snake_case = results
return ret, preds_list, out_label_list
def snake_case ( self , lowerCAmelCase ):
"""simple docstring"""
snake_case ,snake_case ,snake_case = self._eval_end(lowerCAmelCase )
snake_case = ret['log']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def snake_case ( self , lowerCAmelCase ):
"""simple docstring"""
snake_case ,snake_case ,snake_case = self._eval_end(lowerCAmelCase )
snake_case = ret['log']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def snake_case ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
BaseTransformer.add_model_specific_args(lowerCAmelCase , lowerCAmelCase )
parser.add_argument(
'--max_seq_length' , default=1_28 , type=lowerCAmelCase , help=(
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
) , )
parser.add_argument(
'--task' , default='' , type=lowerCAmelCase , required=lowerCAmelCase , help='The GLUE task to run' , )
parser.add_argument(
'--gpus' , default=0 , type=lowerCAmelCase , help='The number of GPUs allocated for this, it is by default 0 meaning none' , )
parser.add_argument(
'--overwrite_cache' , action='store_true' , help='Overwrite the cached training and evaluation sets' )
return parser
def lowerCAmelCase__ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case = argparse.ArgumentParser()
add_generic_args(_UpperCamelCase , os.getcwd() )
snake_case = GLUETransformer.add_model_specific_args(_UpperCamelCase , os.getcwd() )
snake_case = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
snake_case = os.path.join(
'./results' , f"""{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}""" , )
os.makedirs(args.output_dir )
snake_case = GLUETransformer(_UpperCamelCase )
snake_case = generic_train(_UpperCamelCase , _UpperCamelCase )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
snake_case = sorted(glob.glob(os.path.join(args.output_dir , 'checkpoint-epoch=*.ckpt' ) , recursive=_UpperCamelCase ) )
snake_case = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(_UpperCamelCase )
if __name__ == "__main__":
main()
| 104 | """simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
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 DonutImageProcessor
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCAmelCase , lowerCAmelCase=7 , lowerCAmelCase=3 , lowerCAmelCase=18 , lowerCAmelCase=30 , lowerCAmelCase=4_00 , lowerCAmelCase=True , lowerCAmelCase=None , lowerCAmelCase=True , lowerCAmelCase=False , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=[0.5, 0.5, 0.5] , lowerCAmelCase=[0.5, 0.5, 0.5] , ):
"""simple docstring"""
snake_case = parent
snake_case = batch_size
snake_case = num_channels
snake_case = image_size
snake_case = min_resolution
snake_case = max_resolution
snake_case = do_resize
snake_case = size if size is not None else {'height': 18, 'width': 20}
snake_case = do_thumbnail
snake_case = do_align_axis
snake_case = do_pad
snake_case = do_normalize
snake_case = image_mean
snake_case = image_std
def snake_case ( self ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class lowerCAmelCase_ ( lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase : str = DonutImageProcessor if is_vision_available() else None
def snake_case ( self ):
"""simple docstring"""
snake_case = DonutImageProcessingTester(self )
@property
def snake_case ( self ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case ( self ):
"""simple docstring"""
snake_case = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase , 'do_resize' ) )
self.assertTrue(hasattr(lowerCAmelCase , 'size' ) )
self.assertTrue(hasattr(lowerCAmelCase , 'do_thumbnail' ) )
self.assertTrue(hasattr(lowerCAmelCase , 'do_align_long_axis' ) )
self.assertTrue(hasattr(lowerCAmelCase , 'do_pad' ) )
self.assertTrue(hasattr(lowerCAmelCase , 'do_normalize' ) )
self.assertTrue(hasattr(lowerCAmelCase , 'image_mean' ) )
self.assertTrue(hasattr(lowerCAmelCase , 'image_std' ) )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 20} )
snake_case = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
# Previous config had dimensions in (width, height) order
snake_case = self.image_processing_class.from_dict(self.image_processor_dict , size=(42, 84) )
self.assertEqual(image_processor.size , {'height': 84, 'width': 42} )
def snake_case ( self ):
"""simple docstring"""
pass
@is_flaky()
def snake_case ( self ):
"""simple docstring"""
snake_case = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase , Image.Image )
# Test not batched input
snake_case = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
snake_case = image_processing(lowerCAmelCase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def snake_case ( self ):
"""simple docstring"""
snake_case = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase , numpify=lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase , np.ndarray )
# Test not batched input
snake_case = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
snake_case = image_processing(lowerCAmelCase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def snake_case ( self ):
"""simple docstring"""
snake_case = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase , torchify=lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase , torch.Tensor )
# Test not batched input
snake_case = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
snake_case = image_processing(lowerCAmelCase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
| 104 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
if not isinstance(UpperCamelCase , UpperCamelCase ):
_a = f'Input value of [number={number}] must be an integer'
raise TypeError(UpperCamelCase )
if number < 0:
return False
_a = 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()
| 22 | import random
def lowerCamelCase_ ( UpperCamelCase__ : list, UpperCamelCase__ : List[Any] ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = [], [], []
for element in data:
if element < pivot:
less.append(UpperCamelCase__ )
elif element > pivot:
greater.append(UpperCamelCase__ )
else:
equal.append(UpperCamelCase__ )
return less, equal, greater
def lowerCamelCase_ ( UpperCamelCase__ : list, UpperCamelCase__ : int ):
'''simple docstring'''
if index >= len(UpperCamelCase__ ) or index < 0:
return None
UpperCamelCase__ = items[random.randint(0, len(UpperCamelCase__ ) - 1 )]
UpperCamelCase__ = 0
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = _partition(UpperCamelCase__, UpperCamelCase__ )
UpperCamelCase__ = len(UpperCamelCase__ )
UpperCamelCase__ = len(UpperCamelCase__ )
# index is the pivot
if m <= index < m + count:
return pivot
# must be in smaller
elif m > index:
return quick_select(UpperCamelCase__, UpperCamelCase__ )
# must be in larger
else:
return quick_select(UpperCamelCase__, index - (m + count) )
| 240 | 0 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
snake_case_ : str = True
except ImportError:
snake_case_ : Optional[Any] = False
snake_case_ : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowerCamelCase( a__):
return AddNewModelCommand(args.testing ,args.testing_file ,path=args.path)
class A__ ( UpperCamelCase__ ):
@staticmethod
def __UpperCamelCase ( _a : ArgumentParser ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =parser.add_parser('''add-new-model''' )
add_new_model_parser.add_argument('''--testing''' , action='''store_true''' , help='''If in testing mode.''' )
add_new_model_parser.add_argument('''--testing_file''' , type=_a , help='''Configuration file on which to run.''' )
add_new_model_parser.add_argument(
'''--path''' , type=_a , help='''Path to cookiecutter. Should only be used for testing purposes.''' )
add_new_model_parser.set_defaults(func=_a )
def __init__( self : List[str] , _a : bool , _a : str , _a : Any=None , *_a : Union[str, Any] ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =testing
_SCREAMING_SNAKE_CASE =testing_file
_SCREAMING_SNAKE_CASE =path
def __UpperCamelCase ( self : List[Any] ) -> int:
"""simple docstring"""
warnings.warn(
'''The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '''
'''It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '''
'''checks, you should use `transformers-cli add-new-model-like` instead.''' )
if not _has_cookiecutter:
raise ImportError(
'''Model creation dependencies are required to use the `add_new_model` command. Install them by running '''
'''the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n''' )
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
_SCREAMING_SNAKE_CASE =[directory for directory in os.listdir() if '''cookiecutter-template-''' == directory[:22]]
if len(_a ) > 0:
raise ValueError(
'''Several directories starting with `cookiecutter-template-` in current working directory. '''
'''Please clean your directory by removing all folders starting with `cookiecutter-template-` or '''
'''change your working directory.''' )
_SCREAMING_SNAKE_CASE =(
Path(_a ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
)
_SCREAMING_SNAKE_CASE =path_to_transformer_root / '''templates''' / '''adding_a_new_model'''
# Execute cookiecutter
if not self._testing:
cookiecutter(str(_a ) )
else:
with open(self._testing_file , '''r''' ) as configuration_file:
_SCREAMING_SNAKE_CASE =json.load(_a )
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path ) , no_input=_a , extra_context=_a , )
_SCREAMING_SNAKE_CASE =[directory for directory in os.listdir() if '''cookiecutter-template-''' in directory[:22]][0]
# Retrieve configuration
with open(directory + '''/configuration.json''' , '''r''' ) as configuration_file:
_SCREAMING_SNAKE_CASE =json.load(_a )
_SCREAMING_SNAKE_CASE =configuration['''lowercase_modelname''']
_SCREAMING_SNAKE_CASE =configuration['''generate_tensorflow_pytorch_and_flax''']
os.remove(f"{directory}/configuration.json" )
_SCREAMING_SNAKE_CASE ='''PyTorch''' in generate_tensorflow_pytorch_and_flax
_SCREAMING_SNAKE_CASE ='''TensorFlow''' in generate_tensorflow_pytorch_and_flax
_SCREAMING_SNAKE_CASE ='''Flax''' in generate_tensorflow_pytorch_and_flax
_SCREAMING_SNAKE_CASE =f"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
os.makedirs(_a , exist_ok=_a )
os.makedirs(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}" , exist_ok=_a )
# Tests require submodules as they have parent imports
with open(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py" , '''w''' ):
pass
shutil.move(
f"{directory}/__init__.py" , f"{model_dir}/__init__.py" , )
shutil.move(
f"{directory}/configuration_{lowercase_model_name}.py" , f"{model_dir}/configuration_{lowercase_model_name}.py" , )
def remove_copy_lines(_a : int ):
with open(_a , '''r''' ) as f:
_SCREAMING_SNAKE_CASE =f.readlines()
with open(_a , '''w''' ) as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(_a )
if output_pytorch:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_{lowercase_model_name}.py" )
shutil.move(
f"{directory}/modeling_{lowercase_model_name}.py" , f"{model_dir}/modeling_{lowercase_model_name}.py" , )
shutil.move(
f"{directory}/test_modeling_{lowercase_model_name}.py" , f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py" , )
else:
os.remove(f"{directory}/modeling_{lowercase_model_name}.py" )
os.remove(f"{directory}/test_modeling_{lowercase_model_name}.py" )
if output_tensorflow:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_tf_{lowercase_model_name}.py" )
shutil.move(
f"{directory}/modeling_tf_{lowercase_model_name}.py" , f"{model_dir}/modeling_tf_{lowercase_model_name}.py" , )
shutil.move(
f"{directory}/test_modeling_tf_{lowercase_model_name}.py" , f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py" , )
else:
os.remove(f"{directory}/modeling_tf_{lowercase_model_name}.py" )
os.remove(f"{directory}/test_modeling_tf_{lowercase_model_name}.py" )
if output_flax:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_flax_{lowercase_model_name}.py" )
shutil.move(
f"{directory}/modeling_flax_{lowercase_model_name}.py" , f"{model_dir}/modeling_flax_{lowercase_model_name}.py" , )
shutil.move(
f"{directory}/test_modeling_flax_{lowercase_model_name}.py" , f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py" , )
else:
os.remove(f"{directory}/modeling_flax_{lowercase_model_name}.py" )
os.remove(f"{directory}/test_modeling_flax_{lowercase_model_name}.py" )
shutil.move(
f"{directory}/{lowercase_model_name}.md" , f"{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md" , )
shutil.move(
f"{directory}/tokenization_{lowercase_model_name}.py" , f"{model_dir}/tokenization_{lowercase_model_name}.py" , )
shutil.move(
f"{directory}/tokenization_fast_{lowercase_model_name}.py" , f"{model_dir}/tokenization_{lowercase_model_name}_fast.py" , )
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(_a : str , _a : str , _a : List[str] ):
# Create temp file
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =mkstemp()
_SCREAMING_SNAKE_CASE =False
with fdopen(_a , '''w''' ) as new_file:
with open(_a ) as old_file:
for line in old_file:
new_file.write(_a )
if line_to_copy_below in line:
_SCREAMING_SNAKE_CASE =True
for line_to_copy in lines_to_copy:
new_file.write(_a )
if not line_found:
raise ValueError(f"Line {line_to_copy_below} was not found in file." )
# Copy the file permissions from the old file to the new file
copymode(_a , _a )
# Remove original file
remove(_a )
# Move new file
move(_a , _a )
def skip_units(_a : str ):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(_a : Any ):
with open(_a ) as datafile:
_SCREAMING_SNAKE_CASE =[]
_SCREAMING_SNAKE_CASE =False
_SCREAMING_SNAKE_CASE =False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
_SCREAMING_SNAKE_CASE =line.split('''"''' )[1]
_SCREAMING_SNAKE_CASE =skip_units(_a )
elif "# Below: " in line and "##" not in line:
_SCREAMING_SNAKE_CASE =line.split('''"''' )[1]
_SCREAMING_SNAKE_CASE =skip_units(_a )
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(_a , _a , _a )
_SCREAMING_SNAKE_CASE =[]
elif "# Replace with" in line and "##" not in line:
_SCREAMING_SNAKE_CASE =[]
elif "##" not in line:
lines_to_copy.append(_a )
remove(_a )
replace_in_files(f"{directory}/to_replace_{lowercase_model_name}.py" )
os.rmdir(_a ) | 191 |
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
snake_case_ : List[Any] = logging.get_logger(__name__)
snake_case_ : Optional[Any] = '''T5Config'''
class A__ ( UpperCamelCase__ ):
UpperCAmelCase = "mt5"
UpperCAmelCase = MTaConfig
class A__ ( UpperCamelCase__ ):
UpperCAmelCase = "mt5"
UpperCAmelCase = MTaConfig
class A__ ( UpperCamelCase__ ):
UpperCAmelCase = "mt5"
UpperCAmelCase = MTaConfig | 191 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
_a : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def _a (lowercase__ : Union[List, PIL.Image.Image, torch.Tensor] ) -> Optional[int]:
"""simple docstring"""
warnings.warn(
'The preprocess method is deprecated and will be removed in a future version. Please'
' use VaeImageProcessor.preprocess instead' , lowercase__ , )
if isinstance(lowercase__ , torch.Tensor ):
return image
elif isinstance(lowercase__ , PIL.Image.Image ):
__snake_case = [image]
if isinstance(image[0] , PIL.Image.Image ):
__snake_case , __snake_case = image[0].size
__snake_case , __snake_case = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
__snake_case = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
__snake_case = np.concatenate(lowercase__ , axis=0 )
__snake_case = np.array(lowercase__ ).astype(np.floataa ) / 2_55.0
__snake_case = image.transpose(0 , 3 , 1 , 2 )
__snake_case = 2.0 * image - 1.0
__snake_case = torch.from_numpy(lowercase__ )
elif isinstance(image[0] , torch.Tensor ):
__snake_case = torch.cat(lowercase__ , dim=0 )
return image
def _a (lowercase__ : Union[List, PIL.Image.Image, torch.Tensor] ) -> Dict:
"""simple docstring"""
if isinstance(lowercase__ , torch.Tensor ):
return mask
elif isinstance(lowercase__ , PIL.Image.Image ):
__snake_case = [mask]
if isinstance(mask[0] , PIL.Image.Image ):
__snake_case , __snake_case = mask[0].size
__snake_case , __snake_case = (x - x % 3_2 for x in (w, h)) # resize to integer multiple of 32
__snake_case = [np.array(m.convert('L' ).resize((w, h) , resample=PIL_INTERPOLATION['nearest'] ) )[None, :] for m in mask]
__snake_case = np.concatenate(lowercase__ , axis=0 )
__snake_case = mask.astype(np.floataa ) / 2_55.0
__snake_case = 0
__snake_case = 1
__snake_case = torch.from_numpy(lowercase__ )
elif isinstance(mask[0] , torch.Tensor ):
__snake_case = torch.cat(lowercase__ , dim=0 )
return mask
class _lowercase ( __lowercase ):
_SCREAMING_SNAKE_CASE : UNetaDModel
_SCREAMING_SNAKE_CASE : RePaintScheduler
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Tuple ) -> Optional[Any]:
super().__init__()
self.register_modules(unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def __call__( self : List[Any] , SCREAMING_SNAKE_CASE_ : Union[torch.Tensor, PIL.Image.Image] , SCREAMING_SNAKE_CASE_ : Union[torch.Tensor, PIL.Image.Image] , SCREAMING_SNAKE_CASE_ : int = 250 , SCREAMING_SNAKE_CASE_ : float = 0.0 , SCREAMING_SNAKE_CASE_ : int = 10 , SCREAMING_SNAKE_CASE_ : int = 10 , SCREAMING_SNAKE_CASE_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , SCREAMING_SNAKE_CASE_ : Optional[str] = "pil" , SCREAMING_SNAKE_CASE_ : bool = True , ) -> Union[ImagePipelineOutput, Tuple]:
__snake_case = image
__snake_case = _preprocess_image(SCREAMING_SNAKE_CASE_ )
__snake_case = original_image.to(device=self.device , dtype=self.unet.dtype )
__snake_case = _preprocess_mask(SCREAMING_SNAKE_CASE_ )
__snake_case = mask_image.to(device=self.device , dtype=self.unet.dtype )
__snake_case = original_image.shape[0]
# sample gaussian noise to begin the loop
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and len(SCREAMING_SNAKE_CASE_ ) != batch_size:
raise ValueError(
f'You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_ )}, but requested an effective batch'
f' size of {batch_size}. Make sure the batch size matches the length of the generators.' )
__snake_case = original_image.shape
__snake_case = randn_tensor(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.device )
__snake_case = eta
__snake_case = self.scheduler.timesteps[0] + 1
__snake_case = generator[0] if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else generator
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
if t < t_last:
# predict the noise residual
__snake_case = self.unet(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).sample
# compute previous image: x_t -> x_t-1
__snake_case = self.scheduler.step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).prev_sample
else:
# compute the reverse: x_t-1 -> x_t
__snake_case = self.scheduler.undo_step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__snake_case = t
__snake_case = (image / 2 + 0.5).clamp(0 , 1 )
__snake_case = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__snake_case = self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_ )
| 56 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
if is_torch_available():
import torch
from transformers import TimmBackbone, TimmBackboneConfig
from ...test_pipeline_mixin import PipelineTesterMixin
class lowercase_ :
def __init__( self , lowercase_ , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_="resnet50" , lowercase_=3 , lowercase_=32 , lowercase_=3 , lowercase_=True , lowercase_=True , ) -> Union[str, Any]:
a__ =parent
a__ =out_indices if out_indices is not None else [4]
a__ =stage_names
a__ =out_features
a__ =backbone
a__ =batch_size
a__ =image_size
a__ =num_channels
a__ =use_pretrained_backbone
a__ =is_training
def __UpperCamelCase ( self) -> Optional[Any]:
a__ =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
a__ =self.get_config()
return config, pixel_values
def __UpperCamelCase ( self) -> Tuple:
return TimmBackboneConfig(
image_size=self.image_size , num_channels=self.num_channels , out_features=self.out_features , out_indices=self.out_indices , stage_names=self.stage_names , use_pretrained_backbone=self.use_pretrained_backbone , backbone=self.backbone , )
def __UpperCamelCase ( self , lowercase_ , lowercase_) -> str:
a__ =TimmBackbone(config=lowercase_)
model.to(lowercase_)
model.eval()
with torch.no_grad():
a__ =model(lowercase_)
self.parent.assertEqual(
result.feature_map[-1].shape , (self.batch_size, model.channels[-1], 14, 14) , )
def __UpperCamelCase ( self) -> str:
a__ =self.prepare_config_and_inputs()
a__ , a__ =config_and_inputs
a__ ={'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
@require_timm
class lowercase_ (lowercase__ , lowercase__ , lowercase__ , unittest.TestCase ):
snake_case =(TimmBackbone,) if is_torch_available() else ()
snake_case ={'feature-extraction': TimmBackbone} if is_torch_available() else {}
snake_case =False
snake_case =False
snake_case =False
snake_case =False
def __UpperCamelCase ( self) -> Optional[Any]:
a__ =TimmBackboneModelTester(self)
a__ =ConfigTester(self , config_class=lowercase_ , has_text_modality=lowercase_)
def __UpperCamelCase ( self) -> Dict:
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 __UpperCamelCase ( self) -> str:
a__ ='resnet18'
a__ ='microsoft/resnet-18'
a__ =AutoBackbone.from_pretrained(lowercase_ , use_timm_backbone=lowercase_)
a__ =AutoBackbone.from_pretrained(lowercase_)
self.assertEqual(len(timm_model.out_features) , len(transformers_model.out_features))
self.assertEqual(len(timm_model.stage_names) , len(transformers_model.stage_names))
self.assertEqual(timm_model.channels , transformers_model.channels)
# Out indices are set to the last layer by default. For timm models, we don't know
# the number of layers in advance, so we set it to (-1,), whereas for transformers
# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
self.assertEqual(timm_model.out_indices , (-1,))
self.assertEqual(transformers_model.out_indices , [len(timm_model.stage_names) - 1])
a__ =AutoBackbone.from_pretrained(lowercase_ , use_timm_backbone=lowercase_ , out_indices=[1, 2, 3])
a__ =AutoBackbone.from_pretrained(lowercase_ , out_indices=[1, 2, 3])
self.assertEqual(timm_model.out_indices , transformers_model.out_indices)
self.assertEqual(len(timm_model.out_features) , len(transformers_model.out_features))
self.assertEqual(timm_model.channels , transformers_model.channels)
@unittest.skip('TimmBackbone doesn\'t support feed forward chunking')
def __UpperCamelCase ( self) -> int:
pass
@unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute')
def __UpperCamelCase ( self) -> List[str]:
pass
@unittest.skip('TimmBackbone initialization is managed on the timm side')
def __UpperCamelCase ( self) -> Any:
pass
@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds')
def __UpperCamelCase ( self) -> Any:
pass
@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds')
def __UpperCamelCase ( self) -> List[str]:
pass
@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint')
def __UpperCamelCase ( self) -> Optional[int]:
pass
@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
def __UpperCamelCase ( self) -> Union[str, Any]:
pass
@unittest.skip('model weights aren\'t tied in TimmBackbone.')
def __UpperCamelCase ( self) -> Dict:
pass
@unittest.skip('model weights aren\'t tied in TimmBackbone.')
def __UpperCamelCase ( self) -> List[Any]:
pass
@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
def __UpperCamelCase ( self) -> List[str]:
pass
@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
def __UpperCamelCase ( self) -> Union[str, Any]:
pass
@unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.')
def __UpperCamelCase ( self) -> int:
pass
@unittest.skip('TimmBackbone doesn\'t support output_attentions.')
def __UpperCamelCase ( self) -> str:
pass
@unittest.skip('Safetensors is not supported by timm.')
def __UpperCamelCase ( self) -> Optional[int]:
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
def __UpperCamelCase ( self) -> Optional[Any]:
pass
def __UpperCamelCase ( self) -> Any:
a__ , a__ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ =model_class(lowercase_)
a__ =inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a__ =[*signature.parameters.keys()]
a__ =['pixel_values']
self.assertListEqual(arg_names[:1] , lowercase_)
def __UpperCamelCase ( self) -> Any:
a__ , a__ =self.model_tester.prepare_config_and_inputs_for_common()
a__ =True
a__ =self.has_attentions
# no need to test all models as different heads yield the same functionality
a__ =self.all_model_classes[0]
a__ =model_class(lowercase_)
model.to(lowercase_)
a__ =self._prepare_for_class(lowercase_ , lowercase_)
a__ =model(**lowercase_)
a__ =outputs[0][-1]
# Encoder-/Decoder-only models
a__ =outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
a__ =outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=lowercase_)
self.assertIsNotNone(hidden_states.grad)
if self.has_attentions:
self.assertIsNotNone(attentions.grad)
def __UpperCamelCase ( self) -> List[str]:
a__ , a__ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ =model_class(lowercase_)
model.to(lowercase_)
model.eval()
a__ =model(**lowercase_)
self.assertEqual(len(result.feature_maps) , len(config.out_indices))
self.assertEqual(len(model.channels) , len(config.out_indices))
# Check output of last stage is taken if out_features=None, out_indices=None
a__ =copy.deepcopy(lowercase_)
a__ =None
a__ =model_class(lowercase_)
model.to(lowercase_)
model.eval()
a__ =model(**lowercase_)
self.assertEqual(len(result.feature_maps) , 1)
self.assertEqual(len(model.channels) , 1)
# Check backbone can be initialized with fresh weights
a__ =copy.deepcopy(lowercase_)
a__ =False
a__ =model_class(lowercase_)
model.to(lowercase_)
model.eval()
a__ =model(**lowercase_)
| 20 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( lowerCamelCase_):
a__ = [0] * len(lowerCamelCase_)
for i in range(1 , len(lowerCamelCase_)):
# use last results for better performance - dynamic programming
a__ = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
a__ = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
a__ = j
return prefix_result
def SCREAMING_SNAKE_CASE ( lowerCamelCase_):
return max(prefix_function(lowerCamelCase_))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 707 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: List[str] , __A: str , __A: Any=2 , __A: str=3 , __A: Tuple=4 , __A: Dict=2 , __A: List[Any]=7 , __A: Any=True , __A: Any=True , __A: List[str]=True , __A: Optional[int]=True , __A: Optional[int]=99 , __A: Tuple=36 , __A: List[str]=2 , __A: Dict=4 , __A: List[str]=37 , __A: Optional[int]="gelu" , __A: Optional[int]=0.1 , __A: Tuple=0.1 , __A: List[Any]=512 , __A: List[str]=16 , __A: Any=2 , __A: Union[str, Any]=0.0_2 , __A: Optional[int]=6 , __A: Union[str, Any]=6 , __A: Union[str, Any]=3 , __A: Tuple=4 , __A: Optional[int]=None , __A: Optional[Any]=1000 , ):
'''simple docstring'''
a__ = parent
a__ = batch_size
a__ = num_channels
a__ = image_size
a__ = patch_size
a__ = is_training
a__ = use_input_mask
a__ = use_token_type_ids
a__ = use_labels
a__ = vocab_size
a__ = hidden_size
a__ = num_hidden_layers
a__ = num_attention_heads
a__ = intermediate_size
a__ = hidden_act
a__ = hidden_dropout_prob
a__ = attention_probs_dropout_prob
a__ = max_position_embeddings
a__ = type_vocab_size
a__ = type_sequence_label_size
a__ = initializer_range
a__ = coordinate_size
a__ = shape_size
a__ = num_labels
a__ = num_choices
a__ = scope
a__ = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
a__ = text_seq_length
a__ = (image_size // patch_size) ** 2 + 1
a__ = self.text_seq_length + self.image_seq_length
def lowercase ( self: Optional[int] ):
'''simple docstring'''
a__ = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
a__ = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
a__ = bbox.numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
a__ = bbox[i, j, 3]
a__ = bbox[i, j, 1]
a__ = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
a__ = bbox[i, j, 2]
a__ = bbox[i, j, 0]
a__ = tmp_coordinate
a__ = tf.constant(__A )
a__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a__ = None
if self.use_input_mask:
a__ = random_attention_mask([self.batch_size, self.text_seq_length] )
a__ = None
if self.use_token_type_ids:
a__ = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
a__ = None
a__ = None
if self.use_labels:
a__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a__ = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
a__ = LayoutLMvaConfig(
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 , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def lowercase ( self: List[str] , __A: List[str] , __A: List[str] , __A: List[str] , __A: int , __A: Any , __A: Any ):
'''simple docstring'''
a__ = TFLayoutLMvaModel(config=__A )
# text + image
a__ = model(__A , pixel_values=__A , training=__A )
a__ = model(
__A , bbox=__A , pixel_values=__A , attention_mask=__A , token_type_ids=__A , training=__A , )
a__ = model(__A , bbox=__A , pixel_values=__A , training=__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
a__ = model(__A , training=__A )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
a__ = model({'''pixel_values''': pixel_values} , training=__A )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def lowercase ( self: Optional[int] , __A: Any , __A: str , __A: List[str] , __A: List[str] , __A: List[str] , __A: Optional[Any] , __A: Any ):
'''simple docstring'''
a__ = self.num_labels
a__ = TFLayoutLMvaForSequenceClassification(config=__A )
a__ = model(
__A , bbox=__A , pixel_values=__A , attention_mask=__A , token_type_ids=__A , labels=__A , training=__A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase ( self: str , __A: List[str] , __A: int , __A: str , __A: Any , __A: str , __A: str , __A: str ):
'''simple docstring'''
a__ = self.num_labels
a__ = TFLayoutLMvaForTokenClassification(config=__A )
a__ = model(
__A , bbox=__A , pixel_values=__A , attention_mask=__A , token_type_ids=__A , labels=__A , training=__A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def lowercase ( self: Union[str, Any] , __A: Any , __A: List[Any] , __A: Any , __A: List[str] , __A: Any , __A: Optional[int] , __A: Tuple ):
'''simple docstring'''
a__ = 2
a__ = TFLayoutLMvaForQuestionAnswering(config=__A )
a__ = model(
__A , bbox=__A , pixel_values=__A , attention_mask=__A , token_type_ids=__A , start_positions=__A , end_positions=__A , training=__A , )
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 lowercase ( self: int ):
'''simple docstring'''
a__ = self.prepare_config_and_inputs()
((a__) ,(a__) ,(a__) ,(a__) ,(a__) ,(a__) ,(a__) ,(a__)) = config_and_inputs
a__ = {
'''input_ids''': input_ids,
'''bbox''': bbox,
'''pixel_values''': pixel_values,
'''token_type_ids''': token_type_ids,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_tf
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE =(
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
_SCREAMING_SNAKE_CASE =(
{'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
if is_tf_available()
else {}
)
_SCREAMING_SNAKE_CASE =False
_SCREAMING_SNAKE_CASE =False
_SCREAMING_SNAKE_CASE =False
def lowercase ( self: List[str] , __A: Dict , __A: Optional[Any] , __A: str , __A: Union[str, Any] , __A: Optional[Any] ):
'''simple docstring'''
return True
def lowercase ( self: Dict , __A: Optional[Any] , __A: Any , __A: List[Any]=False ):
'''simple docstring'''
a__ = copy.deepcopy(__A )
if model_class in get_values(__A ):
a__ = {
k: tf.tile(tf.expand_dims(__A , 1 ) , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(__A , tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(__A ):
a__ = tf.ones(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(__A ):
a__ = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
a__ = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(__A ):
a__ = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(__A ):
a__ = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=tf.intaa )
return inputs_dict
def lowercase ( self: List[str] ):
'''simple docstring'''
a__ = TFLayoutLMvaModelTester(self )
a__ = ConfigTester(self , config_class=__A , hidden_size=37 )
def lowercase ( self: Dict ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase ( self: Any ):
'''simple docstring'''
a__ ,a__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ = model_class(__A )
if getattr(__A , '''hf_compute_loss''' , __A ):
# The number of elements in the loss should be the same as the number of elements in the label
a__ = self._prepare_for_class(inputs_dict.copy() , __A , return_labels=__A )
a__ = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=__A )[0]
]
a__ = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
a__ = self._prepare_for_class(inputs_dict.copy() , __A , return_labels=__A )
a__ = prepared_for_class.pop('''input_ids''' )
a__ = model(__A , **__A )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
a__ = self._prepare_for_class(inputs_dict.copy() , __A , return_labels=__A )
a__ = prepared_for_class.pop('''input_ids''' )
if "labels" in prepared_for_class:
a__ = prepared_for_class['''labels'''].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
a__ = -100
a__ = tf.convert_to_tensor(__A )
a__ = model(__A , **__A )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
a__ = self._prepare_for_class(inputs_dict.copy() , __A , return_labels=__A )
a__ = model(__A )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
a__ = self._prepare_for_class(inputs_dict.copy() , __A , return_labels=__A )
# Get keys that were added with the _prepare_for_class function
a__ = prepared_for_class.keys() - inputs_dict.keys()
a__ = inspect.signature(model.call ).parameters
a__ = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
a__ = {0: '''input_ids'''}
for label_key in label_keys:
a__ = signature_names.index(__A )
a__ = label_key
a__ = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
a__ = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
a__ = prepared_for_class[value]
a__ = tuple(__A )
# Send to model
a__ = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def lowercase ( self: Optional[int] ):
'''simple docstring'''
(
(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,
) = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(__A , __A , __A , __A , __A , __A )
def lowercase ( self: Dict ):
'''simple docstring'''
(
(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,
) = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
a__ = type
self.model_tester.create_and_check_model(__A , __A , __A , __A , __A , __A )
def lowercase ( self: int ):
'''simple docstring'''
(
(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,
) = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
__A , __A , __A , __A , __A , __A , __A )
def lowercase ( self: List[str] ):
'''simple docstring'''
(
(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,
) = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
__A , __A , __A , __A , __A , __A , __A )
def lowercase ( self: Tuple ):
'''simple docstring'''
(
(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,
) = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
__A , __A , __A , __A , __A , __A , __A )
@slow
def lowercase ( self: Union[str, Any] ):
'''simple docstring'''
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ = TFLayoutLMvaModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def SCREAMING_SNAKE_CASE ( ):
a__ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
return image
@require_tf
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowercase ( self: Any ):
'''simple docstring'''
return LayoutLMvaImageProcessor(apply_ocr=__A ) if is_vision_available() else None
@slow
def lowercase ( self: Union[str, Any] ):
'''simple docstring'''
a__ = TFLayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base''' )
a__ = self.default_image_processor
a__ = prepare_img()
a__ = image_processor(images=__A , return_tensors='''tf''' ).pixel_values
a__ = tf.constant([[1, 2]] )
a__ = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) , axis=0 )
# forward pass
a__ = model(input_ids=__A , bbox=__A , pixel_values=__A , training=__A )
# verify the logits
a__ = (1, 199, 768)
self.assertEqual(outputs.last_hidden_state.shape , __A )
a__ = tf.constant(
[[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , __A , atol=1e-4 ) )
| 200 | 0 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_decord,
require_tf,
require_torch,
require_torch_or_tf,
require_vision,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class A ( unittest.TestCase ):
'''simple docstring'''
A__ = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple ) -> Tuple:
"""simple docstring"""
lowercase__ = hf_hub_download(
repo_id="""nateraw/video-demo""" , filename="""archery.mp4""" , repo_type="""dataset""" )
lowercase__ = VideoClassificationPipeline(model=_UpperCAmelCase , image_processor=_UpperCAmelCase , top_k=2 )
lowercase__ = [
example_video_filepath,
"""https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
]
return video_classifier, examples
def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Tuple ) -> List[Any]:
"""simple docstring"""
for example in examples:
lowercase__ = video_classifier(_UpperCAmelCase )
self.assertEqual(
_UpperCAmelCase , [
{"""score""": ANY(_UpperCAmelCase ), """label""": ANY(_UpperCAmelCase )},
{"""score""": ANY(_UpperCAmelCase ), """label""": ANY(_UpperCAmelCase )},
] , )
@require_torch
def lowerCamelCase__ (self : str ) -> List[Any]:
"""simple docstring"""
lowercase__ = """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
lowercase__ = VideoMAEFeatureExtractor(
size={"""shortest_edge""": 10} , crop_size={"""height""": 10, """width""": 10} )
lowercase__ = pipeline(
"""video-classification""" , model=_UpperCAmelCase , feature_extractor=_UpperCAmelCase , frame_sampling_rate=4 )
lowercase__ = hf_hub_download(repo_id="""nateraw/video-demo""" , filename="""archery.mp4""" , repo_type="""dataset""" )
lowercase__ = video_classifier(_UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(_UpperCAmelCase , decimals=4 ) , [{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}] , )
lowercase__ = video_classifier(
[
video_file_path,
video_file_path,
] , top_k=2 , )
self.assertEqual(
nested_simplify(_UpperCAmelCase , decimals=4 ) , [
[{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}],
[{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}],
] , )
@require_tf
def lowerCamelCase__ (self : Any ) -> Union[str, Any]:
"""simple docstring"""
pass
| 15 |
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 A ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__(self : Any , _UpperCAmelCase : NestedDataStructureLike[PathLike] , _UpperCAmelCase : Optional[NamedSplit] = None , _UpperCAmelCase : Optional[Features] = None , _UpperCAmelCase : str = None , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[int] = None , **_UpperCAmelCase : Optional[int] , ) -> List[str]:
"""simple docstring"""
super().__init__(
_UpperCAmelCase , split=_UpperCAmelCase , features=_UpperCAmelCase , cache_dir=_UpperCAmelCase , keep_in_memory=_UpperCAmelCase , streaming=_UpperCAmelCase , num_proc=_UpperCAmelCase , **_UpperCAmelCase , )
lowercase__ = path_or_paths if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else {self.split: path_or_paths}
lowercase__ = Text(
cache_dir=_UpperCAmelCase , data_files=_UpperCAmelCase , features=_UpperCAmelCase , **_UpperCAmelCase , )
def lowerCamelCase__ (self : Tuple ) -> Any:
"""simple docstring"""
if self.streaming:
lowercase__ = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
lowercase__ = None
lowercase__ = None
lowercase__ = None
lowercase__ = None
self.builder.download_and_prepare(
download_config=_UpperCAmelCase , download_mode=_UpperCAmelCase , verification_mode=_UpperCAmelCase , base_path=_UpperCAmelCase , num_proc=self.num_proc , )
lowercase__ = self.builder.as_dataset(
split=self.split , verification_mode=_UpperCAmelCase , in_memory=self.keep_in_memory )
return dataset
| 15 | 1 |
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__ : Optional[int] = 4
UpperCamelCase__ : int = 3
class __snake_case ( lowerCAmelCase__ ):
pass
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
for shard in shards:
for i in range(_SCREAMING_SNAKE_CASE ):
yield {"i": i, "shard": shard}
def _UpperCAmelCase ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = int(os.environ['RANK'] )
SCREAMING_SNAKE_CASE_ = int(os.environ['WORLD_SIZE'] )
SCREAMING_SNAKE_CASE_ = ArgumentParser()
parser.add_argument('--streaming' , type=_SCREAMING_SNAKE_CASE )
parser.add_argument('--local_rank' , type=_SCREAMING_SNAKE_CASE )
parser.add_argument('--num_workers' , type=_SCREAMING_SNAKE_CASE , default=0 )
SCREAMING_SNAKE_CASE_ = parser.parse_args()
SCREAMING_SNAKE_CASE_ = args.streaming
SCREAMING_SNAKE_CASE_ = args.num_workers
SCREAMING_SNAKE_CASE_ = {'shards': [f"""shard_{shard_idx}""" for shard_idx in range(_SCREAMING_SNAKE_CASE )]}
SCREAMING_SNAKE_CASE_ = IterableDataset.from_generator(_SCREAMING_SNAKE_CASE , gen_kwargs=_SCREAMING_SNAKE_CASE )
if not streaming:
SCREAMING_SNAKE_CASE_ = Dataset.from_list(list(_SCREAMING_SNAKE_CASE ) )
SCREAMING_SNAKE_CASE_ = split_dataset_by_node(_SCREAMING_SNAKE_CASE , rank=_SCREAMING_SNAKE_CASE , world_size=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ = torch.utils.data.DataLoader(_SCREAMING_SNAKE_CASE , num_workers=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ = NUM_SHARDS * NUM_ITEMS_PER_SHARD
SCREAMING_SNAKE_CASE_ = full_size // world_size
expected_local_size += int(rank < (full_size % world_size) )
SCREAMING_SNAKE_CASE_ = 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()
| 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
UpperCamelCase__ : Tuple = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
"processing_trocr": ["TrOCRProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Tuple = [
"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__ : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 620 | 0 |
"""simple docstring"""
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 : Optional[Any] = logging.get_logger(__name__)
if is_vision_available():
import PIL
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = ["""pixel_values"""]
def __init__( self : List[str] , _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 / 255 , _lowercase : bool = True , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : bool = True , **_lowercase : Tuple , ):
super().__init__(**_lowercase )
A = size if size is not None else {'shortest_edge': 224}
A = get_size_dict(_lowercase , default_to_square=_lowercase )
A = crop_size if crop_size is not None else {'height': 224, 'width': 224}
A = get_size_dict(_lowercase , default_to_square=_lowercase , param_name='crop_size' )
A = do_resize
A = size
A = resample
A = do_center_crop
A = crop_size
A = do_rescale
A = rescale_factor
A = do_normalize
A = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
A = image_std if image_std is not None else OPENAI_CLIP_STD
A = do_convert_rgb
def __a ( self : str , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : List[str] , ):
A = 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()}' )
A = 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 __a ( self : int , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : str , ):
A = 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 __a ( self : List[str] , _lowercase : np.ndarray , _lowercase : Union[int, float] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : List[str] , ):
return rescale(_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : List[str] , _lowercase : np.ndarray , _lowercase : Union[float, List[float]] , _lowercase : Union[float, List[float]] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Union[str, Any] , ):
return normalize(_lowercase , mean=_lowercase , std=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : Optional[int] , _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 : int , ):
A = do_resize if do_resize is not None else self.do_resize
A = size if size is not None else self.size
A = get_size_dict(_lowercase , param_name='size' , default_to_square=_lowercase )
A = resample if resample is not None else self.resample
A = do_center_crop if do_center_crop is not None else self.do_center_crop
A = crop_size if crop_size is not None else self.crop_size
A = get_size_dict(_lowercase , param_name='crop_size' , default_to_square=_lowercase )
A = do_rescale if do_rescale is not None else self.do_rescale
A = rescale_factor if rescale_factor is not None else self.rescale_factor
A = do_normalize if do_normalize is not None else self.do_normalize
A = image_mean if image_mean is not None else self.image_mean
A = image_std if image_std is not None else self.image_std
A = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
A = 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:
A = [convert_to_rgb(_lowercase ) for image in images]
# All transformations expect numpy arrays.
A = [to_numpy_array(_lowercase ) for image in images]
if do_resize:
A = [self.resize(image=_lowercase , size=_lowercase , resample=_lowercase ) for image in images]
if do_center_crop:
A = [self.center_crop(image=_lowercase , size=_lowercase ) for image in images]
if do_rescale:
A = [self.rescale(image=_lowercase , scale=_lowercase ) for image in images]
if do_normalize:
A = [self.normalize(image=_lowercase , mean=_lowercase , std=_lowercase ) for image in images]
A = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
A = {'pixel_values': images}
return BatchFeature(data=_lowercase , tensor_type=_lowercase )
| 690 |
"""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 : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : int = {"vocab_file": "sentencepiece.model"}
UpperCamelCase : Union[str, Any] = {
"vocab_file": {
"google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
},
}
UpperCamelCase : Union[str, Any] = {
"google/rembert": 256,
}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Dict , _lowercase : Optional[Any] , _lowercase : Optional[Any]=False , _lowercase : Dict=True , _lowercase : List[str]=True , _lowercase : int="[CLS]" , _lowercase : str="[SEP]" , _lowercase : List[str]="[UNK]" , _lowercase : List[Any]="[SEP]" , _lowercase : Union[str, Any]="[PAD]" , _lowercase : List[str]="[CLS]" , _lowercase : Any="[MASK]" , **_lowercase : Optional[Any] , ):
super().__init__(
do_lower_case=_lowercase , remove_space=_lowercase , keep_accents=_lowercase , bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , **_lowercase , )
A = do_lower_case
A = remove_space
A = keep_accents
A = vocab_file
A = spm.SentencePieceProcessor()
self.sp_model.Load(_lowercase )
@property
def __a ( self : Tuple ):
return len(self.sp_model )
def __a ( self : List[str] ):
A = {self.convert_ids_to_tokens(_lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Tuple ):
A = self.__dict__.copy()
A = None
return state
def __setstate__( self : List[str] , _lowercase : int ):
A = d
A = spm.SentencePieceProcessor()
self.sp_model.Load(self.vocab_file )
def __a ( self : Dict , _lowercase : Union[str, Any] , _lowercase : Dict=False ):
A = self.sp_model.EncodeAsPieces(_lowercase )
return pieces
def __a ( self : Dict , _lowercase : Tuple ):
return self.sp_model.PieceToId(_lowercase )
def __a ( self : str , _lowercase : Optional[int] ):
return self.sp_model.IdToPiece(_lowercase )
def __a ( self : Optional[int] , _lowercase : Optional[int] ):
A = self.sp_model.decode_pieces(_lowercase )
return out_string
def __a ( self : Optional[int] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [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 __a ( self : Any , _lowercase : List[int] , _lowercase : Optional[List[int]] = None , _lowercase : 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(_lowercase )) + [1] + ([0] * len(_lowercase )) + [1]
return [1] + ([0] * len(_lowercase )) + [1]
def __a ( self : str , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
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 __a ( self : Optional[Any] , _lowercase : str , _lowercase : Optional[str] = None ):
if not os.path.isdir(_lowercase ):
logger.error('Vocabulary path ({}) should be a directory'.format(_lowercase ) )
return
A = os.path.join(
_lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ):
copyfile(self.vocab_file , _lowercase )
return (out_vocab_file,)
| 690 | 1 |
'''simple docstring'''
from itertools import permutations
def lowerCAmelCase__ ( UpperCAmelCase ):
"""simple docstring"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
snake_case__ : Union[str, Any] = [7, 11, 13, 17]
for i, test in enumerate(UpperCAmelCase ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def lowerCAmelCase__ ( UpperCAmelCase = 10 ):
"""simple docstring"""
return sum(
int("""""".join(map(UpperCAmelCase , UpperCAmelCase ) ) )
for num in permutations(range(UpperCAmelCase ) )
if is_substring_divisible(UpperCAmelCase ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 701 |
'''simple docstring'''
class _A :
'''simple docstring'''
def __init__( self : List[Any] )-> List[str]:
snake_case__ : List[str] = """"""
snake_case__ : Dict = """"""
snake_case__ : Union[str, Any] = []
def __lowerCAmelCase ( self : Any , lowerCamelCase : int , lowerCamelCase : int )-> int:
if m == -1:
return n + 1
elif n == -1:
return m + 1
elif self.dp[m][n] > -1:
return self.dp[m][n]
else:
if self.worda[m] == self.worda[n]:
snake_case__ : Any = self.__min_dist_top_down_dp(m - 1 , n - 1 )
else:
snake_case__ : List[Any] = self.__min_dist_top_down_dp(lowerCamelCase , n - 1 )
snake_case__ : Any = self.__min_dist_top_down_dp(m - 1 , lowerCamelCase )
snake_case__ : Optional[int] = self.__min_dist_top_down_dp(m - 1 , n - 1 )
snake_case__ : Dict = 1 + min(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self.dp[m][n]
def __lowerCAmelCase ( self : List[str] , lowerCamelCase : str , lowerCamelCase : str )-> int:
snake_case__ : Optional[int] = worda
snake_case__ : List[str] = worda
snake_case__ : List[str] = [[-1 for _ in range(len(lowerCamelCase ) )] for _ in range(len(lowerCamelCase ) )]
return self.__min_dist_top_down_dp(len(lowerCamelCase ) - 1 , len(lowerCamelCase ) - 1 )
def __lowerCAmelCase ( self : List[Any] , lowerCamelCase : str , lowerCamelCase : str )-> int:
snake_case__ : List[str] = worda
snake_case__ : int = worda
snake_case__ : Any = len(lowerCamelCase )
snake_case__ : List[str] = len(lowerCamelCase )
snake_case__ : List[str] = [[0 for _ in range(n + 1 )] for _ in range(m + 1 )]
for i in range(m + 1 ):
for j in range(n + 1 ):
if i == 0: # first string is empty
snake_case__ : Union[str, Any] = j
elif j == 0: # second string is empty
snake_case__ : List[str] = i
elif worda[i - 1] == worda[j - 1]: # last characters are equal
snake_case__ : Tuple = self.dp[i - 1][j - 1]
else:
snake_case__ : int = self.dp[i][j - 1]
snake_case__ : List[Any] = self.dp[i - 1][j]
snake_case__ : List[str] = self.dp[i - 1][j - 1]
snake_case__ : Tuple = 1 + min(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self.dp[m][n]
if __name__ == "__main__":
lowerCAmelCase__ = EditDistance()
print('****************** Testing Edit Distance DP Algorithm ******************')
print()
lowerCAmelCase__ = input('Enter the first string: ').strip()
lowerCAmelCase__ = input('Enter the second string: ').strip()
print()
print(f"""The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}""")
print(f"""The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}""")
print()
print('*************** End of Testing Edit Distance DP Algorithm ***************')
| 172 | 0 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, 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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , __lowercase : int , __lowercase : str=13 , __lowercase : Tuple=7 , __lowercase : Any=True , __lowercase : Optional[int]=True , __lowercase : Any=True , __lowercase : Tuple=True , __lowercase : List[Any]=99 , __lowercase : List[Any]=32 , __lowercase : List[Any]=5 , __lowercase : str=4 , __lowercase : Optional[int]=37 , __lowercase : Any="gelu" , __lowercase : int=0.1 , __lowercase : Dict=0.1 , __lowercase : List[Any]=5_12 , __lowercase : str=16 , __lowercase : Any=2 , __lowercase : List[str]=0.02 , __lowercase : List[str]=3 , __lowercase : List[str]=4 , __lowercase : List[str]=None , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = use_token_type_ids
snake_case_ = use_labels
snake_case_ = vocab_size
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_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_labels
snake_case_ = num_choices
snake_case_ = scope
def snake_case__ ( self : int ):
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_input_mask:
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ = None
if self.use_token_type_ids:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ = None
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case__ ( self : Optional[Any] ):
"""simple docstring"""
return NystromformerConfig(
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=__lowercase , initializer_range=self.initializer_range , )
def snake_case__ ( self : str , __lowercase : Union[str, Any] , __lowercase : List[str] , __lowercase : str , __lowercase : Optional[Any] , __lowercase : Union[str, Any] , __lowercase : Dict , __lowercase : Tuple ):
"""simple docstring"""
snake_case_ = NystromformerModel(config=__lowercase )
model.to(__lowercase )
model.eval()
snake_case_ = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase )
snake_case_ = model(__lowercase , token_type_ids=__lowercase )
snake_case_ = model(__lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case__ ( self : Any , __lowercase : Union[str, Any] , __lowercase : List[str] , __lowercase : Any , __lowercase : int , __lowercase : int , __lowercase : Optional[Any] , __lowercase : int ):
"""simple docstring"""
snake_case_ = NystromformerForMaskedLM(config=__lowercase )
model.to(__lowercase )
model.eval()
snake_case_ = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case__ ( self : Optional[int] , __lowercase : Union[str, Any] , __lowercase : List[str] , __lowercase : Optional[int] , __lowercase : List[Any] , __lowercase : str , __lowercase : Union[str, Any] , __lowercase : Tuple ):
"""simple docstring"""
snake_case_ = NystromformerForQuestionAnswering(config=__lowercase )
model.to(__lowercase )
model.eval()
snake_case_ = model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , start_positions=__lowercase , end_positions=__lowercase , )
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 snake_case__ ( self : int , __lowercase : Dict , __lowercase : str , __lowercase : int , __lowercase : str , __lowercase : Optional[Any] , __lowercase : List[Any] , __lowercase : List[Any] ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = NystromformerForSequenceClassification(__lowercase )
model.to(__lowercase )
model.eval()
snake_case_ = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case__ ( self : Dict , __lowercase : List[Any] , __lowercase : List[Any] , __lowercase : List[Any] , __lowercase : Union[str, Any] , __lowercase : Optional[int] , __lowercase : List[Any] , __lowercase : Dict ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = NystromformerForTokenClassification(config=__lowercase )
model.to(__lowercase )
model.eval()
snake_case_ = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def snake_case__ ( self : Optional[Any] , __lowercase : List[Any] , __lowercase : List[str] , __lowercase : Union[str, Any] , __lowercase : Any , __lowercase : Union[str, Any] , __lowercase : Union[str, Any] , __lowercase : str ):
"""simple docstring"""
snake_case_ = self.num_choices
snake_case_ = NystromformerForMultipleChoice(config=__lowercase )
model.to(__lowercase )
model.eval()
snake_case_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def snake_case__ ( self : Tuple ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
(
(
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) ,
) = config_and_inputs
snake_case_ = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCAmelCase_ = (
{
'''feature-extraction''': NystromformerModel,
'''fill-mask''': NystromformerForMaskedLM,
'''question-answering''': NystromformerForQuestionAnswering,
'''text-classification''': NystromformerForSequenceClassification,
'''token-classification''': NystromformerForTokenClassification,
'''zero-shot''': NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def snake_case__ ( self : Dict ):
"""simple docstring"""
snake_case_ = NystromformerModelTester(self )
snake_case_ = ConfigTester(self , config_class=__lowercase , hidden_size=37 )
def snake_case__ ( self : Any ):
"""simple docstring"""
self.config_tester.run_common_tests()
def snake_case__ ( self : Dict ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase )
def snake_case__ ( self : Optional[int] ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case_ = type
self.model_tester.create_and_check_model(*__lowercase )
def snake_case__ ( self : Tuple ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__lowercase )
def snake_case__ ( self : Dict ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__lowercase )
def snake_case__ ( self : Tuple ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__lowercase )
def snake_case__ ( self : Optional[Any] ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__lowercase )
def snake_case__ ( self : List[Any] ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__lowercase )
@slow
def snake_case__ ( self : Optional[int] ):
"""simple docstring"""
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = NystromformerModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def snake_case__ ( self : Union[str, Any] ):
"""simple docstring"""
snake_case_ = NystromformerModel.from_pretrained("uw-madison/nystromformer-512" )
snake_case_ = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
snake_case_ = model(__lowercase )[0]
snake_case_ = torch.Size((1, 6, 7_68) )
self.assertEqual(output.shape , __lowercase )
snake_case_ = torch.tensor(
[[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __lowercase , atol=1E-4 ) )
@slow
def snake_case__ ( self : int ):
"""simple docstring"""
snake_case_ = "the [MASK] of Belgium is Brussels"
snake_case_ = AutoTokenizer.from_pretrained("uw-madison/nystromformer-512" )
snake_case_ = NystromformerForMaskedLM.from_pretrained("uw-madison/nystromformer-512" )
snake_case_ = tokenizer(__lowercase , return_tensors="pt" )
with torch.no_grad():
snake_case_ = model(encoding.input_ids ).logits
snake_case_ = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(__lowercase ) , "capital" )
| 376 |
import numpy as np
import qiskit
def lowerCamelCase__ ( _A = 8 , _A = None ):
'''simple docstring'''
snake_case_ = np.random.default_rng(seed=_A )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
snake_case_ = 6 * key_len
# Measurement basis for Alice's qubits.
snake_case_ = rng.integers(2 , size=_A )
# The set of states Alice will prepare.
snake_case_ = rng.integers(2 , size=_A )
# Measurement basis for Bob's qubits.
snake_case_ = rng.integers(2 , size=_A )
# Quantum Circuit to simulate BB84
snake_case_ = qiskit.QuantumCircuit(_A , name="BB84" )
# Alice prepares her qubits according to rules above.
for index, _ in enumerate(_A ):
if alice_state[index] == 1:
bbaa_circ.x(_A )
if alice_basis[index] == 1:
bbaa_circ.h(_A )
bbaa_circ.barrier()
# Bob measures the received qubits according to rules above.
for index, _ in enumerate(_A ):
if bob_basis[index] == 1:
bbaa_circ.h(_A )
bbaa_circ.barrier()
bbaa_circ.measure_all()
# Simulate the quantum circuit.
snake_case_ = qiskit.Aer.get_backend("aer_simulator" )
# We only need to run one shot because the key is unique.
# Multiple shots will produce the same key.
snake_case_ = qiskit.execute(_A , _A , shots=1 , seed_simulator=_A )
# Returns the result of measurement.
snake_case_ = job.result().get_counts(_A ).most_frequent()
# Extracting the generated key from the simulation results.
# Only keep measurement results where Alice and Bob chose the same basis.
snake_case_ = "".join(
[
result_bit
for alice_basis_bit, bob_basis_bit, result_bit in zip(
_A , _A , _A )
if alice_basis_bit == bob_basis_bit
] )
# Get final key. Pad with 0 if too short, otherwise truncate.
snake_case_ = gen_key[:key_len] if len(_A ) >= key_len else gen_key.ljust(_A , "0" )
return key
if __name__ == "__main__":
print(f'''The generated key is : {bbaa(8, seed=0)}''')
from doctest import testmod
testmod()
| 376 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
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 MobileViTImageProcessor
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any]=7 , lowerCAmelCase_ : Any=3 , lowerCAmelCase_ : List[Any]=1_8 , lowerCAmelCase_ : str=3_0 , lowerCAmelCase_ : Tuple=4_0_0 , lowerCAmelCase_ : str=True , lowerCAmelCase_ : str=None , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Union[str, Any]=True , ) -> Tuple:
__lowerCAmelCase = size if size is not None else {'shortest_edge': 2_0}
__lowerCAmelCase = crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = num_channels
__lowerCAmelCase = image_size
__lowerCAmelCase = min_resolution
__lowerCAmelCase = max_resolution
__lowerCAmelCase = do_resize
__lowerCAmelCase = size
__lowerCAmelCase = do_center_crop
__lowerCAmelCase = crop_size
__lowerCAmelCase = do_flip_channel_order
def lowercase ( self : Any ) -> List[str]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class _UpperCAmelCase ( _UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
a_ = MobileViTImageProcessor if is_vision_available() else None
def lowercase ( self : Any ) -> str:
__lowerCAmelCase = MobileViTImageProcessingTester(self )
@property
def lowercase ( self : Dict ) -> Tuple:
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase ( self : str ) -> Optional[int]:
__lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase_ , 'do_resize' ) )
self.assertTrue(hasattr(lowerCAmelCase_ , 'size' ) )
self.assertTrue(hasattr(lowerCAmelCase_ , 'do_center_crop' ) )
self.assertTrue(hasattr(lowerCAmelCase_ , 'center_crop' ) )
self.assertTrue(hasattr(lowerCAmelCase_ , 'do_flip_channel_order' ) )
def lowercase ( self : List[str] ) -> Any:
__lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 2_0} )
self.assertEqual(image_processor.crop_size , {'height': 1_8, 'width': 1_8} )
__lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {'shortest_edge': 4_2} )
self.assertEqual(image_processor.crop_size , {'height': 8_4, 'width': 8_4} )
def lowercase ( self : Union[str, Any] ) -> Optional[int]:
pass
def lowercase ( self : List[str] ) -> List[Any]:
# Initialize image_processing
__lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase_ , Image.Image )
# Test not batched input
__lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
__lowerCAmelCase = image_processing(lowerCAmelCase_ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def lowercase ( self : Optional[int] ) -> List[Any]:
# Initialize image_processing
__lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase_ , numpify=lowerCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase_ , np.ndarray )
# Test not batched input
__lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
__lowerCAmelCase = image_processing(lowerCAmelCase_ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def lowercase ( self : Dict ) -> Union[str, Any]:
# Initialize image_processing
__lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase_ , torchify=lowerCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase_ , torch.Tensor )
# Test not batched input
__lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
__lowerCAmelCase = image_processing(lowerCAmelCase_ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
| 704 |
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArguments,
set_seed,
)
def a_ ( ):
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument('--model_ckpt', type=lowerCAmelCase_, default='microsoft/unixcoder-base-nine' )
parser.add_argument('--num_epochs', type=lowerCAmelCase_, default=5 )
parser.add_argument('--batch_size', type=lowerCAmelCase_, default=6 )
parser.add_argument('--gradient_accumulation_steps', type=lowerCAmelCase_, default=1 )
parser.add_argument('--freeze', type=lowerCAmelCase_, default=lowerCAmelCase_ )
parser.add_argument('--learning_rate', type=lowerCAmelCase_, default=5E-4 )
parser.add_argument('--seed', type=lowerCAmelCase_, default=0 )
parser.add_argument('--lr_scheduler_type', type=lowerCAmelCase_, default='cosine' )
parser.add_argument('--num_warmup_steps', type=lowerCAmelCase_, default=10 )
parser.add_argument('--weight_decay', type=lowerCAmelCase_, default=0.01 )
parser.add_argument('--output_dir', type=lowerCAmelCase_, default='./results' )
return parser.parse_args()
_snake_case : Union[str, Any] = load('accuracy')
def a_ ( lowerCAmelCase_ : List[Any] ):
__lowerCAmelCase , __lowerCAmelCase = eval_pred
__lowerCAmelCase = np.argmax(lowerCAmelCase_, axis=1 )
return metric.compute(predictions=lowerCAmelCase_, references=lowerCAmelCase_ )
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase_ : List[Any] ) -> None:
super().__init__()
__lowerCAmelCase = trainer
def lowercase ( self : Union[str, Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : List[str] , **lowerCAmelCase_ : Optional[int] ) -> Dict:
if control.should_evaluate:
__lowerCAmelCase = deepcopy(lowerCAmelCase_ )
self._trainer.evaluate(eval_dataset=self._trainer.train_dataset , metric_key_prefix='train' )
return control_copy
def a_ ( ):
__lowerCAmelCase = get_args()
set_seed(args.seed )
__lowerCAmelCase = load_dataset('codeparrot/codecomplex', split='train' )
__lowerCAmelCase = dataset.train_test_split(test_size=0.2 )
__lowerCAmelCase = train_test['test'].train_test_split(test_size=0.5 )
__lowerCAmelCase = DatasetDict(
{
'train': train_test['train'],
'test': test_validation['train'],
'valid': test_validation['test'],
} )
print('Loading tokenizer and model' )
__lowerCAmelCase = AutoTokenizer.from_pretrained(args.model_ckpt )
__lowerCAmelCase = tokenizer.eos_token
__lowerCAmelCase = AutoModelForSequenceClassification.from_pretrained(args.model_ckpt, num_labels=7 )
__lowerCAmelCase = model.config.eos_token_id
if args.freeze:
for param in model.roberta.parameters():
__lowerCAmelCase = False
__lowerCAmelCase = ClassLabel(num_classes=7, names=list(set(train_test_validation['train']['complexity'] ) ) )
def tokenize(lowerCAmelCase_ : Union[str, Any] ):
__lowerCAmelCase = tokenizer(example['src'], truncation=lowerCAmelCase_, max_length=1024 )
__lowerCAmelCase = labels.straint(example['complexity'] )
return {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"],
"label": label,
}
__lowerCAmelCase = train_test_validation.map(
lowerCAmelCase_, batched=lowerCAmelCase_, remove_columns=train_test_validation['train'].column_names, )
__lowerCAmelCase = DataCollatorWithPadding(tokenizer=lowerCAmelCase_ )
__lowerCAmelCase = TrainingArguments(
output_dir=args.output_dir, learning_rate=args.learning_rate, lr_scheduler_type=args.lr_scheduler_type, evaluation_strategy='epoch', save_strategy='epoch', logging_strategy='epoch', per_device_train_batch_size=args.batch_size, per_device_eval_batch_size=args.batch_size, num_train_epochs=args.num_epochs, gradient_accumulation_steps=args.gradient_accumulation_steps, weight_decay=0.01, metric_for_best_model='accuracy', run_name='complexity-java', report_to='wandb', )
__lowerCAmelCase = Trainer(
model=lowerCAmelCase_, args=lowerCAmelCase_, train_dataset=tokenized_datasets['train'], eval_dataset=tokenized_datasets['valid'], tokenizer=lowerCAmelCase_, data_collator=lowerCAmelCase_, compute_metrics=lowerCAmelCase_, )
print('Training...' )
trainer.add_callback(CustomCallback(lowerCAmelCase_ ) )
trainer.train()
if __name__ == "__main__":
main()
| 421 | 0 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
UpperCAmelCase : List[str] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
UpperCAmelCase : List[str] = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F"""{len(upper_files)} files contain uppercase characters:""")
print('''\n'''.join(upper_files) + '''\n''')
UpperCAmelCase : int = [file for file in filepaths if ''' ''' in file]
if space_files:
print(F"""{len(space_files)} files contain space characters:""")
print('''\n'''.join(space_files) + '''\n''')
UpperCAmelCase : List[Any] = [file for file in filepaths if '''-''' in file]
if hyphen_files:
print(F"""{len(hyphen_files)} files contain hyphen characters:""")
print('''\n'''.join(hyphen_files) + '''\n''')
UpperCAmelCase : Optional[Any] = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F"""{len(nodir_files)} files are not in a directory:""")
print('''\n'''.join(nodir_files) + '''\n''')
UpperCAmelCase : int = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 239 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def a ( __UpperCAmelCase : str , __UpperCAmelCase : str = "cpu" , __UpperCAmelCase : Union[str, None] = None ) -> None:
__magic_name__: List[Any] = torch.load(__UpperCAmelCase , map_location=__UpperCAmelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(__UpperCAmelCase , torch.Tensor ):
raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin""" )
__magic_name__: Any = v.half()
if save_path is None: # overwrite src_path
__magic_name__: List[str] = src_path
torch.save(__UpperCAmelCase , __UpperCAmelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 96 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE( snake_case_ : List[str] ) ->List[str]:
'''simple docstring'''
_lowercase : List[str] = []
_lowercase : Tuple = set({'''(''', '''[''', '''{'''} )
_lowercase : Union[str, Any] = set({''')''', ''']''', '''}'''} )
_lowercase : Dict = {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}
for i in range(len(snake_case_ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(snake_case_ ) == 0 or (len(snake_case_ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(snake_case_ ) == 0
def _SCREAMING_SNAKE_CASE( ) ->Optional[Any]:
'''simple docstring'''
_lowercase : Union[str, Any] = input('''Enter sequence of brackets: ''' )
if is_balanced(snake_case_ ):
print(snake_case_ , '''is balanced''' )
else:
print(snake_case_ , '''is not balanced''' )
if __name__ == "__main__":
main()
| 411 |
'''simple docstring'''
lowerCamelCase__ = 2_56
# Modulus to hash a string
lowerCamelCase__ = 1_00_00_03
def _SCREAMING_SNAKE_CASE( snake_case_ : str , snake_case_ : str ) ->bool:
'''simple docstring'''
_lowercase : int = len(snake_case_ )
_lowercase : str = len(snake_case_ )
if p_len > t_len:
return False
_lowercase : List[str] = 0
_lowercase : Any = 0
_lowercase : Union[str, Any] = 1
# Calculating the hash of pattern and substring of text
for i in range(snake_case_ ):
_lowercase : List[Any] = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
_lowercase : List[str] = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
_lowercase : Optional[Any] = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
_lowercase : Optional[int] = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def _SCREAMING_SNAKE_CASE( ) ->None:
'''simple docstring'''
_lowercase : List[str] = '''abc1abc12'''
_lowercase : int = '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
_lowercase : List[str] = '''alskfjaldsk23adsfabcabc'''
assert rabin_karp(snake_case_ , snake_case_ ) and not rabin_karp(snake_case_ , snake_case_ )
# Test 2)
_lowercase : int = '''ABABX'''
_lowercase : Any = '''ABABZABABYABABX'''
assert rabin_karp(snake_case_ , snake_case_ )
# Test 3)
_lowercase : Tuple = '''AAAB'''
_lowercase : Tuple = '''ABAAAAAB'''
assert rabin_karp(snake_case_ , snake_case_ )
# Test 4)
_lowercase : Dict = '''abcdabcy'''
_lowercase : List[Any] = '''abcxabcdabxabcdabcdabcy'''
assert rabin_karp(snake_case_ , snake_case_ )
# Test 5)
_lowercase : Tuple = '''Lü'''
_lowercase : Any = '''Lüsai'''
assert rabin_karp(snake_case_ , snake_case_ )
_lowercase : Tuple = '''Lue'''
assert not rabin_karp(snake_case_ , snake_case_ )
print('''Success.''' )
if __name__ == "__main__":
test_rabin_karp()
| 411 | 1 |
"""simple docstring"""
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
A_ : List[str] = {
'debug': logging.DEBUG,
'info': logging.INFO,
'warning': logging.WARNING,
'error': logging.ERROR,
'critical': logging.CRITICAL,
}
A_ : int = logging.WARNING
def __snake_case ( ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = os.getenv('DATASETS_VERBOSITY' , __A )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
F"""Unknown option DATASETS_VERBOSITY={env_level_str}, """
F"""has to be one of: { ", ".join(log_levels.keys() ) }""" )
return _default_log_level
def __snake_case ( ) -> Dict:
'''simple docstring'''
return __name__.split('.' )[0]
def __snake_case ( ) -> List[Any]:
'''simple docstring'''
return logging.getLogger(_get_library_name() )
def __snake_case ( ) -> Optional[Any]:
'''simple docstring'''
# Apply our default configuration to the library root logger.
SCREAMING_SNAKE_CASE : str = _get_library_root_logger()
library_root_logger.setLevel(_get_default_logging_level() )
def __snake_case ( ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = _get_library_root_logger()
library_root_logger.setLevel(logging.NOTSET )
def __snake_case ( __A : str = None ) -> Optional[Any]:
'''simple docstring'''
if name is None:
SCREAMING_SNAKE_CASE : Optional[int] = _get_library_name()
return logging.getLogger(__A )
def __snake_case ( ) -> str:
'''simple docstring'''
return _get_library_root_logger().getEffectiveLevel()
def __snake_case ( __A : List[str] ) -> Dict:
'''simple docstring'''
_get_library_root_logger().setLevel(__A )
def __snake_case ( ) -> Any:
'''simple docstring'''
return set_verbosity(__A )
def __snake_case ( ) -> Optional[Any]:
'''simple docstring'''
return set_verbosity(__A )
def __snake_case ( ) -> str:
'''simple docstring'''
return set_verbosity(__A )
def __snake_case ( ) -> List[str]:
'''simple docstring'''
return set_verbosity(__A )
def __snake_case ( ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = False
def __snake_case ( ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = True
# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : List[Any] , *_SCREAMING_SNAKE_CASE : List[Any] , **_SCREAMING_SNAKE_CASE : Optional[Any] ) -> int: # pylint: disable=unused-argument
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = args[0] if args else None
def __iter__( self : List[Any] ) -> Tuple:
"""simple docstring"""
return iter(self._iterator )
def __getattr__( self : int , _SCREAMING_SNAKE_CASE : Union[str, Any] ) -> int:
"""simple docstring"""
def empty_fn(*_SCREAMING_SNAKE_CASE : Optional[Any] , **_SCREAMING_SNAKE_CASE : Union[str, Any] ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self : str ) -> List[str]:
"""simple docstring"""
return self
def __exit__( self : List[str] , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Optional[int] ) -> List[Any]:
"""simple docstring"""
return
A_ : str = True
class lowerCAmelCase__ :
'''simple docstring'''
def __call__( self : Any , *_SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : List[str]=False , **_SCREAMING_SNAKE_CASE : Any ) -> Union[str, Any]:
"""simple docstring"""
if _tqdm_active and not disable:
return tqdm_lib.tqdm(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
else:
return EmptyTqdm(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self : List[Any] , *_SCREAMING_SNAKE_CASE : Optional[Any] , **_SCREAMING_SNAKE_CASE : List[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self : str ) -> Any:
"""simple docstring"""
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
A_ : Dict = _tqdm_cls()
def __snake_case ( ) -> int:
'''simple docstring'''
global _tqdm_active
return bool(_tqdm_active )
def __snake_case ( ) -> Any:
'''simple docstring'''
global _tqdm_active
SCREAMING_SNAKE_CASE : List[str] = True
def __snake_case ( ) -> int:
'''simple docstring'''
global _tqdm_active
SCREAMING_SNAKE_CASE : List[Any] = False
| 265 |
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__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Optional[int] = """decision_transformer"""
_UpperCAmelCase : str = ["""past_key_values"""]
_UpperCAmelCase : Any = {
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , __magic_name__=1_7 , __magic_name__=4 , __magic_name__=1_2_8 , __magic_name__=4_0_9_6 , __magic_name__=True , __magic_name__=1 , __magic_name__=1_0_2_4 , __magic_name__=3 , __magic_name__=1 , __magic_name__=None , __magic_name__="relu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=1e-5 , __magic_name__=0.02 , __magic_name__=True , __magic_name__=True , __magic_name__=5_0_2_5_6 , __magic_name__=5_0_2_5_6 , __magic_name__=False , __magic_name__=False , **__magic_name__ , ):
lowerCamelCase : Optional[int] = state_dim
lowerCamelCase : int = act_dim
lowerCamelCase : int = hidden_size
lowerCamelCase : Union[str, Any] = max_ep_len
lowerCamelCase : Optional[int] = action_tanh
lowerCamelCase : Any = vocab_size
lowerCamelCase : List[str] = n_positions
lowerCamelCase : List[Any] = n_layer
lowerCamelCase : Dict = n_head
lowerCamelCase : Optional[Any] = n_inner
lowerCamelCase : Tuple = activation_function
lowerCamelCase : Tuple = resid_pdrop
lowerCamelCase : str = embd_pdrop
lowerCamelCase : Dict = attn_pdrop
lowerCamelCase : Tuple = layer_norm_epsilon
lowerCamelCase : Tuple = initializer_range
lowerCamelCase : Tuple = scale_attn_weights
lowerCamelCase : str = use_cache
lowerCamelCase : List[Any] = scale_attn_by_inverse_layer_idx
lowerCamelCase : List[str] = reorder_and_upcast_attn
lowerCamelCase : Optional[Any] = bos_token_id
lowerCamelCase : str = eos_token_id
super().__init__(bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
| 681 | 0 |
from importlib import import_module
from .logging import get_logger
__UpperCamelCase : Dict = get_logger(__name__)
class lowerCAmelCase__:
'''simple docstring'''
def __init__( self : Optional[int] , __snake_case : List[str] , __snake_case : str=None ):
'''simple docstring'''
UpperCAmelCase_ : Any = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith('''__''' ):
setattr(self , _lowercase , getattr(_lowercase , _lowercase ) )
UpperCAmelCase_ : Optional[int] = module._original_module if isinstance(_lowercase , _PatchedModuleObj ) else module
class lowerCAmelCase__:
'''simple docstring'''
A_ : Any = []
def __init__( self : Optional[Any] , __snake_case : List[Any] , __snake_case : str , __snake_case : List[str] , __snake_case : Union[str, Any]=None ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = obj
UpperCAmelCase_ : Tuple = target
UpperCAmelCase_ : List[str] = new
UpperCAmelCase_ : Dict = target.split('''.''' )[0]
UpperCAmelCase_ : Optional[Any] = {}
UpperCAmelCase_ : int = attrs or []
def __enter__( self : List[str] ):
'''simple docstring'''
*UpperCAmelCase_ , UpperCAmelCase_ : Union[str, Any] = self.target.split('''.''' )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(_lowercase ) ):
try:
UpperCAmelCase_ : List[Any] = import_module('''.'''.join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
UpperCAmelCase_ : Optional[Any] = getattr(self.obj , _lowercase )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(_lowercase , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
UpperCAmelCase_ : Union[str, Any] = obj_attr
# patch at top level
setattr(self.obj , _lowercase , _PatchedModuleObj(_lowercase , attrs=self.attrs ) )
UpperCAmelCase_ : Optional[Any] = getattr(self.obj , _lowercase )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(_lowercase , _lowercase , _PatchedModuleObj(getattr(_lowercase , _lowercase , _lowercase ) , attrs=self.attrs ) )
UpperCAmelCase_ : str = getattr(_lowercase , _lowercase )
# finally set the target attribute
setattr(_lowercase , _lowercase , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
UpperCAmelCase_ : Dict = getattr(import_module('''.'''.join(_lowercase ) ) , _lowercase )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , _lowercase ) is attr_value:
UpperCAmelCase_ : List[Any] = getattr(self.obj , _lowercase )
setattr(self.obj , _lowercase , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
UpperCAmelCase_ : List[str] = globals()['''__builtins__'''][target_attr]
setattr(self.obj , _lowercase , self.new )
else:
raise RuntimeError(f'''Tried to patch attribute {target_attr} instead of a submodule.''' )
def __exit__( self : List[Any] , *__snake_case : Dict ):
'''simple docstring'''
for attr in list(self.original ):
setattr(self.obj , _lowercase , self.original.pop(_lowercase ) )
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
self.__enter__()
self._active_patches.append(self )
def _lowerCamelCase ( self : List[Any] ):
'''simple docstring'''
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__() | 703 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def snake_case_ ( __lowercase , __lowercase ):
# Load checkpoint
UpperCAmelCase_ : Tuple = torch.load(__lowercase , map_location='''cpu''' )
UpperCAmelCase_ : Optional[int] = chkpt['''model''']
# We have the base model one level deeper than the original XLM repository
UpperCAmelCase_ : str = {}
for k, v in state_dict.items():
if "pred_layer" in k:
UpperCAmelCase_ : Tuple = v
else:
UpperCAmelCase_ : Union[str, Any] = v
UpperCAmelCase_ : int = chkpt['''params''']
UpperCAmelCase_ : Union[str, Any] = {n: v for n, v in config.items() if not isinstance(__lowercase , (torch.FloatTensor, numpy.ndarray) )}
UpperCAmelCase_ : int = chkpt['''dico_word2id''']
UpperCAmelCase_ : List[Any] = {s + '''</w>''' if s.find('''@@''' ) == -1 and i > 1_3 else s.replace('''@@''' , '''''' ): i for s, i in vocab.items()}
# Save pytorch-model
UpperCAmelCase_ : Tuple = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
UpperCAmelCase_ : Tuple = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
UpperCAmelCase_ : Dict = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']
print(F'''Save PyTorch model to {pytorch_weights_dump_path}''' )
torch.save(__lowercase , __lowercase )
print(F'''Save configuration file to {pytorch_config_dump_path}''' )
with open(__lowercase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(__lowercase , indent=2 ) + '''\n''' )
print(F'''Save vocab file to {pytorch_config_dump_path}''' )
with open(__lowercase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(__lowercase , indent=2 ) + '''\n''' )
if __name__ == "__main__":
__UpperCamelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--xlm_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.'
)
__UpperCamelCase : Dict = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path) | 641 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ : List[str] = logging.get_logger(__name__)
UpperCamelCase__ : List[Any] = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
),
}
class lowerCamelCase_ ( a_ ):
SCREAMING_SNAKE_CASE_ = 'van'
def __init__( self : Optional[int] ,__lowerCamelCase : List[Any]=2_24 ,__lowerCamelCase : List[Any]=3 ,__lowerCamelCase : List[str]=[7, 3, 3, 3] ,__lowerCamelCase : Dict=[4, 2, 2, 2] ,__lowerCamelCase : List[Any]=[64, 1_28, 3_20, 5_12] ,__lowerCamelCase : Dict=[3, 3, 12, 3] ,__lowerCamelCase : Optional[int]=[8, 8, 4, 4] ,__lowerCamelCase : Tuple="gelu" ,__lowerCamelCase : str=0.02 ,__lowerCamelCase : List[Any]=1e-6 ,__lowerCamelCase : Any=1e-2 ,__lowerCamelCase : Dict=0.0 ,__lowerCamelCase : Any=0.0 ,**__lowerCamelCase : Optional[int] ,):
'''simple docstring'''
super().__init__(**__lowerCamelCase )
a = image_size
a = num_channels
a = patch_sizes
a = strides
a = hidden_sizes
a = depths
a = mlp_ratios
a = hidden_act
a = initializer_range
a = layer_norm_eps
a = layer_scale_init_value
a = drop_path_rate
a = dropout_rate
| 387 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
UpperCamelCase__ : Any = None
UpperCamelCase__ : List[str] = logging.get_logger(__name__)
UpperCamelCase__ : int = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
UpperCamelCase__ : Union[str, Any] = {
"""vocab_file""": {
"""google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
"""google/bigbird-roberta-large""": (
"""https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
),
"""google/bigbird-base-trivia-itc""": (
"""https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
),
},
"""tokenizer_file""": {
"""google/bigbird-roberta-base""": (
"""https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
),
"""google/bigbird-roberta-large""": (
"""https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
),
"""google/bigbird-base-trivia-itc""": (
"""https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
),
},
}
UpperCamelCase__ : Optional[int] = {
"""google/bigbird-roberta-base""": 4_096,
"""google/bigbird-roberta-large""": 4_096,
"""google/bigbird-base-trivia-itc""": 4_096,
}
UpperCamelCase__ : Tuple = """▁"""
class lowerCamelCase_ ( a_ ):
SCREAMING_SNAKE_CASE_ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ = BigBirdTokenizer
SCREAMING_SNAKE_CASE_ = ['input_ids', 'attention_mask']
SCREAMING_SNAKE_CASE_ = []
def __init__( self : Any ,__lowerCamelCase : List[str]=None ,__lowerCamelCase : Union[str, Any]=None ,__lowerCamelCase : Optional[int]="<unk>" ,__lowerCamelCase : Dict="<s>" ,__lowerCamelCase : Tuple="</s>" ,__lowerCamelCase : List[str]="<pad>" ,__lowerCamelCase : Tuple="[SEP]" ,__lowerCamelCase : List[str]="[MASK]" ,__lowerCamelCase : List[Any]="[CLS]" ,**__lowerCamelCase : List[str] ,):
'''simple docstring'''
a = AddedToken(__lowerCamelCase ,lstrip=__lowerCamelCase ,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase ,__lowerCamelCase ) else bos_token
a = AddedToken(__lowerCamelCase ,lstrip=__lowerCamelCase ,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase ,__lowerCamelCase ) else eos_token
a = AddedToken(__lowerCamelCase ,lstrip=__lowerCamelCase ,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase ,__lowerCamelCase ) else unk_token
a = AddedToken(__lowerCamelCase ,lstrip=__lowerCamelCase ,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase ,__lowerCamelCase ) else pad_token
a = AddedToken(__lowerCamelCase ,lstrip=__lowerCamelCase ,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase ,__lowerCamelCase ) else cls_token
a = AddedToken(__lowerCamelCase ,lstrip=__lowerCamelCase ,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase ,__lowerCamelCase ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
a = AddedToken(__lowerCamelCase ,lstrip=__lowerCamelCase ,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase ,__lowerCamelCase ) else mask_token
super().__init__(
__lowerCamelCase ,tokenizer_file=__lowerCamelCase ,bos_token=__lowerCamelCase ,eos_token=__lowerCamelCase ,unk_token=__lowerCamelCase ,sep_token=__lowerCamelCase ,pad_token=__lowerCamelCase ,cls_token=__lowerCamelCase ,mask_token=__lowerCamelCase ,**__lowerCamelCase ,)
a = vocab_file
a = False if not self.vocab_file else True
def SCREAMING_SNAKE_CASE_ ( self : Dict ,__lowerCamelCase : List[int] ,__lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
a = [self.sep_token_id]
a = [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 SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ,__lowerCamelCase : List[int] ,__lowerCamelCase : Optional[List[int]] = None ,__lowerCamelCase : bool = False ):
'''simple docstring'''
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 None:
return [1] + ([0] * len(__lowerCamelCase )) + [1]
return [1] + ([0] * len(__lowerCamelCase )) + [1] + ([0] * len(__lowerCamelCase )) + [1]
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ,__lowerCamelCase : List[int] ,__lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
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 : Optional[Any] ,__lowerCamelCase : str ,__lowerCamelCase : Optional[str] = None ):
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(__lowerCamelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
a = 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,)
| 387 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Any = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}
class __lowercase ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCAmelCase_ : List[str] = '''markuplm'''
def __init__( self , __UpperCAmelCase=3_05_22 , __UpperCAmelCase=7_68 , __UpperCAmelCase=12 , __UpperCAmelCase=12 , __UpperCAmelCase=30_72 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=5_12 , __UpperCAmelCase=2 , __UpperCAmelCase=0.0_2 , __UpperCAmelCase=1E-12 , __UpperCAmelCase=0 , __UpperCAmelCase=0 , __UpperCAmelCase=2 , __UpperCAmelCase=2_56 , __UpperCAmelCase=10_24 , __UpperCAmelCase=2_16 , __UpperCAmelCase=10_01 , __UpperCAmelCase=32 , __UpperCAmelCase=50 , __UpperCAmelCase="absolute" , __UpperCAmelCase=True , __UpperCAmelCase=None , **__UpperCAmelCase , ) -> Any:
super().__init__(
pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , **__UpperCAmelCase , )
A : Tuple = vocab_size
A : str = hidden_size
A : str = num_hidden_layers
A : Union[str, Any] = num_attention_heads
A : Optional[int] = hidden_act
A : Any = intermediate_size
A : str = hidden_dropout_prob
A : Dict = attention_probs_dropout_prob
A : Optional[Any] = max_position_embeddings
A : int = type_vocab_size
A : Optional[int] = initializer_range
A : List[Any] = layer_norm_eps
A : int = position_embedding_type
A : Optional[Any] = use_cache
A : List[Any] = classifier_dropout
# additional properties
A : str = max_depth
A : Any = max_xpath_tag_unit_embeddings
A : Optional[int] = max_xpath_subs_unit_embeddings
A : Optional[int] = tag_pad_id
A : List[str] = subs_pad_id
A : Any = xpath_unit_hidden_size
| 423 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_SCREAMING_SNAKE_CASE )
class __lowercase ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCAmelCase_ : str = field(default='''summarization''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
UpperCAmelCase_ : ClassVar[Features] = Features({'''text''': Value('''string''' )} )
UpperCAmelCase_ : ClassVar[Features] = Features({'''summary''': Value('''string''' )} )
UpperCAmelCase_ : str = "text"
UpperCAmelCase_ : str = "summary"
@property
def snake_case ( self ) -> Dict[str, str]:
return {self.text_column: "text", self.summary_column: "summary"}
| 423 | 1 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def _UpperCamelCase ( __UpperCamelCase ) -> str:
lowerCamelCase_ = []
for line in lines:
lowerCamelCase_ = re.sub(R'#.*' ,'' ,__UpperCamelCase ) # remove comments
if line:
filtered_lines.append(__UpperCamelCase )
lowerCamelCase_ = '\n'.join(__UpperCamelCase )
# Make a hash from all this code
lowerCamelCase_ = full_str.encode('utf-8' )
return shaaaa(__UpperCamelCase ).hexdigest()
# get importable module names and hash for caching
A_ = {
"csv": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"json": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"pandas": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"parquet": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"arrow": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"text": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"imagefolder": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"audiofolder": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
A_ = {
".csv": ("csv", {}),
".tsv": ("csv", {"sep": "\t"}),
".json": ("json", {}),
".jsonl": ("json", {}),
".parquet": ("parquet", {}),
".arrow": ("arrow", {}),
".txt": ("text", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
A_ = {"imagefolder", "audiofolder"}
# Used to filter data files based on extensions given a module name
A_ = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(".zip")
_MODULE_TO_EXTENSIONS["audiofolder"].append(".zip")
| 42 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __magic_name__ ( __UpperCAmelCase ):
__A : Tuple = ["image_processor", "tokenizer"]
__A : Dict = "BlipImageProcessor"
__A : Dict = "AutoTokenizer"
def __init__( self : Any , snake_case__ : Union[str, Any] , snake_case__ : str ):
'''simple docstring'''
lowercase :Dict = False
super().__init__(snake_case__ , snake_case__ )
lowercase :Union[str, Any] = self.image_processor
def __call__( self : Optional[int] , snake_case__ : ImageInput = None , snake_case__ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , snake_case__ : bool = True , snake_case__ : Union[bool, str, PaddingStrategy] = False , snake_case__ : Union[bool, str, TruncationStrategy] = None , snake_case__ : Optional[int] = None , snake_case__ : int = 0 , snake_case__ : Optional[int] = None , snake_case__ : Optional[bool] = None , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : bool = True , snake_case__ : Optional[Union[str, TensorType]] = None , **snake_case__ : Optional[Any] , ):
'''simple docstring'''
if images is None and text is None:
raise ValueError('''You have to specify either images or text.''' )
# Get only text
if images is None:
lowercase :List[Any] = self.tokenizer
lowercase :str = self.tokenizer(
text=snake_case__ , add_special_tokens=snake_case__ , padding=snake_case__ , truncation=snake_case__ , max_length=snake_case__ , stride=snake_case__ , pad_to_multiple_of=snake_case__ , return_attention_mask=snake_case__ , return_overflowing_tokens=snake_case__ , return_special_tokens_mask=snake_case__ , return_offsets_mapping=snake_case__ , return_token_type_ids=snake_case__ , return_length=snake_case__ , verbose=snake_case__ , return_tensors=snake_case__ , **snake_case__ , )
return text_encoding
# add pixel_values
lowercase :Union[str, Any] = self.image_processor(snake_case__ , return_tensors=snake_case__ )
if text is not None:
lowercase :int = self.tokenizer(
text=snake_case__ , add_special_tokens=snake_case__ , padding=snake_case__ , truncation=snake_case__ , max_length=snake_case__ , stride=snake_case__ , pad_to_multiple_of=snake_case__ , return_attention_mask=snake_case__ , return_overflowing_tokens=snake_case__ , return_special_tokens_mask=snake_case__ , return_offsets_mapping=snake_case__ , return_token_type_ids=snake_case__ , return_length=snake_case__ , verbose=snake_case__ , return_tensors=snake_case__ , **snake_case__ , )
else:
lowercase :Optional[int] = None
if text_encoding is not None:
encoding_image_processor.update(snake_case__ )
return encoding_image_processor
def __snake_case ( self : Tuple , *snake_case__ : List[Any] , **snake_case__ : Tuple ):
'''simple docstring'''
return self.tokenizer.batch_decode(*snake_case__ , **snake_case__ )
def __snake_case ( self : List[str] , *snake_case__ : Dict , **snake_case__ : List[Any] ):
'''simple docstring'''
return self.tokenizer.decode(*snake_case__ , **snake_case__ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def __snake_case ( self : List[Any] ):
'''simple docstring'''
lowercase :List[Any] = self.tokenizer.model_input_names
lowercase :List[Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 677 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForMaskedImageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
)
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
__magic_name__ = 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''')
__magic_name__ = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__magic_name__ = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class a__ :
"""simple docstring"""
A__ : Optional[str] = field(
default='''cifar10''' , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
A__ : Optional[str] = field(
default=_snake_case , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
A__ : Optional[str] = field(
default=_snake_case , metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''} , )
A__ : Optional[str] = field(default=_snake_case , metadata={'''help''': '''A folder containing the training data.'''} )
A__ : Optional[str] = field(default=_snake_case , metadata={'''help''': '''A folder containing the validation data.'''} )
A__ : Optional[float] = field(
default=0.15 , metadata={'''help''': '''Percent to split off of train for validation.'''} )
A__ : int = field(default=32 , metadata={'''help''': '''The size of the square patches to use for masking.'''} )
A__ : float = field(
default=0.6 , metadata={'''help''': '''Percentage of patches to mask.'''} , )
A__ : Optional[int] = field(
default=_snake_case , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
A__ : Optional[int] = field(
default=_snake_case , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def __UpperCAmelCase ( self :Tuple ):
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__ :
"""simple docstring"""
A__ : str = field(
default=_snake_case , metadata={
'''help''': (
'''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
'''checkpoint identifier on the hub. '''
'''Don\'t set if you want to train a model from scratch.'''
)
} , )
A__ : Optional[str] = field(
default=_snake_case , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(_snake_case )} , )
A__ : Optional[str] = field(
default=_snake_case , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
A__ : Optional[str] = field(
default=_snake_case , 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'''
)
} , )
A__ : Optional[str] = field(
default=_snake_case , metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''} , )
A__ : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
A__ : str = field(default=_snake_case , metadata={'''help''': '''Name or path of preprocessor config.'''} )
A__ : bool = field(
default=_snake_case , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
A__ : Optional[int] = field(
default=_snake_case , metadata={
'''help''': (
'''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
)
} , )
A__ : Optional[int] = field(
default=_snake_case , metadata={
'''help''': (
'''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
)
} , )
A__ : Optional[int] = field(
default=_snake_case , metadata={'''help''': '''Stride to use for the encoder.'''} , )
class a__ :
"""simple docstring"""
def __init__( self :Any , lowercase__ :Union[str, Any]=192 , lowercase__ :Tuple=32 , lowercase__ :Optional[int]=4 , lowercase__ :List[str]=0.6 ):
lowercase = input_size
lowercase = mask_patch_size
lowercase = model_patch_size
lowercase = mask_ratio
if self.input_size % self.mask_patch_size != 0:
raise ValueError('Input size must be divisible by mask patch size' )
if self.mask_patch_size % self.model_patch_size != 0:
raise ValueError('Mask patch size must be divisible by model patch size' )
lowercase = self.input_size // self.mask_patch_size
lowercase = self.mask_patch_size // self.model_patch_size
lowercase = self.rand_size**2
lowercase = int(np.ceil(self.token_count * self.mask_ratio ) )
def __call__( self :Any ):
lowercase = np.random.permutation(self.token_count )[: self.mask_count]
lowercase = np.zeros(self.token_count , dtype=lowercase__ )
lowercase = 1
lowercase = mask.reshape((self.rand_size, self.rand_size) )
lowercase = mask.repeat(self.scale , axis=0 ).repeat(self.scale , axis=1 )
return torch.tensor(mask.flatten() )
def __snake_case ( _UpperCAmelCase ):
"""simple docstring"""
lowercase = torch.stack([example['pixel_values'] for example in examples] )
lowercase = torch.stack([example['mask'] for example in examples] )
return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def __snake_case ( ):
"""simple docstring"""
lowercase = 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.
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_mim' , _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()
lowercase = 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.
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 , _UpperCAmelCase ) 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']
# Create config
# 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_or_path:
lowercase = AutoConfig.from_pretrained(model_args.config_name_or_path , **_UpperCAmelCase )
elif model_args.model_name_or_path:
lowercase = AutoConfig.from_pretrained(model_args.model_name_or_path , **_UpperCAmelCase )
else:
lowercase = CONFIG_MAPPING[model_args.model_type]()
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}""" )
# make sure the decoder_type is "simmim" (only relevant for BEiT)
if hasattr(_UpperCAmelCase , 'decoder_type' ):
lowercase = 'simmim'
# adapt config
lowercase = model_args.image_size if model_args.image_size is not None else config.image_size
lowercase = model_args.patch_size if model_args.patch_size is not None else config.patch_size
lowercase = (
model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
)
config.update(
{
'image_size': model_args.image_size,
'patch_size': model_args.patch_size,
'encoder_stride': model_args.encoder_stride,
} )
# create image processor
if model_args.image_processor_name:
lowercase = AutoImageProcessor.from_pretrained(model_args.image_processor_name , **_UpperCAmelCase )
elif model_args.model_name_or_path:
lowercase = AutoImageProcessor.from_pretrained(model_args.model_name_or_path , **_UpperCAmelCase )
else:
lowercase = {
conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
}
lowercase = IMAGE_PROCESSOR_TYPES[model_args.model_type]()
# create model
if model_args.model_name_or_path:
lowercase = AutoModelForMaskedImageModeling.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 , )
else:
logger.info('Training new model from scratch' )
lowercase = AutoModelForMaskedImageModeling.from_config(_UpperCAmelCase )
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 SimMIM paper
# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
lowercase = Compose(
[
Lambda(lambda _UpperCAmelCase : img.convert('RGB' ) if img.mode != "RGB" else img ),
RandomResizedCrop(model_args.image_size , scale=(0.67, 1.0) , ratio=(3.0 / 4.0, 4.0 / 3.0) ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
# create mask generator
lowercase = MaskGenerator(
input_size=model_args.image_size , mask_patch_size=data_args.mask_patch_size , model_patch_size=model_args.patch_size , mask_ratio=data_args.mask_ratio , )
def preprocess_images(_UpperCAmelCase ):
lowercase = [transforms(_UpperCAmelCase ) for image in examples[image_column_name]]
lowercase = [mask_generator() for i in range(len(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(_UpperCAmelCase )
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(_UpperCAmelCase )
# Initialize our trainer
lowercase = Trainer(
model=_UpperCAmelCase , args=_UpperCAmelCase , train_dataset=ds['train'] if training_args.do_train else None , eval_dataset=ds['validation'] if training_args.do_eval else None , tokenizer=_UpperCAmelCase , data_collator=_UpperCAmelCase , )
# 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=_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:
lowercase = trainer.evaluate()
trainer.log_metrics('eval' , _UpperCAmelCase )
trainer.save_metrics('eval' , _UpperCAmelCase )
# Write model card and (optionally) push to hub
lowercase = {
'finetuned_from': model_args.model_name_or_path,
'tasks': 'masked-image-modeling',
'dataset': data_args.dataset_name,
'tags': ['masked-image-modeling'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_UpperCAmelCase )
else:
trainer.create_model_card(**_UpperCAmelCase )
if __name__ == "__main__":
main()
| 717 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'''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
__magic_name__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 314 | 0 |
"""simple docstring"""
import argparse
import os
import re
__UpperCamelCase : Any = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__UpperCamelCase : Any = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__UpperCamelCase : Union[str, Any] = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__UpperCamelCase : Tuple = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__UpperCamelCase : Any = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__UpperCamelCase : List[Any] = re.compile(R'''\[([^\]]+)\]''')
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : Any = _re_indent.search(__UpperCamelCase )
return "" if search is None else search.groups()[0]
def __SCREAMING_SNAKE_CASE ( A_ , A_="" , A_=None , A_=None ):
lowerCAmelCase__ : int = 0
lowerCAmelCase__ : Dict = code.split('''\n''' )
if start_prompt is not None:
while not lines[index].startswith(__UpperCamelCase ):
index += 1
lowerCAmelCase__ : Optional[int] = ["""\n""".join(lines[:index] )]
else:
lowerCAmelCase__ : List[str] = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
lowerCAmelCase__ : Any = [lines[index]]
index += 1
while index < len(__UpperCamelCase ) and (end_prompt is None or not lines[index].startswith(__UpperCamelCase )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(__UpperCamelCase ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + ''' ''' ):
current_block.append(lines[index] )
blocks.append('''\n'''.join(__UpperCamelCase ) )
if index < len(__UpperCamelCase ) - 1:
lowerCAmelCase__ : Union[str, Any] = [lines[index + 1]]
index += 1
else:
lowerCAmelCase__ : Optional[int] = []
else:
blocks.append('''\n'''.join(__UpperCamelCase ) )
lowerCAmelCase__ : Union[str, Any] = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(__UpperCamelCase ) > 0:
blocks.append('''\n'''.join(__UpperCamelCase ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(__UpperCamelCase ):
blocks.append('''\n'''.join(lines[index:] ) )
return blocks
def __SCREAMING_SNAKE_CASE ( A_ ):
def _inner(A_ ):
return key(__UpperCamelCase ).lower().replace('''_''' , '''''' )
return _inner
def __SCREAMING_SNAKE_CASE ( A_ , A_=None ):
def noop(A_ ):
return x
if key is None:
lowerCAmelCase__ : Tuple = noop
# Constants are all uppercase, they go first.
lowerCAmelCase__ : Dict = [obj for obj in objects if key(__UpperCamelCase ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
lowerCAmelCase__ : str = [obj for obj in objects if key(__UpperCamelCase )[0].isupper() and not key(__UpperCamelCase ).isupper()]
# Functions begin with a lowercase, they go last.
lowerCAmelCase__ : int = [obj for obj in objects if not key(__UpperCamelCase )[0].isupper()]
lowerCAmelCase__ : Optional[int] = ignore_underscore(__UpperCamelCase )
return sorted(__UpperCamelCase , key=__UpperCamelCase ) + sorted(__UpperCamelCase , key=__UpperCamelCase ) + sorted(__UpperCamelCase , key=__UpperCamelCase )
def __SCREAMING_SNAKE_CASE ( A_ ):
def _replace(A_ ):
lowerCAmelCase__ : List[Any] = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
lowerCAmelCase__ : Tuple = [part.strip().replace('''\"''' , '''''' ) for part in imports.split(''',''' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
lowerCAmelCase__ : Optional[Any] = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(__UpperCamelCase )] ) + "]"
lowerCAmelCase__ : Optional[Any] = import_statement.split('''\n''' )
if len(__UpperCamelCase ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
lowerCAmelCase__ : Any = 2 if lines[1].strip() == """[""" else 1
lowerCAmelCase__ : Any = [(i, _re_strip_line.search(__UpperCamelCase ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
lowerCAmelCase__ : List[str] = sort_objects(__UpperCamelCase , key=lambda A_ : x[1] )
lowerCAmelCase__ : Union[str, Any] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(__UpperCamelCase ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
lowerCAmelCase__ : Any = _re_bracket_content.sub(_replace , lines[1] )
else:
lowerCAmelCase__ : List[str] = [part.strip().replace('''\"''' , '''''' ) for part in lines[1].split(''',''' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
lowerCAmelCase__ : Tuple = keys[:-1]
lowerCAmelCase__ : List[str] = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(__UpperCamelCase )] )
return "\n".join(__UpperCamelCase )
else:
# Finally we have to deal with imports fitting on one line
lowerCAmelCase__ : Any = _re_bracket_content.sub(_replace , __UpperCamelCase )
return import_statement
def __SCREAMING_SNAKE_CASE ( A_ , A_=True ):
with open(__UpperCamelCase , '''r''' ) as f:
lowerCAmelCase__ : str = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
lowerCAmelCase__ : int = split_code_in_indented_blocks(
__UpperCamelCase , start_prompt='''_import_structure = {''' , end_prompt='''if TYPE_CHECKING:''' )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(__UpperCamelCase ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
lowerCAmelCase__ : List[str] = main_blocks[block_idx]
lowerCAmelCase__ : List[str] = block.split('''\n''' )
# Get to the start of the imports.
lowerCAmelCase__ : int = 0
while line_idx < len(__UpperCamelCase ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
lowerCAmelCase__ : int = len(__UpperCamelCase )
else:
line_idx += 1
if line_idx >= len(__UpperCamelCase ):
continue
# Ignore beginning and last line: they don't contain anything.
lowerCAmelCase__ : Dict = """\n""".join(block_lines[line_idx:-1] )
lowerCAmelCase__ : Tuple = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
lowerCAmelCase__ : List[str] = split_code_in_indented_blocks(__UpperCamelCase , indent_level=__UpperCamelCase )
# We have two categories of import key: list or _import_structure[key].append/extend
lowerCAmelCase__ : Dict = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
lowerCAmelCase__ : str = [(pattern.search(__UpperCamelCase ).groups()[0] if pattern.search(__UpperCamelCase ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
lowerCAmelCase__ : str = [(i, key) for i, key in enumerate(__UpperCamelCase ) if key is not None]
lowerCAmelCase__ : int = [x[0] for x in sorted(__UpperCamelCase , key=lambda A_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
lowerCAmelCase__ : int = 0
lowerCAmelCase__ : int = []
for i in range(len(__UpperCamelCase ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
lowerCAmelCase__ : str = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(__UpperCamelCase )
count += 1
# And we put our main block back together with its first and last line.
lowerCAmelCase__ : Any = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(__UpperCamelCase ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(__UpperCamelCase , '''w''' ) as f:
f.write('''\n'''.join(__UpperCamelCase ) )
def __SCREAMING_SNAKE_CASE ( A_=True ):
lowerCAmelCase__ : Any = []
for root, _, files in os.walk(__UpperCamelCase ):
if "__init__.py" in files:
lowerCAmelCase__ : Tuple = sort_imports(os.path.join(__UpperCamelCase , '''__init__.py''' ) , check_only=__UpperCamelCase )
if result:
lowerCAmelCase__ : List[str] = [os.path.join(__UpperCamelCase , '''__init__.py''' )]
if len(__UpperCamelCase ) > 0:
raise ValueError(f'Would overwrite {len(__UpperCamelCase )} files, run `make style`.' )
if __name__ == "__main__":
__UpperCamelCase : Tuple = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__UpperCamelCase : Dict = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 450 |
"""simple docstring"""
# Copyright 2023 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.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCAmelCase : List[Any] = {
'''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VivitConfig'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : List[Any] = ['''VivitImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : List[Any] = [
'''VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''VivitModel''',
'''VivitPreTrainedModel''',
'''VivitForVideoClassification''',
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
__lowerCAmelCase : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 58 | 0 |
"""simple docstring"""
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
_lowerCAmelCase = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'''{bindir}/../../examples/pytorch/translation'''):
from run_translation import main # noqa
set_seed(4_2)
_lowerCAmelCase = """sshleifer/student_marian_en_ro_6_1"""
_lowerCAmelCase = """sshleifer/tiny-mbart"""
@require_torch
class __UpperCamelCase ( a__ ):
def __lowerCamelCase ( self ,_A=False ,_A=None ,_A=True ,_A=True ,_A=True ,_A=True ,):
'''simple docstring'''
_lowerCAmelCase : Any = self.run_trainer(
eval_steps=1 ,max_len=12 ,model_name=_A ,num_train_epochs=1 ,distributed=_A ,extra_args_str=_A ,predict_with_generate=_A ,do_train=_A ,do_eval=_A ,do_predict=_A ,)
_lowerCAmelCase : Optional[Any] = TrainerState.load_from_json(os.path.join(_A ,'trainer_state.json' ) ).log_history
if not do_eval:
return
_lowerCAmelCase : Tuple = [log for log in logs if 'eval_loss' in log.keys()]
_lowerCAmelCase : Union[str, Any] = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
_lowerCAmelCase : List[str] = eval_metrics[-1]
assert isinstance(last_step_stats['eval_bleu'] ,_A )
assert not math.isnan(float(last_step_stats['eval_loss'] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def __lowerCamelCase ( self ):
'''simple docstring'''
self.run_seqaseq_quick()
@require_torch_multi_gpu
def __lowerCamelCase ( self ):
'''simple docstring'''
self.run_seqaseq_quick(distributed=_A )
@require_torch_multi_gpu
def __lowerCamelCase ( self ):
'''simple docstring'''
self.run_seqaseq_quick(distributed=_A )
@unittest.skip('Requires an update of the env running those tests' )
@require_torch_multi_gpu
@require_fairscale
def __lowerCamelCase ( self ):
'''simple docstring'''
self.run_seqaseq_quick(distributed=_A ,extra_args_str='--sharded_ddp simple' )
@unittest.skip('Requires an update of the env running those tests' )
@require_torch_multi_gpu
@require_fairscale
def __lowerCamelCase ( self ):
'''simple docstring'''
self.run_seqaseq_quick(distributed=_A ,extra_args_str='--sharded_ddp simple --fp16' )
@unittest.skip('Requires an update of the env running those tests' )
@require_torch_multi_gpu
@require_fairscale
def __lowerCamelCase ( self ):
'''simple docstring'''
self.run_seqaseq_quick(distributed=_A ,extra_args_str='--sharded_ddp zero_dp_2' ,predict_with_generate=_A )
@unittest.skip('Requires an update of the env running those tests' )
@require_torch_multi_gpu
@require_fairscale
def __lowerCamelCase ( self ):
'''simple docstring'''
self.run_seqaseq_quick(
distributed=_A ,extra_args_str='--sharded_ddp zero_dp_2 --fp16' ,predict_with_generate=_A )
@require_apex
@require_torch_gpu
def __lowerCamelCase ( self ):
'''simple docstring'''
self.run_seqaseq_quick(distributed=_A ,extra_args_str='--fp16 --fp16_backend=apex' )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=_A ,extra_args_str='--fp16 --fp16_backend=apex' )
@parameterized.expand(['base', 'low', 'high', 'mixed'] )
@require_torch_multi_gpu
def __lowerCamelCase ( self ,_A ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = {
# test with the default log_level - should be info and thus log info once
'base': {'extra_args_str': '', 'n_matches': 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
'low': {'extra_args_str': '--log_level debug --log_level_replica debug', 'n_matches': 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
'high': {'extra_args_str': '--log_level error --log_level_replica debug', 'n_matches': 1},
# test with high log_level and log_level_replica - should be quiet on all processes
'mixed': {'extra_args_str': '--log_level error --log_level_replica error', 'n_matches': 0},
}
_lowerCAmelCase : int = experiments[experiment_id]
_lowerCAmelCase : List[Any] = {'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
_lowerCAmelCase : Tuple = 'Running training'
with CaptureStderr() as cl:
self.run_seqaseq_quick(**_A ,extra_args_str=data['extra_args_str'] )
_lowerCAmelCase : int = len(re.findall(_A ,cl.err ) )
self.assertEqual(_A ,data['n_matches'] )
@slow
def __lowerCamelCase ( self ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.run_trainer(
eval_steps=2 ,max_len=128 ,model_name=_A ,learning_rate=3E-4 ,num_train_epochs=10 ,distributed=_A ,)
# Check metrics
_lowerCAmelCase : Tuple = TrainerState.load_from_json(os.path.join(_A ,'trainer_state.json' ) ).log_history
_lowerCAmelCase : Union[str, Any] = [log for log in logs if 'eval_loss' in log.keys()]
_lowerCAmelCase : int = eval_metrics[0]
_lowerCAmelCase : List[str] = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats['eval_bleu'] ,_A )
# test if do_predict saves generations and metrics
_lowerCAmelCase : str = os.listdir(_A )
_lowerCAmelCase : Dict = {os.path.basename(_A ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def __lowerCamelCase ( self ):
'''simple docstring'''
from transformers.training_args import OptimizerNames
def train_and_return_metrics(_A ) -> Tuple[int, float]:
_lowerCAmelCase : List[str] = '--skip_memory_metrics 0'
_lowerCAmelCase : str = self.run_trainer(
max_len=128 ,model_name=_A ,learning_rate=3E-4 ,num_train_epochs=1 ,optim=_A ,distributed=_A ,extra_args_str=_A ,do_eval=_A ,do_predict=_A ,n_gpus_to_use=1 ,)
# Check metrics
_lowerCAmelCase : str = TrainerState.load_from_json(Path(_A ,'trainer_state.json' ) ).log_history
_lowerCAmelCase : List[Any] = int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20 )
_lowerCAmelCase : List[str] = int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20 )
_lowerCAmelCase : Optional[Any] = logs[0]['train_loss']
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase : Tuple = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase : str = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
_lowerCAmelCase : Union[str, Any] = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
_lowerCAmelCase : List[str] = gpu_peak_mem_orig + gpu_alloc_mem_orig
_lowerCAmelCase : List[Any] = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
_lowerCAmelCase : List[str] = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
_lowerCAmelCase : Any = 120
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
_A ,_A ,'should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'
F""" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"""
F""" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB""" ,)
self.assertGreater(
_A ,_A ,'should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'
F""" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"""
F""" gpu_total_mem_bnb={gpu_total_mem_bnb}MB""" ,)
self.assertEqual(
_A ,_A ,F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}""" )
def __lowerCamelCase ( self ,_A ,_A ,_A ,_A = 3E-3 ,_A = "adafactor" ,_A = False ,_A = None ,_A = 0 ,_A = True ,_A = True ,_A = True ,_A = True ,_A = None ,):
'''simple docstring'''
_lowerCAmelCase : int = self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
_lowerCAmelCase : Optional[int] = self.get_auto_remove_tmp_dir()
_lowerCAmelCase : Tuple = F"""
--model_name_or_path {model_name}
--train_file {data_dir}/train.json
--validation_file {data_dir}/val.json
--test_file {data_dir}/test.json
--output_dir {output_dir}
--overwrite_output_dir
--max_train_samples 8
--max_source_length {max_len}
--max_target_length {max_len}
--do_train
--num_train_epochs {str(_A )}
--per_device_train_batch_size 4
--learning_rate {learning_rate}
--warmup_steps 8
--logging_steps 0
--logging_strategy no
--save_steps {str(_A )}
--group_by_length
--label_smoothing_factor 0.1
--target_lang ro_RO
--source_lang en_XX
""".split()
_lowerCAmelCase : Dict = F"""
--do_eval
--per_device_eval_batch_size 4
--max_eval_samples 8
--val_max_target_length {max_len}
--evaluation_strategy steps
--eval_steps {str(_A )}
""".split()
_lowerCAmelCase : List[str] = '\n --do_predict\n '.split()
_lowerCAmelCase : int = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += F"""--optim {optim}""".split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
_lowerCAmelCase : List[Any] = get_gpu_count()
_lowerCAmelCase : List[str] = get_torch_dist_unique_port()
_lowerCAmelCase : Tuple = F"""
-m torch.distributed.run
--nproc_per_node={n_gpus_to_use}
--master_port={master_port}
{self.examples_dir_str}/pytorch/translation/run_translation.py
""".split()
_lowerCAmelCase : Optional[int] = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(_A ,env=self.get_env() )
else:
_lowerCAmelCase : Optional[Any] = ['run_translation.py'] + args
with patch.object(_A ,'argv' ,_A ):
main()
return output_dir
| 16 |
"""simple docstring"""
import argparse
import struct
import unittest
class __UpperCamelCase :
def __init__( self ,_A ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = data
# Initialize hash values
_lowerCAmelCase : Any = [
0x6A09_E667,
0xBB67_AE85,
0x3C6E_F372,
0xA54F_F53A,
0x510E_527F,
0x9B05_688C,
0x1F83_D9AB,
0x5BE0_CD19,
]
# Initialize round constants
_lowerCAmelCase : str = [
0x428A_2F98,
0x7137_4491,
0xB5C0_FBCF,
0xE9B5_DBA5,
0x3956_C25B,
0x59F1_11F1,
0x923F_82A4,
0xAB1C_5ED5,
0xD807_AA98,
0x1283_5B01,
0x2431_85BE,
0x550C_7DC3,
0x72BE_5D74,
0x80DE_B1FE,
0x9BDC_06A7,
0xC19B_F174,
0xE49B_69C1,
0xEFBE_4786,
0x0FC1_9DC6,
0x240C_A1CC,
0x2DE9_2C6F,
0x4A74_84AA,
0x5CB0_A9DC,
0x76F9_88DA,
0x983E_5152,
0xA831_C66D,
0xB003_27C8,
0xBF59_7FC7,
0xC6E0_0BF3,
0xD5A7_9147,
0x06CA_6351,
0x1429_2967,
0x27B7_0A85,
0x2E1B_2138,
0x4D2C_6DFC,
0x5338_0D13,
0x650A_7354,
0x766A_0ABB,
0x81C2_C92E,
0x9272_2C85,
0xA2BF_E8A1,
0xA81A_664B,
0xC24B_8B70,
0xC76C_51A3,
0xD192_E819,
0xD699_0624,
0xF40E_3585,
0x106A_A070,
0x19A4_C116,
0x1E37_6C08,
0x2748_774C,
0x34B0_BCB5,
0x391C_0CB3,
0x4ED8_AA4A,
0x5B9C_CA4F,
0x682E_6FF3,
0x748F_82EE,
0x78A5_636F,
0x84C8_7814,
0x8CC7_0208,
0x90BE_FFFA,
0xA450_6CEB,
0xBEF9_A3F7,
0xC671_78F2,
]
_lowerCAmelCase : Any = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def __lowerCamelCase ( _A ):
'''simple docstring'''
_lowerCAmelCase : int = b'\x80' + (b'\x00' * (63 - (len(_A ) + 8) % 64))
_lowerCAmelCase : Any = struct.pack('>Q' ,(len(_A ) * 8) )
return data + padding + big_endian_integer
def __lowerCamelCase ( self ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = [
self.preprocessed_data[x : x + 64]
for x in range(0 ,len(self.preprocessed_data ) ,64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
_lowerCAmelCase : int = list(struct.unpack('>16L' ,_A ) )
# add 48 0-ed integers
words += [0] * 48
_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase : Tuple = self.hashes
for index in range(0 ,64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
_lowerCAmelCase : List[str] = (
self.ror(words[index - 15] ,7 )
^ self.ror(words[index - 15] ,18 )
^ (words[index - 15] >> 3)
)
_lowerCAmelCase : Tuple = (
self.ror(words[index - 2] ,17 )
^ self.ror(words[index - 2] ,19 )
^ (words[index - 2] >> 10)
)
_lowerCAmelCase : str = (
words[index - 16] + sa + words[index - 7] + sa
) % 0x1_0000_0000
# Compression
_lowerCAmelCase : Optional[Any] = self.ror(_A ,6 ) ^ self.ror(_A ,11 ) ^ self.ror(_A ,25 )
_lowerCAmelCase : int = (e & f) ^ ((~e & 0xFFFF_FFFF) & g)
_lowerCAmelCase : int = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x1_0000_0000
_lowerCAmelCase : Union[str, Any] = self.ror(_A ,2 ) ^ self.ror(_A ,13 ) ^ self.ror(_A ,22 )
_lowerCAmelCase : Any = (a & b) ^ (a & c) ^ (b & c)
_lowerCAmelCase : Any = (sa + maj) % 0x1_0000_0000
_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase : Tuple = (
g,
f,
e,
((d + tempa) % 0x1_0000_0000),
c,
b,
a,
((tempa + tempa) % 0x1_0000_0000),
)
_lowerCAmelCase : Any = [a, b, c, d, e, f, g, h]
# Modify final values
_lowerCAmelCase : int = [
((element + mutated_hash_values[index]) % 0x1_0000_0000)
for index, element in enumerate(self.hashes )
]
_lowerCAmelCase : List[str] = ''.join([hex(_A )[2:].zfill(8 ) for value in self.hashes] )
def __lowerCamelCase ( self ,_A ,_A ):
'''simple docstring'''
return 0xFFFF_FFFF & (value << (32 - rotations)) | (value >> rotations)
class __UpperCamelCase ( unittest.TestCase ):
def __lowerCamelCase ( self ):
'''simple docstring'''
import hashlib
_lowerCAmelCase : Any = bytes('Test String' ,'utf-8' )
self.assertEqual(SHAaaa(_A ).hash ,hashlib.shaaaa(_A ).hexdigest() )
def lowerCamelCase__ ( ):
'''simple docstring'''
import doctest
doctest.testmod()
_lowerCAmelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
'-s' , '--string' , dest='input_string' , default='Hello World!! Welcome to Cryptography' , help='Hash the string' , )
parser.add_argument(
'-f' , '--file' , dest='input_file' , help='Hash contents of a file' )
_lowerCAmelCase : Tuple = parser.parse_args()
_lowerCAmelCase : List[str] = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , 'rb' ) as f:
_lowerCAmelCase : int = f.read()
else:
_lowerCAmelCase : int = bytes(_lowerCamelCase , 'utf-8' )
print(SHAaaa(_lowerCamelCase ).hash )
if __name__ == "__main__":
main()
| 16 | 1 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
_a = get_logger(__name__)
class _UpperCAmelCase:
lowercase__ = """dummy_data"""
lowercase__ = """datasets"""
lowercase__ = False
def __init__( self , __a , __a , __a , __a = None , __a = False , __a = True , __a = None , ) -> int:
'''simple docstring'''
_UpperCamelCase = 0
_UpperCamelCase = dataset_name
_UpperCamelCase = cache_dir
_UpperCamelCase = use_local_dummy_data
_UpperCamelCase = config
# download_callbacks take a single url as input
_UpperCamelCase = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
_UpperCamelCase = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
_UpperCamelCase = str(lowerCAmelCase_)
# to be downloaded
_UpperCamelCase = None
_UpperCamelCase = None
@property
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
if self._dummy_file is None:
_UpperCamelCase = self.download_dummy_data()
return self._dummy_file
@property
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join('''dummy''' , self.config.name , self.version_name)
# structure is dummy / version_name
return os.path.join('''dummy''' , self.version_name)
@property
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return os.path.join(self.dummy_data_folder , '''dummy_data.zip''')
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
_UpperCamelCase = cached_path(
lowerCAmelCase_ , cache_dir=self.cache_dir , extract_compressed_file=lowerCAmelCase_ , force_extract=lowerCAmelCase_)
return os.path.join(lowerCAmelCase_ , self.dummy_file_name)
@property
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file)
@property
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
if self._bucket_url is None:
_UpperCamelCase = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , '''/'''))
return self._bucket_url
@property
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
if os.path.isdir(self.dummy_file):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , '''/''').split('''/''')[:-1])
def UpperCAmelCase ( self , __a , *__a) -> str:
'''simple docstring'''
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
_UpperCamelCase = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
_UpperCamelCase = self.dummy_file_name
# special case when data_url is a dict
if isinstance(lowerCAmelCase_ , lowerCAmelCase_):
return self.create_dummy_data_dict(lowerCAmelCase_ , lowerCAmelCase_)
elif isinstance(lowerCAmelCase_ , (list, tuple)):
return self.create_dummy_data_list(lowerCAmelCase_ , lowerCAmelCase_)
else:
return self.create_dummy_data_single(lowerCAmelCase_ , lowerCAmelCase_)
def UpperCAmelCase ( self , __a , *__a) -> Optional[int]:
'''simple docstring'''
return self.download_and_extract(lowerCAmelCase_)
def UpperCAmelCase ( self , __a , __a) -> Optional[Any]:
'''simple docstring'''
return self.download_and_extract(lowerCAmelCase_)
def UpperCAmelCase ( self , __a , *__a , **__a) -> Optional[Any]:
'''simple docstring'''
return path
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return {}
def UpperCAmelCase ( self , __a , __a) -> Tuple:
'''simple docstring'''
_UpperCamelCase = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(lowerCAmelCase_ , lowerCAmelCase_):
for single_url in single_urls:
download_callback(lowerCAmelCase_)
else:
_UpperCamelCase = single_urls
download_callback(lowerCAmelCase_)
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(lowerCAmelCase_ , lowerCAmelCase_):
_UpperCamelCase = [os.path.join(lowerCAmelCase_ , urllib.parse.quote_plus(Path(lowerCAmelCase_).name)) for x in single_urls]
else:
_UpperCamelCase = single_urls
_UpperCamelCase = os.path.join(lowerCAmelCase_ , urllib.parse.quote_plus(Path(lowerCAmelCase_).name))
_UpperCamelCase = value
# make sure that values are unique
if all(isinstance(lowerCAmelCase_ , lowerCAmelCase_) for i in dummy_data_dict.values()) and len(set(dummy_data_dict.values())) < len(
dummy_data_dict.values()):
# append key to value to make its name unique
_UpperCamelCase = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def UpperCAmelCase ( self , __a , __a) -> str:
'''simple docstring'''
_UpperCamelCase = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
_UpperCamelCase = all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}''' , lowerCAmelCase_)) for url in data_url)
_UpperCamelCase = all(
url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed''') for url in data_url)
if data_url and (is_tf_records or is_pubmed_records):
_UpperCamelCase = [data_url[0]] * len(lowerCAmelCase_)
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(lowerCAmelCase_)
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_UpperCamelCase = os.path.join(lowerCAmelCase_ , urllib.parse.quote_plus(single_url.split('''/''')[-1]))
dummy_data_list.append(lowerCAmelCase_)
return dummy_data_list
def UpperCAmelCase ( self , __a , __a) -> Optional[int]:
'''simple docstring'''
for download_callback in self.download_callbacks:
download_callback(lowerCAmelCase_)
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_UpperCamelCase = os.path.join(lowerCAmelCase_ , urllib.parse.quote_plus(data_url.split('''/''')[-1]))
if os.path.exists(lowerCAmelCase_) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
pass
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
pass
def UpperCAmelCase ( self , __a) -> List[Any]:
'''simple docstring'''
def _iter_archive_members(__a):
# this preserves the order of the members inside the ZIP archive
_UpperCamelCase = Path(self.dummy_file).parent
_UpperCamelCase = path.relative_to(lowerCAmelCase_)
with ZipFile(self.local_path_to_dummy_data) as zip_file:
_UpperCamelCase = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix()):
yield dummy_parent_path.joinpath(lowerCAmelCase_)
_UpperCamelCase = Path(lowerCAmelCase_)
_UpperCamelCase = _iter_archive_members(lowerCAmelCase_) if self.use_local_dummy_data else path.rglob('''*''')
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''')):
yield file_path.relative_to(lowerCAmelCase_).as_posix(), file_path.open('''rb''')
def UpperCAmelCase ( self , __a) -> Optional[int]:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_):
_UpperCamelCase = [paths]
for path in paths:
if os.path.isfile(lowerCAmelCase_):
if os.path.basename(lowerCAmelCase_).startswith(('''.''', '''__''')):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(lowerCAmelCase_):
if os.path.basename(lowerCAmelCase_).startswith(('''.''', '''__''')):
continue
dirnames.sort()
for filename in sorted(lowerCAmelCase_):
if filename.startswith(('''.''', '''__''')):
continue
yield os.path.join(lowerCAmelCase_ , lowerCAmelCase_)
| 19 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
"stable diffusion controlnet",
"0.22.0",
"Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.",
standard_warn=False,
stacklevel=3,
)
| 393 | 0 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowercase = logging.get_logger(__name__)
class UpperCamelCase_ ( snake_case_ ):
'''simple docstring'''
def __init__( self , *a , **a ) -> None:
warnings.warn(
'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use YolosImageProcessor instead.' , a , )
super().__init__(*a , **a )
| 607 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoTokenizer,
TFAutoModelForCausalLM,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSpeechSeqaSeq,
TFAutoModelForVisionaSeq,
TFBartForConditionalGeneration,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
tf_top_k_top_p_filtering,
)
if is_tensorflow_text_available():
import tensorflow_text as text
@require_tf
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ) -> str:
snake_case_ = tf.convert_to_tensor(
[
[
8.2_220_991, # 3rd highest value; idx. 0
-0.5_620_044,
5.23_229_752,
4.0_386_393,
-6.8_798_378,
-0.54_785_802,
-3.2_012_153,
2.92_777_176,
1.88_171_953,
7.35_341_276, # 5th highest value; idx. 9
8.43_207_833, # 2nd highest value; idx. 10
-9.85_711_836,
-5.96_209_236,
-1.13_039_161,
-7.1_115_294,
-0.8_369_633,
-5.3_186_408,
7.06_427_407,
0.81_369_344,
-0.82_023_817,
-5.9_179_796,
0.58_813_443,
-6.99_778_438,
4.71_551_189,
-0.18_771_637,
7.44_020_759, # 4th highest value; idx. 25
9.38_450_987, # 1st highest value; idx. 26
2.12_662_941,
-9.32_562_038,
2.35_652_522,
], # cummulative prob of 5 highest values <= 0.6
[
0.58_425_518,
4.53_139_238,
-5.57_510_464,
-6.28_030_699,
-7.19_529_503,
-4.02_122_551,
1.39_337_037,
-6.06_707_057,
1.59_480_517,
-9.643_119,
0.03_907_799,
0.67_231_762,
-8.88_206_726,
6.27_115_922, # 4th highest value; idx. 13
2.28_520_723,
4.82_767_506,
4.30_421_368,
8.8_275_313, # 2nd highest value; idx. 17
5.44_029_958, # 5th highest value; idx. 18
-4.4_735_794,
7.38_579_536, # 3rd highest value; idx. 20
-2.91_051_663,
2.61_946_077,
-2.5_674_762,
-9.48_959_302,
-4.02_922_645,
-1.35_416_918,
9.67_702_323, # 1st highest value; idx. 27
-5.89_478_553,
1.85_370_467,
], # cummulative prob of 5 highest values <= 0.6
] , dtype=tf.floataa , )
snake_case_ = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] , dtype=tf.intaa , ) # expected non filtered idx as noted above
snake_case_ = tf.convert_to_tensor(
[8.222_099, 7.3_534_126, 8.432_078, 7.4_402_075, 9.38_451, 6.271_159, 8.827_531, 5.4_402_995, 7.3_857_956, 9.677_023] , dtype=tf.floataa , ) # expected non filtered values as noted above
snake_case_ = tf_top_k_top_p_filtering(a , top_k=10 , top_p=0.6 , min_tokens_to_keep=4 )
snake_case_ = output[output != -float('inf' )]
snake_case_ = tf.cast(
tf.where(tf.not_equal(a , tf.constant(-float('inf' ) , dtype=tf.floataa ) ) ) , dtype=tf.intaa , )
tf.debugging.assert_near(a , a , rtol=1E-12 )
tf.debugging.assert_equal(a , a )
@require_tf
class UpperCamelCase_ ( unittest.TestCase , snake_case_ ):
'''simple docstring'''
if is_tf_available():
lowerCAmelCase = {
'''AutoModelForCausalLM''': TFAutoModelForCausalLM,
'''AutoModelForSpeechSeq2Seq''': TFAutoModelForSpeechSeqaSeq,
'''AutoModelForSeq2SeqLM''': TFAutoModelForSeqaSeqLM,
'''AutoModelForVision2Seq''': TFAutoModelForVisionaSeq,
'''LogitsProcessorList''': TFLogitsProcessorList,
'''MinLengthLogitsProcessor''': TFMinLengthLogitsProcessor,
'''create_tensor_fn''': tf.convert_to_tensor,
'''floats_tensor''': floats_tensor,
'''return_tensors''': '''tf''',
}
@slow
def _UpperCamelCase ( self ) -> Optional[int]:
# TF-only test: tf.saved_model export
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 2
snake_case_ = 2
class UpperCamelCase_ ( tf.Module ):
'''simple docstring'''
def __init__( self , a ) -> Any:
super(a , self ).__init__()
snake_case_ = model
@tf.function(
input_signature=(
tf.TensorSpec((None, input_length) , tf.intaa , name='input_ids' ),
tf.TensorSpec((None, input_length) , tf.intaa , name='attention_mask' ),
) , jit_compile=a , )
def _UpperCamelCase ( self , a , a ) -> Optional[Any]:
snake_case_ = self.model.generate(
input_ids=a , attention_mask=a , max_new_tokens=a , return_dict_in_generate=a , )
return {"sequences": outputs["sequences"]}
snake_case_ = [[2, 0], [1_02, 1_03]]
snake_case_ = [[1, 0], [1, 1]]
snake_case_ = DummyModel(model=a )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(a , a , signatures={'serving_default': dummy_model.serving} )
snake_case_ = tf.saved_model.load(a ).signatures['serving_default']
for batch_size in range(1 , len(a ) + 1 ):
snake_case_ = {
'input_ids': tf.constant(dummy_input_ids[:batch_size] ),
'attention_mask': tf.constant(dummy_attention_masks[:batch_size] ),
}
snake_case_ = serving_func(**a )['sequences']
snake_case_ = test_model.generate(**a , max_new_tokens=a )
tf.debugging.assert_equal(a , a )
@slow
def _UpperCamelCase ( self ) -> Dict:
# TF-only test: tf.saved_model export
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 1
snake_case_ = 2
class UpperCamelCase_ ( tf.Module ):
'''simple docstring'''
def __init__( self , a ) -> int:
super(a , self ).__init__()
snake_case_ = model
@tf.function(
input_signature=(
tf.TensorSpec((batch_size, None) , tf.intaa , name='input_ids' ),
tf.TensorSpec((batch_size, None) , tf.intaa , name='attention_mask' ),
) , jit_compile=a , )
def _UpperCamelCase ( self , a , a ) -> Union[str, Any]:
snake_case_ = self.model.generate(
input_ids=a , attention_mask=a , max_new_tokens=a , return_dict_in_generate=a , )
return {"sequences": outputs["sequences"]}
snake_case_ = [[2], [1_02, 1_03]]
snake_case_ = [[1], [1, 1]]
snake_case_ = DummyModel(model=a )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(a , a , signatures={'serving_default': dummy_model.serving} )
snake_case_ = tf.saved_model.load(a ).signatures['serving_default']
for input_row in range(len(a ) ):
snake_case_ = {
'input_ids': tf.constant([dummy_input_ids[input_row]] ),
'attention_mask': tf.constant([dummy_attention_masks[input_row]] ),
}
snake_case_ = serving_func(**a )['sequences']
snake_case_ = test_model.generate(**a , max_new_tokens=a )
tf.debugging.assert_equal(a , a )
@slow
@require_tensorflow_text
def _UpperCamelCase ( self ) -> Any:
# TF-only test: tf.saved_model export
with tempfile.TemporaryDirectory() as tmp_dir:
# file needed to load the TF tokenizer
hf_hub_download(repo_id='google/flan-t5-small' , filename='spiece.model' , local_dir=a )
class UpperCamelCase_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self ) -> Any:
super().__init__()
snake_case_ = text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(a , 'spiece.model' ) , 'rb' ).read() )
snake_case_ = TFAutoModelForSeqaSeqLM.from_pretrained('hf-internal-testing/tiny-random-t5' )
def _UpperCamelCase ( self , a , *a , **a ) -> int:
snake_case_ = self.tokenizer.tokenize(a )
snake_case_ , snake_case_ = text.pad_model_inputs(
a , max_seq_length=64 , pad_value=self.model.config.pad_token_id )
snake_case_ = self.model.generate(input_ids=a , attention_mask=a )
return self.tokenizer.detokenize(a )
snake_case_ = CompleteSentenceTransformer()
snake_case_ = tf.keras.layers.Input(shape=(1,) , dtype=tf.string , name='inputs' )
snake_case_ = complete_model(a )
snake_case_ = tf.keras.Model(a , a )
keras_model.save(a )
def _UpperCamelCase ( self ) -> Union[str, Any]:
# Has PT equivalent: this test relies on random sampling
snake_case_ = {
'do_sample': True,
'num_beams': 1,
'top_p': 0.7,
'top_k': 10,
'temperature': 0.7,
}
snake_case_ = 14
snake_case_ = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 'Hello, my dog is cute and'
snake_case_ = tokenizer(a , return_tensors='tf' )
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 6_38
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(':/CPU:0' ):
tf.random.set_seed(0 )
snake_case_ = model.generate(**a , eos_token_id=a , **a )
self.assertTrue(expectation == len(generated_tokens[0] ) )
snake_case_ = [6_38, 1_98]
with tf.device(':/CPU:0' ):
tf.random.set_seed(0 )
snake_case_ = model.generate(**a , eos_token_id=a , **a )
self.assertTrue(expectation == len(generated_tokens[0] ) )
def _UpperCamelCase ( self ) -> Any:
# Has PT equivalent: ample use of framework-specific code
snake_case_ = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = 'Hugging Face is a technology company based in New York and Paris.'
snake_case_ = bart_tokenizer(a , return_tensors='tf' ).input_ids
snake_case_ = TFBartForConditionalGeneration.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = bart_model.generate(a ).numpy()
class UpperCamelCase_ ( snake_case_ ):
'''simple docstring'''
def _UpperCamelCase ( self , a , a=None , **a ) -> List[str]:
return super().call(a , **a )
snake_case_ = FakeBart.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = bart_model.generate(a , foo='bar' ).numpy()
self.assertTrue(np.array_equal(a , a ) )
class UpperCamelCase_ ( bart_model.model.encoder.__class__ ):
'''simple docstring'''
def _UpperCamelCase ( self , a , **a ) -> List[Any]:
return super().call(a , **a )
snake_case_ = FakeEncoder(bart_model.config , bart_model.model.shared )
snake_case_ = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
snake_case_ = bart_model.generate(a ).numpy()
with self.assertRaises(a ):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(a , foo='bar' )
| 607 | 1 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase )
class __lowerCAmelCase ( _UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Tuple , **_lowerCAmelCase : str ) -> List[str]:
"""simple docstring"""
super().__init__(**SCREAMING_SNAKE_CASE_ )
if self.framework == "tf":
raise ValueError(F'''The {self.__class__} is only available in PyTorch.''' )
requires_backends(self , "vision" )
self.check_model_type(SCREAMING_SNAKE_CASE_ )
def __call__( self : Any , _lowerCAmelCase : Tuple , _lowerCAmelCase : Tuple = None , **_lowerCAmelCase : List[str] , ) -> Optional[Any]:
"""simple docstring"""
if "text_queries" in kwargs:
snake_case_ = kwargs.pop("text_queries" )
if isinstance(SCREAMING_SNAKE_CASE_ , (str, Image.Image) ):
snake_case_ = {'image': image, 'candidate_labels': candidate_labels}
else:
snake_case_ = image
snake_case_ = super().__call__(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
return results
def lowerCAmelCase__ ( self : Optional[int] , **_lowerCAmelCase : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
snake_case_ = {}
if "threshold" in kwargs:
snake_case_ = kwargs['threshold']
if "top_k" in kwargs:
snake_case_ = kwargs['top_k']
return {}, {}, postprocess_params
def lowerCAmelCase__ ( self : Tuple , _lowerCAmelCase : Dict ) -> List[str]:
"""simple docstring"""
snake_case_ = load_image(inputs["image"] )
snake_case_ = inputs['candidate_labels']
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
snake_case_ = candidate_labels.split("," )
snake_case_ = torch.tensor([[image.height, image.width]] , dtype=torch.intaa )
for i, candidate_label in enumerate(SCREAMING_SNAKE_CASE_ ):
snake_case_ = self.tokenizer(SCREAMING_SNAKE_CASE_ , return_tensors=self.framework )
snake_case_ = self.image_processor(SCREAMING_SNAKE_CASE_ , return_tensors=self.framework )
yield {
"is_last": i == len(SCREAMING_SNAKE_CASE_ ) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def lowerCAmelCase__ ( self : List[str] , _lowerCAmelCase : Optional[int] ) -> Any:
"""simple docstring"""
snake_case_ = model_inputs.pop("target_size" )
snake_case_ = model_inputs.pop("candidate_label" )
snake_case_ = model_inputs.pop("is_last" )
snake_case_ = self.model(**SCREAMING_SNAKE_CASE_ )
snake_case_ = {'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
return model_outputs
def lowerCAmelCase__ ( self : List[str] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict=0.1 , _lowerCAmelCase : Optional[Any]=None ) -> Tuple:
"""simple docstring"""
snake_case_ = []
for model_output in model_outputs:
snake_case_ = model_output['candidate_label']
snake_case_ = BaseModelOutput(SCREAMING_SNAKE_CASE_ )
snake_case_ = self.image_processor.post_process_object_detection(
outputs=SCREAMING_SNAKE_CASE_ , threshold=SCREAMING_SNAKE_CASE_ , target_sizes=model_output["target_size"] )[0]
for index in outputs["scores"].nonzero():
snake_case_ = outputs['scores'][index].item()
snake_case_ = self._get_bounding_box(outputs["boxes"][index][0] )
snake_case_ = {'score': score, 'label': label, 'box': box}
results.append(SCREAMING_SNAKE_CASE_ )
snake_case_ = sorted(SCREAMING_SNAKE_CASE_ , key=lambda _lowerCAmelCase : x["score"] , reverse=SCREAMING_SNAKE_CASE_ )
if top_k:
snake_case_ = results[:top_k]
return results
def lowerCAmelCase__ ( self : str , _lowerCAmelCase : List[Any] ) -> Dict[str, int]:
"""simple docstring"""
if self.framework != "pt":
raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch." )
snake_case_ = box.int().tolist()
snake_case_ = {
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax,
}
return bbox
| 283 |
'''simple docstring'''
from collections import namedtuple
import requests
from lxml import html # type: ignore
A__ : Tuple = namedtuple("""covid_data""", """cases deaths recovered""")
def UpperCAmelCase__ ( UpperCAmelCase_ : str = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
__lowerCamelCase : Union[str, Any] = '//div[@class = "maincounter-number"]/span/text()'
return covid_data(*html.fromstring(requests.get(UpperCAmelCase_ ).content ).xpath(UpperCAmelCase_ ) )
A__ : str = """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""
print(fmt.format(*covid_stats()))
| 13 | 0 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class UpperCamelCase ( snake_case_ ):
def __init__( self ,__UpperCamelCase = "▁" ,__UpperCamelCase = True ,__UpperCamelCase = "<unk>" ,__UpperCamelCase = "</s>" ,__UpperCamelCase = "<pad>" ,) -> Any:
'''simple docstring'''
lowercase_ : str = {
'pad': {'id': 0, 'token': pad_token},
'eos': {'id': 1, 'token': eos_token},
'unk': {'id': 2, 'token': unk_token},
}
lowercase_ : List[str] = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
lowercase_ : Dict = token_dict['token']
lowercase_ : Tuple = Tokenizer(Unigram() )
lowercase_ : Tuple = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(' {2,}' ) ,' ' ),
normalizers.Lowercase(),
] )
lowercase_ : Dict = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=__UpperCamelCase ,add_prefix_space=__UpperCamelCase ),
pre_tokenizers.Digits(individual_digits=__UpperCamelCase ),
pre_tokenizers.Punctuation(),
] )
lowercase_ : Any = decoders.Metaspace(replacement=__UpperCamelCase ,add_prefix_space=__UpperCamelCase )
lowercase_ : List[Any] = TemplateProcessing(
single=f'''$A {self.special_tokens["eos"]["token"]}''' ,special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])] ,)
lowercase_ : int = {
'model': 'SentencePieceUnigram',
'replacement': replacement,
'add_prefix_space': add_prefix_space,
}
super().__init__(__UpperCamelCase ,__UpperCamelCase )
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase = 8000 ,__UpperCamelCase = True ,) -> Tuple:
'''simple docstring'''
lowercase_ : Union[str, Any] = trainers.UnigramTrainer(
vocab_size=__UpperCamelCase ,special_tokens=self.special_tokens_list ,show_progress=__UpperCamelCase ,)
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
lowercase_ : str = [files]
self._tokenizer.train(__UpperCamelCase ,trainer=__UpperCamelCase )
self.add_unk_id()
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase = 8000 ,__UpperCamelCase = True ,) -> List[Any]:
'''simple docstring'''
lowercase_ : str = trainers.UnigramTrainer(
vocab_size=__UpperCamelCase ,special_tokens=self.special_tokens_list ,show_progress=__UpperCamelCase ,)
self._tokenizer.train_from_iterator(__UpperCamelCase ,trainer=__UpperCamelCase )
self.add_unk_id()
def _UpperCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ : List[str] = json.loads(self._tokenizer.to_str() )
lowercase_ : List[Any] = self.special_tokens['unk']['id']
lowercase_ : Tuple = Tokenizer.from_str(json.dumps(__UpperCamelCase ) )
| 701 | """simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ={
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class UpperCamelCase ( lowercase_ ):
lowercase = 'wav2vec2'
def __init__( self ,__UpperCamelCase=32 ,__UpperCamelCase=768 ,__UpperCamelCase=12 ,__UpperCamelCase=12 ,__UpperCamelCase=3072 ,__UpperCamelCase="gelu" ,__UpperCamelCase=0.1 ,__UpperCamelCase=0.1 ,__UpperCamelCase=0.1 ,__UpperCamelCase=0.0 ,__UpperCamelCase=0.0 ,__UpperCamelCase=0.1 ,__UpperCamelCase=0.1 ,__UpperCamelCase=0.02 ,__UpperCamelCase=1e-5 ,__UpperCamelCase="group" ,__UpperCamelCase="gelu" ,__UpperCamelCase=(512, 512, 512, 512, 512, 512, 512) ,__UpperCamelCase=(5, 2, 2, 2, 2, 2, 2) ,__UpperCamelCase=(10, 3, 3, 3, 3, 2, 2) ,__UpperCamelCase=False ,__UpperCamelCase=128 ,__UpperCamelCase=16 ,__UpperCamelCase=False ,__UpperCamelCase=True ,__UpperCamelCase=0.05 ,__UpperCamelCase=10 ,__UpperCamelCase=2 ,__UpperCamelCase=0.0 ,__UpperCamelCase=10 ,__UpperCamelCase=0 ,__UpperCamelCase=320 ,__UpperCamelCase=2 ,__UpperCamelCase=0.1 ,__UpperCamelCase=100 ,__UpperCamelCase=256 ,__UpperCamelCase=256 ,__UpperCamelCase=0.1 ,__UpperCamelCase="sum" ,__UpperCamelCase=False ,__UpperCamelCase=False ,__UpperCamelCase=256 ,__UpperCamelCase=(512, 512, 512, 512, 1500) ,__UpperCamelCase=(5, 3, 3, 1, 1) ,__UpperCamelCase=(1, 2, 3, 1, 1) ,__UpperCamelCase=512 ,__UpperCamelCase=0 ,__UpperCamelCase=1 ,__UpperCamelCase=2 ,__UpperCamelCase=False ,__UpperCamelCase=3 ,__UpperCamelCase=2 ,__UpperCamelCase=3 ,__UpperCamelCase=None ,__UpperCamelCase=None ,**__UpperCamelCase ,) -> Optional[Any]:
'''simple docstring'''
super().__init__(**__UpperCamelCase ,pad_token_id=__UpperCamelCase ,bos_token_id=__UpperCamelCase ,eos_token_id=__UpperCamelCase )
lowercase_ : Optional[Any] = hidden_size
lowercase_ : Tuple = feat_extract_norm
lowercase_ : Dict = feat_extract_activation
lowercase_ : List[str] = list(__UpperCamelCase )
lowercase_ : str = list(__UpperCamelCase )
lowercase_ : Dict = list(__UpperCamelCase )
lowercase_ : Optional[Any] = conv_bias
lowercase_ : Dict = num_conv_pos_embeddings
lowercase_ : List[str] = num_conv_pos_embedding_groups
lowercase_ : Optional[Any] = len(self.conv_dim )
lowercase_ : Any = num_hidden_layers
lowercase_ : List[Any] = intermediate_size
lowercase_ : List[Any] = hidden_act
lowercase_ : Optional[int] = num_attention_heads
lowercase_ : int = hidden_dropout
lowercase_ : Dict = attention_dropout
lowercase_ : Union[str, Any] = activation_dropout
lowercase_ : Tuple = feat_proj_dropout
lowercase_ : List[str] = final_dropout
lowercase_ : Union[str, Any] = layerdrop
lowercase_ : List[str] = layer_norm_eps
lowercase_ : Optional[int] = initializer_range
lowercase_ : List[Any] = vocab_size
lowercase_ : Optional[int] = do_stable_layer_norm
lowercase_ : Union[str, Any] = use_weighted_layer_sum
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)`, but is `len(config.conv_dim) ='
f''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
f''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowercase_ : Dict = apply_spec_augment
lowercase_ : Optional[int] = mask_time_prob
lowercase_ : Union[str, Any] = mask_time_length
lowercase_ : List[str] = mask_time_min_masks
lowercase_ : List[str] = mask_feature_prob
lowercase_ : Any = mask_feature_length
lowercase_ : List[Any] = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
lowercase_ : List[Any] = num_codevectors_per_group
lowercase_ : Optional[int] = num_codevector_groups
lowercase_ : Dict = contrastive_logits_temperature
lowercase_ : int = feat_quantizer_dropout
lowercase_ : Optional[int] = num_negatives
lowercase_ : str = codevector_dim
lowercase_ : str = proj_codevector_dim
lowercase_ : Optional[Any] = diversity_loss_weight
# ctc loss
lowercase_ : Tuple = ctc_loss_reduction
lowercase_ : int = ctc_zero_infinity
# adapter
lowercase_ : int = add_adapter
lowercase_ : Dict = adapter_kernel_size
lowercase_ : List[str] = adapter_stride
lowercase_ : Dict = num_adapter_layers
lowercase_ : Dict = output_hidden_size or hidden_size
lowercase_ : Optional[Any] = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
lowercase_ : Dict = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
lowercase_ : Any = list(__UpperCamelCase )
lowercase_ : str = list(__UpperCamelCase )
lowercase_ : Any = list(__UpperCamelCase )
lowercase_ : Tuple = xvector_output_dim
@property
def _UpperCAmelCase ( self ) -> str:
'''simple docstring'''
return functools.reduce(operator.mul ,self.conv_stride ,1 )
| 477 | 0 |
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