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"""simple docstring"""
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class __SCREAMING_SNAKE_CASE ( UpperCAmelCase_ ):
'''simple docstring'''
def __init__( self : Dict , *__a : List[str] , __a : Any=None , __a : Any=None , **__a : int ) -> Tuple:
super().__init__(*_snake_case , **_snake_case )
_UpperCamelCase : Optional[Any] = eval_examples
_UpperCamelCase : Dict = post_process_function
def __SCREAMING_SNAKE_CASE ( self : int , __a : Optional[Dataset] = None , __a : int=None , __a : Optional[List[str]] = None , __a : str = "eval" , **__a : Dict , ) -> int:
_UpperCamelCase : int = gen_kwargs.copy()
_UpperCamelCase : Dict = (
gen_kwargs["max_length"] if gen_kwargs.get("max_length" ) is not None else self.args.generation_max_length
)
_UpperCamelCase : int = (
gen_kwargs["num_beams"] if gen_kwargs.get("num_beams" ) is not None else self.args.generation_num_beams
)
_UpperCamelCase : Dict = gen_kwargs
_UpperCamelCase : Dict = self.eval_dataset if eval_dataset is None else eval_dataset
_UpperCamelCase : Union[str, Any] = self.get_eval_dataloader(_snake_case )
_UpperCamelCase : Optional[Any] = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_UpperCamelCase : Optional[int] = self.compute_metrics
_UpperCamelCase : Tuple = None
_UpperCamelCase : int = time.time()
_UpperCamelCase : Union[str, Any] = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_UpperCamelCase : List[str] = eval_loop(
_snake_case , description="Evaluation" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_snake_case , metric_key_prefix=_snake_case , )
finally:
_UpperCamelCase : List[Any] = compute_metrics
_UpperCamelCase : Any = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_snake_case , _snake_case , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
_UpperCamelCase : int = self.post_process_function(_snake_case , _snake_case , _snake_case )
_UpperCamelCase : Optional[Any] = self.compute_metrics(_snake_case )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
_UpperCamelCase : Tuple = metrics.pop(_snake_case )
metrics.update(output.metrics )
else:
_UpperCamelCase : int = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_snake_case )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_UpperCamelCase : Optional[Any] = self.callback_handler.on_evaluate(self.args , self.state , self.control , _snake_case )
return metrics
def __SCREAMING_SNAKE_CASE ( self : Tuple , __a : Union[str, Any] , __a : Optional[int] , __a : str=None , __a : str = "test" , **__a : List[str] ) -> Dict:
_UpperCamelCase : Tuple = gen_kwargs.copy()
_UpperCamelCase : Tuple = self.get_test_dataloader(_snake_case )
# Temporarily disable metric computation, we will do it in the loop here.
_UpperCamelCase : int = self.compute_metrics
_UpperCamelCase : List[str] = None
_UpperCamelCase : Optional[int] = time.time()
_UpperCamelCase : Any = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_UpperCamelCase : int = eval_loop(
_snake_case , description="Prediction" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_snake_case , metric_key_prefix=_snake_case , )
finally:
_UpperCamelCase : Tuple = compute_metrics
_UpperCamelCase : List[str] = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_snake_case , _snake_case , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
_UpperCamelCase : Any = self.post_process_function(_snake_case , _snake_case , _snake_case , "predict" )
_UpperCamelCase : Any = self.compute_metrics(_snake_case )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
_UpperCamelCase : Dict = metrics.pop(_snake_case )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=_snake_case )
| 624 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def A ( __UpperCamelCase ) -> Union[str, Any]:
if hor == 128:
A__ = ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
A__ = (32, 128, 256)
A__ = ('UpResnetBlock1D', 'UpResnetBlock1D')
elif hor == 32:
A__ = ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
A__ = (32, 64, 128, 256)
A__ = ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
A__ = torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch''' )
A__ = model.state_dict()
A__ = {
'down_block_types': down_block_types,
'block_out_channels': block_out_channels,
'up_block_types': up_block_types,
'layers_per_block': 1,
'use_timestep_embedding': True,
'out_block_type': 'OutConv1DBlock',
'norm_num_groups': 8,
'downsample_each_block': False,
'in_channels': 14,
'out_channels': 14,
'extra_in_channels': 0,
'time_embedding_type': 'positional',
'flip_sin_to_cos': False,
'freq_shift': 1,
'sample_size': 65_536,
'mid_block_type': 'MidResTemporalBlock1D',
'act_fn': 'mish',
}
A__ = UNetaDModel(**__UpperCamelCase )
print(f'''length of state dict: {len(state_dict.keys() )}''' )
print(f'''length of value function dict: {len(hf_value_function.state_dict().keys() )}''' )
A__ = dict(zip(model.state_dict().keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
A__ = state_dict.pop(__UpperCamelCase )
hf_value_function.load_state_dict(__UpperCamelCase )
torch.save(hf_value_function.state_dict() , f'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin''' )
with open(f'''hub/hopper-medium-v2/unet/hor{hor}/config.json''' , 'w' ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
def A ( ) -> List[str]:
A__ = {
'in_channels': 14,
'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
'up_block_types': (),
'out_block_type': 'ValueFunction',
'mid_block_type': 'ValueFunctionMidBlock1D',
'block_out_channels': (32, 64, 128, 256),
'layers_per_block': 1,
'downsample_each_block': True,
'sample_size': 65_536,
'out_channels': 14,
'extra_in_channels': 0,
'time_embedding_type': 'positional',
'use_timestep_embedding': True,
'flip_sin_to_cos': False,
'freq_shift': 1,
'norm_num_groups': 8,
'act_fn': 'mish',
}
A__ = torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch' )
A__ = model
A__ = UNetaDModel(**__UpperCamelCase )
print(f'''length of state dict: {len(state_dict.keys() )}''' )
print(f'''length of value function dict: {len(hf_value_function.state_dict().keys() )}''' )
A__ = dict(zip(state_dict.keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
A__ = state_dict.pop(__UpperCamelCase )
hf_value_function.load_state_dict(__UpperCamelCase )
torch.save(hf_value_function.state_dict() , 'hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin' )
with open('hub/hopper-medium-v2/value_function/config.json' , 'w' ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
if __name__ == "__main__":
unet(3_2)
# unet(128)
value_function()
| 9 | 0 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
UpperCamelCase = input('Enter image url: ').strip()
print(F'''Downloading image from {url} ...''')
UpperCamelCase = BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL is in the content field of the first meta tag with property og:image
UpperCamelCase = soup.find('meta', {'property': 'og:image'})['content']
UpperCamelCase = requests.get(image_url).content
UpperCamelCase = F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
with open(file_name, 'wb') as fp:
fp.write(image_data)
print(F'''Done. Image saved to disk as {file_name}.''')
| 387 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class _a ( lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : Union[str, Any] = """gpt_neox"""
def __init__( self , __UpperCAmelCase=50_432 , __UpperCAmelCase=6_144 , __UpperCAmelCase=44 , __UpperCAmelCase=64 , __UpperCAmelCase=24_576 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.25 , __UpperCAmelCase=10_000 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.1 , __UpperCAmelCase=2_048 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1e-5 , __UpperCAmelCase=True , __UpperCAmelCase=0 , __UpperCAmelCase=2 , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=None , **__UpperCAmelCase , ):
super().__init__(bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , **__UpperCAmelCase )
__A : Optional[int] = vocab_size
__A : List[Any] = max_position_embeddings
__A : Any = hidden_size
__A : str = num_hidden_layers
__A : List[str] = num_attention_heads
__A : Dict = intermediate_size
__A : List[Any] = hidden_act
__A : Tuple = rotary_pct
__A : Optional[int] = rotary_emb_base
__A : int = attention_dropout
__A : Optional[int] = hidden_dropout
__A : List[Any] = classifier_dropout
__A : Optional[Any] = initializer_range
__A : Optional[int] = layer_norm_eps
__A : str = use_cache
__A : Optional[int] = tie_word_embeddings
__A : Any = use_parallel_residual
__A : List[Any] = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
"The hidden size is not divisble by the number of attention heads! Make sure to update them!" )
def __UpperCAmelCase( self ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __UpperCAmelCase ) or len(self.rope_scaling ) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
F"got {self.rope_scaling}" )
__A : Dict = self.rope_scaling.get("type" , __UpperCAmelCase )
__A : Dict = self.rope_scaling.get("factor" , __UpperCAmelCase )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" )
if rope_scaling_factor is None or not isinstance(__UpperCAmelCase , __UpperCAmelCase ) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 387 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
__snake_case : str =logging.get_logger(__name__)
@dataclass
class lowerCamelCase__ ( lowerCamelCase__):
'''simple docstring'''
snake_case_ =[
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__(self ,**__lowerCamelCase ) -> Optional[int]:
"""simple docstring"""
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowerCAmelCase__ : List[Any] = deprecated_arg[3:]
setattr(self ,__lowerCamelCase ,not kwargs.pop(__lowerCamelCase ) )
logger.warning(
f"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
f""" {positive_arg}={kwargs[positive_arg]}""" )
lowerCAmelCase__ : str = kwargs.pop('''torchscript''' ,self.torchscript )
lowerCAmelCase__ : Any = kwargs.pop('''torch_xla_tpu_print_metrics''' ,self.torch_xla_tpu_print_metrics )
lowerCAmelCase__ : int = kwargs.pop('''fp16_opt_level''' ,self.fpaa_opt_level )
super().__init__(**__lowerCamelCase )
snake_case_ =field(default=lowerCamelCase__ , metadata={"""help""": """Trace the models using torchscript"""})
snake_case_ =field(default=lowerCamelCase__ , metadata={"""help""": """Print Xla/PyTorch tpu metrics"""})
snake_case_ =field(
default="""O1""" , metadata={
"""help""": (
"""For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
"""See details at https://nvidia.github.io/apex/amp.html"""
)
} , )
@cached_property
def lowerCAmelCase__ (self ) -> Tuple["torch.device", int]:
"""simple docstring"""
requires_backends(self ,['''torch'''] )
logger.info('''PyTorch: setting up devices''' )
if not self.cuda:
lowerCAmelCase__ : Any = torch.device('''cpu''' )
lowerCAmelCase__ : Optional[int] = 0
elif is_torch_tpu_available():
lowerCAmelCase__ : Optional[Any] = xm.xla_device()
lowerCAmelCase__ : int = 0
else:
lowerCAmelCase__ : Tuple = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
lowerCAmelCase__ : List[str] = torch.cuda.device_count()
return device, n_gpu
@property
def lowerCAmelCase__ (self ) -> Optional[Any]:
"""simple docstring"""
return is_torch_tpu_available() and self.tpu
@property
def lowerCAmelCase__ (self ) -> int:
"""simple docstring"""
requires_backends(self ,['''torch'''] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def lowerCAmelCase__ (self ) -> "torch.device":
"""simple docstring"""
requires_backends(self ,['''torch'''] )
return self._setup_devices[0]
@property
def lowerCAmelCase__ (self ) -> List[Any]:
"""simple docstring"""
requires_backends(self ,['''torch'''] )
return self._setup_devices[1]
@property
def lowerCAmelCase__ (self ) -> Dict:
"""simple docstring"""
return self.n_gpu > 0
| 647 |
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_video_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VivitImageProcessor
class lowerCamelCase__ ( unittest.TestCase):
'''simple docstring'''
def __init__(self ,__lowerCamelCase ,__lowerCamelCase=7 ,__lowerCamelCase=3 ,__lowerCamelCase=10 ,__lowerCamelCase=18 ,__lowerCamelCase=30 ,__lowerCamelCase=4_00 ,__lowerCamelCase=True ,__lowerCamelCase=None ,__lowerCamelCase=True ,__lowerCamelCase=[0.5, 0.5, 0.5] ,__lowerCamelCase=[0.5, 0.5, 0.5] ,__lowerCamelCase=None ,) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ : List[Any] = size if size is not None else {'''shortest_edge''': 18}
lowerCAmelCase__ : int = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
lowerCAmelCase__ : Optional[int] = parent
lowerCAmelCase__ : Tuple = batch_size
lowerCAmelCase__ : Union[str, Any] = num_channels
lowerCAmelCase__ : str = num_frames
lowerCAmelCase__ : Optional[Any] = image_size
lowerCAmelCase__ : str = min_resolution
lowerCAmelCase__ : Optional[Any] = max_resolution
lowerCAmelCase__ : Optional[Any] = do_resize
lowerCAmelCase__ : List[str] = size
lowerCAmelCase__ : Union[str, Any] = do_normalize
lowerCAmelCase__ : int = image_mean
lowerCAmelCase__ : Optional[int] = image_std
lowerCAmelCase__ : Optional[Any] = crop_size
def lowerCAmelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class lowerCamelCase__ ( lowerCamelCase__ , unittest.TestCase):
'''simple docstring'''
snake_case_ =VivitImageProcessor if is_vision_available() else None
def lowerCAmelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ : Tuple = VivitImageProcessingTester(self )
@property
def lowerCAmelCase__ (self ) -> Optional[int]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase__ (self ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ : List[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowerCamelCase ,'''image_mean''' ) )
self.assertTrue(hasattr(__lowerCamelCase ,'''image_std''' ) )
self.assertTrue(hasattr(__lowerCamelCase ,'''do_normalize''' ) )
self.assertTrue(hasattr(__lowerCamelCase ,'''do_resize''' ) )
self.assertTrue(hasattr(__lowerCamelCase ,'''do_center_crop''' ) )
self.assertTrue(hasattr(__lowerCamelCase ,'''size''' ) )
def lowerCAmelCase__ (self ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{'''shortest_edge''': 18} )
self.assertEqual(image_processor.crop_size ,{'''height''': 18, '''width''': 18} )
lowerCAmelCase__ : Any = self.image_processing_class.from_dict(self.image_processor_dict ,size=42 ,crop_size=84 )
self.assertEqual(image_processor.size ,{'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size ,{'''height''': 84, '''width''': 84} )
def lowerCAmelCase__ (self ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL videos
lowerCAmelCase__ : str = prepare_video_inputs(self.image_processor_tester ,equal_resolution=__lowerCamelCase )
for video in video_inputs:
self.assertIsInstance(__lowerCamelCase ,__lowerCamelCase )
self.assertIsInstance(video[0] ,Image.Image )
# Test not batched input
lowerCAmelCase__ : Optional[int] = image_processing(video_inputs[0] ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape ,(
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
# Test batched
lowerCAmelCase__ : Optional[int] = image_processing(__lowerCamelCase ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
def lowerCAmelCase__ (self ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase__ : Tuple = prepare_video_inputs(self.image_processor_tester ,equal_resolution=__lowerCamelCase ,numpify=__lowerCamelCase )
for video in video_inputs:
self.assertIsInstance(__lowerCamelCase ,__lowerCamelCase )
self.assertIsInstance(video[0] ,np.ndarray )
# Test not batched input
lowerCAmelCase__ : Any = image_processing(video_inputs[0] ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape ,(
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
# Test batched
lowerCAmelCase__ : Dict = image_processing(__lowerCamelCase ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
def lowerCAmelCase__ (self ) -> Tuple:
"""simple docstring"""
lowerCAmelCase__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase__ : int = prepare_video_inputs(self.image_processor_tester ,equal_resolution=__lowerCamelCase ,torchify=__lowerCamelCase )
for video in video_inputs:
self.assertIsInstance(__lowerCamelCase ,__lowerCamelCase )
self.assertIsInstance(video[0] ,torch.Tensor )
# Test not batched input
lowerCAmelCase__ : Any = image_processing(video_inputs[0] ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape ,(
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
# Test batched
lowerCAmelCase__ : Optional[Any] = image_processing(__lowerCamelCase ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
| 647 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
snake_case_ : str = logging.get_logger(__name__)
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : Union[str, Any], SCREAMING_SNAKE_CASE__ : List[str], SCREAMING_SNAKE_CASE__ : List[Any] ) -> List[Any]:
UpperCAmelCase_ : Union[str, Any] = WavaVecaForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__, config=SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : Dict = downstream_dict['''projector.weight''']
UpperCAmelCase_ : int = downstream_dict['''projector.bias''']
UpperCAmelCase_ : List[Any] = downstream_dict['''model.post_net.linear.weight''']
UpperCAmelCase_ : Any = downstream_dict['''model.post_net.linear.bias''']
return model
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : Dict, SCREAMING_SNAKE_CASE__ : Optional[Any], SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> List[Any]:
UpperCAmelCase_ : Dict = WavaVecaForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE__, config=SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : str = downstream_dict['''model.linear.weight''']
UpperCAmelCase_ : Optional[int] = downstream_dict['''model.linear.bias''']
return model
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : Any, SCREAMING_SNAKE_CASE__ : Union[str, Any], SCREAMING_SNAKE_CASE__ : int ) -> Any:
UpperCAmelCase_ : int = WavaVecaForXVector.from_pretrained(SCREAMING_SNAKE_CASE__, config=SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : List[Any] = downstream_dict['''connector.weight''']
UpperCAmelCase_ : Any = downstream_dict['''connector.bias''']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
UpperCAmelCase_ : List[Any] = downstream_dict[
F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
]
UpperCAmelCase_ : Optional[Any] = downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
UpperCAmelCase_ : Union[str, Any] = downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
UpperCAmelCase_ : int = downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
UpperCAmelCase_ : Dict = downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
UpperCAmelCase_ : Dict = downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
UpperCAmelCase_ : Optional[Any] = downstream_dict['''objective.W''']
return model
@torch.no_grad()
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : Tuple, SCREAMING_SNAKE_CASE__ : Optional[Any], SCREAMING_SNAKE_CASE__ : Any, SCREAMING_SNAKE_CASE__ : int ) -> Union[str, Any]:
UpperCAmelCase_ : Optional[int] = torch.load(SCREAMING_SNAKE_CASE__, map_location='''cpu''' )
UpperCAmelCase_ : List[str] = checkpoint['''Downstream''']
UpperCAmelCase_ : List[str] = WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : Any = WavaVecaFeatureExtractor.from_pretrained(
SCREAMING_SNAKE_CASE__, return_attention_mask=SCREAMING_SNAKE_CASE__, do_normalize=SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : Tuple = hf_config.architectures[0]
if arch.endswith('''ForSequenceClassification''' ):
UpperCAmelCase_ : List[Any] = convert_classification(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
elif arch.endswith('''ForAudioFrameClassification''' ):
UpperCAmelCase_ : int = convert_diarization(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
elif arch.endswith('''ForXVector''' ):
UpperCAmelCase_ : Tuple = convert_xvector(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
else:
raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}""" )
if hf_config.use_weighted_layer_sum:
UpperCAmelCase_ : str = checkpoint['''Featurizer''']['''weights''']
hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__ )
hf_model.save_pretrained(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
snake_case_ : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
"--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
)
parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
snake_case_ : Any = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 701 |
'''simple docstring'''
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__":
snake_case_ : List[Any] = pd.read_csv("sample_data.csv", header=None)
snake_case_ : Optional[Any] = df.shape[:1][0]
# If you're using some other dataset input the target column
snake_case_ : Any = df.iloc[:, 1:2]
snake_case_ : str = actual_data.values.reshape(len_data, 1)
snake_case_ : Optional[Any] = MinMaxScaler().fit_transform(actual_data)
snake_case_ : List[str] = 10
snake_case_ : Any = 5
snake_case_ : Any = 20
snake_case_ : Tuple = len_data - periods * look_back
snake_case_ : str = actual_data[:division]
snake_case_ : Optional[int] = actual_data[division - look_back :]
snake_case_ ,snake_case_ : Any = [], []
snake_case_ ,snake_case_ : Union[str, 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])
snake_case_ : Any = np.array(train_x)
snake_case_ : Optional[Any] = np.array(test_x)
snake_case_ : Optional[Any] = np.array([list(i.ravel()) for i in train_y])
snake_case_ : List[str] = np.array([list(i.ravel()) for i in test_y])
snake_case_ : List[Any] = Sequential()
model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(1_28, 1)))
model.add(Dense(forward_days))
model.compile(loss="mean_squared_error", optimizer="adam")
snake_case_ : Dict = model.fit(
x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
)
snake_case_ : Optional[Any] = model.predict(x_test)
| 644 | 0 |
'''simple docstring'''
import re
def __lowerCamelCase ( _UpperCamelCase : str ):
'''simple docstring'''
UpperCAmelCase_ = re.compile(
R'''^(?:0|94|\+94|0{2}94)''' R'''7(0|1|2|4|5|6|7|8)''' R'''(-| |)''' R'''\d{7}$''' )
return bool(re.search(_UpperCamelCase , _UpperCamelCase ) )
if __name__ == "__main__":
lowercase__ : int = "0094702343221"
print(is_sri_lankan_phone_number(phone))
| 390 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ : Optional[int] = logging.get_logger(__name__)
lowercase__ : Optional[Any] = {
"google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
"google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
"google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class lowerCamelCase ( lowerCamelCase ):
'''simple docstring'''
lowerCAmelCase__ = '''big_bird'''
def __init__( self : List[Any] , UpperCAmelCase__ : Tuple=5_0358 , UpperCAmelCase__ : List[Any]=768 , UpperCAmelCase__ : Dict=12 , UpperCAmelCase__ : str=12 , UpperCAmelCase__ : Union[str, Any]=3072 , UpperCAmelCase__ : Dict="gelu_new" , UpperCAmelCase__ : List[Any]=0.1 , UpperCAmelCase__ : Any=0.1 , UpperCAmelCase__ : Any=4096 , UpperCAmelCase__ : Any=2 , UpperCAmelCase__ : int=0.02 , UpperCAmelCase__ : List[Any]=1e-12 , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : int=0 , UpperCAmelCase__ : int=1 , UpperCAmelCase__ : Optional[Any]=2 , UpperCAmelCase__ : List[Any]=66 , UpperCAmelCase__ : Dict="block_sparse" , UpperCAmelCase__ : Union[str, Any]=True , UpperCAmelCase__ : Dict=False , UpperCAmelCase__ : Optional[int]=64 , UpperCAmelCase__ : Union[str, Any]=3 , UpperCAmelCase__ : Optional[Any]=None , **UpperCAmelCase__ : Optional[Any] , ) ->Dict:
super().__init__(
pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , sep_token_id=UpperCAmelCase__ , **UpperCAmelCase__ , )
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = max_position_embeddings
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = type_vocab_size
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = use_cache
UpperCAmelCase_ = rescale_embeddings
UpperCAmelCase_ = attention_type
UpperCAmelCase_ = use_bias
UpperCAmelCase_ = block_size
UpperCAmelCase_ = num_random_blocks
UpperCAmelCase_ = classifier_dropout
class lowerCamelCase ( lowerCamelCase ):
'''simple docstring'''
@property
def lowerCAmelCase__ ( self : Optional[Any] ) ->Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
UpperCAmelCase_ = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
UpperCAmelCase_ = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 390 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
A = logging.get_logger(__name__)
A = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class __a ( __A ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = """dpt"""
def __init__( self , UpperCamelCase__=768 , UpperCamelCase__=12 , UpperCamelCase__=12 , UpperCamelCase__=3072 , UpperCamelCase__="gelu" , UpperCamelCase__=0.0 , UpperCamelCase__=0.0 , UpperCamelCase__=0.02 , UpperCamelCase__=1E-12 , UpperCamelCase__=384 , UpperCamelCase__=16 , UpperCamelCase__=3 , UpperCamelCase__=False , UpperCamelCase__=True , UpperCamelCase__=[2, 5, 8, 11] , UpperCamelCase__="project" , UpperCamelCase__=[4, 2, 1, 0.5] , UpperCamelCase__=[96, 192, 384, 768] , UpperCamelCase__=256 , UpperCamelCase__=-1 , UpperCamelCase__=False , UpperCamelCase__=True , UpperCamelCase__=0.4 , UpperCamelCase__=255 , UpperCamelCase__=0.1 , UpperCamelCase__=[1, 1024, 24, 24] , UpperCamelCase__=[0, 1] , UpperCamelCase__=None , **UpperCamelCase__ , ):
super().__init__(**UpperCamelCase_ )
SCREAMING_SNAKE_CASE_ : List[Any] = hidden_size
SCREAMING_SNAKE_CASE_ : Optional[int] = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info('Initializing the config with a `BiT` backbone.' )
SCREAMING_SNAKE_CASE_ : Optional[int] = {
'global_padding': 'same',
'layer_type': 'bottleneck',
'depths': [3, 4, 9],
'out_features': ['stage1', 'stage2', 'stage3'],
'embedding_dynamic_padding': True,
}
SCREAMING_SNAKE_CASE_ : List[str] = BitConfig(**UpperCamelCase_ )
elif isinstance(UpperCamelCase_ , UpperCamelCase_ ):
logger.info('Initializing the config with a `BiT` backbone.' )
SCREAMING_SNAKE_CASE_ : List[Any] = BitConfig(**UpperCamelCase_ )
elif isinstance(UpperCamelCase_ , UpperCamelCase_ ):
SCREAMING_SNAKE_CASE_ : Any = backbone_config
else:
raise ValueError(
F'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.''' )
SCREAMING_SNAKE_CASE_ : Optional[int] = backbone_featmap_shape
SCREAMING_SNAKE_CASE_ : str = neck_ignore_stages
if readout_type != "project":
raise ValueError('Readout type must be \'project\' when using `DPT-hybrid` mode.' )
else:
SCREAMING_SNAKE_CASE_ : str = None
SCREAMING_SNAKE_CASE_ : List[str] = None
SCREAMING_SNAKE_CASE_ : Any = []
SCREAMING_SNAKE_CASE_ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE_ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE_ : Any = intermediate_size
SCREAMING_SNAKE_CASE_ : Tuple = hidden_act
SCREAMING_SNAKE_CASE_ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ : List[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ : List[str] = initializer_range
SCREAMING_SNAKE_CASE_ : Union[str, Any] = layer_norm_eps
SCREAMING_SNAKE_CASE_ : Tuple = image_size
SCREAMING_SNAKE_CASE_ : List[Any] = patch_size
SCREAMING_SNAKE_CASE_ : int = num_channels
SCREAMING_SNAKE_CASE_ : Union[str, Any] = qkv_bias
SCREAMING_SNAKE_CASE_ : int = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError('Readout_type must be one of [\'ignore\', \'add\', \'project\']' )
SCREAMING_SNAKE_CASE_ : Any = readout_type
SCREAMING_SNAKE_CASE_ : str = reassemble_factors
SCREAMING_SNAKE_CASE_ : Union[str, Any] = neck_hidden_sizes
SCREAMING_SNAKE_CASE_ : Any = fusion_hidden_size
SCREAMING_SNAKE_CASE_ : Optional[int] = head_in_index
SCREAMING_SNAKE_CASE_ : str = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
SCREAMING_SNAKE_CASE_ : Optional[Any] = use_auxiliary_head
SCREAMING_SNAKE_CASE_ : Any = auxiliary_loss_weight
SCREAMING_SNAKE_CASE_ : Tuple = semantic_loss_ignore_index
SCREAMING_SNAKE_CASE_ : Optional[int] = semantic_classifier_dropout
def __snake_case ( self ):
SCREAMING_SNAKE_CASE_ : Tuple = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
SCREAMING_SNAKE_CASE_ : Optional[int] = self.backbone_config.to_dict()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.__class__.model_type
return output
| 704 |
import math
def _lowerCamelCase( lowerCAmelCase__ : float , lowerCAmelCase__ : float ):
'''simple docstring'''
return math.pow(lowerCAmelCase__ , 2 ) - a
def _lowerCamelCase( lowerCAmelCase__ : float ):
'''simple docstring'''
return 2 * x
def _lowerCamelCase( lowerCAmelCase__ : float ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Optional[Any] = 2.0
while start <= a:
SCREAMING_SNAKE_CASE_ : str = math.pow(lowerCAmelCase__ , 2 )
return start
def _lowerCamelCase( lowerCAmelCase__ : float , lowerCAmelCase__ : int = 9999 , lowerCAmelCase__ : float = 0.00_000_000_000_001 ):
'''simple docstring'''
if a < 0:
raise ValueError('math domain error' )
SCREAMING_SNAKE_CASE_ : Optional[int] = get_initial_point(lowerCAmelCase__ )
for _ in range(lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ : Tuple = value
SCREAMING_SNAKE_CASE_ : Union[str, Any] = value - fx(lowerCAmelCase__ , lowerCAmelCase__ ) / fx_derivative(lowerCAmelCase__ )
if abs(prev_value - value ) < tolerance:
return value
return value
if __name__ == "__main__":
from doctest import testmod
testmod() | 97 | 0 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Optional[int] , lowerCAmelCase : Any , lowerCAmelCase : Dict=None , lowerCAmelCase : Optional[int]=None ):
A_ = start
A_ = end
A_ = val
A_ = (start + end) // 2
A_ = left
A_ = right
def __repr__( self : Dict ):
return F"SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : int , lowerCAmelCase : Sequence , lowerCAmelCase : Optional[Any] ):
A_ = collection
A_ = function
if self.collection:
A_ = self._build_tree(0 , len(lowerCAmelCase ) - 1 )
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase : List[Any] , lowerCAmelCase : Union[str, Any] ):
self._update_tree(self.root , lowerCAmelCase , lowerCAmelCase )
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase : Dict , lowerCAmelCase : Any ):
return self._query_range(self.root , lowerCAmelCase , lowerCAmelCase )
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase : List[str] , lowerCAmelCase : Optional[Any] ):
if start == end:
return SegmentTreeNode(lowerCAmelCase , lowerCAmelCase , self.collection[start] )
A_ = (start + end) // 2
A_ = self._build_tree(lowerCAmelCase , lowerCAmelCase )
A_ = self._build_tree(mid + 1 , lowerCAmelCase )
return SegmentTreeNode(lowerCAmelCase , lowerCAmelCase , self.fn(left.val , right.val ) , lowerCAmelCase , lowerCAmelCase )
def _UpperCAmelCase ( self : Tuple , lowerCAmelCase : int , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Dict ):
if node.start == i and node.end == i:
A_ = val
return
if i <= node.mid:
self._update_tree(node.left , lowerCAmelCase , lowerCAmelCase )
else:
self._update_tree(node.right , lowerCAmelCase , lowerCAmelCase )
A_ = self.fn(node.left.val , node.right.val )
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Dict , lowerCAmelCase : List[Any] ):
if node.start == i and node.end == j:
return node.val
if i <= node.mid:
if j <= node.mid:
# range in left child tree
return self._query_range(node.left , lowerCAmelCase , lowerCAmelCase )
else:
# range in left child tree and right child tree
return self.fn(
self._query_range(node.left , lowerCAmelCase , node.mid ) , self._query_range(node.right , node.mid + 1 , lowerCAmelCase ) , )
else:
# range in right child tree
return self._query_range(node.right , lowerCAmelCase , lowerCAmelCase )
def _UpperCAmelCase ( self : Optional[int] ):
if self.root is not None:
A_ = Queue()
queue.put(self.root )
while not queue.empty():
A_ = queue.get()
yield node
if node.left is not None:
queue.put(node.left )
if node.right is not None:
queue.put(node.right )
if __name__ == "__main__":
import operator
for fn in [operator.add, max, min]:
print('''*''' * 50)
__SCREAMING_SNAKE_CASE : Any = SegmentTree([2, 1, 5, 3, 4], fn)
for node in arr.traverse():
print(node)
print()
arr.update(1, 5)
for node in arr.traverse():
print(node)
print()
print(arr.query_range(3, 4)) # 7
print(arr.query_range(2, 2)) # 5
print(arr.query_range(1, 3)) # 13
print()
| 452 |
'''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 convert_to_rgb, 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
if is_vision_available():
import PIL
__SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
class __lowerCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] =["pixel_values"]
def __init__( self : Optional[int] , lowerCAmelCase : bool = True , lowerCAmelCase : Dict[str, int] = None , lowerCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , lowerCAmelCase : bool = True , lowerCAmelCase : Union[int, float] = 1 / 2_55 , lowerCAmelCase : bool = True , lowerCAmelCase : Optional[Union[float, List[float]]] = None , lowerCAmelCase : Optional[Union[float, List[float]]] = None , lowerCAmelCase : bool = True , **lowerCAmelCase : List[Any] , ):
super().__init__(**lowerCAmelCase )
A_ = size if size is not None else {"height": 3_84, "width": 3_84}
A_ = get_size_dict(lowerCAmelCase , default_to_square=lowerCAmelCase )
A_ = do_resize
A_ = size
A_ = resample
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 _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase : np.ndarray , lowerCAmelCase : Dict[str, int] , lowerCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase : Tuple , ):
A_ = get_size_dict(lowerCAmelCase , default_to_square=lowerCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}" )
A_ = (size["height"], size["width"])
return resize(lowerCAmelCase , size=lowerCAmelCase , resample=lowerCAmelCase , data_format=lowerCAmelCase , **lowerCAmelCase )
def _UpperCAmelCase ( self : str , lowerCAmelCase : np.ndarray , lowerCAmelCase : Union[int, float] , lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase : str , ):
return rescale(lowerCAmelCase , scale=lowerCAmelCase , data_format=lowerCAmelCase , **lowerCAmelCase )
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase : np.ndarray , lowerCAmelCase : Union[float, List[float]] , lowerCAmelCase : Union[float, List[float]] , lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase : Dict , ):
return normalize(lowerCAmelCase , mean=lowerCAmelCase , std=lowerCAmelCase , data_format=lowerCAmelCase , **lowerCAmelCase )
def _UpperCAmelCase ( self : Tuple , lowerCAmelCase : ImageInput , lowerCAmelCase : Optional[bool] = None , lowerCAmelCase : Optional[Dict[str, int]] = None , lowerCAmelCase : PILImageResampling = None , lowerCAmelCase : Optional[bool] = None , lowerCAmelCase : Optional[float] = None , lowerCAmelCase : Optional[bool] = None , lowerCAmelCase : Optional[Union[float, List[float]]] = None , lowerCAmelCase : Optional[Union[float, List[float]]] = None , lowerCAmelCase : Optional[Union[str, TensorType]] = None , lowerCAmelCase : bool = None , lowerCAmelCase : ChannelDimension = ChannelDimension.FIRST , **lowerCAmelCase : List[str] , ):
A_ = do_resize if do_resize is not None else self.do_resize
A_ = resample if resample is not None else self.resample
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_ = size if size is not None else self.size
A_ = get_size_dict(lowerCAmelCase , default_to_square=lowerCAmelCase )
A_ = 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_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(lowerCAmelCase ) for image in images]
# All transformations expect numpy arrays.
A_ = [to_numpy_array(lowerCAmelCase ) for image in images]
if do_resize:
A_ = [self.resize(image=lowerCAmelCase , size=lowerCAmelCase , resample=lowerCAmelCase ) for image in images]
if do_rescale:
A_ = [self.rescale(image=lowerCAmelCase , scale=lowerCAmelCase ) for image in images]
if do_normalize:
A_ = [self.normalize(image=lowerCAmelCase , mean=lowerCAmelCase , std=lowerCAmelCase ) for image in images]
A_ = [to_channel_dimension_format(lowerCAmelCase , lowerCAmelCase ) for image in images]
A_ = BatchFeature(data={"pixel_values": images} , tensor_type=lowerCAmelCase )
return encoded_outputs
| 452 | 1 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket" )
@patch("builtins.open" )
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
UpperCAmelCase = Mock()
UpperCAmelCase = conn, Mock()
UpperCAmelCase = iter([1, None] )
UpperCAmelCase = lambda _lowerCAmelCase : next(_lowerCAmelCase )
# ===== invoke =====
send_file(filename="mytext.txt" , testing=_lowerCAmelCase )
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| 705 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowerCAmelCase =logging.get_logger(__name__)
__lowerCAmelCase ={"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
__lowerCAmelCase ={
"tokenizer_file": {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
},
}
__lowerCAmelCase ={
"gpt-neox-20b": 2048,
}
class __magic_name__ ( _a):
_UpperCAmelCase : int = VOCAB_FILES_NAMES
_UpperCAmelCase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase : Tuple = ['input_ids', 'attention_mask']
def __init__( self : Optional[int] ,__SCREAMING_SNAKE_CASE : str=None ,__SCREAMING_SNAKE_CASE : Union[str, Any]=None ,__SCREAMING_SNAKE_CASE : List[str]=None ,__SCREAMING_SNAKE_CASE : List[Any]="<|endoftext|>" ,__SCREAMING_SNAKE_CASE : Any="<|endoftext|>" ,__SCREAMING_SNAKE_CASE : Dict="<|endoftext|>" ,__SCREAMING_SNAKE_CASE : Union[str, Any]=False ,**__SCREAMING_SNAKE_CASE : Tuple ,):
super().__init__(
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,tokenizer_file=__SCREAMING_SNAKE_CASE ,unk_token=__SCREAMING_SNAKE_CASE ,bos_token=__SCREAMING_SNAKE_CASE ,eos_token=__SCREAMING_SNAKE_CASE ,add_prefix_space=__SCREAMING_SNAKE_CASE ,**__SCREAMING_SNAKE_CASE ,)
UpperCAmelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" ,__SCREAMING_SNAKE_CASE ) != add_prefix_space:
UpperCAmelCase = getattr(__SCREAMING_SNAKE_CASE ,pre_tok_state.pop("type" ) )
UpperCAmelCase = add_prefix_space
UpperCAmelCase = pre_tok_class(**__SCREAMING_SNAKE_CASE )
UpperCAmelCase = add_prefix_space
def _UpperCAmelCase ( self : int ,__SCREAMING_SNAKE_CASE : str ,__SCREAMING_SNAKE_CASE : Optional[str] = None ):
UpperCAmelCase = self._tokenizer.model.save(__SCREAMING_SNAKE_CASE ,name=__SCREAMING_SNAKE_CASE )
return tuple(__SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self : str ,__SCREAMING_SNAKE_CASE : "Conversation" ):
UpperCAmelCase = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__SCREAMING_SNAKE_CASE ,add_special_tokens=__SCREAMING_SNAKE_CASE ) + [self.eos_token_id] )
if len(__SCREAMING_SNAKE_CASE ) > self.model_max_length:
UpperCAmelCase = input_ids[-self.model_max_length :]
return input_ids
| 405 | 0 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def __UpperCamelCase ( self ):
'''simple docstring'''
__A =AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip''' )
__A =AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip''' )
model.to(lowercase__ )
from datasets import load_dataset
__A =load_dataset('''nielsr/rvlcdip-demo''' )
__A =dataset['''train'''][0]['''image'''].convert('''RGB''' )
__A =image_processor(lowercase__ , return_tensors='''pt''' ).to(lowercase__ )
# forward pass
with torch.no_grad():
__A =model(**lowercase__ )
__A =outputs.logits
__A =torch.Size((1, 1_6) )
self.assertEqual(logits.shape , lowercase__ )
__A =torch.tensor(
[-0.4158, -0.4092, -0.4347] , device=lowercase__ , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , lowercase__ , atol=1E-4 ) )
| 184 |
def __lowerCAmelCase ( A , A , A , A ):
# Return True if there is node that has not iterated.
UpperCAmelCase_ = [False] * len(A )
UpperCAmelCase_ = []
queue.append(A )
UpperCAmelCase_ = True
while queue:
UpperCAmelCase_ = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(A )
UpperCAmelCase_ = True
UpperCAmelCase_ = u
return visited[t]
def __lowerCAmelCase ( A , A , A ):
# This array is filled by BFS and to store path
UpperCAmelCase_ = [-1] * (len(A ))
UpperCAmelCase_ = 0
while bfs(A , A , A , A ):
UpperCAmelCase_ = float("Inf" )
UpperCAmelCase_ = sink
while s != source:
# Find the minimum value in select path
UpperCAmelCase_ = min(A , graph[parent[s]][s] )
UpperCAmelCase_ = parent[s]
max_flow += path_flow
UpperCAmelCase_ = sink
while v != source:
UpperCAmelCase_ = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
UpperCAmelCase_ = parent[v]
return max_flow
_a: Any = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
_a , _a: Optional[int] = 0, 5
print(ford_fulkerson(graph, source, sink)) | 162 | 0 |
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def A__ ( lowerCamelCase="" ) -> str:
UpperCamelCase_: List[str] = tempfile.mkdtemp()
return os.path.join(lowerCamelCase , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self : int ):
UpperCamelCase_: Optional[int] = torch.rand(12 , dtype=torch.floataa ) - 0.5
UpperCamelCase_: List[Any] = AgentAudio(snake_case_ )
UpperCamelCase_: str = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(snake_case_ , agent_type.to_raw() , atol=1e-4 ) )
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(snake_case_ ) )
# Ensure that the file contains the same value as the original tensor
UpperCamelCase_, UpperCamelCase_: Optional[Any] = sf.read(snake_case_ )
self.assertTrue(torch.allclose(snake_case_ , torch.tensor(snake_case_ ) , atol=1e-4 ) )
def lowerCAmelCase__ ( self : Optional[int] ):
UpperCamelCase_: List[Any] = torch.rand(12 , dtype=torch.floataa ) - 0.5
UpperCamelCase_: Optional[Any] = get_new_path(suffix=""".wav""" )
sf.write(snake_case_ , snake_case_ , 1_6000 )
UpperCamelCase_: Optional[Any] = AgentAudio(snake_case_ )
self.assertTrue(torch.allclose(snake_case_ , agent_type.to_raw() , atol=1e-4 ) )
self.assertEqual(agent_type.to_string() , snake_case_ )
@require_vision
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self : Any ):
UpperCamelCase_: Any = torch.randint(0 , 256 , (64, 64, 3) )
UpperCamelCase_: Tuple = AgentImage(snake_case_ )
UpperCamelCase_: Dict = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(snake_case_ , agent_type._tensor , atol=1e-4 ) )
self.assertIsInstance(agent_type.to_raw() , Image.Image )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(snake_case_ ) )
def lowerCAmelCase__ ( self : List[Any] ):
UpperCamelCase_: Any = Path(get_tests_dir("""fixtures/tests_samples/COCO""" ) ) / """000000039769.png"""
UpperCamelCase_: Dict = Image.open(snake_case_ )
UpperCamelCase_: List[str] = AgentImage(snake_case_ )
self.assertTrue(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(snake_case_ ) )
def lowerCAmelCase__ ( self : Union[str, Any] ):
UpperCamelCase_: Optional[int] = Path(get_tests_dir("""fixtures/tests_samples/COCO""" ) ) / """000000039769.png"""
UpperCamelCase_: List[Any] = Image.open(snake_case_ )
UpperCamelCase_: int = AgentImage(snake_case_ )
self.assertFalse(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(snake_case_ ) )
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self : Dict ):
UpperCamelCase_: Optional[Any] = """Hey!"""
UpperCamelCase_: int = AgentText(snake_case_ )
self.assertEqual(snake_case_ , agent_type.to_string() )
self.assertEqual(snake_case_ , agent_type.to_raw() )
self.assertEqual(snake_case_ , snake_case_ )
| 670 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCamelCase_ : Dict = logging.get_logger(__name__)
class _UpperCamelCase ( _A ):
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : Tuple=None , **snake_case_ : List[str] ):
warnings.warn(
"""`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
"""instead.""" , snake_case_ , )
super().__init__(args=snake_case_ , **snake_case_ )
| 670 | 1 |
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
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = 42
lowerCAmelCase_ = 42
lowerCAmelCase_ = None
class __A ( lowerCAmelCase , lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = 2
@register_to_config
def __init__( self , __lowerCAmelCase = 0.02 , __lowerCAmelCase = 1_0_0 , __lowerCAmelCase = 1.007 , __lowerCAmelCase = 8_0 , __lowerCAmelCase = 0.05 , __lowerCAmelCase = 5_0 , ):
'''simple docstring'''
lowerCamelCase__ = sigma_max
# setable values
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None # sigma(t_i)
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase = None ):
'''simple docstring'''
return sample
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase = None ):
'''simple docstring'''
lowerCamelCase__ = num_inference_steps
lowerCamelCase__ = np.arange(0 , self.num_inference_steps )[::-1].copy()
lowerCamelCase__ = torch.from_numpy(__lowerCAmelCase ).to(__lowerCAmelCase )
lowerCamelCase__ = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
lowerCamelCase__ = torch.tensor(__lowerCAmelCase , dtype=torch.floataa , device=__lowerCAmelCase )
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None ):
'''simple docstring'''
if self.config.s_min <= sigma <= self.config.s_max:
lowerCamelCase__ = min(self.config.s_churn / self.num_inference_steps , 2**0.5 - 1 )
else:
lowerCamelCase__ = 0
# sample eps ~ N(0, S_noise^2 * I)
lowerCamelCase__ = self.config.s_noise * randn_tensor(sample.shape , generator=__lowerCAmelCase ).to(sample.device )
lowerCamelCase__ = sigma + gamma * sigma
lowerCamelCase__ = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = True , ):
'''simple docstring'''
lowerCamelCase__ = sample_hat + sigma_hat * model_output
lowerCamelCase__ = (sample_hat - pred_original_sample) / sigma_hat
lowerCamelCase__ = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=__lowerCAmelCase , derivative=__lowerCAmelCase , pred_original_sample=__lowerCAmelCase )
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = True , ):
'''simple docstring'''
lowerCamelCase__ = sample_prev + sigma_prev * model_output
lowerCamelCase__ = (sample_prev - pred_original_sample) / sigma_prev
lowerCamelCase__ = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=__lowerCAmelCase , derivative=__lowerCAmelCase , pred_original_sample=__lowerCAmelCase )
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
raise NotImplementedError()
| 481 |
def lowerCAmelCase__(__snake_case ) -> list:
'''simple docstring'''
def merge(__snake_case ,__snake_case ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(__snake_case ) <= 1:
return collection
lowerCamelCase__ = len(__snake_case ) // 2
return merge(merge_sort(collection[:mid] ) ,merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
_a = input("Enter numbers separated by a comma:\n").strip()
_a = [int(item) for item in user_input.split(",")]
print(*merge_sort(unsorted), sep=",")
| 481 | 1 |
"""simple docstring"""
import cva
import numpy as np
class UpperCamelCase :
def __init__( self , snake_case__ , snake_case__ ):
"""simple docstring"""
if k in (0.04, 0.06):
_SCREAMING_SNAKE_CASE : Optional[Any] = k
_SCREAMING_SNAKE_CASE : Optional[int] = window_size
else:
raise ValueError("invalid k value" )
def __str__( self ):
"""simple docstring"""
return str(self.k )
def __SCREAMING_SNAKE_CASE ( self , snake_case__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Optional[int] = cva.imread(snake_case__ , 0 )
_SCREAMING_SNAKE_CASE : Any = img.shape
_SCREAMING_SNAKE_CASE : list[list[int]] = []
_SCREAMING_SNAKE_CASE : str = img.copy()
_SCREAMING_SNAKE_CASE : List[Any] = cva.cvtColor(snake_case__ , cva.COLOR_GRAY2RGB )
_SCREAMING_SNAKE_CASE : str = np.gradient(snake_case__ )
_SCREAMING_SNAKE_CASE : str = dx**2
_SCREAMING_SNAKE_CASE : Any = dy**2
_SCREAMING_SNAKE_CASE : Optional[int] = dx * dy
_SCREAMING_SNAKE_CASE : List[Any] = 0.04
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.window_size // 2
for y in range(snake_case__ , h - offset ):
for x in range(snake_case__ , w - offset ):
_SCREAMING_SNAKE_CASE : Optional[Any] = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_SCREAMING_SNAKE_CASE : Tuple = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_SCREAMING_SNAKE_CASE : List[str] = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_SCREAMING_SNAKE_CASE : Optional[int] = (wxx * wyy) - (wxy**2)
_SCREAMING_SNAKE_CASE : Union[str, Any] = wxx + wyy
_SCREAMING_SNAKE_CASE : Any = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) , 0 )
color_img.itemset((y, x, 1) , 0 )
color_img.itemset((y, x, 2) , 255 )
return color_img, corner_list
if __name__ == "__main__":
lowercase_ : Optional[int] = HarrisCorner(0.0_4, 3)
lowercase_ : Union[str, Any] = edge_detect.detect('''path_to_image''')
cva.imwrite('''detect.png''', color_img)
| 720 |
"""simple docstring"""
from __future__ import annotations
lowercase_ : List[str] = '''#'''
class UpperCamelCase :
def __init__( self ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : dict = {}
def __SCREAMING_SNAKE_CASE ( self , snake_case__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : str = self._trie
for char in text:
if char not in trie:
_SCREAMING_SNAKE_CASE : List[str] = {}
_SCREAMING_SNAKE_CASE : List[str] = trie[char]
_SCREAMING_SNAKE_CASE : Optional[Any] = True
def __SCREAMING_SNAKE_CASE ( self , snake_case__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Optional[Any] = self._trie
for char in prefix:
if char in trie:
_SCREAMING_SNAKE_CASE : str = trie[char]
else:
return []
return self._elements(snake_case__ )
def __SCREAMING_SNAKE_CASE ( self , snake_case__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : str = []
for c, v in d.items():
_SCREAMING_SNAKE_CASE : int = [" "] if c == END else [(c + s) for s in self._elements(snake_case__ )]
result.extend(snake_case__ )
return tuple(snake_case__ )
lowercase_ : Union[str, Any] = Trie()
lowercase_ : Optional[Any] = ('''depart''', '''detergent''', '''daring''', '''dog''', '''deer''', '''deal''')
for word in words:
trie.insert_word(word)
def _lowerCAmelCase ( lowerCamelCase__ : str ) -> tuple:
_SCREAMING_SNAKE_CASE : Dict = trie.find_word(lowerCamelCase__ )
return tuple(string + word for word in suffixes )
def _lowerCAmelCase ( ) -> None:
print(autocomplete_using_trie("de" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 295 | 0 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed
__a = logging.getLogger(__name__)
def lowerCamelCase__ ( _lowercase=2 , _lowercase=3 , _lowercase=16 , _lowercase = 10 , _lowercase = 2 ):
'''simple docstring'''
def get_dataset(_lowercase ):
UpperCAmelCase_ : Optional[int] = torch.randn(batch_size * n_batches , 1 )
return TensorDataset(_lowercase , a * x + b + 0.1 * torch.randn(batch_size * n_batches , 1 ) )
UpperCAmelCase_ : Tuple = get_dataset(_lowercase )
UpperCAmelCase_ : List[Any] = get_dataset(_lowercase )
UpperCAmelCase_ : Optional[int] = DataLoader(_lowercase , shuffle=_lowercase , batch_size=_lowercase , num_workers=4 )
UpperCAmelCase_ : int = DataLoader(_lowercase , shuffle=_lowercase , batch_size=_lowercase , num_workers=4 )
return (train_dataloader, valid_dataloader)
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase=None ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = []
for epoch in range(_lowercase ):
# Train quickly
model.train()
for batch in dataloader:
UpperCAmelCase_, UpperCAmelCase_ : int = batch
UpperCAmelCase_ : List[Any] = model(_lowercase )
UpperCAmelCase_ : Dict = torch.nn.functional.mse_loss(_lowercase , _lowercase )
accelerator.backward(_lowercase )
optimizer.step()
optimizer.zero_grad()
rands.append(random.random() ) # Introduce some randomness
if scheduler is not None:
scheduler.step()
return rands
class __a( nn.Module ):
"""simple docstring"""
def __init__( self ) -> Dict:
super().__init__()
UpperCAmelCase_ : List[str] = nn.Parameter(torch.randn(1 ) )
UpperCAmelCase_ : int = nn.Parameter(torch.randn(1 ) )
def a__ ( self ,_SCREAMING_SNAKE_CASE ) -> Optional[int]:
return x * self.a + self.b
class __a( unittest.TestCase ):
"""simple docstring"""
def a__ ( self ) -> Optional[int]:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
UpperCAmelCase_ : Tuple = DummyModel()
UpperCAmelCase_ : str = torch.optim.Adam(params=model.parameters() ,lr=1e-3 )
UpperCAmelCase_, UpperCAmelCase_ : str = dummy_dataloaders()
UpperCAmelCase_ : Any = ProjectConfiguration(total_limit=1 ,project_dir=_SCREAMING_SNAKE_CASE ,automatic_checkpoint_naming=_SCREAMING_SNAKE_CASE )
# Train baseline
UpperCAmelCase_ : str = Accelerator(project_config=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ : int = accelerator.prepare(
_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
# Save initial
accelerator.save_state()
# Save second state
accelerator.save_state()
self.assertEqual(len(os.listdir(accelerator.project_dir ) ) ,1 )
def a__ ( self ) -> Any:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
UpperCAmelCase_ : Optional[int] = DummyModel()
UpperCAmelCase_ : Union[str, Any] = torch.optim.Adam(params=model.parameters() ,lr=1e-3 )
UpperCAmelCase_, UpperCAmelCase_ : Optional[Any] = dummy_dataloaders()
# Train baseline
UpperCAmelCase_ : Union[str, Any] = Accelerator()
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ : int = accelerator.prepare(
_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
# Save initial
UpperCAmelCase_ : Union[str, Any] = os.path.join(_SCREAMING_SNAKE_CASE ,'''initial''' )
accelerator.save_state(_SCREAMING_SNAKE_CASE )
((UpperCAmelCase_), (UpperCAmelCase_)) : Optional[Any] = model.a.item(), model.b.item()
UpperCAmelCase_ : List[Any] = optimizer.state_dict()
UpperCAmelCase_ : Optional[Any] = train(3 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
((UpperCAmelCase_), (UpperCAmelCase_)) : int = model.a.item(), model.b.item()
UpperCAmelCase_ : Tuple = optimizer.state_dict()
# Train partially
set_seed(42 )
UpperCAmelCase_ : Tuple = DummyModel()
UpperCAmelCase_ : Dict = torch.optim.Adam(params=model.parameters() ,lr=1e-3 )
UpperCAmelCase_, UpperCAmelCase_ : List[Any] = dummy_dataloaders()
UpperCAmelCase_ : List[str] = Accelerator()
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ : Union[str, Any] = accelerator.prepare(
_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
accelerator.load_state(_SCREAMING_SNAKE_CASE )
((UpperCAmelCase_), (UpperCAmelCase_)) : Union[str, Any] = model.a.item(), model.b.item()
UpperCAmelCase_ : Any = optimizer.state_dict()
self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : str = train(2 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
# Save everything
UpperCAmelCase_ : Union[str, Any] = os.path.join(_SCREAMING_SNAKE_CASE ,'''checkpoint''' )
accelerator.save_state(_SCREAMING_SNAKE_CASE )
# Load everything back in and make sure all states work
accelerator.load_state(_SCREAMING_SNAKE_CASE )
test_rands += train(1 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
((UpperCAmelCase_), (UpperCAmelCase_)) : Union[str, Any] = model.a.item(), model.b.item()
UpperCAmelCase_ : Any = optimizer.state_dict()
self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
def a__ ( self ) -> Optional[int]:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
UpperCAmelCase_ : str = DummyModel()
UpperCAmelCase_ : List[str] = torch.optim.Adam(params=model.parameters() ,lr=1e-3 )
UpperCAmelCase_, UpperCAmelCase_ : int = dummy_dataloaders()
UpperCAmelCase_ : Union[str, Any] = ProjectConfiguration(automatic_checkpoint_naming=_SCREAMING_SNAKE_CASE )
# Train baseline
UpperCAmelCase_ : Optional[int] = Accelerator(project_dir=_SCREAMING_SNAKE_CASE ,project_config=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ : str = accelerator.prepare(
_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
# Save initial
accelerator.save_state()
((UpperCAmelCase_), (UpperCAmelCase_)) : int = model.a.item(), model.b.item()
UpperCAmelCase_ : List[Any] = optimizer.state_dict()
UpperCAmelCase_ : int = train(3 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
((UpperCAmelCase_), (UpperCAmelCase_)) : Optional[int] = model.a.item(), model.b.item()
UpperCAmelCase_ : List[str] = optimizer.state_dict()
# Train partially
set_seed(42 )
UpperCAmelCase_ : Optional[int] = DummyModel()
UpperCAmelCase_ : str = torch.optim.Adam(params=model.parameters() ,lr=1e-3 )
UpperCAmelCase_, UpperCAmelCase_ : Optional[Any] = dummy_dataloaders()
UpperCAmelCase_ : List[str] = ProjectConfiguration(iteration=1 ,automatic_checkpoint_naming=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : str = Accelerator(project_dir=_SCREAMING_SNAKE_CASE ,project_config=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ : Union[str, Any] = accelerator.prepare(
_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
accelerator.load_state(os.path.join(_SCREAMING_SNAKE_CASE ,'''checkpoints''' ,'''checkpoint_0''' ) )
((UpperCAmelCase_), (UpperCAmelCase_)) : Dict = model.a.item(), model.b.item()
UpperCAmelCase_ : List[Any] = optimizer.state_dict()
self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Dict = train(2 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
# Save everything
accelerator.save_state()
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(_SCREAMING_SNAKE_CASE ,'''checkpoints''' ,'''checkpoint_1''' ) )
test_rands += train(1 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
((UpperCAmelCase_), (UpperCAmelCase_)) : Optional[int] = model.a.item(), model.b.item()
UpperCAmelCase_ : str = optimizer.state_dict()
self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
def a__ ( self ) -> Optional[int]:
UpperCAmelCase_ : str = torch.tensor([1, 2, 3] )
UpperCAmelCase_ : Any = torch.tensor([2, 3, 4] )
UpperCAmelCase_ : int = DummyModel()
UpperCAmelCase_ : Tuple = torch.optim.Adam(net.parameters() )
UpperCAmelCase_ : Optional[int] = Accelerator()
with self.assertRaises(_SCREAMING_SNAKE_CASE ) as ve:
accelerator.register_for_checkpointing(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Optional[int] = str(ve.exception )
self.assertTrue('''Item at index 0''' in message )
self.assertTrue('''Item at index 1''' in message )
self.assertFalse('''Item at index 2''' in message )
self.assertFalse('''Item at index 3''' in message )
def a__ ( self ) -> List[Any]:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
UpperCAmelCase_ : Any = DummyModel()
UpperCAmelCase_ : Any = torch.optim.Adam(params=model.parameters() ,lr=1e-3 )
UpperCAmelCase_ : Union[str, Any] = torch.optim.lr_scheduler.StepLR(_SCREAMING_SNAKE_CASE ,step_size=1 ,gamma=0.99 )
UpperCAmelCase_, UpperCAmelCase_ : Tuple = dummy_dataloaders()
UpperCAmelCase_ : str = ProjectConfiguration(automatic_checkpoint_naming=_SCREAMING_SNAKE_CASE )
# Train baseline
UpperCAmelCase_ : List[str] = Accelerator(project_dir=_SCREAMING_SNAKE_CASE ,project_config=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ : List[Any] = accelerator.prepare(
_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
# Save initial
accelerator.save_state()
UpperCAmelCase_ : int = scheduler.state_dict()
train(3 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
self.assertNotEqual(_SCREAMING_SNAKE_CASE ,scheduler.state_dict() )
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(_SCREAMING_SNAKE_CASE ,'''checkpoints''' ,'''checkpoint_0''' ) )
self.assertEqual(_SCREAMING_SNAKE_CASE ,scheduler.state_dict() )
def a__ ( self ) -> str:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
UpperCAmelCase_ : List[str] = DummyModel()
UpperCAmelCase_ : Dict = ProjectConfiguration(automatic_checkpoint_naming=_SCREAMING_SNAKE_CASE ,total_limit=2 )
# Train baseline
UpperCAmelCase_ : Tuple = Accelerator(project_dir=_SCREAMING_SNAKE_CASE ,project_config=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Union[str, Any] = accelerator.prepare(_SCREAMING_SNAKE_CASE )
# Save 3 states:
for _ in range(11 ):
accelerator.save_state()
self.assertTrue(not os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE ,'''checkpoints''' ,'''checkpoint_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE ,'''checkpoints''' ,'''checkpoint_9''' ) ) )
self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE ,'''checkpoints''' ,'''checkpoint_10''' ) ) )
@require_cuda
def a__ ( self ) -> Optional[Any]:
UpperCAmelCase_ : List[Any] = ['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
execute_subprocess_async(_SCREAMING_SNAKE_CASE ,env=os.environ.copy() )
if __name__ == "__main__":
__a = '/tmp/accelerate/state_checkpointing'
__a = DummyModel()
__a = torch.optim.Adam(params=model.parameters(), lr=1E-3)
__a = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
__a ,__a = dummy_dataloaders()
__a = ProjectConfiguration(automatic_checkpoint_naming=True)
# Train baseline
__a = Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
if accelerator.process_index == 0:
if os.path.exists(savedir):
shutil.rmtree(savedir)
os.makedirs(savedir)
__a ,__a ,__a ,__a ,__a = accelerator.prepare(
model, optimizer, train_dataloader, valid_dataloader, scheduler
)
__a ,__a = accelerator.prepare(model, optimizer)
train(3, model, train_dataloader, optimizer, accelerator, scheduler)
# Check that the intial optimizer is loaded on the GPU
for group in optimizer.param_groups:
__a = group['params'][0].device
break
assert param_device.type == accelerator.device.type
__a = model.cpu()
accelerator.wait_for_everyone()
accelerator.save_state()
accelerator.wait_for_everyone()
# Check CPU state
accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='cpu')
for group in optimizer.param_groups:
__a = group['params'][0].device
break
assert (
param_device.type == torch.device('cpu').type
), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"
# Check device state
model.to(accelerator.device)
accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
for group in optimizer.param_groups:
__a = group['params'][0].device
break
assert (
param_device.type == accelerator.device.type
), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"
# Check error
with pytest.raises(TypeError, match='Unsupported optimizer map location passed'):
accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='invalid')
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
shutil.rmtree(savedir)
accelerator.wait_for_everyone() | 30 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = '''trocr'''
__lowerCAmelCase = ['''past_key_values''']
__lowerCAmelCase = {
'''num_attention_heads''': '''decoder_attention_heads''',
'''hidden_size''': '''d_model''',
'''num_hidden_layers''': '''decoder_layers''',
}
def __init__( self , _UpperCAmelCase=50265 , _UpperCAmelCase=1024 , _UpperCAmelCase=12 , _UpperCAmelCase=16 , _UpperCAmelCase=4096 , _UpperCAmelCase="gelu" , _UpperCAmelCase=512 , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.0 , _UpperCAmelCase=0.0 , _UpperCAmelCase=2 , _UpperCAmelCase=0.0_2 , _UpperCAmelCase=0.0 , _UpperCAmelCase=True , _UpperCAmelCase=False , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=1 , _UpperCAmelCase=0 , _UpperCAmelCase=2 , **_UpperCAmelCase , ):
__a : List[str] = vocab_size
__a : Optional[Any] = d_model
__a : Optional[Any] = decoder_layers
__a : Union[str, Any] = decoder_attention_heads
__a : int = decoder_ffn_dim
__a : List[Any] = activation_function
__a : Any = max_position_embeddings
__a : Dict = dropout
__a : List[Any] = attention_dropout
__a : Optional[Any] = activation_dropout
__a : str = init_std
__a : List[str] = decoder_layerdrop
__a : Union[str, Any] = use_cache
__a : Optional[Any] = scale_embedding
__a : List[Any] = use_learned_position_embeddings
__a : Optional[int] = layernorm_embedding
super().__init__(
pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , decoder_start_token_id=_UpperCAmelCase , **_UpperCAmelCase , ) | 52 | 0 |
from maths.prime_check import is_prime
def _lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ = f"Input value of [number={number}] must be an integer"
raise TypeError(SCREAMING_SNAKE_CASE )
if is_prime(SCREAMING_SNAKE_CASE ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 563 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__lowercase = logging.get_logger(__name__)
__lowercase = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
__lowercase = {
"""vocab_file""": {
"""yjernite/retribert-base-uncased""": (
"""https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""yjernite/retribert-base-uncased""": (
"""https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"""
),
},
}
__lowercase = {
"""yjernite/retribert-base-uncased""": 512,
}
__lowercase = {
"""yjernite/retribert-base-uncased""": {"""do_lower_case""": True},
}
class _lowercase ( __lowerCamelCase ):
_lowercase : Union[str, Any] = VOCAB_FILES_NAMES
_lowercase : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
_lowercase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowercase : Tuple = PRETRAINED_INIT_CONFIGURATION
_lowercase : List[Any] = RetriBertTokenizer
_lowercase : Optional[int] = ['input_ids', 'attention_mask']
def __init__( self : Optional[Any] , lowerCamelCase__ : int=None , lowerCamelCase__ : Tuple=None , lowerCamelCase__ : Dict=True , lowerCamelCase__ : Union[str, Any]="[UNK]" , lowerCamelCase__ : Optional[Any]="[SEP]" , lowerCamelCase__ : List[Any]="[PAD]" , lowerCamelCase__ : Tuple="[CLS]" , lowerCamelCase__ : List[Any]="[MASK]" , lowerCamelCase__ : Dict=True , lowerCamelCase__ : str=None , **lowerCamelCase__ : List[Any] , ) -> Dict:
"""simple docstring"""
super().__init__(
lowerCamelCase__ , tokenizer_file=lowerCamelCase__ , do_lower_case=lowerCamelCase__ , unk_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , cls_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , tokenize_chinese_chars=lowerCamelCase__ , strip_accents=lowerCamelCase__ , **lowerCamelCase__ , )
A_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , lowerCamelCase__ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , lowerCamelCase__ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , lowerCamelCase__ ) != tokenize_chinese_chars
):
A_ = getattr(lowerCamelCase__ , normalizer_state.pop('''type''' ) )
A_ = do_lower_case
A_ = strip_accents
A_ = tokenize_chinese_chars
A_ = normalizer_class(**lowerCamelCase__ )
A_ = do_lower_case
def UpperCamelCase ( self : Optional[Any] , lowerCamelCase__ : int , lowerCamelCase__ : List[str]=None ) -> Union[str, Any]:
"""simple docstring"""
A_ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase ( self : Any , lowerCamelCase__ : List[int] , lowerCamelCase__ : Optional[List[int]] = None ) -> List[int]:
"""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 UpperCamelCase ( self : Union[str, Any] , lowerCamelCase__ : str , lowerCamelCase__ : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
A_ = self._tokenizer.model.save(lowerCamelCase__ , name=lowerCamelCase__ )
return tuple(lowerCamelCase__ )
| 563 | 1 |
import random
def __lowerCAmelCase ( _A ,_A ):
"""simple docstring"""
_lowercase , _lowercase , _lowercase = [], [], []
for element in data:
if element < pivot:
less.append(_lowercase )
elif element > pivot:
greater.append(_lowercase )
else:
equal.append(_lowercase )
return less, equal, greater
def __lowerCAmelCase ( _A ,_A ):
"""simple docstring"""
if index >= len(_lowercase ) or index < 0:
return None
_lowercase = items[random.randint(0 ,len(_lowercase ) - 1 )]
_lowercase = 0
_lowercase , _lowercase , _lowercase = _partition(_lowercase ,_lowercase )
_lowercase = len(_lowercase )
_lowercase = len(_lowercase )
# index is the pivot
if m <= index < m + count:
return pivot
# must be in smaller
elif m > index:
return quick_select(_lowercase ,_lowercase )
# must be in larger
else:
return quick_select(_lowercase ,index - (m + count) )
| 398 |
"""simple docstring"""
def __snake_case ( _lowercase ):
"""simple docstring"""
UpperCamelCase = [0 for i in range(len(_lowercase ) )]
# initialize interval's left pointer and right pointer
UpperCamelCase , UpperCamelCase = 0, 0
for i in range(1 ,len(_lowercase ) ):
# case when current index is inside the interval
if i <= right_pointer:
UpperCamelCase = min(right_pointer - i + 1 ,z_result[i - left_pointer] )
UpperCamelCase = min_edge
while go_next(_lowercase ,_lowercase ,_lowercase ):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
UpperCamelCase , UpperCamelCase = i, i + z_result[i] - 1
return z_result
def __snake_case ( _lowercase ,_lowercase ,_lowercase ):
"""simple docstring"""
return i + z_result[i] < len(_lowercase ) and s[z_result[i]] == s[i + z_result[i]]
def __snake_case ( _lowercase ,_lowercase ):
"""simple docstring"""
UpperCamelCase = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
UpperCamelCase = z_function(pattern + input_str )
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(_lowercase ):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod() | 34 | 0 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
a= logging.getLogger()
def _UpperCamelCase ( ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument('-f' )
__UpperCamelCase : Dict = parser.parse_args()
return args.f
def _UpperCamelCase ( _a : List[Any] ):
"""simple docstring"""
__UpperCamelCase : str = {}
__UpperCamelCase : List[str] = os.path.join(_lowercase , 'all_results.json' )
if os.path.exists(_lowercase ):
with open(_lowercase , 'r' ) as f:
__UpperCamelCase : Optional[int] = json.load(_lowercase )
else:
raise ValueError(f"""can't find {path}""" )
return results
def _UpperCamelCase ( ):
"""simple docstring"""
__UpperCamelCase : str = torch.cuda.is_available() and torch_device == 'cuda'
return is_using_cuda and is_apex_available()
a= logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class __lowercase ( _lowerCamelCase ):
"""simple docstring"""
@classmethod
def lowerCAmelCase ( cls ):
# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
__UpperCamelCase : Dict = tempfile.mkdtemp()
__UpperCamelCase : List[Any] = os.path.join(cls.tmpdir , 'default_config.yml' )
write_basic_config(save_location=cls.configPath )
__UpperCamelCase : str = ['accelerate', 'launch', '--config_file', cls.configPath]
@classmethod
def lowerCAmelCase ( cls ):
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def lowerCAmelCase ( self ):
__UpperCamelCase : Optional[int] = self.get_auto_remove_tmp_dir()
__UpperCamelCase : Tuple = f"""
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--seed=42
--checkpointing_steps epoch
--with_tracking
""".split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__UpperCamelCase : str = get_results(__A )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertTrue(os.path.exists(os.path.join(__A , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__A , 'glue_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def lowerCAmelCase ( self ):
__UpperCamelCase : Optional[int] = self.get_auto_remove_tmp_dir()
__UpperCamelCase : str = f"""
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
""".split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
__UpperCamelCase : List[str] = get_results(__A )
self.assertLess(result['perplexity'] , 1_0_0 )
self.assertTrue(os.path.exists(os.path.join(__A , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__A , 'clm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def lowerCAmelCase ( self ):
__UpperCamelCase : Tuple = self.get_auto_remove_tmp_dir()
__UpperCamelCase : Tuple = f"""
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : int = get_results(__A )
self.assertLess(result['perplexity'] , 4_2 )
self.assertTrue(os.path.exists(os.path.join(__A , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__A , 'mlm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def lowerCAmelCase ( self ):
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
__UpperCamelCase : int = 7 if get_gpu_count() > 1 else 2
__UpperCamelCase : Tuple = self.get_auto_remove_tmp_dir()
__UpperCamelCase : Any = f"""
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : Optional[Any] = get_results(__A )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertLess(result['train_loss'] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(__A , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__A , 'ner_no_trainer' ) ) )
@unittest.skip(reason='Fix me @muellerzr' )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def lowerCAmelCase ( self ):
__UpperCamelCase : Tuple = self.get_auto_remove_tmp_dir()
__UpperCamelCase : List[str] = f"""
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : str = get_results(__A )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result['eval_f1'] , 2_8 )
self.assertGreaterEqual(result['eval_exact'] , 2_8 )
self.assertTrue(os.path.exists(os.path.join(__A , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__A , 'qa_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def lowerCAmelCase ( self ):
__UpperCamelCase : List[str] = self.get_auto_remove_tmp_dir()
__UpperCamelCase : Dict = f"""
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
""".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : Optional[int] = get_results(__A )
self.assertGreaterEqual(result['eval_accuracy'] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(__A , 'swag_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def lowerCAmelCase ( self ):
__UpperCamelCase : Dict = self.get_auto_remove_tmp_dir()
__UpperCamelCase : List[Any] = f"""
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : Optional[Any] = get_results(__A )
self.assertGreaterEqual(result['eval_rouge1'] , 1_0 )
self.assertGreaterEqual(result['eval_rouge2'] , 2 )
self.assertGreaterEqual(result['eval_rougeL'] , 7 )
self.assertGreaterEqual(result['eval_rougeLsum'] , 7 )
self.assertTrue(os.path.exists(os.path.join(__A , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__A , 'summarization_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def lowerCAmelCase ( self ):
__UpperCamelCase : List[str] = self.get_auto_remove_tmp_dir()
__UpperCamelCase : Any = f"""
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : List[str] = get_results(__A )
self.assertGreaterEqual(result['eval_bleu'] , 3_0 )
self.assertTrue(os.path.exists(os.path.join(__A , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__A , 'translation_no_trainer' ) ) )
@slow
def lowerCAmelCase ( self ):
__UpperCamelCase : Dict = logging.StreamHandler(sys.stdout )
logger.addHandler(__A )
__UpperCamelCase : Optional[Any] = self.get_auto_remove_tmp_dir()
__UpperCamelCase : Tuple = f"""
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
""".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : Optional[int] = get_results(__A )
self.assertGreaterEqual(result['eval_overall_accuracy'] , 0.1_0 )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def lowerCAmelCase ( self ):
__UpperCamelCase : List[Any] = self.get_auto_remove_tmp_dir()
__UpperCamelCase : Tuple = f"""
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
""".split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__UpperCamelCase : int = get_results(__A )
# The base model scores a 25%
self.assertGreaterEqual(result['eval_accuracy'] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(__A , 'step_1' ) ) )
self.assertTrue(os.path.exists(os.path.join(__A , 'image_classification_no_trainer' ) ) )
| 718 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a= {
'''configuration_bridgetower''': [
'''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BridgeTowerConfig''',
'''BridgeTowerTextConfig''',
'''BridgeTowerVisionConfig''',
],
'''processing_bridgetower''': ['''BridgeTowerProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a= ['''BridgeTowerImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a= [
'''BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BridgeTowerForContrastiveLearning''',
'''BridgeTowerForImageAndTextRetrieval''',
'''BridgeTowerForMaskedLM''',
'''BridgeTowerModel''',
'''BridgeTowerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_bridgetower import (
BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
BridgeTowerConfig,
BridgeTowerTextConfig,
BridgeTowerVisionConfig,
)
from .processing_bridgetower import BridgeTowerProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_bridgetower import BridgeTowerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bridgetower import (
BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
BridgeTowerForContrastiveLearning,
BridgeTowerForImageAndTextRetrieval,
BridgeTowerForMaskedLM,
BridgeTowerModel,
BridgeTowerPreTrainedModel,
)
else:
import sys
a= _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 287 | 0 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class a__ ( a__ ):
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ ) -> Union[str, Any]:
with open(lowerCamelCase_ , encoding='''utf-8''' ) as input_file:
lowerCAmelCase__ = re.compile(r'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)''' )
lowerCAmelCase__ = input_file.read()
lowerCAmelCase__ = regexp.search(lowerCamelCase_ )
return match
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ ) -> List[Any]:
with open(lowerCamelCase_ , encoding='''utf-8''' ) as input_file:
lowerCAmelCase__ = re.compile(r'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()''' , re.DOTALL )
lowerCAmelCase__ = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
lowerCAmelCase__ = regexp.finditer(lowerCamelCase_ )
lowerCAmelCase__ = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
lowerCAmelCase__ = Path('''./datasets''' )
lowerCAmelCase__ = list(dataset_paths.absolute().glob('''**/*.py''' ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(lowerCamelCase_ ) ):
raise AssertionError(F"""open(...) must use utf-8 encoding in {dataset}""" )
def __SCREAMING_SNAKE_CASE ( self ) -> Any:
lowerCAmelCase__ = Path('''./datasets''' )
lowerCAmelCase__ = list(dataset_paths.absolute().glob('''**/*.py''' ) )
for dataset in dataset_files:
if self._no_print_statements(str(lowerCamelCase_ ) ):
raise AssertionError(F"""print statement found in {dataset}. Use datasets.logger/logging instead.""" ) | 90 | # 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
UpperCamelCase__ = float('nan')
class A :
def __init__(self : str , __UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase__ = sys.stdout
UpperCAmelCase__ = open(__UpperCAmelCase , "a" )
def __getattr__(self : str , __UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
return getattr(self.stdout , __UpperCAmelCase )
def lowercase_ (self : List[Any] , __UpperCAmelCase : int ) -> Dict:
"""simple docstring"""
self.stdout.write(__UpperCAmelCase )
# strip tqdm codes
self.file.write(re.sub(r"^.*\r" , "" , __UpperCAmelCase , 0 , re.M ) )
def lowerCAmelCase_ ( __A=80, __A=False ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ = []
# deal with critical env vars
UpperCAmelCase__ = ["CUDA_VISIBLE_DEVICES"]
for key in env_keys:
UpperCAmelCase__ = os.environ.get(__A, __A )
if val is not None:
cmd.append(f"""{key}={val}""" )
# python executable (not always needed if the script is executable)
UpperCAmelCase__ = sys.executable if full_python_path else sys.executable.split("/" )[-1]
cmd.append(__A )
# now the normal args
cmd += list(map(shlex.quote, sys.argv ) )
# split up into up to MAX_WIDTH lines with shell multi-line escapes
UpperCAmelCase__ = []
UpperCAmelCase__ = ""
while len(__A ) > 0:
current_line += f"""{cmd.pop(0 )} """
if len(__A ) == 0 or len(__A ) + len(cmd[0] ) + 1 > max_width - 1:
lines.append(__A )
UpperCAmelCase__ = ""
return "\\\n".join(__A )
def lowerCAmelCase_ ( __A, __A ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase__ = re.sub(r"[\\\n]+", " ", args.base_cmd )
# remove --output_dir if any and set our own
UpperCAmelCase__ = re.sub("--output_dir\s+[^\s]+", "", args.base_cmd )
args.base_cmd += f""" --output_dir {output_dir}"""
# ensure we have --overwrite_output_dir
UpperCAmelCase__ = re.sub("--overwrite_output_dir\s+", "", args.base_cmd )
args.base_cmd += " --overwrite_output_dir"
return [sys.executable] + shlex.split(args.base_cmd )
def lowerCAmelCase_ ( __A, __A, __A, __A, __A, __A, __A ) -> List[Any]:
'''simple docstring'''
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] )}, )
UpperCAmelCase__ = subprocess.run(__A, capture_output=__A, text=__A )
if verbose:
print("STDOUT", result.stdout )
print("STDERR", result.stderr )
# save the streams
UpperCAmelCase__ = variation.replace(" ", "-" )
with open(Path(__A ) / f"""log.{prefix}.stdout.txt""", "w" ) as f:
f.write(result.stdout )
with open(Path(__A ) / 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:
UpperCAmelCase__ = json.load(__A )
# filter out just the keys we want
return {k: v for k, v in metrics.items() if k in metric_keys}
def lowerCAmelCase_ ( __A, __A, __A, __A, __A, __A, __A, __A, __A, __A, ) -> int:
'''simple docstring'''
UpperCAmelCase__ = []
UpperCAmelCase__ = []
UpperCAmelCase__ = f"""{id}: {variation:<{longest_variation_len}}"""
UpperCAmelCase__ = f"""{preamble}: """
UpperCAmelCase__ = set(report_metric_keys + [target_metric_key] )
for i in tqdm(range(__A ), desc=__A, leave=__A ):
UpperCAmelCase__ = process_run_single(
__A, __A, __A, __A, __A, __A, __A )
UpperCAmelCase__ = single_run_metrics[target_metric_key]
if not math.isnan(__A ):
metrics.append(__A )
results.append(__A )
outcome += "✓"
else:
outcome += "✘"
UpperCAmelCase__ = f"""\33[2K\r{outcome}"""
if len(__A ) > 0:
UpperCAmelCase__ = {k: fmean([x[k] for x in metrics] ) for k in metrics[0].keys()}
UpperCAmelCase__ = round(mean_metrics[target_metric_key], 2 )
UpperCAmelCase__ = f"""{outcome} {mean_target}"""
if len(__A ) > 1:
results_str += f""" {tuple(round(__A, 2 ) for x in results )}"""
print(__A )
UpperCAmelCase__ = variation
return mean_metrics
else:
print(__A )
return {variation_key: variation, target_metric_key: nan}
def lowerCAmelCase_ ( ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase__ = torch.cuda.get_device_properties(torch.device("cuda" ) )
return f"""
Datetime : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S" )}
Software:
transformers: {transformers.__version__}
torch : {torch.__version__}
cuda : {torch.version.cuda}
python : {platform.python_version()}
Hardware:
{torch.cuda.device_count()} GPUs : {properties.name}, {properties.total_memory/2**30:0.2f}GB
"""
def lowerCAmelCase_ ( __A, __A, __A, __A, __A ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase__ = pd.DataFrame(__A )
UpperCAmelCase__ = "variation"
UpperCAmelCase__ = "diff_%"
UpperCAmelCase__ = nan
if base_variation is not None and len(df[df[variation_key] == base_variation] ):
# this may still return nan
UpperCAmelCase__ = df.loc[df[variation_key] == base_variation][target_metric_key].item()
if math.isnan(__A ):
# as a fallback, use the minimal value as the sentinel
UpperCAmelCase__ = df.loc[df[target_metric_key] != nan][target_metric_key].min()
# create diff column if possible
if not math.isnan(__A ):
UpperCAmelCase__ = df.apply(
lambda __A : 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
UpperCAmelCase__ = [variation_key, target_metric_key, diff_key, *report_metric_keys]
UpperCAmelCase__ = df.reindex(__A, axis="columns" ) # reorder cols
# capitalize
UpperCAmelCase__ = df.rename(str.capitalize, axis="columns" )
# make the cols as narrow as possible
UpperCAmelCase__ = df.rename(lambda __A : c.replace("_", "<br>" ), axis="columns" )
UpperCAmelCase__ = df.rename(lambda __A : c.replace("_", "\n" ), axis="columns" )
UpperCAmelCase__ = ["", "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=__A, 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=__A, floatfmt=".2f" )]
print("\n\n".join(__A ) )
def lowerCAmelCase_ ( ) -> int:
'''simple docstring'''
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument(
"--base-cmd", default=__A, type=__A, required=__A, help="Base cmd", )
parser.add_argument(
"--variations", default=__A, type=__A, nargs="+", required=__A, help="Multi-dimensional variations, example: '|--fp16|--bf16' '|--tf32'", )
parser.add_argument(
"--base-variation", default=__A, type=__A, 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=__A, type=__A, required=__A, help="Target metric key in output_dir/all_results.json, e.g., train_samples_per_second", )
parser.add_argument(
"--report-metric-keys", default="", type=__A, 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=__A, help="How many times to re-run each variation - an average will be reported", )
parser.add_argument(
"--output_dir", default="output_benchmark", type=__A, 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=__A, action="store_true", help="Whether to show the outputs of each run or just the benchmark progress", )
UpperCAmelCase__ = parser.parse_args()
UpperCAmelCase__ = args.output_dir
Path(__A ).mkdir(exist_ok=__A )
UpperCAmelCase__ = get_base_command(__A, __A )
# split each dimension into its --foo variations
UpperCAmelCase__ = [list(map(str.strip, re.split(r"\|", __A ) ) ) 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
UpperCAmelCase__ = list(map(str.strip, map(" ".join, itertools.product(*__A ) ) ) )
UpperCAmelCase__ = max(len(__A ) for x in variations )
# split wanted keys
UpperCAmelCase__ = args.report_metric_keys.split()
# capture prints into a log file for convenience
UpperCAmelCase__ = 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}""" )
UpperCAmelCase__ = Tee(__A )
print(f"""\n*** Running {len(__A )} benchmarks:""" )
print(f"""Base command: {" ".join(__A )}""" )
UpperCAmelCase__ = "variation"
UpperCAmelCase__ = []
for id, variation in enumerate(tqdm(__A, desc="Total completion: ", leave=__A ) ):
UpperCAmelCase__ = base_cmd + variation.split()
results.append(
process_run(
id + 1, __A, __A, __A, __A, args.target_metric_key, __A, args.repeat_times, __A, args.verbose, ) )
process_results(__A, args.target_metric_key, __A, args.base_variation, __A )
if __name__ == "__main__":
main()
| 486 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"
),
}
class A ( _UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase = 'gptsan-japanese'
lowerCamelCase = [
'past_key_values',
]
lowerCamelCase = {
'hidden_size': 'd_model',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : List[str],lowercase_ : str=3_6_0_0_0,lowercase_ : Any=1_2_8_0,lowercase_ : int=1_0_2_4,lowercase_ : str=8_1_9_2,lowercase_ : Union[str, Any]=4_0_9_6,lowercase_ : Optional[int]=1_2_8,lowercase_ : Optional[Any]=1_0,lowercase_ : int=0,lowercase_ : int=1_6,lowercase_ : Any=1_6,lowercase_ : Optional[Any]=1_2_8,lowercase_ : Any=0.0,lowercase_ : Any=1E-5,lowercase_ : Tuple=False,lowercase_ : Dict=0.0,lowercase_ : Union[str, Any]="float32",lowercase_ : Union[str, Any]=False,lowercase_ : List[Any]=False,lowercase_ : Union[str, Any]=False,lowercase_ : str=0.002,lowercase_ : Optional[Any]=False,lowercase_ : str=True,lowercase_ : Dict=3_5_9_9_8,lowercase_ : Any=3_5_9_9_5,lowercase_ : Union[str, Any]=3_5_9_9_9,**lowercase_ : Optional[Any],)-> Dict:
'''simple docstring'''
A__ = vocab_size
A__ = max_position_embeddings
A__ = d_model
A__ = d_ff
A__ = d_ext
A__ = d_spout
A__ = num_switch_layers
A__ = num_ext_layers
A__ = num_switch_layers + num_ext_layers
A__ = num_heads
A__ = num_experts
A__ = expert_capacity
A__ = dropout_rate
A__ = layer_norm_epsilon
A__ = router_bias
A__ = router_jitter_noise
A__ = router_dtype
A__ = router_ignore_padding_tokens
A__ = output_hidden_states
A__ = output_attentions
A__ = initializer_factor
A__ = output_router_logits
A__ = use_cache
super().__init__(
separator_token_id=lowercase_,pad_token_id=lowercase_,eos_token_id=lowercase_,**lowercase_,)
| 708 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
"google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
"google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class A ( _UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase = 'big_bird'
def __init__( self : Dict,lowercase_ : Optional[int]=5_0_3_5_8,lowercase_ : Union[str, Any]=7_6_8,lowercase_ : str=1_2,lowercase_ : int=1_2,lowercase_ : Optional[int]=3_0_7_2,lowercase_ : Dict="gelu_new",lowercase_ : Dict=0.1,lowercase_ : Any=0.1,lowercase_ : Optional[Any]=4_0_9_6,lowercase_ : List[Any]=2,lowercase_ : List[str]=0.02,lowercase_ : List[Any]=1E-12,lowercase_ : Dict=True,lowercase_ : List[Any]=0,lowercase_ : int=1,lowercase_ : Optional[int]=2,lowercase_ : List[Any]=6_6,lowercase_ : List[Any]="block_sparse",lowercase_ : List[Any]=True,lowercase_ : Union[str, Any]=False,lowercase_ : List[str]=6_4,lowercase_ : Optional[Any]=3,lowercase_ : Optional[int]=None,**lowercase_ : Optional[int],)-> str:
'''simple docstring'''
super().__init__(
pad_token_id=lowercase_,bos_token_id=lowercase_,eos_token_id=lowercase_,sep_token_id=lowercase_,**lowercase_,)
A__ = vocab_size
A__ = max_position_embeddings
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__ = initializer_range
A__ = type_vocab_size
A__ = layer_norm_eps
A__ = use_cache
A__ = rescale_embeddings
A__ = attention_type
A__ = use_bias
A__ = block_size
A__ = num_random_blocks
A__ = classifier_dropout
class A ( _UpperCAmelCase ):
"""simple docstring"""
@property
def snake_case__ ( self : List[str] )-> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
A__ = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
A__ = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 586 | 0 |
import argparse
import os
import re
A : Any = """src/transformers/models/auto"""
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
A : Dict = re.compile(R'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
A : List[str] = re.compile(R'''\s*\(\s*\"(\S[^\"]+)\"''')
def __lowerCamelCase ( __a :int , __a :bool = False ) -> List[str]:
"""simple docstring"""
with open(_lowercase , """r""" , encoding="""utf-8""" ) as f:
A__ = f.read()
A__ = content.split("""\n""" )
A__ = []
A__ = 0
while line_idx < len(_lowercase ):
if _re_intro_mapping.search(lines[line_idx] ) is not None:
A__ = len(re.search(R"""^(\s*)\S""" , lines[line_idx] ).groups()[0] ) + 8
# Start of a new mapping!
while not lines[line_idx].startswith(""" """ * indent + """(""" ):
new_lines.append(lines[line_idx] )
line_idx += 1
A__ = []
while lines[line_idx].strip() != "]":
# Blocks either fit in one line or not
if lines[line_idx].strip() == "(":
A__ = line_idx
while not lines[line_idx].startswith(""" """ * indent + """)""" ):
line_idx += 1
blocks.append("""\n""".join(lines[start_idx : line_idx + 1] ) )
else:
blocks.append(lines[line_idx] )
line_idx += 1
# Sort blocks by their identifiers
A__ = sorted(_lowercase , key=lambda __a : _re_identifier.search(_lowercase ).groups()[0] )
new_lines += blocks
else:
new_lines.append(lines[line_idx] )
line_idx += 1
if overwrite:
with open(_lowercase , """w""" , encoding="""utf-8""" ) as f:
f.write("""\n""".join(_lowercase ) )
elif "\n".join(_lowercase ) != content:
return True
def __lowerCamelCase ( __a :bool = False ) -> List[Any]:
"""simple docstring"""
A__ = [os.path.join(_lowercase , _lowercase ) for f in os.listdir(_lowercase ) if f.endswith(""".py""" )]
A__ = [sort_auto_mapping(_lowercase , overwrite=_lowercase ) for fname in fnames]
if not overwrite and any(_lowercase ):
A__ = [f for f, d in zip(_lowercase , _lowercase ) if d]
raise ValueError(
F'The following files have auto mappings that need sorting: {", ".join(_lowercase )}. Run `make style` to fix'
""" this.""" )
if __name__ == "__main__":
A : Tuple = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
A : List[Any] = parser.parse_args()
sort_all_auto_mappings(not args.check_only)
| 176 |
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( _UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
_A = PriorTransformer
_A = "hidden_states"
@property
def snake_case__( self: Union[str, Any] ):
lowercase__ : List[Any] = 4
lowercase__ : Optional[int] = 8
lowercase__ : List[str] = 7
lowercase__ : Optional[Any] = floats_tensor((batch_size, embedding_dim) ).to(lowerCamelCase_ )
lowercase__ : Union[str, Any] = floats_tensor((batch_size, embedding_dim) ).to(lowerCamelCase_ )
lowercase__ : Any = floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(lowerCamelCase_ )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def snake_case__( self: int, lowerCamelCase_: Dict=0 ):
torch.manual_seed(lowerCamelCase_ )
lowercase__ : Tuple = 4
lowercase__ : List[Any] = 8
lowercase__ : List[str] = 7
lowercase__ : Union[str, Any] = torch.randn((batch_size, embedding_dim) ).to(lowerCamelCase_ )
lowercase__ : Optional[int] = torch.randn((batch_size, embedding_dim) ).to(lowerCamelCase_ )
lowercase__ : List[str] = torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowerCamelCase_ )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
@property
def snake_case__( self: str ):
return (4, 8)
@property
def snake_case__( self: List[str] ):
return (4, 8)
def snake_case__( self: Dict ):
lowercase__ : int = {
'num_attention_heads': 2,
'attention_head_dim': 4,
'num_layers': 2,
'embedding_dim': 8,
'num_embeddings': 7,
'additional_embeddings': 4,
}
lowercase__ : Optional[Any] = self.dummy_input
return init_dict, inputs_dict
def snake_case__( self: int ):
lowercase__ , lowercase__ : Dict = PriorTransformer.from_pretrained(
'hf-internal-testing/prior-dummy', output_loading_info=lowerCamelCase_ )
self.assertIsNotNone(lowerCamelCase_ )
self.assertEqual(len(loading_info['missing_keys'] ), 0 )
model.to(lowerCamelCase_ )
lowercase__ : Tuple = model(**self.dummy_input )[0]
assert hidden_states is not None, "Make sure output is not None"
def snake_case__( self: str ):
lowercase__ , lowercase__ : List[Any] = self.prepare_init_args_and_inputs_for_common()
lowercase__ : Optional[int] = self.model_class(**lowerCamelCase_ )
lowercase__ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Dict = [*signature.parameters.keys()]
lowercase__ : List[str] = ['hidden_states', 'timestep']
self.assertListEqual(arg_names[:2], lowerCamelCase_ )
def snake_case__( self: Union[str, Any] ):
lowercase__ : str = PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy' )
lowercase__ : Optional[int] = model.to(lowerCamelCase_ )
if hasattr(lowerCamelCase_, 'set_default_attn_processor' ):
model.set_default_attn_processor()
lowercase__ : List[str] = self.get_dummy_seed_input()
with torch.no_grad():
lowercase__ : str = model(**lowerCamelCase_ )[0]
lowercase__ : int = output[0, :5].flatten().cpu()
print(lowerCamelCase_ )
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
lowercase__ : List[str] = torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9] )
self.assertTrue(torch_all_close(lowerCamelCase_, lowerCamelCase_, rtol=1E-2 ) )
@slow
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def snake_case__( self: Tuple, lowerCamelCase_: Union[str, Any]=1, lowerCamelCase_: Tuple=768, lowerCamelCase_: Dict=77, lowerCamelCase_: Union[str, Any]=0 ):
torch.manual_seed(lowerCamelCase_ )
lowercase__ : Dict = batch_size
lowercase__ : Dict = embedding_dim
lowercase__ : Dict = num_embeddings
lowercase__ : int = torch.randn((batch_size, embedding_dim) ).to(lowerCamelCase_ )
lowercase__ : str = torch.randn((batch_size, embedding_dim) ).to(lowerCamelCase_ )
lowercase__ : List[str] = torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowerCamelCase_ )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def snake_case__( self: str ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@parameterized.expand(
[
# fmt: off
[13, [-0.5_8_6_1, 0.1_2_8_3, -0.0_9_3_1, 0.0_8_8_2, 0.4_4_7_6, 0.1_3_2_9, -0.0_4_9_8, 0.0_6_4_0]],
[37, [-0.4_9_1_3, 0.0_1_1_0, -0.0_4_8_3, 0.0_5_4_1, 0.4_9_5_4, -0.0_1_7_0, 0.0_3_5_4, 0.1_6_5_1]],
# fmt: on
] )
def snake_case__( self: Optional[Any], lowerCamelCase_: List[str], lowerCamelCase_: str ):
lowercase__ : List[Any] = PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior', subfolder='prior' )
model.to(lowerCamelCase_ )
lowercase__ : Optional[int] = self.get_dummy_seed_input(seed=lowerCamelCase_ )
with torch.no_grad():
lowercase__ : List[str] = model(**lowerCamelCase_ )[0]
assert list(sample.shape ) == [1, 768]
lowercase__ : Union[str, Any] = sample[0, :8].flatten().cpu()
print(lowerCamelCase_ )
lowercase__ : Optional[Any] = torch.tensor(lowerCamelCase_ )
assert torch_all_close(lowerCamelCase_, lowerCamelCase_, atol=1E-3 )
| 266 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( __a ) -> bool:
'''simple docstring'''
_UpperCamelCase :str =[int(__a ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(__a ) == 4 and all(0 <= int(__a ) <= 2_54 for octet in octets )
if __name__ == "__main__":
_lowerCamelCase : List[str] = input().strip()
_lowerCamelCase : Optional[Any] = """valid""" if is_ip_va_address_valid(ip) else """invalid"""
print(f"{ip} is a {valid_or_invalid} IP v4 address.") | 512 | '''simple docstring'''
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class lowerCamelCase__ ( __snake_case , unittest.TestCase ):
__UpperCAmelCase = BertTokenizer
__UpperCAmelCase = BertTokenizerFast
__UpperCAmelCase = True
__UpperCAmelCase = True
__UpperCAmelCase = filter_non_english
def _UpperCamelCase ( self ) -> str:
"""simple docstring"""
super().setUp()
_UpperCamelCase :Optional[Any] =[
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
_UpperCamelCase :Optional[Any] =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def _UpperCamelCase ( self , lowerCAmelCase__ ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase :Union[str, Any] ="""UNwant\u00E9d,running"""
_UpperCamelCase :Optional[int] ="""unwanted, running"""
return input_text, output_text
def _UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
_UpperCamelCase :Tuple =self.tokenizer_class(self.vocab_file )
_UpperCamelCase :int =tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(lowerCAmelCase__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , [9, 6, 7, 12, 10, 11] )
def _UpperCamelCase ( self ) -> int:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
_UpperCamelCase :List[str] =self.get_tokenizer()
_UpperCamelCase :Dict =self.get_rust_tokenizer()
_UpperCamelCase :Tuple ="""UNwant\u00E9d,running"""
_UpperCamelCase :Optional[Any] =tokenizer.tokenize(lowerCAmelCase__ )
_UpperCamelCase :Tuple =rust_tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase :Any =tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
_UpperCamelCase :Union[str, Any] =rust_tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase :Any =self.get_rust_tokenizer()
_UpperCamelCase :Optional[int] =tokenizer.encode(lowerCAmelCase__ )
_UpperCamelCase :int =rust_tokenizer.encode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# With lower casing
_UpperCamelCase :str =self.get_tokenizer(do_lower_case=lowerCAmelCase__ )
_UpperCamelCase :Optional[Any] =self.get_rust_tokenizer(do_lower_case=lowerCAmelCase__ )
_UpperCamelCase :Union[str, Any] ="""UNwant\u00E9d,running"""
_UpperCamelCase :Union[str, Any] =tokenizer.tokenize(lowerCAmelCase__ )
_UpperCamelCase :Tuple =rust_tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase :Dict =tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
_UpperCamelCase :str =rust_tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase :Optional[int] =self.get_rust_tokenizer()
_UpperCamelCase :Dict =tokenizer.encode(lowerCAmelCase__ )
_UpperCamelCase :Dict =rust_tokenizer.encode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def _UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase :Union[str, Any] =BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )
def _UpperCamelCase ( self ) -> int:
"""simple docstring"""
_UpperCamelCase :str =BasicTokenizer(do_lower_case=lowerCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def _UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase :List[Any] =BasicTokenizer(do_lower_case=lowerCAmelCase__ , strip_accents=lowerCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] )
def _UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase :int =BasicTokenizer(do_lower_case=lowerCAmelCase__ , strip_accents=lowerCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def _UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase :Tuple =BasicTokenizer(do_lower_case=lowerCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def _UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
_UpperCamelCase :Dict =BasicTokenizer(do_lower_case=lowerCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def _UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
_UpperCamelCase :Any =BasicTokenizer(do_lower_case=lowerCAmelCase__ , strip_accents=lowerCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def _UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase :Optional[Any] =BasicTokenizer(do_lower_case=lowerCAmelCase__ , strip_accents=lowerCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def _UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase :Optional[Any] =BasicTokenizer(do_lower_case=lowerCAmelCase__ , never_split=["""[UNK]"""] )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )
def _UpperCamelCase ( self ) -> int:
"""simple docstring"""
_UpperCamelCase :Optional[int] =BasicTokenizer()
_UpperCamelCase :List[Any] ="""a\n'll !!to?'d of, can't."""
_UpperCamelCase :Optional[Any] =["""a""", """'""", """ll""", """!""", """!""", """to""", """?""", """'""", """d""", """of""", """,""", """can""", """'""", """t""", """."""]
self.assertListEqual(tokenizer.tokenize(lowerCAmelCase__ ) , lowerCAmelCase__ )
def _UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase :Tuple =["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
_UpperCamelCase :Tuple ={}
for i, token in enumerate(lowerCAmelCase__ ):
_UpperCamelCase :Any =i
_UpperCamelCase :List[Any] =WordpieceTokenizer(vocab=lowerCAmelCase__ , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] )
def _UpperCamelCase ( self ) -> int:
"""simple docstring"""
self.assertTrue(_is_whitespace(""" """ ) )
self.assertTrue(_is_whitespace("""\t""" ) )
self.assertTrue(_is_whitespace("""\r""" ) )
self.assertTrue(_is_whitespace("""\n""" ) )
self.assertTrue(_is_whitespace("""\u00A0""" ) )
self.assertFalse(_is_whitespace("""A""" ) )
self.assertFalse(_is_whitespace("""-""" ) )
def _UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
self.assertTrue(_is_control("""\u0005""" ) )
self.assertFalse(_is_control("""A""" ) )
self.assertFalse(_is_control(""" """ ) )
self.assertFalse(_is_control("""\t""" ) )
self.assertFalse(_is_control("""\r""" ) )
def _UpperCamelCase ( self ) -> Any:
"""simple docstring"""
self.assertTrue(_is_punctuation("""-""" ) )
self.assertTrue(_is_punctuation("""$""" ) )
self.assertTrue(_is_punctuation("""`""" ) )
self.assertTrue(_is_punctuation(""".""" ) )
self.assertFalse(_is_punctuation("""A""" ) )
self.assertFalse(_is_punctuation(""" """ ) )
def _UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase :Dict =self.get_tokenizer()
_UpperCamelCase :Optional[Any] =self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(lowerCAmelCase__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
self.assertListEqual(
[rust_tokenizer.tokenize(lowerCAmelCase__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
@slow
def _UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase :List[Any] =self.tokenizer_class.from_pretrained("""bert-base-uncased""" )
_UpperCamelCase :List[str] =tokenizer.encode("""sequence builders""" , add_special_tokens=lowerCAmelCase__ )
_UpperCamelCase :str =tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowerCAmelCase__ )
_UpperCamelCase :Tuple =tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ )
_UpperCamelCase :Any =tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ , lowerCAmelCase__ )
assert encoded_sentence == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_a + [102]
def _UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_UpperCamelCase :Tuple =self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
_UpperCamelCase :int =f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
_UpperCamelCase :Any =tokenizer_r.encode_plus(
lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , )
_UpperCamelCase :Union[str, Any] =tokenizer_r.do_lower_case if hasattr(lowerCAmelCase__ , """do_lower_case""" ) else False
_UpperCamelCase :str =(
[
((0, 0), tokenizer_r.cls_token),
((0, 1), """A"""),
((1, 2), ""","""),
((3, 5), """na"""),
((5, 6), """##ï"""),
((6, 8), """##ve"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """Allen"""),
((21, 23), """##NL"""),
((23, 24), """##P"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), """a"""),
((1, 2), ""","""),
((3, 8), """naive"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """allen"""),
((21, 23), """##nl"""),
((23, 24), """##p"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["""offset_mapping"""] )
def _UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase :int =["""的""", """人""", """有"""]
_UpperCamelCase :int ="""""".join(lowerCAmelCase__ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_UpperCamelCase :Optional[int] =True
_UpperCamelCase :Tuple =self.tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
_UpperCamelCase :Any =self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
_UpperCamelCase :Union[str, Any] =tokenizer_p.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
_UpperCamelCase :str =tokenizer_r.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
_UpperCamelCase :Any =tokenizer_r.convert_ids_to_tokens(lowerCAmelCase__ )
_UpperCamelCase :Dict =tokenizer_p.convert_ids_to_tokens(lowerCAmelCase__ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase :Optional[Any] =False
_UpperCamelCase :int =self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
_UpperCamelCase :Dict =self.tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
_UpperCamelCase :Any =tokenizer_r.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
_UpperCamelCase :List[str] =tokenizer_p.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
_UpperCamelCase :Union[str, Any] =tokenizer_r.convert_ids_to_tokens(lowerCAmelCase__ )
_UpperCamelCase :str =tokenizer_p.convert_ids_to_tokens(lowerCAmelCase__ )
# it is expected that only the first Chinese character is not preceded by "##".
_UpperCamelCase :Optional[int] =[
f'''##{token}''' if idx != 0 else token for idx, token in enumerate(lowerCAmelCase__ )
]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ ) | 512 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
),
"""microsoft/deberta-v2-xxlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
),
}
class lowercase__ ( A_ ):
__UpperCAmelCase = '''deberta-v2'''
def __init__( self , SCREAMING_SNAKE_CASE=12_8100 , SCREAMING_SNAKE_CASE=1536 , SCREAMING_SNAKE_CASE=24 , SCREAMING_SNAKE_CASE=24 , SCREAMING_SNAKE_CASE=6144 , SCREAMING_SNAKE_CASE="gelu" , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=512 , SCREAMING_SNAKE_CASE=0 , SCREAMING_SNAKE_CASE=0.02 , SCREAMING_SNAKE_CASE=1e-7 , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=-1 , SCREAMING_SNAKE_CASE=0 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=0 , SCREAMING_SNAKE_CASE="gelu" , **SCREAMING_SNAKE_CASE , ) -> Tuple:
super().__init__(**SCREAMING_SNAKE_CASE)
_lowerCamelCase : Optional[Any] = hidden_size
_lowerCamelCase : Dict = num_hidden_layers
_lowerCamelCase : int = num_attention_heads
_lowerCamelCase : int = intermediate_size
_lowerCamelCase : List[str] = hidden_act
_lowerCamelCase : List[str] = hidden_dropout_prob
_lowerCamelCase : List[Any] = attention_probs_dropout_prob
_lowerCamelCase : List[Any] = max_position_embeddings
_lowerCamelCase : Tuple = type_vocab_size
_lowerCamelCase : Tuple = initializer_range
_lowerCamelCase : Optional[int] = relative_attention
_lowerCamelCase : Optional[int] = max_relative_positions
_lowerCamelCase : Optional[int] = pad_token_id
_lowerCamelCase : Any = position_biased_input
# Backwards compatibility
if type(SCREAMING_SNAKE_CASE) == str:
_lowerCamelCase : Optional[int] = [x.strip() for x in pos_att_type.lower().split("""|""")]
_lowerCamelCase : int = pos_att_type
_lowerCamelCase : Optional[Any] = vocab_size
_lowerCamelCase : Optional[Any] = layer_norm_eps
_lowerCamelCase : Optional[Any] = kwargs.get("""pooler_hidden_size""" , SCREAMING_SNAKE_CASE)
_lowerCamelCase : Any = pooler_dropout
_lowerCamelCase : Optional[int] = pooler_hidden_act
class lowercase__ ( A_ ):
@property
def UpperCamelCase_ ( self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_lowerCamelCase : List[str] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_lowerCamelCase : int = {0: """batch""", 1: """sequence"""}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)])
else:
return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)])
@property
def UpperCamelCase_ ( self) -> int:
return 12
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = -1 , SCREAMING_SNAKE_CASE = -1 , SCREAMING_SNAKE_CASE = -1 , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = 3 , SCREAMING_SNAKE_CASE = 40 , SCREAMING_SNAKE_CASE = 40 , SCREAMING_SNAKE_CASE = None , ) -> Mapping[str, Any]:
_lowerCamelCase : int = super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE , framework=SCREAMING_SNAKE_CASE)
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 88 |
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
lowerCamelCase = logging.get_logger(__name__)
if is_vision_available():
import PIL
class A ( UpperCamelCase_ ):
UpperCamelCase__ : List[str] =['pixel_values']
def __init__( self : Any , 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_ : str , ) -> None:
"""simple docstring"""
super().__init__(**lowercase_ )
_lowerCamelCase : Optional[int] =size if size is not None else {'shortest_edge': 224}
_lowerCamelCase : List[Any] =get_size_dict(lowercase_ , default_to_square=lowercase_ )
_lowerCamelCase : str =crop_size if crop_size is not None else {'height': 224, 'width': 224}
_lowerCamelCase : str =get_size_dict(lowercase_ , default_to_square=lowercase_ , param_name='crop_size' )
_lowerCamelCase : Dict =do_resize
_lowerCamelCase : int =size
_lowerCamelCase : Optional[Any] =resample
_lowerCamelCase : Optional[int] =do_center_crop
_lowerCamelCase : Tuple =crop_size
_lowerCamelCase : Optional[int] =do_rescale
_lowerCamelCase : Optional[Any] =rescale_factor
_lowerCamelCase : Union[str, Any] =do_normalize
_lowerCamelCase : Optional[int] =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
_lowerCamelCase : Any =image_std if image_std is not None else OPENAI_CLIP_STD
_lowerCamelCase : Any =do_convert_rgb
def lowerCamelCase ( self : int , lowercase_ : np.ndarray , lowercase_ : Dict[str, int] , lowercase_ : PILImageResampling = PILImageResampling.BICUBIC , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : Any , ) -> np.ndarray:
"""simple docstring"""
_lowerCamelCase : int =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()}''' )
_lowerCamelCase : Union[str, Any] =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 lowerCamelCase ( self : Union[str, Any] , lowercase_ : np.ndarray , lowercase_ : Dict[str, int] , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : Tuple , ) -> np.ndarray:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] =get_size_dict(lowercase_ )
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(lowercase_ , size=(size['height'], size['width']) , data_format=lowercase_ , **lowercase_ )
def lowerCamelCase ( self : str , lowercase_ : np.ndarray , lowercase_ : Union[int, float] , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : int , ) -> str:
"""simple docstring"""
return rescale(lowercase_ , scale=lowercase_ , data_format=lowercase_ , **lowercase_ )
def lowerCamelCase ( self : Optional[int] , lowercase_ : np.ndarray , lowercase_ : Union[float, List[float]] , lowercase_ : Union[float, List[float]] , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : Tuple , ) -> np.ndarray:
"""simple docstring"""
return normalize(lowercase_ , mean=lowercase_ , std=lowercase_ , data_format=lowercase_ , **lowercase_ )
def lowerCamelCase ( self : List[str] , lowercase_ : ImageInput , lowercase_ : bool = None , lowercase_ : Dict[str, int] = None , lowercase_ : PILImageResampling = None , lowercase_ : bool = None , lowercase_ : int = None , lowercase_ : bool = None , lowercase_ : float = None , lowercase_ : bool = None , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : bool = None , lowercase_ : Optional[Union[str, TensorType]] = None , lowercase_ : Optional[ChannelDimension] = ChannelDimension.FIRST , **lowercase_ : Union[str, Any] , ) -> PIL.Image.Image:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] =do_resize if do_resize is not None else self.do_resize
_lowerCamelCase : List[str] =size if size is not None else self.size
_lowerCamelCase : Any =get_size_dict(lowercase_ , param_name='size' , default_to_square=lowercase_ )
_lowerCamelCase : str =resample if resample is not None else self.resample
_lowerCamelCase : List[Any] =do_center_crop if do_center_crop is not None else self.do_center_crop
_lowerCamelCase : Optional[Any] =crop_size if crop_size is not None else self.crop_size
_lowerCamelCase : Dict =get_size_dict(lowercase_ , param_name='crop_size' , default_to_square=lowercase_ )
_lowerCamelCase : str =do_rescale if do_rescale is not None else self.do_rescale
_lowerCamelCase : Tuple =rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCamelCase : List[str] =do_normalize if do_normalize is not None else self.do_normalize
_lowerCamelCase : Tuple =image_mean if image_mean is not None else self.image_mean
_lowerCamelCase : int =image_std if image_std is not None else self.image_std
_lowerCamelCase : Tuple =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
_lowerCamelCase : Any =make_list_of_images(lowercase_ )
if not valid_images(lowercase_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
_lowerCamelCase : Tuple =[convert_to_rgb(lowercase_ ) for image in images]
# All transformations expect numpy arrays.
_lowerCamelCase : Optional[Any] =[to_numpy_array(lowercase_ ) for image in images]
if do_resize:
_lowerCamelCase : Optional[Any] =[self.resize(image=lowercase_ , size=lowercase_ , resample=lowercase_ ) for image in images]
if do_center_crop:
_lowerCamelCase : Optional[int] =[self.center_crop(image=lowercase_ , size=lowercase_ ) for image in images]
if do_rescale:
_lowerCamelCase : Optional[Any] =[self.rescale(image=lowercase_ , scale=lowercase_ ) for image in images]
if do_normalize:
_lowerCamelCase : List[Any] =[self.normalize(image=lowercase_ , mean=lowercase_ , std=lowercase_ ) for image in images]
_lowerCamelCase : List[str] =[to_channel_dimension_format(lowercase_ , lowercase_ ) for image in images]
_lowerCamelCase : Tuple ={'pixel_values': images}
return BatchFeature(data=lowercase_ , tensor_type=lowercase_ )
| 464 | 0 |
'''simple docstring'''
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
A ={
'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt',
'tiny': 'https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt',
'base.en': 'https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt',
'base': 'https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt',
'small.en': 'https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt',
'small': 'https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt',
'medium.en': 'https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt',
'medium': 'https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt',
'large': 'https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt',
'large-v2': 'https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt',
}
def snake_case_ (_a : Tuple ):
UpperCAmelCase = ['''layers''', '''blocks''']
for k in ignore_keys:
state_dict.pop(_a , _a )
A ={
'blocks': 'layers',
'mlp.0': 'fc1',
'mlp.2': 'fc2',
'mlp_ln': 'final_layer_norm',
'.attn.query': '.self_attn.q_proj',
'.attn.key': '.self_attn.k_proj',
'.attn.value': '.self_attn.v_proj',
'.attn_ln': '.self_attn_layer_norm',
'.attn.out': '.self_attn.out_proj',
'.cross_attn.query': '.encoder_attn.q_proj',
'.cross_attn.key': '.encoder_attn.k_proj',
'.cross_attn.value': '.encoder_attn.v_proj',
'.cross_attn_ln': '.encoder_attn_layer_norm',
'.cross_attn.out': '.encoder_attn.out_proj',
'decoder.ln.': 'decoder.layer_norm.',
'encoder.ln.': 'encoder.layer_norm.',
'token_embedding': 'embed_tokens',
'encoder.positional_embedding': 'encoder.embed_positions.weight',
'decoder.positional_embedding': 'decoder.embed_positions.weight',
'ln_post': 'layer_norm',
}
def snake_case_ (_a : List[Any] ):
UpperCAmelCase = list(s_dict.keys() )
for key in keys:
UpperCAmelCase = key
for k, v in WHISPER_MAPPING.items():
if k in key:
UpperCAmelCase = new_key.replace(_a , _a )
print(F"{key} -> {new_key}" )
UpperCAmelCase = s_dict.pop(_a )
return s_dict
def snake_case_ (_a : int ):
UpperCAmelCase , UpperCAmelCase = emb.weight.shape
UpperCAmelCase = nn.Linear(_a , _a , bias=_a )
UpperCAmelCase = emb.weight.data
return lin_layer
def snake_case_ (_a : str , _a : str ):
os.makedirs(_a , exist_ok=_a )
UpperCAmelCase = os.path.basename(_a )
UpperCAmelCase = url.split('''/''' )[-2]
UpperCAmelCase = os.path.join(_a , _a )
if os.path.exists(_a ) and not os.path.isfile(_a ):
raise RuntimeError(F"{download_target} exists and is not a regular file" )
if os.path.isfile(_a ):
UpperCAmelCase = open(_a , '''rb''' ).read()
if hashlib.shaaaa(_a ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(F"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" )
with urllib.request.urlopen(_a ) as source, open(_a , '''wb''' ) as output:
with tqdm(
total=int(source.info().get('''Content-Length''' ) ) , ncols=8_0 , unit='''iB''' , unit_scale=_a , unit_divisor=1_0_2_4 ) as loop:
while True:
UpperCAmelCase = source.read(8_1_9_2 )
if not buffer:
break
output.write(_a )
loop.update(len(_a ) )
UpperCAmelCase = open(_a , '''rb''' ).read()
if hashlib.shaaaa(_a ).hexdigest() != expected_shaaaa:
raise RuntimeError(
'''Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.''' )
return model_bytes
def snake_case_ (_a : int , _a : Optional[Any] ):
if ".pt" not in checkpoint_path:
UpperCAmelCase = _download(_MODELS[checkpoint_path] )
else:
UpperCAmelCase = torch.load(_a , map_location='''cpu''' )
UpperCAmelCase = original_checkpoint['''dims''']
UpperCAmelCase = original_checkpoint['''model_state_dict''']
UpperCAmelCase = state_dict['''decoder.token_embedding.weight''']
remove_ignore_keys_(_a )
rename_keys(_a )
UpperCAmelCase = True
UpperCAmelCase = state_dict['''decoder.layers.0.fc1.weight'''].shape[0]
UpperCAmelCase = WhisperConfig(
vocab_size=dimensions['''n_vocab'''] , encoder_ffn_dim=_a , decoder_ffn_dim=_a , num_mel_bins=dimensions['''n_mels'''] , d_model=dimensions['''n_audio_state'''] , max_target_positions=dimensions['''n_text_ctx'''] , encoder_layers=dimensions['''n_audio_layer'''] , encoder_attention_heads=dimensions['''n_audio_head'''] , decoder_layers=dimensions['''n_text_layer'''] , decoder_attention_heads=dimensions['''n_text_state'''] , max_source_positions=dimensions['''n_audio_ctx'''] , )
UpperCAmelCase = WhisperForConditionalGeneration(_a )
UpperCAmelCase , UpperCAmelCase = model.model.load_state_dict(_a , strict=_a )
if len(_a ) > 0 and not set(_a ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
F" but all the following weights are missing {missing}" )
if tie_embeds:
UpperCAmelCase = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
UpperCAmelCase = proj_out_weights
model.save_pretrained(_a )
if __name__ == "__main__":
A =argparse.ArgumentParser()
# # Required parameters
parser.add_argument('--checkpoint_path', type=str, help='Patht to the downloaded checkpoints')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
A =parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 358 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class _a ( unittest.TestCase ):
def __init__( self : List[Any] , lowercase : Dict , lowercase : List[str]=13 , lowercase : str=7 , lowercase : List[str]=True , lowercase : List[str]=True , lowercase : Optional[Any]=True , lowercase : Optional[Any]=True , lowercase : Any=99 , lowercase : Any=32 , lowercase : Any=5 , lowercase : Tuple=4 , lowercase : List[Any]=37 , lowercase : List[Any]="gelu" , lowercase : int=0.1 , lowercase : Any=0.1 , lowercase : Optional[int]=512 , lowercase : List[str]=16 , lowercase : Union[str, Any]=2 , lowercase : int=0.02 , lowercase : int=4 , ):
'''simple docstring'''
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = seq_length
UpperCAmelCase = is_training
UpperCAmelCase = use_attention_mask
UpperCAmelCase = use_token_type_ids
UpperCAmelCase = use_labels
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = type_vocab_size
UpperCAmelCase = type_sequence_label_size
UpperCAmelCase = initializer_range
UpperCAmelCase = num_choices
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase = None
if self.use_attention_mask:
UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase = None
if self.use_token_type_ids:
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase = RobertaPreLayerNormConfig(
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 , )
return config, input_ids, token_type_ids, attention_mask
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = config_and_inputs
UpperCAmelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = config_and_inputs
UpperCAmelCase = True
UpperCAmelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class _a ( __a , unittest.TestCase ):
__a : Any = True
__a : str = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = FlaxRobertaPreLayerNormModelTester(self )
@slow
def A ( self : Optional[int] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase = model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' , from_pt=lowercase )
UpperCAmelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowercase )
@require_flax
class _a ( unittest.TestCase ):
@slow
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' , from_pt=lowercase )
UpperCAmelCase = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
UpperCAmelCase = model(lowercase )[0]
UpperCAmelCase = [1, 11, 50_265]
self.assertEqual(list(output.shape ) , lowercase )
# compare the actual values for a slice.
UpperCAmelCase = np.array(
[[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , lowercase , atol=1E-4 ) )
@slow
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' , from_pt=lowercase )
UpperCAmelCase = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
UpperCAmelCase = model(lowercase )[0]
# compare the actual values for a slice.
UpperCAmelCase = np.array(
[[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , lowercase , atol=1E-4 ) )
| 358 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"""sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class __a ( __magic_name__ ):
"""simple docstring"""
__UpperCamelCase : Any = 'poolformer'
def __init__( self , snake_case=3 , snake_case=16 , snake_case=16 , snake_case=3 , snake_case=4.0 , snake_case=[2, 2, 6, 2] , snake_case=[64, 128, 320, 512] , snake_case=[7, 3, 3, 3] , snake_case=[4, 2, 2, 2] , snake_case=[2, 1, 1, 1] , snake_case=4 , snake_case=0.0 , snake_case="gelu" , snake_case=True , snake_case=1e-5 , snake_case=0.02 , **snake_case , ):
"""simple docstring"""
lowerCAmelCase__ : str = num_channels
lowerCAmelCase__ : int = patch_size
lowerCAmelCase__ : Optional[Any] = stride
lowerCAmelCase__ : List[Any] = padding
lowerCAmelCase__ : Tuple = pool_size
lowerCAmelCase__ : Any = hidden_sizes
lowerCAmelCase__ : Optional[int] = mlp_ratio
lowerCAmelCase__ : List[Any] = depths
lowerCAmelCase__ : Union[str, Any] = patch_sizes
lowerCAmelCase__ : Optional[Any] = strides
lowerCAmelCase__ : Tuple = num_encoder_blocks
lowerCAmelCase__ : int = drop_path_rate
lowerCAmelCase__ : str = hidden_act
lowerCAmelCase__ : Union[str, Any] = use_layer_scale
lowerCAmelCase__ : Optional[Any] = layer_scale_init_value
lowerCAmelCase__ : List[str] = initializer_range
super().__init__(**snake_case )
class __a ( __magic_name__ ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] = version.parse('1.11' )
@property
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
return 2e-3
| 453 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( lowercase__ ) -> float:
return 1_0 - x * x
def SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(lowercase__ ) * equation(lowercase__ ) >= 0:
raise ValueError("Wrong space!" )
lowerCAmelCase__ : Union[str, Any] = a
while (b - a) >= 0.01:
# Find middle point
lowerCAmelCase__ : int = (a + b) / 2
# Check if middle point is root
if equation(lowercase__ ) == 0.0:
break
# Decide the side to repeat the steps
if equation(lowercase__ ) * equation(lowercase__ ) < 0:
lowerCAmelCase__ : str = c
else:
lowerCAmelCase__ : List[str] = c
return c
if __name__ == "__main__":
import doctest
doctest.testmod()
print(bisection(-2, 5))
print(bisection(0, 6))
| 453 | 1 |
def lowerCamelCase_ ( _lowercase = 100 ) -> int:
__A : List[Any] = set()
__A : Any = 0
__A : Optional[Any] = n + 1 # maximum limit
for a in range(2 , _lowercase ):
for b in range(2 , _lowercase ):
__A : Optional[int] = a**b # calculates the current power
collect_powers.add(_lowercase ) # adds the result to the set
return len(_lowercase )
if __name__ == "__main__":
print('Number of terms ', solution(int(str(input()).strip())))
| 387 | import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def lowerCamelCase_ ( _lowercase , _lowercase , _lowercase , _lowercase=5 ) -> str:
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count("<mask>" ) == 1
__A : Optional[Any] = torch.tensor(tokenizer.encode(_lowercase , add_special_tokens=_lowercase ) ).unsqueeze(0 ) # Batch size 1
__A : List[str] = model(_lowercase )[0] # The last hidden-state is the first element of the output tuple
__A : Optional[int] = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
__A : Optional[Any] = logits[0, masked_index, :]
__A : int = logits.softmax(dim=0 )
__A , __A : Union[str, Any] = prob.topk(k=_lowercase , dim=0 )
__A : Dict = " ".join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(_lowercase ) )] )
__A : Dict = tokenizer.mask_token
__A : str = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" " ) ):
__A : int = predicted_token_bpe.replace("\u2581" , " " )
if " {0}".format(_lowercase ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(" {0}".format(_lowercase ) , _lowercase ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(_lowercase , _lowercase ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
UpperCamelCase = CamembertTokenizer.from_pretrained('camembert-base')
UpperCamelCase = CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()
UpperCamelCase = 'Le camembert est <mask> :)'
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 387 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase =["model.decoder.embed_positions.weights"]
def __lowerCAmelCase ( UpperCamelCase__ ) -> Union[str, Any]:
if "emb" in name:
__lowerCamelCase = name.replace('''emb''' , '''model.decoder.embed_tokens''' )
if "transformer" in name:
__lowerCamelCase = name.replace('''transformer''' , '''model.decoder''' )
if "cross_attention" in name:
__lowerCamelCase = name.replace('''cross_attention''' , '''encoder_attn''' )
if "linear1" in name:
__lowerCamelCase = name.replace('''linear1''' , '''fc1''' )
if "linear2" in name:
__lowerCamelCase = name.replace('''linear2''' , '''fc2''' )
if "norm1" in name:
__lowerCamelCase = name.replace('''norm1''' , '''self_attn_layer_norm''' )
if "norm_cross" in name:
__lowerCamelCase = name.replace('''norm_cross''' , '''encoder_attn_layer_norm''' )
if "norm2" in name:
__lowerCamelCase = name.replace('''norm2''' , '''final_layer_norm''' )
if "out_norm" in name:
__lowerCamelCase = name.replace('''out_norm''' , '''model.decoder.layer_norm''' )
if "linears" in name:
__lowerCamelCase = name.replace('''linears''' , '''lm_heads''' )
if "condition_provider.conditioners.description.output_proj" in name:
__lowerCamelCase = name.replace('''condition_provider.conditioners.description.output_proj''' , '''enc_to_dec_proj''' )
return name
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> Tuple[Dict, Dict]:
__lowerCamelCase = list(state_dict.keys() )
__lowerCamelCase = {}
for key in keys:
__lowerCamelCase = state_dict.pop(__lowerCAmelCase )
__lowerCamelCase = rename_keys(__lowerCAmelCase )
if "in_proj_weight" in key:
# split fused qkv proj
__lowerCamelCase = val[:hidden_size, :]
__lowerCamelCase = val[hidden_size : 2 * hidden_size, :]
__lowerCamelCase = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
__lowerCamelCase = val
else:
__lowerCamelCase = val
return state_dict, enc_dec_proj_state_dict
def __lowerCAmelCase ( UpperCamelCase__ ) -> MusicgenDecoderConfig:
if checkpoint == "small":
# default config values
__lowerCamelCase = 10_24
__lowerCamelCase = 24
__lowerCamelCase = 16
elif checkpoint == "medium":
__lowerCamelCase = 15_36
__lowerCamelCase = 48
__lowerCamelCase = 24
elif checkpoint == "large":
__lowerCamelCase = 20_48
__lowerCamelCase = 48
__lowerCamelCase = 32
else:
raise ValueError(f"""Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.""" )
__lowerCamelCase = MusicgenDecoderConfig(
hidden_size=__lowerCAmelCase , ffn_dim=hidden_size * 4 , num_hidden_layers=__lowerCAmelCase , num_attention_heads=__lowerCAmelCase , )
return config
@torch.no_grad()
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__="cpu" ) -> Optional[int]:
__lowerCamelCase = MusicGen.get_pretrained(__lowerCAmelCase , device=__lowerCAmelCase )
__lowerCamelCase = decoder_config_from_checkpoint(__lowerCAmelCase )
__lowerCamelCase = fairseq_model.lm.state_dict()
__lowerCamelCase = rename_state_dict(
__lowerCAmelCase , hidden_size=decoder_config.hidden_size )
__lowerCamelCase = TaEncoderModel.from_pretrained('''t5-base''' )
__lowerCamelCase = EncodecModel.from_pretrained('''facebook/encodec_32khz''' )
__lowerCamelCase = MusicgenForCausalLM(__lowerCAmelCase ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
__lowerCamelCase = decoder.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
for key in missing_keys.copy():
if key.startswith(('''text_encoder''', '''audio_encoder''') ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(__lowerCAmelCase )
if len(__lowerCAmelCase ) > 0:
raise ValueError(f"""Missing key(s) in state_dict: {missing_keys}""" )
if len(__lowerCAmelCase ) > 0:
raise ValueError(f"""Unexpected key(s) in state_dict: {unexpected_keys}""" )
# init the composite model
__lowerCamelCase = MusicgenForConditionalGeneration(text_encoder=__lowerCAmelCase , audio_encoder=__lowerCAmelCase , decoder=__lowerCAmelCase )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(__lowerCAmelCase )
# check we can do a forward pass
__lowerCamelCase = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
__lowerCamelCase = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
__lowerCamelCase = model(input_ids=__lowerCAmelCase , decoder_input_ids=__lowerCAmelCase ).logits
if logits.shape != (8, 1, 20_48):
raise ValueError('''Incorrect shape for logits''' )
# now construct the processor
__lowerCamelCase = AutoTokenizer.from_pretrained('''t5-base''' )
__lowerCamelCase = AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz''' , padding_side='''left''' )
__lowerCamelCase = MusicgenProcessor(feature_extractor=__lowerCAmelCase , tokenizer=__lowerCAmelCase )
# set the appropriate bos/pad token ids
__lowerCamelCase = 20_48
__lowerCamelCase = 20_48
# set other default generation config params
__lowerCamelCase = int(30 * audio_encoder.config.frame_rate )
__lowerCamelCase = True
__lowerCamelCase = 3.0
if pytorch_dump_folder is not None:
Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
logger.info(f"""Saving model {checkpoint} to {pytorch_dump_folder}""" )
model.save_pretrained(__lowerCAmelCase )
processor.save_pretrained(__lowerCAmelCase )
if repo_id:
logger.info(f"""Pushing model {checkpoint} to {repo_id}""" )
model.push_to_hub(__lowerCAmelCase )
processor.push_to_hub(__lowerCAmelCase )
if __name__ == "__main__":
__UpperCAmelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint",
default="small",
type=str,
help="Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.",
)
parser.add_argument(
"--pytorch_dump_folder",
required=True,
default=None,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
parser.add_argument(
"--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
)
__UpperCAmelCase =parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 546 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
UpperCamelCase = '\nimport os\n'
UpperCamelCase = '\ndef foo():\n import os\n return False\n'
UpperCamelCase = '\ndef foo():\n def bar():\n if True:\n import os\n return False\n return bar()\n'
UpperCamelCase = '\nimport os\n\ntry:\n import bar\nexcept ImportError:\n raise ValueError()\n'
UpperCamelCase = '\nimport os\n\ndef foo():\n try:\n import bar\n except ImportError:\n raise ValueError()\n'
UpperCamelCase = '\nimport os\n\ntry:\n import bar\nexcept (ImportError, AttributeError):\n raise ValueError()\n'
UpperCamelCase = '\nimport os\n\ntry:\n import bar\nexcept ImportError as e:\n raise ValueError()\n'
UpperCamelCase = '\nimport os\n\ntry:\n import bar\nexcept:\n raise ValueError()\n'
UpperCamelCase = '\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n raise ValueError()\n'
UpperCamelCase = '\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n x = 1\n raise ValueError()\n'
UpperCamelCase = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize("""case""" , __lowerCAmelCase )
def __lowerCamelCase ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] ) -> str:
__UpperCamelCase : str = os.path.join(__lowerCAmelCase , """test_file.py""" )
with open(__lowerCAmelCase , """w""" ) as _tmp_file:
_tmp_file.write(__lowerCAmelCase )
__UpperCamelCase : Optional[int] = get_imports(__lowerCAmelCase )
assert parsed_imports == ["os"]
| 269 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( _a , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = PhobertTokenizer
_SCREAMING_SNAKE_CASE = False
def A ( self : Optional[int] ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCamelCase = ['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
UpperCamelCase = dict(zip(UpperCamelCase__ , range(len(UpperCamelCase__ ) ) ) )
UpperCamelCase = ['#version: 0.2', 'l à</w>']
UpperCamelCase = {'unk_token': '<unk>'}
UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
for token in vocab_tokens:
fp.write(f"""{token} {vocab_tokens[token]}\n""" )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(UpperCamelCase__ ) )
def A ( self : Any , **UpperCamelCase__ : int ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return PhobertTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase__ )
def A ( self : Optional[Any] , UpperCamelCase__ : Dict ):
"""simple docstring"""
UpperCamelCase = 'Tôi là VinAI Research'
UpperCamelCase = 'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
return input_text, output_text
def A ( self : Any ):
"""simple docstring"""
UpperCamelCase = PhobertTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
UpperCamelCase = 'Tôi là VinAI Research'
UpperCamelCase = 'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
UpperCamelCase = tokenizer.tokenize(UpperCamelCase__ )
print(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
UpperCamelCase = tokens + [tokenizer.unk_token]
UpperCamelCase = [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__ ) , UpperCamelCase__ )
| 707 |
'''simple docstring'''
def __lowerCamelCase ( A__ , A__ , A__ ) -> float:
"""simple docstring"""
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
if years_to_repay <= 0 or not isinstance(A__ , A__ ):
raise Exception('Years to repay must be an integer > 0' )
# Yearly rate is divided by 12 to get monthly rate
UpperCamelCase = rate_per_annum / 12
# Years to repay is multiplied by 12 to get number of payments as payment is monthly
UpperCamelCase = years_to_repay * 12
return (
principal
* rate_per_month
* (1 + rate_per_month) ** number_of_payments
/ ((1 + rate_per_month) ** number_of_payments - 1)
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class a (_lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : str = DiTPipeline
__UpperCAmelCase : Union[str, Any] = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
__UpperCAmelCase : List[Any] = PipelineTesterMixin.required_optional_params - {
"latents",
"num_images_per_prompt",
"callback",
"callback_steps",
}
__UpperCAmelCase : Optional[int] = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
__UpperCAmelCase : Tuple = False
def __snake_case ( self : int ) -> Any:
torch.manual_seed(0 )
__snake_case : Dict = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=lowerCamelCase , activation_fn="gelu-approximate" , num_embeds_ada_norm=1000 , norm_type="ada_norm_zero" , norm_elementwise_affine=lowerCamelCase , )
__snake_case : Optional[int] = AutoencoderKL()
__snake_case : str = DDIMScheduler()
__snake_case : Union[str, Any] = {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
return components
def __snake_case ( self : List[str] , lowerCamelCase : Any , lowerCamelCase : List[Any]=0 ) -> List[Any]:
if str(lowerCamelCase ).startswith("mps" ):
__snake_case : Optional[int] = torch.manual_seed(lowerCamelCase )
else:
__snake_case : int = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
__snake_case : Tuple = {
"class_labels": [1],
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def __snake_case ( self : List[Any] ) -> List[Any]:
__snake_case : Dict = "cpu"
__snake_case : Optional[int] = self.get_dummy_components()
__snake_case : Dict = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__snake_case : Optional[Any] = self.get_dummy_inputs(lowerCamelCase )
__snake_case : Tuple = pipe(**lowerCamelCase ).images
__snake_case : List[str] = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
__snake_case : int = np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57] )
__snake_case : int = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase , 1E-3 )
def __snake_case ( self : Any ) -> Optional[Any]:
self._test_inference_batch_single_identical(relax_max_difference=lowerCamelCase , expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def __snake_case ( self : Optional[Any] ) -> Optional[int]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class a (unittest.TestCase ):
"""simple docstring"""
def __snake_case ( self : Optional[Any] ) -> Tuple:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __snake_case ( self : List[Any] ) -> List[Any]:
__snake_case : Union[str, Any] = torch.manual_seed(0 )
__snake_case : Any = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256" )
pipe.to("cuda" )
__snake_case : Dict = ["vase", "umbrella", "white shark", "white wolf"]
__snake_case : Any = pipe.get_label_ids(lowerCamelCase )
__snake_case : Tuple = pipe(lowerCamelCase , generator=lowerCamelCase , num_inference_steps=40 , output_type="np" ).images
for word, image in zip(lowerCamelCase , lowerCamelCase ):
__snake_case : Union[str, Any] = load_numpy(
F'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1E-2
def __snake_case ( self : Dict ) -> Optional[int]:
__snake_case : List[str] = DiTPipeline.from_pretrained("facebook/DiT-XL-2-512" )
__snake_case : Dict = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("cuda" )
__snake_case : List[str] = ["vase", "umbrella"]
__snake_case : int = pipe.get_label_ids(lowerCamelCase )
__snake_case : Optional[Any] = torch.manual_seed(0 )
__snake_case : Dict = pipe(lowerCamelCase , generator=lowerCamelCase , num_inference_steps=25 , output_type="np" ).images
for word, image in zip(lowerCamelCase , lowerCamelCase ):
__snake_case : Tuple = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
F'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1E-1
| 81 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE__ ( __snake_case ):
_lowerCAmelCase = "blip_2_vision_model"
def __init__(self , _lowercase=1408 , _lowercase=6144 , _lowercase=39 , _lowercase=16 , _lowercase=224 , _lowercase=14 , _lowercase="gelu" , _lowercase=0.0_0001 , _lowercase=0.0 , _lowercase=1e-10 , _lowercase=True , **_lowercase , ):
'''simple docstring'''
super().__init__(**_lowercase )
__a : Tuple = hidden_size
__a : Any = intermediate_size
__a : Dict = num_hidden_layers
__a : Optional[Any] = num_attention_heads
__a : str = patch_size
__a : Union[str, Any] = image_size
__a : List[Any] = initializer_range
__a : List[str] = attention_dropout
__a : Union[str, Any] = layer_norm_eps
__a : Optional[int] = hidden_act
__a : int = qkv_bias
@classmethod
def lowerCAmelCase__(cls , _lowercase , **_lowercase ):
'''simple docstring'''
cls._set_token_in_kwargs(_lowercase )
__a , __a : Optional[int] = cls.get_config_dict(_lowercase , **_lowercase )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get("""model_type""" ) == "blip-2":
__a : Tuple = 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(_lowercase , **_lowercase )
class SCREAMING_SNAKE_CASE__ ( __snake_case ):
_lowerCAmelCase = "blip_2_qformer"
def __init__(self , _lowercase=30522 , _lowercase=768 , _lowercase=12 , _lowercase=12 , _lowercase=3072 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=0.02 , _lowercase=1e-12 , _lowercase=0 , _lowercase="absolute" , _lowercase=2 , _lowercase=1408 , **_lowercase , ):
'''simple docstring'''
super().__init__(pad_token_id=_lowercase , **_lowercase )
__a : int = vocab_size
__a : Union[str, Any] = hidden_size
__a : Union[str, Any] = num_hidden_layers
__a : Any = num_attention_heads
__a : List[str] = hidden_act
__a : Union[str, Any] = intermediate_size
__a : Optional[int] = hidden_dropout_prob
__a : Any = attention_probs_dropout_prob
__a : List[str] = max_position_embeddings
__a : Union[str, Any] = initializer_range
__a : Union[str, Any] = layer_norm_eps
__a : Any = position_embedding_type
__a : Union[str, Any] = cross_attention_frequency
__a : str = encoder_hidden_size
@classmethod
def lowerCAmelCase__(cls , _lowercase , **_lowercase ):
'''simple docstring'''
cls._set_token_in_kwargs(_lowercase )
__a , __a : Optional[int] = cls.get_config_dict(_lowercase , **_lowercase )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get("""model_type""" ) == "blip-2":
__a : Optional[int] = config_dict["""qformer_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(_lowercase , **_lowercase )
class SCREAMING_SNAKE_CASE__ ( __snake_case ):
_lowerCAmelCase = "blip-2"
_lowerCAmelCase = True
def __init__(self , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=32 , **_lowercase ):
'''simple docstring'''
super().__init__(**_lowercase )
if vision_config is None:
__a : List[Any] = {}
logger.info("""vision_config is None. initializing the Blip2VisionConfig with default values.""" )
if qformer_config is None:
__a : Optional[int] = {}
logger.info("""qformer_config is None. Initializing the Blip2QFormerConfig with default values.""" )
if text_config is None:
__a : Optional[int] = {}
logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`).""" )
__a : List[Any] = BlipaVisionConfig(**_lowercase )
__a : List[str] = BlipaQFormerConfig(**_lowercase )
__a : str = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
__a : Optional[Any] = CONFIG_MAPPING[text_model_type](**_lowercase )
__a : Dict = self.text_config.tie_word_embeddings
__a : Optional[Any] = self.text_config.is_encoder_decoder
__a : Union[str, Any] = num_query_tokens
__a : Union[str, Any] = self.vision_config.hidden_size
__a : Tuple = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
__a : Optional[Any] = 1.0
__a : List[str] = 0.02
@classmethod
def lowerCAmelCase__(cls , _lowercase , _lowercase , _lowercase , **_lowercase , ):
'''simple docstring'''
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **_lowercase , )
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : Union[str, Any] = copy.deepcopy(self.__dict__ )
__a : Union[str, Any] = self.vision_config.to_dict()
__a : int = self.qformer_config.to_dict()
__a : List[Any] = self.text_config.to_dict()
__a : int = self.__class__.model_type
return output
| 581 | 0 |
'''simple docstring'''
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForWholeWordMask,
HfArgumentParser,
LineByLineTextDataset,
LineByLineWithRefDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
UpperCamelCase__ : Optional[Any] = logging.getLogger(__name__)
UpperCamelCase__ : List[Any] = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
UpperCamelCase__ : List[str] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _lowercase :
'''simple docstring'''
UpperCAmelCase_ : Optional[str] = field(
default=lowerCAmelCase ,metadata={
'''help''': (
'''The model checkpoint for weights initialization. Leave None if you want to train a model from'''
''' scratch.'''
)
} ,)
UpperCAmelCase_ : Optional[str] = field(
default=lowerCAmelCase ,metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(lowerCAmelCase )} ,)
UpperCAmelCase_ : Optional[str] = field(
default=lowerCAmelCase ,metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCAmelCase_ : Optional[str] = field(
default=lowerCAmelCase ,metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCAmelCase_ : Optional[str] = field(
default=lowerCAmelCase ,metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,)
@dataclass
class _lowercase :
'''simple docstring'''
UpperCAmelCase_ : Optional[str] = field(
default=lowerCAmelCase ,metadata={'''help''': '''The input training data file (a text file).'''} )
UpperCAmelCase_ : Optional[str] = field(
default=lowerCAmelCase ,metadata={
'''help''': (
'''The input training data files (multiple files in glob format). '''
'''Very often splitting large files to smaller files can prevent tokenizer going out of memory'''
)
} ,)
UpperCAmelCase_ : Optional[str] = field(
default=lowerCAmelCase ,metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} ,)
UpperCAmelCase_ : Optional[str] = field(
default=lowerCAmelCase ,metadata={'''help''': '''An optional input train ref data file for whole word mask in Chinese.'''} ,)
UpperCAmelCase_ : Optional[str] = field(
default=lowerCAmelCase ,metadata={'''help''': '''An optional input eval ref data file for whole word mask in Chinese.'''} ,)
UpperCAmelCase_ : bool = field(
default=lowerCAmelCase ,metadata={'''help''': '''Whether distinct lines of text in the dataset are to be handled as distinct sequences.'''} ,)
UpperCAmelCase_ : bool = field(
default=lowerCAmelCase ,metadata={'''help''': '''Train with masked-language modeling loss instead of language modeling.'''} )
UpperCAmelCase_ : bool = field(default=lowerCAmelCase ,metadata={'''help''': '''Whether ot not to use whole word mask.'''} )
UpperCAmelCase_ : float = field(
default=0.1_5 ,metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''} )
UpperCAmelCase_ : float = field(
default=1 / 6 ,metadata={
'''help''': (
'''Ratio of length of a span of masked tokens to surrounding context length for permutation language'''
''' modeling.'''
)
} ,)
UpperCAmelCase_ : int = field(
default=5 ,metadata={'''help''': '''Maximum length of a span of masked tokens for permutation language modeling.'''} )
UpperCAmelCase_ : int = field(
default=-1 ,metadata={
'''help''': (
'''Optional input sequence length after tokenization.'''
'''The training dataset will be truncated in block of this size for training.'''
'''Default to the model max input length for single sentence inputs (take into account special tokens).'''
)
} ,)
UpperCAmelCase_ : bool = field(
default=lowerCAmelCase ,metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def __UpperCamelCase( _A : DataTrainingArguments , _A : PreTrainedTokenizer , _A : bool = False , _A : Optional[str] = None , ):
'''simple docstring'''
def _dataset(_A : Dict , _A : int=None ):
if args.line_by_line:
if ref_path is not None:
if not args.whole_word_mask or not args.mlm:
raise ValueError('''You need to set world whole masking and mlm to True for Chinese Whole Word Mask''' )
return LineByLineWithRefDataset(
tokenizer=_A , file_path=_A , block_size=args.block_size , ref_path=_A , )
return LineByLineTextDataset(tokenizer=_A , file_path=_A , block_size=args.block_size )
else:
return TextDataset(
tokenizer=_A , file_path=_A , block_size=args.block_size , overwrite_cache=args.overwrite_cache , cache_dir=_A , )
if evaluate:
return _dataset(args.eval_data_file , args.eval_ref_file )
elif args.train_data_files:
return ConcatDataset([_dataset(_A ) for f in glob(args.train_data_files )] )
else:
return _dataset(args.train_data_file , args.train_ref_file )
def __UpperCamelCase( ):
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCAmelCase__ : List[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : int = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
'''Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '''
'''or remove the --do_eval argument.''' )
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , _A )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
UpperCAmelCase__ : Union[str, Any] = AutoConfig.from_pretrained(model_args.config_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
UpperCAmelCase__ : Dict = AutoConfig.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
UpperCAmelCase__ : Dict = CONFIG_MAPPING[model_args.model_type]()
logger.warning('''You are instantiating a new config instance from scratch.''' )
if model_args.tokenizer_name:
UpperCAmelCase__ : str = AutoTokenizer.from_pretrained(model_args.tokenizer_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
UpperCAmelCase__ : List[str] = AutoTokenizer.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
raise ValueError(
'''You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'''
''' script, save it,and load it from here, using --tokenizer_name''' )
if model_args.model_name_or_path:
UpperCAmelCase__ : int = AutoModelWithLMHead.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=_A , cache_dir=model_args.cache_dir , )
else:
logger.info('''Training new model from scratch''' )
UpperCAmelCase__ : Tuple = AutoModelWithLMHead.from_config(_A )
model.resize_token_embeddings(len(_A ) )
if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
raise ValueError(
'''BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'''
'''--mlm flag (masked language modeling).''' )
if data_args.block_size <= 0:
UpperCAmelCase__ : str = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
UpperCAmelCase__ : List[str] = min(data_args.block_size , tokenizer.max_len )
# Get datasets
UpperCAmelCase__ : Dict = (
get_dataset(_A , tokenizer=_A , cache_dir=model_args.cache_dir ) if training_args.do_train else None
)
UpperCAmelCase__ : Dict = (
get_dataset(_A , tokenizer=_A , evaluate=_A , cache_dir=model_args.cache_dir )
if training_args.do_eval
else None
)
if config.model_type == "xlnet":
UpperCAmelCase__ : Tuple = DataCollatorForPermutationLanguageModeling(
tokenizer=_A , plm_probability=data_args.plm_probability , max_span_length=data_args.max_span_length , )
else:
if data_args.mlm and data_args.whole_word_mask:
UpperCAmelCase__ : Optional[int] = DataCollatorForWholeWordMask(
tokenizer=_A , mlm_probability=data_args.mlm_probability )
else:
UpperCAmelCase__ : Tuple = DataCollatorForLanguageModeling(
tokenizer=_A , mlm=data_args.mlm , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
UpperCAmelCase__ : Dict = Trainer(
model=_A , args=_A , data_collator=_A , train_dataset=_A , eval_dataset=_A , prediction_loss_only=_A , )
# Training
if training_args.do_train:
UpperCAmelCase__ : List[str] = (
model_args.model_name_or_path
if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
else None
)
trainer.train(model_path=_A )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
UpperCAmelCase__ : str = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
UpperCAmelCase__ : List[Any] = trainer.evaluate()
UpperCAmelCase__ : List[Any] = math.exp(eval_output['''eval_loss'''] )
UpperCAmelCase__ : int = {'''perplexity''': perplexity}
UpperCAmelCase__ : List[Any] = os.path.join(training_args.output_dir , '''eval_results_lm.txt''' )
if trainer.is_world_master():
with open(_A , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key in sorted(result.keys() ):
logger.info(''' %s = %s''' , _A , str(result[key] ) )
writer.write('''%s = %s\n''' % (key, str(result[key] )) )
results.update(_A )
return results
def __UpperCamelCase( _A : int ):
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 496 | '''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCamelCase__ : str = '▁'
UpperCamelCase__ : Any = {
'vocab_file': 'vocab.json',
'spm_file': 'sentencepiece.bpe.model',
'tokenizer_config_file': 'tokenizer_config.json',
}
UpperCamelCase__ : Union[str, Any] = {
'vocab_file': {
'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
},
'spm_file': {
'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
},
'tokenizer_config_file': {
'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
},
}
UpperCamelCase__ : Dict = {
'facebook/m2m100_418M': 1_024,
}
# fmt: off
UpperCamelCase__ : Optional[int] = {
'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}
class _lowercase ( lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = VOCAB_FILES_NAMES
UpperCAmelCase_ : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase_ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase_ : Optional[int] = ['''input_ids''', '''attention_mask''']
UpperCAmelCase_ : List[int] = []
UpperCAmelCase_ : List[int] = []
def __init__( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_=None ,lowerCamelCase_=None ,lowerCamelCase_="<s>" ,lowerCamelCase_="</s>" ,lowerCamelCase_="</s>" ,lowerCamelCase_="<pad>" ,lowerCamelCase_="<unk>" ,lowerCamelCase_="m2m100" ,lowerCamelCase_ = None ,lowerCamelCase_=8 ,**lowerCamelCase_ ,) -> None:
'''simple docstring'''
UpperCAmelCase__ : str = {} if sp_model_kwargs is None else sp_model_kwargs
UpperCAmelCase__ : Dict = language_codes
UpperCAmelCase__ : Union[str, Any] = FAIRSEQ_LANGUAGE_CODES[language_codes]
UpperCAmelCase__ : Union[str, Any] = {lang_code: f'''__{lang_code}__''' for lang_code in fairseq_language_code}
UpperCAmelCase__ : Any = kwargs.get('''additional_special_tokens''' ,[] )
kwargs["additional_special_tokens"] += [
self.get_lang_token(lowerCamelCase_ )
for lang_code in fairseq_language_code
if self.get_lang_token(lowerCamelCase_ ) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=lowerCamelCase_ ,tgt_lang=lowerCamelCase_ ,bos_token=lowerCamelCase_ ,eos_token=lowerCamelCase_ ,sep_token=lowerCamelCase_ ,unk_token=lowerCamelCase_ ,pad_token=lowerCamelCase_ ,language_codes=lowerCamelCase_ ,sp_model_kwargs=self.sp_model_kwargs ,num_madeup_words=lowerCamelCase_ ,**lowerCamelCase_ ,)
UpperCAmelCase__ : Optional[int] = vocab_file
UpperCAmelCase__ : Optional[Any] = load_json(lowerCamelCase_ )
UpperCAmelCase__ : List[str] = {v: k for k, v in self.encoder.items()}
UpperCAmelCase__ : List[Any] = spm_file
UpperCAmelCase__ : Any = load_spm(lowerCamelCase_ ,self.sp_model_kwargs )
UpperCAmelCase__ : int = len(self.encoder )
UpperCAmelCase__ : Optional[int] = {
self.get_lang_token(lowerCamelCase_ ): self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase_ )
}
UpperCAmelCase__ : List[Any] = {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase_ )}
UpperCAmelCase__ : List[str] = {v: k for k, v in self.lang_token_to_id.items()}
UpperCAmelCase__ : Optional[int] = src_lang if src_lang is not None else '''en'''
UpperCAmelCase__ : int = tgt_lang
UpperCAmelCase__ : int = self.get_lang_id(self._src_lang )
self.set_src_lang_special_tokens(self._src_lang )
UpperCAmelCase__ : Optional[int] = num_madeup_words
@property
def lowerCAmelCase__ ( self ) -> int:
'''simple docstring'''
return len(self.encoder ) + len(self.lang_token_to_id )
@property
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
return self._src_lang
@src_lang.setter
def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> None:
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(lowerCamelCase_ ,out_type=lowerCamelCase_ )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> Optional[int]:
'''simple docstring'''
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(lowerCamelCase_ ,self.encoder[self.unk_token] )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> str:
'''simple docstring'''
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(lowerCamelCase_ ,self.unk_token )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> Any:
'''simple docstring'''
UpperCAmelCase__ : Any = []
UpperCAmelCase__ : str = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(lowerCamelCase_ ) + token
UpperCAmelCase__ : str = []
else:
current_sub_tokens.append(lowerCamelCase_ )
out_string += self.sp_model.decode(lowerCamelCase_ )
return out_string.strip()
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = None ,lowerCamelCase_ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase_ ,token_ids_a=lowerCamelCase_ ,already_has_special_tokens=lowerCamelCase_ )
UpperCAmelCase__ : Dict = [1] * len(self.prefix_tokens )
UpperCAmelCase__ : Optional[Any] = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(lowerCamelCase_ )) + suffix_ones
return prefix_ones + ([0] * len(lowerCamelCase_ )) + ([0] * len(lowerCamelCase_ )) + suffix_ones
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase__ : Tuple = {self.convert_ids_to_tokens(lowerCamelCase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.__dict__.copy()
UpperCAmelCase__ : str = None
return state
def __setstate__( self ,lowerCamelCase_ ) -> None:
'''simple docstring'''
UpperCAmelCase__ : int = d
# for backward compatibility
if not hasattr(self ,'''sp_model_kwargs''' ):
UpperCAmelCase__ : Dict = {}
UpperCAmelCase__ : int = load_spm(self.spm_file ,self.sp_model_kwargs )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = None ) -> Tuple[str]:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = Path(lowerCamelCase_ )
if not save_dir.is_dir():
raise OSError(f'''{save_directory} should be a directory''' )
UpperCAmelCase__ : Optional[int] = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
)
UpperCAmelCase__ : str = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
)
save_json(self.encoder ,lowerCamelCase_ )
if os.path.abspath(self.spm_file ) != os.path.abspath(lowerCamelCase_ ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file ,lowerCamelCase_ )
elif not os.path.isfile(self.spm_file ):
with open(lowerCamelCase_ ,'''wb''' ) as fi:
UpperCAmelCase__ : str = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase_ )
return (str(lowerCamelCase_ ), str(lowerCamelCase_ ))
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = "en" ,lowerCamelCase_ = None ,lowerCamelCase_ = "ro" ,**lowerCamelCase_ ,) -> BatchEncoding:
'''simple docstring'''
UpperCAmelCase__ : int = src_lang
UpperCAmelCase__ : Tuple = tgt_lang
self.set_src_lang_special_tokens(self.src_lang )
return super().prepare_seqaseq_batch(lowerCamelCase_ ,lowerCamelCase_ ,**lowerCamelCase_ )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,**lowerCamelCase_ ) -> Optional[int]:
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
UpperCAmelCase__ : List[str] = src_lang
UpperCAmelCase__ : List[str] = self(lowerCamelCase_ ,add_special_tokens=lowerCamelCase_ ,**lowerCamelCase_ )
UpperCAmelCase__ : Optional[Any] = self.get_lang_id(lowerCamelCase_ )
UpperCAmelCase__ : Dict = tgt_lang_id
return inputs
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
self.set_src_lang_special_tokens(self.src_lang )
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
self.set_tgt_lang_special_tokens(self.tgt_lang )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> None:
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.get_lang_token(lowerCamelCase_ )
UpperCAmelCase__ : List[Any] = self.lang_token_to_id[lang_token]
UpperCAmelCase__ : Union[str, Any] = [self.cur_lang_id]
UpperCAmelCase__ : Dict = [self.eos_token_id]
def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> None:
'''simple docstring'''
UpperCAmelCase__ : Any = self.get_lang_token(lowerCamelCase_ )
UpperCAmelCase__ : Optional[int] = self.lang_token_to_id[lang_token]
UpperCAmelCase__ : Tuple = [self.cur_lang_id]
UpperCAmelCase__ : str = [self.eos_token_id]
def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> str:
'''simple docstring'''
return self.lang_code_to_token[lang]
def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> int:
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = self.get_lang_token(lowerCamelCase_ )
return self.lang_token_to_id[lang_token]
def __UpperCamelCase( _A : str , _A : Dict[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = sentencepiece.SentencePieceProcessor(**_A )
spm.Load(str(_A ) )
return spm
def __UpperCamelCase( _A : str ):
'''simple docstring'''
with open(_A , '''r''' ) as f:
return json.load(_A )
def __UpperCamelCase( _A : List[str] , _A : str ):
'''simple docstring'''
with open(_A , '''w''' ) as f:
json.dump(_A , _A , indent=2 )
| 496 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class lowercase__ :
lowercase__ = LEDConfig
lowercase__ = {}
lowercase__ = """gelu"""
def __init__( self : Tuple ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Any=13 ,lowerCamelCase__ : List[Any]=7 ,lowerCamelCase__ : Tuple=True ,lowerCamelCase__ : Tuple=False ,lowerCamelCase__ : Tuple=99 ,lowerCamelCase__ : List[Any]=32 ,lowerCamelCase__ : Optional[Any]=2 ,lowerCamelCase__ : Optional[Any]=4 ,lowerCamelCase__ : List[Any]=37 ,lowerCamelCase__ : Dict=0.1 ,lowerCamelCase__ : Optional[Any]=0.1 ,lowerCamelCase__ : Tuple=20 ,lowerCamelCase__ : Optional[int]=2 ,lowerCamelCase__ : List[Any]=1 ,lowerCamelCase__ : str=0 ,lowerCamelCase__ : Union[str, Any]=4 ,):
'''simple docstring'''
_UpperCamelCase : Any = parent
_UpperCamelCase : Optional[int] = batch_size
_UpperCamelCase : int = seq_length
_UpperCamelCase : Optional[int] = is_training
_UpperCamelCase : Optional[int] = use_labels
_UpperCamelCase : Optional[int] = vocab_size
_UpperCamelCase : List[str] = hidden_size
_UpperCamelCase : Optional[int] = num_hidden_layers
_UpperCamelCase : str = num_attention_heads
_UpperCamelCase : List[str] = intermediate_size
_UpperCamelCase : Optional[int] = hidden_dropout_prob
_UpperCamelCase : Any = attention_probs_dropout_prob
_UpperCamelCase : Any = max_position_embeddings
_UpperCamelCase : Dict = eos_token_id
_UpperCamelCase : Tuple = pad_token_id
_UpperCamelCase : Tuple = bos_token_id
_UpperCamelCase : Any = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
_UpperCamelCase : int = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
_UpperCamelCase : Dict = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
_UpperCamelCase : int = ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size )
_UpperCamelCase : Union[str, Any] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) ,1 )
_UpperCamelCase : Any = tf.concat([input_ids, eos_tensor] ,axis=1 )
_UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_UpperCamelCase : Any = self.config_cls(
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 ,max_position_embeddings=self.max_position_embeddings ,eos_token_ids=[2] ,bos_token_id=self.bos_token_id ,pad_token_id=self.pad_token_id ,decoder_start_token_id=self.pad_token_id ,attention_window=self.attention_window ,**self.config_updates ,)
_UpperCamelCase : int = prepare_led_inputs_dict(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
_UpperCamelCase : Any = tf.concat(
[tf.zeros_like(lowerCamelCase__ )[:, :-1], tf.ones_like(lowerCamelCase__ )[:, -1:]] ,axis=-1 ,)
_UpperCamelCase : Optional[int] = global_attention_mask
return config, inputs_dict
def UpperCamelCase_ ( self : str ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Tuple ):
'''simple docstring'''
_UpperCamelCase : Dict = TFLEDModel(config=lowerCamelCase__ ).get_decoder()
_UpperCamelCase : str = inputs_dict['input_ids']
_UpperCamelCase : Union[str, Any] = input_ids[:1, :]
_UpperCamelCase : str = inputs_dict['attention_mask'][:1, :]
_UpperCamelCase : str = 1
# first forward pass
_UpperCamelCase : int = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,use_cache=lowerCamelCase__ )
_UpperCamelCase , _UpperCamelCase : int = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_UpperCamelCase : int = ids_tensor((self.batch_size, 3) ,config.vocab_size )
_UpperCamelCase : Optional[Any] = tf.cast(ids_tensor((self.batch_size, 3) ,2 ) ,tf.inta )
# append to next input_ids and
_UpperCamelCase : Optional[int] = tf.concat([input_ids, next_tokens] ,axis=-1 )
_UpperCamelCase : Tuple = tf.concat([attention_mask, next_attn_mask] ,axis=-1 )
_UpperCamelCase : Any = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ )[0]
_UpperCamelCase : Tuple = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,past_key_values=lowerCamelCase__ )[0]
self.parent.assertEqual(next_tokens.shape[1] ,output_from_past.shape[1] )
# select random slice
_UpperCamelCase : List[str] = int(ids_tensor((1,) ,output_from_past.shape[-1] ) )
_UpperCamelCase : List[str] = output_from_no_past[:, -3:, random_slice_idx]
_UpperCamelCase : Tuple = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(lowerCamelCase__ ,lowerCamelCase__ ,rtol=1E-3 )
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=None , ):
if attention_mask is None:
_UpperCamelCase : int = tf.cast(tf.math.not_equal(UpperCAmelCase_ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
_UpperCamelCase : Optional[int] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
_UpperCamelCase : Dict = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_UpperCamelCase : List[Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class lowercase__ ( lowercase , lowercase , unittest.TestCase ):
lowercase__ = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
lowercase__ = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
lowercase__ = (
{
"""conversational""": TFLEDForConditionalGeneration,
"""feature-extraction""": TFLEDModel,
"""summarization""": TFLEDForConditionalGeneration,
"""text2text-generation""": TFLEDForConditionalGeneration,
"""translation""": TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowercase__ = True
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
_UpperCamelCase : str = TFLEDModelTester(self )
_UpperCamelCase : int = ConfigTester(self ,config_class=lowerCamelCase__ )
def UpperCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase__ )
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCamelCase : Tuple = tf.zeros_like(inputs_dict['attention_mask'] )
_UpperCamelCase : Union[str, Any] = 2
_UpperCamelCase : Tuple = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices ,1 ,inputs_dict['global_attention_mask'] ,)
_UpperCamelCase : Optional[int] = True
_UpperCamelCase : int = self.model_tester.seq_length
_UpperCamelCase : Optional[Any] = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(lowerCamelCase__ : Any ):
_UpperCamelCase : Union[str, Any] = outputs.decoder_attentions
self.assertEqual(len(lowerCamelCase__ ) ,self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads, seq_length, seq_length] ,)
def check_encoder_attentions_output(lowerCamelCase__ : List[str] ):
_UpperCamelCase : Optional[int] = [t.numpy() for t in outputs.encoder_attentions]
_UpperCamelCase : Optional[int] = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(lowerCamelCase__ ) ,self.model_tester.num_hidden_layers )
self.assertEqual(len(lowerCamelCase__ ) ,self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads, seq_length, seq_length] ,)
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] ,)
for model_class in self.all_model_classes:
_UpperCamelCase : str = True
_UpperCamelCase : Dict = False
_UpperCamelCase : str = False
_UpperCamelCase : Optional[Any] = model_class(lowerCamelCase__ )
_UpperCamelCase : Dict = model(self._prepare_for_class(lowerCamelCase__ ,lowerCamelCase__ ) )
_UpperCamelCase : Optional[int] = len(lowerCamelCase__ )
self.assertEqual(config.output_hidden_states ,lowerCamelCase__ )
check_encoder_attentions_output(lowerCamelCase__ )
if self.is_encoder_decoder:
_UpperCamelCase : Optional[int] = model_class(lowerCamelCase__ )
_UpperCamelCase : Optional[int] = model(self._prepare_for_class(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(config.output_hidden_states ,lowerCamelCase__ )
check_decoder_attentions_output(lowerCamelCase__ )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
_UpperCamelCase : int = True
_UpperCamelCase : List[str] = model_class(lowerCamelCase__ )
_UpperCamelCase : Dict = model(self._prepare_for_class(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(config.output_hidden_states ,lowerCamelCase__ )
check_encoder_attentions_output(lowerCamelCase__ )
# Check attention is always last and order is fine
_UpperCamelCase : Tuple = True
_UpperCamelCase : Any = True
_UpperCamelCase : List[Any] = model_class(lowerCamelCase__ )
_UpperCamelCase : Optional[int] = model(self._prepare_for_class(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) ,len(lowerCamelCase__ ) )
self.assertEqual(model.config.output_hidden_states ,lowerCamelCase__ )
check_encoder_attentions_output(lowerCamelCase__ )
@unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.' )
def UpperCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
pass
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
# TODO: Head-masking not yet implement
pass
def A__ ( UpperCAmelCase_ ):
return tf.constant(UpperCAmelCase_ , dtype=tf.intaa )
snake_case_ : Optional[int] = 1e-4
@slow
@require_tf
class lowercase__ ( unittest.TestCase ):
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
_UpperCamelCase : str = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' ).led
# change to intended input here
_UpperCamelCase : Dict = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
_UpperCamelCase : Tuple = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
_UpperCamelCase : Tuple = prepare_led_inputs_dict(model.config ,lowerCamelCase__ ,lowerCamelCase__ )
_UpperCamelCase : Optional[Any] = model(**lowerCamelCase__ )[0]
_UpperCamelCase : Union[str, Any] = (1, 1024, 768)
self.assertEqual(output.shape ,lowerCamelCase__ )
# change to expected output here
_UpperCamelCase : int = tf.convert_to_tensor(
[[2.3_0_5_0, 2.8_2_7_9, 0.6_5_3_1], [-1.8_4_5_7, -0.1_4_5_5, -3.5_6_6_1], [-1.0_1_8_6, 0.4_5_8_6, -2.2_0_4_3]] ,)
tf.debugging.assert_near(output[:, :3, :3] ,lowerCamelCase__ ,atol=1E-3 )
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
_UpperCamelCase : str = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' )
# change to intended input here
_UpperCamelCase : Dict = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
_UpperCamelCase : int = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
_UpperCamelCase : Optional[int] = prepare_led_inputs_dict(model.config ,lowerCamelCase__ ,lowerCamelCase__ )
_UpperCamelCase : Optional[int] = model(**lowerCamelCase__ )[0]
_UpperCamelCase : Optional[Any] = (1, 1024, model.config.vocab_size)
self.assertEqual(output.shape ,lowerCamelCase__ )
# change to expected output here
_UpperCamelCase : str = tf.convert_to_tensor(
[[3_3.6_5_0_7, 6.4_5_7_2, 1_6.8_0_8_9], [5.8_7_3_9, -2.4_2_3_8, 1_1.2_9_0_2], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]] ,)
tf.debugging.assert_near(output[:, :3, :3] ,lowerCamelCase__ ,atol=1E-3 ,rtol=1E-3 )
| 195 |
'''simple docstring'''
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from transformers.models.bert.modeling_flax_bert import FlaxBertModel
from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel
@require_flax
class lowercase__ ( unittest.TestCase ):
@slow
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
for model_name in ["bert-base-cased", "bert-large-uncased"]:
with self.subTest(lowerCamelCase__ ):
_UpperCamelCase : Optional[int] = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
_UpperCamelCase : Dict = FlaxAutoModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
@slow
def UpperCamelCase_ ( self : List[Any] ):
'''simple docstring'''
for model_name in ["roberta-base", "roberta-large"]:
with self.subTest(lowerCamelCase__ ):
_UpperCamelCase : Any = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
_UpperCamelCase : Any = FlaxAutoModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
@slow
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
for model_name in ["bert-base-cased", "bert-large-uncased"]:
_UpperCamelCase : Optional[int] = AutoTokenizer.from_pretrained(lowerCamelCase__ )
_UpperCamelCase : List[Any] = FlaxBertModel.from_pretrained(lowerCamelCase__ )
_UpperCamelCase : Optional[int] = tokenizer('Do you support jax jitted function?' ,return_tensors=TensorType.JAX )
@jax.jit
def eval(**lowerCamelCase__ : Union[str, Any] ):
return model(**lowerCamelCase__ )
eval(**lowerCamelCase__ ).block_until_ready()
@slow
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
for model_name in ["roberta-base", "roberta-large"]:
_UpperCamelCase : Optional[int] = AutoTokenizer.from_pretrained(lowerCamelCase__ )
_UpperCamelCase : Tuple = FlaxRobertaModel.from_pretrained(lowerCamelCase__ )
_UpperCamelCase : Optional[Any] = tokenizer('Do you support jax jitted function?' ,return_tensors=TensorType.JAX )
@jax.jit
def eval(**lowerCamelCase__ : Union[str, Any] ):
return model(**lowerCamelCase__ )
eval(**lowerCamelCase__ ).block_until_ready()
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,'bert-base is not a local folder and is not a valid model identifier' ):
_UpperCamelCase : int = FlaxAutoModel.from_pretrained('bert-base' )
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
_UpperCamelCase : Tuple = FlaxAutoModel.from_pretrained(lowerCamelCase__ ,revision='aaaaaa' )
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack' ,):
_UpperCamelCase : List[Any] = FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model' )
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
with self.assertRaisesRegex(lowerCamelCase__ ,'Use `from_pt=True` to load this model' ):
_UpperCamelCase : Tuple = FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only' )
| 195 | 1 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
SCREAMING_SNAKE_CASE__ = """\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
and Jeffrey Dean},
year={2016},
eprint={1609.08144},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
SCREAMING_SNAKE_CASE__ = """\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
"""
SCREAMING_SNAKE_CASE__ = """\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.
Args:
predictions (list of str): list of translations to score.
Each translation should be tokenized into a list of tokens.
references (list of list of str): list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.
Returns:
\'google_bleu\': google_bleu score
Examples:
Example 1:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results[\"google_bleu\"], 2))
0.44
Example 2:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results[\"google_bleu\"], 2))
0.61
Example 3:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
>>> print(round(results[\"google_bleu\"], 2))
0.53
Example 4:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
>>> print(round(results[\"google_bleu\"], 2))
0.4
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCamelCase ( datasets.Metric ):
"""simple docstring"""
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ),
"references": datasets.Sequence(
datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ) , id="references" ),
} ) , )
def A__ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 1 , UpperCAmelCase = 4 , ) -> Optional[int]:
'''simple docstring'''
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=_snake_case , hypotheses=_snake_case , min_len=_snake_case , max_len=_snake_case )
}
| 708 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: float , __lowerCamelCase: float , __lowerCamelCase: float , ):
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif stress < 0:
raise ValueError("Stress cannot be negative" )
elif tangential_force < 0:
raise ValueError("Tangential Force cannot be negative" )
elif area < 0:
raise ValueError("Area cannot be negative" )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 601 | 0 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase_ ( __UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : Tuple = LongformerTokenizer
UpperCAmelCase : List[Any] = True
UpperCAmelCase : str = LongformerTokenizerFast
UpperCAmelCase : int = True
def lowerCAmelCase_ ( self : List[Any] ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_A = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
_A = dict(zip(_UpperCAmelCase , range(len(_UpperCAmelCase ) ) ) )
_A = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
_A = {"""unk_token""": """<unk>"""}
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(_UpperCAmelCase ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Optional[int] , **_UpperCAmelCase : Union[str, Any] ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] , **_UpperCAmelCase : Optional[Any] ):
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : Optional[int] ):
_A = """lower newer"""
_A = """lower newer"""
return input_text, output_text
def lowerCAmelCase_ ( self : List[Any] ):
_A = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
_A = """lower newer"""
_A = ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
_A = tokenizer.tokenize(_UpperCAmelCase ) # , add_prefix_space=True)
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
_A = tokens + [tokenizer.unk_token]
_A = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Any ):
_A = self.get_tokenizer()
self.assertListEqual(tokenizer.encode('Hello world!' , add_special_tokens=_UpperCAmelCase ) , [0, 31_414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode('Hello world! cécé herlolip 418' , add_special_tokens=_UpperCAmelCase ) , [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2] , )
@slow
def lowerCAmelCase_ ( self : Tuple ):
_A = self.tokenizer_class.from_pretrained('allenai/longformer-base-4096' )
_A = tokenizer.encode('sequence builders' , add_special_tokens=_UpperCAmelCase )
_A = tokenizer.encode('multi-sequence build' , add_special_tokens=_UpperCAmelCase )
_A = tokenizer.encode(
'sequence builders' , add_special_tokens=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase )
_A = tokenizer.encode(
'sequence builders' , 'multi-sequence build' , add_special_tokens=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase )
_A = tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase )
_A = tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase , _UpperCAmelCase )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def lowerCAmelCase_ ( self : Dict ):
_A = self.get_tokenizer()
_A = """Encode this sequence."""
_A = tokenizer.byte_encoder[""" """.encode('utf-8' )[0]]
# Testing encoder arguments
_A = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase )
_A = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(_UpperCAmelCase , _UpperCAmelCase )
_A = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase )
_A = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
tokenizer.add_special_tokens({'bos_token': '<s>'} )
_A = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
_A = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(_UpperCAmelCase , _UpperCAmelCase )
# Testing spaces after special tokens
_A = """<mask>"""
tokenizer.add_special_tokens(
{'mask_token': AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase )} ) # mask token has a left space
_A = tokenizer.convert_tokens_to_ids(_UpperCAmelCase )
_A = """Encode <mask> sequence"""
_A = """Encode <mask>sequence"""
_A = tokenizer.encode(_UpperCAmelCase )
_A = encoded.index(_UpperCAmelCase )
_A = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
_A = tokenizer.encode(_UpperCAmelCase )
_A = encoded.index(_UpperCAmelCase )
_A = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] ):
pass
def lowerCAmelCase_ ( self : Any ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_A = self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase , **_UpperCAmelCase )
_A = self.tokenizer_class.from_pretrained(_UpperCAmelCase , **_UpperCAmelCase )
_A = """A, <mask> AllenNLP sentence."""
_A = tokenizer_r.encode_plus(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase , return_token_type_ids=_UpperCAmelCase )
_A = tokenizer_p.encode_plus(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase , return_token_type_ids=_UpperCAmelCase )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r['token_type_ids'] ) , sum(tokens_p['token_type_ids'] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] ) , sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] ) , )
_A = tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
_A = tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p['input_ids'] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(tokens_r['input_ids'] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(
_UpperCAmelCase , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
self.assertSequenceEqual(
_UpperCAmelCase , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
def lowerCAmelCase_ ( self : int ):
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
_A = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase , trim_offsets=_UpperCAmelCase )
_A = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
_A = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state['add_prefix_space'] , _UpperCAmelCase )
self.assertEqual(post_processor_state['add_prefix_space'] , _UpperCAmelCase )
self.assertEqual(post_processor_state['trim_offsets'] , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] ):
# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
# `trim_offsets`
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_A = """hello""" # `hello` is a token in the vocabulary of `pretrained_name`
_A = F'''{text_of_1_token} {text_of_1_token}'''
_A = self.rust_tokenizer_class.from_pretrained(
_UpperCAmelCase , use_fast=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase , trim_offsets=_UpperCAmelCase )
_A = tokenizer_r(_UpperCAmelCase , return_offsets_mapping=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_UpperCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_UpperCAmelCase ) + 1, len(_UpperCAmelCase ) + 1 + len(_UpperCAmelCase )) , )
_A = self.rust_tokenizer_class.from_pretrained(
_UpperCAmelCase , use_fast=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase , trim_offsets=_UpperCAmelCase )
_A = tokenizer_r(_UpperCAmelCase , return_offsets_mapping=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_UpperCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_UpperCAmelCase ) + 1, len(_UpperCAmelCase ) + 1 + len(_UpperCAmelCase )) , )
_A = self.rust_tokenizer_class.from_pretrained(
_UpperCAmelCase , use_fast=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase , trim_offsets=_UpperCAmelCase )
_A = tokenizer_r(_UpperCAmelCase , return_offsets_mapping=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_UpperCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_UpperCAmelCase ), len(_UpperCAmelCase ) + 1 + len(_UpperCAmelCase )) , )
_A = self.rust_tokenizer_class.from_pretrained(
_UpperCAmelCase , use_fast=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase , trim_offsets=_UpperCAmelCase )
_A = tokenizer_r(_UpperCAmelCase , return_offsets_mapping=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_UpperCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_UpperCAmelCase ), len(_UpperCAmelCase ) + 1 + len(_UpperCAmelCase )) , )
_A = F''' {text}'''
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
_A = self.rust_tokenizer_class.from_pretrained(
_UpperCAmelCase , use_fast=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase , trim_offsets=_UpperCAmelCase )
_A = tokenizer_r(_UpperCAmelCase , return_offsets_mapping=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(_UpperCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(_UpperCAmelCase ) + 1, 1 + len(_UpperCAmelCase ) + 1 + len(_UpperCAmelCase )) , )
_A = self.rust_tokenizer_class.from_pretrained(
_UpperCAmelCase , use_fast=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase , trim_offsets=_UpperCAmelCase )
_A = tokenizer_r(_UpperCAmelCase , return_offsets_mapping=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(_UpperCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(_UpperCAmelCase ), 1 + len(_UpperCAmelCase ) + 1 + len(_UpperCAmelCase )) , )
_A = self.rust_tokenizer_class.from_pretrained(
_UpperCAmelCase , use_fast=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase , trim_offsets=_UpperCAmelCase )
_A = tokenizer_r(_UpperCAmelCase , return_offsets_mapping=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(_UpperCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(_UpperCAmelCase ), 1 + len(_UpperCAmelCase ) + 1 + len(_UpperCAmelCase )) , )
| 7 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase :int = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
'''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
'''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
'''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase :Optional[int] = [
'''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Wav2Vec2ForAudioFrameClassification''',
'''Wav2Vec2ForCTC''',
'''Wav2Vec2ForMaskedLM''',
'''Wav2Vec2ForPreTraining''',
'''Wav2Vec2ForSequenceClassification''',
'''Wav2Vec2ForXVector''',
'''Wav2Vec2Model''',
'''Wav2Vec2PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase :List[Any] = [
'''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWav2Vec2ForCTC''',
'''TFWav2Vec2Model''',
'''TFWav2Vec2PreTrainedModel''',
'''TFWav2Vec2ForSequenceClassification''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase :Any = [
'''FlaxWav2Vec2ForCTC''',
'''FlaxWav2Vec2ForPreTraining''',
'''FlaxWav2Vec2Model''',
'''FlaxWav2Vec2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
lowerCamelCase :Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 667 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , snake_case : int ):
'''simple docstring'''
A__ : Dict = value
A__ : Node | None = None
A__ : Node | None = None
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[str] , snake_case : Node ):
'''simple docstring'''
A__ : Tuple = tree
def _UpperCamelCase ( self : List[str] , snake_case : Node | None ):
'''simple docstring'''
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self : List[str] ):
'''simple docstring'''
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 718 |
"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
A_ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( UpperCamelCase ):
snake_case_ = ['input_values', 'attention_mask']
def __init__( self : Union[str, Any] , snake_case : int = 1 , snake_case : int = 1_6000 , snake_case : float = 0.0 , snake_case : bool = False , snake_case : int = 80 , snake_case : int = 16 , snake_case : int = 64 , snake_case : str = "hann_window" , snake_case : float = 1.0 , snake_case : float = 80 , snake_case : float = 7600 , snake_case : float = 1e-10 , snake_case : int = 2 , snake_case : bool = True , **snake_case : Dict , ):
'''simple docstring'''
super().__init__(feature_size=snake_case , sampling_rate=snake_case , padding_value=snake_case , **snake_case )
A__ : Optional[int] = do_normalize
A__ : List[Any] = return_attention_mask
A__ : Tuple = num_mel_bins
A__ : Optional[int] = hop_length
A__ : List[str] = win_length
A__ : List[Any] = win_function
A__ : Tuple = frame_signal_scale
A__ : Optional[Any] = fmin
A__ : str = fmax
A__ : str = mel_floor
A__ : Dict = reduction_factor
A__ : int = win_length * sampling_rate // 1000
A__ : Optional[Any] = hop_length * sampling_rate // 1000
A__ : Optional[int] = optimal_fft_length(self.sample_size )
A__ : List[Any] = (self.n_fft // 2) + 1
A__ : str = window_function(window_length=self.sample_size , name=self.win_function , periodic=snake_case )
A__ : Optional[Any] = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.num_mel_bins , min_frequency=self.fmin , max_frequency=self.fmax , sampling_rate=self.sampling_rate , norm="""slaney""" , mel_scale="""slaney""" , )
if frame_signal_scale != 1.0:
warnings.warn(
"""The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers""" , snake_case , )
if reduction_factor != 2.0:
warnings.warn(
"""The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers""" , snake_case , )
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def _UpperCamelCase ( snake_case : List[np.ndarray] , snake_case : List[np.ndarray] , snake_case : float = 0.0 ):
'''simple docstring'''
if attention_mask is not None:
A__ : Tuple = np.array(snake_case , np.intaa )
A__ : List[Any] = []
for vector, length in zip(snake_case , attention_mask.sum(-1 ) ):
A__ : str = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
if length < normed_slice.shape[0]:
A__ : int = padding_value
normed_input_values.append(snake_case )
else:
A__ : List[Any] = [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]
return normed_input_values
def _UpperCamelCase ( self : Optional[int] , snake_case : np.ndarray , ):
'''simple docstring'''
A__ : List[Any] = spectrogram(
snake_case , window=self.window , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , mel_filters=self.mel_filters , mel_floor=self.mel_floor , log_mel="""log10""" , )
return log_mel_spec.T
def __call__( self : str , snake_case : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , snake_case : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , snake_case : Union[bool, str, PaddingStrategy] = False , snake_case : Optional[int] = None , snake_case : bool = False , snake_case : Optional[int] = None , snake_case : Optional[bool] = None , snake_case : Optional[Union[str, TensorType]] = None , snake_case : Optional[int] = None , **snake_case : Union[str, Any] , ):
'''simple docstring'''
if audio is None and audio_target is None:
raise ValueError("""You must provide either `audio` or `audio_target` values.""" )
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
F' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"""It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
if audio is not None:
A__ : Dict = self._process_audio(
snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , **snake_case , )
else:
A__ : Dict = None
if audio_target is not None:
A__ : Union[str, Any] = self._process_audio(
snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , **snake_case , )
if inputs is None:
return inputs_target
else:
A__ : Union[str, Any] = inputs_target["""input_values"""]
A__ : str = inputs_target.get("""attention_mask""" )
if decoder_attention_mask is not None:
A__ : List[str] = decoder_attention_mask
return inputs
def _UpperCamelCase ( self : str , snake_case : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , snake_case : bool = False , snake_case : Union[bool, str, PaddingStrategy] = False , snake_case : Optional[int] = None , snake_case : bool = False , snake_case : Optional[int] = None , snake_case : Optional[bool] = None , snake_case : Optional[Union[str, TensorType]] = None , **snake_case : Union[str, Any] , ):
'''simple docstring'''
A__ : List[str] = isinstance(snake_case , np.ndarray ) and len(speech.shape ) > 1
if is_batched_numpy and len(speech.shape ) > 2:
raise ValueError(F'Only mono-channel audio is supported for input to {self}' )
A__ : Tuple = is_batched_numpy or (
isinstance(snake_case , (list, tuple) ) and (isinstance(speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
A__ : Tuple = [np.asarray(snake_case , dtype=np.floataa ) for speech in speech]
elif not is_batched and not isinstance(snake_case , np.ndarray ):
A__ : Tuple = np.asarray(snake_case , dtype=np.floataa )
elif isinstance(snake_case , np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
A__ : str = speech.astype(np.floataa )
# always return batch
if not is_batched:
A__ : Optional[Any] = [speech]
# needed to make pad() work on spectrogram inputs
A__ : str = self.feature_size
# convert into correct format for padding
if is_target:
A__ : str = [self._extract_mel_features(snake_case ) for waveform in speech]
A__ : List[str] = BatchFeature({"""input_values""": features} )
A__ : Dict = self.num_mel_bins
else:
A__ : Tuple = BatchFeature({"""input_values""": speech} )
A__ : List[str] = self.pad(
snake_case , padding=snake_case , max_length=snake_case , truncation=snake_case , pad_to_multiple_of=snake_case , return_attention_mask=snake_case , **snake_case , )
A__ : List[str] = feature_size_hack
# convert input values to correct format
A__ : str = padded_inputs["""input_values"""]
if not isinstance(input_values[0] , np.ndarray ):
A__ : Tuple = [np.asarray(snake_case , dtype=np.floataa ) for array in input_values]
elif (
not isinstance(snake_case , np.ndarray )
and isinstance(input_values[0] , np.ndarray )
and input_values[0].dtype is np.dtype(np.floataa )
):
A__ : Dict = [array.astype(np.floataa ) for array in input_values]
elif isinstance(snake_case , np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
A__ : Any = input_values.astype(np.floataa )
# convert attention_mask to correct format
A__ : List[Any] = padded_inputs.get("""attention_mask""" )
if attention_mask is not None:
A__ : Any = [np.asarray(snake_case , dtype=np.intaa ) for array in attention_mask]
# zero-mean and unit-variance normalization
if not is_target and self.do_normalize:
A__ : Optional[int] = (
attention_mask
if self._get_padding_strategies(snake_case , max_length=snake_case ) is not PaddingStrategy.DO_NOT_PAD
else None
)
A__ : Optional[int] = self.zero_mean_unit_var_norm(
padded_inputs["""input_values"""] , attention_mask=snake_case , padding_value=self.padding_value )
if return_tensors is not None:
A__ : Any = padded_inputs.convert_to_tensors(snake_case )
return padded_inputs
def _UpperCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
A__ : List[str] = super().to_dict()
# Don't serialize these as they are derived from the other properties.
A__ : Dict = ["""window""", """mel_filters""", """sample_size""", """sample_stride""", """n_fft""", """n_freqs"""]
for name in names:
if name in output:
del output[name]
return output
| 498 | 0 |
def _a ( lowercase__ : int , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
return round(float(moles / volume ) * nfactor )
def _a ( lowercase__ : float , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
return round(float((moles * 0.0821 * temperature) / (volume) ) )
def _a ( lowercase__ : float , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
return round(float((moles * 0.0821 * temperature) / (pressure) ) )
def _a ( lowercase__ : float , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
return round(float((pressure * volume) / (0.0821 * moles) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 85 |
def _lowerCAmelCase ( _lowerCAmelCase = 1000 ) -> int:
'''simple docstring'''
__snake_case = 2**power
__snake_case = str(_lowerCAmelCase )
__snake_case = list(_lowerCAmelCase )
__snake_case = 0
for i in list_num:
sum_of_num += int(_lowerCAmelCase )
return sum_of_num
if __name__ == "__main__":
A : Optional[Any] = int(input('Enter the power of 2: ').strip())
print('2 ^ ', power, ' = ', 2**power)
A : List[Any] = solution(power)
print('Sum of the digits is: ', result)
| 371 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'glpn'
def __init__( self , snake_case_=3 , snake_case_=4 , snake_case_=[2, 2, 2, 2] , snake_case_=[8, 4, 2, 1] , snake_case_=[32, 64, 1_60, 2_56] , snake_case_=[7, 3, 3, 3] , snake_case_=[4, 2, 2, 2] , snake_case_=[1, 2, 5, 8] , snake_case_=[4, 4, 4, 4] , snake_case_="gelu" , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.02 , snake_case_=0.1 , snake_case_=1E-6 , snake_case_=64 , snake_case_=10 , snake_case_=-1 , **snake_case_ , ):
super().__init__(**snake_case_ )
lowercase =num_channels
lowercase =num_encoder_blocks
lowercase =depths
lowercase =sr_ratios
lowercase =hidden_sizes
lowercase =patch_sizes
lowercase =strides
lowercase =mlp_ratios
lowercase =num_attention_heads
lowercase =hidden_act
lowercase =hidden_dropout_prob
lowercase =attention_probs_dropout_prob
lowercase =initializer_range
lowercase =drop_path_rate
lowercase =layer_norm_eps
lowercase =decoder_hidden_size
lowercase =max_depth
lowercase =head_in_index
| 145 |
'''simple docstring'''
import argparse
import json
import subprocess
def UpperCamelCase ( lowercase_ : List[Any] , lowercase_ : Tuple ) -> Union[str, Any]:
'''simple docstring'''
lowercase =[]
lowercase =(
f'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
''' https://api.github.com/repos/huggingface/transformers/actions/runners'''
)
lowercase =subprocess.run(lowercase_ , shell=lowercase_ , stdout=subprocess.PIPE )
lowercase =output.stdout.decode('''utf-8''' )
lowercase =json.loads(lowercase_ )
lowercase =status['''runners''']
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(lowercase_ )
# save the result so we can report them on Slack
with open('''offline_runners.txt''' , '''w''' ) as fp:
fp.write(json.dumps(lowercase_ ) )
if len(lowercase_ ) > 0:
lowercase ='''\n'''.join([x['''name'''] for x in offline_runners] )
raise ValueError(f'The following runners are offline:\n{failed}' )
if __name__ == "__main__":
def UpperCamelCase ( lowercase_ : int ) -> Optional[int]:
'''simple docstring'''
return values.split(''',''' )
_UpperCAmelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--target_runners''',
default=None,
type=list_str,
required=True,
help='''Comma-separated list of runners to check status.''',
)
parser.add_argument(
'''--token''', default=None, type=str, required=True, help='''A token that has actions:read permission.'''
)
_UpperCAmelCase : Optional[int] = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 145 | 1 |
"""simple docstring"""
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
snake_case = HfApi()
snake_case = {}
# fmt: off
snake_case = torch.tensor([
-0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7_4_6_7,
1.2_3_4_2, -2.2_4_8_5, 0.4_6_3_6, 0.8_0_7_6, -0.7_9_9_1, 0.3_9_6_9, 0.8_4_9_8, 0.9_1_8_9,
-1.8_8_8_7, -3.3_5_2_2, 0.7_6_3_9, 0.2_0_4_0, 0.6_2_7_1, -2.7_1_4_8, -1.6_3_1_6, 3.0_8_3_9,
0.3_1_8_6, 0.2_7_2_1, -0.9_7_5_9, -1.2_4_6_1, 2.6_2_5_7, 1.3_5_5_7
])
snake_case = torch.tensor([
-2.3_6_3_9, -2.5_3_4_4, 0.0_0_5_4, -0.6_6_7_4, 1.5_9_9_0, 1.0_1_5_8, 0.3_1_2_4, -2.1_4_3_6,
1.8_7_9_5, -2.5_4_2_9, -0.1_5_6_6, -0.3_9_7_3, 1.2_4_9_0, 2.6_4_4_7, 1.2_2_8_3, -0.5_2_0_8,
-2.8_1_5_4, -3.5_1_1_9, 2.3_8_3_8, 1.2_0_3_3, 1.7_2_0_1, -2.1_2_5_6, -1.4_5_7_6, 2.7_9_4_8,
2.4_2_0_4, -0.9_7_5_2, -1.2_5_4_6, 0.8_0_2_7, 3.2_7_5_8, 3.1_3_6_5
])
snake_case = torch.tensor([
-0.6_5_3_1, -0.6_8_9_1, -0.3_1_7_2, -0.5_3_7_5, -0.9_1_4_0, -0.5_3_6_7, -0.1_1_7_5, -0.7_8_6_9,
-0.3_8_0_8, -0.4_5_1_3, -0.2_0_9_8, -0.0_0_8_3, 0.3_1_8_3, 0.5_1_4_0, 0.2_2_4_7, -0.1_3_0_4,
-0.1_3_0_2, -0.2_8_0_2, -0.2_0_8_4, -0.2_0_2_5, -0.4_9_6_7, -0.4_8_7_3, -0.0_8_6_1, 0.6_9_2_5,
0.0_2_5_0, 0.1_2_9_0, -0.1_5_4_3, 0.6_3_1_6, 1.0_4_6_0, 1.4_9_4_3
])
snake_case = torch.tensor([
0.0_9_1_1, 0.1_1_0_7, 0.0_1_8_2, 0.0_4_3_5, -0.0_8_0_5, -0.0_6_0_8, 0.0_3_8_1, 0.2_1_7_2,
-0.0_2_8_0, 0.1_3_2_7, -0.0_2_9_9, -0.0_2_5_5, -0.0_0_5_0, -0.1_1_7_0, -0.1_0_4_6, 0.0_3_0_9,
0.1_3_6_7, 0.1_7_2_8, -0.0_5_3_3, -0.0_7_4_8, -0.0_5_3_4, 0.1_6_2_4, 0.0_3_8_4, -0.1_8_0_5,
-0.0_7_0_7, 0.0_6_4_2, 0.0_2_2_0, -0.0_1_3_4, -0.1_3_3_3, -0.1_5_0_5
])
snake_case = torch.tensor([
0.1_3_2_1, 0.1_3_3_7, 0.0_4_4_0, 0.0_6_2_2, -0.0_5_9_1, -0.0_3_7_0, 0.0_5_0_3, 0.2_1_3_3,
-0.0_1_7_7, 0.1_4_1_5, -0.0_1_1_6, -0.0_1_1_2, 0.0_0_4_4, -0.0_9_8_0, -0.0_7_8_9, 0.0_3_9_5,
0.1_5_0_2, 0.1_7_8_5, -0.0_4_8_8, -0.0_5_1_4, -0.0_4_0_4, 0.1_5_3_9, 0.0_4_5_4, -0.1_5_5_9,
-0.0_6_6_5, 0.0_6_5_9, 0.0_3_8_3, -0.0_0_0_5, -0.1_2_6_6, -0.1_3_8_6
])
snake_case = torch.tensor([
0.1_1_5_4, 0.1_2_1_8, 0.0_3_0_7, 0.0_5_2_6, -0.0_7_1_1, -0.0_5_4_1, 0.0_3_6_6, 0.2_0_7_8,
-0.0_2_6_7, 0.1_3_1_7, -0.0_2_2_6, -0.0_1_9_3, -0.0_0_1_4, -0.1_0_5_5, -0.0_9_0_2, 0.0_3_3_0,
0.1_3_9_1, 0.1_7_0_9, -0.0_5_6_2, -0.0_6_9_3, -0.0_5_6_0, 0.1_4_8_2, 0.0_3_8_1, -0.1_6_8_3,
-0.0_6_8_1, 0.0_6_6_1, 0.0_3_3_1, -0.0_0_4_6, -0.1_2_6_8, -0.1_4_3_1
])
snake_case = torch.tensor([
0.1_1_9_2, 0.1_2_4_0, 0.0_4_1_4, 0.0_6_0_6, -0.0_5_5_7, -0.0_4_1_2, 0.0_4_3_0, 0.2_0_4_2,
-0.0_2_0_0, 0.1_3_8_5, -0.0_1_1_5, -0.0_1_3_2, 0.0_0_1_7, -0.0_9_6_5, -0.0_8_0_2, 0.0_3_9_8,
0.1_4_3_3, 0.1_7_4_7, -0.0_4_5_8, -0.0_5_3_3, -0.0_4_0_7, 0.1_5_4_5, 0.0_4_1_9, -0.1_5_7_4,
-0.0_6_4_5, 0.0_6_2_6, 0.0_3_4_1, -0.0_0_1_0, -0.1_1_9_9, -0.1_3_9_0
])
snake_case = torch.tensor([
0.1_0_7_5, 0.1_0_7_4, 0.0_2_0_5, 0.0_4_3_1, -0.0_7_7_4, -0.0_6_0_7, 0.0_2_9_8, 0.2_0_4_2,
-0.0_3_2_0, 0.1_2_6_7, -0.0_2_8_1, -0.0_2_5_0, -0.0_0_6_4, -0.1_0_9_1, -0.0_9_4_6, 0.0_2_9_0,
0.1_3_2_8, 0.1_6_5_0, -0.0_5_8_0, -0.0_7_3_8, -0.0_5_8_6, 0.1_4_4_0, 0.0_3_3_7, -0.1_7_4_6,
-0.0_7_1_2, 0.0_6_0_5, 0.0_2_5_0, -0.0_0_9_9, -0.1_3_1_6, -0.1_4_7_3
])
snake_case = torch.tensor([
-1.4_5_7_2, -2.0_4_8_1, -0.0_4_1_4, -0.6_0_0_5, 1.4_1_3_6, 0.5_8_4_8, 0.4_0_2_8, -2.7_3_3_0,
1.2_2_1_2, -2.1_2_2_8, 0.2_1_5_5, 0.4_0_3_9, 0.7_6_6_2, 2.0_5_3_5, 0.7_4_7_7, -0.3_2_4_3,
-2.1_7_5_8, -2.7_6_4_8, 1.6_9_4_7, 0.7_0_2_6, 1.2_3_3_8, -1.6_0_7_8, -0.8_6_8_2, 2.2_8_1_0,
1.8_5_7_4, -0.5_7_1_8, -0.5_5_8_6, -0.0_1_8_6, 2.3_4_1_5, 2.1_2_5_1])
snake_case = torch.tensor([
-1.3_6_9_0, -1.9_7_2_0, -0.4_0_9_0, -0.6_9_6_6, 1.4_6_6_0, 0.9_9_3_8, -0.1_3_8_5, -2.7_3_2_4,
0.7_7_3_6, -1.8_9_1_7, 0.2_9_2_3, 0.4_2_9_3, 0.1_6_9_3, 1.4_1_1_2, 1.1_8_8_7, -0.3_1_8_1,
-2.2_1_6_0, -2.6_3_8_1, 1.3_1_7_0, 0.8_1_6_3, 0.9_2_4_0, -1.6_5_4_4, -0.6_0_9_9, 2.5_2_5_9,
1.6_4_3_0, -0.9_0_9_0, -0.9_3_9_2, -0.0_1_2_6, 2.4_2_6_8, 2.3_2_6_6
])
snake_case = torch.tensor([
-1.3_5_2_5, -1.9_6_2_8, -0.3_9_5_6, -0.6_8_6_0, 1.4_6_6_4, 1.0_0_1_4, -0.1_2_5_9, -2.7_2_1_2,
0.7_7_7_2, -1.8_8_1_1, 0.2_9_9_6, 0.4_3_8_8, 0.1_7_0_4, 1.4_0_2_9, 1.1_7_0_1, -0.3_0_2_7,
-2.2_0_5_3, -2.6_2_8_7, 1.3_3_5_0, 0.8_1_3_1, 0.9_2_7_4, -1.6_2_9_2, -0.6_0_9_8, 2.5_1_3_1,
1.6_5_0_5, -0.8_9_5_8, -0.9_2_9_8, -0.0_1_5_1, 2.4_2_5_7, 2.3_3_5_5
])
snake_case = torch.tensor([
-2.0_5_8_5, -2.7_8_9_7, -0.2_8_5_0, -0.8_9_4_0, 1.9_0_5_2, 0.5_7_0_2, 0.6_3_4_5, -3.8_9_5_9,
1.5_9_3_2, -3.2_3_1_9, 0.1_9_7_4, 0.0_2_8_7, 1.7_5_6_6, 2.6_5_4_3, 0.8_3_8_7, -0.5_3_5_1,
-3.2_7_3_6, -4.3_3_7_5, 2.9_0_2_9, 1.6_3_9_0, 1.4_6_4_0, -2.1_7_0_1, -1.9_0_1_3, 2.9_3_4_1,
3.4_9_8_1, -0.6_2_5_5, -1.1_6_4_4, -0.1_5_9_1, 3.7_0_9_7, 3.2_0_6_6
])
snake_case = torch.tensor([
-2.3_1_3_9, -2.5_5_9_4, -0.0_1_9_7, -0.6_7_8_5, 1.7_0_0_1, 1.1_6_0_6, 0.3_0_7_5, -2.1_7_4_0,
1.8_0_7_1, -2.5_6_3_0, -0.0_9_2_6, -0.3_8_1_1, 1.2_1_1_6, 2.6_2_4_6, 1.2_7_3_1, -0.5_3_9_8,
-2.8_1_5_3, -3.6_1_4_0, 2.3_8_9_3, 1.3_2_6_2, 1.6_2_5_8, -2.1_8_5_6, -1.3_2_6_7, 2.8_3_9_5,
2.3_7_7_9, -1.0_6_2_3, -1.2_4_6_8, 0.8_9_5_9, 3.3_3_6_7, 3.2_2_4_3
])
snake_case = torch.tensor([
-2.0_6_2_8, -2.7_6_6_7, -0.2_0_8_9, -0.8_2_6_3, 2.0_5_3_9, 0.5_9_9_2, 0.6_4_9_5, -3.8_3_3_6,
1.6_0_2_5, -3.2_8_1_7, 0.1_7_2_1, -0.0_6_3_3, 1.7_5_1_6, 2.7_0_3_9, 0.8_1_0_0, -0.5_9_0_8,
-3.2_1_1_3, -4.4_3_4_3, 2.9_2_5_7, 1.3_6_3_2, 1.5_5_6_2, -2.1_4_8_9, -1.9_8_9_4, 3.0_5_6_0,
3.3_3_9_6, -0.7_3_2_8, -1.0_4_1_7, 0.0_3_8_3, 3.7_0_9_3, 3.2_3_4_3
])
snake_case = torch.tensor([
-1.4_5_7_4, -2.0_5_6_9, -0.0_4_7_3, -0.6_1_1_7, 1.4_0_1_8, 0.5_7_6_9, 0.4_1_2_9, -2.7_3_4_4,
1.2_2_4_1, -2.1_3_9_7, 0.2_0_0_0, 0.3_9_3_7, 0.7_6_1_6, 2.0_4_5_3, 0.7_3_2_4, -0.3_3_9_1,
-2.1_7_4_6, -2.7_7_4_4, 1.6_9_6_3, 0.6_9_2_1, 1.2_1_8_7, -1.6_1_7_2, -0.8_8_7_7, 2.2_4_3_9,
1.8_4_7_1, -0.5_8_3_9, -0.5_6_0_5, -0.0_4_6_4, 2.3_2_5_0, 2.1_2_1_9
])
# fmt: on
snake_case = api.list_models(filter='''diffusers''')
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
snake_case = '''/home/patrick/google_checkpoints/''' + mod.modelId.split('''/''')[-1]
print(F"Started running {mod.modelId}!!!")
if mod.modelId.startswith('''CompVis'''):
snake_case = UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''')
else:
snake_case = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
snake_case = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
snake_case = torch.tensor([1_0] * noise.shape[0])
with torch.no_grad():
snake_case = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :3_0], results['''_'''.join('''_'''.join(mod.modelId.split('''/''')).split('''-'''))], atol=1E-3
)
print(F"{mod.modelId} has passed successfully!!!")
| 103 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
def _lowerCamelCase ( self :Tuple , a :float ) -> float:
return 0.0
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : np.ndarray , _lowerCamelCase : int) -> tuple[int | float, int | float]:
'''simple docstring'''
__UpperCamelCase : List[str] = min([-20, np.min(fft_results[1 : samplerate // 2 - 1])])
__UpperCamelCase : Any = max([20, np.max(fft_results[1 : samplerate // 2 - 1])])
return lowest, highest
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : FilterType , _lowerCamelCase : int) -> None:
'''simple docstring'''
__UpperCamelCase : List[str] = 512
__UpperCamelCase : List[Any] = [1] + [0] * (size - 1)
__UpperCamelCase : List[Any] = [filter_type.process(_lowerCamelCase) for item in inputs]
__UpperCamelCase : Optional[Any] = [0] * (samplerate - size) # zero-padding
outputs += filler
__UpperCamelCase : Optional[int] = np.abs(np.fft.fft(_lowerCamelCase))
__UpperCamelCase : Optional[int] = 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
__UpperCamelCase : Optional[Any] = get_bounds(_lowerCamelCase , _lowerCamelCase)
plt.ylim(max([-80, bounds[0]]) , min([80, bounds[1]]))
plt.ylabel("Gain (dB)")
plt.plot(_lowerCamelCase)
plt.show()
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : FilterType , _lowerCamelCase : int) -> None:
'''simple docstring'''
__UpperCamelCase : Any = 512
__UpperCamelCase : Dict = [1] + [0] * (size - 1)
__UpperCamelCase : Tuple = [filter_type.process(_lowerCamelCase) for item in inputs]
__UpperCamelCase : Dict = [0] * (samplerate - size) # zero-padding
outputs += filler
__UpperCamelCase : Optional[int] = 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() | 557 | 0 |
import sys
from collections import defaultdict
class _UpperCamelCase :
def __init__( self :List[Any] ) -> str:
UpperCAmelCase__ = []
def UpperCAmelCase_ ( self :str , lowerCamelCase :Union[str, Any] ) -> Union[str, Any]:
return self.node_position[vertex]
def UpperCAmelCase_ ( self :Union[str, Any] , lowerCamelCase :Optional[int] , lowerCamelCase :Tuple ) -> Tuple:
UpperCAmelCase__ = pos
def UpperCAmelCase_ ( self :Union[str, Any] , lowerCamelCase :int , lowerCamelCase :Union[str, Any] , lowerCamelCase :Tuple , lowerCamelCase :Optional[Any] ) -> Optional[int]:
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
UpperCAmelCase__ = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
UpperCAmelCase__ = 2 * start + 1
else:
UpperCAmelCase__ = 2 * start + 2
if heap[smallest_child] < heap[start]:
UpperCAmelCase__ , UpperCAmelCase__ = heap[smallest_child], positions[smallest_child]
UpperCAmelCase__ , UpperCAmelCase__ = (
heap[start],
positions[start],
)
UpperCAmelCase__ , UpperCAmelCase__ = temp, tempa
UpperCAmelCase__ = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , lowerCamelCase )
self.top_to_bottom(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
def UpperCAmelCase_ ( self :Union[str, Any] , lowerCamelCase :Union[str, Any] , lowerCamelCase :List[str] , lowerCamelCase :List[Any] , lowerCamelCase :Tuple ) -> int:
UpperCAmelCase__ = position[index]
while index != 0:
UpperCAmelCase__ = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
UpperCAmelCase__ = heap[parent]
UpperCAmelCase__ = position[parent]
self.set_position(position[parent] , lowerCamelCase )
else:
UpperCAmelCase__ = val
UpperCAmelCase__ = temp
self.set_position(lowerCamelCase , lowerCamelCase )
break
UpperCAmelCase__ = parent
else:
UpperCAmelCase__ = val
UpperCAmelCase__ = temp
self.set_position(lowerCamelCase , 0 )
def UpperCAmelCase_ ( self :List[str] , lowerCamelCase :Dict , lowerCamelCase :Optional[int] ) -> Optional[int]:
UpperCAmelCase__ = len(lowerCamelCase ) // 2 - 1
for i in range(lowerCamelCase , -1 , -1 ):
self.top_to_bottom(lowerCamelCase , lowerCamelCase , len(lowerCamelCase ) , lowerCamelCase )
def UpperCAmelCase_ ( self :str , lowerCamelCase :List[Any] , lowerCamelCase :Union[str, Any] ) -> List[Any]:
UpperCAmelCase__ = positions[0]
UpperCAmelCase__ = sys.maxsize
self.top_to_bottom(lowerCamelCase , 0 , len(lowerCamelCase ) , lowerCamelCase )
return temp
def lowerCAmelCase ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = Heap()
UpperCAmelCase__ = [0] * len(_lowerCAmelCase )
UpperCAmelCase__ = [-1] * len(_lowerCAmelCase ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
UpperCAmelCase__ = [] # Heap of Distance of vertices from their neighboring vertex
UpperCAmelCase__ = []
for vertex in range(len(_lowerCAmelCase ) ):
distance_tv.append(sys.maxsize )
positions.append(_lowerCAmelCase )
heap.node_position.append(_lowerCAmelCase )
UpperCAmelCase__ = []
UpperCAmelCase__ = 1
UpperCAmelCase__ = sys.maxsize
for neighbor, distance in adjacency_list[0]:
UpperCAmelCase__ = 0
UpperCAmelCase__ = distance
heap.heapify(_lowerCAmelCase , _lowerCAmelCase )
for _ in range(1 , len(_lowerCAmelCase ) ):
UpperCAmelCase__ = heap.delete_minimum(_lowerCAmelCase , _lowerCAmelCase )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
UpperCAmelCase__ = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(_lowerCAmelCase )]
):
UpperCAmelCase__ = distance
heap.bottom_to_top(
_lowerCAmelCase , heap.get_position(_lowerCAmelCase ) , _lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase__ = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
_lowerCAmelCase : Optional[int] = int(input("Enter number of edges: ").strip())
_lowerCAmelCase : List[Any] = defaultdict(list)
for _ in range(edges_number):
_lowerCAmelCase : List[str] = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 364 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
_lowerCAmelCase : int = "facebook/wmt19-en-de"
_lowerCAmelCase : int = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCAmelCase : Dict = FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
_lowerCAmelCase : List[Any] = FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')
# Test
_lowerCAmelCase : Optional[Any] = tokenizer(["Making tiny model"], return_tensors="pt")
_lowerCAmelCase : Optional[Any] = tiny_model(**batch)
print("test output:", len(outputs.logits[0]))
# Save
_lowerCAmelCase : Optional[Any] = "tiny-wmt19-en-de"
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F'''Generated {mname_tiny}''')
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 364 | 1 |
'''simple docstring'''
from decimal import Decimal, getcontext
from math import ceil, factorial
def UpperCAmelCase__ ( UpperCAmelCase_ : int ) -> str:
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise TypeError('Undefined for non-integers' )
elif precision < 1:
raise ValueError('Undefined for non-natural numbers' )
__lowerCamelCase : List[Any] = precision
__lowerCamelCase : Tuple = ceil(precision / 14 )
__lowerCamelCase : Union[str, Any] = 42_68_80 * Decimal(1_00_05 ).sqrt()
__lowerCamelCase : List[str] = 1
__lowerCamelCase : Dict = 13_59_14_09
__lowerCamelCase : Union[str, Any] = Decimal(UpperCAmelCase_ )
for k in range(1 , UpperCAmelCase_ ):
__lowerCamelCase : List[Any] = factorial(6 * k ) // (factorial(3 * k ) * factorial(UpperCAmelCase_ ) ** 3)
linear_term += 5_45_14_01_34
exponential_term *= -26_25_37_41_26_40_76_80_00
partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
return str(constant_term / partial_sum )[:-1]
if __name__ == "__main__":
A__ : Any = 50
print(f'''The first {n} digits of pi is: {pi(n)}''')
| 13 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def UpperCAmelCase__ ( UpperCAmelCase_ : bytes ) -> bytes:
if len(UpperCAmelCase_ ) != 32:
raise ValueError('Input must be of length 32' )
__lowerCamelCase : Dict = B''
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def UpperCAmelCase__ ( UpperCAmelCase_ : int ) -> bytes:
if i < 0:
raise ValueError('Input must be non-negative' )
__lowerCamelCase : Union[str, Any] = format(UpperCAmelCase_ , '08x' )[-8:]
__lowerCamelCase : str = B''
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('utf-8' )
return little_endian_hex
def UpperCAmelCase__ ( UpperCAmelCase_ : bytes ) -> bytes:
__lowerCamelCase : Optional[Any] = B''
for char in message:
bit_string += format(UpperCAmelCase_ , '08b' ).encode('utf-8' )
__lowerCamelCase : List[str] = format(len(UpperCAmelCase_ ) , '064b' ).encode('utf-8' )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(UpperCAmelCase_ ) % 5_12 != 4_48:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )
return bit_string
def UpperCAmelCase__ ( UpperCAmelCase_ : bytes ) -> Generator[list[int], None, None]:
if len(UpperCAmelCase_ ) % 5_12 != 0:
raise ValueError('Input must have length that\'s a multiple of 512' )
for pos in range(0 , len(UpperCAmelCase_ ) , 5_12 ):
__lowerCamelCase : Any = bit_string[pos : pos + 5_12]
__lowerCamelCase : Optional[int] = []
for i in range(0 , 5_12 , 32 ):
block_words.append(int(to_little_endian(block[i : i + 32] ) , 2 ) )
yield block_words
def UpperCAmelCase__ ( UpperCAmelCase_ : int ) -> int:
if i < 0:
raise ValueError('Input must be non-negative' )
__lowerCamelCase : List[Any] = format(UpperCAmelCase_ , '032b' )
__lowerCamelCase : Optional[int] = ''
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(UpperCAmelCase_ , 2 )
def UpperCAmelCase__ ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> int:
return (a + b) % 2**32
def UpperCAmelCase__ ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> int:
if i < 0:
raise ValueError('Input must be non-negative' )
if shift < 0:
raise ValueError('Shift must be non-negative' )
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def UpperCAmelCase__ ( UpperCAmelCase_ : bytes ) -> bytes:
__lowerCamelCase : Optional[Any] = preprocess(UpperCAmelCase_ )
__lowerCamelCase : Union[str, Any] = [int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]
# Starting states
__lowerCamelCase : Dict = 0x67_45_23_01
__lowerCamelCase : Union[str, Any] = 0xef_cd_ab_89
__lowerCamelCase : Optional[Any] = 0x98_ba_dc_fe
__lowerCamelCase : Union[str, Any] = 0x10_32_54_76
__lowerCamelCase : List[str] = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(UpperCAmelCase_ ):
__lowerCamelCase : Dict = aa
__lowerCamelCase : Tuple = ba
__lowerCamelCase : List[Any] = ca
__lowerCamelCase : Dict = da
# Hash current chunk
for i in range(64 ):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
__lowerCamelCase : List[str] = d ^ (b & (c ^ d))
__lowerCamelCase : Optional[int] = i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
__lowerCamelCase : Optional[int] = c ^ (d & (b ^ c))
__lowerCamelCase : Tuple = (5 * i + 1) % 16
elif i <= 47:
__lowerCamelCase : str = b ^ c ^ d
__lowerCamelCase : Any = (3 * i + 5) % 16
else:
__lowerCamelCase : Union[str, Any] = c ^ (b | not_aa(UpperCAmelCase_ ))
__lowerCamelCase : int = (7 * i) % 16
__lowerCamelCase : Optional[int] = (f + a + added_consts[i] + block_words[g]) % 2**32
__lowerCamelCase : Optional[Any] = d
__lowerCamelCase : Tuple = c
__lowerCamelCase : Optional[int] = b
__lowerCamelCase : List[str] = sum_aa(UpperCAmelCase_ , left_rotate_aa(UpperCAmelCase_ , shift_amounts[i] ) )
# Add hashed chunk to running total
__lowerCamelCase : int = sum_aa(UpperCAmelCase_ , UpperCAmelCase_ )
__lowerCamelCase : str = sum_aa(UpperCAmelCase_ , UpperCAmelCase_ )
__lowerCamelCase : List[Any] = sum_aa(UpperCAmelCase_ , UpperCAmelCase_ )
__lowerCamelCase : List[str] = sum_aa(UpperCAmelCase_ , UpperCAmelCase_ )
__lowerCamelCase : Dict = reformat_hex(UpperCAmelCase_ ) + reformat_hex(UpperCAmelCase_ ) + reformat_hex(UpperCAmelCase_ ) + reformat_hex(UpperCAmelCase_ )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 13 | 1 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase :Tuple = logging.get_logger(__name__)
def A ( UpperCAmelCase , UpperCAmelCase ):
_snake_case : Tuple = RobertaPreLayerNormConfig.from_pretrained(
UpperCAmelCase , architectures=["RobertaPreLayerNormForMaskedLM"] )
# convert state_dict
_snake_case : Optional[Any] = torch.load(hf_hub_download(repo_id=UpperCAmelCase , filename="pytorch_model.bin" ) )
_snake_case : Optional[Any] = {}
for tensor_key, tensor_value in original_state_dict.items():
# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
if tensor_key.startswith("roberta." ):
_snake_case : str = "roberta_prelayernorm." + tensor_key[len("roberta." ) :]
# The original implementation contains weights which are not used, remove them from the state_dict
if tensor_key.endswith(".self.LayerNorm.weight" ) or tensor_key.endswith(".self.LayerNorm.bias" ):
continue
_snake_case : Union[str, Any] = tensor_value
_snake_case : Optional[Any] = RobertaPreLayerNormForMaskedLM.from_pretrained(
pretrained_model_name_or_path=UpperCAmelCase , config=UpperCAmelCase , state_dict=UpperCAmelCase )
model.save_pretrained(UpperCAmelCase )
# convert tokenizer
_snake_case : List[Any] = AutoTokenizer.from_pretrained(UpperCAmelCase )
tokenizer.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
__lowerCAmelCase :List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint-repo',
default=None,
type=str,
required=True,
help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
__lowerCAmelCase :Optional[Any] = parser.parse_args()
convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path) | 701 |
import sys
import turtle
def A ( UpperCAmelCase , UpperCAmelCase ):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def A ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , ):
my_pen.up()
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.down()
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.goto(vertexa[0] , vertexa[1] )
if depth == 0:
return
triangle(UpperCAmelCase , get_mid(UpperCAmelCase , UpperCAmelCase ) , get_mid(UpperCAmelCase , UpperCAmelCase ) , depth - 1 )
triangle(UpperCAmelCase , get_mid(UpperCAmelCase , UpperCAmelCase ) , get_mid(UpperCAmelCase , UpperCAmelCase ) , depth - 1 )
triangle(UpperCAmelCase , get_mid(UpperCAmelCase , UpperCAmelCase ) , get_mid(UpperCAmelCase , UpperCAmelCase ) , depth - 1 )
if __name__ == "__main__":
if len(sys.argv) != 2:
raise ValueError(
'Correct format for using this script: '
'python fractals.py <int:depth_for_fractal>'
)
__lowerCAmelCase :Any = turtle.Turtle()
my_pen.ht()
my_pen.speed(5)
my_pen.pencolor('red')
__lowerCAmelCase :Optional[int] = [(-1_75, -1_25), (0, 1_75), (1_75, -1_25)] # vertices of triangle
triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1])) | 278 | 0 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
lowercase = logging.get_logger(__name__) # pylint: disable=invalid-name
def __UpperCAmelCase ( a_):
if not path:
return "pipe"
for ext in PipelineDataFormat.SUPPORTED_FORMATS:
if path.endswith(a_):
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 ( a_):
snake_case_ = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
snake_case_ = try_infer_format_from_ext(args.input) if args.format == 'infer' else args.format
snake_case_ = PipelineDataFormat.from_str(
format=a_ , output_path=args.output , input_path=args.input , column=args.column if args.column else nlp.default_input_names , overwrite=args.overwrite , )
return RunCommand(a_ , a_)
class UpperCamelCase_ ( snake_case_ ):
'''simple docstring'''
def __init__( self , a , a ) -> Dict:
snake_case_ = nlp
snake_case_ = reader
@staticmethod
def _UpperCamelCase ( a ) -> Dict:
snake_case_ = 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=a , help='Path to the file to use for inference' )
run_parser.add_argument('--output' , type=a , help='Path to the file that will be used post to write results.' )
run_parser.add_argument('--model' , type=a , help='Name or path to the model to instantiate.' )
run_parser.add_argument('--config' , type=a , help='Name or path to the model\'s config to instantiate.' )
run_parser.add_argument(
'--tokenizer' , type=a , help='Name of the tokenizer to use. (default: same as the model name)' )
run_parser.add_argument(
'--column' , type=a , help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)' , )
run_parser.add_argument(
'--format' , type=a , default='infer' , choices=PipelineDataFormat.SUPPORTED_FORMATS , help='Input format to read from' , )
run_parser.add_argument(
'--device' , type=a , 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=a )
def _UpperCamelCase ( self ) -> Tuple:
snake_case_ , snake_case_ = self._nlp, []
for entry in self._reader:
snake_case_ = nlp(**a ) if self._reader.is_multi_columns else nlp(a )
if isinstance(a , a ):
outputs.append(a )
else:
outputs += output
# Saving data
if self._nlp.binary_output:
snake_case_ = self._reader.save_binary(a )
logger.warning(F'''Current pipeline requires output to be in binary format, saving at {binary_path}''' )
else:
self._reader.save(a )
| 198 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case_ )
class UpperCamelCase_ ( snake_case_ ):
'''simple docstring'''
lowerCAmelCase = field(default='''language-modeling''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
lowerCAmelCase = Features({'''text''': Value('''string''' )} )
lowerCAmelCase = Features({} )
lowerCAmelCase = "text"
@property
def _UpperCamelCase ( self ) -> Dict[str, str]:
return {self.text_column: "text"}
| 198 | 1 |
from __future__ import annotations
import math
def _lowerCamelCase ( __A : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__A ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
SCREAMING_SNAKE_CASE = [num for num in range(3, 100001, 2) if not is_prime(num)]
def _lowerCamelCase ( __A : int ) -> list[int]:
if not isinstance(__A , __A ):
raise ValueError('''n must be an integer''' )
if n <= 0:
raise ValueError('''n must be >= 0''' )
_UpperCAmelCase : Tuple = []
for num in range(len(__A ) ):
_UpperCAmelCase : List[str] = 0
while 2 * i * i <= odd_composites[num]:
_UpperCAmelCase : Dict = odd_composites[num] - 2 * i * i
if is_prime(__A ):
break
i += 1
else:
list_nums.append(odd_composites[num] )
if len(__A ) == n:
return list_nums
return []
def _lowerCamelCase ( ) -> int:
return compute_nums(1 )[0]
if __name__ == "__main__":
print(F'{solution() = }')
| 701 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase ( __A : int , __A : Optional[Any] , __A : int ) -> int:
# Initialise PyTorch model
_UpperCAmelCase : Dict = RemBertConfig.from_json_file(__A )
print('''Building PyTorch model from configuration: {}'''.format(str(__A ) ) )
_UpperCAmelCase : int = RemBertModel(__A )
# Load weights from tf checkpoint
load_tf_weights_in_rembert(__A , __A , __A )
# Save pytorch-model
print('''Save PyTorch model to {}'''.format(__A ) )
torch.save(model.state_dict() , __A )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--rembert_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained RemBERT model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
SCREAMING_SNAKE_CASE = parser.parse_args()
convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)
| 186 | 0 |
import qiskit
def _a ( UpperCAmelCase = 2 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] = qubits
# Using Aer's simulator
lowerCamelCase__ : str = qiskit.Aer.get_backend('''aer_simulator''' )
# Creating a Quantum Circuit acting on the q register
lowerCamelCase__ : str = qiskit.QuantumCircuit(UpperCAmelCase , UpperCAmelCase )
# Adding a H gate on qubit 0 (now q0 in superposition)
circuit.h(0 )
for i in range(1 , UpperCAmelCase ):
# Adding CX (CNOT) gate
circuit.cx(i - 1 , UpperCAmelCase )
# Mapping the quantum measurement to the classical bits
circuit.measure(list(range(UpperCAmelCase ) ) , list(range(UpperCAmelCase ) ) )
# Now measuring any one qubit would affect other qubits to collapse
# their super position and have same state as the measured one.
# Executing the circuit on the simulator
lowerCamelCase__ : List[str] = qiskit.execute(UpperCAmelCase , UpperCAmelCase , shots=1000 )
return job.result().get_counts(UpperCAmelCase )
if __name__ == "__main__":
print(F'''Total count for various states are: {quantum_entanglement(3)}''')
| 315 |
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 | 1 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler
from transformers import GPTaLMHeadModel
def __UpperCAmelCase ( _UpperCAmelCase : List[Any]=32 , _UpperCAmelCase : Optional[int]=10 , _UpperCAmelCase : List[str]=1_00 , _UpperCAmelCase : Optional[int]=10_26 , _UpperCAmelCase : str=True , _UpperCAmelCase : Dict="data/tokenized_stories_train_wikitext103.jbl" , _UpperCAmelCase : Optional[Any]="igf_context_pairs.jbl" , ) -> Any:
set_seed(3 )
# generate train_data and objective_set
__snake_case , __snake_case = generate_datasets(
_UpperCAmelCase , _UpperCAmelCase , number=_UpperCAmelCase , min_len=10_26 , trim=_UpperCAmelCase )
# keeps model same across runs
set_seed(4 )
# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
# can we train on GPU?
__snake_case = torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
# load pretrained model
__snake_case = load_gpta("gpt2" ).to(_UpperCAmelCase )
print("computing perplexity on objective set" )
__snake_case = compute_perplexity(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).item()
print("perplexity on objective set:" , _UpperCAmelCase )
# collect igf pairs and save to file demo.jbl
collect_objective_set(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# clean up, delete model and data we don't need anymore
del model, train_data, objective_set
torch.cuda.empty_cache()
def __UpperCAmelCase ( _UpperCAmelCase : Dict , _UpperCAmelCase : Any=15 , _UpperCAmelCase : Union[str, Any]=1_28 , _UpperCAmelCase : List[str]=1_00 , _UpperCAmelCase : Optional[Any]="igf_model.pt" , ) -> Any:
set_seed(42 )
# Load pre-trained model
__snake_case = GPTaLMHeadModel.from_pretrained("gpt2" )
# Initialize secondary learner to use embedding weights of model
__snake_case = SecondaryLearner(_UpperCAmelCase )
# Train secondary learner
__snake_case = train_secondary_learner(
_UpperCAmelCase , _UpperCAmelCase , max_epochs=_UpperCAmelCase , batch_size=_UpperCAmelCase , eval_freq=1_00 , igf_model_path=_UpperCAmelCase , )
del model, secondary_learner_train_data
torch.cuda.empty_cache()
return secondary_learner
def __UpperCAmelCase ( _UpperCAmelCase : Any , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Union[str, Any]=32 , _UpperCAmelCase : List[str]=10_00 , _UpperCAmelCase : List[str]=16 , _UpperCAmelCase : Union[str, Any]=1.0 , _UpperCAmelCase : Optional[int]=recopy_gpta , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : int=10 , _UpperCAmelCase : str="gpt2_finetuned.pt" , ) -> int:
__snake_case = torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
__snake_case = RandomSampler(_UpperCAmelCase )
__snake_case = DataLoader(_UpperCAmelCase , sampler=_UpperCAmelCase )
__snake_case = max_steps // (len(_UpperCAmelCase )) + 1
__snake_case = 0
__snake_case = torch.zeros((1, context_len) , dtype=torch.long , device=_UpperCAmelCase )
__snake_case , __snake_case , __snake_case = recopy_model(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
model.train()
if secondary_learner is not None:
secondary_learner.to(_UpperCAmelCase )
secondary_learner.eval()
__snake_case = []
__snake_case = 0
__snake_case = []
__snake_case = []
# Compute the performance of the transformer model at the beginning
__snake_case = compute_perplexity(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
test_perps.append(_UpperCAmelCase )
print("Test perplexity, step" , _UpperCAmelCase , ":" , _UpperCAmelCase )
for epoch in range(int(_UpperCAmelCase ) ):
for step, example in enumerate(_UpperCAmelCase ):
torch.cuda.empty_cache()
__snake_case = random.randint(0 , example.size(2 ) - context_len - 1 )
__snake_case = example[0, 0, start : start + context_len]
lm_optimizer.zero_grad()
__snake_case = model(_UpperCAmelCase , labels=_UpperCAmelCase )
__snake_case = True
if secondary_learner is not None:
__snake_case = secondary_learner.forward(
torch.tensor(_UpperCAmelCase , dtype=torch.long , device=_UpperCAmelCase ).unsqueeze(0 ) )[0].item()
observed_qs.append(float(_UpperCAmelCase ) )
# Here we implement the simple non-constant threshold for the predicted IG(X) value
# We will decay the selectivity of our secondary learner filter from
# 1 standard deviation above average to 1 below average after 10 batches.
if global_step == 10:
__snake_case = -1
if predicted_q < threshold:
__snake_case = False
# If we passed the filter, add the context to the batch!
if do_backprop:
contexts.append(np.array(context.cpu() ) )
__snake_case = outputs[0]
lm_loss.backward()
examples += 1
del outputs
# Once the batch is filled with enough contexts, backprop on the batch.
if examples == batch_size:
torch.cuda.empty_cache()
__snake_case = 0
# Do LM backprop
torch.nn.utils.clip_grad_norm_(model.parameters() , 3.0 )
lm_optimizer.step()
lm_scheduler.step() # Update learning rate schedule
global_step += 1
# Compute the performance of the transformer model at this batch
if global_step % eval_interval == 0:
__snake_case = compute_perplexity(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
test_perps.append(_UpperCAmelCase )
print("Test perplexity, step" , _UpperCAmelCase , ":" , _UpperCAmelCase )
# Break out of the loop after 60 batches
if max_steps > 0 and global_step > 60:
break
if max_steps > 0 and global_step > 60:
break
# save finetuned transformer model
torch.save(model.state_dict() , _UpperCAmelCase )
torch.cuda.empty_cache()
# Do some cleaning up so we can reinitialize for the next run of this function
del lm_optimizer
del lm_scheduler
return model
def __UpperCAmelCase ( ) -> int:
__snake_case = argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task" )
# Required parameters
parser.add_argument(
"--data_dir" , default=_UpperCAmelCase , type=_UpperCAmelCase , required=_UpperCAmelCase , help="The input data dir. Should contain data files for WikiText." , )
parser.add_argument(
"--model_name_or_path" , default=_UpperCAmelCase , type=_UpperCAmelCase , required=_UpperCAmelCase , help="Path to pretrained model or model identifier from huggingface.co/models" , )
parser.add_argument(
"--data_file" , type=_UpperCAmelCase , default=_UpperCAmelCase , help=(
"A jbl file containing tokenized data which can be split as objective dataset, "
"train_dataset and test_dataset."
) , )
parser.add_argument(
"--igf_data_file" , type=_UpperCAmelCase , default=_UpperCAmelCase , help="A jbl file containing the context and information gain pairs to train secondary learner." , )
parser.add_argument(
"--output_dir" , default=_UpperCAmelCase , type=_UpperCAmelCase , required=_UpperCAmelCase , help="The output directory where the final fine-tuned model is stored." , )
parser.add_argument(
"--tokenizer_name" , default=_UpperCAmelCase , type=_UpperCAmelCase , help="Pretrained tokenizer name or path if not the same as model_name" , )
parser.add_argument("--seed" , type=_UpperCAmelCase , default=_UpperCAmelCase , help="A seed for reproducible training." )
parser.add_argument(
"--context_len" , default=32 , type=_UpperCAmelCase , help=(
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
) , )
parser.add_argument(
"--size_objective_set" , default=1_00 , type=_UpperCAmelCase , help="number of articles that are long enough to be used as our objective set" , )
parser.add_argument(
"--eval_freq" , default=1_00 , type=_UpperCAmelCase , help="secondary model evaluation is triggered at eval_freq" )
parser.add_argument("--max_steps" , default=10_00 , type=_UpperCAmelCase , help="To calculate training epochs" )
parser.add_argument(
"--secondary_learner_batch_size" , default=1_28 , type=_UpperCAmelCase , help="batch size of training data for secondary learner" , )
parser.add_argument(
"--batch_size" , default=16 , type=_UpperCAmelCase , help="batch size of training data of language model(gpt2) " )
parser.add_argument(
"--eval_interval" , default=10 , type=_UpperCAmelCase , help=(
"decay the selectivity of our secondary learner filter from"
"1 standard deviation above average to 1 below average after 10 batches"
) , )
parser.add_argument(
"--number" , default=1_00 , type=_UpperCAmelCase , help="The number of examples split to be used as objective_set/test_data" )
parser.add_argument(
"--min_len" , default=10_26 , type=_UpperCAmelCase , help="The minimum length of the article to be used as objective set" )
parser.add_argument(
"--secondary_learner_max_epochs" , default=15 , type=_UpperCAmelCase , help="number of epochs to train secondary learner" )
parser.add_argument("--trim" , default=_UpperCAmelCase , type=_UpperCAmelCase , help="truncate the example if it exceeds context length" )
parser.add_argument(
"--threshold" , default=1.0 , type=_UpperCAmelCase , help=(
"The threshold value used by secondary learner to filter the train_data and allow only"
" informative data as input to the model"
) , )
parser.add_argument("--finetuned_model_name" , default="gpt2_finetuned.pt" , type=_UpperCAmelCase , help="finetuned_model_name" )
parser.add_argument(
"--recopy_model" , default=_UpperCAmelCase , type=_UpperCAmelCase , help="Reset the model to the original pretrained GPT-2 weights after each iteration" , )
# function calls
# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
generate_n_pairs(
context_len=32 , max_steps=10 , size_objective_set=1_00 , min_len=10_26 , trim=_UpperCAmelCase , data_file="data/tokenized_stories_train_wikitext103.jbl" , igf_data_file="igf_context_pairs.jbl" , )
# Load train data for secondary learner
__snake_case = joblib.load("data/IGF_values.jbl" )
# Train secondary learner
__snake_case = training_secondary_learner(
_UpperCAmelCase , secondary_learner_max_epochs=15 , secondary_learner_batch_size=1_28 , eval_freq=1_00 , igf_model_path="igf_model.pt" , )
# load pretrained gpt2 model
__snake_case = GPTaLMHeadModel.from_pretrained("gpt2" )
set_seed(42 )
# Generate train and test data to train and evaluate gpt2 model
__snake_case , __snake_case = generate_datasets(
context_len=32 , file="data/tokenized_stories_train_wikitext103.jbl" , number=1_00 , min_len=10_26 , trim=_UpperCAmelCase )
# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
finetune(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , context_len=32 , max_steps=10_00 , batch_size=16 , threshold=1.0 , recopy_model=_UpperCAmelCase , secondary_learner=_UpperCAmelCase , eval_interval=10 , finetuned_model_name="gpt2_finetuned.pt" , )
if __name__ == "__main__":
main()
| 680 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a : Optional[Any] = False
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def A ( self : int ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : List[Any] ):
"""simple docstring"""
__snake_case = VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion" )
# remove text_unet
pipe.remove_unused_weights()
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
__snake_case = "A painting of a squirrel eating a burger "
__snake_case = torch.manual_seed(0 )
__snake_case = pipe(
prompt=a_ , generator=a_ , guidance_scale=7.5 , num_inference_steps=2 , output_type="numpy" ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(a_ )
__snake_case = VersatileDiffusionTextToImagePipeline.from_pretrained(a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
__snake_case = generator.manual_seed(0 )
__snake_case = pipe(
prompt=a_ , generator=a_ , guidance_scale=7.5 , num_inference_steps=2 , output_type="numpy" ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def A ( self : Optional[int] ):
"""simple docstring"""
__snake_case = VersatileDiffusionTextToImagePipeline.from_pretrained(
"shi-labs/versatile-diffusion" , torch_dtype=torch.floataa )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
__snake_case = "A painting of a squirrel eating a burger "
__snake_case = torch.manual_seed(0 )
__snake_case = pipe(
prompt=a_ , generator=a_ , guidance_scale=7.5 , num_inference_steps=50 , output_type="numpy" ).images
__snake_case = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__snake_case = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 680 | 1 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class _a :
def __init__( self : Dict , _lowercase : Dict , _lowercase : Optional[int]=13 , _lowercase : Tuple=7 , _lowercase : int=True , _lowercase : Optional[int]=True , _lowercase : int=True , _lowercase : Union[str, Any]=True , _lowercase : str=True , _lowercase : List[str]=False , _lowercase : Dict=False , _lowercase : Optional[Any]=False , _lowercase : Dict=2 , _lowercase : Union[str, Any]=99 , _lowercase : Optional[Any]=0 , _lowercase : List[str]=32 , _lowercase : Optional[int]=5 , _lowercase : int=4 , _lowercase : Tuple=0.1 , _lowercase : List[str]=0.1 , _lowercase : List[str]=512 , _lowercase : List[Any]=2 , _lowercase : Any=0.02 , _lowercase : Tuple=2 , _lowercase : List[Any]=4 , _lowercase : List[Any]="last" , _lowercase : List[Any]=True , _lowercase : Tuple=None , _lowercase : int=0 , ) -> Optional[int]:
snake_case : int = parent
snake_case : Optional[Any] = batch_size
snake_case : Optional[int] = seq_length
snake_case : Union[str, Any] = is_training
snake_case : int = use_input_lengths
snake_case : Optional[Any] = use_token_type_ids
snake_case : str = use_labels
snake_case : Optional[Any] = gelu_activation
snake_case : int = sinusoidal_embeddings
snake_case : Any = causal
snake_case : List[str] = asm
snake_case : str = n_langs
snake_case : Optional[int] = vocab_size
snake_case : List[Any] = n_special
snake_case : List[str] = hidden_size
snake_case : Optional[Any] = num_hidden_layers
snake_case : Dict = num_attention_heads
snake_case : List[Any] = hidden_dropout_prob
snake_case : str = attention_probs_dropout_prob
snake_case : List[Any] = max_position_embeddings
snake_case : Tuple = type_sequence_label_size
snake_case : Optional[int] = initializer_range
snake_case : Union[str, Any] = num_labels
snake_case : Union[str, Any] = num_choices
snake_case : str = summary_type
snake_case : str = use_proj
snake_case : Optional[Any] = scope
snake_case : List[Any] = bos_token_id
def __lowercase ( self : str ) -> List[Any]:
snake_case : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case : int = None
if self.use_input_lengths:
snake_case : Optional[Any] = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
snake_case : List[str] = None
if self.use_token_type_ids:
snake_case : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
snake_case : Optional[Any] = None
snake_case : Tuple = None
snake_case : Union[str, Any] = None
if self.use_labels:
snake_case : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case : int = ids_tensor([self.batch_size] , 2 ).float()
snake_case : Optional[int] = ids_tensor([self.batch_size] , self.num_choices )
snake_case : Any = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def __lowercase ( self : Any ) -> Optional[Any]:
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def __lowercase ( self : str , _lowercase : int , _lowercase : Any , _lowercase : List[Any] , _lowercase : Dict , _lowercase : Union[str, Any] , _lowercase : List[str] , _lowercase : List[str] , _lowercase : Any , _lowercase : List[Any] , ) -> Tuple:
snake_case : Union[str, Any] = XLMModel(config=_lowercase )
model.to(_lowercase )
model.eval()
snake_case : str = model(_lowercase , lengths=_lowercase , langs=_lowercase )
snake_case : Optional[Any] = model(_lowercase , langs=_lowercase )
snake_case : Dict = model(_lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase ( self : List[Any] , _lowercase : int , _lowercase : Dict , _lowercase : List[Any] , _lowercase : str , _lowercase : Tuple , _lowercase : List[Any] , _lowercase : List[Any] , _lowercase : List[str] , _lowercase : str , ) -> List[Any]:
snake_case : Union[str, Any] = XLMWithLMHeadModel(_lowercase )
model.to(_lowercase )
model.eval()
snake_case : int = model(_lowercase , token_type_ids=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowercase ( self : Optional[Any] , _lowercase : Dict , _lowercase : Any , _lowercase : List[str] , _lowercase : str , _lowercase : Optional[int] , _lowercase : str , _lowercase : int , _lowercase : Union[str, Any] , _lowercase : Union[str, Any] , ) -> List[str]:
snake_case : Any = XLMForQuestionAnsweringSimple(_lowercase )
model.to(_lowercase )
model.eval()
snake_case : Union[str, Any] = model(_lowercase )
snake_case : Optional[Any] = model(_lowercase , start_positions=_lowercase , end_positions=_lowercase )
snake_case : int = outputs
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 : Any , _lowercase : List[str] , _lowercase : str , _lowercase : int , _lowercase : Dict , _lowercase : str , _lowercase : Any , _lowercase : Tuple , _lowercase : Tuple , _lowercase : Tuple , ) -> str:
snake_case : Dict = XLMForQuestionAnswering(_lowercase )
model.to(_lowercase )
model.eval()
snake_case : Dict = model(_lowercase )
snake_case : Union[str, Any] = model(
_lowercase , start_positions=_lowercase , end_positions=_lowercase , cls_index=_lowercase , is_impossible=_lowercase , p_mask=_lowercase , )
snake_case : Union[str, Any] = model(
_lowercase , start_positions=_lowercase , end_positions=_lowercase , cls_index=_lowercase , is_impossible=_lowercase , )
((snake_case) , ) : Dict = result_with_labels.to_tuple()
snake_case : Any = model(_lowercase , start_positions=_lowercase , end_positions=_lowercase )
((snake_case) , ) : Optional[int] = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def __lowercase ( self : Tuple , _lowercase : int , _lowercase : str , _lowercase : Dict , _lowercase : str , _lowercase : int , _lowercase : List[Any] , _lowercase : Any , _lowercase : Union[str, Any] , _lowercase : Dict , ) -> Optional[Any]:
snake_case : Optional[Any] = XLMForSequenceClassification(_lowercase )
model.to(_lowercase )
model.eval()
snake_case : Optional[int] = model(_lowercase )
snake_case : Optional[int] = model(_lowercase , labels=_lowercase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowercase ( self : Any , _lowercase : List[Any] , _lowercase : Tuple , _lowercase : Optional[int] , _lowercase : Dict , _lowercase : List[str] , _lowercase : Any , _lowercase : Optional[Any] , _lowercase : Any , _lowercase : Optional[Any] , ) -> Tuple:
snake_case : Any = self.num_labels
snake_case : Optional[Any] = XLMForTokenClassification(_lowercase )
model.to(_lowercase )
model.eval()
snake_case : Any = model(_lowercase , attention_mask=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowercase ( self : Tuple , _lowercase : List[Any] , _lowercase : Any , _lowercase : int , _lowercase : Dict , _lowercase : Dict , _lowercase : List[Any] , _lowercase : Optional[Any] , _lowercase : int , _lowercase : Union[str, Any] , ) -> Optional[Any]:
snake_case : Optional[Any] = self.num_choices
snake_case : int = XLMForMultipleChoice(config=_lowercase )
model.to(_lowercase )
model.eval()
snake_case : Any = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case : int = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case : Tuple = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case : List[str] = model(
_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowercase ( self : Optional[int] ) -> int:
snake_case : Any = self.prepare_config_and_inputs()
(
(
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) ,
) : int = config_and_inputs
snake_case : str = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
return config, inputs_dict
@require_torch
class _a ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase):
__magic_name__ = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
__magic_name__ = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
__magic_name__ = (
{
"""feature-extraction""": XLMModel,
"""fill-mask""": XLMWithLMHeadModel,
"""question-answering""": XLMForQuestionAnsweringSimple,
"""text-classification""": XLMForSequenceClassification,
"""text-generation""": XLMWithLMHeadModel,
"""token-classification""": XLMForTokenClassification,
"""zero-shot""": XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def __lowercase ( self : Optional[Any] , _lowercase : Dict , _lowercase : Any , _lowercase : List[Any] , _lowercase : Optional[int] , _lowercase : List[str] ) -> Optional[Any]:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def __lowercase ( self : Any , _lowercase : List[Any] , _lowercase : int , _lowercase : Tuple=False ) -> int:
snake_case : Dict = super()._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
snake_case : List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_lowercase )
snake_case : int = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_lowercase )
return inputs_dict
def __lowercase ( self : Dict ) -> Tuple:
snake_case : Union[str, Any] = XLMModelTester(self )
snake_case : int = ConfigTester(self , config_class=_lowercase , emb_dim=37 )
def __lowercase ( self : int ) -> Optional[int]:
self.config_tester.run_common_tests()
def __lowercase ( self : int ) -> Optional[int]:
snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*_lowercase )
def __lowercase ( self : int ) -> List[str]:
snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*_lowercase )
def __lowercase ( self : str ) -> Union[str, Any]:
snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*_lowercase )
def __lowercase ( self : str ) -> int:
snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*_lowercase )
def __lowercase ( self : Union[str, Any] ) -> Optional[int]:
snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*_lowercase )
def __lowercase ( self : Any ) -> List[Any]:
snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*_lowercase )
def __lowercase ( self : str ) -> Dict:
snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*_lowercase )
def __lowercase ( self : Optional[Any] , _lowercase : int , _lowercase : Dict , _lowercase : int , _lowercase : str , _lowercase : Dict , _lowercase : int=False , _lowercase : Optional[int]=1 ) -> str:
self.assertIsInstance(_lowercase , _lowercase )
self.assertListEqual(
[isinstance(_lowercase , _lowercase ) for iter_attentions in attentions] , [True] * len(_lowercase ) )
self.assertEqual(len(_lowercase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(_lowercase ):
# adds PAD dummy token
snake_case : Tuple = min_length + idx + 1
snake_case : List[Any] = min_length + idx + 1
snake_case : Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(_lowercase ) )
def __lowercase ( self : int , _lowercase : Any , _lowercase : str , _lowercase : List[str] , _lowercase : Dict , _lowercase : Optional[int] , _lowercase : Optional[Any]=False , _lowercase : Union[str, Any]=1 ) -> Any:
self.assertIsInstance(_lowercase , _lowercase )
self.assertListEqual(
[isinstance(_lowercase , _lowercase ) for iter_hidden_states in hidden_states] , [True] * len(_lowercase ) , )
self.assertEqual(len(_lowercase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(_lowercase ):
# adds PAD dummy token
snake_case : Dict = min_length + idx + 1
snake_case : str = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(_lowercase ) , )
pass
@slow
def __lowercase ( self : Optional[int] ) -> Union[str, Any]:
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case : str = XLMModel.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
@require_torch
class _a ( unittest.TestCase):
@slow
def __lowercase ( self : List[str] ) -> Optional[int]:
snake_case : str = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048" )
model.to(_lowercase )
snake_case : Optional[Any] = torch.tensor([[14, 447]] , dtype=torch.long , device=_lowercase ) # the president
snake_case : Optional[int] = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
snake_case : Union[str, Any] = model.generate(_lowercase , do_sample=_lowercase )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , _lowercase )
| 449 |
"""simple docstring"""
A = 8.31_4462 # Unit - J mol-1 K-1
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: float , lowerCamelCase_: float , lowerCamelCase_: float ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: float , lowerCamelCase_: float , lowerCamelCase_: float ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 449 | 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_video_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VivitImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self :Dict , _lowerCamelCase :Optional[int] , _lowerCamelCase :str=7 , _lowerCamelCase :List[str]=3 , _lowerCamelCase :Optional[int]=1_0 , _lowerCamelCase :Dict=1_8 , _lowerCamelCase :int=3_0 , _lowerCamelCase :Optional[Any]=4_0_0 , _lowerCamelCase :Tuple=True , _lowerCamelCase :List[str]=None , _lowerCamelCase :Tuple=True , _lowerCamelCase :Optional[Any]=[0.5, 0.5, 0.5] , _lowerCamelCase :int=[0.5, 0.5, 0.5] , _lowerCamelCase :Optional[Any]=None , ):
__SCREAMING_SNAKE_CASE : List[Any] = size if size is not None else {'''shortest_edge''': 1_8}
__SCREAMING_SNAKE_CASE : List[Any] = crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
__SCREAMING_SNAKE_CASE : int = parent
__SCREAMING_SNAKE_CASE : int = batch_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = num_channels
__SCREAMING_SNAKE_CASE : int = num_frames
__SCREAMING_SNAKE_CASE : Tuple = image_size
__SCREAMING_SNAKE_CASE : Dict = min_resolution
__SCREAMING_SNAKE_CASE : str = max_resolution
__SCREAMING_SNAKE_CASE : str = do_resize
__SCREAMING_SNAKE_CASE : Optional[Any] = size
__SCREAMING_SNAKE_CASE : Tuple = do_normalize
__SCREAMING_SNAKE_CASE : List[str] = image_mean
__SCREAMING_SNAKE_CASE : List[Any] = image_std
__SCREAMING_SNAKE_CASE : str = crop_size
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = VivitImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : str = VivitImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self :Any ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : List[str] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''image_mean''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''image_std''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_center_crop''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''size''' ) )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 1_8} )
self.assertEqual(image_processor.crop_size , {'''height''': 1_8, '''width''': 1_8} )
__SCREAMING_SNAKE_CASE : Optional[Any] = 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 SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL videos
__SCREAMING_SNAKE_CASE : Tuple = prepare_video_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for video in video_inputs:
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertIsInstance(video[0] , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE : Optional[Any] = image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__SCREAMING_SNAKE_CASE : Tuple = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE : Any = prepare_video_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for video in video_inputs:
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertIsInstance(video[0] , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE : List[Any] = image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__SCREAMING_SNAKE_CASE : Optional[int] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def SCREAMING_SNAKE_CASE_ ( self :int ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : Dict = prepare_video_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for video in video_inputs:
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertIsInstance(video[0] , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__SCREAMING_SNAKE_CASE : Optional[Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
| 705 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''SpeechT5FeatureExtractor'''
lowerCamelCase__ = '''SpeechT5Tokenizer'''
def __init__( self :List[Any] , _lowerCamelCase :Optional[int] , _lowerCamelCase :str ):
super().__init__(_lowerCamelCase , _lowerCamelCase )
def __call__( self :Optional[int] , *_lowerCamelCase :Dict , **_lowerCamelCase :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Any = kwargs.pop('''audio''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = kwargs.pop('''text''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = kwargs.pop('''text_target''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = kwargs.pop('''audio_target''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = 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:
__SCREAMING_SNAKE_CASE : Optional[int] = self.feature_extractor(_lowerCamelCase , *_lowerCamelCase , sampling_rate=_lowerCamelCase , **_lowerCamelCase )
elif text is not None:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer(_lowerCamelCase , **_lowerCamelCase )
else:
__SCREAMING_SNAKE_CASE : Tuple = None
if audio_target is not None:
__SCREAMING_SNAKE_CASE : Tuple = self.feature_extractor(audio_target=_lowerCamelCase , *_lowerCamelCase , sampling_rate=_lowerCamelCase , **_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = targets['''input_values''']
elif text_target is not None:
__SCREAMING_SNAKE_CASE : str = self.tokenizer(_lowerCamelCase , **_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = targets['''input_ids''']
else:
__SCREAMING_SNAKE_CASE : List[Any] = None
if inputs is None:
return targets
if targets is not None:
__SCREAMING_SNAKE_CASE : int = labels
__SCREAMING_SNAKE_CASE : Dict = targets.get('''attention_mask''' )
if decoder_attention_mask is not None:
__SCREAMING_SNAKE_CASE : Any = decoder_attention_mask
return inputs
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , *_lowerCamelCase :Dict , **_lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = kwargs.pop('''input_values''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = kwargs.pop('''input_ids''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = 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:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.feature_extractor.pad(_lowerCamelCase , *_lowerCamelCase , **_lowerCamelCase )
elif input_ids is not None:
__SCREAMING_SNAKE_CASE : int = self.tokenizer.pad(_lowerCamelCase , **_lowerCamelCase )
else:
__SCREAMING_SNAKE_CASE : Any = None
if labels is not None:
if "input_ids" in labels or (isinstance(_lowerCamelCase , _lowerCamelCase ) and "input_ids" in labels[0]):
__SCREAMING_SNAKE_CASE : Any = self.tokenizer.pad(_lowerCamelCase , **_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = targets['''input_ids''']
else:
__SCREAMING_SNAKE_CASE : Any = self.feature_extractor.feature_size
__SCREAMING_SNAKE_CASE : Any = self.feature_extractor.num_mel_bins
__SCREAMING_SNAKE_CASE : Any = self.feature_extractor.pad(_lowerCamelCase , *_lowerCamelCase , **_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = feature_size_hack
__SCREAMING_SNAKE_CASE : Any = targets['''input_values''']
else:
__SCREAMING_SNAKE_CASE : Dict = None
if inputs is None:
return targets
if targets is not None:
__SCREAMING_SNAKE_CASE : List[Any] = labels
__SCREAMING_SNAKE_CASE : int = targets.get('''attention_mask''' )
if decoder_attention_mask is not None:
__SCREAMING_SNAKE_CASE : List[Any] = decoder_attention_mask
return inputs
def SCREAMING_SNAKE_CASE_ ( self :Tuple , *_lowerCamelCase :Tuple , **_lowerCamelCase :Union[str, Any] ):
return self.tokenizer.batch_decode(*_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , *_lowerCamelCase :List[Any] , **_lowerCamelCase :List[str] ):
return self.tokenizer.decode(*_lowerCamelCase , **_lowerCamelCase )
| 401 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def lowercase (snake_case__ : int , snake_case__ : int , snake_case__ : bool , snake_case__ : list[int] , snake_case__ : float ) -> int:
'''simple docstring'''
if depth < 0:
raise ValueError("""Depth cannot be less than 0""" )
if len(snake_case__ ) == 0:
raise ValueError("""Scores cannot be empty""" )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1 , node_index * 2 , snake_case__ , snake_case__ , snake_case__ ) , minimax(depth + 1 , node_index * 2 + 1 , snake_case__ , snake_case__ , snake_case__ ) , )
return min(
minimax(depth + 1 , node_index * 2 , snake_case__ , snake_case__ , snake_case__ ) , minimax(depth + 1 , node_index * 2 + 1 , snake_case__ , snake_case__ , snake_case__ ) , )
def lowercase () -> None:
'''simple docstring'''
lowerCAmelCase = [90, 23, 6, 33, 21, 65, 123, 34_423]
lowerCAmelCase = math.log(len(snake_case__ ) , 2 )
print("""Optimal value : """ , end="""""" )
print(minimax(0 , 0 , snake_case__ , snake_case__ , snake_case__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 169 |
"""simple docstring"""
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class SCREAMING_SNAKE_CASE__ ( _a , unittest.TestCase ):
_a = BertJapaneseTokenizer
_a = False
_a = True
def __lowercase ( self : Any ):
super().setUp()
lowerCAmelCase = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""こんにちは""",
"""こん""",
"""にちは""",
"""ばんは""",
"""##こん""",
"""##にちは""",
"""##ばんは""",
"""世界""",
"""##世界""",
"""、""",
"""##、""",
"""。""",
"""##。""",
]
lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def __lowercase ( self : int , lowerCAmelCase : List[Any] ):
lowerCAmelCase = """こんにちは、世界。 \nこんばんは、世界。"""
lowerCAmelCase = """こんにちは 、 世界 。 こんばんは 、 世界 。"""
return input_text, output_text
def __lowercase ( self : Optional[Any] , lowerCAmelCase : List[Any] ):
lowerCAmelCase , lowerCAmelCase = self.get_input_output_texts(lowerCAmelCase )
lowerCAmelCase = tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
lowerCAmelCase = tokenizer.decode(lowerCAmelCase , clean_up_tokenization_spaces=lowerCAmelCase )
return text, ids
def __lowercase ( self : List[str] ):
pass # TODO add if relevant
def __lowercase ( self : Optional[Any] ):
pass # TODO add if relevant
def __lowercase ( self : Any ):
pass # TODO add if relevant
def __lowercase ( self : List[Any] ):
lowerCAmelCase = self.tokenizer_class(self.vocab_file )
lowerCAmelCase = tokenizer.tokenize("""こんにちは、世界。\nこんばんは、世界。""" )
self.assertListEqual(lowerCAmelCase , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
def __lowercase ( self : int ):
lowerCAmelCase = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""mecab""" )
self.assertIsNotNone(lowerCAmelCase )
lowerCAmelCase = """こんにちは、世界。\nこんばんは、世界。"""
lowerCAmelCase = tokenizer.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
lowerCAmelCase = os.path.join(self.tmpdirname , """tokenizer.bin""" )
with open(lowerCAmelCase , """wb""" ) as handle:
pickle.dump(lowerCAmelCase , lowerCAmelCase )
with open(lowerCAmelCase , """rb""" ) as handle:
lowerCAmelCase = pickle.load(lowerCAmelCase )
lowerCAmelCase = tokenizer_new.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
def __lowercase ( self : str ):
lowerCAmelCase = MecabTokenizer(mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップルストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def __lowercase ( self : int ):
try:
lowerCAmelCase = MecabTokenizer(mecab_dic="""unidic_lite""" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def __lowercase ( self : Optional[int] ):
try:
lowerCAmelCase = MecabTokenizer(mecab_dic="""unidic""" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def __lowercase ( self : Any ):
lowerCAmelCase = MecabTokenizer(do_lower_case=lowerCAmelCase , mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップルストア""", """で""", """iphone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def __lowercase ( self : Optional[int] ):
try:
lowerCAmelCase = MecabTokenizer(
do_lower_case=lowerCAmelCase , normalize_text=lowerCAmelCase , mecab_option="""-d /usr/local/lib/mecab/dic/jumandic""" )
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
def __lowercase ( self : int ):
lowerCAmelCase = MecabTokenizer(normalize_text=lowerCAmelCase , mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れ""", """た""", """ """, """。"""] , )
@require_sudachi
def __lowercase ( self : List[Any] ):
lowerCAmelCase = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""sudachi""" )
self.assertIsNotNone(lowerCAmelCase )
lowerCAmelCase = """こんにちは、世界。\nこんばんは、世界。"""
lowerCAmelCase = tokenizer.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
lowerCAmelCase = os.path.join(self.tmpdirname , """tokenizer.bin""" )
with open(lowerCAmelCase , """wb""" ) as handle:
pickle.dump(lowerCAmelCase , lowerCAmelCase )
with open(lowerCAmelCase , """rb""" ) as handle:
lowerCAmelCase = pickle.load(lowerCAmelCase )
lowerCAmelCase = tokenizer_new.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
@require_sudachi
def __lowercase ( self : str ):
lowerCAmelCase = SudachiTokenizer(sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , [""" """, """\t""", """アップル""", """ストア""", """で""", """iPhone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """] , )
@require_sudachi
def __lowercase ( self : str ):
lowerCAmelCase = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""A""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ) , ["""外国""", """人""", """参政""", """権"""] )
@require_sudachi
def __lowercase ( self : Dict ):
lowerCAmelCase = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""B""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ) , ["""外国人""", """参政権"""] )
@require_sudachi
def __lowercase ( self : int ):
lowerCAmelCase = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""C""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ) , ["""外国人参政権"""] )
@require_sudachi
def __lowercase ( self : str ):
lowerCAmelCase = SudachiTokenizer(do_lower_case=lowerCAmelCase , sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , [""" """, """\t""", """アップル""", """ストア""", """で""", """iphone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """] , )
@require_sudachi
def __lowercase ( self : Tuple ):
lowerCAmelCase = SudachiTokenizer(normalize_text=lowerCAmelCase , sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , [""" """, """\t""", """ｱｯﾌﾟﾙ""", """ストア""", """で""", """iPhone""", """８""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """\u3000""", """。""", """ """, """ """] , )
@require_sudachi
def __lowercase ( self : List[Any] ):
lowerCAmelCase = SudachiTokenizer(trim_whitespace=lowerCAmelCase , sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
@require_jumanpp
def __lowercase ( self : List[Any] ):
lowerCAmelCase = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""jumanpp""" )
self.assertIsNotNone(lowerCAmelCase )
lowerCAmelCase = """こんにちは、世界。\nこんばんは、世界。"""
lowerCAmelCase = tokenizer.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
lowerCAmelCase = os.path.join(self.tmpdirname , """tokenizer.bin""" )
with open(lowerCAmelCase , """wb""" ) as handle:
pickle.dump(lowerCAmelCase , lowerCAmelCase )
with open(lowerCAmelCase , """rb""" ) as handle:
lowerCAmelCase = pickle.load(lowerCAmelCase )
lowerCAmelCase = tokenizer_new.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
@require_jumanpp
def __lowercase ( self : Optional[Any] ):
lowerCAmelCase = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def __lowercase ( self : Optional[Any] ):
lowerCAmelCase = JumanppTokenizer(do_lower_case=lowerCAmelCase )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iphone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def __lowercase ( self : int ):
lowerCAmelCase = JumanppTokenizer(normalize_text=lowerCAmelCase )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""ｱ""", """ｯ""", """ﾌ""", """ﾟ""", """ﾙ""", """ストア""", """で""", """iPhone""", """８""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def __lowercase ( self : Any ):
lowerCAmelCase = JumanppTokenizer(trim_whitespace=lowerCAmelCase )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れた""", """。"""] , )
@require_jumanpp
def __lowercase ( self : Tuple ):
lowerCAmelCase = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize("""ありがとうございますm(_ _)ｍ見つけるのが大変です。""" ) , ["""ありがとう""", """ございます""", """m(_ _)m""", """見つける""", """の""", """が""", """大変です""", """。"""] , )
def __lowercase ( self : str ):
lowerCAmelCase = ["""[UNK]""", """[CLS]""", """[SEP]""", """こんにちは""", """こん""", """にちは""", """ばんは""", """##こん""", """##にちは""", """##ばんは"""]
lowerCAmelCase = {}
for i, token in enumerate(lowerCAmelCase ):
lowerCAmelCase = i
lowerCAmelCase = WordpieceTokenizer(vocab=lowerCAmelCase , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""こんにちは""" ) , ["""こんにちは"""] )
self.assertListEqual(tokenizer.tokenize("""こんばんは""" ) , ["""こん""", """##ばんは"""] )
self.assertListEqual(tokenizer.tokenize("""こんばんは こんばんにちは こんにちは""" ) , ["""こん""", """##ばんは""", """[UNK]""", """こんにちは"""] )
def __lowercase ( self : Dict ):
lowerCAmelCase = BertJapaneseTokenizer.from_pretrained("""nlp-waseda/roberta-base-japanese-with-auto-jumanpp""" )
lowerCAmelCase = tokenizer.subword_tokenizer
lowerCAmelCase = subword_tokenizer.tokenize("""国境 の 長い トンネル を 抜ける と 雪国 であった 。""" )
self.assertListEqual(lowerCAmelCase , ["""▁国境""", """▁の""", """▁長い""", """▁トンネル""", """▁を""", """▁抜ける""", """▁と""", """▁雪""", """国""", """▁であった""", """▁。"""] )
lowerCAmelCase = subword_tokenizer.tokenize("""こんばんは こんばん にち は こんにちは""" )
self.assertListEqual(lowerCAmelCase , ["""▁こん""", """ばん""", """は""", """▁こん""", """ばん""", """▁に""", """ち""", """▁は""", """▁こんにちは"""] )
def __lowercase ( self : str ):
lowerCAmelCase = self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese""" )
lowerCAmelCase = tokenizer.encode("""ありがとう。""" , add_special_tokens=lowerCAmelCase )
lowerCAmelCase = tokenizer.encode("""どういたしまして。""" , add_special_tokens=lowerCAmelCase )
lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase )
lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase , lowerCAmelCase )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class SCREAMING_SNAKE_CASE__ ( _a , unittest.TestCase ):
_a = BertJapaneseTokenizer
_a = False
def __lowercase ( self : Union[str, Any] ):
super().setUp()
lowerCAmelCase = ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]
lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def __lowercase ( self : Optional[int] , **lowerCAmelCase : Optional[Any] ):
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type="""character""" , **lowerCAmelCase )
def __lowercase ( self : List[str] , lowerCAmelCase : Union[str, Any] ):
lowerCAmelCase = """こんにちは、世界。 \nこんばんは、世界。"""
lowerCAmelCase = """こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"""
return input_text, output_text
def __lowercase ( self : List[Any] ):
pass # TODO add if relevant
def __lowercase ( self : Optional[Any] ):
pass # TODO add if relevant
def __lowercase ( self : int ):
pass # TODO add if relevant
def __lowercase ( self : Union[str, Any] ):
lowerCAmelCase = self.tokenizer_class(self.vocab_file , subword_tokenizer_type="""character""" )
lowerCAmelCase = tokenizer.tokenize("""こんにちは、世界。 \nこんばんは、世界。""" )
self.assertListEqual(
lowerCAmelCase , ["""こ""", """ん""", """に""", """ち""", """は""", """、""", """世""", """界""", """。""", """こ""", """ん""", """ば""", """ん""", """は""", """、""", """世""", """界""", """。"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase ) , [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] )
def __lowercase ( self : Any ):
lowerCAmelCase = ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]
lowerCAmelCase = {}
for i, token in enumerate(lowerCAmelCase ):
lowerCAmelCase = i
lowerCAmelCase = CharacterTokenizer(vocab=lowerCAmelCase , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""こんにちは""" ) , ["""こ""", """ん""", """に""", """ち""", """は"""] )
self.assertListEqual(tokenizer.tokenize("""こんにちほ""" ) , ["""こ""", """ん""", """に""", """ち""", """[UNK]"""] )
def __lowercase ( self : Tuple ):
lowerCAmelCase = self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese-char""" )
lowerCAmelCase = tokenizer.encode("""ありがとう。""" , add_special_tokens=lowerCAmelCase )
lowerCAmelCase = tokenizer.encode("""どういたしまして。""" , add_special_tokens=lowerCAmelCase )
lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase )
lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase , lowerCAmelCase )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowercase ( self : Optional[int] ):
lowerCAmelCase = """cl-tohoku/bert-base-japanese"""
lowerCAmelCase = AutoTokenizer.from_pretrained(lowerCAmelCase )
self.assertIsInstance(lowerCAmelCase , lowerCAmelCase )
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowercase ( self : List[str] ):
lowerCAmelCase = """cl-tohoku/bert-base-japanese"""
with self.assertLogs("""transformers""" , level="""WARNING""" ) as cm:
BertTokenizer.from_pretrained(lowerCAmelCase )
self.assertTrue(
cm.records[0].message.startswith(
"""The tokenizer class you load from this checkpoint is not the same type as the class this function"""
""" is called from.""" ) )
lowerCAmelCase = """bert-base-cased"""
with self.assertLogs("""transformers""" , level="""WARNING""" ) as cm:
BertJapaneseTokenizer.from_pretrained(lowerCAmelCase )
self.assertTrue(
cm.records[0].message.startswith(
"""The tokenizer class you load from this checkpoint is not the same type as the class this function"""
""" is called from.""" ) )
| 169 | 1 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def _lowercase ( a_ : Optional[int] ,a_ : List[Any] ,a_ : Any ) -> str:
'''simple docstring'''
if isinstance(a_ ,torch.Tensor ):
return image
elif isinstance(a_ ,PIL.Image.Image ):
__magic_name__ = [image]
if isinstance(image[0] ,PIL.Image.Image ):
__magic_name__ = [np.array(i.resize((w, h) ,resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
__magic_name__ = np.concatenate(a_ ,axis=0 )
__magic_name__ = np.array(a_ ).astype(np.floataa ) / 255.0
__magic_name__ = image.transpose(0 ,3 ,1 ,2 )
__magic_name__ = 2.0 * image - 1.0
__magic_name__ = torch.from_numpy(a_ )
elif isinstance(image[0] ,torch.Tensor ):
__magic_name__ = torch.cat(a_ ,dim=0 )
return image
def _lowercase ( a_ : Dict ,a_ : Optional[Any] ,a_ : Any ,a_ : int=0.9995 ) -> Optional[Any]:
'''simple docstring'''
if not isinstance(a_ ,np.ndarray ):
__magic_name__ = True
__magic_name__ = va.device
__magic_name__ = va.cpu().numpy()
__magic_name__ = va.cpu().numpy()
__magic_name__ = np.sum(va * va / (np.linalg.norm(a_ ) * np.linalg.norm(a_ )) )
if np.abs(a_ ) > DOT_THRESHOLD:
__magic_name__ = (1 - t) * va + t * va
else:
__magic_name__ = np.arccos(a_ )
__magic_name__ = np.sin(a_ )
__magic_name__ = theta_a * t
__magic_name__ = np.sin(a_ )
__magic_name__ = np.sin(theta_a - theta_t ) / sin_theta_a
__magic_name__ = sin_theta_t / sin_theta_a
__magic_name__ = sa * va + sa * va
if inputs_are_torch:
__magic_name__ = torch.from_numpy(a_ ).to(a_ )
return va
def _lowercase ( a_ : Tuple ,a_ : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
__magic_name__ = F.normalize(a_ ,dim=-1 )
__magic_name__ = F.normalize(a_ ,dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def _lowercase ( a_ : Any ,a_ : str ) -> Any:
'''simple docstring'''
for param in model.parameters():
__magic_name__ = value
class __UpperCamelCase ( SCREAMING_SNAKE_CASE ):
def __init__( self: Optional[Any] , __UpperCamelCase: AutoencoderKL , __UpperCamelCase: CLIPTextModel , __UpperCamelCase: CLIPModel , __UpperCamelCase: CLIPTokenizer , __UpperCamelCase: UNetaDConditionModel , __UpperCamelCase: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler] , __UpperCamelCase: CLIPFeatureExtractor , __UpperCamelCase: Tuple=None , __UpperCamelCase: Union[str, Any]=None , __UpperCamelCase: Optional[int]=None , ):
'''simple docstring'''
super().__init__()
self.register_modules(
vae=__UpperCamelCase , text_encoder=__UpperCamelCase , clip_model=__UpperCamelCase , tokenizer=__UpperCamelCase , unet=__UpperCamelCase , scheduler=__UpperCamelCase , feature_extractor=__UpperCamelCase , coca_model=__UpperCamelCase , coca_tokenizer=__UpperCamelCase , coca_transform=__UpperCamelCase , )
__magic_name__ = (
feature_extractor.size
if isinstance(feature_extractor.size , __UpperCamelCase )
else feature_extractor.size['shortest_edge']
)
__magic_name__ = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , __UpperCamelCase )
set_requires_grad(self.clip_model , __UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any] , __UpperCamelCase: Optional[Union[str, int]] = "auto" ):
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
__magic_name__ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( self: int ):
'''simple docstring'''
self.enable_attention_slicing(__UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( self: int ):
'''simple docstring'''
set_requires_grad(self.vae , __UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( self: Dict ):
'''simple docstring'''
set_requires_grad(self.vae , __UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( self: str ):
'''simple docstring'''
set_requires_grad(self.unet , __UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( self: Dict ):
'''simple docstring'''
set_requires_grad(self.unet , __UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( self: List[Any] , __UpperCamelCase: Dict , __UpperCamelCase: List[str] , __UpperCamelCase: Any ):
'''simple docstring'''
__magic_name__ = min(int(num_inference_steps * strength ) , __UpperCamelCase )
__magic_name__ = max(num_inference_steps - init_timestep , 0 )
__magic_name__ = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def _SCREAMING_SNAKE_CASE ( self: str , __UpperCamelCase: List[str] , __UpperCamelCase: Union[str, Any] , __UpperCamelCase: List[Any] , __UpperCamelCase: Union[str, Any] , __UpperCamelCase: Optional[int] , __UpperCamelCase: Tuple=None ):
'''simple docstring'''
if not isinstance(__UpperCamelCase , torch.Tensor ):
raise ValueError(F'`image` has to be of type `torch.Tensor` but is {type(__UpperCamelCase )}' )
__magic_name__ = image.to(device=__UpperCamelCase , dtype=__UpperCamelCase )
if isinstance(__UpperCamelCase , __UpperCamelCase ):
__magic_name__ = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(__UpperCamelCase )
]
__magic_name__ = torch.cat(__UpperCamelCase , dim=0 )
else:
__magic_name__ = self.vae.encode(__UpperCamelCase ).latent_dist.sample(__UpperCamelCase )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
__magic_name__ = 0.18215 * init_latents
__magic_name__ = init_latents.repeat_interleave(__UpperCamelCase , dim=0 )
__magic_name__ = randn_tensor(init_latents.shape , generator=__UpperCamelCase , device=__UpperCamelCase , dtype=__UpperCamelCase )
# get latents
__magic_name__ = self.scheduler.add_noise(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
__magic_name__ = init_latents
return latents
def _SCREAMING_SNAKE_CASE ( self: int , __UpperCamelCase: int ):
'''simple docstring'''
__magic_name__ = self.coca_transform(__UpperCamelCase ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
__magic_name__ = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
__magic_name__ = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split('<end_of_text>' )[0].replace('<start_of_text>' , '' ).rstrip(' .,' )
def _SCREAMING_SNAKE_CASE ( self: Dict , __UpperCamelCase: Tuple , __UpperCamelCase: Union[str, Any] ):
'''simple docstring'''
__magic_name__ = self.feature_extractor.preprocess(__UpperCamelCase )
__magic_name__ = torch.from_numpy(clip_image_input['pixel_values'][0] ).unsqueeze(0 ).to(self.device ).half()
__magic_name__ = self.clip_model.get_image_features(__UpperCamelCase )
__magic_name__ = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=__UpperCamelCase )
__magic_name__ = image_embeddings_clip.repeat_interleave(__UpperCamelCase , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def _SCREAMING_SNAKE_CASE ( self: List[Any] , __UpperCamelCase: Optional[int] , __UpperCamelCase: Optional[Any] , __UpperCamelCase: Tuple , __UpperCamelCase: Optional[int] , __UpperCamelCase: int , __UpperCamelCase: Optional[int] , __UpperCamelCase: Optional[int] , ):
'''simple docstring'''
__magic_name__ = latents.detach().requires_grad_()
__magic_name__ = self.scheduler.scale_model_input(__UpperCamelCase , __UpperCamelCase )
# predict the noise residual
__magic_name__ = self.unet(__UpperCamelCase , __UpperCamelCase , encoder_hidden_states=__UpperCamelCase ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
__magic_name__ = self.scheduler.alphas_cumprod[timestep]
__magic_name__ = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
__magic_name__ = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
__magic_name__ = torch.sqrt(__UpperCamelCase )
__magic_name__ = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , __UpperCamelCase ):
__magic_name__ = self.scheduler.sigmas[index]
__magic_name__ = latents - sigma * noise_pred
else:
raise ValueError(F'scheduler type {type(self.scheduler )} not supported' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
__magic_name__ = 1 / 0.18215 * sample
__magic_name__ = self.vae.decode(__UpperCamelCase ).sample
__magic_name__ = (image / 2 + 0.5).clamp(0 , 1 )
__magic_name__ = transforms.Resize(self.feature_extractor_size )(__UpperCamelCase )
__magic_name__ = self.normalize(__UpperCamelCase ).to(latents.dtype )
__magic_name__ = self.clip_model.get_image_features(__UpperCamelCase )
__magic_name__ = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=__UpperCamelCase )
__magic_name__ = spherical_dist_loss(__UpperCamelCase , __UpperCamelCase ).mean() * clip_guidance_scale
__magic_name__ = -torch.autograd.grad(__UpperCamelCase , __UpperCamelCase )[0]
if isinstance(self.scheduler , __UpperCamelCase ):
__magic_name__ = latents.detach() + grads * (sigma**2)
__magic_name__ = noise_pred_original
else:
__magic_name__ = noise_pred_original - torch.sqrt(__UpperCamelCase ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self: Dict , __UpperCamelCase: Union[torch.FloatTensor, PIL.Image.Image] , __UpperCamelCase: Union[torch.FloatTensor, PIL.Image.Image] , __UpperCamelCase: Optional[str] = None , __UpperCamelCase: Optional[str] = None , __UpperCamelCase: Optional[int] = 5_12 , __UpperCamelCase: Optional[int] = 5_12 , __UpperCamelCase: float = 0.6 , __UpperCamelCase: Optional[int] = 50 , __UpperCamelCase: Optional[float] = 7.5 , __UpperCamelCase: Optional[int] = 1 , __UpperCamelCase: float = 0.0 , __UpperCamelCase: Optional[float] = 1_00 , __UpperCamelCase: Optional[torch.Generator] = None , __UpperCamelCase: Optional[str] = "pil" , __UpperCamelCase: bool = True , __UpperCamelCase: float = 0.8 , __UpperCamelCase: float = 0.1 , __UpperCamelCase: float = 0.1 , ):
'''simple docstring'''
if isinstance(__UpperCamelCase , __UpperCamelCase ) and len(__UpperCamelCase ) != batch_size:
raise ValueError(F'You have passed {batch_size} batch_size, but only {len(__UpperCamelCase )} generators.' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.' )
if isinstance(__UpperCamelCase , torch.Generator ) and batch_size > 1:
__magic_name__ = [generator] + [None] * (batch_size - 1)
__magic_name__ = [
('model', self.coca_model is None),
('tokenizer', self.coca_tokenizer is None),
('transform', self.coca_transform is None),
]
__magic_name__ = [x[0] for x in coca_is_none if x[1]]
__magic_name__ = ', '.join(__UpperCamelCase )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(__UpperCamelCase ):
raise ValueError(
F'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
F'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.' )
__magic_name__ = self.get_image_description(__UpperCamelCase )
if style_prompt is None:
if len(__UpperCamelCase ):
raise ValueError(
F'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
F' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.' )
__magic_name__ = self.get_image_description(__UpperCamelCase )
# get prompt text embeddings for content and style
__magic_name__ = self.tokenizer(
__UpperCamelCase , padding='max_length' , max_length=self.tokenizer.model_max_length , truncation=__UpperCamelCase , return_tensors='pt' , )
__magic_name__ = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
__magic_name__ = self.tokenizer(
__UpperCamelCase , padding='max_length' , max_length=self.tokenizer.model_max_length , truncation=__UpperCamelCase , return_tensors='pt' , )
__magic_name__ = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
__magic_name__ = slerp(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# duplicate text embeddings for each generation per prompt
__magic_name__ = text_embeddings.repeat_interleave(__UpperCamelCase , dim=0 )
# set timesteps
__magic_name__ = 'offset' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
__magic_name__ = {}
if accepts_offset:
__magic_name__ = 1
self.scheduler.set_timesteps(__UpperCamelCase , **__UpperCamelCase )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
__magic_name__, __magic_name__ = self.get_timesteps(__UpperCamelCase , __UpperCamelCase , self.device )
__magic_name__ = timesteps[:1].repeat(__UpperCamelCase )
# Preprocess image
__magic_name__ = preprocess(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
__magic_name__ = self.prepare_latents(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , text_embeddings.dtype , self.device , __UpperCamelCase )
__magic_name__ = preprocess(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
__magic_name__ = self.prepare_latents(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , text_embeddings.dtype , self.device , __UpperCamelCase )
__magic_name__ = slerp(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if clip_guidance_scale > 0:
__magic_name__ = self.get_clip_image_embeddings(__UpperCamelCase , __UpperCamelCase )
__magic_name__ = self.get_clip_image_embeddings(__UpperCamelCase , __UpperCamelCase )
__magic_name__ = slerp(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
__magic_name__ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
__magic_name__ = content_text_input.input_ids.shape[-1]
__magic_name__ = self.tokenizer([''] , padding='max_length' , max_length=__UpperCamelCase , return_tensors='pt' )
__magic_name__ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
__magic_name__ = uncond_embeddings.repeat_interleave(__UpperCamelCase , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
__magic_name__ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
__magic_name__ = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
__magic_name__ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
__magic_name__ = torch.randn(__UpperCamelCase , generator=__UpperCamelCase , device='cpu' , dtype=__UpperCamelCase ).to(
self.device )
else:
__magic_name__ = torch.randn(__UpperCamelCase , generator=__UpperCamelCase , device=self.device , dtype=__UpperCamelCase )
else:
if latents.shape != latents_shape:
raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
__magic_name__ = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
__magic_name__ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
__magic_name__ = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
__magic_name__ = {}
if accepts_eta:
__magic_name__ = eta
# check if the scheduler accepts generator
__magic_name__ = 'generator' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
__magic_name__ = generator
with self.progress_bar(total=__UpperCamelCase ):
for i, t in enumerate(__UpperCamelCase ):
# expand the latents if we are doing classifier free guidance
__magic_name__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
__magic_name__ = self.scheduler.scale_model_input(__UpperCamelCase , __UpperCamelCase )
# predict the noise residual
__magic_name__ = self.unet(__UpperCamelCase , __UpperCamelCase , encoder_hidden_states=__UpperCamelCase ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
__magic_name__, __magic_name__ = noise_pred.chunk(2 )
__magic_name__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
__magic_name__ = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
__magic_name__, __magic_name__ = self.cond_fn(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )
# compute the previous noisy sample x_t -> x_t-1
__magic_name__ = self.scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
__magic_name__ = 1 / 0.18215 * latents
__magic_name__ = self.vae.decode(__UpperCamelCase ).sample
__magic_name__ = (image / 2 + 0.5).clamp(0 , 1 )
__magic_name__ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__magic_name__ = self.numpy_to_pil(__UpperCamelCase )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=__UpperCamelCase , nsfw_content_detected=__UpperCamelCase )
| 184 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
"umberto-commoncrawl-cased-v1": (
"https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
),
"umberto-wikipedia-uncased-v1": (
"https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
),
}
class __UpperCamelCase ( SCREAMING_SNAKE_CASE ):
_lowercase : Optional[int] = "camembert"
def __init__( self: Optional[Any] , __UpperCamelCase: Any=3_05_22 , __UpperCamelCase: Tuple=7_68 , __UpperCamelCase: str=12 , __UpperCamelCase: Optional[int]=12 , __UpperCamelCase: List[str]=30_72 , __UpperCamelCase: Any="gelu" , __UpperCamelCase: Optional[int]=0.1 , __UpperCamelCase: Any=0.1 , __UpperCamelCase: str=5_12 , __UpperCamelCase: Dict=2 , __UpperCamelCase: str=0.02 , __UpperCamelCase: List[Any]=1E-12 , __UpperCamelCase: List[Any]=1 , __UpperCamelCase: Any=0 , __UpperCamelCase: Union[str, Any]=2 , __UpperCamelCase: Dict="absolute" , __UpperCamelCase: Any=True , __UpperCamelCase: Any=None , **__UpperCamelCase: Union[str, Any] , ):
'''simple docstring'''
super().__init__(pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
__magic_name__ = vocab_size
__magic_name__ = hidden_size
__magic_name__ = num_hidden_layers
__magic_name__ = num_attention_heads
__magic_name__ = hidden_act
__magic_name__ = intermediate_size
__magic_name__ = hidden_dropout_prob
__magic_name__ = attention_probs_dropout_prob
__magic_name__ = max_position_embeddings
__magic_name__ = type_vocab_size
__magic_name__ = initializer_range
__magic_name__ = layer_norm_eps
__magic_name__ = position_embedding_type
__magic_name__ = use_cache
__magic_name__ = classifier_dropout
class __UpperCamelCase ( SCREAMING_SNAKE_CASE ):
@property
def _SCREAMING_SNAKE_CASE ( self: Dict ):
'''simple docstring'''
if self.task == "multiple-choice":
__magic_name__ = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
__magic_name__ = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 184 | 1 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def a_ ( UpperCamelCase_ : Any , UpperCamelCase_ : int = "cpu" , UpperCamelCase_ : Any = None ) -> None:
"""simple docstring"""
lowerCamelCase = torch.load(__A , map_location=__A )
for k, v in tqdm(state_dict.items() ):
if not isinstance(__A , torch.Tensor ):
raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin' )
lowerCamelCase = v.half()
if save_path is None: # overwrite src_path
lowerCamelCase = src_path
torch.save(__A , __A )
if __name__ == "__main__":
fire.Fire(convert)
| 246 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def __snake_case ( __A ) -> Union[str, Any]:
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4_E00 and cp <= 0x9_FFF)
or (cp >= 0x3_400 and cp <= 0x4_DBF) #
or (cp >= 0x20_000 and cp <= 0x2A_6DF) #
or (cp >= 0x2A_700 and cp <= 0x2B_73F) #
or (cp >= 0x2B_740 and cp <= 0x2B_81F) #
or (cp >= 0x2B_820 and cp <= 0x2C_EAF) #
or (cp >= 0xF_900 and cp <= 0xF_AFF)
or (cp >= 0x2F_800 and cp <= 0x2F_A1F) #
): #
return True
return False
def __snake_case ( __A ) -> Optional[Any]:
# word like '180' or '身高' or '神'
for char in word:
lowercase : List[Any] = ord(__A )
if not _is_chinese_char(__A ):
return 0
return 1
def __snake_case ( __A ) -> Tuple:
lowercase : List[str] = set()
for token in tokens:
lowercase : Optional[Any] = len(__A ) > 1 and is_chinese(__A )
if chinese_word:
word_set.add(__A )
lowercase : List[str] = list(__A )
return word_list
def __snake_case ( __A ,__A ) -> Union[str, Any]:
if not chinese_word_set:
return bert_tokens
lowercase : Union[str, Any] = max([len(__A ) for w in chinese_word_set] )
lowercase : int = bert_tokens
lowercase , lowercase : Optional[Any] = 0, len(__A )
while start < end:
lowercase : Any = True
if is_chinese(bert_word[start] ):
lowercase : Dict = min(end - start ,__A )
for i in range(__A ,1 ,-1 ):
lowercase : List[Any] = """""".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 ,start + i ):
lowercase : Optional[int] = """##""" + bert_word[j]
lowercase : Tuple = start + i
lowercase : int = False
break
if single_word:
start += 1
return bert_word
def __snake_case ( __A ,__A ,__A ) -> List[str]:
lowercase : Any = []
for i in range(0 ,len(__A ) ,100 ):
lowercase : List[str] = ltp_tokenizer.pipeline(lines[i : i + 100] ,tasks=["""cws"""] ).cws
lowercase : Tuple = [get_chinese_word(__A ) for r in res]
ltp_res.extend(__A )
assert len(__A ) == len(__A )
lowercase : Optional[Any] = []
for i in range(0 ,len(__A ) ,100 ):
lowercase : Tuple = bert_tokenizer(lines[i : i + 100] ,add_special_tokens=__A ,truncation=__A ,max_length=512 )
bert_res.extend(res["""input_ids"""] )
assert len(__A ) == len(__A )
lowercase : Optional[Any] = []
for input_ids, chinese_word in zip(__A ,__A ):
lowercase : List[str] = []
for id in input_ids:
lowercase : int = bert_tokenizer._convert_id_to_token(__A )
input_tokens.append(__A )
lowercase : Dict = add_sub_symbol(__A ,__A )
lowercase : List[str] = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__A ):
if token[:2] == "##":
lowercase : Dict = token[2:]
# save chinese tokens' pos
if len(__A ) == 1 and _is_chinese_char(ord(__A ) ):
ref_id.append(__A )
ref_ids.append(__A )
assert len(__A ) == len(__A )
return ref_ids
def __snake_case ( __A ) -> Dict:
# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
with open(args.file_name ,"""r""" ,encoding="""utf-8""" ) as f:
lowercase : int = f.readlines()
lowercase : Union[str, Any] = [line.strip() for line in data if len(__A ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
lowercase : str = LTP(args.ltp ) # faster in GPU device
lowercase : Union[str, Any] = BertTokenizer.from_pretrained(args.bert )
lowercase : List[str] = prepare_ref(__A ,__A ,__A )
with open(args.save_path ,"""w""" ,encoding="""utf-8""" ) as f:
lowercase : int = [json.dumps(__A ) + """\n""" for ref in ref_ids]
f.writelines(__A )
if __name__ == "__main__":
lowerCAmelCase: Dict =argparse.ArgumentParser(description="prepare_chinese_ref")
parser.add_argument(
"--file_name",
required=False,
type=str,
default="./resources/chinese-demo.txt",
help="file need process, same as training data in lm",
)
parser.add_argument(
"--ltp",
required=False,
type=str,
default="./resources/ltp",
help="resources for LTP tokenizer, usually a path",
)
parser.add_argument(
"--bert",
required=False,
type=str,
default="./resources/robert",
help="resources for Bert tokenizer",
)
parser.add_argument(
"--save_path",
required=False,
type=str,
default="./resources/ref.txt",
help="path to save res",
)
lowerCAmelCase: str =parser.parse_args()
main(args)
| 607 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase = logging.get_logger(__name__)
class __lowerCamelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case__ : Optional[Any] = '''maskformer-swin'''
snake_case__ : List[str] = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , a__=224 , a__=4 , a__=3 , a__=96 , a__=[2, 2, 6, 2] , a__=[3, 6, 12, 24] , a__=7 , a__=4.0 , a__=True , a__=0.0 , a__=0.0 , a__=0.1 , a__="gelu" , a__=False , a__=0.02 , a__=1e-5 , a__=None , a__=None , **a__ , ):
super().__init__(**a__ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_size
__SCREAMING_SNAKE_CASE : List[Any] = patch_size
__SCREAMING_SNAKE_CASE : Tuple = num_channels
__SCREAMING_SNAKE_CASE : Dict = embed_dim
__SCREAMING_SNAKE_CASE : str = depths
__SCREAMING_SNAKE_CASE : List[Any] = len(a__ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = num_heads
__SCREAMING_SNAKE_CASE : Optional[int] = window_size
__SCREAMING_SNAKE_CASE : List[str] = mlp_ratio
__SCREAMING_SNAKE_CASE : Any = qkv_bias
__SCREAMING_SNAKE_CASE : List[Any] = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : Optional[int] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Dict = drop_path_rate
__SCREAMING_SNAKE_CASE : Dict = hidden_act
__SCREAMING_SNAKE_CASE : int = use_absolute_embeddings
__SCREAMING_SNAKE_CASE : str = layer_norm_eps
__SCREAMING_SNAKE_CASE : int = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__SCREAMING_SNAKE_CASE : Union[str, Any] = int(embed_dim * 2 ** (len(a__ ) - 1) )
__SCREAMING_SNAKE_CASE : int = ["stem"] + [f'stage{idx}' for idx in range(1 , len(a__ ) + 1 )]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Union[str, Any] = get_aligned_output_features_output_indices(
out_features=a__ , out_indices=a__ , stage_names=self.stage_names )
| 564 |
'''simple docstring'''
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
lowercase = logging.get_logger(__name__)
lowercase = '''T5Config'''
class __lowerCamelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case__ : Optional[int] = '''mt5'''
snake_case__ : Dict = MTaConfig
class __lowerCamelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case__ : List[str] = '''mt5'''
snake_case__ : List[str] = MTaConfig
class __lowerCamelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case__ : Optional[int] = '''mt5'''
snake_case__ : Union[str, Any] = MTaConfig
| 564 | 1 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
lowerCamelCase__ = logging.getLogger(__name__)
def lowerCAmelCase__ ( a__ ) ->Any:
'''simple docstring'''
_UpperCamelCase = git.Repo(search_parent_directories=a__ )
_UpperCamelCase = {
"repo_id": str(a__ ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
}
with open(os.path.join(a__ , "git_log.json" ) , "w" ) as f:
json.dump(a__ , a__ , indent=4 )
def lowerCAmelCase__ ( a__ ) ->Optional[int]:
'''simple docstring'''
if params.n_gpu <= 0:
_UpperCamelCase = 0
_UpperCamelCase = -1
_UpperCamelCase = True
_UpperCamelCase = False
return
assert torch.cuda.is_available()
logger.info("Initializing GPUs" )
if params.n_gpu > 1:
assert params.local_rank != -1
_UpperCamelCase = int(os.environ["WORLD_SIZE"] )
_UpperCamelCase = int(os.environ["N_GPU_NODE"] )
_UpperCamelCase = int(os.environ["RANK"] )
# number of nodes / node ID
_UpperCamelCase = params.world_size // params.n_gpu_per_node
_UpperCamelCase = params.global_rank // params.n_gpu_per_node
_UpperCamelCase = True
assert params.n_nodes == int(os.environ["N_NODES"] )
assert params.node_id == int(os.environ["NODE_RANK"] )
# local job (single GPU)
else:
assert params.local_rank == -1
_UpperCamelCase = 1
_UpperCamelCase = 0
_UpperCamelCase = 0
_UpperCamelCase = 0
_UpperCamelCase = 1
_UpperCamelCase = 1
_UpperCamelCase = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
_UpperCamelCase = params.node_id == 0 and params.local_rank == 0
_UpperCamelCase = params.n_nodes > 1
# summary
_UpperCamelCase = f'--- Global rank: {params.global_rank} - '
logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes )
logger.info(PREFIX + "Node ID : %i" % params.node_id )
logger.info(PREFIX + "Local rank : %i" % params.local_rank )
logger.info(PREFIX + "World size : %i" % params.world_size )
logger.info(PREFIX + "GPUs per node : %i" % params.n_gpu_per_node )
logger.info(PREFIX + "Master : %s" % str(params.is_master ) )
logger.info(PREFIX + "Multi-node : %s" % str(params.multi_node ) )
logger.info(PREFIX + "Multi-GPU : %s" % str(params.multi_gpu ) )
logger.info(PREFIX + "Hostname : %s" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("Initializing PyTorch distributed" )
torch.distributed.init_process_group(
init_method="env://" , backend="nccl" , )
def lowerCAmelCase__ ( a__ ) ->str:
'''simple docstring'''
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 547 | def lowerCAmelCase__ ( a__ , a__ , a__ , a__ , a__ ) ->int:
'''simple docstring'''
if index == number_of_items:
return 0
_UpperCamelCase = 0
_UpperCamelCase = 0
_UpperCamelCase = knapsack(a__ , a__ , a__ , a__ , index + 1 )
if weights[index] <= max_weight:
_UpperCamelCase = values[index] + knapsack(
a__ , a__ , a__ , max_weight - weights[index] , index + 1 )
return max(a__ , a__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 547 | 1 |
def lowerCamelCase_ ( UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def lowerCamelCase_ ( UpperCAmelCase_ : int ) -> bool:
'''simple docstring'''
_UpperCamelCase : List[Any] = 0
_UpperCamelCase : str = number
while duplicate > 0:
_UpperCamelCase , _UpperCamelCase : int = divmod(UpperCAmelCase_ , 1_0 )
fact_sum += factorial(UpperCAmelCase_ )
return fact_sum == number
if __name__ == "__main__":
print("""Program to check whether a number is a Krisnamurthy Number or not.""")
lowerCAmelCase__ = int(input("""Enter number: """).strip())
print(
f'{number} is {"" if krishnamurthy(number) else "not "}a Krishnamurthy Number.'
)
| 648 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
lowerCAmelCase__ = {
"""sample_size""": 3_2,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 2,
"""num_class_embeds""": 1_0_0_0,
"""block_out_channels""": [3_2, 6_4],
"""attention_head_dim""": 8,
"""down_block_types""": [
"""ResnetDownsampleBlock2D""",
"""AttnDownBlock2D""",
],
"""up_block_types""": [
"""AttnUpBlock2D""",
"""ResnetUpsampleBlock2D""",
],
"""resnet_time_scale_shift""": """scale_shift""",
"""upsample_type""": """resnet""",
"""downsample_type""": """resnet""",
}
lowerCAmelCase__ = {
"""sample_size""": 6_4,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 3,
"""num_class_embeds""": 1_0_0_0,
"""block_out_channels""": [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
"""attention_head_dim""": 6_4,
"""down_block_types""": [
"""ResnetDownsampleBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
],
"""up_block_types""": [
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""ResnetUpsampleBlock2D""",
],
"""resnet_time_scale_shift""": """scale_shift""",
"""upsample_type""": """resnet""",
"""downsample_type""": """resnet""",
}
lowerCAmelCase__ = {
"""sample_size""": 2_5_6,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 2,
"""num_class_embeds""": None,
"""block_out_channels""": [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
"""attention_head_dim""": 6_4,
"""down_block_types""": [
"""ResnetDownsampleBlock2D""",
"""ResnetDownsampleBlock2D""",
"""ResnetDownsampleBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
],
"""up_block_types""": [
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""ResnetUpsampleBlock2D""",
"""ResnetUpsampleBlock2D""",
"""ResnetUpsampleBlock2D""",
],
"""resnet_time_scale_shift""": """default""",
"""upsample_type""": """resnet""",
"""downsample_type""": """resnet""",
}
lowerCAmelCase__ = {
"""num_train_timesteps""": 4_0,
"""sigma_min""": 0.0_02,
"""sigma_max""": 80.0,
}
lowerCAmelCase__ = {
"""num_train_timesteps""": 2_0_1,
"""sigma_min""": 0.0_02,
"""sigma_max""": 80.0,
}
lowerCAmelCase__ = {
"""num_train_timesteps""": 1_5_1,
"""sigma_min""": 0.0_02,
"""sigma_max""": 80.0,
}
def lowerCamelCase_ ( UpperCAmelCase_ : int ) -> List[str]:
'''simple docstring'''
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError('boolean value expected' )
def lowerCamelCase_ ( UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : str , UpperCAmelCase_ : Union[str, Any]=False ) -> str:
'''simple docstring'''
_UpperCamelCase : Optional[int] = checkpoint[F'''{old_prefix}.in_layers.0.weight''']
_UpperCamelCase : str = checkpoint[F'''{old_prefix}.in_layers.0.bias''']
_UpperCamelCase : str = checkpoint[F'''{old_prefix}.in_layers.2.weight''']
_UpperCamelCase : Union[str, Any] = checkpoint[F'''{old_prefix}.in_layers.2.bias''']
_UpperCamelCase : Dict = checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
_UpperCamelCase : Optional[int] = checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
_UpperCamelCase : Tuple = checkpoint[F'''{old_prefix}.out_layers.0.weight''']
_UpperCamelCase : List[Any] = checkpoint[F'''{old_prefix}.out_layers.0.bias''']
_UpperCamelCase : Optional[Any] = checkpoint[F'''{old_prefix}.out_layers.3.weight''']
_UpperCamelCase : Union[str, Any] = checkpoint[F'''{old_prefix}.out_layers.3.bias''']
if has_skip:
_UpperCamelCase : Tuple = checkpoint[F'''{old_prefix}.skip_connection.weight''']
_UpperCamelCase : Optional[int] = checkpoint[F'''{old_prefix}.skip_connection.bias''']
return new_checkpoint
def lowerCamelCase_ ( UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Any=None ) -> int:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : Optional[Any] = checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3 , dim=0 )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : Optional[Any] = checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3 , dim=0 )
_UpperCamelCase : Dict = checkpoint[F'''{old_prefix}.norm.weight''']
_UpperCamelCase : Optional[int] = checkpoint[F'''{old_prefix}.norm.bias''']
_UpperCamelCase : List[str] = weight_q.squeeze(-1 ).squeeze(-1 )
_UpperCamelCase : Dict = bias_q.squeeze(-1 ).squeeze(-1 )
_UpperCamelCase : Any = weight_k.squeeze(-1 ).squeeze(-1 )
_UpperCamelCase : List[Any] = bias_k.squeeze(-1 ).squeeze(-1 )
_UpperCamelCase : Dict = weight_v.squeeze(-1 ).squeeze(-1 )
_UpperCamelCase : Tuple = bias_v.squeeze(-1 ).squeeze(-1 )
_UpperCamelCase : Optional[Any] = (
checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1 ).squeeze(-1 )
)
_UpperCamelCase : Dict = checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def lowerCamelCase_ ( UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[int] ) -> Tuple:
'''simple docstring'''
_UpperCamelCase : Any = torch.load(UpperCAmelCase_ , map_location='cpu' )
_UpperCamelCase : Union[str, Any] = {}
_UpperCamelCase : Optional[int] = checkpoint['time_embed.0.weight']
_UpperCamelCase : List[Any] = checkpoint['time_embed.0.bias']
_UpperCamelCase : Dict = checkpoint['time_embed.2.weight']
_UpperCamelCase : Optional[Any] = checkpoint['time_embed.2.bias']
if unet_config["num_class_embeds"] is not None:
_UpperCamelCase : List[str] = checkpoint['label_emb.weight']
_UpperCamelCase : Optional[int] = checkpoint['input_blocks.0.0.weight']
_UpperCamelCase : Union[str, Any] = checkpoint['input_blocks.0.0.bias']
_UpperCamelCase : Optional[int] = unet_config['down_block_types']
_UpperCamelCase : Optional[Any] = unet_config['layers_per_block']
_UpperCamelCase : Dict = unet_config['attention_head_dim']
_UpperCamelCase : List[str] = unet_config['block_out_channels']
_UpperCamelCase : str = 1
_UpperCamelCase : Optional[int] = channels_list[0]
for i, layer_type in enumerate(UpperCAmelCase_ ):
_UpperCamelCase : List[str] = channels_list[i]
_UpperCamelCase : str = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(UpperCAmelCase_ ):
_UpperCamelCase : str = F'''down_blocks.{i}.resnets.{j}'''
_UpperCamelCase : List[Any] = F'''input_blocks.{current_layer}.0'''
_UpperCamelCase : Any = True if j == 0 and downsample_block_has_skip else False
_UpperCamelCase : str = convert_resnet(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , has_skip=UpperCAmelCase_ )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(UpperCAmelCase_ ):
_UpperCamelCase : List[str] = F'''down_blocks.{i}.resnets.{j}'''
_UpperCamelCase : str = F'''input_blocks.{current_layer}.0'''
_UpperCamelCase : int = True if j == 0 and downsample_block_has_skip else False
_UpperCamelCase : Any = convert_resnet(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , has_skip=UpperCAmelCase_ )
_UpperCamelCase : Dict = F'''down_blocks.{i}.attentions.{j}'''
_UpperCamelCase : Optional[int] = F'''input_blocks.{current_layer}.1'''
_UpperCamelCase : Dict = convert_attention(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
current_layer += 1
if i != len(UpperCAmelCase_ ) - 1:
_UpperCamelCase : int = F'''down_blocks.{i}.downsamplers.0'''
_UpperCamelCase : Optional[int] = F'''input_blocks.{current_layer}.0'''
_UpperCamelCase : List[Any] = convert_resnet(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
current_layer += 1
_UpperCamelCase : Tuple = current_channels
# hardcoded the mid-block for now
_UpperCamelCase : Any = 'mid_block.resnets.0'
_UpperCamelCase : Optional[Any] = 'middle_block.0'
_UpperCamelCase : Tuple = convert_resnet(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
_UpperCamelCase : Optional[Any] = 'mid_block.attentions.0'
_UpperCamelCase : Tuple = 'middle_block.1'
_UpperCamelCase : Union[str, Any] = convert_attention(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
_UpperCamelCase : Tuple = 'mid_block.resnets.1'
_UpperCamelCase : str = 'middle_block.2'
_UpperCamelCase : List[str] = convert_resnet(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
_UpperCamelCase : List[Any] = 0
_UpperCamelCase : Optional[int] = unet_config['up_block_types']
for i, layer_type in enumerate(UpperCAmelCase_ ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
_UpperCamelCase : Optional[Any] = F'''up_blocks.{i}.resnets.{j}'''
_UpperCamelCase : Optional[int] = F'''output_blocks.{current_layer}.0'''
_UpperCamelCase : str = convert_resnet(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , has_skip=UpperCAmelCase_ )
current_layer += 1
if i != len(UpperCAmelCase_ ) - 1:
_UpperCamelCase : List[Any] = F'''up_blocks.{i}.upsamplers.0'''
_UpperCamelCase : Dict = F'''output_blocks.{current_layer-1}.1'''
_UpperCamelCase : Optional[int] = convert_resnet(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
_UpperCamelCase : str = F'''up_blocks.{i}.resnets.{j}'''
_UpperCamelCase : Union[str, Any] = F'''output_blocks.{current_layer}.0'''
_UpperCamelCase : Optional[int] = convert_resnet(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , has_skip=UpperCAmelCase_ )
_UpperCamelCase : int = F'''up_blocks.{i}.attentions.{j}'''
_UpperCamelCase : List[Any] = F'''output_blocks.{current_layer}.1'''
_UpperCamelCase : Optional[int] = convert_attention(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
current_layer += 1
if i != len(UpperCAmelCase_ ) - 1:
_UpperCamelCase : List[Any] = F'''up_blocks.{i}.upsamplers.0'''
_UpperCamelCase : Union[str, Any] = F'''output_blocks.{current_layer-1}.2'''
_UpperCamelCase : List[str] = convert_resnet(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
_UpperCamelCase : List[Any] = checkpoint['out.0.weight']
_UpperCamelCase : str = checkpoint['out.0.bias']
_UpperCamelCase : int = checkpoint['out.2.weight']
_UpperCamelCase : List[Any] = checkpoint['out.2.bias']
return new_checkpoint
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("""--unet_path""", default=None, type=str, required=True, help="""Path to the unet.pt to convert.""")
parser.add_argument(
"""--dump_path""", default=None, type=str, required=True, help="""Path to output the converted UNet model."""
)
parser.add_argument("""--class_cond""", default=True, type=str, help="""Whether the model is class-conditional.""")
lowerCAmelCase__ = parser.parse_args()
lowerCAmelCase__ = strabool(args.class_cond)
lowerCAmelCase__ = os.path.basename(args.unet_path)
print(f'Checkpoint: {ckpt_name}')
# Get U-Net config
if "imagenet64" in ckpt_name:
lowerCAmelCase__ = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
lowerCAmelCase__ = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
lowerCAmelCase__ = TEST_UNET_CONFIG
else:
raise ValueError(f'Checkpoint type {ckpt_name} is not currently supported.')
if not args.class_cond:
lowerCAmelCase__ = None
lowerCAmelCase__ = con_pt_to_diffuser(args.unet_path, unet_config)
lowerCAmelCase__ = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
lowerCAmelCase__ = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
lowerCAmelCase__ = CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
lowerCAmelCase__ = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(f'Checkpoint type {ckpt_name} is not currently supported.')
lowerCAmelCase__ = CMStochasticIterativeScheduler(**scheduler_config)
lowerCAmelCase__ = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 648 | 1 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class __UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def snake_case_ ( self ):
__a = FlaxXLMRobertaModel.from_pretrained("""xlm-roberta-base""" )
__a = AutoTokenizer.from_pretrained("""xlm-roberta-base""" )
__a = """The dog is cute and lives in the garden house"""
__a = jnp.array([tokenizer.encode(__A )] )
__a = (1, 12, 768) # batch_size, sequence_length, embedding_vector_dim
__a = jnp.array(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
__a = model(__A )["""last_hidden_state"""]
self.assertEqual(output.shape , __A )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] , __A , atol=1E-3 ) )
| 99 |
class __UpperCAmelCase :
"""simple docstring"""
def __init__( self , __A ):
__a = set_counts
__a = max(__A )
__a = len(__A )
__a = [1] * num_sets
__a = list(range(__A ) )
def snake_case_ ( self , __A , __A ):
__a = self.get_parent(__A )
__a = self.get_parent(__A )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
__a = 0
__a = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
__a = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
__a = 0
__a = src_parent
__a = self.set_counts[src_parent]
__a = max(self.max_set , __A )
return True
def snake_case_ ( self , __A ):
if self.parents[disj_set] == disj_set:
return disj_set
__a = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 99 | 1 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class _lowerCamelCase :
"""simple docstring"""
snake_case = LEDConfig
snake_case = {}
snake_case = "gelu"
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=37 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=20 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=4 , )->Optional[Any]:
'''simple docstring'''
A_ : Any = parent
A_ : Union[str, Any] = batch_size
A_ : Optional[Any] = seq_length
A_ : Any = is_training
A_ : Tuple = use_labels
A_ : Dict = vocab_size
A_ : str = hidden_size
A_ : int = num_hidden_layers
A_ : int = num_attention_heads
A_ : Dict = intermediate_size
A_ : int = hidden_dropout_prob
A_ : Optional[int] = attention_probs_dropout_prob
A_ : List[Any] = max_position_embeddings
A_ : Any = eos_token_id
A_ : int = pad_token_id
A_ : Union[str, Any] = bos_token_id
A_ : Optional[int] = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
A_ : int = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
A_ : Tuple = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def _snake_case ( self )->Tuple:
'''simple docstring'''
A_ : Any = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
A_ : Optional[Any] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
A_ : List[str] = tf.concat([input_ids, eos_tensor] , axis=1 )
A_ : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ : int = self.config_cls(
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 , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
A_ : Dict = prepare_led_inputs_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : Union[str, Any] = tf.concat(
[tf.zeros_like(_SCREAMING_SNAKE_CASE )[:, :-1], tf.ones_like(_SCREAMING_SNAKE_CASE )[:, -1:]] , axis=-1 , )
A_ : List[str] = global_attention_mask
return config, inputs_dict
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->Any:
'''simple docstring'''
A_ : int = TFLEDModel(config=_SCREAMING_SNAKE_CASE ).get_decoder()
A_ : str = inputs_dict['''input_ids''']
A_ : int = input_ids[:1, :]
A_ : List[str] = inputs_dict['''attention_mask'''][:1, :]
A_ : str = 1
# first forward pass
A_ : Tuple = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , use_cache=_SCREAMING_SNAKE_CASE )
A_ , A_ : Optional[int] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
A_ : Dict = ids_tensor((self.batch_size, 3) , config.vocab_size )
A_ : int = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
A_ : Optional[Any] = tf.concat([input_ids, next_tokens] , axis=-1 )
A_ : Tuple = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
A_ : Dict = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )[0]
A_ : Any = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , past_key_values=_SCREAMING_SNAKE_CASE )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
A_ : Any = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
A_ : Optional[int] = output_from_no_past[:, -3:, random_slice_idx]
A_ : Union[str, Any] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , rtol=1e-3 )
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , ):
if attention_mask is None:
A_ : List[str] = tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
A_ : Any = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
A_ : str = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
A_ : Any = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class _lowerCamelCase ( UpperCamelCase , UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
snake_case = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
snake_case = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
snake_case = (
{
"conversational": TFLEDForConditionalGeneration,
"feature-extraction": TFLEDModel,
"summarization": TFLEDForConditionalGeneration,
"text2text-generation": TFLEDForConditionalGeneration,
"translation": TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
snake_case = True
snake_case = False
snake_case = False
snake_case = False
def _snake_case ( self )->Tuple:
'''simple docstring'''
A_ : Any = TFLEDModelTester(self )
A_ : str = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE )
def _snake_case ( self )->Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def _snake_case ( self )->Dict:
'''simple docstring'''
A_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE )
def _snake_case ( self )->Tuple:
'''simple docstring'''
A_ , A_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Any = tf.zeros_like(inputs_dict['''attention_mask'''] )
A_ : int = 2
A_ : Union[str, Any] = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict['''global_attention_mask'''] , )
A_ : Optional[Any] = True
A_ : Tuple = self.model_tester.seq_length
A_ : Union[str, Any] = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(_SCREAMING_SNAKE_CASE ):
A_ : int = outputs.decoder_attentions
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(_SCREAMING_SNAKE_CASE ):
A_ : Union[str, Any] = [t.numpy() for t in outputs.encoder_attentions]
A_ : Optional[Any] = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
A_ : Optional[int] = True
A_ : Optional[Any] = False
A_ : Any = False
A_ : Tuple = model_class(_SCREAMING_SNAKE_CASE )
A_ : Optional[int] = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
A_ : Optional[Any] = len(_SCREAMING_SNAKE_CASE )
self.assertEqual(config.output_hidden_states , _SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(_SCREAMING_SNAKE_CASE )
if self.is_encoder_decoder:
A_ : Tuple = model_class(_SCREAMING_SNAKE_CASE )
A_ : Optional[Any] = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
self.assertEqual(config.output_hidden_states , _SCREAMING_SNAKE_CASE )
check_decoder_attentions_output(_SCREAMING_SNAKE_CASE )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
A_ : List[str] = True
A_ : int = model_class(_SCREAMING_SNAKE_CASE )
A_ : List[str] = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
self.assertEqual(config.output_hidden_states , _SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(_SCREAMING_SNAKE_CASE )
# Check attention is always last and order is fine
A_ : List[Any] = True
A_ : Optional[int] = True
A_ : Optional[int] = model_class(_SCREAMING_SNAKE_CASE )
A_ : int = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_SCREAMING_SNAKE_CASE ) )
self.assertEqual(model.config.output_hidden_states , _SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(_SCREAMING_SNAKE_CASE )
@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.''' )
def _snake_case ( self )->Dict:
'''simple docstring'''
pass
def _snake_case ( self )->Dict:
'''simple docstring'''
pass
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
return tf.constant(SCREAMING_SNAKE_CASE , dtype=tf.intaa )
UpperCamelCase = 1e-4
@slow
@require_tf
class _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self )->Optional[int]:
'''simple docstring'''
A_ : Optional[int] = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' ).led
# change to intended input here
A_ : Union[str, Any] = _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
A_ : Union[str, Any] = _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
A_ : Dict = prepare_led_inputs_dict(model.config , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : Union[str, Any] = model(**_SCREAMING_SNAKE_CASE )[0]
A_ : Optional[int] = (1, 1024, 768)
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
# change to expected output here
A_ : Optional[Any] = tf.convert_to_tensor(
[[2.3_0_5_0, 2.8_2_7_9, 0.6_5_3_1], [-1.8_4_5_7, -0.1_4_5_5, -3.5_6_6_1], [-1.0_1_8_6, 0.4_5_8_6, -2.2_0_4_3]] , )
tf.debugging.assert_near(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1e-3 )
def _snake_case ( self )->Optional[int]:
'''simple docstring'''
A_ : Tuple = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' )
# change to intended input here
A_ : Union[str, Any] = _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
A_ : str = _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
A_ : Dict = prepare_led_inputs_dict(model.config , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : Tuple = model(**_SCREAMING_SNAKE_CASE )[0]
A_ : Any = (1, 1024, model.config.vocab_size)
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
# change to expected output here
A_ : Optional[int] = tf.convert_to_tensor(
[[3_3.6_5_0_7, 6.4_5_7_2, 1_6.8_0_8_9], [5.8_7_3_9, -2.4_2_3_8, 1_1.2_9_0_2], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]] , )
tf.debugging.assert_near(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1e-3 , rtol=1e-3 )
| 152 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase )
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
snake_case = field(default="summarization" , metadata={"include_in_asdict_even_if_is_default": True} )
snake_case = Features({"text": Value("string" )} )
snake_case = Features({"summary": Value("string" )} )
snake_case = "text"
snake_case = "summary"
@property
def _snake_case ( self )->Dict[str, str]:
'''simple docstring'''
return {self.text_column: "text", self.summary_column: "summary"}
| 152 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_snake_case = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
'''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''UniSpeechForCTC''',
'''UniSpeechForPreTraining''',
'''UniSpeechForSequenceClassification''',
'''UniSpeechModel''',
'''UniSpeechPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 340 |
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
_snake_case = logging.get_logger(__name__)
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
'''simple docstring'''
try:
import torch # noqa: F401
except ImportError:
logger.error(
"Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
" instructions." )
raise
if not is_sharded:
lowerCamelCase : Any = os.path.abspath(SCREAMING_SNAKE_CASE_ )
logger.info(f"""Loading PyTorch weights from {pt_path}""" )
lowerCamelCase : Optional[int] = torch.load(SCREAMING_SNAKE_CASE_ , map_location="cpu" )
logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.""" )
lowerCamelCase : List[str] = convert_pytorch_state_dict_to_flax(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
else:
# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
lowerCamelCase : Dict = convert_pytorch_sharded_state_dict_to_flax(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return flax_state_dict
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
'''simple docstring'''
def is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_ ) -> bool:
return len(set(SCREAMING_SNAKE_CASE_ ) & {key, (model_prefix,) + key} ) > 0
# layer norm
lowerCamelCase : Optional[Any] = pt_tuple_key[:-1] + ("scale",)
if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_ ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer mean
lowerCamelCase : Tuple = pt_tuple_key[:-1] + ("mean",)
if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_ ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer var
lowerCamelCase : Any = pt_tuple_key[:-1] + ("var",)
if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_ ):
return renamed_pt_tuple_key, pt_tensor
# embedding
lowerCamelCase : List[str] = pt_tuple_key[:-1] + ("embedding",)
if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_ ):
return renamed_pt_tuple_key, pt_tensor
# conv layer
lowerCamelCase : int = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_ ):
lowerCamelCase : int = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
lowerCamelCase : str = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_ ):
lowerCamelCase : Any = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
lowerCamelCase : Optional[int] = pt_tuple_key[:-1] + ("weight",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
lowerCamelCase : str = pt_tuple_key[:-1] + ("bias",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
lowerCamelCase : Optional[int] = None
if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
lowerCamelCase : Optional[Any] = pt_tuple_key[-2] + "_g"
elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
lowerCamelCase : List[Any] = pt_tuple_key[-2] + "_v"
if name is not None:
lowerCamelCase : str = pt_tuple_key[:-3] + (name,)
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : Union[str, Any] = {k: v.numpy() for k, v in pt_state_dict.items()}
lowerCamelCase : Optional[int] = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers
if "params" in flax_model.params:
lowerCamelCase : Union[str, Any] = flax_model.params["params"]
else:
lowerCamelCase : int = flax_model.params
lowerCamelCase : List[str] = flatten_dict(SCREAMING_SNAKE_CASE_ )
# add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
lowerCamelCase : Any = flatten_dict(flax_model.params["batch_stats"] )
random_flax_state_dict.update(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Optional[Any] = {}
lowerCamelCase : Optional[int] = (model_prefix not in flax_model_params) and (
model_prefix in {k.split("." )[0] for k in pt_state_dict.keys()}
)
lowerCamelCase : Optional[Any] = (model_prefix in flax_model_params) and (
model_prefix not in {k.split("." )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowerCamelCase : List[Any] = tuple(pt_key.split("." ) )
# remove base model prefix if necessary
lowerCamelCase : List[str] = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
lowerCamelCase : Union[str, Any] = pt_tuple_key[1:]
# Correctly rename weight parameters
lowerCamelCase , lowerCamelCase : List[str] = rename_key_and_reshape_tensor(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# add model prefix if necessary
lowerCamelCase : Union[str, Any] = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
lowerCamelCase : Union[str, Any] = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1] or "var" in flax_key[-1]:
lowerCamelCase : Tuple = jnp.asarray(SCREAMING_SNAKE_CASE_ )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
continue
# also add unexpected weight so that warning is thrown
lowerCamelCase : List[Any] = jnp.asarray(SCREAMING_SNAKE_CASE_ )
else:
# also add unexpected weight so that warning is thrown
lowerCamelCase : List[Any] = jnp.asarray(SCREAMING_SNAKE_CASE_ )
return unflatten_dict(SCREAMING_SNAKE_CASE_ )
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
import torch
# Load the index
lowerCamelCase : str = {}
for shard_file in shard_filenames:
# load using msgpack utils
lowerCamelCase : str = torch.load(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Tuple = {k: v.numpy() for k, v in pt_state_dict.items()}
lowerCamelCase : str = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
lowerCamelCase : Optional[int] = flax_model.params["params"]
lowerCamelCase : Any = flatten_dict(SCREAMING_SNAKE_CASE_ )
random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"] ) )
else:
lowerCamelCase : Optional[Any] = flax_model.params
lowerCamelCase : Tuple = flatten_dict(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Any = (model_prefix not in flax_model_params) and (
model_prefix in {k.split("." )[0] for k in pt_state_dict.keys()}
)
lowerCamelCase : Union[str, Any] = (model_prefix in flax_model_params) and (
model_prefix not in {k.split("." )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowerCamelCase : List[Any] = tuple(pt_key.split("." ) )
# remove base model prefix if necessary
lowerCamelCase : Optional[Any] = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
lowerCamelCase : int = pt_tuple_key[1:]
# Correctly rename weight parameters
lowerCamelCase , lowerCamelCase : str = rename_key_and_reshape_tensor(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# add model prefix if necessary
lowerCamelCase : Optional[int] = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
lowerCamelCase : List[str] = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1]:
lowerCamelCase : str = jnp.asarray(SCREAMING_SNAKE_CASE_ )
continue
if "var" in flax_key[-1]:
lowerCamelCase : Dict = jnp.asarray(SCREAMING_SNAKE_CASE_ )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
continue
# also add unexpected weight so that warning is thrown
lowerCamelCase : Optional[Any] = jnp.asarray(SCREAMING_SNAKE_CASE_ )
else:
# also add unexpected weight so that warning is thrown
lowerCamelCase : Optional[int] = jnp.asarray(SCREAMING_SNAKE_CASE_ )
return unflatten_dict(SCREAMING_SNAKE_CASE_ )
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : List[str] = os.path.abspath(SCREAMING_SNAKE_CASE_ )
logger.info(f"""Loading Flax weights from {flax_checkpoint_path}""" )
# import correct flax class
lowerCamelCase : str = getattr(SCREAMING_SNAKE_CASE_ , "Flax" + model.__class__.__name__ )
# load flax weight dict
with open(SCREAMING_SNAKE_CASE_ , "rb" ) as state_f:
try:
lowerCamelCase : Any = from_bytes(SCREAMING_SNAKE_CASE_ , state_f.read() )
except UnpicklingError:
raise EnvironmentError(f"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """ )
return load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
try:
import torch # noqa: F401
except ImportError:
logger.error(
"Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
" instructions." )
raise
# check if we have bf16 weights
lowerCamelCase : Union[str, Any] = flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE_ : x.dtype == jnp.bfloataa , SCREAMING_SNAKE_CASE_ ) ).values()
if any(SCREAMING_SNAKE_CASE_ ):
# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
"Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
"before loading those in PyTorch model." )
lowerCamelCase : List[str] = jax.tree_util.tree_map(
lambda SCREAMING_SNAKE_CASE_ : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , SCREAMING_SNAKE_CASE_ )
lowerCamelCase : int = flatten_dict(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Dict = pt_model.state_dict()
lowerCamelCase : Optional[Any] = (pt_model.base_model_prefix in flax_state) and (
pt_model.base_model_prefix not in {k.split("." )[0] for k in pt_model_dict.keys()}
)
lowerCamelCase : Any = (pt_model.base_model_prefix not in flax_state) and (
pt_model.base_model_prefix in {k.split("." )[0] for k in pt_model_dict.keys()}
)
# keep track of unexpected & missing keys
lowerCamelCase : Optional[int] = []
lowerCamelCase : Tuple = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
lowerCamelCase : Optional[int] = flax_key_tuple[0] == pt_model.base_model_prefix
lowerCamelCase : Tuple = ".".join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict
# adapt flax_key to prepare for loading from/to base model only
if load_model_with_head_into_base_model and has_base_model_prefix:
lowerCamelCase : Dict = flax_key_tuple[1:]
elif load_base_model_into_model_with_head and require_base_model_prefix:
lowerCamelCase : List[Any] = (pt_model.base_model_prefix,) + flax_key_tuple
# rename flax weights to PyTorch format
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(SCREAMING_SNAKE_CASE_ ) not in pt_model_dict:
# conv layer
lowerCamelCase : List[str] = flax_key_tuple[:-1] + ("weight",)
lowerCamelCase : Tuple = jnp.transpose(SCREAMING_SNAKE_CASE_ , (3, 2, 0, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(SCREAMING_SNAKE_CASE_ ) not in pt_model_dict:
# linear layer
lowerCamelCase : Tuple = flax_key_tuple[:-1] + ("weight",)
lowerCamelCase : Union[str, Any] = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
lowerCamelCase : Tuple = flax_key_tuple[:-1] + ("weight",)
# adding batch stats from flax batch norm to pt
elif "mean" in flax_key_tuple[-1]:
lowerCamelCase : str = flax_key_tuple[:-1] + ("running_mean",)
elif "var" in flax_key_tuple[-1]:
lowerCamelCase : List[str] = flax_key_tuple[:-1] + ("running_var",)
if "batch_stats" in flax_state:
lowerCamelCase : List[str] = ".".join(flax_key_tuple[1:] ) # Remove the params/batch_stats header
else:
lowerCamelCase : Any = ".".join(SCREAMING_SNAKE_CASE_ )
# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
lowerCamelCase : Tuple = {}
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
for key in pt_model_dict:
lowerCamelCase : Any = key.split("." )
lowerCamelCase : str = None
if key_components[-3::2] == ["parametrizations", "original0"]:
lowerCamelCase : Any = key_components[-2] + "_g"
elif key_components[-3::2] == ["parametrizations", "original1"]:
lowerCamelCase : Tuple = key_components[-2] + "_v"
if name is not None:
lowerCamelCase : Any = key_components[:-3] + [name]
lowerCamelCase : Any = ".".join(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Optional[int] = key
if flax_key in special_pt_names:
lowerCamelCase : Any = special_pt_names[flax_key]
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
else:
# add weight to pytorch dict
lowerCamelCase : Optional[Any] = np.asarray(SCREAMING_SNAKE_CASE_ ) if not isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ) else flax_tensor
lowerCamelCase : int = torch.from_numpy(SCREAMING_SNAKE_CASE_ )
# remove from missing keys
missing_keys.remove(SCREAMING_SNAKE_CASE_ )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(SCREAMING_SNAKE_CASE_ )
pt_model.load_state_dict(SCREAMING_SNAKE_CASE_ )
# re-transform missing_keys to list
lowerCamelCase : List[Any] = list(SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
logger.warning(
"Some weights of the Flax model were not used when initializing the PyTorch model"
f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
" (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
" FlaxBertForSequenceClassification model)." )
else:
logger.warning(f"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n""" )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
logger.warning(
f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
" use it for predictions and inference." )
else:
logger.warning(
f"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
"If your task is similar to the task the model of the checkpoint was trained on, "
f"""you can already use {pt_model.__class__.__name__} for predictions without further training.""" )
return pt_model
| 340 | 1 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowercase__ ( UpperCamelCase_):
UpperCamelCase_ = ComputeEnvironment.AMAZON_SAGEMAKER
UpperCamelCase_ = True
UpperCamelCase_ = """ml.p3.2xlarge"""
UpperCamelCase_ = """accelerate_sagemaker_execution_role"""
UpperCamelCase_ = """hf-sm"""
UpperCamelCase_ = """us-east-1"""
UpperCamelCase_ = 1
UpperCamelCase_ = """accelerate-sagemaker-1"""
UpperCamelCase_ = """1.6"""
UpperCamelCase_ = """4.4"""
UpperCamelCase_ = """train.py"""
UpperCamelCase_ = [
"""--model_name_or_path""",
"""bert""",
"""--do_train""",
"""False""",
"""--epochs""",
"""3""",
"""--learning_rate""",
"""5e-5""",
"""--max_steps""",
"""50.5""",
]
UpperCamelCase_ = [
"""--model_name_or_path""",
"""bert""",
"""--do_train""",
"""--do_test""",
"""False""",
"""--do_predict""",
"""--epochs""",
"""3""",
"""--learning_rate""",
"""5e-5""",
"""--max_steps""",
"""50.5""",
]
class lowercase__ ( unittest.TestCase):
def __A ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args['''model_name_or_path'''] , UpperCamelCase__ )
assert isinstance(converted_args['''do_train'''] , UpperCamelCase__ )
assert isinstance(converted_args['''epochs'''] , UpperCamelCase__ )
assert isinstance(converted_args['''learning_rate'''] , UpperCamelCase__ )
assert isinstance(converted_args['''max_steps'''] , UpperCamelCase__ )
with pytest.raises(UpperCamelCase__ ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args )
| 34 | from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
# TODO Update this
__UpperCamelCase : List[str] = {
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.json',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class lowercase__ ( UpperCamelCase_):
UpperCamelCase_ = """esm"""
def __init__( self : Tuple , UpperCamelCase__ : Optional[Any]=None , UpperCamelCase__ : int=None , UpperCamelCase__ : List[str]=None , UpperCamelCase__ : Any=768 , UpperCamelCase__ : Optional[Any]=12 , UpperCamelCase__ : str=12 , UpperCamelCase__ : Optional[int]=3072 , UpperCamelCase__ : Optional[Any]=0.1 , UpperCamelCase__ : Any=0.1 , UpperCamelCase__ : Union[str, Any]=1026 , UpperCamelCase__ : Dict=0.02 , UpperCamelCase__ : Any=1E-12 , UpperCamelCase__ : Dict="absolute" , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : int=None , UpperCamelCase__ : Optional[int]=False , UpperCamelCase__ : Tuple=False , UpperCamelCase__ : List[str]=None , UpperCamelCase__ : Optional[Any]=None , **UpperCamelCase__ : Any , ):
'''simple docstring'''
super().__init__(pad_token_id=UpperCamelCase__ , mask_token_id=UpperCamelCase__ , **UpperCamelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = vocab_size
SCREAMING_SNAKE_CASE : Any = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : Dict = num_attention_heads
SCREAMING_SNAKE_CASE : Any = intermediate_size
SCREAMING_SNAKE_CASE : str = hidden_dropout_prob
SCREAMING_SNAKE_CASE : List[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : str = max_position_embeddings
SCREAMING_SNAKE_CASE : Tuple = initializer_range
SCREAMING_SNAKE_CASE : Optional[int] = layer_norm_eps
SCREAMING_SNAKE_CASE : Dict = position_embedding_type
SCREAMING_SNAKE_CASE : Any = use_cache
SCREAMING_SNAKE_CASE : Dict = emb_layer_norm_before
SCREAMING_SNAKE_CASE : List[str] = token_dropout
SCREAMING_SNAKE_CASE : List[Any] = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('''No esmfold_config supplied for folding model, using default values.''' )
SCREAMING_SNAKE_CASE : List[Any] = EsmFoldConfig()
elif isinstance(UpperCamelCase__ , UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : Union[str, Any] = EsmFoldConfig(**UpperCamelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = esmfold_config
if vocab_list is None:
logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!''' )
SCREAMING_SNAKE_CASE : Optional[int] = get_default_vocab_list()
else:
SCREAMING_SNAKE_CASE : Optional[Any] = vocab_list
else:
SCREAMING_SNAKE_CASE : str = None
SCREAMING_SNAKE_CASE : int = None
if self.esmfold_config is not None and getattr(self.esmfold_config , '''use_esm_attn_map''' , UpperCamelCase__ ):
raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!''' )
def __A ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = super().to_dict()
if isinstance(self.esmfold_config , UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : Optional[Any] = self.esmfold_config.to_dict()
return output
@dataclass
class lowercase__ :
UpperCamelCase_ = None
UpperCamelCase_ = True
UpperCamelCase_ = False
UpperCamelCase_ = False
UpperCamelCase_ = False
UpperCamelCase_ = 0
UpperCamelCase_ = True
UpperCamelCase_ = False
UpperCamelCase_ = 128
UpperCamelCase_ = None
def __A ( self : Optional[int] ):
'''simple docstring'''
if self.trunk is None:
SCREAMING_SNAKE_CASE : Optional[Any] = TrunkConfig()
elif isinstance(self.trunk , UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : Tuple = TrunkConfig(**self.trunk )
def __A ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = asdict(self )
SCREAMING_SNAKE_CASE : Tuple = self.trunk.to_dict()
return output
@dataclass
class lowercase__ :
UpperCamelCase_ = 48
UpperCamelCase_ = 1_024
UpperCamelCase_ = 128
UpperCamelCase_ = 32
UpperCamelCase_ = 32
UpperCamelCase_ = 32
UpperCamelCase_ = 0
UpperCamelCase_ = 0
UpperCamelCase_ = False
UpperCamelCase_ = 4
UpperCamelCase_ = 128
UpperCamelCase_ = None
def __A ( self : Any ):
'''simple docstring'''
if self.structure_module is None:
SCREAMING_SNAKE_CASE : Optional[int] = StructureModuleConfig()
elif isinstance(self.structure_module , UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : Optional[Any] = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(f"""`max_recycles` should be positive, got {self.max_recycles}.""" )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
f""" {self.sequence_state_dim} and {self.sequence_state_dim}.""" )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
f""" {self.pairwise_state_dim} and {self.pairwise_state_dim}.""" )
SCREAMING_SNAKE_CASE : Dict = self.sequence_state_dim // self.sequence_head_width
SCREAMING_SNAKE_CASE : Tuple = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
f""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.""" )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
f""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.""" )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.""" )
if self.dropout >= 0.4:
raise ValueError(f"""`dropout` should not be greater than 0.4, got {self.dropout}.""" )
def __A ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = asdict(self )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.structure_module.to_dict()
return output
@dataclass
class lowercase__ :
UpperCamelCase_ = 384
UpperCamelCase_ = 128
UpperCamelCase_ = 16
UpperCamelCase_ = 128
UpperCamelCase_ = 12
UpperCamelCase_ = 4
UpperCamelCase_ = 8
UpperCamelCase_ = 0.1
UpperCamelCase_ = 8
UpperCamelCase_ = 1
UpperCamelCase_ = 2
UpperCamelCase_ = 7
UpperCamelCase_ = 10
UpperCamelCase_ = 1E-8
UpperCamelCase_ = 1E5
def __A ( self : Dict ):
'''simple docstring'''
return asdict(self )
def A ( ):
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 34 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=True , _lowerCAmelCase="pt" ) -> str:
"""simple docstring"""
A : Any = {"""add_prefix_space""": True} if isinstance(_lowerCAmelCase , _lowerCAmelCase ) and not line.startswith(""" """ ) else {}
A : Optional[int] = padding_side
return tokenizer(
[line] , max_length=_lowerCAmelCase , padding="""max_length""" if pad_to_max_length else None , truncation=_lowerCAmelCase , return_tensors=_lowerCAmelCase , add_special_tokens=_lowerCAmelCase , **_lowerCAmelCase , )
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=None , ) -> int:
"""simple docstring"""
A : Dict = input_ids.ne(_lowerCAmelCase ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__( self, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__="train", lowerCamelCase__=None, lowerCamelCase__=None, lowerCamelCase__=None, lowerCamelCase__="", ):
super().__init__()
A : List[str] = Path(lowerCamelCase__ ).joinpath(type_path + """.source""" )
A : List[Any] = Path(lowerCamelCase__ ).joinpath(type_path + """.target""" )
A : int = self.get_char_lens(self.src_file )
A : List[Any] = max_source_length
A : Optional[Any] = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
A : str = tokenizer
A : str = prefix
if n_obs is not None:
A : Optional[int] = self.src_lens[:n_obs]
A : Optional[Any] = src_lang
A : Tuple = tgt_lang
def __len__( self ):
return len(self.src_lens )
def __getitem__( self, lowerCamelCase__ ):
A : List[Any] = index + 1 # linecache starts at 1
A : int = self.prefix + linecache.getline(str(self.src_file ), lowerCamelCase__ ).rstrip("""\n""" )
A : int = linecache.getline(str(self.tgt_file ), lowerCamelCase__ ).rstrip("""\n""" )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer, lowerCamelCase__ ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
A : str = (
self.tokenizer.question_encoder if isinstance(self.tokenizer, lowerCamelCase__ ) else self.tokenizer
)
A : int = self.tokenizer.generator if isinstance(self.tokenizer, lowerCamelCase__ ) else self.tokenizer
A : Any = encode_line(lowerCamelCase__, lowerCamelCase__, self.max_source_length, """right""" )
A : List[Any] = encode_line(lowerCamelCase__, lowerCamelCase__, self.max_target_length, """right""" )
A : List[str] = source_inputs["""input_ids"""].squeeze()
A : str = target_inputs["""input_ids"""].squeeze()
A : Any = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def _lowerCAmelCase ( lowerCamelCase__ ):
return [len(lowerCamelCase__ ) for x in Path(lowerCamelCase__ ).open().readlines()]
def _lowerCAmelCase ( self, lowerCamelCase__ ):
A : Union[str, Any] = torch.stack([x["""input_ids"""] for x in batch] )
A : Tuple = torch.stack([x["""attention_mask"""] for x in batch] )
A : List[str] = torch.stack([x["""decoder_input_ids"""] for x in batch] )
A : str = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer, lowerCamelCase__ )
else self.tokenizer.pad_token_id
)
A : List[str] = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer, lowerCamelCase__ )
else self.tokenizer.pad_token_id
)
A : Tuple = trim_batch(lowerCamelCase__, lowerCamelCase__ )
A , A : Tuple = trim_batch(lowerCamelCase__, lowerCamelCase__, attention_mask=lowerCamelCase__ )
A : Any = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
SCREAMING_SNAKE_CASE_:Optional[Any] = getLogger(__name__)
def __UpperCamelCase ( _lowerCAmelCase ) -> Any:
"""simple docstring"""
return list(itertools.chain.from_iterable(_lowerCAmelCase ) )
def __UpperCamelCase ( _lowerCAmelCase ) -> None:
"""simple docstring"""
A : Any = get_git_info()
save_json(_lowerCAmelCase , os.path.join(_lowerCAmelCase , """git_log.json""" ) )
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=4 , **_lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
with open(_lowerCAmelCase , """w""" ) as f:
json.dump(_lowerCAmelCase , _lowerCAmelCase , indent=_lowerCAmelCase , **_lowerCAmelCase )
def __UpperCamelCase ( _lowerCAmelCase ) -> Optional[int]:
"""simple docstring"""
with open(_lowerCAmelCase ) as f:
return json.load(_lowerCAmelCase )
def __UpperCamelCase ( ) -> int:
"""simple docstring"""
A : List[str] = git.Repo(search_parent_directories=_lowerCAmelCase )
A : Dict = {
"""repo_id""": str(_lowerCAmelCase ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
"""hostname""": str(socket.gethostname() ),
}
return repo_infos
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> List:
"""simple docstring"""
return list(map(_lowerCAmelCase , _lowerCAmelCase ) )
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
"""simple docstring"""
with open(_lowerCAmelCase , """wb""" ) as f:
return pickle.dump(_lowerCAmelCase , _lowerCAmelCase )
def __UpperCamelCase ( _lowerCAmelCase ) -> Optional[int]:
"""simple docstring"""
def remove_articles(_lowerCAmelCase ):
return re.sub(R"""\b(a|an|the)\b""" , """ """ , _lowerCAmelCase )
def white_space_fix(_lowerCAmelCase ):
return " ".join(text.split() )
def remove_punc(_lowerCAmelCase ):
A : str = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_lowerCAmelCase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase ) ) ) )
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Tuple:
"""simple docstring"""
A : Dict = normalize_answer(_lowerCAmelCase ).split()
A : Tuple = normalize_answer(_lowerCAmelCase ).split()
A : Optional[Any] = Counter(_lowerCAmelCase ) & Counter(_lowerCAmelCase )
A : Dict = sum(common.values() )
if num_same == 0:
return 0
A : Union[str, Any] = 1.0 * num_same / len(_lowerCAmelCase )
A : Dict = 1.0 * num_same / len(_lowerCAmelCase )
A : Optional[int] = (2 * precision * recall) / (precision + recall)
return fa
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
return normalize_answer(_lowerCAmelCase ) == normalize_answer(_lowerCAmelCase )
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
"""simple docstring"""
assert len(_lowerCAmelCase ) == len(_lowerCAmelCase )
A : Dict = 0
for hypo, pred in zip(_lowerCAmelCase , _lowerCAmelCase ):
em += exact_match_score(_lowerCAmelCase , _lowerCAmelCase )
if len(_lowerCAmelCase ) > 0:
em /= len(_lowerCAmelCase )
return {"em": em}
def __UpperCamelCase ( _lowerCAmelCase ) -> int:
"""simple docstring"""
return model_prefix.startswith("""rag""" )
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> int:
"""simple docstring"""
A : Dict = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
A : List[Any] = """dropout_rate"""
for p in extra_params:
if getattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
if not hasattr(_lowerCAmelCase , _lowerCAmelCase ) and not hasattr(_lowerCAmelCase , equivalent_param[p] ):
logger.info("""config doesn't have a `{}` attribute""".format(_lowerCAmelCase ) )
delattr(_lowerCAmelCase , _lowerCAmelCase )
continue
A : List[Any] = p if hasattr(_lowerCAmelCase , _lowerCAmelCase ) else equivalent_param[p]
setattr(_lowerCAmelCase , _lowerCAmelCase , getattr(_lowerCAmelCase , _lowerCAmelCase ) )
delattr(_lowerCAmelCase , _lowerCAmelCase )
return hparams, config
| 662 |
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self, lowerCamelCase__ ):
# we need a list not a string, so do something to change the type
A : List[Any] = arr.split(""",""" )
def _lowerCAmelCase ( self ):
A : int = [int(self.array[0] )] * len(self.array )
A : Optional[Any] = [int(self.array[0] )] * len(self.array )
for i in range(1, len(self.array ) ):
A : Union[str, Any] = max(
int(self.array[i] ) + sum_value[i - 1], int(self.array[i] ) )
A : Dict = max(sum_value[i], rear[i - 1] )
return rear[len(self.array ) - 1]
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_:int = input("""please input some numbers:""")
SCREAMING_SNAKE_CASE_:Dict = SubArray(whole_array)
SCREAMING_SNAKE_CASE_:Optional[int] = array.solve_sub_array()
print(("""the results is:""", re))
| 662 | 1 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCAmelCase ( UpperCamelCase__ : int ):
"""simple docstring"""
def wrapper(*UpperCamelCase__ : Tuple , **UpperCamelCase__ : List[Any] ):
__UpperCAmelCase = timeit.default_timer()
__UpperCAmelCase = func(*UpperCamelCase__ , **UpperCamelCase__ )
__UpperCAmelCase = timeit.default_timer() - starttime
return delta
__UpperCAmelCase = func.__name__
return wrapper
def lowerCAmelCase ( UpperCamelCase__ : dict , UpperCamelCase__ : str=1_0_0 , UpperCamelCase__ : Union[str, Any]=None ):
"""simple docstring"""
__UpperCAmelCase = []
__UpperCAmelCase = seq_shapes or {}
for i in range(UpperCamelCase__ ):
__UpperCAmelCase = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(UpperCamelCase__ , _ArrayXD ):
__UpperCAmelCase = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(UpperCamelCase__ , datasets.Value ):
if v.dtype == "string":
__UpperCAmelCase = '''The small grey turtle was surprisingly fast when challenged.'''
else:
__UpperCAmelCase = np.random.randint(1_0 , size=1 ).astype(v.dtype ).item()
elif isinstance(UpperCamelCase__ , datasets.Sequence ):
while isinstance(UpperCamelCase__ , datasets.Sequence ):
__UpperCAmelCase = v.feature
__UpperCAmelCase = seq_shapes[k]
__UpperCAmelCase = np.random.rand(*UpperCamelCase__ ).astype(v.dtype )
__UpperCAmelCase = data
dummy_data.append((i, example) )
return dummy_data
def lowerCAmelCase ( UpperCamelCase__ : Tuple , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Dict=1_0_0 , UpperCamelCase__ : Optional[Any]=None ):
"""simple docstring"""
__UpperCAmelCase = generate_examples(UpperCamelCase__ , num_examples=UpperCamelCase__ , seq_shapes=UpperCamelCase__ )
with ArrowWriter(features=UpperCamelCase__ , path=UpperCamelCase__ ) as writer:
for key, record in dummy_data:
__UpperCAmelCase = features.encode_example(UpperCamelCase__ )
writer.write(UpperCamelCase__ )
__UpperCAmelCase , __UpperCAmelCase = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.""" )
__UpperCAmelCase = datasets.Dataset.from_file(filename=UpperCamelCase__ , info=datasets.DatasetInfo(features=UpperCamelCase__ ) )
return dataset
| 654 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
class A ( UpperCAmelCase ):
a_ = '''bert-generation'''
def __init__( self : str , __a : str=5_0_3_5_8 , __a : int=1_0_2_4 , __a : Optional[Any]=2_4 , __a : Any=1_6 , __a : int=4_0_9_6 , __a : Any="gelu" , __a : Union[str, Any]=0.1 , __a : Any=0.1 , __a : Union[str, Any]=5_1_2 , __a : int=0.0_2 , __a : str=1e-12 , __a : List[str]=0 , __a : Optional[int]=2 , __a : Tuple=1 , __a : str="absolute" , __a : Optional[Any]=True , **__a : Tuple , ) -> Any:
super().__init__(pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a )
__UpperCAmelCase = vocab_size
__UpperCAmelCase = hidden_size
__UpperCAmelCase = num_hidden_layers
__UpperCAmelCase = num_attention_heads
__UpperCAmelCase = hidden_act
__UpperCAmelCase = intermediate_size
__UpperCAmelCase = hidden_dropout_prob
__UpperCAmelCase = attention_probs_dropout_prob
__UpperCAmelCase = max_position_embeddings
__UpperCAmelCase = initializer_range
__UpperCAmelCase = layer_norm_eps
__UpperCAmelCase = position_embedding_type
__UpperCAmelCase = use_cache
| 654 | 1 |
'''simple docstring'''
import random
def __a(SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
_lowerCAmelCase = num - 1
_lowerCAmelCase = 0
while s % 2 == 0:
_lowerCAmelCase = s // 2
t += 1
for _ in range(5 ):
_lowerCAmelCase = random.randrange(2 , num - 1 )
_lowerCAmelCase = pow(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if v != 1:
_lowerCAmelCase = 0
while v != (num - 1):
if i == t - 1:
return False
else:
_lowerCAmelCase = i + 1
_lowerCAmelCase = (v**2) % num
return True
def __a(SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
if num < 2:
return False
_lowerCAmelCase = [
2,
3,
5,
7,
11,
13,
17,
19,
23,
29,
31,
37,
41,
43,
47,
53,
59,
61,
67,
71,
73,
79,
83,
89,
97,
101,
103,
107,
109,
113,
127,
131,
137,
139,
149,
151,
157,
163,
167,
173,
179,
181,
191,
193,
197,
199,
211,
223,
227,
229,
233,
239,
241,
251,
257,
263,
269,
271,
277,
281,
283,
293,
307,
311,
313,
317,
331,
337,
347,
349,
353,
359,
367,
373,
379,
383,
389,
397,
401,
409,
419,
421,
431,
433,
439,
443,
449,
457,
461,
463,
467,
479,
487,
491,
499,
503,
509,
521,
523,
541,
547,
557,
563,
569,
571,
577,
587,
593,
599,
601,
607,
613,
617,
619,
631,
641,
643,
647,
653,
659,
661,
673,
677,
683,
691,
701,
709,
719,
727,
733,
739,
743,
751,
757,
761,
769,
773,
787,
797,
809,
811,
821,
823,
827,
829,
839,
853,
857,
859,
863,
877,
881,
883,
887,
907,
911,
919,
929,
937,
941,
947,
953,
967,
971,
977,
983,
991,
997,
]
if num in low_primes:
return True
for prime in low_primes:
if (num % prime) == 0:
return False
return rabin_miller(SCREAMING_SNAKE_CASE_ )
def __a(SCREAMING_SNAKE_CASE_ : int = 1024 ):
'''simple docstring'''
while True:
_lowerCAmelCase = random.randrange(2 ** (keysize - 1) , 2 ** (keysize) )
if is_prime_low_num(SCREAMING_SNAKE_CASE_ ):
return num
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = generate_large_prime()
print(("Prime number:", num))
print(("is_prime_low_num:", is_prime_low_num(num)))
| 18 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 387 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( A : int , A : Optional[int] , A : Optional[int] , A : str , A : List[Any] , A : Tuple ) -> Union[str, Any]:
if index == r:
for j in range(A ):
print(data[j] , end=''' ''' )
print(''' ''' )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
UpperCAmelCase_ : Optional[Any] = arr[i]
combination_util(A , A , A , index + 1 , A , i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(A , A , A , A , A , i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def __UpperCAmelCase ( A : Union[str, Any] , A : Optional[int] , A : Union[str, Any] ) -> int:
# A temporary array to store all combination one by one
UpperCAmelCase_ : str = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(A , A , A , 0 , A , 0 )
if __name__ == "__main__":
# Driver code to check the function above
_UpperCamelCase : List[Any] = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 216 |
'''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 ( A : str , A : List[Any] , A : Tuple ) -> str:
return params[F"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]
def __UpperCAmelCase ( A : int , A : Any , A : Dict , A : Any="attention" ) -> Union[str, Any]:
UpperCAmelCase_ : Dict = np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :] )
UpperCAmelCase_ : int = k_tmp.reshape(k_tmp.shape[0] , k_tmp.shape[1] * k_tmp.shape[2] )
UpperCAmelCase_ : Dict = np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :] )
UpperCAmelCase_ : Optional[int] = o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] , o_tmp.shape[2] )
UpperCAmelCase_ : List[Any] = np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :] )
UpperCAmelCase_ : int = q_tmp.reshape(q_tmp.shape[0] , q_tmp.shape[1] * q_tmp.shape[2] )
UpperCAmelCase_ : Tuple = np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :] )
UpperCAmelCase_ : List[Any] = v_tmp.reshape(v_tmp.shape[0] , v_tmp.shape[1] * v_tmp.shape[2] )
return k, o, q, v
def __UpperCAmelCase ( A : Optional[Any] , A : Tuple , A : Optional[int] , A : str=False ) -> Dict:
if split_mlp_wi:
UpperCAmelCase_ : List[Any] = params[F"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
UpperCAmelCase_ : str = params[F"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
UpperCAmelCase_ : Tuple = (wi_a, wi_a)
else:
UpperCAmelCase_ : List[str] = params[F"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]
UpperCAmelCase_ : Dict = params[F"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
return wi, wo
def __UpperCAmelCase ( A : Tuple , A : int , A : Optional[Any] , A : int ) -> Dict:
return params[F"{prefix}/{prefix}/{layer_name}/scale"][:, i]
def __UpperCAmelCase ( A : dict , *, A : int , A : bool , A : bool = False ) -> Any:
UpperCAmelCase_ : int = traverse_util.flatten_dict(variables['''target'''] )
UpperCAmelCase_ : Optional[int] = {'''/'''.join(A ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
UpperCAmelCase_ : int = '''encoder/encoder/mlp/wi_0/kernel''' in old
print('''Split MLP:''' , A )
UpperCAmelCase_ : Any = collections.OrderedDict()
# Shared embeddings.
UpperCAmelCase_ : int = old['''token_embedder/embedding''']
# Encoder.
for i in range(A ):
# Block i, layer 0 (Self Attention).
UpperCAmelCase_ : List[str] = tax_layer_norm_lookup(A , A , '''encoder''' , '''pre_attention_layer_norm''' )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : int = tax_attention_lookup(A , A , '''encoder''' , '''attention''' )
UpperCAmelCase_ : int = layer_norm
UpperCAmelCase_ : Union[str, Any] = k.T
UpperCAmelCase_ : str = o.T
UpperCAmelCase_ : List[Any] = q.T
UpperCAmelCase_ : Dict = v.T
# Block i, layer 1 (MLP).
UpperCAmelCase_ : str = tax_layer_norm_lookup(A , A , '''encoder''' , '''pre_mlp_layer_norm''' )
UpperCAmelCase_ , UpperCAmelCase_ : Optional[int] = tax_mlp_lookup(A , A , '''encoder''' , A )
UpperCAmelCase_ : List[Any] = layer_norm
if split_mlp_wi:
UpperCAmelCase_ : Dict = wi[0].T
UpperCAmelCase_ : Dict = wi[1].T
else:
UpperCAmelCase_ : Tuple = wi.T
UpperCAmelCase_ : Any = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
UpperCAmelCase_ : Optional[Any] = tax_relpos_bias_lookup(
A , A , '''encoder''' ).T
UpperCAmelCase_ : Any = old['''encoder/encoder_norm/scale''']
if not scalable_attention:
UpperCAmelCase_ : Optional[Any] = tax_relpos_bias_lookup(
A , 0 , '''encoder''' ).T
UpperCAmelCase_ : List[str] = tax_relpos_bias_lookup(
A , 0 , '''decoder''' ).T
if not is_encoder_only:
# Decoder.
for i in range(A ):
# Block i, layer 0 (Self Attention).
UpperCAmelCase_ : Optional[int] = tax_layer_norm_lookup(A , A , '''decoder''' , '''pre_self_attention_layer_norm''' )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Tuple = tax_attention_lookup(A , A , '''decoder''' , '''self_attention''' )
UpperCAmelCase_ : int = layer_norm
UpperCAmelCase_ : Any = k.T
UpperCAmelCase_ : Optional[int] = o.T
UpperCAmelCase_ : List[Any] = q.T
UpperCAmelCase_ : str = v.T
# Block i, layer 1 (Cross Attention).
UpperCAmelCase_ : str = tax_layer_norm_lookup(A , A , '''decoder''' , '''pre_cross_attention_layer_norm''' )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : List[str] = tax_attention_lookup(A , A , '''decoder''' , '''encoder_decoder_attention''' )
UpperCAmelCase_ : Any = layer_norm
UpperCAmelCase_ : Optional[Any] = k.T
UpperCAmelCase_ : Union[str, Any] = o.T
UpperCAmelCase_ : List[str] = q.T
UpperCAmelCase_ : Any = v.T
# Block i, layer 2 (MLP).
UpperCAmelCase_ : Dict = tax_layer_norm_lookup(A , A , '''decoder''' , '''pre_mlp_layer_norm''' )
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = tax_mlp_lookup(A , A , '''decoder''' , A )
UpperCAmelCase_ : Optional[int] = layer_norm
if split_mlp_wi:
UpperCAmelCase_ : Optional[int] = wi[0].T
UpperCAmelCase_ : int = wi[1].T
else:
UpperCAmelCase_ : Any = wi.T
UpperCAmelCase_ : Any = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
UpperCAmelCase_ : List[str] = tax_relpos_bias_lookup(A , A , '''decoder''' ).T
UpperCAmelCase_ : Optional[Any] = 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:
UpperCAmelCase_ : int = old['''decoder/logits_dense/kernel'''].T
return new
def __UpperCAmelCase ( A : Tuple , A : bool ) -> List[str]:
UpperCAmelCase_ : str = 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:
UpperCAmelCase_ : Optional[Any] = state_dict['''shared.weight''']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
UpperCAmelCase_ : Optional[int] = 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.''' )
UpperCAmelCase_ : int = state_dict['''shared.weight''']
return state_dict
def __UpperCAmelCase ( A : Any , A : Optional[Any] , A : Optional[Any] , A : str , A : Optional[int] ) -> Dict:
UpperCAmelCase_ : List[str] = checkpoints.load_tax_checkpoint(A )
UpperCAmelCase_ : str = convert_tax_to_pytorch(
A , num_layers=config.num_layers , is_encoder_only=A , scalable_attention=A )
UpperCAmelCase_ : Union[str, Any] = make_state_dict(A , A )
model.load_state_dict(A , strict=A )
def __UpperCAmelCase ( A : str , A : int , A : List[str] , A : bool = False , A : bool = False , ) -> Any:
UpperCAmelCase_ : Union[str, Any] = MTaConfig.from_json_file(A )
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:
UpperCAmelCase_ : Dict = UMTaEncoderModel(A )
else:
UpperCAmelCase_ : Dict = UMTaForConditionalGeneration(A )
# Load weights from tf checkpoint
load_tax_weights_in_ta(A , A , A , A , A )
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}" )
model.save_pretrained(A )
# Verify that we can load the checkpoint.
model.from_pretrained(A )
print('''Done''' )
if __name__ == "__main__":
_UpperCamelCase : Optional[Any] = 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,
)
_UpperCamelCase : Optional[int] = 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,
)
| 216 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase=False):
SCREAMING_SNAKE_CASE = []
for i in range(config.num_hidden_layers):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''module.blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight'''))
rename_keys.append((F'''module.blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias'''))
rename_keys.append(
(F'''module.blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight'''))
rename_keys.append((F'''module.blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias'''))
rename_keys.append((F'''module.blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight'''))
rename_keys.append((F'''module.blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias'''))
rename_keys.append((F'''module.blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight'''))
rename_keys.append((F'''module.blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias'''))
rename_keys.append((F'''module.blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight'''))
rename_keys.append((F'''module.blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias'''))
# projection layer + position embeddings
rename_keys.extend(
[
('module.cls_token', 'vit.embeddings.cls_token'),
('module.patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
('module.patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
('module.pos_embed', 'vit.embeddings.position_embeddings'),
])
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('module.norm.weight', 'layernorm.weight'),
('module.norm.bias', 'layernorm.bias'),
])
# if just the base model, we should remove "vit" from all keys that start with "vit"
SCREAMING_SNAKE_CASE = [(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'),
])
return rename_keys
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=False):
for i in range(config.num_hidden_layers):
if base_model:
SCREAMING_SNAKE_CASE = ''
else:
SCREAMING_SNAKE_CASE = 'vit.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE = state_dict.pop(F'''module.blocks.{i}.attn.qkv.weight''')
SCREAMING_SNAKE_CASE = state_dict.pop(F'''module.blocks.{i}.attn.qkv.bias''')
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE = in_proj_weight[
: config.hidden_size, :
]
SCREAMING_SNAKE_CASE = in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE = in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE = in_proj_bias[-config.hidden_size :]
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase , _UpperCAmelCase)
def lowerCamelCase__ (_UpperCAmelCase):
# projection head is used in the self-supervised pre-training in MSN,
# for downstream task it's not needed.
SCREAMING_SNAKE_CASE = [
'module.fc.fc1.weight',
'module.fc.fc1.bias',
'module.fc.bn1.weight',
'module.fc.bn1.bias',
'module.fc.bn1.running_mean',
'module.fc.bn1.running_var',
'module.fc.bn1.num_batches_tracked',
'module.fc.fc2.weight',
'module.fc.fc2.bias',
'module.fc.bn2.weight',
'module.fc.bn2.bias',
'module.fc.bn2.running_mean',
'module.fc.bn2.running_var',
'module.fc.bn2.num_batches_tracked',
'module.fc.fc3.weight',
'module.fc.fc3.bias',
]
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase , _UpperCAmelCase)
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = dct.pop(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = val
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = ViTMSNConfig()
SCREAMING_SNAKE_CASE = 1000
SCREAMING_SNAKE_CASE = 'datasets/huggingface/label-files'
SCREAMING_SNAKE_CASE = 'imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(_UpperCAmelCase , _UpperCAmelCase) , 'r'))
SCREAMING_SNAKE_CASE = {int(_UpperCAmelCase): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
SCREAMING_SNAKE_CASE = 384
SCREAMING_SNAKE_CASE = 1536
SCREAMING_SNAKE_CASE = 6
elif "l16" in checkpoint_url:
SCREAMING_SNAKE_CASE = 1024
SCREAMING_SNAKE_CASE = 4096
SCREAMING_SNAKE_CASE = 24
SCREAMING_SNAKE_CASE = 16
SCREAMING_SNAKE_CASE = 0.1
elif "b4" in checkpoint_url:
SCREAMING_SNAKE_CASE = 4
elif "l7" in checkpoint_url:
SCREAMING_SNAKE_CASE = 7
SCREAMING_SNAKE_CASE = 1024
SCREAMING_SNAKE_CASE = 4096
SCREAMING_SNAKE_CASE = 24
SCREAMING_SNAKE_CASE = 16
SCREAMING_SNAKE_CASE = 0.1
SCREAMING_SNAKE_CASE = ViTMSNModel(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(_UpperCAmelCase , map_location='cpu')['target_encoder']
SCREAMING_SNAKE_CASE = ViTImageProcessor(size=config.image_size)
remove_projection_head(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = create_rename_keys(_UpperCAmelCase , base_model=_UpperCAmelCase)
for src, dest in rename_keys:
rename_key(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase)
read_in_q_k_v(_UpperCAmelCase , _UpperCAmelCase , base_model=_UpperCAmelCase)
model.load_state_dict(_UpperCAmelCase)
model.eval()
SCREAMING_SNAKE_CASE = 'http://images.cocodataset.org/val2017/000000039769.jpg'
SCREAMING_SNAKE_CASE = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase).raw)
SCREAMING_SNAKE_CASE = ViTImageProcessor(
size=config.image_size , image_mean=_UpperCAmelCase , image_std=_UpperCAmelCase)
SCREAMING_SNAKE_CASE = image_processor(images=_UpperCAmelCase , return_tensors='pt')
# forward pass
torch.manual_seed(2)
SCREAMING_SNAKE_CASE = model(**_UpperCAmelCase)
SCREAMING_SNAKE_CASE = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
SCREAMING_SNAKE_CASE = torch.tensor([[-1.09_15, -1.48_76, -1.18_09]])
elif "b16" in checkpoint_url:
SCREAMING_SNAKE_CASE = torch.tensor([[14.28_89, -18.90_45, 11.72_81]])
elif "l16" in checkpoint_url:
SCREAMING_SNAKE_CASE = torch.tensor([[41.50_28, -22.86_81, 45.64_75]])
elif "b4" in checkpoint_url:
SCREAMING_SNAKE_CASE = torch.tensor([[-4.38_68, 5.29_32, -0.41_37]])
else:
SCREAMING_SNAKE_CASE = torch.tensor([[-0.17_92, -0.64_65, 2.42_63]])
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , _UpperCAmelCase , atol=1e-4)
print(F'''Saving model 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 __name__ == "__main__":
a_ : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar',
type=str,
help='URL of the checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
a_ : List[Any] = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 73 |
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> float:
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(snake_case_ ) )
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> float:
"""simple docstring"""
a = 0.0
for coeff in reversed(snake_case_ ):
a = result * x + coeff
return result
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = (0.0, 0.0, 5.0, 9.3, 7.0)
UpperCamelCase__ : int = 1_0.0
print(evaluate_poly(poly, x))
print(horner(poly, x))
| 387 | 0 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
__lowerCamelCase : Optional[Any] = logging.getLogger(__name__)
@dataclass(frozen=__snake_case )
class A__ :
_UpperCAmelCase :str
_UpperCAmelCase :str
_UpperCAmelCase :Optional[str] = None
_UpperCAmelCase :Optional[str] = None
_UpperCAmelCase :Optional[str] = None
@dataclass(frozen=__snake_case )
class A__ :
_UpperCAmelCase :List[int]
_UpperCAmelCase :Optional[List[int]] = None
_UpperCAmelCase :Optional[List[int]] = None
_UpperCAmelCase :Optional[Union[int, float]] = None
_UpperCAmelCase :Optional[int] = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class A__ ( __snake_case ):
_UpperCAmelCase :List[InputFeatures]
def __init__( self , A_ , A_ , A_ , A_ = None , A_=False , A_ = False , ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = hans_processors[task]()
UpperCamelCase : Optional[Any] = os.path.join(
A_ , "cached_{}_{}_{}_{}".format(
"dev" if evaluate else "train" , tokenizer.__class__.__name__ , str(A_ ) , A_ , ) , )
UpperCamelCase : List[str] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
UpperCamelCase , UpperCamelCase : int = label_list[2], label_list[1]
UpperCamelCase : Dict = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
UpperCamelCase : int = cached_features_file + ".lock"
with FileLock(A_ ):
if os.path.exists(A_ ) and not overwrite_cache:
logger.info(F"""Loading features from cached file {cached_features_file}""" )
UpperCamelCase : List[str] = torch.load(A_ )
else:
logger.info(F"""Creating features from dataset file at {data_dir}""" )
UpperCamelCase : List[Any] = (
processor.get_dev_examples(A_ ) if evaluate else processor.get_train_examples(A_ )
)
logger.info("Training examples: %s" , len(A_ ) )
UpperCamelCase : Tuple = hans_convert_examples_to_features(A_ , A_ , A_ , A_ )
logger.info("Saving features into cached file %s" , A_ )
torch.save(self.features , A_ )
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self , A_ ):
'''simple docstring'''
return self.features[i]
def __UpperCamelCase( self ):
'''simple docstring'''
return self.label_list
if is_tf_available():
import tensorflow as tf
class A__ :
_UpperCAmelCase :List[InputFeatures]
def __init__( self , A_ , A_ , A_ , A_ = 128 , A_=False , A_ = False , ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = hans_processors[task]()
UpperCamelCase : Optional[int] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
UpperCamelCase , UpperCamelCase : Any = label_list[2], label_list[1]
UpperCamelCase : Tuple = label_list
UpperCamelCase : Optional[Any] = processor.get_dev_examples(A_ ) if evaluate else processor.get_train_examples(A_ )
UpperCamelCase : str = hans_convert_examples_to_features(A_ , A_ , A_ , A_ )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc="convert examples to features" ):
if ex_index % 1_0000 == 0:
logger.info("Writing example %d of %d" % (ex_index, len(A_ )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
UpperCamelCase : str = tf.data.Dataset.from_generator(
A_ , (
{
"example_id": tf.intaa,
"input_ids": tf.intaa,
"attention_mask": tf.intaa,
"token_type_ids": tf.intaa,
},
tf.intaa,
) , (
{
"example_id": tf.TensorShape([] ),
"input_ids": tf.TensorShape([None, None] ),
"attention_mask": tf.TensorShape([None, None] ),
"token_type_ids": tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def __UpperCamelCase( self ):
'''simple docstring'''
return self.dataset
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self , A_ ):
'''simple docstring'''
return self.features[i]
def __UpperCamelCase( self ):
'''simple docstring'''
return self.label_list
class A__ ( __snake_case ):
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(A_ , "heuristics_train_set.txt" ) ) , "train" )
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(A_ , "heuristics_evaluation_set.txt" ) ) , "dev" )
def __UpperCamelCase( self ):
'''simple docstring'''
return ["contradiction", "entailment", "neutral"]
def __UpperCamelCase( self , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = []
for i, line in enumerate(A_ ):
if i == 0:
continue
UpperCamelCase : Union[str, Any] = "%s-%s" % (set_type, line[0])
UpperCamelCase : Optional[int] = line[5]
UpperCamelCase : Tuple = line[6]
UpperCamelCase : int = line[7][2:] if line[7].startswith("ex" ) else line[7]
UpperCamelCase : Optional[Any] = line[0]
examples.append(InputExample(guid=A_ , text_a=A_ , text_b=A_ , label=A_ , pairID=A_ ) )
return examples
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ) -> int:
UpperCamelCase : Optional[int] = {label: i for i, label in enumerate(_lowerCAmelCase )}
UpperCamelCase : List[str] = []
for ex_index, example in tqdm.tqdm(enumerate(_lowerCAmelCase ) , desc="convert examples to features" ):
if ex_index % 1_0000 == 0:
logger.info("Writing example %d" % (ex_index) )
UpperCamelCase : List[str] = tokenizer(
example.text_a , example.text_b , add_special_tokens=_lowerCAmelCase , max_length=_lowerCAmelCase , padding="max_length" , truncation=_lowerCAmelCase , return_overflowing_tokens=_lowerCAmelCase , )
UpperCamelCase : str = label_map[example.label] if example.label in label_map else 0
UpperCamelCase : Tuple = int(example.pairID )
features.append(InputFeatures(**_lowerCAmelCase , label=_lowerCAmelCase , pairID=_lowerCAmelCase ) )
for i, example in enumerate(examples[:5] ):
logger.info("*** Example ***" )
logger.info(F"""guid: {example}""" )
logger.info(F"""features: {features[i]}""" )
return features
__lowerCamelCase : Tuple = {
"""hans""": 3,
}
__lowerCamelCase : List[Any] = {
"""hans""": HansProcessor,
}
| 38 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class A__ :
def __init__( self , A_ , A_=13 , A_=7 , A_=True , A_=True , A_=True , A_=True , A_=99 , A_=32 , A_=2 , A_=4 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=16 , A_=2 , A_=0.02 , A_=3 , A_=4 , A_=None , ):
'''simple docstring'''
UpperCamelCase : Dict = parent
UpperCamelCase : str = 13
UpperCamelCase : int = 7
UpperCamelCase : str = True
UpperCamelCase : Dict = True
UpperCamelCase : str = True
UpperCamelCase : Tuple = True
UpperCamelCase : List[str] = 99
UpperCamelCase : Optional[Any] = 384
UpperCamelCase : Tuple = 2
UpperCamelCase : Union[str, Any] = 4
UpperCamelCase : Dict = 37
UpperCamelCase : Any = "gelu"
UpperCamelCase : List[Any] = 0.1
UpperCamelCase : int = 0.1
UpperCamelCase : Tuple = 512
UpperCamelCase : List[Any] = 16
UpperCamelCase : int = 2
UpperCamelCase : Dict = 0.02
UpperCamelCase : Optional[Any] = 3
UpperCamelCase : List[Any] = 4
UpperCamelCase : Dict = 128
UpperCamelCase : Optional[Any] = 2
UpperCamelCase : Optional[int] = 9
UpperCamelCase : Optional[int] = 1
UpperCamelCase : Union[str, Any] = None
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : str = None
if self.use_input_mask:
UpperCamelCase : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Tuple = None
if self.use_token_type_ids:
UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase : Optional[int] = None
UpperCamelCase : Optional[int] = None
UpperCamelCase : List[Any] = None
if self.use_labels:
UpperCamelCase : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : Any = ConvBertConfig(
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 , return_dict=A_ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : str = TFConvBertModel(config=A_ )
UpperCamelCase : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
UpperCamelCase : Optional[int] = [input_ids, input_mask]
UpperCamelCase : Any = model(A_ )
UpperCamelCase : int = model(A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : Tuple = TFConvBertForMaskedLM(config=A_ )
UpperCamelCase : int = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCamelCase : Dict = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : Dict = self.num_labels
UpperCamelCase : int = TFConvBertForSequenceClassification(config=A_ )
UpperCamelCase : List[Any] = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCamelCase : Optional[Any] = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : List[str] = self.num_choices
UpperCamelCase : str = TFConvBertForMultipleChoice(config=A_ )
UpperCamelCase : List[Any] = tf.tile(tf.expand_dims(A_ , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase : Dict = tf.tile(tf.expand_dims(A_ , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase : Any = tf.tile(tf.expand_dims(A_ , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase : List[str] = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
UpperCamelCase : Optional[Any] = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : Dict = self.num_labels
UpperCamelCase : str = TFConvBertForTokenClassification(config=A_ )
UpperCamelCase : List[Any] = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCamelCase : str = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : List[str] = TFConvBertForQuestionAnswering(config=A_ )
UpperCamelCase : Union[str, Any] = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCamelCase : Union[str, Any] = model(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 __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : Optional[Any] = config_and_inputs
UpperCamelCase : int = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class A__ ( __snake_case , __snake_case , unittest.TestCase ):
_UpperCAmelCase :Dict = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
_UpperCAmelCase :Optional[Any] = (
{
'feature-extraction': TFConvBertModel,
'fill-mask': TFConvBertForMaskedLM,
'question-answering': TFConvBertForQuestionAnswering,
'text-classification': TFConvBertForSequenceClassification,
'token-classification': TFConvBertForTokenClassification,
'zero-shot': TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
_UpperCAmelCase :Any = False
_UpperCAmelCase :int = False
_UpperCAmelCase :str = False
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Dict = TFConvBertModelTester(self )
UpperCamelCase : Dict = ConfigTester(self , config_class=A_ , hidden_size=37 )
def __UpperCamelCase( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A_ )
@slow
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase : Optional[Any] = True
UpperCamelCase : Any = True
if hasattr(A_ , "use_cache" ):
UpperCamelCase : List[str] = True
UpperCamelCase : List[Any] = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
UpperCamelCase : Any = getattr(self.model_tester , "key_length" , A_ )
for model_class in self.all_model_classes:
UpperCamelCase : List[Any] = self._prepare_for_class(A_ , A_ )
UpperCamelCase : Dict = model_class(A_ )
UpperCamelCase : Optional[int] = len(model(A_ ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(A_ , saved_model=A_ )
UpperCamelCase : Union[str, Any] = os.path.join(A_ , "saved_model" , "1" )
UpperCamelCase : Dict = tf.keras.models.load_model(A_ )
UpperCamelCase : str = model(A_ )
if self.is_encoder_decoder:
UpperCamelCase : Union[str, Any] = outputs["encoder_hidden_states"]
UpperCamelCase : Any = outputs["encoder_attentions"]
else:
UpperCamelCase : Any = outputs["hidden_states"]
UpperCamelCase : List[str] = outputs["attentions"]
self.assertEqual(len(A_ ) , A_ )
UpperCamelCase : int = getattr(
self.model_tester , "expected_num_hidden_layers" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(A_ ) , A_ )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(A_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
self.assertIsNotNone(A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase : Dict = True
UpperCamelCase : int = getattr(self.model_tester , "decoder_seq_length" , self.model_tester.seq_length )
UpperCamelCase : Optional[int] = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
UpperCamelCase : Optional[int] = getattr(self.model_tester , "key_length" , A_ )
UpperCamelCase : Optional[Any] = getattr(self.model_tester , "key_length" , A_ )
def check_decoder_attentions_output(A_ ):
UpperCamelCase : Optional[Any] = len(A_ )
self.assertEqual(out_len % 2 , 0 )
UpperCamelCase : Any = outputs.decoder_attentions
self.assertEqual(len(A_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(A_ ):
UpperCamelCase : Dict = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(A_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
UpperCamelCase : Union[str, Any] = True
UpperCamelCase : List[Any] = False
UpperCamelCase : Dict = model_class(A_ )
UpperCamelCase : Dict = model(self._prepare_for_class(A_ , A_ ) )
UpperCamelCase : List[str] = len(A_ )
self.assertEqual(config.output_hidden_states , A_ )
check_encoder_attentions_output(A_ )
if self.is_encoder_decoder:
UpperCamelCase : int = model_class(A_ )
UpperCamelCase : Tuple = model(self._prepare_for_class(A_ , A_ ) )
self.assertEqual(config.output_hidden_states , A_ )
check_decoder_attentions_output(A_ )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
UpperCamelCase : Tuple = True
UpperCamelCase : int = model_class(A_ )
UpperCamelCase : Dict = model(self._prepare_for_class(A_ , A_ ) )
self.assertEqual(config.output_hidden_states , A_ )
check_encoder_attentions_output(A_ )
# Check attention is always last and order is fine
UpperCamelCase : Optional[int] = True
UpperCamelCase : List[str] = True
UpperCamelCase : Optional[int] = model_class(A_ )
UpperCamelCase : Optional[Any] = model(self._prepare_for_class(A_ , A_ ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(A_ ) )
self.assertEqual(model.config.output_hidden_states , A_ )
check_encoder_attentions_output(A_ )
@require_tf
class A__ ( unittest.TestCase ):
@slow
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : str = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
UpperCamelCase : str = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCamelCase : List[str] = model(A_ )[0]
UpperCamelCase : int = [1, 6, 768]
self.assertEqual(output.shape , A_ )
UpperCamelCase : List[str] = tf.constant(
[
[
[-0.03_47_54_93, -0.4_68_60_34, -0.30_63_88_32],
[0.22_63_72_48, -0.26_98_86_46, -0.7_42_34_24],
[0.10_32_48_68, -0.45_01_35_08, -0.58_28_07_84],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , A_ , atol=1e-4 )
| 38 | 1 |
from typing import Any
import numpy as np
def _lowerCamelCase ( __lowerCamelCase ) -> bool:
'''simple docstring'''
return np.array_equal(__lowerCamelCase , matrix.conjugate().T )
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase ) -> Any:
'''simple docstring'''
UpperCAmelCase__ : List[Any] = v.conjugate().T
UpperCAmelCase__ : int = v_star.dot(__lowerCamelCase )
assert isinstance(__lowerCamelCase , np.ndarray )
return (v_star_dot.dot(__lowerCamelCase )) / (v_star.dot(__lowerCamelCase ))
def _lowerCamelCase ( ) -> None:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = np.array([[2, 2 + 1J, 4], [2 - 1J, 3, 1J], [4, -1J, 1]] )
UpperCAmelCase__ : int = np.array([[1], [2], [3]] )
assert is_hermitian(__lowerCamelCase ), F"{a} is not hermitian."
print(rayleigh_quotient(__lowerCamelCase , __lowerCamelCase ) )
UpperCAmelCase__ : Dict = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]] )
assert is_hermitian(__lowerCamelCase ), F"{a} is not hermitian."
assert rayleigh_quotient(__lowerCamelCase , __lowerCamelCase ) == float(3 )
if __name__ == "__main__":
import doctest
doctest.testmod()
tests()
| 79 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
A_ : Optional[int] ="""pt"""
elif is_tf_available():
A_ : int ="""tf"""
else:
A_ : Tuple ="""jax"""
class __a ( lowerCAmelCase__ , unittest.TestCase ):
SCREAMING_SNAKE_CASE__ : Dict = ByTaTokenizer
SCREAMING_SNAKE_CASE__ : List[str] = False
def snake_case_ ( self ):
super().setUp()
_lowerCamelCase = ByTaTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def snake_case_ ( self ):
return ByTaTokenizer.from_pretrained('google/byt5-small' )
def snake_case_ ( self , **a__ ):
return self.tokenizer_class.from_pretrained(self.tmpdirname , **a__ )
def snake_case_ ( self , a__ , a__=False , a__=20 , a__=5 ):
# XXX The default common tokenizer tests assume that every ID is decodable on its own.
# This assumption is invalid for ByT5 because single bytes might not be
# valid utf-8 (byte 128 for instance).
# Here we're overriding the smallest possible method to provide
# a clean sequence without making the same assumption.
_lowerCamelCase = []
for i in range(len(a__ ) ):
try:
_lowerCamelCase = tokenizer.decode([i] , clean_up_tokenization_spaces=a__ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
_lowerCamelCase = list(filter(lambda a__ : re.match(r'^[ a-zA-Z]+$' , t[1] ) , a__ ) )
_lowerCamelCase = list(filter(lambda a__ : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=a__ ) , a__ ) )
if max_length is not None and len(a__ ) > max_length:
_lowerCamelCase = toks[:max_length]
if min_length is not None and len(a__ ) < min_length and len(a__ ) > 0:
while len(a__ ) < min_length:
_lowerCamelCase = toks + toks
# toks_str = [t[1] for t in toks]
_lowerCamelCase = [t[0] for t in toks]
# Ensure consistency
_lowerCamelCase = tokenizer.decode(a__ , clean_up_tokenization_spaces=a__ )
if " " not in output_txt and len(a__ ) > 1:
_lowerCamelCase = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=a__ )
+ ' '
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=a__ )
)
if with_prefix_space:
_lowerCamelCase = ' ' + output_txt
_lowerCamelCase = tokenizer.encode(a__ , add_special_tokens=a__ )
return output_txt, output_ids
def snake_case_ ( self ):
_lowerCamelCase = self.ta_base_tokenizer
_lowerCamelCase = tokenizer(['hi</s>', 'I went to the gym</s>', '</s>'] )
_lowerCamelCase = tokenizer(['hi', 'I went to the gym', ''] )
self.assertListEqual(batch_with_eos_added['input_ids'] , batch_without_eos_added['input_ids'] )
def snake_case_ ( self ):
_lowerCamelCase = self.ta_base_tokenizer
_lowerCamelCase = 'Unicode €.'
_lowerCamelCase = tokenizer(a__ )
_lowerCamelCase = [88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
self.assertEqual(encoded['input_ids'] , a__ )
# decoding
_lowerCamelCase = tokenizer.decode(a__ )
self.assertEqual(a__ , 'Unicode €.</s>' )
_lowerCamelCase = tokenizer('e è é ê ë' )
_lowerCamelCase = [1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
self.assertEqual(encoded['input_ids'] , a__ )
# decoding
_lowerCamelCase = tokenizer.decode(a__ )
self.assertEqual(a__ , 'e è é ê ë</s>' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë' ) ) , 'e è é ê ë</s>' )
def snake_case_ ( self ):
_lowerCamelCase = self.ta_base_tokenizer
_lowerCamelCase = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
# fmt: off
_lowerCamelCase = [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
# fmt: on
_lowerCamelCase = tokenizer(a__ , padding=a__ , return_tensors=a__ )
self.assertIsInstance(a__ , a__ )
if FRAMEWORK != "jax":
_lowerCamelCase = list(batch.input_ids.numpy()[0] )
else:
_lowerCamelCase = list(batch.input_ids.tolist()[0] )
self.assertListEqual(a__ , a__ )
self.assertEqual((2, 37) , batch.input_ids.shape )
self.assertEqual((2, 37) , batch.attention_mask.shape )
def snake_case_ ( self ):
_lowerCamelCase = self.ta_base_tokenizer
_lowerCamelCase = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
_lowerCamelCase = tokenizer(a__ , padding=a__ , return_tensors=a__ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('input_ids' , a__ )
self.assertIn('attention_mask' , a__ )
self.assertNotIn('decoder_input_ids' , a__ )
self.assertNotIn('decoder_attention_mask' , a__ )
def snake_case_ ( self ):
_lowerCamelCase = self.ta_base_tokenizer
_lowerCamelCase = [
'Summary of the text.',
'Another summary.',
]
_lowerCamelCase = tokenizer(
text_target=a__ , max_length=32 , padding='max_length' , truncation=a__ , return_tensors=a__ )
self.assertEqual(32 , targets['input_ids'].shape[1] )
def snake_case_ ( self ):
_lowerCamelCase = self.ta_base_tokenizer
_lowerCamelCase = ['A long paragraph for summarization. </s>']
_lowerCamelCase = ['Summary of the text. </s>']
# fmt: off
_lowerCamelCase = [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
_lowerCamelCase = [86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
# fmt: on
_lowerCamelCase = tokenizer(a__ , text_target=a__ )
self.assertEqual(a__ , batch['input_ids'][0] )
self.assertEqual(a__ , batch['labels'][0] )
def snake_case_ ( self ):
# safety check on max_len default value so we are sure the test works
_lowerCamelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
_lowerCamelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
# Isolate this from the other tests because we save additional tokens/etc
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = ' He is very happy, UNwant\u00E9d,running'
_lowerCamelCase = tokenizer.encode(a__ , add_special_tokens=a__ )
tokenizer.save_pretrained(a__ )
_lowerCamelCase = tokenizer.__class__.from_pretrained(a__ )
_lowerCamelCase = after_tokenizer.encode(a__ , add_special_tokens=a__ )
self.assertListEqual(a__ , a__ )
shutil.rmtree(a__ )
_lowerCamelCase = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
# Isolate this from the other tests because we save additional tokens/etc
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = ' He is very happy, UNwant\u00E9d,running'
tokenizer.add_tokens(['bim', 'bambam'] )
_lowerCamelCase = tokenizer.additional_special_tokens
additional_special_tokens.append('new_additional_special_token' )
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
_lowerCamelCase = tokenizer.encode(a__ , add_special_tokens=a__ )
tokenizer.save_pretrained(a__ )
_lowerCamelCase = tokenizer.__class__.from_pretrained(a__ )
_lowerCamelCase = after_tokenizer.encode(a__ , add_special_tokens=a__ )
self.assertListEqual(a__ , a__ )
self.assertIn('new_additional_special_token' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
_lowerCamelCase = tokenizer.__class__.from_pretrained(a__ , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(a__ )
def snake_case_ ( self ):
_lowerCamelCase = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(a__ )
with open(os.path.join(a__ , 'special_tokens_map.json' ) , encoding='utf-8' ) as json_file:
_lowerCamelCase = json.load(a__ )
with open(os.path.join(a__ , 'tokenizer_config.json' ) , encoding='utf-8' ) as json_file:
_lowerCamelCase = json.load(a__ )
_lowerCamelCase = [F'<extra_id_{i}>' for i in range(1_25 )]
_lowerCamelCase = added_tokens_extra_ids + [
'an_additional_special_token'
]
_lowerCamelCase = added_tokens_extra_ids + [
'an_additional_special_token'
]
with open(os.path.join(a__ , 'special_tokens_map.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(a__ , a__ )
with open(os.path.join(a__ , 'tokenizer_config.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(a__ , a__ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
_lowerCamelCase = tokenizer_class.from_pretrained(
a__ , )
self.assertIn(
'an_additional_special_token' , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
['an_additional_special_token'] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token'] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
_lowerCamelCase = added_tokens_extra_ids + [AddedToken('a_new_additional_special_token' , lstrip=a__ )]
_lowerCamelCase = tokenizer_class.from_pretrained(
a__ , additional_special_tokens=a__ , )
self.assertIn('a_new_additional_special_token' , tokenizer.additional_special_tokens )
self.assertEqual(
['a_new_additional_special_token'] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['a_new_additional_special_token'] ) ) , )
def snake_case_ ( self ):
_lowerCamelCase = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(a__ )
_lowerCamelCase = tokenizer_class.from_pretrained(a__ )
self.assertTrue(tokenizer.decode([2_55] ) == '' )
def snake_case_ ( self ):
pass
def snake_case_ ( self ):
pass
def snake_case_ ( self ):
pass
def snake_case_ ( self ):
pass
def snake_case_ ( self ):
# The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
# and special added tokens as tokens
_lowerCamelCase = self.get_tokenizers(fast=a__ , do_lower_case=a__ )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
_lowerCamelCase = ['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
_lowerCamelCase = tokenizer.convert_tokens_to_string(a__ )
self.assertIsInstance(a__ , a__ )
def snake_case_ ( self ):
_lowerCamelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
_lowerCamelCase = [
'bos_token',
'eos_token',
'unk_token',
'sep_token',
'pad_token',
'cls_token',
'mask_token',
]
_lowerCamelCase = 0
_lowerCamelCase = tokenizer.convert_ids_to_tokens(
a__ , skip_special_tokens=a__ )
for attr in attributes_list:
setattr(a__ , attr + '_id' , a__ )
self.assertEqual(getattr(a__ , a__ ) , a__ )
self.assertEqual(getattr(a__ , attr + '_id' ) , a__ )
setattr(a__ , attr + '_id' , a__ )
self.assertEqual(getattr(a__ , a__ ) , a__ )
self.assertEqual(getattr(a__ , attr + '_id' ) , a__ )
setattr(a__ , 'additional_special_tokens_ids' , [] )
self.assertListEqual(getattr(a__ , 'additional_special_tokens' ) , [] )
self.assertListEqual(getattr(a__ , 'additional_special_tokens_ids' ) , [] )
setattr(a__ , 'additional_special_tokens_ids' , [token_id_to_test_setters] )
self.assertListEqual(getattr(a__ , 'additional_special_tokens' ) , [token_to_test_setters] )
self.assertListEqual(getattr(a__ , 'additional_special_tokens_ids' ) , [token_id_to_test_setters] )
| 650 | 0 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class snake_case_ :
"""simple docstring"""
def __init__( self ,lowercase ,lowercase=13 ,lowercase=32 ,lowercase=3 ,lowercase=4 ,lowercase=[10, 20, 30, 40] ,lowercase=[2, 2, 3, 2] ,lowercase=True ,lowercase=True ,lowercase=37 ,lowercase="gelu" ,lowercase=10 ,lowercase=0.02 ,lowercase=["stage2", "stage3", "stage4"] ,lowercase=[2, 3, 4] ,lowercase=None ,):
"""simple docstring"""
UpperCAmelCase_ : List[str] = parent
UpperCAmelCase_ : Tuple = batch_size
UpperCAmelCase_ : Optional[Any] = image_size
UpperCAmelCase_ : Optional[Any] = num_channels
UpperCAmelCase_ : Dict = num_stages
UpperCAmelCase_ : int = hidden_sizes
UpperCAmelCase_ : Union[str, Any] = depths
UpperCAmelCase_ : Dict = is_training
UpperCAmelCase_ : Dict = use_labels
UpperCAmelCase_ : List[str] = intermediate_size
UpperCAmelCase_ : Dict = hidden_act
UpperCAmelCase_ : Tuple = num_labels
UpperCAmelCase_ : Any = initializer_range
UpperCAmelCase_ : List[Any] = out_features
UpperCAmelCase_ : Dict = out_indices
UpperCAmelCase_ : str = scope
def A_ ( self):
"""simple docstring"""
UpperCAmelCase_ : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
UpperCAmelCase_ : Dict = None
if self.use_labels:
UpperCAmelCase_ : int = ids_tensor([self.batch_size] ,self.num_labels)
UpperCAmelCase_ : int = self.get_config()
return config, pixel_values, labels
def A_ ( self):
"""simple docstring"""
return ConvNextConfig(
num_channels=self.num_channels ,hidden_sizes=self.hidden_sizes ,depths=self.depths ,num_stages=self.num_stages ,hidden_act=self.hidden_act ,is_decoder=lowercase ,initializer_range=self.initializer_range ,out_features=self.out_features ,out_indices=self.out_indices ,num_labels=self.num_labels ,)
def A_ ( self ,lowercase ,lowercase ,lowercase):
"""simple docstring"""
UpperCAmelCase_ : Tuple = ConvNextModel(config=lowercase)
model.to(lowercase)
model.eval()
UpperCAmelCase_ : Dict = model(lowercase)
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,)
def A_ ( self ,lowercase ,lowercase ,lowercase):
"""simple docstring"""
UpperCAmelCase_ : int = ConvNextForImageClassification(lowercase)
model.to(lowercase)
model.eval()
UpperCAmelCase_ : Any = model(lowercase ,labels=lowercase)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels))
def A_ ( self ,lowercase ,lowercase ,lowercase):
"""simple docstring"""
UpperCAmelCase_ : List[str] = ConvNextBackbone(config=lowercase)
model.to(lowercase)
model.eval()
UpperCAmelCase_ : Optional[Any] = model(lowercase)
# verify hidden states
self.parent.assertEqual(len(result.feature_maps) ,len(config.out_features))
self.parent.assertListEqual(list(result.feature_maps[0].shape) ,[self.batch_size, self.hidden_sizes[1], 4, 4])
# verify channels
self.parent.assertEqual(len(model.channels) ,len(config.out_features))
self.parent.assertListEqual(model.channels ,config.hidden_sizes[1:])
# verify backbone works with out_features=None
UpperCAmelCase_ : Tuple = None
UpperCAmelCase_ : Tuple = ConvNextBackbone(config=lowercase)
model.to(lowercase)
model.eval()
UpperCAmelCase_ : Optional[int] = model(lowercase)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps) ,1)
self.parent.assertListEqual(list(result.feature_maps[0].shape) ,[self.batch_size, self.hidden_sizes[-1], 1, 1])
# verify channels
self.parent.assertEqual(len(model.channels) ,1)
self.parent.assertListEqual(model.channels ,[config.hidden_sizes[-1]])
def A_ ( self):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = self.prepare_config_and_inputs()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Union[str, Any] = config_and_inputs
UpperCAmelCase_ : str = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class snake_case_ (lowercase__ , lowercase__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
_lowerCamelCase = (
{"""feature-extraction""": ConvNextModel, """image-classification""": ConvNextForImageClassification}
if is_torch_available()
else {}
)
_lowerCamelCase = True
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
def A_ ( self):
"""simple docstring"""
UpperCAmelCase_ : Dict = ConvNextModelTester(self)
UpperCAmelCase_ : Union[str, Any] = ConfigTester(self ,config_class=lowercase ,has_text_modality=lowercase ,hidden_size=37)
def A_ ( self):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A_ ( self):
"""simple docstring"""
return
@unittest.skip(reason="ConvNext does not use inputs_embeds")
def A_ ( self):
"""simple docstring"""
pass
@unittest.skip(reason="ConvNext does not support input and output embeddings")
def A_ ( self):
"""simple docstring"""
pass
@unittest.skip(reason="ConvNext does not use feedforward chunking")
def A_ ( self):
"""simple docstring"""
pass
def A_ ( self):
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ : Optional[Any] = model_class(lowercase)
UpperCAmelCase_ : Dict = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase_ : str = [*signature.parameters.keys()]
UpperCAmelCase_ : str = ["pixel_values"]
self.assertListEqual(arg_names[:1] ,lowercase)
def A_ ( self):
"""simple docstring"""
UpperCAmelCase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase)
def A_ ( self):
"""simple docstring"""
UpperCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*lowercase)
def A_ ( self):
"""simple docstring"""
def check_hidden_states_output(lowercase ,lowercase ,lowercase):
UpperCAmelCase_ : Optional[int] = model_class(lowercase)
model.to(lowercase)
model.eval()
with torch.no_grad():
UpperCAmelCase_ : Any = model(**self._prepare_for_class(lowercase ,lowercase))
UpperCAmelCase_ : Union[str, Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
UpperCAmelCase_ : Tuple = self.model_tester.num_stages
self.assertEqual(len(lowercase) ,expected_num_stages + 1)
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]) ,[self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,)
UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ : int = True
check_hidden_states_output(lowercase ,lowercase ,lowercase)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase_ : Any = True
check_hidden_states_output(lowercase ,lowercase ,lowercase)
def A_ ( self):
"""simple docstring"""
UpperCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase)
@slow
def A_ ( self):
"""simple docstring"""
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase_ : Tuple = ConvNextModel.from_pretrained(lowercase)
self.assertIsNotNone(lowercase)
def _snake_case ( ) -> str:
'''simple docstring'''
UpperCAmelCase_ : List[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class snake_case_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def A_ ( self):
"""simple docstring"""
return AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224") if is_vision_available() else None
@slow
def A_ ( self):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224").to(lowercase)
UpperCAmelCase_ : Tuple = self.default_image_processor
UpperCAmelCase_ : Any = prepare_img()
UpperCAmelCase_ : Tuple = image_processor(images=lowercase ,return_tensors="pt").to(lowercase)
# forward pass
with torch.no_grad():
UpperCAmelCase_ : List[str] = model(**lowercase)
# verify the logits
UpperCAmelCase_ : Any = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape ,lowercase)
UpperCAmelCase_ : str = torch.tensor([-0.0260, -0.4739, 0.1911]).to(lowercase)
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,lowercase ,atol=1E-4))
@require_torch
class snake_case_ (unittest.TestCase , lowercase__ ):
"""simple docstring"""
_lowerCamelCase = (ConvNextBackbone,) if is_torch_available() else ()
_lowerCamelCase = ConvNextConfig
_lowerCamelCase = False
def A_ ( self):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = ConvNextModelTester(self)
| 455 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class snake_case_ (lowercase__ ):
"""simple docstring"""
_lowerCamelCase = 42
_lowerCamelCase = 42
class snake_case_ (lowercase__ , lowercase__ ):
"""simple docstring"""
_lowerCamelCase = 1
@register_to_config
def __init__( self ,lowercase = 2000 ,lowercase = 0.15 ,lowercase = 0.01 ,lowercase = 1348.0 ,lowercase = 1E-5 ,lowercase = 1 ,):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = sigma_max
# setable values
UpperCAmelCase_ : Optional[int] = None
self.set_sigmas(lowercase ,lowercase ,lowercase ,lowercase)
def A_ ( self ,lowercase ,lowercase = None):
"""simple docstring"""
return sample
def A_ ( self ,lowercase ,lowercase = None ,lowercase = None):
"""simple docstring"""
UpperCAmelCase_ : int = sampling_eps if sampling_eps is not None else self.config.sampling_eps
UpperCAmelCase_ : List[Any] = torch.linspace(1 ,lowercase ,lowercase ,device=lowercase)
def A_ ( self ,lowercase ,lowercase = None ,lowercase = None ,lowercase = None):
"""simple docstring"""
UpperCAmelCase_ : Any = sigma_min if sigma_min is not None else self.config.sigma_min
UpperCAmelCase_ : int = sigma_max if sigma_max is not None else self.config.sigma_max
UpperCAmelCase_ : Union[str, Any] = sampling_eps if sampling_eps is not None else self.config.sampling_eps
if self.timesteps is None:
self.set_timesteps(lowercase ,lowercase)
UpperCAmelCase_ : Union[str, Any] = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
UpperCAmelCase_ : Optional[int] = torch.exp(torch.linspace(math.log(lowercase) ,math.log(lowercase) ,lowercase))
UpperCAmelCase_ : Any = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])
def A_ ( self ,lowercase ,lowercase):
"""simple docstring"""
return torch.where(
timesteps == 0 ,torch.zeros_like(t.to(timesteps.device)) ,self.discrete_sigmas[timesteps - 1].to(timesteps.device) ,)
def A_ ( self ,lowercase ,lowercase ,lowercase ,lowercase = None ,lowercase = True ,):
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler")
UpperCAmelCase_ : Optional[int] = timestep * torch.ones(
sample.shape[0] ,device=sample.device) # torch.repeat_interleave(timestep, sample.shape[0])
UpperCAmelCase_ : Tuple = (timestep * (len(self.timesteps) - 1)).long()
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
UpperCAmelCase_ : Optional[int] = timesteps.to(self.discrete_sigmas.device)
UpperCAmelCase_ : Optional[Any] = self.discrete_sigmas[timesteps].to(sample.device)
UpperCAmelCase_ : Optional[Any] = self.get_adjacent_sigma(lowercase ,lowercase).to(sample.device)
UpperCAmelCase_ : Any = torch.zeros_like(lowercase)
UpperCAmelCase_ : Dict = (sigma**2 - adjacent_sigma**2) ** 0.5
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
UpperCAmelCase_ : Dict = diffusion.flatten()
while len(diffusion.shape) < len(sample.shape):
UpperCAmelCase_ : List[str] = diffusion.unsqueeze(-1)
UpperCAmelCase_ : List[Any] = drift - diffusion**2 * model_output
# equation 6: sample noise for the diffusion term of
UpperCAmelCase_ : Union[str, Any] = randn_tensor(
sample.shape ,layout=sample.layout ,generator=lowercase ,device=sample.device ,dtype=sample.dtype)
UpperCAmelCase_ : Any = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
UpperCAmelCase_ : Tuple = prev_sample_mean + diffusion * noise # add impact of diffusion field g
if not return_dict:
return (prev_sample, prev_sample_mean)
return SdeVeOutput(prev_sample=lowercase ,prev_sample_mean=lowercase)
def A_ ( self ,lowercase ,lowercase ,lowercase = None ,lowercase = True ,):
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler")
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
# sample noise for correction
UpperCAmelCase_ : int = randn_tensor(sample.shape ,layout=sample.layout ,generator=lowercase).to(sample.device)
# compute step size from the model_output, the noise, and the snr
UpperCAmelCase_ : Union[str, Any] = torch.norm(model_output.reshape(model_output.shape[0] ,-1) ,dim=-1).mean()
UpperCAmelCase_ : Optional[Any] = torch.norm(noise.reshape(noise.shape[0] ,-1) ,dim=-1).mean()
UpperCAmelCase_ : List[Any] = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
UpperCAmelCase_ : Optional[Any] = step_size * torch.ones(sample.shape[0]).to(sample.device)
# self.repeat_scalar(step_size, sample.shape[0])
# compute corrected sample: model_output term and noise term
UpperCAmelCase_ : Any = step_size.flatten()
while len(step_size.shape) < len(sample.shape):
UpperCAmelCase_ : Tuple = step_size.unsqueeze(-1)
UpperCAmelCase_ : Dict = sample + step_size * model_output
UpperCAmelCase_ : int = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=lowercase)
def A_ ( self ,lowercase ,lowercase ,lowercase ,):
"""simple docstring"""
UpperCAmelCase_ : Any = timesteps.to(original_samples.device)
UpperCAmelCase_ : List[str] = self.discrete_sigmas.to(original_samples.device)[timesteps]
UpperCAmelCase_ : Tuple = (
noise * sigmas[:, None, None, None]
if noise is not None
else torch.randn_like(lowercase) * sigmas[:, None, None, None]
)
UpperCAmelCase_ : Tuple = noise + original_samples
return noisy_samples
def __len__( self):
"""simple docstring"""
return self.config.num_train_timesteps
| 455 | 1 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
AccelerateTestCase,
TempDirTestCase,
execute_subprocess_async,
require_cuda,
require_fsdp,
require_multi_gpu,
slow,
)
from accelerate.utils.constants import (
FSDP_AUTO_WRAP_POLICY,
FSDP_BACKWARD_PREFETCH,
FSDP_SHARDING_STRATEGY,
FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment
set_seed(42)
a__ : Dict = 'bert-base-cased'
a__ : List[str] = 'fp16'
a__ : Optional[Any] = 'bf16'
a__ : Dict = [FPaa, BFaa]
@require_fsdp
@require_cuda
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
def __UpperCAmelCase ( self ):
"""simple docstring"""
super().setUp()
A_ = dict(
ACCELERATE_USE_FSDP='''true''' ,MASTER_ADDR='''localhost''' ,MASTER_PORT='''10999''' ,RANK='''0''' ,LOCAL_RANK='''0''' ,WORLD_SIZE='''1''' ,)
def __UpperCAmelCase ( self ):
"""simple docstring"""
from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy
for i, strategy in enumerate(__snake_case ):
A_ = self.dist_env.copy()
A_ = f'{i + 1}'
A_ = strategy
with mockenv_context(**__snake_case ):
A_ = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.sharding_strategy ,ShardingStrategy(i + 1 ) )
def __UpperCAmelCase ( self ):
"""simple docstring"""
from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch
for i, prefetch_policy in enumerate(__snake_case ):
A_ = self.dist_env.copy()
A_ = prefetch_policy
with mockenv_context(**__snake_case ):
A_ = FullyShardedDataParallelPlugin()
if prefetch_policy == "NO_PREFETCH":
self.assertIsNone(fsdp_plugin.backward_prefetch )
else:
self.assertEqual(fsdp_plugin.backward_prefetch ,BackwardPrefetch(i + 1 ) )
def __UpperCAmelCase ( self ):
"""simple docstring"""
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
for i, state_dict_type in enumerate(__snake_case ):
A_ = self.dist_env.copy()
A_ = state_dict_type
with mockenv_context(**__snake_case ):
A_ = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.state_dict_type ,StateDictType(i + 1 ) )
if state_dict_type == "FULL_STATE_DICT":
self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu )
self.assertTrue(fsdp_plugin.state_dict_config.ranka_only )
def __UpperCAmelCase ( self ):
"""simple docstring"""
A_ = AutoModel.from_pretrained(__snake_case )
for policy in FSDP_AUTO_WRAP_POLICY:
A_ = self.dist_env.copy()
A_ = policy
if policy == "TRANSFORMER_BASED_WRAP":
A_ = '''BertLayer'''
elif policy == "SIZE_BASED_WRAP":
A_ = '''2000'''
with mockenv_context(**__snake_case ):
A_ = FullyShardedDataParallelPlugin()
fsdp_plugin.set_auto_wrap_policy(__snake_case )
if policy == "NO_WRAP":
self.assertIsNone(fsdp_plugin.auto_wrap_policy )
else:
self.assertIsNotNone(fsdp_plugin.auto_wrap_policy )
A_ = self.dist_env.copy()
A_ = '''TRANSFORMER_BASED_WRAP'''
A_ = '''T5Layer'''
with mockenv_context(**__snake_case ):
A_ = FullyShardedDataParallelPlugin()
with self.assertRaises(__snake_case ) as cm:
fsdp_plugin.set_auto_wrap_policy(__snake_case )
self.assertTrue('''Could not find the transformer layer class to wrap in the model.''' in str(cm.exception ) )
A_ = self.dist_env.copy()
A_ = '''SIZE_BASED_WRAP'''
A_ = '''0'''
with mockenv_context(**__snake_case ):
A_ = FullyShardedDataParallelPlugin()
fsdp_plugin.set_auto_wrap_policy(__snake_case )
self.assertIsNone(fsdp_plugin.auto_wrap_policy )
def __UpperCAmelCase ( self ):
"""simple docstring"""
from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
for mp_dtype in dtypes:
A_ = self.dist_env.copy()
A_ = mp_dtype
with mockenv_context(**__snake_case ):
A_ = Accelerator()
if mp_dtype == "fp16":
A_ = torch.floataa
elif mp_dtype == "bf16":
A_ = torch.bfloataa
A_ = MixedPrecision(param_dtype=__snake_case ,reduce_dtype=__snake_case ,buffer_dtype=__snake_case )
self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy ,__snake_case )
if mp_dtype == FPaa:
self.assertTrue(isinstance(accelerator.scaler ,__snake_case ) )
elif mp_dtype == BFaa:
self.assertIsNone(accelerator.scaler )
AcceleratorState._reset_state(__snake_case )
def __UpperCAmelCase ( self ):
"""simple docstring"""
from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload
for flag in [True, False]:
A_ = self.dist_env.copy()
A_ = str(__snake_case ).lower()
with mockenv_context(**__snake_case ):
A_ = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.cpu_offload ,CPUOffload(offload_params=__snake_case ) )
@require_fsdp
@require_multi_gpu
@slow
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
def __UpperCAmelCase ( self ):
"""simple docstring"""
super().setUp()
A_ = 0.82
A_ = [
'''fsdp_shard_grad_op_transformer_based_wrap''',
'''fsdp_full_shard_transformer_based_wrap''',
]
A_ = {
'''multi_gpu_fp16''': 3_2_0_0,
'''fsdp_shard_grad_op_transformer_based_wrap_fp16''': 2_0_0_0,
'''fsdp_full_shard_transformer_based_wrap_fp16''': 1_9_0_0,
# Disabling below test as it overwhelms the RAM memory usage
# on CI self-hosted runner leading to tests getting killed.
# "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500, # fp16 was leading to indefinite hang
}
A_ = 1_6_0
A_ = 1_6_0
A_ = inspect.getfile(accelerate.test_utils )
A_ = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''external_deps'''] )
def __UpperCAmelCase ( self ):
"""simple docstring"""
A_ = os.path.join(self.test_scripts_folder ,'''test_performance.py''' )
A_ = ['''accelerate''', '''launch''', '''--num_processes=2''', '''--num_machines=1''', '''--machine_rank=0''', '''--use_fsdp''']
for config in self.performance_configs:
A_ = cmd.copy()
for i, strategy in enumerate(__snake_case ):
if strategy.lower() in config:
cmd_config.append(f'--fsdp_sharding_strategy={i+1}' )
break
if "fp32" in config:
cmd_config.append('''--mixed_precision=no''' )
else:
cmd_config.append('''--mixed_precision=fp16''' )
if "cpu_offload" in config:
cmd_config.append('''--fsdp_offload_params=True''' )
for policy in FSDP_AUTO_WRAP_POLICY:
if policy.lower() in config:
cmd_config.append(f'--fsdp_auto_wrap_policy={policy}' )
break
if policy == "TRANSFORMER_BASED_WRAP":
cmd_config.append('''--fsdp_transformer_layer_cls_to_wrap=BertLayer''' )
elif policy == "SIZE_BASED_WRAP":
cmd_config.append('''--fsdp_min_num_params=2000''' )
cmd_config.extend(
[
self.test_file_path,
f'--output_dir={self.tmpdir}',
f'--performance_lower_bound={self.performance_lower_bound}',
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__snake_case ,env=os.environ.copy() )
def __UpperCAmelCase ( self ):
"""simple docstring"""
A_ = os.path.join(self.test_scripts_folder ,'''test_checkpointing.py''' )
A_ = [
'''accelerate''',
'''launch''',
'''--num_processes=2''',
'''--num_machines=1''',
'''--machine_rank=0''',
'''--use_fsdp''',
'''--mixed_precision=fp16''',
'''--fsdp_transformer_layer_cls_to_wrap=BertLayer''',
]
for i, strategy in enumerate(__snake_case ):
A_ = cmd.copy()
cmd_config.append(f'--fsdp_sharding_strategy={i+1}' )
if strategy != "FULL_SHARD":
continue
A_ = len(__snake_case )
for state_dict_type in FSDP_STATE_DICT_TYPE:
A_ = cmd_config[:state_dict_config_index]
cmd_config.append(f'--fsdp_state_dict_type={state_dict_type}' )
cmd_config.extend(
[
self.test_file_path,
f'--output_dir={self.tmpdir}',
'''--partial_train_epoch=1''',
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__snake_case ,env=os.environ.copy() )
A_ = cmd_config[:-1]
A_ = os.path.join(self.tmpdir ,'''epoch_0''' )
cmd_config.extend(
[
f'--resume_from_checkpoint={resume_from_checkpoint}',
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__snake_case ,env=os.environ.copy() )
def __UpperCAmelCase ( self ):
"""simple docstring"""
A_ = os.path.join(self.test_scripts_folder ,'''test_peak_memory_usage.py''' )
A_ = [
'''accelerate''',
'''launch''',
'''--num_processes=2''',
'''--num_machines=1''',
'''--machine_rank=0''',
]
for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
A_ = cmd.copy()
if "fp16" in spec:
cmd_config.extend(['''--mixed_precision=fp16'''] )
else:
cmd_config.extend(['''--mixed_precision=no'''] )
if "multi_gpu" in spec:
continue
else:
cmd_config.extend(['''--use_fsdp'''] )
for i, strategy in enumerate(__snake_case ):
if strategy.lower() in spec:
cmd_config.append(f'--fsdp_sharding_strategy={i+1}' )
break
if "cpu_offload" in spec:
cmd_config.append('''--fsdp_offload_params=True''' )
for policy in FSDP_AUTO_WRAP_POLICY:
if policy.lower() in spec:
cmd_config.append(f'--fsdp_auto_wrap_policy={policy}' )
break
if policy == "TRANSFORMER_BASED_WRAP":
cmd_config.append('''--fsdp_transformer_layer_cls_to_wrap=BertLayer''' )
elif policy == "SIZE_BASED_WRAP":
cmd_config.append('''--fsdp_min_num_params=2000''' )
cmd_config.extend(
[
self.test_file_path,
f'--output_dir={self.tmpdir}',
f'--peak_memory_upper_bound={peak_mem_upper_bound}',
f'--n_train={self.n_train}',
f'--n_val={self.n_val}',
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__snake_case ,env=os.environ.copy() )
| 188 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : str = {
'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
'tokenization_roformer': ['RoFormerTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : List[Any] = ['RoFormerTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : List[str] = [
'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'RoFormerForCausalLM',
'RoFormerForMaskedLM',
'RoFormerForMultipleChoice',
'RoFormerForQuestionAnswering',
'RoFormerForSequenceClassification',
'RoFormerForTokenClassification',
'RoFormerLayer',
'RoFormerModel',
'RoFormerPreTrainedModel',
'load_tf_weights_in_roformer',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : List[Any] = [
'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRoFormerForCausalLM',
'TFRoFormerForMaskedLM',
'TFRoFormerForMultipleChoice',
'TFRoFormerForQuestionAnswering',
'TFRoFormerForSequenceClassification',
'TFRoFormerForTokenClassification',
'TFRoFormerLayer',
'TFRoFormerModel',
'TFRoFormerPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Dict = [
'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'FlaxRoFormerForMaskedLM',
'FlaxRoFormerForMultipleChoice',
'FlaxRoFormerForQuestionAnswering',
'FlaxRoFormerForSequenceClassification',
'FlaxRoFormerForTokenClassification',
'FlaxRoFormerModel',
'FlaxRoFormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
from .tokenization_roformer import RoFormerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roformer_fast import RoFormerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roformer import (
FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
FlaxRoFormerPreTrainedModel,
)
else:
import sys
a__ : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 188 | 1 |
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ):
if index == r:
for j in range(__snake_case ):
print(data[j] , end=''' ''' )
print(''' ''' )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
_UpperCamelCase = arr[i]
combination_util(__snake_case , __snake_case , __snake_case , index + 1 , __snake_case , i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def _snake_case ( __snake_case , __snake_case , __snake_case ):
# A temporary array to store all combination one by one
_UpperCamelCase = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(__snake_case , __snake_case , __snake_case , 0 , __snake_case , 0 )
if __name__ == "__main__":
# Driver code to check the function above
_lowerCAmelCase = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 71 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = "gpt_neox"
def __init__( self : Union[str, Any] , _A : Union[str, Any]=5_0432 , _A : List[Any]=6144 , _A : int=44 , _A : int=64 , _A : Optional[Any]=2_4576 , _A : Any="gelu" , _A : Tuple=0.25 , _A : Union[str, Any]=1_0000 , _A : Tuple=0.0 , _A : Any=0.0 , _A : int=0.1 , _A : List[str]=2048 , _A : Dict=0.02 , _A : Optional[Any]=1e-5 , _A : Tuple=True , _A : List[Any]=0 , _A : Optional[int]=2 , _A : Optional[int]=False , _A : List[Any]=True , _A : Any=None , **_A : Any , ):
super().__init__(bos_token_id=_A , eos_token_id=_A , **_A )
_UpperCamelCase = vocab_size
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = rotary_pct
_UpperCamelCase = rotary_emb_base
_UpperCamelCase = attention_dropout
_UpperCamelCase = hidden_dropout
_UpperCamelCase = classifier_dropout
_UpperCamelCase = initializer_range
_UpperCamelCase = layer_norm_eps
_UpperCamelCase = use_cache
_UpperCamelCase = tie_word_embeddings
_UpperCamelCase = use_parallel_residual
_UpperCamelCase = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )
def UpperCamelCase_ ( self : str ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , _A ) or len(self.rope_scaling ) != 2:
raise ValueError(
'''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
F"""got {self.rope_scaling}""" )
_UpperCamelCase = self.rope_scaling.get('''type''' , _A )
_UpperCamelCase = self.rope_scaling.get('''factor''' , _A )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}""" )
if rope_scaling_factor is None or not isinstance(_A , _A ) or rope_scaling_factor <= 1.0:
raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}""" )
| 71 | 1 |
import math
from numpy import inf
from scipy.integrate import quad
def snake_case_ (__A : float ) -> float:
if num <= 0:
raise ValueError("""math domain error""" )
return quad(__A , 0 , __A , args=(__A) )[0]
def snake_case_ (__A : float , __A : float ) -> float:
return math.pow(__A , z - 1 ) * math.exp(-x )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 651 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# 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/text-classification/requirements.txt""")
__UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
lowerCamelCase : Optional[int] =field(
default=128 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
lowerCamelCase : bool =field(
default=a_ , metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
lowerCamelCase : bool =field(
default=a_ , metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
} , )
lowerCamelCase : Optional[int] =field(
default=a_ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
lowerCamelCase : Optional[int] =field(
default=a_ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
lowerCamelCase : Optional[int] =field(
default=a_ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
)
} , )
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
lowerCamelCase : str =field(
default=a_ , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
lowerCamelCase : str =field(
default=a_ , metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."} )
lowerCamelCase : Optional[str] =field(
default=a_ , metadata={"help": "Train language if it is different from the evaluation language."} )
lowerCamelCase : Optional[str] =field(
default=a_ , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
lowerCamelCase : Optional[str] =field(
default=a_ , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
lowerCamelCase : Optional[str] =field(
default=a_ , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
lowerCamelCase : Optional[bool] =field(
default=a_ , metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"} , )
lowerCamelCase : bool =field(
default=a_ , metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."} , )
lowerCamelCase : str =field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
lowerCamelCase : bool =field(
default=a_ , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
lowerCamelCase : bool =field(
default=a_ , metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."} , )
def snake_case_ () -> List[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__lowerCAmelCase : List[str] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase : List[str] = 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_xnli""" , __A )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
__lowerCAmelCase : int = training_args.get_process_log_level()
logger.setLevel(__A )
datasets.utils.logging.set_verbosity(__A )
transformers.utils.logging.set_verbosity(__A )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ f'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(f'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
__lowerCAmelCase : Optional[int] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCAmelCase : Union[str, Any] = 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:
logger.info(
f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
__lowerCAmelCase : Union[str, Any] = load_dataset(
"""xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
__lowerCAmelCase : List[str] = load_dataset(
"""xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
__lowerCAmelCase : Dict = train_dataset.features["""label"""].names
if training_args.do_eval:
__lowerCAmelCase : Tuple = load_dataset(
"""xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
__lowerCAmelCase : Optional[int] = eval_dataset.features["""label"""].names
if training_args.do_predict:
__lowerCAmelCase : List[Any] = load_dataset(
"""xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
__lowerCAmelCase : Optional[Any] = predict_dataset.features["""label"""].names
# Labels
__lowerCAmelCase : Optional[int] = len(__A )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowerCAmelCase : int = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__A , idalabel={str(__A ): label for i, label in enumerate(__A )} , labelaid={label: i for i, label in enumerate(__A )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
__lowerCAmelCase : Union[str, Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
__lowerCAmelCase : Dict = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
__lowerCAmelCase : List[str] = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
__lowerCAmelCase : int = False
def preprocess_function(__A : List[str] ):
# Tokenize the texts
return tokenizer(
examples["""premise"""] , examples["""hypothesis"""] , padding=__A , max_length=data_args.max_seq_length , truncation=__A , )
if training_args.do_train:
if data_args.max_train_samples is not None:
__lowerCAmelCase : Union[str, Any] = min(len(__A ) , data_args.max_train_samples )
__lowerCAmelCase : List[str] = train_dataset.select(range(__A ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
__lowerCAmelCase : Dict = train_dataset.map(
__A , batched=__A , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(__A ) ) , 3 ):
logger.info(f'''Sample {index} of the training set: {train_dataset[index]}.''' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
__lowerCAmelCase : str = min(len(__A ) , data_args.max_eval_samples )
__lowerCAmelCase : Optional[int] = eval_dataset.select(range(__A ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
__lowerCAmelCase : List[Any] = eval_dataset.map(
__A , batched=__A , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
__lowerCAmelCase : Dict = min(len(__A ) , data_args.max_predict_samples )
__lowerCAmelCase : Optional[int] = predict_dataset.select(range(__A ) )
with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ):
__lowerCAmelCase : Union[str, Any] = predict_dataset.map(
__A , batched=__A , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , )
# Get the metric function
__lowerCAmelCase : Optional[Any] = evaluate.load("""xnli""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__A : EvalPrediction ):
__lowerCAmelCase : Optional[int] = p.predictions[0] if isinstance(p.predictions , __A ) else p.predictions
__lowerCAmelCase : Tuple = np.argmax(__A , axis=1 )
return metric.compute(predictions=__A , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
__lowerCAmelCase : Dict = default_data_collator
elif training_args.fpaa:
__lowerCAmelCase : Union[str, Any] = DataCollatorWithPadding(__A , pad_to_multiple_of=8 )
else:
__lowerCAmelCase : Optional[int] = None
# Initialize our Trainer
__lowerCAmelCase : Optional[int] = Trainer(
model=__A , args=__A , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__A , tokenizer=__A , data_collator=__A , )
# Training
if training_args.do_train:
__lowerCAmelCase : Optional[int] = None
if training_args.resume_from_checkpoint is not None:
__lowerCAmelCase : int = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__lowerCAmelCase : Tuple = last_checkpoint
__lowerCAmelCase : List[str] = trainer.train(resume_from_checkpoint=__A )
__lowerCAmelCase : int = train_result.metrics
__lowerCAmelCase : Optional[Any] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__A )
)
__lowerCAmelCase : Optional[int] = min(__A , len(__A ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , __A )
trainer.save_metrics("""train""" , __A )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__lowerCAmelCase : List[str] = trainer.evaluate(eval_dataset=__A )
__lowerCAmelCase : List[str] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__A )
__lowerCAmelCase : List[Any] = min(__A , len(__A ) )
trainer.log_metrics("""eval""" , __A )
trainer.save_metrics("""eval""" , __A )
# Prediction
if training_args.do_predict:
logger.info("""*** Predict ***""" )
__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase : Any = trainer.predict(__A , metric_key_prefix="""predict""" )
__lowerCAmelCase : List[Any] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__A )
)
__lowerCAmelCase : str = min(__A , len(__A ) )
trainer.log_metrics("""predict""" , __A )
trainer.save_metrics("""predict""" , __A )
__lowerCAmelCase : Any = np.argmax(__A , axis=1 )
__lowerCAmelCase : int = os.path.join(training_args.output_dir , """predictions.txt""" )
if trainer.is_world_process_zero():
with open(__A , """w""" ) as writer:
writer.write("""index\tprediction\n""" )
for index, item in enumerate(__A ):
__lowerCAmelCase : List[Any] = label_list[item]
writer.write(f'''{index}\t{item}\n''' )
if __name__ == "__main__":
main()
| 651 | 1 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
snake_case__ : int = datasets.utils.logging.get_logger(__name__)
class snake_case_( folder_based_builder.FolderBasedBuilderConfig ):
__UpperCamelCase = None
__UpperCamelCase = None
class snake_case_( folder_based_builder.FolderBasedBuilder ):
__UpperCamelCase = datasets.Audio()
__UpperCamelCase = '''audio'''
__UpperCamelCase = AudioFolderConfig
__UpperCamelCase = 42 # definition at the bottom of the script
__UpperCamelCase = AudioClassification(audio_column='''audio''' , label_column='''label''' )
snake_case__ : Union[str, Any] = [
'''.aiff''',
'''.au''',
'''.avr''',
'''.caf''',
'''.flac''',
'''.htk''',
'''.svx''',
'''.mat4''',
'''.mat5''',
'''.mpc2k''',
'''.ogg''',
'''.paf''',
'''.pvf''',
'''.raw''',
'''.rf64''',
'''.sd2''',
'''.sds''',
'''.ircam''',
'''.voc''',
'''.w64''',
'''.wav''',
'''.nist''',
'''.wavex''',
'''.wve''',
'''.xi''',
'''.mp3''',
'''.opus''',
]
snake_case__ : str = AUDIO_EXTENSIONS
| 637 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def _snake_case ( _snake_case : Tuple , _snake_case : Union[str, Any]=10 ):
lowerCAmelCase : Dict = []
for _ in range(_snake_case ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def _snake_case ( _snake_case : Optional[int] , _snake_case : int=10 ):
lowerCAmelCase : Optional[int] = []
for step in range(_snake_case ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase : List[str] = os.path.join(_snake_case , '''schedule.bin''' )
torch.save(scheduler.state_dict() , _snake_case )
lowerCAmelCase : List[Any] = torch.load(_snake_case )
scheduler.load_state_dict(_snake_case )
return lrs
@require_torch
class snake_case_( unittest.TestCase ):
def lowerCamelCase__ ( self : Optional[int] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : str , UpperCamelCase_ : Any ):
self.assertEqual(len(UpperCamelCase_ ) , len(UpperCamelCase_ ) )
for a, b in zip(UpperCamelCase_ , UpperCamelCase_ ):
self.assertAlmostEqual(UpperCamelCase_ , UpperCamelCase_ , delta=UpperCamelCase_ )
def lowerCamelCase__ ( self : Tuple ):
lowerCAmelCase : Any = torch.tensor([0.1, -0.2, -0.1] , requires_grad=UpperCamelCase_ )
lowerCAmelCase : List[str] = torch.tensor([0.4, 0.2, -0.5] )
lowerCAmelCase : List[Any] = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
lowerCAmelCase : Union[str, Any] = AdamW(params=[w] , lr=2E-1 , weight_decay=0.0 )
for _ in range(1_0_0 ):
lowerCAmelCase : Union[str, Any] = criterion(UpperCamelCase_ , UpperCamelCase_ )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
def lowerCamelCase__ ( self : Union[str, Any] ):
lowerCAmelCase : Tuple = torch.tensor([0.1, -0.2, -0.1] , requires_grad=UpperCamelCase_ )
lowerCAmelCase : Union[str, Any] = torch.tensor([0.4, 0.2, -0.5] )
lowerCAmelCase : Optional[int] = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
lowerCAmelCase : Any = Adafactor(
params=[w] , lr=1E-2 , eps=(1E-30, 1E-3) , clip_threshold=1.0 , decay_rate=-0.8 , betaa=UpperCamelCase_ , weight_decay=0.0 , relative_step=UpperCamelCase_ , scale_parameter=UpperCamelCase_ , warmup_init=UpperCamelCase_ , )
for _ in range(1_0_0_0 ):
lowerCAmelCase : List[Any] = criterion(UpperCamelCase_ , UpperCamelCase_ )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
@require_torch
class snake_case_( unittest.TestCase ):
__UpperCamelCase = nn.Linear(50 , 50 ) if is_torch_available() else None
__UpperCamelCase = AdamW(m.parameters() , lr=10.0 ) if is_torch_available() else None
__UpperCamelCase = 10
def lowerCamelCase__ ( self : Union[str, Any] , UpperCamelCase_ : str , UpperCamelCase_ : str , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Union[str, Any]=None ):
self.assertEqual(len(UpperCamelCase_ ) , len(UpperCamelCase_ ) )
for a, b in zip(UpperCamelCase_ , UpperCamelCase_ ):
self.assertAlmostEqual(UpperCamelCase_ , UpperCamelCase_ , delta=UpperCamelCase_ , msg=UpperCamelCase_ )
def lowerCamelCase__ ( self : Union[str, Any] ):
lowerCAmelCase : Tuple = {'''num_warmup_steps''': 2, '''num_training_steps''': 1_0}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
lowerCAmelCase : Optional[Any] = {
get_constant_schedule: ({}, [10.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{'''num_warmup_steps''': 4},
[0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, '''num_cycles''': 2},
[0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, '''power''': 2.0, '''lr_end''': 1E-7},
[0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
),
get_inverse_sqrt_schedule: (
{'''num_warmup_steps''': 2},
[0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
),
}
for scheduler_func, data in scheds.items():
lowerCAmelCase, lowerCAmelCase : Union[str, Any] = data
lowerCAmelCase : List[Any] = scheduler_func(self.optimizer , **UpperCamelCase_ )
self.assertEqual(len([scheduler.get_lr()[0]] ) , 1 )
lowerCAmelCase : str = unwrap_schedule(UpperCamelCase_ , self.num_steps )
self.assertListAlmostEqual(
UpperCamelCase_ , UpperCamelCase_ , tol=1E-2 , msg=F'''failed for {scheduler_func} in normal scheduler''' , )
lowerCAmelCase : Optional[int] = scheduler_func(self.optimizer , **UpperCamelCase_ )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(UpperCamelCase_ ) # wrap to test picklability of the schedule
lowerCAmelCase : List[Any] = unwrap_and_save_reload_schedule(UpperCamelCase_ , self.num_steps )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ , msg=F'''failed for {scheduler_func} in save and reload''' )
class snake_case_:
def __init__( self : List[Any] , UpperCamelCase_ : Any ):
lowerCAmelCase : Tuple = fn
def __call__( self : Union[str, Any] , *UpperCamelCase_ : Optional[Any] , **UpperCamelCase_ : List[Any] ):
return self.fn(*UpperCamelCase_ , **UpperCamelCase_ )
@classmethod
def lowerCamelCase__ ( self : Any , UpperCamelCase_ : Optional[int] ):
lowerCAmelCase : Union[str, Any] = list(map(self , scheduler.lr_lambdas ) )
| 637 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCAmelCase = {
"""configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
"""tokenization_canine""": ["""CanineTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
"""CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CanineForMultipleChoice""",
"""CanineForQuestionAnswering""",
"""CanineForSequenceClassification""",
"""CanineForTokenClassification""",
"""CanineLayer""",
"""CanineModel""",
"""CaninePreTrainedModel""",
"""load_tf_weights_in_canine""",
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
_UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 699 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def _lowerCAmelCase ( _lowerCAmelCase )-> Optional[Any]:
return 1.0 / (1.0 + np.exp(-_outputs ))
def _lowerCAmelCase ( _lowerCAmelCase )-> str:
__UpperCAmelCase = np.max(_outputs , axis=-1 , keepdims=_lowerCAmelCase )
__UpperCAmelCase = np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=_lowerCAmelCase )
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : List[str] = """sigmoid"""
_A : Optional[Any] = """softmax"""
_A : Optional[int] = """none"""
@add_end_docstrings(
UpperCAmelCase_ , R"""
return_all_scores (`bool`, *optional*, defaults to `False`):
Whether to return all prediction scores or just the one of the predicted class.
function_to_apply (`str`, *optional*, defaults to `\"default\"`):
The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:
- `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
has several labels, will apply the softmax function on the output.
- `\"sigmoid\"`: Applies the sigmoid function on the output.
- `\"softmax\"`: Applies the softmax function on the output.
- `\"none\"`: Does not apply any function on the output.
""" , )
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : Dict = False
_A : Optional[int] = ClassificationFunction.NONE
def __init__( self , **__A ):
super().__init__(**__A )
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == 'tf'
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )
def __lowerCamelCase ( self , __A=None , __A=None , __A="" , **__A ):
# Using "" as default argument because we're going to use `top_k=None` in user code to declare
# "No top_k"
__UpperCAmelCase = tokenizer_kwargs
__UpperCAmelCase = {}
if hasattr(self.model.config , 'return_all_scores' ) and return_all_scores is None:
__UpperCAmelCase = self.model.config.return_all_scores
if isinstance(__A , __A ) or top_k is None:
__UpperCAmelCase = top_k
__UpperCAmelCase = False
elif return_all_scores is not None:
warnings.warn(
'`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of'
' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.' , __A , )
if return_all_scores:
__UpperCAmelCase = None
else:
__UpperCAmelCase = 1
if isinstance(__A , __A ):
__UpperCAmelCase = ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
__UpperCAmelCase = function_to_apply
return preprocess_params, {}, postprocess_params
def __call__( self , *__A , **__A ):
__UpperCAmelCase = super().__call__(*__A , **__A )
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
__UpperCAmelCase = 'top_k' not in kwargs
if isinstance(args[0] , __A ) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def __lowerCamelCase ( self , __A , **__A ):
__UpperCAmelCase = self.framework
if isinstance(__A , __A ):
return self.tokenizer(**__A , return_tensors=__A , **__A )
elif isinstance(__A , __A ) and len(__A ) == 1 and isinstance(inputs[0] , __A ) and len(inputs[0] ) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0] , text_pair=inputs[0][1] , return_tensors=__A , **__A )
elif isinstance(__A , __A ):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.' )
return self.tokenizer(__A , return_tensors=__A , **__A )
def __lowerCamelCase ( self , __A ):
return self.model(**__A )
def __lowerCamelCase ( self , __A , __A=None , __A=1 , __A=True ):
# `_legacy` is used to determine if we're running the naked pipeline and in backward
# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
# the more natural result containing the list.
# Default value before `set_parameters`
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
__UpperCAmelCase = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
__UpperCAmelCase = ClassificationFunction.SOFTMAX
elif hasattr(self.model.config , 'function_to_apply' ) and function_to_apply is None:
__UpperCAmelCase = self.model.config.function_to_apply
else:
__UpperCAmelCase = ClassificationFunction.NONE
__UpperCAmelCase = model_outputs['logits'][0]
__UpperCAmelCase = outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
__UpperCAmelCase = sigmoid(__A )
elif function_to_apply == ClassificationFunction.SOFTMAX:
__UpperCAmelCase = softmax(__A )
elif function_to_apply == ClassificationFunction.NONE:
__UpperCAmelCase = outputs
else:
raise ValueError(f'Unrecognized `function_to_apply` argument: {function_to_apply}' )
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
__UpperCAmelCase = [
{'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(__A )
]
if not _legacy:
dict_scores.sort(key=lambda __A : x["score"] , reverse=__A )
if top_k is not None:
__UpperCAmelCase = dict_scores[:top_k]
return dict_scores
| 126 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 720 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ ( __magic_name__ , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : List[Any] = XGLMTokenizer
__SCREAMING_SNAKE_CASE : List[Any] = XGLMTokenizerFast
__SCREAMING_SNAKE_CASE : List[Any] = True
__SCREAMING_SNAKE_CASE : int = True
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowercase_ = XGLMTokenizer(UpperCamelCase__ , keep_accents=UpperCamelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase__ ( self : Tuple ):
'''simple docstring'''
lowercase_ = """<pad>"""
lowercase_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__ ) , UpperCamelCase__ )
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
lowercase_ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(len(UpperCamelCase__ ) , 1_008 )
def UpperCAmelCase__ ( self : int ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1_008 )
def UpperCAmelCase__ ( self : Tuple ):
'''simple docstring'''
lowercase_ = XGLMTokenizer(UpperCamelCase__ , keep_accents=UpperCamelCase__ )
lowercase_ = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(UpperCamelCase__ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCamelCase__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
lowercase_ = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCamelCase__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
lowercase_ = tokenizer.convert_tokens_to_ids(UpperCamelCase__ )
self.assertListEqual(
UpperCamelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
lowercase_ = tokenizer.convert_ids_to_tokens(UpperCamelCase__ )
self.assertListEqual(
UpperCamelCase__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
@cached_property
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
return XGLMTokenizer.from_pretrained("""facebook/xglm-564M""" )
def UpperCAmelCase__ ( self : Dict ):
'''simple docstring'''
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(UpperCamelCase__ , f.name )
lowercase_ = XGLMTokenizer(f.name , keep_accents=UpperCamelCase__ )
lowercase_ = pickle.dumps(UpperCamelCase__ )
pickle.loads(UpperCamelCase__ )
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
lowercase_ = self.get_tokenizer()
lowercase_ = self.get_rust_tokenizer()
lowercase_ = """I was born in 92000, and this is falsé."""
lowercase_ = tokenizer.tokenize(UpperCamelCase__ )
lowercase_ = rust_tokenizer.tokenize(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
lowercase_ = tokenizer.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ )
lowercase_ = rust_tokenizer.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
lowercase_ = self.get_rust_tokenizer()
lowercase_ = tokenizer.encode(UpperCamelCase__ )
lowercase_ = rust_tokenizer.encode(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
@slow
def UpperCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase_ = """Hello World!"""
lowercase_ = [2, 31_227, 4_447, 35]
self.assertListEqual(UpperCamelCase__ , self.big_tokenizer.encode(UpperCamelCase__ ) )
@slow
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
lowercase_ = (
"""This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
""" add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"""
)
# fmt: off
lowercase_ = [2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
# fmt: on
self.assertListEqual(UpperCamelCase__ , self.big_tokenizer.encode(UpperCamelCase__ ) )
@slow
def UpperCAmelCase__ ( self : Any ):
'''simple docstring'''
lowercase_ = {
"""input_ids""": [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 5]],
"""attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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]]
} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCamelCase__ , model_name="""facebook/xglm-564M""" , padding=UpperCamelCase__ , )
| 650 | 0 |
'''simple docstring'''
import math
def lowerCamelCase_ ( A_ ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(A_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCamelCase_ ( A_ = 1_00_01 ):
try:
__lowerCamelCase = int(A_ )
except (TypeError, ValueError):
raise TypeError('''Parameter nth must be int or castable to int.''' ) from None
if nth <= 0:
raise ValueError('''Parameter nth must be greater than or equal to one.''' )
__lowerCamelCase = []
__lowerCamelCase = 2
while len(A_ ) < nth:
if is_prime(A_ ):
primes.append(A_ )
num += 1
else:
num += 1
return primes[len(A_ ) - 1]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 316 |
'''simple docstring'''
def lowerCamelCase_ ( A_ , A_ ):
__lowerCamelCase = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__lowerCamelCase = n - k
# Calculate C(n,k)
for i in range(A_ ):
result *= n - i
result //= i + 1
return result
def lowerCamelCase_ ( A_ ):
return binomial_coefficient(2 * node_count , A_ ) // (node_count + 1)
def lowerCamelCase_ ( A_ ):
if n < 0:
raise ValueError('''factorial() not defined for negative values''' )
__lowerCamelCase = 1
for i in range(1 , n + 1 ):
result *= i
return result
def lowerCamelCase_ ( A_ ):
return catalan_number(A_ ) * factorial(A_ )
if __name__ == "__main__":
_UpperCamelCase : Dict =int(input("Enter the number of nodes: ").strip() or 0)
if node_count <= 0:
raise ValueError("We need some nodes to work with.")
print(
f'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
f'''binary trees and {catalan_number(node_count)} binary search trees.'''
)
| 316 | 1 |
"""simple docstring"""
def snake_case (A_ :Optional[Any] ):
'''simple docstring'''
a : str = len(A_ )
for i in range(length - 1 ):
a : Tuple = i
for k in range(i + 1 , A_ ):
if collection[k] < collection[least]:
a : List[str] = k
if least != i:
a : List[str] = (collection[i], collection[least])
return collection
if __name__ == "__main__":
_UpperCamelCase : int = input('Enter numbers separated by a comma:\n').strip()
_UpperCamelCase : List[str] = [int(item) for item in user_input.split(',')]
print(selection_sort(unsorted))
| 720 |
"""simple docstring"""
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
_UpperCamelCase : int = '3'
print('Python version:', sys.version)
print('OS platform:', platform.platform())
print('OS architecture:', platform.machine())
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
except ImportError:
print('Torch version:', None)
try:
import transformers
print('transformers version:', transformers.__version__)
except ImportError:
print('transformers version:', None)
| 118 | 0 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
# load base model
_UpperCamelCase : str = StableDiffusionPipeline.from_pretrained(UpperCAmelCase_ , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
_UpperCamelCase : Optional[Any] = load_file(UpperCAmelCase_ )
_UpperCamelCase : int = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
_UpperCamelCase : Union[str, Any] = key.split('.' )[0].split(LORA_PREFIX_TEXT_ENCODER + '_' )[-1].split('_' )
_UpperCamelCase : List[str] = pipeline.text_encoder
else:
_UpperCamelCase : List[str] = key.split('.' )[0].split(LORA_PREFIX_UNET + '_' )[-1].split('_' )
_UpperCamelCase : List[Any] = pipeline.unet
# find the target layer
_UpperCamelCase : List[Any] = layer_infos.pop(0 )
while len(UpperCAmelCase_ ) > -1:
try:
_UpperCamelCase : List[str] = curr_layer.__getattr__(UpperCAmelCase_ )
if len(UpperCAmelCase_ ) > 0:
_UpperCamelCase : Any = layer_infos.pop(0 )
elif len(UpperCAmelCase_ ) == 0:
break
except Exception:
if len(UpperCAmelCase_ ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
_UpperCamelCase : Optional[int] = layer_infos.pop(0 )
_UpperCamelCase : Union[str, Any] = []
if "lora_down" in key:
pair_keys.append(key.replace('lora_down' , 'lora_up' ) )
pair_keys.append(UpperCAmelCase_ )
else:
pair_keys.append(UpperCAmelCase_ )
pair_keys.append(key.replace('lora_up' , 'lora_down' ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
_UpperCamelCase : int = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
_UpperCamelCase : Any = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(UpperCAmelCase_ , UpperCAmelCase_ ).unsqueeze(2 ).unsqueeze(3 )
else:
_UpperCamelCase : int = state_dict[pair_keys[0]].to(torch.floataa )
_UpperCamelCase : Optional[Any] = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(UpperCAmelCase_ , UpperCAmelCase_ )
# update visited list
for item in pair_keys:
visited.append(UpperCAmelCase_ )
return pipeline
if __name__ == "__main__":
snake_case_ : int = argparse.ArgumentParser()
parser.add_argument(
'--base_model_path', default=None, type=str, required=True, help='Path to the base model in diffusers format.'
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--lora_prefix_unet', default='lora_unet', type=str, help='The prefix of UNet weight in safetensors'
)
parser.add_argument(
'--lora_prefix_text_encoder',
default='lora_te',
type=str,
help='The prefix of text encoder weight in safetensors',
)
parser.add_argument('--alpha', default=0.75, type=float, help='The merging ratio in W = W0 + alpha * deltaW')
parser.add_argument(
'--to_safetensors', action='store_true', help='Whether to store pipeline in safetensors format or not.'
)
parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
snake_case_ : Tuple = parser.parse_args()
snake_case_ : List[str] = args.base_model_path
snake_case_ : Union[str, Any] = args.checkpoint_path
snake_case_ : List[Any] = args.dump_path
snake_case_ : Tuple = args.lora_prefix_unet
snake_case_ : Dict = args.lora_prefix_text_encoder
snake_case_ : str = args.alpha
snake_case_ : Dict = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
snake_case_ : List[Any] = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 195 |
'''simple docstring'''
def A__ ( UpperCAmelCase_ = 1_0_0_0 ):
_UpperCamelCase : List[str] = 3
_UpperCamelCase : Any = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 1_5 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(F"""{solution() = }""")
| 195 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
def lowercase ( a__ : str ) -> Dict:
_UpperCamelCase = DPTConfig(embedding_type='''hybrid''' )
if "large" in checkpoint_url:
_UpperCamelCase = 1024
_UpperCamelCase = 4096
_UpperCamelCase = 24
_UpperCamelCase = 16
_UpperCamelCase = [5, 11, 17, 23]
_UpperCamelCase = [256, 512, 1024, 1024]
_UpperCamelCase = (1, 384, 384)
if "nyu" or "midas" in checkpoint_url:
_UpperCamelCase = 768
_UpperCamelCase = [1, 1, 1, 0.5]
_UpperCamelCase = [256, 512, 768, 768]
_UpperCamelCase = 150
_UpperCamelCase = 16
_UpperCamelCase = (1, 384, 384)
_UpperCamelCase = False
_UpperCamelCase = '''project'''
if "ade" in checkpoint_url:
_UpperCamelCase = True
_UpperCamelCase = 768
_UpperCamelCase = [1, 1, 1, 0.5]
_UpperCamelCase = 150
_UpperCamelCase = 16
_UpperCamelCase = '''huggingface/label-files'''
_UpperCamelCase = '''ade20k-id2label.json'''
_UpperCamelCase = json.load(open(cached_download(hf_hub_url(a__ , a__ , repo_type='''dataset''' ) ) , '''r''' ) )
_UpperCamelCase = {int(a__ ): v for k, v in idalabel.items()}
_UpperCamelCase = idalabel
_UpperCamelCase = {v: k for k, v in idalabel.items()}
_UpperCamelCase = [1, 150, 480, 480]
return config, expected_shape
def lowercase ( a__ : Dict ) -> Optional[int]:
_UpperCamelCase = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
for k in ignore_keys:
state_dict.pop(a__ , a__ )
def lowercase ( a__ : Tuple ) -> Union[str, Any]:
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
_UpperCamelCase = name.replace('''pretrained.model''' , '''dpt.encoder''' )
if "pretrained.model" in name:
_UpperCamelCase = name.replace('''pretrained.model''' , '''dpt.embeddings''' )
if "patch_embed" in name:
_UpperCamelCase = name.replace('''patch_embed''' , '''''' )
if "pos_embed" in name:
_UpperCamelCase = name.replace('''pos_embed''' , '''position_embeddings''' )
if "attn.proj" in name:
_UpperCamelCase = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "proj" in name and "project" not in name:
_UpperCamelCase = name.replace('''proj''' , '''projection''' )
if "blocks" in name:
_UpperCamelCase = name.replace('''blocks''' , '''layer''' )
if "mlp.fc1" in name:
_UpperCamelCase = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
_UpperCamelCase = name.replace('''mlp.fc2''' , '''output.dense''' )
if "norm1" in name and "backbone" not in name:
_UpperCamelCase = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name and "backbone" not in name:
_UpperCamelCase = name.replace('''norm2''' , '''layernorm_after''' )
if "scratch.output_conv" in name:
_UpperCamelCase = name.replace('''scratch.output_conv''' , '''head''' )
if "scratch" in name:
_UpperCamelCase = name.replace('''scratch''' , '''neck''' )
if "layer1_rn" in name:
_UpperCamelCase = name.replace('''layer1_rn''' , '''convs.0''' )
if "layer2_rn" in name:
_UpperCamelCase = name.replace('''layer2_rn''' , '''convs.1''' )
if "layer3_rn" in name:
_UpperCamelCase = name.replace('''layer3_rn''' , '''convs.2''' )
if "layer4_rn" in name:
_UpperCamelCase = name.replace('''layer4_rn''' , '''convs.3''' )
if "refinenet" in name:
_UpperCamelCase = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
_UpperCamelCase = name.replace(F'''refinenet{layer_idx}''' , F'''fusion_stage.layers.{abs(layer_idx-4 )}''' )
if "out_conv" in name:
_UpperCamelCase = name.replace('''out_conv''' , '''projection''' )
if "resConfUnit1" in name:
_UpperCamelCase = name.replace('''resConfUnit1''' , '''residual_layer1''' )
if "resConfUnit2" in name:
_UpperCamelCase = name.replace('''resConfUnit2''' , '''residual_layer2''' )
if "conv1" in name:
_UpperCamelCase = name.replace('''conv1''' , '''convolution1''' )
if "conv2" in name:
_UpperCamelCase = name.replace('''conv2''' , '''convolution2''' )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
_UpperCamelCase = name.replace('''pretrained.act_postprocess1.0.project.0''' , '''neck.reassemble_stage.readout_projects.0.0''' )
if "pretrained.act_postprocess2.0.project.0" in name:
_UpperCamelCase = name.replace('''pretrained.act_postprocess2.0.project.0''' , '''neck.reassemble_stage.readout_projects.1.0''' )
if "pretrained.act_postprocess3.0.project.0" in name:
_UpperCamelCase = name.replace('''pretrained.act_postprocess3.0.project.0''' , '''neck.reassemble_stage.readout_projects.2.0''' )
if "pretrained.act_postprocess4.0.project.0" in name:
_UpperCamelCase = name.replace('''pretrained.act_postprocess4.0.project.0''' , '''neck.reassemble_stage.readout_projects.3.0''' )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
_UpperCamelCase = name.replace('''pretrained.act_postprocess1.3''' , '''neck.reassemble_stage.layers.0.projection''' )
if "pretrained.act_postprocess1.4" in name:
_UpperCamelCase = name.replace('''pretrained.act_postprocess1.4''' , '''neck.reassemble_stage.layers.0.resize''' )
if "pretrained.act_postprocess2.3" in name:
_UpperCamelCase = name.replace('''pretrained.act_postprocess2.3''' , '''neck.reassemble_stage.layers.1.projection''' )
if "pretrained.act_postprocess2.4" in name:
_UpperCamelCase = name.replace('''pretrained.act_postprocess2.4''' , '''neck.reassemble_stage.layers.1.resize''' )
if "pretrained.act_postprocess3.3" in name:
_UpperCamelCase = name.replace('''pretrained.act_postprocess3.3''' , '''neck.reassemble_stage.layers.2.projection''' )
if "pretrained.act_postprocess4.3" in name:
_UpperCamelCase = name.replace('''pretrained.act_postprocess4.3''' , '''neck.reassemble_stage.layers.3.projection''' )
if "pretrained.act_postprocess4.4" in name:
_UpperCamelCase = name.replace('''pretrained.act_postprocess4.4''' , '''neck.reassemble_stage.layers.3.resize''' )
if "pretrained" in name:
_UpperCamelCase = name.replace('''pretrained''' , '''dpt''' )
if "bn" in name:
_UpperCamelCase = name.replace('''bn''' , '''batch_norm''' )
if "head" in name:
_UpperCamelCase = name.replace('''head''' , '''head.head''' )
if "encoder.norm" in name:
_UpperCamelCase = name.replace('''encoder.norm''' , '''layernorm''' )
if "auxlayer" in name:
_UpperCamelCase = name.replace('''auxlayer''' , '''auxiliary_head.head''' )
if "backbone" in name:
_UpperCamelCase = name.replace('''backbone''' , '''backbone.bit.encoder''' )
if ".." in name:
_UpperCamelCase = name.replace('''..''' , '''.''' )
if "stem.conv" in name:
_UpperCamelCase = name.replace('''stem.conv''' , '''bit.embedder.convolution''' )
if "blocks" in name:
_UpperCamelCase = name.replace('''blocks''' , '''layers''' )
if "convolution" in name and "backbone" in name:
_UpperCamelCase = name.replace('''convolution''' , '''conv''' )
if "layer" in name and "backbone" in name:
_UpperCamelCase = name.replace('''layer''' , '''layers''' )
if "backbone.bit.encoder.bit" in name:
_UpperCamelCase = name.replace('''backbone.bit.encoder.bit''' , '''backbone.bit''' )
if "embedder.conv" in name:
_UpperCamelCase = name.replace('''embedder.conv''' , '''embedder.convolution''' )
if "backbone.bit.encoder.stem.norm" in name:
_UpperCamelCase = name.replace('''backbone.bit.encoder.stem.norm''' , '''backbone.bit.embedder.norm''' )
return name
def lowercase ( a__ : Optional[int] , a__ : str ) -> Any:
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_UpperCamelCase = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
_UpperCamelCase = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
_UpperCamelCase = in_proj_weight[: config.hidden_size, :]
_UpperCamelCase = in_proj_bias[: config.hidden_size]
_UpperCamelCase = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_UpperCamelCase = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_UpperCamelCase = in_proj_weight[
-config.hidden_size :, :
]
_UpperCamelCase = in_proj_bias[-config.hidden_size :]
def lowercase ( ) -> Optional[Any]:
_UpperCamelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_UpperCamelCase = Image.open(requests.get(a__ , stream=a__ ).raw )
return im
@torch.no_grad()
def lowercase ( a__ : Optional[int] , a__ : Tuple , a__ : Optional[int] , a__ : Dict , a__ : str ) -> Dict:
_UpperCamelCase , _UpperCamelCase = get_dpt_config(a__ )
# load original state_dict from URL
# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
_UpperCamelCase = torch.load(a__ , map_location='''cpu''' )
# remove certain keys
remove_ignore_keys_(a__ )
# rename keys
for key in state_dict.copy().keys():
_UpperCamelCase = state_dict.pop(a__ )
_UpperCamelCase = val
# read in qkv matrices
read_in_q_k_v(a__ , a__ )
# load HuggingFace model
_UpperCamelCase = DPTForSemanticSegmentation(a__ ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(a__ )
model.load_state_dict(a__ )
model.eval()
# Check outputs on an image
_UpperCamelCase = 480 if '''ade''' in checkpoint_url else 384
_UpperCamelCase = DPTImageProcessor(size=a__ )
_UpperCamelCase = prepare_img()
_UpperCamelCase = image_processor(a__ , return_tensors='''pt''' )
# forward pass
_UpperCamelCase = model(**a__ ).logits if '''ade''' in checkpoint_url else model(**a__ ).predicted_depth
if show_prediction:
_UpperCamelCase = (
torch.nn.functional.interpolate(
outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode='''bicubic''' , align_corners=a__ , )
.squeeze()
.cpu()
.numpy()
)
Image.fromarray((prediction / prediction.max()) * 255 ).show()
if pytorch_dump_folder_path is not None:
Path(a__ ).mkdir(exist_ok=a__ )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(a__ )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(a__ )
if push_to_hub:
model.push_to_hub('''ybelkada/dpt-hybrid-midas''' )
image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas''' )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
type=str,
help="""URL of the original DPT checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=False,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
)
parser.add_argument(
"""--model_name""",
default="""dpt-large""",
type=str,
help="""Name of the model, in case you're pushing to the hub.""",
)
parser.add_argument(
"""--show_prediction""",
action="""store_true""",
)
UpperCAmelCase = parser.parse_args()
convert_dpt_checkpoint(
args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
)
| 342 | """simple docstring"""
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
def lowercase ( a__ : Union[str, Any] , a__ : str ) -> int:
_UpperCamelCase = []
for i in range(encoder_config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'''encoder.deit.blocks.{i}.norm1.weight''', F'''encoder.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''encoder.deit.blocks.{i}.norm1.bias''', F'''encoder.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(F'''encoder.deit.blocks.{i}.attn.proj.weight''', F'''encoder.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(F'''encoder.deit.blocks.{i}.attn.proj.bias''', F'''encoder.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append(
(F'''encoder.deit.blocks.{i}.norm2.weight''', F'''encoder.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''encoder.deit.blocks.{i}.norm2.bias''', F'''encoder.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append(
(F'''encoder.deit.blocks.{i}.mlp.fc1.weight''', F'''encoder.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append(
(F'''encoder.deit.blocks.{i}.mlp.fc1.bias''', F'''encoder.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append(
(F'''encoder.deit.blocks.{i}.mlp.fc2.weight''', F'''encoder.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''encoder.deit.blocks.{i}.mlp.fc2.bias''', F'''encoder.encoder.layer.{i}.output.dense.bias''') )
# cls token, position embeddings and patch embeddings of encoder
rename_keys.extend(
[
('''encoder.deit.cls_token''', '''encoder.embeddings.cls_token'''),
('''encoder.deit.pos_embed''', '''encoder.embeddings.position_embeddings'''),
('''encoder.deit.patch_embed.proj.weight''', '''encoder.embeddings.patch_embeddings.projection.weight'''),
('''encoder.deit.patch_embed.proj.bias''', '''encoder.embeddings.patch_embeddings.projection.bias'''),
('''encoder.deit.norm.weight''', '''encoder.layernorm.weight'''),
('''encoder.deit.norm.bias''', '''encoder.layernorm.bias'''),
] )
return rename_keys
def lowercase ( a__ : List[str] , a__ : List[Any] ) -> Optional[Any]:
for i in range(encoder_config.num_hidden_layers ):
# queries, keys and values (only weights, no biases)
_UpperCamelCase = state_dict.pop(F'''encoder.deit.blocks.{i}.attn.qkv.weight''' )
_UpperCamelCase = in_proj_weight[
: encoder_config.hidden_size, :
]
_UpperCamelCase = in_proj_weight[
encoder_config.hidden_size : encoder_config.hidden_size * 2, :
]
_UpperCamelCase = in_proj_weight[
-encoder_config.hidden_size :, :
]
def lowercase ( a__ : List[Any] , a__ : List[str] , a__ : Dict ) -> str:
_UpperCamelCase = dct.pop(a__ )
_UpperCamelCase = val
def lowercase ( a__ : List[Any] ) -> Union[str, Any]:
if "handwritten" in checkpoint_url:
_UpperCamelCase = '''https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg''' # industry
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" #
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
_UpperCamelCase = '''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
_UpperCamelCase = Image.open(requests.get(a__ , stream=a__ ).raw ).convert('''RGB''' )
return im
@torch.no_grad()
def lowercase ( a__ : Any , a__ : List[str] ) -> Tuple:
_UpperCamelCase = ViTConfig(image_size=384 , qkv_bias=a__ )
_UpperCamelCase = TrOCRConfig()
# size of the architecture
if "base" in checkpoint_url:
_UpperCamelCase = 768
elif "large" in checkpoint_url:
# use ViT-large encoder
_UpperCamelCase = 1024
_UpperCamelCase = 4096
_UpperCamelCase = 24
_UpperCamelCase = 16
_UpperCamelCase = 1024
else:
raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL''' )
# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
_UpperCamelCase = False
_UpperCamelCase = '''relu'''
_UpperCamelCase = 1024
_UpperCamelCase = True
_UpperCamelCase = False
_UpperCamelCase = False
# load HuggingFace model
_UpperCamelCase = ViTModel(a__ , add_pooling_layer=a__ )
_UpperCamelCase = TrOCRForCausalLM(a__ )
_UpperCamelCase = VisionEncoderDecoderModel(encoder=a__ , decoder=a__ )
model.eval()
# load state_dict of original model, rename some keys
_UpperCamelCase = torch.hub.load_state_dict_from_url(a__ , map_location='''cpu''' , check_hash=a__ )['''model''']
_UpperCamelCase = create_rename_keys(a__ , a__ )
for src, dest in rename_keys:
rename_key(a__ , a__ , a__ )
read_in_q_k_v(a__ , a__ )
# remove parameters we don't need
del state_dict["encoder.deit.head.weight"]
del state_dict["encoder.deit.head.bias"]
del state_dict["decoder.version"]
# add prefix to decoder keys
for key, val in state_dict.copy().items():
_UpperCamelCase = state_dict.pop(a__ )
if key.startswith('''decoder''' ) and "output_projection" not in key:
_UpperCamelCase = val
else:
_UpperCamelCase = val
# load state dict
model.load_state_dict(a__ )
# Check outputs on an image
_UpperCamelCase = ViTImageProcessor(size=encoder_config.image_size )
_UpperCamelCase = RobertaTokenizer.from_pretrained('''roberta-large''' )
_UpperCamelCase = TrOCRProcessor(a__ , a__ )
_UpperCamelCase = processor(images=prepare_img(a__ ) , return_tensors='''pt''' ).pixel_values
# verify logits
_UpperCamelCase = torch.tensor([[model.config.decoder.decoder_start_token_id]] )
_UpperCamelCase = model(pixel_values=a__ , decoder_input_ids=a__ )
_UpperCamelCase = outputs.logits
_UpperCamelCase = torch.Size([1, 1, 50265] )
if "trocr-base-handwritten" in checkpoint_url:
_UpperCamelCase = torch.tensor(
[-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311] )
elif "trocr-large-handwritten" in checkpoint_url:
_UpperCamelCase = torch.tensor(
[-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170] )
elif "trocr-base-printed" in checkpoint_url:
_UpperCamelCase = torch.tensor(
[-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210] )
elif "trocr-large-printed" in checkpoint_url:
_UpperCamelCase = torch.tensor(
[-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535] )
if "stage1" not in checkpoint_url:
assert logits.shape == expected_shape, "Shape of logits not as expected"
assert torch.allclose(logits[0, 0, :10] , a__ , atol=1e-3 ), "First elements of logits not as expected"
Path(a__ ).mkdir(exist_ok=a__ )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(a__ )
print(F'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(a__ )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt""",
type=str,
help="""URL to the original PyTorch checkpoint (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
UpperCAmelCase = parser.parse_args()
convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 342 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ) -> float:
SCREAMING_SNAKE_CASE__ = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError("All input parameters must be positive" )
if any(p > 1 for p in parameters[1:4] ):
raise ValueError("Relative densities cannot be greater than one" )
else:
SCREAMING_SNAKE_CASE__ = 1 - (matter_density + radiation_density + dark_energy)
SCREAMING_SNAKE_CASE__ = (
radiation_density * (redshift + 1) ** 4
+ matter_density * (redshift + 1) ** 3
+ curvature * (redshift + 1) ** 2
+ dark_energy
)
SCREAMING_SNAKE_CASE__ = hubble_constant * e_a ** (1 / 2)
return hubble
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo LCDM approximation
_A = 0.3
print(
hubble_parameter(
hubble_constant=68.3,
radiation_density=1E-4,
matter_density=matter_density,
dark_energy=1 - matter_density,
redshift=0,
)
)
| 159 |
import math
snake_case__ = 10
snake_case__ = 7
snake_case__ = BALLS_PER_COLOUR * NUM_COLOURS
def lowerCamelCase__ ( a : int = 20 ) -> str:
"""simple docstring"""
a__ :List[str] = math.comb(a , a )
a__ :Optional[int] = math.comb(NUM_BALLS - BALLS_PER_COLOUR , a )
a__ :Union[str, Any] = NUM_COLOURS * (1 - missing_colour / total)
return F'''{result:.9f}'''
if __name__ == "__main__":
print(solution(20))
| 395 | 0 |
def _UpperCAmelCase ( a : list ):
snake_case__ = False
while is_sorted is False: # Until all the indices are traversed keep looping
snake_case__ = True
for i in range(0 , len(a ) - 1 , 2 ): # iterating over all even indices
if input_list[i] > input_list[i + 1]:
snake_case__ , snake_case__ = input_list[i + 1], input_list[i]
# swapping if elements not in order
snake_case__ = False
for i in range(1 , len(a ) - 1 , 2 ): # iterating over all odd indices
if input_list[i] > input_list[i + 1]:
snake_case__ , snake_case__ = input_list[i + 1], input_list[i]
# swapping if elements not in order
snake_case__ = False
return input_list
if __name__ == "__main__":
print("""Enter list to be sorted""")
a__ = [int(x) for x in input().split()]
# inputing elements of the list in one line
a__ = odd_even_sort(input_list)
print("""The sorted list is""")
print(sorted_list)
| 99 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
a__ = logging.get_logger(__name__)
@dataclass
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Dict = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self : str , **UpperCamelCase__ : Dict):
'''simple docstring'''
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
snake_case__ = deprecated_arg[3:]
setattr(self , UpperCamelCase__ , not kwargs.pop(UpperCamelCase__))
logger.warning(
F'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
F''' {positive_arg}={kwargs[positive_arg]}''')
snake_case__ = kwargs.pop("""torchscript""" , self.torchscript)
snake_case__ = kwargs.pop("""torch_xla_tpu_print_metrics""" , self.torch_xla_tpu_print_metrics)
snake_case__ = kwargs.pop("""fp16_opt_level""" , self.fpaa_opt_level)
super().__init__(**UpperCamelCase__)
_lowercase : bool = field(default=lowercase_ , metadata={'''help''': '''Trace the models using torchscript'''} )
_lowercase : bool = field(default=lowercase_ , metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''} )
_lowercase : str = field(
default='''O1''' , metadata={
'''help''': (
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
'''See details at https://nvidia.github.io/apex/amp.html'''
)
} , )
@cached_property
def __magic_name__ ( self : Tuple):
'''simple docstring'''
requires_backends(self , ["""torch"""])
logger.info("""PyTorch: setting up devices""")
if not self.cuda:
snake_case__ = torch.device("""cpu""")
snake_case__ = 0
elif is_torch_tpu_available():
snake_case__ = xm.xla_device()
snake_case__ = 0
else:
snake_case__ = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""")
snake_case__ = torch.cuda.device_count()
return device, n_gpu
@property
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
return is_torch_tpu_available() and self.tpu
@property
def __magic_name__ ( self : List[str]):
'''simple docstring'''
requires_backends(self , ["""torch"""])
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
requires_backends(self , ["""torch"""])
return self._setup_devices[0]
@property
def __magic_name__ ( self : str):
'''simple docstring'''
requires_backends(self , ["""torch"""])
return self._setup_devices[1]
@property
def __magic_name__ ( self : str):
'''simple docstring'''
return self.n_gpu > 0
| 99 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class snake_case_ ( __A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = "dandelin/vilt-b32-finetuned-vqa"
SCREAMING_SNAKE_CASE : Any = (
"This is a tool that answers a question about an image. It takes an input named `image` which should be the "
"image containing the information, as well as a `question` which should be the question in English. It "
"returns a text that is the answer to the question."
)
SCREAMING_SNAKE_CASE : Tuple = "image_qa"
SCREAMING_SNAKE_CASE : str = AutoProcessor
SCREAMING_SNAKE_CASE : int = AutoModelForVisualQuestionAnswering
SCREAMING_SNAKE_CASE : str = ["image", "text"]
SCREAMING_SNAKE_CASE : Optional[int] = ["text"]
def __init__( self : List[Any] , *_UpperCamelCase : Dict , **_UpperCamelCase : List[Any] ) ->int:
requires_backends(self , ['''vision'''] )
super().__init__(*_UpperCamelCase , **_UpperCamelCase )
def snake_case__( self : Optional[int] , _UpperCamelCase : "Image" , _UpperCamelCase : str ) ->Union[str, Any]:
return self.pre_processor(_UpperCamelCase , _UpperCamelCase , return_tensors='''pt''' )
def snake_case__( self : Any , _UpperCamelCase : Union[str, Any] ) ->str:
with torch.no_grad():
return self.model(**_UpperCamelCase ).logits
def snake_case__( self : Union[str, Any] , _UpperCamelCase : Dict ) ->Any:
snake_case_ = outputs.argmax(-1 ).item()
return self.model.config.idalabel[idx] | 39 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Optional[int] = {
"""configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :str = ["""RemBertTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :Optional[Any] = ["""RemBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :Dict = [
"""REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RemBertForCausalLM""",
"""RemBertForMaskedLM""",
"""RemBertForMultipleChoice""",
"""RemBertForQuestionAnswering""",
"""RemBertForSequenceClassification""",
"""RemBertForTokenClassification""",
"""RemBertLayer""",
"""RemBertModel""",
"""RemBertPreTrainedModel""",
"""load_tf_weights_in_rembert""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :Optional[int] = [
"""TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFRemBertForCausalLM""",
"""TFRemBertForMaskedLM""",
"""TFRemBertForMultipleChoice""",
"""TFRemBertForQuestionAnswering""",
"""TFRemBertForSequenceClassification""",
"""TFRemBertForTokenClassification""",
"""TFRemBertLayer""",
"""TFRemBertModel""",
"""TFRemBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE :List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 628 | 0 |
"""simple docstring"""
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def SCREAMING_SNAKE_CASE ( snake_case, snake_case, snake_case, snake_case):
__snake_case = multiprocessing.Manager()
__snake_case = manager.list()
__snake_case = multiprocessing.Process(target=snake_case, args=(check_program, result, timeout))
p.start()
p.join(timeout=timeout + 1)
if p.is_alive():
p.kill()
if not result:
result.append('''timed out''')
return {
"task_id": task_id,
"passed": result[0] == "passed",
"result": result[0],
"completion_id": completion_id,
}
def SCREAMING_SNAKE_CASE ( snake_case, snake_case, snake_case):
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
__snake_case = shutil.rmtree
__snake_case = os.rmdir
__snake_case = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
__snake_case = {}
with swallow_io():
with time_limit(snake_case):
exec(snake_case, snake_case)
result.append('''passed''')
except TimeoutException:
result.append('''timed out''')
except BaseException as e:
result.append(f"failed: {e}")
# Needed for cleaning up.
__snake_case = rmtree
__snake_case = rmdir
__snake_case = chdir
@contextlib.contextmanager
def SCREAMING_SNAKE_CASE ( snake_case):
def signal_handler(snake_case, snake_case):
raise TimeoutException('''Timed out!''')
signal.setitimer(signal.ITIMER_REAL, snake_case)
signal.signal(signal.SIGALRM, snake_case)
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL, 0)
@contextlib.contextmanager
def SCREAMING_SNAKE_CASE ( ):
__snake_case = WriteOnlyStringIO()
with contextlib.redirect_stdout(snake_case):
with contextlib.redirect_stderr(snake_case):
with redirect_stdin(snake_case):
yield
@contextlib.contextmanager
def SCREAMING_SNAKE_CASE ( ):
with tempfile.TemporaryDirectory() as dirname:
with chdir(snake_case):
yield dirname
class _A ( _UpperCAmelCase ):
"""simple docstring"""
pass
class _A ( io.StringIO ):
"""simple docstring"""
def lowercase ( self : int , *A_ : List[Any] , **A_ : Tuple ) -> str:
raise OSError
def lowercase ( self : Dict , *A_ : Dict , **A_ : Optional[Any] ) -> int:
raise OSError
def lowercase ( self : List[str] , *A_ : str , **A_ : Any ) -> List[Any]:
raise OSError
def lowercase ( self : Optional[Any] , *A_ : List[str] , **A_ : Optional[int] ) -> Tuple:
return False
class _A ( contextlib._RedirectStream ): # type: ignore
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = '''stdin'''
@contextlib.contextmanager
def SCREAMING_SNAKE_CASE ( snake_case):
if root == ".":
yield
return
__snake_case = os.getcwd()
os.chdir(snake_case)
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(snake_case)
def SCREAMING_SNAKE_CASE ( snake_case=None):
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes))
resource.setrlimit(resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes))
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK, (maximum_memory_bytes, maximum_memory_bytes))
faulthandler.disable()
import builtins
__snake_case = None
__snake_case = None
import os
__snake_case = '''1'''
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
import shutil
__snake_case = None
__snake_case = None
__snake_case = None
import subprocess
__snake_case = None # type: ignore
__snake_case = None
import sys
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None | 93 | """simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPSegProcessor, ViTImageProcessor
@require_vision
class _A ( unittest.TestCase ):
"""simple docstring"""
def lowercase ( self : Optional[int] ) -> List[str]:
__snake_case = tempfile.mkdtemp()
# fmt: off
__snake_case = ['''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
__snake_case = dict(zip(A_ , range(len(A_ ) ) ) )
__snake_case = ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
__snake_case = {'''unk_token''': '''<unk>'''}
__snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
__snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(A_ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A_ ) )
__snake_case = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''do_normalize''': True,
'''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
}
__snake_case = os.path.join(self.tmpdirname , A_ )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(A_ , A_ )
def lowercase ( self : Optional[Any] , **A_ : Dict ) -> Any:
return CLIPTokenizer.from_pretrained(self.tmpdirname , **A_ )
def lowercase ( self : Optional[int] , **A_ : str ) -> str:
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **A_ )
def lowercase ( self : Any , **A_ : Tuple ) -> Tuple:
return ViTImageProcessor.from_pretrained(self.tmpdirname , **A_ )
def lowercase ( self : Optional[int] ) -> Optional[Any]:
shutil.rmtree(self.tmpdirname )
def lowercase ( self : int ) -> Optional[Any]:
__snake_case = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__snake_case = [Image.fromarray(np.moveaxis(A_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowercase ( self : Optional[Any] ) -> Optional[Any]:
__snake_case = self.get_tokenizer()
__snake_case = self.get_rust_tokenizer()
__snake_case = self.get_image_processor()
__snake_case = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
processor_slow.save_pretrained(self.tmpdirname )
__snake_case = CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=A_ )
__snake_case = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
processor_fast.save_pretrained(self.tmpdirname )
__snake_case = CLIPSegProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , A_ )
self.assertIsInstance(processor_fast.tokenizer , A_ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , A_ )
self.assertIsInstance(processor_fast.image_processor , A_ )
def lowercase ( self : Union[str, Any] ) -> Any:
__snake_case = CLIPSegProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__snake_case = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__snake_case = self.get_image_processor(do_normalize=A_ , padding_value=1.0 )
__snake_case = CLIPSegProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=A_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , A_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , A_ )
def lowercase ( self : Any ) -> str:
__snake_case = self.get_image_processor()
__snake_case = self.get_tokenizer()
__snake_case = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
__snake_case = self.prepare_image_inputs()
__snake_case = image_processor(A_ , return_tensors='''np''' )
__snake_case = processor(images=A_ , 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 lowercase ( self : List[str] ) -> List[Any]:
__snake_case = self.get_image_processor()
__snake_case = self.get_tokenizer()
__snake_case = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
__snake_case = '''lower newer'''
__snake_case = processor(text=A_ )
__snake_case = tokenizer(A_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase ( self : List[Any] ) -> str:
__snake_case = self.get_image_processor()
__snake_case = self.get_tokenizer()
__snake_case = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
__snake_case = '''lower newer'''
__snake_case = self.prepare_image_inputs()
__snake_case = processor(text=A_ , images=A_ )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(A_ ):
processor()
def lowercase ( self : Union[str, Any] ) -> Any:
__snake_case = self.get_image_processor()
__snake_case = self.get_tokenizer()
__snake_case = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
__snake_case = self.prepare_image_inputs()
__snake_case = self.prepare_image_inputs()
__snake_case = processor(images=A_ , visual_prompt=A_ )
self.assertListEqual(list(inputs.keys() ) , ['''pixel_values''', '''conditional_pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(A_ ):
processor()
def lowercase ( self : Optional[int] ) -> Dict:
__snake_case = self.get_image_processor()
__snake_case = self.get_tokenizer()
__snake_case = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
__snake_case = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__snake_case = processor.batch_decode(A_ )
__snake_case = tokenizer.batch_decode(A_ )
self.assertListEqual(A_ , A_ ) | 93 | 1 |
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __A ( _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
def is_in_circle(_SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float ) -> bool:
__SCREAMING_SNAKE_CASE : str = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
__SCREAMING_SNAKE_CASE : Union[str, Any] = mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(_SCREAMING_SNAKE_CASE ) )
# The ratio of the area for circle to square is pi/4.
__SCREAMING_SNAKE_CASE : Tuple = proportion * 4
print(f'The estimated value of pi is {pi_estimate}' )
print(f'The numpy value of pi is {pi}' )
print(f'The total error is {abs(pi - pi_estimate )}' )
def __A ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Callable[[float], float] , _SCREAMING_SNAKE_CASE : float = 0.0 , _SCREAMING_SNAKE_CASE : float = 1.0 , ):
"""simple docstring"""
return mean(
function_to_integrate(uniform(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ) for _ in range(_SCREAMING_SNAKE_CASE ) ) * (max_value - min_value)
def __A ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : float = 0.0 , _SCREAMING_SNAKE_CASE : float = 1.0 ):
"""simple docstring"""
def identity_function(_SCREAMING_SNAKE_CASE : float ) -> float:
return x
__SCREAMING_SNAKE_CASE : Union[str, Any] = area_under_curve_estimator(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE : Union[str, Any] = (max_value * max_value - min_value * min_value) / 2
print("******************" )
print(f'Estimating area under y=x where x varies from {min_value} to {max_value}' )
print(f'Estimated value is {estimated_value}' )
print(f'Expected value is {expected_value}' )
print(f'Total error is {abs(estimated_value - expected_value )}' )
print("******************" )
def __A ( _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
def function_to_integrate(_SCREAMING_SNAKE_CASE : float ) -> float:
return sqrt(4.0 - x * x )
__SCREAMING_SNAKE_CASE : Optional[int] = area_under_curve_estimator(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 0.0 , 2.0 )
print("******************" )
print("Estimating pi using area_under_curve_estimator" )
print(f'Estimated value is {estimated_value}' )
print(f'Expected value is {pi}' )
print(f'Total error is {abs(estimated_value - pi )}' )
print("******************" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 211 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_MODEL_FOR_PRETRAINING_MAPPING,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
@require_tf
class __lowerCamelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case__ : int = (
(
TFMobileBertModel,
TFMobileBertForMaskedLM,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertForMultipleChoice,
)
if is_tf_available()
else ()
)
snake_case__ : Union[str, Any] = (
{
'''feature-extraction''': TFMobileBertModel,
'''fill-mask''': TFMobileBertForMaskedLM,
'''question-answering''': TFMobileBertForQuestionAnswering,
'''text-classification''': TFMobileBertForSequenceClassification,
'''token-classification''': TFMobileBertForTokenClassification,
'''zero-shot''': TFMobileBertForSequenceClassification,
}
if is_tf_available()
else {}
)
snake_case__ : Dict = False
snake_case__ : Optional[int] = False
def a_ ( self , a__ , a__ , a__=False ):
__SCREAMING_SNAKE_CASE : Tuple = super()._prepare_for_class(a__ , a__ , return_labels=a__ )
if return_labels:
if model_class in get_values(a__ ):
__SCREAMING_SNAKE_CASE : Optional[int] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
return inputs_dict
class __lowerCamelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , a__ , a__=13 , a__=7 , a__=True , a__=True , a__=True , a__=True , a__=99 , a__=32 , a__=32 , a__=2 , a__=4 , a__=37 , a__="gelu" , a__=0.1 , a__=0.1 , a__=512 , a__=16 , a__=2 , a__=0.02 , a__=3 , a__=4 , a__=None , ):
__SCREAMING_SNAKE_CASE : Tuple = parent
__SCREAMING_SNAKE_CASE : str = batch_size
__SCREAMING_SNAKE_CASE : int = seq_length
__SCREAMING_SNAKE_CASE : Any = is_training
__SCREAMING_SNAKE_CASE : Optional[Any] = use_input_mask
__SCREAMING_SNAKE_CASE : Union[str, Any] = use_token_type_ids
__SCREAMING_SNAKE_CASE : Dict = use_labels
__SCREAMING_SNAKE_CASE : List[Any] = vocab_size
__SCREAMING_SNAKE_CASE : str = hidden_size
__SCREAMING_SNAKE_CASE : List[Any] = num_hidden_layers
__SCREAMING_SNAKE_CASE : Optional[Any] = num_attention_heads
__SCREAMING_SNAKE_CASE : Any = intermediate_size
__SCREAMING_SNAKE_CASE : List[str] = hidden_act
__SCREAMING_SNAKE_CASE : Optional[Any] = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : Optional[int] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Tuple = max_position_embeddings
__SCREAMING_SNAKE_CASE : int = type_vocab_size
__SCREAMING_SNAKE_CASE : Any = type_sequence_label_size
__SCREAMING_SNAKE_CASE : List[str] = initializer_range
__SCREAMING_SNAKE_CASE : Optional[int] = num_labels
__SCREAMING_SNAKE_CASE : Any = num_choices
__SCREAMING_SNAKE_CASE : List[str] = scope
__SCREAMING_SNAKE_CASE : Optional[int] = embedding_size
def a_ ( self ):
__SCREAMING_SNAKE_CASE : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__SCREAMING_SNAKE_CASE : Dict = None
if self.use_input_mask:
__SCREAMING_SNAKE_CASE : List[str] = 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 : List[str] = None
__SCREAMING_SNAKE_CASE : List[Any] = None
__SCREAMING_SNAKE_CASE : Any = None
if self.use_labels:
__SCREAMING_SNAKE_CASE : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__SCREAMING_SNAKE_CASE : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__SCREAMING_SNAKE_CASE : Optional[int] = ids_tensor([self.batch_size] , self.num_choices )
__SCREAMING_SNAKE_CASE : Optional[Any] = MobileBertConfig(
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 , embedding_size=self.embedding_size , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
__SCREAMING_SNAKE_CASE : List[str] = TFMobileBertModel(config=a__ )
__SCREAMING_SNAKE_CASE : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
__SCREAMING_SNAKE_CASE : Union[str, Any] = model(a__ )
__SCREAMING_SNAKE_CASE : Any = [input_ids, input_mask]
__SCREAMING_SNAKE_CASE : str = model(a__ )
__SCREAMING_SNAKE_CASE : int = model(a__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def a_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
__SCREAMING_SNAKE_CASE : Tuple = TFMobileBertForMaskedLM(config=a__ )
__SCREAMING_SNAKE_CASE : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
__SCREAMING_SNAKE_CASE : Union[str, Any] = model(a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
__SCREAMING_SNAKE_CASE : str = TFMobileBertForNextSentencePrediction(config=a__ )
__SCREAMING_SNAKE_CASE : int = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
__SCREAMING_SNAKE_CASE : Tuple = model(a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def a_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
__SCREAMING_SNAKE_CASE : Optional[Any] = TFMobileBertForPreTraining(config=a__ )
__SCREAMING_SNAKE_CASE : Any = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
__SCREAMING_SNAKE_CASE : Optional[int] = model(a__ )
self.parent.assertEqual(
result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def a_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
__SCREAMING_SNAKE_CASE : Any = self.num_labels
__SCREAMING_SNAKE_CASE : Optional[int] = TFMobileBertForSequenceClassification(config=a__ )
__SCREAMING_SNAKE_CASE : Tuple = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
__SCREAMING_SNAKE_CASE : Dict = model(a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
__SCREAMING_SNAKE_CASE : List[Any] = self.num_choices
__SCREAMING_SNAKE_CASE : int = TFMobileBertForMultipleChoice(config=a__ )
__SCREAMING_SNAKE_CASE : Dict = tf.tile(tf.expand_dims(a__ , 1 ) , (1, self.num_choices, 1) )
__SCREAMING_SNAKE_CASE : Any = tf.tile(tf.expand_dims(a__ , 1 ) , (1, self.num_choices, 1) )
__SCREAMING_SNAKE_CASE : List[str] = tf.tile(tf.expand_dims(a__ , 1 ) , (1, self.num_choices, 1) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
__SCREAMING_SNAKE_CASE : Union[str, Any] = model(a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self.num_labels
__SCREAMING_SNAKE_CASE : int = TFMobileBertForTokenClassification(config=a__ )
__SCREAMING_SNAKE_CASE : Any = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
__SCREAMING_SNAKE_CASE : Any = model(a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
__SCREAMING_SNAKE_CASE : Dict = TFMobileBertForQuestionAnswering(config=a__ )
__SCREAMING_SNAKE_CASE : Tuple = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
__SCREAMING_SNAKE_CASE : Optional[Any] = model(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 a_ ( self ):
__SCREAMING_SNAKE_CASE : str = self.prepare_config_and_inputs()
(
(
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) ,
) : Union[str, Any] = config_and_inputs
__SCREAMING_SNAKE_CASE : str = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
def a_ ( self ):
__SCREAMING_SNAKE_CASE : int = TFMobileBertModelTest.TFMobileBertModelTester(self )
__SCREAMING_SNAKE_CASE : int = ConfigTester(self , config_class=a__ , hidden_size=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*a__ )
def a_ ( self ):
__SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*a__ )
def a_ ( self ):
__SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*a__ )
def a_ ( self ):
__SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*a__ )
def a_ ( self ):
__SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*a__ )
def a_ ( self ):
__SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*a__ )
def a_ ( self ):
__SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*a__ )
def a_ ( self ):
__SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*a__ )
@slow
def a_ ( self ):
# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["google/mobilebert-uncased"]:
__SCREAMING_SNAKE_CASE : Any = TFMobileBertModel.from_pretrained(a__ )
self.assertIsNotNone(a__ )
@require_tf
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def a_ ( self ):
__SCREAMING_SNAKE_CASE : List[str] = TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased" )
__SCREAMING_SNAKE_CASE : Optional[Any] = tf.constant([[0, 1, 2, 3, 4, 5]] )
__SCREAMING_SNAKE_CASE : str = model(a__ )[0]
__SCREAMING_SNAKE_CASE : Dict = [1, 6, 30522]
self.assertEqual(output.shape , a__ )
__SCREAMING_SNAKE_CASE : str = tf.constant(
[
[
[-4.5919547, -9.248295, -9.645256],
[-6.7306175, -6.440284, -6.6052837],
[-7.2743506, -6.7847915, -6.024673],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , a__ , atol=1e-4 )
| 211 | 1 |
"""simple docstring"""
import math
import sys
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
if number != int(_lowerCamelCase ):
raise ValueError("the value of input must be a natural number" )
if number < 0:
raise ValueError("the value of input must not be a negative number" )
if number == 0:
return 1
_lowerCamelCase : Optional[int] = [-1] * (number + 1)
_lowerCamelCase : int = 0
for i in range(1 , number + 1 ):
_lowerCamelCase : Optional[Any] = sys.maxsize
_lowerCamelCase : Tuple = int(math.sqrt(_lowerCamelCase ) )
for j in range(1 , root + 1 ):
_lowerCamelCase : Dict = 1 + answers[i - (j**2)]
_lowerCamelCase : Union[str, Any] = min(_lowerCamelCase , _lowerCamelCase )
_lowerCamelCase : Optional[int] = answer
return answers[number]
if __name__ == "__main__":
import doctest
doctest.testmod() | 386 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel | 386 | 1 |
from abc import ABC, abstractmethod
from typing import List, Optional
class lowerCamelCase__ ( _A):
"""simple docstring"""
def __init__( self : Dict ) -> Tuple:
# test for the above condition
self.test()
def snake_case_ ( self : List[str] ) -> Dict:
_A = 0
_A = False
while not completed:
if counter == 1:
self.reset()
_A = self.advance()
if not self.does_advance(__lowerCAmelCase ):
raise Exception(
'''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.''' )
_A , _A , _A = self.update(__lowerCAmelCase )
counter += 1
if counter > 1_00_00:
raise Exception('''update() does not fulfill the constraint.''' )
if self.remaining() != 0:
raise Exception('''Custom Constraint is not defined correctly.''' )
@abstractmethod
def snake_case_ ( self : Dict ) -> str:
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def snake_case_ ( self : List[Any] , __lowerCAmelCase : int ) -> Any:
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def snake_case_ ( self : Union[str, Any] , __lowerCAmelCase : int ) -> Tuple:
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def snake_case_ ( self : List[str] ) -> Optional[int]:
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def snake_case_ ( self : List[Any] ) -> int:
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def snake_case_ ( self : Tuple , __lowerCAmelCase : Union[str, Any]=False ) -> Optional[Any]:
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class lowerCamelCase__ ( _A):
"""simple docstring"""
def __init__( self : List[Any] , __lowerCAmelCase : List[int] ) -> Any:
super(__lowerCAmelCase , self ).__init__()
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or len(__lowerCAmelCase ) == 0:
raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.''' )
if any((not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or token_id < 0) for token_id in token_ids ):
raise ValueError(f'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.''' )
_A = token_ids
_A = len(self.token_ids )
_A = -1 # the index of the currently fulfilled step
_A = False
def snake_case_ ( self : Optional[int] ) -> str:
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def snake_case_ ( self : Optional[Any] , __lowerCAmelCase : int ) -> str:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(__lowerCAmelCase )}''' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def snake_case_ ( self : Dict , __lowerCAmelCase : int ) -> str:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(__lowerCAmelCase )}''' )
_A = False
_A = False
_A = False
if self.does_advance(__lowerCAmelCase ):
self.fulfilled_idx += 1
_A = True
if self.fulfilled_idx == (self.seqlen - 1):
_A = True
_A = completed
else:
# failed to make progress.
_A = True
self.reset()
return stepped, completed, reset
def snake_case_ ( self : Union[str, Any] ) -> int:
_A = False
_A = 0
def snake_case_ ( self : Union[str, Any] ) -> Any:
return self.seqlen - (self.fulfilled_idx + 1)
def snake_case_ ( self : Optional[int] , __lowerCAmelCase : Dict=False ) -> str:
_A = PhrasalConstraint(self.token_ids )
if stateful:
_A = self.seqlen
_A = self.fulfilled_idx
_A = self.completed
return new_constraint
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : Union[str, Any] , __lowerCAmelCase : List[List[int]] , __lowerCAmelCase : Optional[Any]=True ) -> Any:
_A = max([len(__lowerCAmelCase ) for one in nested_token_ids] )
_A = {}
for token_ids in nested_token_ids:
_A = root
for tidx, token_id in enumerate(__lowerCAmelCase ):
if token_id not in level:
_A = {}
_A = level[token_id]
if no_subsets and self.has_subsets(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError(
'''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
f''' {nested_token_ids}.''' )
_A = root
def snake_case_ ( self : Dict , __lowerCAmelCase : str ) -> List[str]:
_A = self.trie
for current_token in current_seq:
_A = start[current_token]
_A = list(start.keys() )
return next_tokens
def snake_case_ ( self : List[str] , __lowerCAmelCase : str ) -> int:
_A = self.next_tokens(__lowerCAmelCase )
return len(__lowerCAmelCase ) == 0
def snake_case_ ( self : List[str] , __lowerCAmelCase : int ) -> Optional[Any]:
_A = list(root.values() )
if len(__lowerCAmelCase ) == 0:
return 1
else:
return sum([self.count_leaves(__lowerCAmelCase ) for nn in next_nodes] )
def snake_case_ ( self : Optional[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : str ) -> int:
_A = self.count_leaves(__lowerCAmelCase )
return len(__lowerCAmelCase ) != leaf_count
class lowerCamelCase__ ( _A):
"""simple docstring"""
def __init__( self : Optional[int] , __lowerCAmelCase : List[List[int]] ) -> Union[str, Any]:
super(__lowerCAmelCase , self ).__init__()
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or len(__lowerCAmelCase ) == 0:
raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' )
if any(not isinstance(__lowerCAmelCase , __lowerCAmelCase ) for token_ids in nested_token_ids ):
raise ValueError(f'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.''' )
if any(
any((not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
f'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.''' )
_A = DisjunctiveTrie(__lowerCAmelCase )
_A = nested_token_ids
_A = self.trie.max_height
_A = []
_A = False
def snake_case_ ( self : str ) -> str:
_A = self.trie.next_tokens(self.current_seq )
if len(__lowerCAmelCase ) == 0:
return None
else:
return token_list
def snake_case_ ( self : List[Any] , __lowerCAmelCase : int ) -> List[str]:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(__lowerCAmelCase )}''' )
_A = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def snake_case_ ( self : List[Any] , __lowerCAmelCase : int ) -> Tuple:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(__lowerCAmelCase )}''' )
_A = False
_A = False
_A = False
if self.does_advance(__lowerCAmelCase ):
self.current_seq.append(__lowerCAmelCase )
_A = True
else:
_A = True
self.reset()
_A = self.trie.reached_leaf(self.current_seq )
_A = completed
return stepped, completed, reset
def snake_case_ ( self : Tuple ) -> int:
_A = False
_A = []
def snake_case_ ( self : Any ) -> List[str]:
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def snake_case_ ( self : str , __lowerCAmelCase : Dict=False ) -> Optional[int]:
_A = DisjunctiveConstraint(self.token_ids )
if stateful:
_A = self.seqlen
_A = self.current_seq
_A = self.completed
return new_constraint
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : Union[str, Any] , __lowerCAmelCase : List[Constraint] ) -> Optional[Any]:
_A = constraints
# max # of steps required to fulfill a given constraint
_A = max([c.seqlen for c in constraints] )
_A = len(__lowerCAmelCase )
_A = False
self.init_state()
def snake_case_ ( self : int ) -> str:
_A = []
_A = None
_A = [constraint.copy(stateful=__lowerCAmelCase ) for constraint in self.constraints]
def snake_case_ ( self : int ) -> Any:
_A = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def snake_case_ ( self : Any ) -> str:
_A = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
_A = constraint.advance()
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
token_list.append(__lowerCAmelCase )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
token_list.extend(__lowerCAmelCase )
else:
_A = self.inprogress_constraint.advance()
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
token_list.append(__lowerCAmelCase )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
token_list.extend(__lowerCAmelCase )
if len(__lowerCAmelCase ) == 0:
return None
else:
return token_list
def snake_case_ ( self : List[str] , __lowerCAmelCase : Optional[List[int]] ) -> Dict:
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
_A , _A = self.add(__lowerCAmelCase )
# the entire list of constraints are fulfilled
if self.completed:
break
def snake_case_ ( self : Any , __lowerCAmelCase : int ) -> Optional[int]:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.''' )
_A , _A = False, False
if self.completed:
_A = True
_A = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
_A , _A , _A = self.inprogress_constraint.update(__lowerCAmelCase )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=__lowerCAmelCase ) )
_A = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
_A = None
if len(self.pending_constraints ) == 0:
# we're done!
_A = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(__lowerCAmelCase ):
_A , _A , _A = pending_constraint.update(__lowerCAmelCase )
if not stepped:
raise Exception(
'''`constraint.update(token_id)` is not yielding incremental progress, '''
'''even though `constraint.does_advance(token_id)` is true.''' )
if complete:
self.complete_constraints.append(__lowerCAmelCase )
_A = None
if not complete and stepped:
_A = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
_A = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
_A = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def snake_case_ ( self : Tuple , __lowerCAmelCase : Union[str, Any]=True ) -> Optional[Any]:
_A = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
_A = [
constraint.copy(stateful=__lowerCAmelCase ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
_A = self.inprogress_constraint.copy(stateful=__lowerCAmelCase )
_A = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 2 |
"""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.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def _UpperCAmelCase ( __lowerCamelCase : Optional[int] ) -> str:
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def _UpperCAmelCase ( __lowerCamelCase : Tuple ) -> Dict:
_snake_case = create_tensor(__lowerCamelCase )
_snake_case = gather(__lowerCamelCase )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def _UpperCAmelCase ( __lowerCamelCase : Tuple ) -> Tuple:
_snake_case = [state.process_index]
_snake_case = gather_object(__lowerCamelCase )
assert len(__lowerCamelCase ) == state.num_processes, f'''{gathered_obj}, {len(__lowerCamelCase )} != {state.num_processes}'''
assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''
def _UpperCAmelCase ( __lowerCamelCase : int ) -> Union[str, Any]:
_snake_case = create_tensor(__lowerCamelCase )
_snake_case = broadcast(__lowerCamelCase )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def _UpperCAmelCase ( __lowerCamelCase : str ) -> int:
# We need to pad the tensor with one more element if we are the main process
# to ensure that we can pad
if state.is_main_process:
_snake_case = torch.arange(state.num_processes + 1 ).to(state.device )
else:
_snake_case = torch.arange(state.num_processes ).to(state.device )
_snake_case = pad_across_processes(__lowerCamelCase )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def _UpperCAmelCase ( __lowerCamelCase : Any ) -> List[str]:
# For now runs on only two processes
if state.num_processes != 2:
return
_snake_case = create_tensor(__lowerCamelCase )
_snake_case = reduce(__lowerCamelCase , '''sum''' )
_snake_case = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(__lowerCamelCase , __lowerCamelCase ), f'''{reduced_tensor} != {truth_tensor}'''
def _UpperCAmelCase ( __lowerCamelCase : int ) -> Optional[int]:
# For now runs on only two processes
if state.num_processes != 2:
return
_snake_case = create_tensor(__lowerCamelCase )
_snake_case = reduce(__lowerCamelCase , '''mean''' )
_snake_case = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(__lowerCamelCase , __lowerCamelCase ), f'''{reduced_tensor} != {truth_tensor}'''
def _UpperCAmelCase ( __lowerCamelCase : Dict ) -> List[Any]:
# For xla_spawn (TPUs)
main()
def _UpperCAmelCase ( ) -> Optional[Any]:
_snake_case = PartialState()
state.print(f'''State: {state}''' )
state.print('''testing gather''' )
test_gather(__lowerCamelCase )
state.print('''testing gather_object''' )
test_gather_object(__lowerCamelCase )
state.print('''testing broadcast''' )
test_broadcast(__lowerCamelCase )
state.print('''testing pad_across_processes''' )
test_pad_across_processes(__lowerCamelCase )
state.print('''testing reduce_sum''' )
test_reduce_sum(__lowerCamelCase )
state.print('''testing reduce_mean''' )
test_reduce_mean(__lowerCamelCase )
if __name__ == "__main__":
main()
| 224 | 0 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs
lowerCAmelCase : Dict = imread(r"""digital_image_processing/image_data/lena_small.jpg""")
lowerCAmelCase : int = cvtColor(img, COLOR_BGR2GRAY)
def a__ ( ) -> Optional[int]:
lowerCamelCase = cn.convert_to_negative(snake_case__ )
# assert negative_img array for at least one True
assert negative_img.any()
def a__ ( ) -> Dict:
with Image.open("""digital_image_processing/image_data/lena_small.jpg""" ) as img:
# Work around assertion for response
assert str(cc.change_contrast(snake_case__ , 1_10 ) ).startswith(
"""<PIL.Image.Image image mode=RGB size=100x100 at""" )
def a__ ( ) -> str:
lowerCamelCase = canny.gen_gaussian_kernel(9 , sigma=1.4 )
# Assert ambiguous array
assert resp.all()
def a__ ( ) -> Tuple:
lowerCamelCase = imread("""digital_image_processing/image_data/lena_small.jpg""" , 0 )
# assert ambiguous array for all == True
assert canny_img.all()
lowerCamelCase = canny.canny(snake_case__ )
# assert canny array for at least one True
assert canny_array.any()
def a__ ( ) -> Optional[int]:
assert gg.gaussian_filter(snake_case__ , 5 , sigma=0.9 ).all()
def a__ ( ) -> Union[str, Any]:
# laplace diagonals
lowerCamelCase = array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
lowerCamelCase = conv.img_convolve(snake_case__ , snake_case__ ).astype(snake_case__ )
assert res.any()
def a__ ( ) -> int:
assert med.median_filter(snake_case__ , 3 ).any()
def a__ ( ) -> str:
lowerCamelCase , lowerCamelCase = sob.sobel_filter(snake_case__ )
assert grad.any() and theta.any()
def a__ ( ) -> str:
lowerCamelCase = sp.make_sepia(snake_case__ , 20 )
assert sepia.all()
def a__ ( snake_case__ = "digital_image_processing/image_data/lena_small.jpg" ) -> Dict:
lowerCamelCase = bs.Burkes(imread(snake_case__ , 1 ) , 1_20 )
burkes.process()
assert burkes.output_img.any()
def a__ ( snake_case__ = "digital_image_processing/image_data/lena_small.jpg" , ) -> List[str]:
lowerCamelCase = rs.NearestNeighbour(imread(snake_case__ , 1 ) , 4_00 , 2_00 )
nn.process()
assert nn.output.any()
def a__ ( ) -> Optional[Any]:
lowerCamelCase = """digital_image_processing/image_data/lena.jpg"""
# Reading the image and converting it to grayscale.
lowerCamelCase = imread(snake_case__ , 0 )
# Test for get_neighbors_pixel function() return not None
lowerCamelCase = 0
lowerCamelCase = 0
lowerCamelCase = image[x_coordinate][y_coordinate]
lowerCamelCase = lbp.get_neighbors_pixel(
snake_case__ , snake_case__ , snake_case__ , snake_case__ )
assert neighbors_pixels is not None
# Test for local_binary_pattern function()
# Create a numpy array as the same height and width of read image
lowerCamelCase = np.zeros((image.shape[0], image.shape[1]) )
# Iterating through the image and calculating the local binary pattern value
# for each pixel.
for i in range(0 , image.shape[0] ):
for j in range(0 , image.shape[1] ):
lowerCamelCase = lbp.local_binary_value(snake_case__ , snake_case__ , snake_case__ )
assert lbp_image.any()
| 718 |
"""simple docstring"""
from __future__ import annotations
def a__ ( snake_case__ , snake_case__ = None , snake_case__ = None ) -> None:
if start is None:
lowerCamelCase = 0
if end is None:
lowerCamelCase = len(snake_case__ ) - 1
if start >= end:
return
lowerCamelCase = (start + end) // 2
slowsort(snake_case__ , snake_case__ , snake_case__ )
slowsort(snake_case__ , mid + 1 , snake_case__ )
if sequence[end] < sequence[mid]:
lowerCamelCase , lowerCamelCase = sequence[mid], sequence[end]
slowsort(snake_case__ , snake_case__ , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 533 | 0 |
def lowercase_ ( SCREAMING_SNAKE_CASE : int = 60_08_51_47_51_43 ):
"""simple docstring"""
try:
snake_case__ : Optional[int] =int(UpperCamelCase__ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''' )
snake_case__ : str =1
snake_case__ : str =2
while i * i <= n:
while n % i == 0:
snake_case__ : List[str] =i
n //= i
i += 1
if n > 1:
snake_case__ : Any =n
return int(UpperCamelCase__ )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 381 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCamelCase_ ( UpperCamelCase__ ):
lowerCamelCase_ = ["image_processor", "tokenizer"]
lowerCamelCase_ = "AutoImageProcessor"
lowerCamelCase_ = "AutoTokenizer"
def __init__( self :Optional[int] , __A :Optional[Any] , __A :Dict ) -> Dict:
"""simple docstring"""
super().__init__(__A , __A )
SCREAMING_SNAKE_CASE__ = self.image_processor
def __call__( self :int , __A :str=None , __A :int=None , __A :Union[str, Any]=None , **__A :str ) -> Optional[Any]:
"""simple docstring"""
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
SCREAMING_SNAKE_CASE__ = self.tokenizer(__A , return_tensors=__A , **__A )
if images is not None:
SCREAMING_SNAKE_CASE__ = self.image_processor(__A , return_tensors=__A , **__A )
if text is not None and images is not None:
SCREAMING_SNAKE_CASE__ = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__A ) , tensor_type=__A )
def _snake_case ( self :str , *__A :List[str] , **__A :List[str] ) -> List[Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*__A , **__A )
def _snake_case ( self :List[str] , *__A :Any , **__A :Any ) -> Tuple:
"""simple docstring"""
return self.tokenizer.decode(*__A , **__A )
@property
def _snake_case ( self :Dict ) -> List[Any]:
"""simple docstring"""
return ["input_ids", "attention_mask", "pixel_values"] | 6 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def lowercase__ ( snake_case_ :Optional[Any] ):
__UpperCAmelCase = 384
__UpperCAmelCase = 7
if "tiny" in model_name:
__UpperCAmelCase = 96
__UpperCAmelCase = (2, 2, 6, 2)
__UpperCAmelCase = (3, 6, 12, 24)
elif "small" in model_name:
__UpperCAmelCase = 96
__UpperCAmelCase = (2, 2, 18, 2)
__UpperCAmelCase = (3, 6, 12, 24)
elif "base" in model_name:
__UpperCAmelCase = 128
__UpperCAmelCase = (2, 2, 18, 2)
__UpperCAmelCase = (4, 8, 16, 32)
__UpperCAmelCase = 12
__UpperCAmelCase = 512
elif "large" in model_name:
__UpperCAmelCase = 192
__UpperCAmelCase = (2, 2, 18, 2)
__UpperCAmelCase = (6, 12, 24, 48)
__UpperCAmelCase = 12
__UpperCAmelCase = 768
# set label information
__UpperCAmelCase = 150
__UpperCAmelCase = 'huggingface/label-files'
__UpperCAmelCase = 'ade20k-id2label.json'
__UpperCAmelCase = json.load(open(hf_hub_download(_lowercase , _lowercase , repo_type='''dataset''' ) , '''r''' ) )
__UpperCAmelCase = {int(_lowercase ): v for k, v in idalabel.items()}
__UpperCAmelCase = {v: k for k, v in idalabel.items()}
__UpperCAmelCase = SwinConfig(
embed_dim=_lowercase , depths=_lowercase , num_heads=_lowercase , window_size=_lowercase , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] , )
__UpperCAmelCase = UperNetConfig(
backbone_config=_lowercase , auxiliary_in_channels=_lowercase , num_labels=_lowercase , idalabel=_lowercase , labelaid=_lowercase , )
return config
def lowercase__ ( snake_case_ :Union[str, Any] ):
__UpperCAmelCase = []
# fmt: off
# stem
rename_keys.append(('''backbone.patch_embed.projection.weight''', '''backbone.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''backbone.patch_embed.projection.bias''', '''backbone.embeddings.patch_embeddings.projection.bias''') )
rename_keys.append(('''backbone.patch_embed.norm.weight''', '''backbone.embeddings.norm.weight''') )
rename_keys.append(('''backbone.patch_embed.norm.bias''', '''backbone.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.stages.{i}.blocks.{j}.norm1.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''') )
rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm1.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''') )
rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''') )
rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''') )
rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''') )
rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''') )
rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm2.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''') )
rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm2.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''') )
rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''') )
rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''') )
rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight''') )
rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias''') )
if i < 3:
rename_keys.append((F'''backbone.stages.{i}.downsample.reduction.weight''', F'''backbone.encoder.layers.{i}.downsample.reduction.weight''') )
rename_keys.append((F'''backbone.stages.{i}.downsample.norm.weight''', F'''backbone.encoder.layers.{i}.downsample.norm.weight''') )
rename_keys.append((F'''backbone.stages.{i}.downsample.norm.bias''', F'''backbone.encoder.layers.{i}.downsample.norm.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 lowercase__ ( snake_case_ :List[str] , snake_case_ :Optional[Any] , snake_case_ :List[Any] ):
__UpperCAmelCase = dct.pop(_lowercase )
__UpperCAmelCase = val
def lowercase__ ( snake_case_ :Tuple , snake_case_ :str ):
__UpperCAmelCase = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
__UpperCAmelCase = 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)
__UpperCAmelCase = state_dict.pop(F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight''' )
__UpperCAmelCase = state_dict.pop(F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
__UpperCAmelCase = in_proj_weight[:dim, :]
__UpperCAmelCase = in_proj_bias[: dim]
__UpperCAmelCase = in_proj_weight[
dim : dim * 2, :
]
__UpperCAmelCase = in_proj_bias[
dim : dim * 2
]
__UpperCAmelCase = in_proj_weight[
-dim :, :
]
__UpperCAmelCase = in_proj_bias[-dim :]
# fmt: on
def lowercase__ ( snake_case_ :Tuple ):
__UpperCAmelCase = x.shape
__UpperCAmelCase = x.reshape(_lowercase , 4 , in_channel // 4 )
__UpperCAmelCase = x[:, [0, 2, 1, 3], :].transpose(1 , 2 ).reshape(_lowercase , _lowercase )
return x
def lowercase__ ( snake_case_ :List[Any] ):
__UpperCAmelCase = x.shape
__UpperCAmelCase = x.reshape(_lowercase , in_channel // 4 , 4 )
__UpperCAmelCase = x[:, :, [0, 2, 1, 3]].transpose(1 , 2 ).reshape(_lowercase , _lowercase )
return x
def lowercase__ ( snake_case_ :List[str] ):
__UpperCAmelCase = x.shape[0]
__UpperCAmelCase = x.reshape(4 , in_channel // 4 )
__UpperCAmelCase = x[[0, 2, 1, 3], :].transpose(0 , 1 ).reshape(_lowercase )
return x
def lowercase__ ( snake_case_ :Any ):
__UpperCAmelCase = x.shape[0]
__UpperCAmelCase = x.reshape(in_channel // 4 , 4 )
__UpperCAmelCase = x[:, [0, 2, 1, 3]].transpose(0 , 1 ).reshape(_lowercase )
return x
def lowercase__ ( snake_case_ :Optional[int] , snake_case_ :Dict , snake_case_ :str ):
__UpperCAmelCase = {
'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
}
__UpperCAmelCase = model_name_to_url[model_name]
__UpperCAmelCase = torch.hub.load_state_dict_from_url(_lowercase , map_location='''cpu''' , file_name=_lowercase )[
'state_dict'
]
for name, param in state_dict.items():
print(_lowercase , param.shape )
__UpperCAmelCase = get_upernet_config(_lowercase )
__UpperCAmelCase = UperNetForSemanticSegmentation(_lowercase )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
__UpperCAmelCase = state_dict.pop(_lowercase )
if "bn" in key:
__UpperCAmelCase = key.replace('''bn''' , '''batch_norm''' )
__UpperCAmelCase = val
# rename keys
__UpperCAmelCase = create_rename_keys(_lowercase )
for src, dest in rename_keys:
rename_key(_lowercase , _lowercase , _lowercase )
read_in_q_k_v(_lowercase , config.backbone_config )
# fix downsample parameters
for key, value in state_dict.items():
if "downsample" in key:
if "reduction" in key:
__UpperCAmelCase = reverse_correct_unfold_reduction_order(_lowercase )
if "norm" in key:
__UpperCAmelCase = reverse_correct_unfold_norm_order(_lowercase )
model.load_state_dict(_lowercase )
# verify on image
__UpperCAmelCase = 'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
__UpperCAmelCase = Image.open(requests.get(_lowercase , stream=_lowercase ).raw ).convert('''RGB''' )
__UpperCAmelCase = SegformerImageProcessor()
__UpperCAmelCase = processor(_lowercase , return_tensors='''pt''' ).pixel_values
with torch.no_grad():
__UpperCAmelCase = model(_lowercase )
__UpperCAmelCase = outputs.logits
print(logits.shape )
print('''First values of logits:''' , logits[0, 0, :3, :3] )
# assert values
if model_name == "upernet-swin-tiny":
__UpperCAmelCase = torch.tensor(
[[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]] )
elif model_name == "upernet-swin-small":
__UpperCAmelCase = torch.tensor(
[[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]] )
elif model_name == "upernet-swin-base":
__UpperCAmelCase = torch.tensor(
[[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]] )
elif model_name == "upernet-swin-large":
__UpperCAmelCase = torch.tensor(
[[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]] )
print('''Logits:''' , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , _lowercase , 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(_lowercase )
print(F'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(_lowercase )
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__":
_lowercase : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='upernet-swin-tiny',
type=str,
choices=[f"""upernet-swin-{size}""" for size in ['tiny', 'small', 'base', 'large']],
help='Name of the Swin + 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.'
)
_lowercase : Optional[Any] = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 704 |
"""simple docstring"""
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
AlbertConfig,
BartConfig,
BertConfig,
CamembertConfig,
CTRLConfig,
DistilBertConfig,
DPRConfig,
ElectraConfig,
FlaubertConfig,
GPTaConfig,
LayoutLMConfig,
LxmertConfig,
OpenAIGPTConfig,
RobertaConfig,
TaConfig,
TFAlbertForPreTraining,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFCamembertForMaskedLM,
TFCTRLLMHeadModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
TFElectraForPreTraining,
TFFlaubertWithLMHeadModel,
TFGPTaLMHeadModel,
TFLayoutLMForMaskedLM,
TFLxmertForPreTraining,
TFLxmertVisualFeatureEncoder,
TFOpenAIGPTLMHeadModel,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForSequenceClassification,
TFTaForConditionalGeneration,
TFTransfoXLLMHeadModel,
TFWavaVecaModel,
TFXLMRobertaForMaskedLM,
TFXLMWithLMHeadModel,
TFXLNetLMHeadModel,
TransfoXLConfig,
WavaVecaConfig,
WavaVecaModel,
XLMConfig,
XLMRobertaConfig,
XLNetConfig,
is_torch_available,
load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging
if is_torch_available():
import numpy as np
import torch
from . import (
AlbertForPreTraining,
BartForConditionalGeneration,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
CamembertForMaskedLM,
CTRLLMHeadModel,
DistilBertForMaskedLM,
DistilBertForQuestionAnswering,
DPRContextEncoder,
DPRQuestionEncoder,
DPRReader,
ElectraForPreTraining,
FlaubertWithLMHeadModel,
GPTaLMHeadModel,
LayoutLMForMaskedLM,
LxmertForPreTraining,
LxmertVisualFeatureEncoder,
OpenAIGPTLMHeadModel,
RobertaForMaskedLM,
RobertaForSequenceClassification,
TaForConditionalGeneration,
TransfoXLLMHeadModel,
XLMRobertaForMaskedLM,
XLMWithLMHeadModel,
XLNetLMHeadModel,
)
logging.set_verbosity_info()
_lowercase : List[str] = {
'bart': (
BartConfig,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
BartForConditionalGeneration,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'bert': (
BertConfig,
TFBertForPreTraining,
BertForPreTraining,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'bert-base-cased-finetuned-mrpc': (
BertConfig,
TFBertForSequenceClassification,
BertForSequenceClassification,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'dpr': (
DPRConfig,
TFDPRQuestionEncoder,
TFDPRContextEncoder,
TFDPRReader,
DPRQuestionEncoder,
DPRContextEncoder,
DPRReader,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'gpt2': (
GPTaConfig,
TFGPTaLMHeadModel,
GPTaLMHeadModel,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'xlnet': (
XLNetConfig,
TFXLNetLMHeadModel,
XLNetLMHeadModel,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'xlm': (
XLMConfig,
TFXLMWithLMHeadModel,
XLMWithLMHeadModel,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'xlm-roberta': (
XLMRobertaConfig,
TFXLMRobertaForMaskedLM,
XLMRobertaForMaskedLM,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'transfo-xl': (
TransfoXLConfig,
TFTransfoXLLMHeadModel,
TransfoXLLMHeadModel,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'openai-gpt': (
OpenAIGPTConfig,
TFOpenAIGPTLMHeadModel,
OpenAIGPTLMHeadModel,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'roberta': (
RobertaConfig,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
RobertaForMaskedLM,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'layoutlm': (
LayoutLMConfig,
TFLayoutLMForMaskedLM,
LayoutLMForMaskedLM,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'roberta-large-mnli': (
RobertaConfig,
TFRobertaForSequenceClassification,
RobertaForSequenceClassification,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'camembert': (
CamembertConfig,
TFCamembertForMaskedLM,
CamembertForMaskedLM,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'flaubert': (
FlaubertConfig,
TFFlaubertWithLMHeadModel,
FlaubertWithLMHeadModel,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'distilbert': (
DistilBertConfig,
TFDistilBertForMaskedLM,
DistilBertForMaskedLM,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'distilbert-base-distilled-squad': (
DistilBertConfig,
TFDistilBertForQuestionAnswering,
DistilBertForQuestionAnswering,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'lxmert': (
LxmertConfig,
TFLxmertForPreTraining,
LxmertForPreTraining,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'lxmert-visual-feature-encoder': (
LxmertConfig,
TFLxmertVisualFeatureEncoder,
LxmertVisualFeatureEncoder,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'ctrl': (
CTRLConfig,
TFCTRLLMHeadModel,
CTRLLMHeadModel,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'albert': (
AlbertConfig,
TFAlbertForPreTraining,
AlbertForPreTraining,
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
't5': (
TaConfig,
TFTaForConditionalGeneration,
TaForConditionalGeneration,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'electra': (
ElectraConfig,
TFElectraForPreTraining,
ElectraForPreTraining,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'wav2vec2': (
WavaVecaConfig,
TFWavaVecaModel,
WavaVecaModel,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
}
def lowercase__ ( snake_case_ :str , snake_case_ :Union[str, Any] , snake_case_ :Tuple , snake_case_ :List[str] , snake_case_ :List[Any]=False , snake_case_ :List[Any]=True ):
if model_type not in MODEL_CLASSES:
raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys() )}.''' )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = MODEL_CLASSES[model_type]
# Initialise TF model
if config_file in aws_config_map:
__UpperCAmelCase = cached_file(snake_case_ , snake_case_ , force_download=not use_cached_models )
__UpperCAmelCase = config_class.from_json_file(snake_case_ )
__UpperCAmelCase = True
__UpperCAmelCase = True
print(F'''Building TensorFlow model from configuration: {config}''' )
__UpperCAmelCase = model_class(snake_case_ )
# Load weights from tf checkpoint
if pytorch_checkpoint_path in aws_config_map.keys():
__UpperCAmelCase = cached_file(
snake_case_ , snake_case_ , force_download=not use_cached_models )
# Load PyTorch checkpoint in tf2 model:
__UpperCAmelCase = load_pytorch_checkpoint_in_tfa_model(snake_case_ , snake_case_ )
if compare_with_pt_model:
__UpperCAmelCase = tf_model(tf_model.dummy_inputs , training=snake_case_ ) # build the network
__UpperCAmelCase = torch.load(snake_case_ , map_location='''cpu''' )
__UpperCAmelCase = pt_model_class.from_pretrained(
pretrained_model_name_or_path=snake_case_ , config=snake_case_ , state_dict=snake_case_ )
with torch.no_grad():
__UpperCAmelCase = pt_model(**pt_model.dummy_inputs )
__UpperCAmelCase = pto[0].numpy()
__UpperCAmelCase = tfo[0].numpy()
__UpperCAmelCase = np.amax(np.abs(np_pt - np_tf ) )
print(F'''Max absolute difference between models outputs {diff}''' )
assert diff <= 2E-2, F'''Error, model absolute difference is >2e-2: {diff}'''
# Save pytorch-model
print(F'''Save TensorFlow model to {tf_dump_path}''' )
tf_model.save_weights(snake_case_ , save_format='''h5''' )
def lowercase__ ( snake_case_ :Union[str, Any] , snake_case_ :List[str] , snake_case_ :int=None , snake_case_ :Optional[int]=None , snake_case_ :List[str]=False , snake_case_ :Optional[int]=False , snake_case_ :Dict=False , snake_case_ :List[Any]=False , ):
if args_model_type is None:
__UpperCAmelCase = list(MODEL_CLASSES.keys() )
else:
__UpperCAmelCase = [args_model_type]
for j, model_type in enumerate(snake_case_ , start=1 ):
print('''=''' * 100 )
print(F''' Converting model type {j}/{len(snake_case_ )}: {model_type}''' )
print('''=''' * 100 )
if model_type not in MODEL_CLASSES:
raise ValueError(F'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys() )}.''' )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = MODEL_CLASSES[model_type]
if model_shortcut_names_or_path is None:
__UpperCAmelCase = list(aws_model_maps.keys() )
if config_shortcut_names_or_path is None:
__UpperCAmelCase = model_shortcut_names_or_path
for i, (model_shortcut_name, config_shortcut_name) in enumerate(
zip(snake_case_ , snake_case_ ) , start=1 ):
print('''-''' * 100 )
if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
if not only_convert_finetuned_models:
print(F''' Skipping finetuned checkpoint {model_shortcut_name}''' )
continue
__UpperCAmelCase = model_shortcut_name
elif only_convert_finetuned_models:
print(F''' Skipping not finetuned checkpoint {model_shortcut_name}''' )
continue
print(
F''' Converting checkpoint {i}/{len(snake_case_ )}: {model_shortcut_name} - model_type {model_type}''' )
print('''-''' * 100 )
if config_shortcut_name in aws_config_map:
__UpperCAmelCase = cached_file(snake_case_ , snake_case_ , force_download=not use_cached_models )
else:
__UpperCAmelCase = config_shortcut_name
if model_shortcut_name in aws_model_maps:
__UpperCAmelCase = cached_file(snake_case_ , snake_case_ , force_download=not use_cached_models )
else:
__UpperCAmelCase = model_shortcut_name
if os.path.isfile(snake_case_ ):
__UpperCAmelCase = '''converted_model'''
convert_pt_checkpoint_to_tf(
model_type=snake_case_ , pytorch_checkpoint_path=snake_case_ , config_file=snake_case_ , tf_dump_path=os.path.join(snake_case_ , model_shortcut_name + '''-tf_model.h5''' ) , compare_with_pt_model=snake_case_ , )
if remove_cached_files:
os.remove(snake_case_ )
os.remove(snake_case_ )
if __name__ == "__main__":
_lowercase : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
)
parser.add_argument(
'--model_type',
default=None,
type=str,
help=(
f"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
'convert all the models from AWS.'
),
)
parser.add_argument(
'--pytorch_checkpoint_path',
default=None,
type=str,
help=(
'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
'If not given, will download and convert all the checkpoints from AWS.'
),
)
parser.add_argument(
'--config_file',
default=None,
type=str,
help=(
'The config json file corresponding to the pre-trained model. \n'
'This specifies the model architecture. If not given and '
'--pytorch_checkpoint_path is not given or is a shortcut name '
'use the configuration associated to the shortcut name on the AWS'
),
)
parser.add_argument(
'--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
)
parser.add_argument(
'--use_cached_models',
action='store_true',
help='Use cached models if possible instead of updating to latest checkpoint versions.',
)
parser.add_argument(
'--remove_cached_files',
action='store_true',
help='Remove pytorch models after conversion (save memory when converting in batches).',
)
parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
_lowercase : List[Any] = parser.parse_args()
# if args.pytorch_checkpoint_path is not None:
# convert_pt_checkpoint_to_tf(args.model_type.lower(),
# args.pytorch_checkpoint_path,
# args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
# args.tf_dump_path,
# compare_with_pt_model=args.compare_with_pt_model,
# use_cached_models=args.use_cached_models)
# else:
convert_all_pt_checkpoints_to_tf(
args.model_type.lower() if args.model_type is not None else None,
args.tf_dump_path,
model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
if args.pytorch_checkpoint_path is not None
else None,
config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
compare_with_pt_model=args.compare_with_pt_model,
use_cached_models=args.use_cached_models,
remove_cached_files=args.remove_cached_files,
only_convert_finetuned_models=args.only_convert_finetuned_models,
)
| 397 | 0 |
'''simple docstring'''
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase__ : List[str] = _modexpt(UpperCamelCase__ , exponent // 2 , UpperCamelCase__ ) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modexpt(UpperCamelCase__ , exponent - 1 , UpperCamelCase__ )) % modulo_value
def _UpperCamelCase ( UpperCamelCase__ = 1_7_7_7 , UpperCamelCase__ = 1_8_5_5 , UpperCamelCase__ = 8 ):
UpperCAmelCase__ : List[str] = base
for _ in range(1 , UpperCamelCase__ ):
UpperCAmelCase__ : Dict = _modexpt(UpperCamelCase__ , UpperCamelCase__ , 1_0**digits )
return result
if __name__ == "__main__":
print(f"""{solution() = }""") | 407 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__A =True
except (ImportError, ModuleNotFoundError):
__A =False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def _UpperCamelCase ( UpperCamelCase__ ):
re.sub("""<n>""" , """""" , UpperCamelCase__ ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(UpperCamelCase__ ) ) | 407 | 1 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
snake_case : int = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
snake_case : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 657 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
snake_case : Dict = logging.get_logger(__name__)
snake_case : Any = {name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}
def __lowercase ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] ):
if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
raise ValueError(F'Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys() )}.' )
if tokenizer_name is None:
a__ = TOKENIZER_CLASSES
else:
a__ = {tokenizer_name: getattr(__lowerCAmelCase , tokenizer_name + 'Fast' )}
logger.info(F'Loading tokenizer classes: {tokenizer_names}' )
for tokenizer_name in tokenizer_names:
a__ = TOKENIZER_CLASSES[tokenizer_name]
a__ = True
if checkpoint_name is None:
a__ = list(tokenizer_class.max_model_input_sizes.keys() )
else:
a__ = [checkpoint_name]
logger.info(F'For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}' )
for checkpoint in checkpoint_names:
logger.info(F'Loading {tokenizer_class.__class__.__name__} {checkpoint}' )
# Load tokenizer
a__ = tokenizer_class.from_pretrained(__lowerCAmelCase , force_download=__lowerCAmelCase )
# Save fast tokenizer
logger.info(F'Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}' )
# For organization names we create sub-directories
if "/" in checkpoint:
a__ , a__ = checkpoint.split('/' )
a__ = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
elif add_prefix:
a__ = checkpoint
a__ = dump_path
else:
a__ = None
a__ = dump_path
logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}' )
if checkpoint in list(tokenizer.pretrained_vocab_files_map.values() )[0]:
a__ = list(tokenizer.pretrained_vocab_files_map.values() )[0][checkpoint]
a__ = file_path.split(__lowerCAmelCase )[-1][0]
if next_char == "/":
a__ = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
a__ = None
logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}' )
a__ = tokenizer.save_pretrained(
__lowerCAmelCase , legacy_format=__lowerCAmelCase , filename_prefix=__lowerCAmelCase )
logger.info(F'=> File names {file_names}' )
for file_name in file_names:
if not file_name.endswith('tokenizer.json' ):
os.remove(__lowerCAmelCase )
logger.info(F'=> removing {file_name}' )
if __name__ == "__main__":
snake_case : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
)
parser.add_argument(
'''--tokenizer_name''',
default=None,
type=str,
help=(
f"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
'''download and convert all the checkpoints from AWS.'''
),
)
parser.add_argument(
'''--checkpoint_name''',
default=None,
type=str,
help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
)
parser.add_argument(
'''--force_download''',
action='''store_true''',
help='''Re-download checkpoints.''',
)
snake_case : List[str] = parser.parse_args()
convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
| 657 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
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
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : str , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any=7 , __lowerCAmelCase : str=3 , __lowerCAmelCase : Any=18 , __lowerCAmelCase : Optional[int]=30 , __lowerCAmelCase : List[str]=400 , __lowerCAmelCase : Any=True , __lowerCAmelCase : List[Any]=None , __lowerCAmelCase : Union[str, Any]=True , ):
"""simple docstring"""
_lowerCAmelCase = size if size is not None else {'''height''': 18, '''width''': 18}
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = image_size
_lowerCAmelCase = min_resolution
_lowerCAmelCase = max_resolution
_lowerCAmelCase = do_resize
_lowerCAmelCase = size
_lowerCAmelCase = do_normalize
def a ( self : Union[str, Any] ):
"""simple docstring"""
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
[-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
] ),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( __lowercase , unittest.TestCase ):
"""simple docstring"""
__A = ImageGPTImageProcessor if is_vision_available() else None
def a ( self : Dict ):
"""simple docstring"""
_lowerCAmelCase = ImageGPTImageProcessingTester(self )
@property
def a ( self : List[str] ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def a ( self : List[str] ):
"""simple docstring"""
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_A , 'clusters' ) )
self.assertTrue(hasattr(_A , 'do_resize' ) )
self.assertTrue(hasattr(_A , 'size' ) )
self.assertTrue(hasattr(_A , 'do_normalize' ) )
def a ( self : int ):
"""simple docstring"""
_lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 18} )
_lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
def a ( self : str ):
"""simple docstring"""
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
_lowerCAmelCase = json.loads(image_processor.to_json_string() )
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(_A , obj[key] ) )
else:
self.assertEqual(obj[key] , _A )
def a ( self : List[Any] ):
"""simple docstring"""
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCAmelCase = os.path.join(_A , 'image_processor.json' )
image_processor_first.to_json_file(_A )
_lowerCAmelCase = self.image_processing_class.from_json_file(_A ).to_dict()
_lowerCAmelCase = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(_A , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , _A )
def a ( self : List[str] ):
"""simple docstring"""
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(_A )
_lowerCAmelCase = self.image_processing_class.from_pretrained(_A ).to_dict()
_lowerCAmelCase = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(_A , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , _A )
@unittest.skip('ImageGPT requires clusters at initialization' )
def a ( self : Tuple ):
"""simple docstring"""
pass
def A_ ( ):
_lowerCAmelCase = load_dataset('hf-internal-testing/fixtures_image_utils' , split='test' )
_lowerCAmelCase = Image.open(dataset[4]['file'] )
_lowerCAmelCase = Image.open(dataset[5]['file'] )
_lowerCAmelCase = [imagea, imagea]
return images
@require_vision
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@slow
def a ( self : List[Any] ):
"""simple docstring"""
_lowerCAmelCase = ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small' )
_lowerCAmelCase = prepare_images()
# test non-batched
_lowerCAmelCase = image_processing(images[0] , return_tensors='pt' )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (1, 1024) )
_lowerCAmelCase = [306, 191, 191]
self.assertEqual(encoding.input_ids[0, :3].tolist() , _A )
# test batched
_lowerCAmelCase = image_processing(_A , return_tensors='pt' )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (2, 1024) )
_lowerCAmelCase = [303, 13, 13]
self.assertEqual(encoding.input_ids[1, -3:].tolist() , _A )
| 309 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class A_ ( __lowercase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Any = "switch_transformers"
_SCREAMING_SNAKE_CASE : int = ["past_key_values"]
_SCREAMING_SNAKE_CASE : Optional[Any] = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__( self , _A=32128 , _A=768 , _A=64 , _A=2048 , _A=64 , _A=12 , _A=3 , _A=12 , _A=3 , _A=12 , _A=8 , _A=False , _A=0.01 , _A="float32" , _A=False , _A=32 , _A=128 , _A=0.1 , _A=1e-6 , _A=0.001 , _A=0.001 , _A=1.0 , _A="relu" , _A=True , _A=False , _A=True , _A=0 , _A=1 , **_A , ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : str = vocab_size
_UpperCAmelCase : Tuple = d_model
_UpperCAmelCase : Dict = d_kv
_UpperCAmelCase : str = d_ff
_UpperCAmelCase : int = num_sparse_encoder_layers
_UpperCAmelCase : Dict = num_layers
_UpperCAmelCase : int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
_UpperCAmelCase : Dict = num_sparse_decoder_layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_encoder_layers > 0:
_UpperCAmelCase : int = self.num_layers // self.num_sparse_encoder_layers
else:
_UpperCAmelCase : Optional[int] = self.num_layers # HACK: this will create 0 sparse layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_decoder_layers > 0:
_UpperCAmelCase : Optional[Any] = self.num_decoder_layers // self.num_sparse_decoder_layers
else:
_UpperCAmelCase : Dict = self.num_decoder_layers # HACK: this will create 0 sparse layers
_UpperCAmelCase : Any = num_heads
_UpperCAmelCase : List[Any] = num_experts
_UpperCAmelCase : List[str] = expert_capacity
_UpperCAmelCase : List[str] = router_bias
_UpperCAmelCase : Optional[Any] = router_jitter_noise
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}''')
_UpperCAmelCase : List[str] = router_dtype
_UpperCAmelCase : Any = router_ignore_padding_tokens
_UpperCAmelCase : Optional[Any] = relative_attention_num_buckets
_UpperCAmelCase : Optional[int] = relative_attention_max_distance
_UpperCAmelCase : List[Any] = dropout_rate
_UpperCAmelCase : Optional[int] = layer_norm_epsilon
_UpperCAmelCase : Union[str, Any] = initializer_factor
_UpperCAmelCase : int = feed_forward_proj
_UpperCAmelCase : List[str] = use_cache
_UpperCAmelCase : Optional[int] = add_router_probs
_UpperCAmelCase : Optional[int] = router_z_loss_coef
_UpperCAmelCase : List[str] = router_aux_loss_coef
_UpperCAmelCase : Union[str, Any] = self.feed_forward_proj.split('''-''')
_UpperCAmelCase : int = act_info[-1]
_UpperCAmelCase : int = act_info[0] == '''gated'''
if len(_A) > 1 and act_info[0] != "gated" or len(_A) > 2:
raise ValueError(
f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
'''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
'''\'gated-gelu\' or \'relu\'''')
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
_UpperCAmelCase : Optional[Any] = '''gelu_new'''
super().__init__(
pad_token_id=_A , eos_token_id=_A , is_encoder_decoder=_A , **_A , )
| 485 | 0 |
'''simple docstring'''
import argparse
import struct
import unittest
class lowerCamelCase :
def __init__( self , a_ ):
lowerCAmelCase : Dict = data
# Initialize hash values
lowerCAmelCase : Any = [
0x6a09e667,
0xbb67ae85,
0x3c6ef372,
0xa54ff53a,
0x510e527f,
0x9b05688c,
0x1f83d9ab,
0x5be0cd19,
]
# Initialize round constants
lowerCAmelCase : Optional[int] = [
0x428a2f98,
0x71374491,
0xb5c0fbcf,
0xe9b5dba5,
0x3956c25b,
0x59f111f1,
0x923f82a4,
0xab1c5ed5,
0xd807aa98,
0x12835b01,
0x243185be,
0x550c7dc3,
0x72be5d74,
0x80deb1fe,
0x9bdc06a7,
0xc19bf174,
0xe49b69c1,
0xefbe4786,
0x0fc19dc6,
0x240ca1cc,
0x2de92c6f,
0x4a7484aa,
0x5cb0a9dc,
0x76f988da,
0x983e5152,
0xa831c66d,
0xb00327c8,
0xbf597fc7,
0xc6e00bf3,
0xd5a79147,
0x06ca6351,
0x14292967,
0x27b70a85,
0x2e1b2138,
0x4d2c6dfc,
0x53380d13,
0x650a7354,
0x766a0abb,
0x81c2c92e,
0x92722c85,
0xa2bfe8a1,
0xa81a664b,
0xc24b8b70,
0xc76c51a3,
0xd192e819,
0xd6990624,
0xf40e3585,
0x106aa070,
0x19a4c116,
0x1e376c08,
0x2748774c,
0x34b0bcb5,
0x391c0cb3,
0x4ed8aa4a,
0x5b9cca4f,
0x682e6ff3,
0x748f82ee,
0x78a5636f,
0x84c87814,
0x8cc70208,
0x90befffa,
0xa4506ceb,
0xbef9a3f7,
0xc67178f2,
]
lowerCAmelCase : str = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def _lowerCamelCase ( a_ ):
lowerCAmelCase : Any = B"\x80" + (B"\x00" * (63 - (len(a_ ) + 8) % 64))
lowerCAmelCase : str = struct.pack(">Q" , (len(a_ ) * 8) )
return data + padding + big_endian_integer
def _lowerCamelCase ( self ):
# Convert into blocks of 64 bytes
lowerCAmelCase : List[str] = [
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 : List[str] = 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 : Dict = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
lowerCAmelCase : Optional[int] = (
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
) % 0x100000000
# Compression
lowerCAmelCase : List[str] = self.ror(a_ , 6 ) ^ self.ror(a_ , 11 ) ^ self.ror(a_ , 25 )
lowerCAmelCase : int = (e & f) ^ ((~e & 0xffffffff) & g)
lowerCAmelCase : Tuple = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x100000000
lowerCAmelCase : Union[str, Any] = self.ror(a_ , 2 ) ^ self.ror(a_ , 13 ) ^ self.ror(a_ , 22 )
lowerCAmelCase : List[str] = (a & b) ^ (a & c) ^ (b & c)
lowerCAmelCase : Optional[Any] = (sa + maj) % 0x100000000
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : List[Any] = (
g,
f,
e,
((d + tempa) % 0x100000000),
c,
b,
a,
((tempa + tempa) % 0x100000000),
)
lowerCAmelCase : str = [a, b, c, d, e, f, g, h]
# Modify final values
lowerCAmelCase : List[Any] = [
((element + mutated_hash_values[index]) % 0x100000000)
for index, element in enumerate(self.hashes )
]
lowerCAmelCase : int = "".join([hex(a_ )[2:].zfill(8 ) for value in self.hashes] )
def _lowerCamelCase ( self , a_ , a_ ):
return 0xffffffff & (value << (32 - rotations)) | (value >> rotations)
class lowerCamelCase ( unittest.TestCase ):
def _lowerCamelCase ( self ):
import hashlib
lowerCAmelCase : Any = bytes("Test String" , "utf-8" )
self.assertEqual(SHAaaa(a_ ).hash , hashlib.shaaaa(a_ ).hexdigest() )
def __A ( ):
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 : Dict = parser.parse_args()
lowerCAmelCase : Tuple = 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 : Union[str, Any] = bytes(a_ ,"utf-8" )
print(SHAaaa(a_ ).hash )
if __name__ == "__main__":
main()
| 717 |
'''simple docstring'''
def __A ( a_ : int ):
assert (
isinstance(a_ ,a_ ) and number_of_steps > 0
), f'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_steps == 1:
return 1
lowerCAmelCase , lowerCAmelCase : int = 1, 1
for _ in range(number_of_steps - 1 ):
lowerCAmelCase , lowerCAmelCase : Union[str, Any] = current + previous, current
return current
if __name__ == "__main__":
import doctest
doctest.testmod()
| 551 | 0 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class A__ ( __SCREAMING_SNAKE_CASE ):
lowerCamelCase__ : Optional[Any] =ComputeEnvironment.AMAZON_SAGEMAKER
lowerCamelCase__ : int =True
lowerCamelCase__ : List[Any] ="ml.p3.2xlarge"
lowerCamelCase__ : Tuple ="accelerate_sagemaker_execution_role"
lowerCamelCase__ : Tuple ="hf-sm"
lowerCamelCase__ : Tuple ="us-east-1"
lowerCamelCase__ : int =1
lowerCamelCase__ : Any ="accelerate-sagemaker-1"
lowerCamelCase__ : Optional[Any] ="1.6"
lowerCamelCase__ : Dict ="4.4"
lowerCamelCase__ : Any ="train.py"
lowerCamelCase__ : Optional[int] =[
"--model_name_or_path",
"bert",
"--do_train",
"False",
"--epochs",
"3",
"--learning_rate",
"5e-5",
"--max_steps",
"50.5",
]
lowerCamelCase__ : List[Any] =[
"--model_name_or_path",
"bert",
"--do_train",
"--do_test",
"False",
"--do_predict",
"--epochs",
"3",
"--learning_rate",
"5e-5",
"--max_steps",
"50.5",
]
class A__ ( unittest.TestCase ):
def lowercase ( self ) -> Optional[int]:
"""simple docstring"""
__magic_name__ : Optional[int] = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args['''model_name_or_path'''] , lowerCamelCase )
assert isinstance(converted_args['''do_train'''] , lowerCamelCase )
assert isinstance(converted_args['''epochs'''] , lowerCamelCase )
assert isinstance(converted_args['''learning_rate'''] , lowerCamelCase )
assert isinstance(converted_args['''max_steps'''] , lowerCamelCase )
with pytest.raises(lowerCamelCase ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args )
| 154 |
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 PoolFormerImageProcessor
class A__ ( unittest.TestCase ):
def __init__( self , lowerCamelCase , lowerCamelCase=7 , lowerCamelCase=3 , lowerCamelCase=30 , lowerCamelCase=400 , lowerCamelCase=True , lowerCamelCase=None , lowerCamelCase=0.9 , lowerCamelCase=None , lowerCamelCase=True , lowerCamelCase=[0.5, 0.5, 0.5] , lowerCamelCase=[0.5, 0.5, 0.5] , ) -> int:
"""simple docstring"""
__magic_name__ : int = size if size is not None else {'''shortest_edge''': 30}
__magic_name__ : Union[str, Any] = crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
__magic_name__ : Dict = parent
__magic_name__ : List[str] = batch_size
__magic_name__ : Dict = num_channels
__magic_name__ : List[str] = min_resolution
__magic_name__ : str = max_resolution
__magic_name__ : int = do_resize_and_center_crop
__magic_name__ : Dict = size
__magic_name__ : Union[str, Any] = crop_pct
__magic_name__ : str = crop_size
__magic_name__ : Tuple = do_normalize
__magic_name__ : Union[str, Any] = image_mean
__magic_name__ : Optional[Any] = image_std
def lowercase ( self ) -> Dict:
"""simple docstring"""
return {
"size": self.size,
"do_resize_and_center_crop": self.do_resize_and_center_crop,
"crop_pct": self.crop_pct,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class A__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
lowerCamelCase__ : str =PoolFormerImageProcessor if is_vision_available() else None
def lowercase ( self ) -> List[str]:
"""simple docstring"""
__magic_name__ : int = PoolFormerImageProcessingTester(self )
@property
def lowercase ( self ) -> Any:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase ( self ) -> List[Any]:
"""simple docstring"""
__magic_name__ : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase , '''do_resize_and_center_crop''' ) )
self.assertTrue(hasattr(lowerCamelCase , '''size''' ) )
self.assertTrue(hasattr(lowerCamelCase , '''crop_pct''' ) )
self.assertTrue(hasattr(lowerCamelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(lowerCamelCase , '''image_mean''' ) )
self.assertTrue(hasattr(lowerCamelCase , '''image_std''' ) )
def lowercase ( self ) -> Tuple:
"""simple docstring"""
__magic_name__ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 30} )
self.assertEqual(image_processor.crop_size , {'''height''': 30, '''width''': 30} )
__magic_name__ : int = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def lowercase ( self ) -> int:
"""simple docstring"""
pass
def lowercase ( self ) -> str:
"""simple docstring"""
__magic_name__ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__magic_name__ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , Image.Image )
# Test not batched input
__magic_name__ : Any = 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
__magic_name__ : Dict = 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 ) -> str:
"""simple docstring"""
__magic_name__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__magic_name__ : List[str] = 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
__magic_name__ : List[str] = 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
__magic_name__ : Any = 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:
"""simple docstring"""
__magic_name__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__magic_name__ : List[Any] = 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
__magic_name__ : int = 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
__magic_name__ : Union[str, Any] = 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'''],
) , )
| 154 | 1 |
from math import factorial
def lowercase__ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase) -> float:
"""simple docstring"""
if successes > trials:
raise ValueError('successes must be lower or equal to trials')
if trials < 0 or successes < 0:
raise ValueError('the function is defined for non-negative integers')
if not isinstance(UpperCamelCase__ , UpperCamelCase__) or not isinstance(UpperCamelCase__ , UpperCamelCase__):
raise ValueError('the function is defined for non-negative integers')
if not 0 < prob < 1:
raise ValueError('prob has to be in range of 1 - 0')
UpperCamelCase = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
UpperCamelCase = float(factorial(UpperCamelCase__))
coefficient /= factorial(UpperCamelCase__) * factorial(trials - successes)
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print('''Probability of 2 successes out of 4 trails''')
print('''with probability of 0.75 is:''', end=''' ''')
print(binomial_distribution(2, 4, 0.75))
| 703 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__magic_name__ : Union[str, Any] = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
class A__ ( __snake_case , unittest.TestCase ):
'''simple docstring'''
snake_case__ = XLMProphetNetTokenizer
snake_case__ = False
snake_case__ = True
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
UpperCamelCase = XLMProphetNetTokenizer(_SCREAMING_SNAKE_CASE , keep_accents=_SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(self.tmpdirname )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
"""simple docstring"""
UpperCamelCase = '[PAD]'
UpperCamelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '[PAD]' )
self.assertEqual(vocab_keys[1] , '[CLS]' )
self.assertEqual(vocab_keys[-1] , 'j' )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , 1012 )
def _SCREAMING_SNAKE_CASE ( self : int ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1012 )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = XLMProphetNetTokenizer(_SCREAMING_SNAKE_CASE , keep_accents=_SCREAMING_SNAKE_CASE )
UpperCamelCase = tokenizer.tokenize('This is a test' )
self.assertListEqual(_SCREAMING_SNAKE_CASE , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
UpperCamelCase = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
_SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
UpperCamelCase = tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE )
self.assertListEqual(
_SCREAMING_SNAKE_CASE , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
] , )
UpperCamelCase = tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE )
self.assertListEqual(
_SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'[UNK]',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'[UNK]',
'.',
] , )
@cached_property
def _SCREAMING_SNAKE_CASE ( self : int ):
"""simple docstring"""
return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased' )
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = 'Hello World!'
UpperCamelCase = [3_5389, 6672, 49, 2]
self.assertListEqual(_SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(_SCREAMING_SNAKE_CASE ) )
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = {'input_ids': [[1_1073, 8_2783, 18, 26, 8_2783, 549, 5_1540, 248, 1_7209, 1301, 217, 20, 21_5186, 1325, 147, 1_7209, 1301, 217, 20, 5_6370, 53, 12_2020, 20, 1_6477, 27, 8_7355, 4548, 20, 4728, 7_8392, 17, 15_9969, 18, 26, 2_4491, 629, 15, 538, 2_2704, 5439, 15, 2788, 2_4491, 9885, 15, 4_3534, 605, 15, 814, 1_8403, 3_3200, 29, 15, 4_3534, 2_4458, 1_2410, 111, 2_4966, 8_3669, 9637, 14_4068, 26, 850, 2_2346, 27, 147, 2_4966, 8_3669, 8_3490, 26, 3_9113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 12_2020, 11_5785, 34, 816, 1339, 4_6887, 18, 147, 5_3905, 1951, 4_2238, 4_1170, 1_7732, 834, 436, 15, 2_7523, 9_8733, 217, 147, 5542, 4981, 930, 1_7347, 16, 2], [2_0091, 629, 94, 8_2786, 58, 490, 20, 1528, 84, 5_3905, 344, 8_0592, 11_0128, 1_8822, 5267, 1306, 62, 15_2537, 308, 7997, 401, 12_4427, 549, 3_5442, 225, 109, 1_5055, 2_5748, 147, 7119, 4_3712, 34, 767, 13_5366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 6_3784, 11_9466, 17, 14_7808, 8_8214, 18, 656, 81, 32, 3296, 1_0280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=_SCREAMING_SNAKE_CASE , model_name='microsoft/xprophetnet-large-wiki100-cased' , revision='1acad1643ddd54a44df6a1b797ada8373685d90e' , )
| 410 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVariationPipeline,
UnCLIPScheduler,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _lowerCAmelCase ( _a , unittest.TestCase ):
"""simple docstring"""
__magic_name__ :Optional[int] = UnCLIPImageVariationPipeline
__magic_name__ :Dict = IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
__magic_name__ :Optional[Any] = IMAGE_VARIATION_BATCH_PARAMS
__magic_name__ :Tuple = [
"""generator""",
"""return_dict""",
"""decoder_num_inference_steps""",
"""super_res_num_inference_steps""",
]
__magic_name__ :Optional[Any] = False
@property
def snake_case ( self ):
'''simple docstring'''
return 3_2
@property
def snake_case ( self ):
'''simple docstring'''
return 3_2
@property
def snake_case ( self ):
'''simple docstring'''
return self.time_input_dim
@property
def snake_case ( self ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def snake_case ( self ):
'''simple docstring'''
return 1_0_0
@property
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def snake_case ( self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase__ :List[str] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
return CLIPTextModelWithProjection(snake_case_ )
@property
def snake_case ( self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase__ :Optional[Any] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , num_hidden_layers=5 , num_attention_heads=4 , image_size=3_2 , intermediate_size=3_7 , patch_size=1 , )
return CLIPVisionModelWithProjection(snake_case_ )
@property
def snake_case ( self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase__ :Optional[int] = {
"""clip_embeddings_dim""": self.text_embedder_hidden_size,
"""time_embed_dim""": self.time_embed_dim,
"""cross_attention_dim""": self.cross_attention_dim,
}
lowerCAmelCase__ :Optional[Any] = UnCLIPTextProjModel(**snake_case_ )
return model
@property
def snake_case ( self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase__ :List[Any] = {
"""sample_size""": 3_2,
# RGB in channels
"""in_channels""": 3,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 6,
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": """identity""",
}
lowerCAmelCase__ :Union[str, Any] = UNetaDConditionModel(**snake_case_ )
return model
@property
def snake_case ( self ):
'''simple docstring'''
return {
"sample_size": 6_4,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def snake_case ( self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase__ :Optional[Any] = UNetaDModel(**self.dummy_super_res_kwargs )
return model
@property
def snake_case ( self ):
'''simple docstring'''
torch.manual_seed(1 )
lowerCAmelCase__ :List[str] = UNetaDModel(**self.dummy_super_res_kwargs )
return model
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = self.dummy_decoder
lowerCAmelCase__ :Optional[int] = self.dummy_text_proj
lowerCAmelCase__ :Any = self.dummy_text_encoder
lowerCAmelCase__ :List[Any] = self.dummy_tokenizer
lowerCAmelCase__ :Dict = self.dummy_super_res_first
lowerCAmelCase__ :List[Any] = self.dummy_super_res_last
lowerCAmelCase__ :Tuple = UnCLIPScheduler(
variance_type='learned_range' , prediction_type='epsilon' , num_train_timesteps=1_0_0_0 , )
lowerCAmelCase__ :List[Any] = UnCLIPScheduler(
variance_type='fixed_small_log' , prediction_type='epsilon' , num_train_timesteps=1_0_0_0 , )
lowerCAmelCase__ :str = CLIPImageProcessor(crop_size=3_2 , size=3_2 )
lowerCAmelCase__ :int = self.dummy_image_encoder
return {
"decoder": decoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_proj": text_proj,
"feature_extractor": feature_extractor,
"image_encoder": image_encoder,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase=0 , __UpperCAmelCase=True ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(snake_case_ ) ).to(snake_case_ )
if str(snake_case_ ).startswith('mps' ):
lowerCAmelCase__ :Any = torch.manual_seed(snake_case_ )
else:
lowerCAmelCase__ :Dict = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
if pil_image:
lowerCAmelCase__ :Optional[Any] = input_image * 0.5 + 0.5
lowerCAmelCase__ :List[Any] = input_image.clamp(0 , 1 )
lowerCAmelCase__ :Optional[int] = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
lowerCAmelCase__ :Dict = DiffusionPipeline.numpy_to_pil(snake_case_ )[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = """cpu"""
lowerCAmelCase__ :List[Any] = self.get_dummy_components()
lowerCAmelCase__ :str = self.pipeline_class(**snake_case_ )
lowerCAmelCase__ :List[str] = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
lowerCAmelCase__ :Optional[Any] = self.get_dummy_inputs(snake_case_ , pil_image=snake_case_ )
lowerCAmelCase__ :Dict = pipe(**snake_case_ )
lowerCAmelCase__ :List[str] = output.images
lowerCAmelCase__ :str = self.get_dummy_inputs(snake_case_ , pil_image=snake_case_ )
lowerCAmelCase__ :Optional[Any] = pipe(
**snake_case_ , return_dict=snake_case_ , )[0]
lowerCAmelCase__ :List[Any] = image[0, -3:, -3:, -1]
lowerCAmelCase__ :List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
lowerCAmelCase__ :List[str] = np.array(
[
0.99_97,
0.00_02,
0.99_97,
0.99_97,
0.99_69,
0.00_23,
0.99_97,
0.99_69,
0.99_70,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = """cpu"""
lowerCAmelCase__ :Optional[int] = self.get_dummy_components()
lowerCAmelCase__ :Tuple = self.pipeline_class(**snake_case_ )
lowerCAmelCase__ :Any = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
lowerCAmelCase__ :List[str] = self.get_dummy_inputs(snake_case_ , pil_image=snake_case_ )
lowerCAmelCase__ :Optional[Any] = pipe(**snake_case_ )
lowerCAmelCase__ :List[Any] = output.images
lowerCAmelCase__ :Optional[int] = self.get_dummy_inputs(snake_case_ , pil_image=snake_case_ )
lowerCAmelCase__ :Optional[Any] = pipe(
**snake_case_ , return_dict=snake_case_ , )[0]
lowerCAmelCase__ :List[str] = image[0, -3:, -3:, -1]
lowerCAmelCase__ :Optional[int] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
lowerCAmelCase__ :Optional[Any] = np.array([0.99_97, 0.00_03, 0.99_97, 0.99_97, 0.99_70, 0.00_24, 0.99_97, 0.99_71, 0.99_71] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Tuple = """cpu"""
lowerCAmelCase__ :List[str] = self.get_dummy_components()
lowerCAmelCase__ :Dict = self.pipeline_class(**snake_case_ )
lowerCAmelCase__ :int = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
lowerCAmelCase__ :Any = self.get_dummy_inputs(snake_case_ , pil_image=snake_case_ )
lowerCAmelCase__ :str = [
pipeline_inputs["""image"""],
pipeline_inputs["""image"""],
]
lowerCAmelCase__ :str = pipe(**snake_case_ )
lowerCAmelCase__ :List[str] = output.images
lowerCAmelCase__ :List[Any] = self.get_dummy_inputs(snake_case_ , pil_image=snake_case_ )
lowerCAmelCase__ :Any = [
tuple_pipeline_inputs["""image"""],
tuple_pipeline_inputs["""image"""],
]
lowerCAmelCase__ :List[str] = pipe(
**snake_case_ , return_dict=snake_case_ , )[0]
lowerCAmelCase__ :Tuple = image[0, -3:, -3:, -1]
lowerCAmelCase__ :Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (2, 6_4, 6_4, 3)
lowerCAmelCase__ :Optional[int] = np.array(
[
0.99_97,
0.99_89,
0.00_08,
0.00_21,
0.99_60,
0.00_18,
0.00_14,
0.00_02,
0.99_33,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Dict = torch.device('cpu' )
class _lowerCAmelCase :
"""simple docstring"""
__magic_name__ :Union[str, Any] = 1
lowerCAmelCase__ :Union[str, Any] = self.get_dummy_components()
lowerCAmelCase__ :Union[str, Any] = self.pipeline_class(**snake_case_ )
lowerCAmelCase__ :Tuple = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
lowerCAmelCase__ :Optional[int] = torch.Generator(device=snake_case_ ).manual_seed(0 )
lowerCAmelCase__ :Union[str, Any] = pipe.decoder.dtype
lowerCAmelCase__ :List[str] = 1
lowerCAmelCase__ :str = (
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
lowerCAmelCase__ :List[Any] = pipe.prepare_latents(
snake_case_ , dtype=snake_case_ , device=snake_case_ , generator=snake_case_ , latents=snake_case_ , scheduler=DummyScheduler() )
lowerCAmelCase__ :List[str] = (
batch_size,
pipe.super_res_first.config.in_channels // 2,
pipe.super_res_first.config.sample_size,
pipe.super_res_first.config.sample_size,
)
lowerCAmelCase__ :Union[str, Any] = pipe.prepare_latents(
snake_case_ , dtype=snake_case_ , device=snake_case_ , generator=snake_case_ , latents=snake_case_ , scheduler=DummyScheduler() )
lowerCAmelCase__ :Dict = self.get_dummy_inputs(snake_case_ , pil_image=snake_case_ )
lowerCAmelCase__ :Dict = pipe(
**snake_case_ , decoder_latents=snake_case_ , super_res_latents=snake_case_ ).images
lowerCAmelCase__ :int = self.get_dummy_inputs(snake_case_ , pil_image=snake_case_ )
# Don't pass image, instead pass embedding
lowerCAmelCase__ :Union[str, Any] = pipeline_inputs.pop('image' )
lowerCAmelCase__ :List[Any] = pipe.image_encoder(snake_case_ ).image_embeds
lowerCAmelCase__ :str = pipe(
**snake_case_ , decoder_latents=snake_case_ , super_res_latents=snake_case_ , image_embeddings=snake_case_ , ).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_a - img_out_a ).max() < 1E-4
@skip_mps
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = torch_device == """cpu"""
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
lowerCAmelCase__ :Any = 1E-2
self._test_attention_slicing_forward_pass(
test_max_difference=snake_case_ , expected_max_diff=snake_case_ )
@skip_mps
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :List[str] = torch_device == """cpu"""
lowerCAmelCase__ :Any = True
lowerCAmelCase__ :List[str] = [
"""decoder_num_inference_steps""",
"""super_res_num_inference_steps""",
]
self._test_inference_batch_single_identical(
test_max_difference=snake_case_ , relax_max_difference=snake_case_ , additional_params_copy_to_batched_inputs=snake_case_ , )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Dict = [
"""decoder_num_inference_steps""",
"""super_res_num_inference_steps""",
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
lowerCAmelCase__ :Any = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=snake_case_ , additional_params_copy_to_batched_inputs=snake_case_ , )
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=snake_case_ )
@skip_mps
def snake_case ( self ):
'''simple docstring'''
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def snake_case ( self ):
'''simple docstring'''
return super().test_save_load_local()
@skip_mps
def snake_case ( self ):
'''simple docstring'''
return super().test_save_load_optional_components()
@slow
@require_torch_gpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png' )
lowerCAmelCase__ :Dict = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/unclip/karlo_v1_alpha_cat_variation_fp16.npy' )
lowerCAmelCase__ :Optional[Any] = UnCLIPImageVariationPipeline.from_pretrained(
'kakaobrain/karlo-v1-alpha-image-variations' , torch_dtype=torch.floataa )
lowerCAmelCase__ :Any = pipeline.to(snake_case_ )
pipeline.set_progress_bar_config(disable=snake_case_ )
lowerCAmelCase__ :Tuple = torch.Generator(device='cpu' ).manual_seed(0 )
lowerCAmelCase__ :List[Any] = pipeline(
snake_case_ , generator=snake_case_ , output_type='np' , )
lowerCAmelCase__ :Union[str, Any] = output.images[0]
assert image.shape == (2_5_6, 2_5_6, 3)
assert_mean_pixel_difference(snake_case_ , snake_case_ , 1_5 )
| 93 |
'''simple docstring'''
import argparse
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
########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__a = 16
__a = 32
def __snake_case( _lowerCAmelCase , _lowerCAmelCase = 16 ) -> Optional[Any]:
snake_case__ : Optional[int] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
snake_case__ : Optional[int] = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(_lowerCAmelCase ):
# max_length=None => use the model max length (it's actually the default)
snake_case__ : Union[str, Any] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
snake_case__ : List[str] = datasets.map(
_lowerCAmelCase , batched=_lowerCAmelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case__ : Any = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(_lowerCAmelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
snake_case__ : List[str] = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
snake_case__ : Optional[Any] = 16
elif accelerator.mixed_precision != "no":
snake_case__ : Tuple = 8
else:
snake_case__ : int = None
return tokenizer.pad(
_lowerCAmelCase , padding="""longest""" , max_length=_lowerCAmelCase , pad_to_multiple_of=_lowerCAmelCase , return_tensors="""pt""" , )
# Instantiate dataloaders.
snake_case__ : List[Any] = DataLoader(
tokenized_datasets["""train"""] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
snake_case__ : Dict = DataLoader(
tokenized_datasets["""validation"""] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__a = mocked_dataloaders # noqa: F811
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> int:
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , _lowerCAmelCase ) == "1":
snake_case__ : int = 2
# New Code #
snake_case__ : Any = int(args.gradient_accumulation_steps )
# Initialize accelerator
snake_case__ : Any = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=_lowerCAmelCase )
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
"""Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case__ : List[Any] = config["""lr"""]
snake_case__ : Optional[Any] = int(config["""num_epochs"""] )
snake_case__ : Union[str, Any] = int(config["""seed"""] )
snake_case__ : List[str] = int(config["""batch_size"""] )
snake_case__ : Union[str, Any] = evaluate.load("""glue""" , """mrpc""" )
set_seed(_lowerCAmelCase )
snake_case__ , snake_case__ : Tuple = get_dataloaders(_lowerCAmelCase , _lowerCAmelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case__ : Optional[int] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=_lowerCAmelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
snake_case__ : Tuple = model.to(accelerator.device )
# Instantiate optimizer
snake_case__ : Any = AdamW(params=model.parameters() , lr=_lowerCAmelCase )
# Instantiate scheduler
snake_case__ : List[Any] = get_linear_schedule_with_warmup(
optimizer=_lowerCAmelCase , num_warmup_steps=100 , num_training_steps=(len(_lowerCAmelCase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ : List[Any] = accelerator.prepare(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# Now we train the model
for epoch in range(_lowerCAmelCase ):
model.train()
for step, batch in enumerate(_lowerCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(_lowerCAmelCase ):
snake_case__ : Any = model(**_lowerCAmelCase )
snake_case__ : str = output.loss
accelerator.backward(_lowerCAmelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_lowerCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case__ : str = model(**_lowerCAmelCase )
snake_case__ : Optional[int] = outputs.logits.argmax(dim=-1 )
snake_case__ , snake_case__ : List[Any] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=_lowerCAmelCase , references=_lowerCAmelCase , )
snake_case__ : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"epoch {epoch}:" , _lowerCAmelCase )
def __snake_case( ) -> List[str]:
snake_case__ : List[str] = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=_lowerCAmelCase , default=_lowerCAmelCase , 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.""" , )
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" , type=_lowerCAmelCase , default=1 , help="""The number of minibatches to be ran before gradients are accumulated.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
snake_case__ : Tuple = parser.parse_args()
snake_case__ : Dict = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(_lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
main()
| 374 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : Optional[int] ={"""configuration_timm_backbone""": ["""TimmBackboneConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Tuple =["""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
UpperCAmelCase : Union[str, Any] =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 702 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase : List[str] =get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
UpperCAmelCase : int =25_0004
UpperCAmelCase : Dict =25_0020
@require_sentencepiece
@require_tokenizers
class _lowercase (a_ , unittest.TestCase ):
'''simple docstring'''
lowercase__ = MBartTokenizer
lowercase__ = MBartTokenizerFast
lowercase__ = True
lowercase__ = True
def _lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
UpperCamelCase_ = MBartTokenizer(snake_case__ , keep_accents=snake_case__ )
tokenizer.save_pretrained(self.tmpdirname )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = MBartTokenizer(snake_case__ , keep_accents=snake_case__ )
UpperCamelCase_ = tokenizer.tokenize("This is a test" )
self.assertListEqual(snake_case__ , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(snake_case__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
UpperCamelCase_ = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
snake_case__ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
UpperCamelCase_ = tokenizer.convert_tokens_to_ids(snake_case__ )
self.assertListEqual(
snake_case__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
UpperCamelCase_ = tokenizer.convert_ids_to_tokens(snake_case__ )
self.assertListEqual(
snake_case__ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def _lowerCamelCase ( self ):
'''simple docstring'''
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
UpperCamelCase_ = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
UpperCamelCase_ = self.rust_tokenizer_class.from_pretrained(snake_case__ , **snake_case__ )
UpperCamelCase_ = self.tokenizer_class.from_pretrained(snake_case__ , **snake_case__ )
UpperCamelCase_ = tempfile.mkdtemp()
UpperCamelCase_ = tokenizer_r.save_pretrained(snake_case__ )
UpperCamelCase_ = tokenizer_p.save_pretrained(snake_case__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
UpperCamelCase_ = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(snake_case__ , snake_case__ )
# Checks everything loads correctly in the same way
UpperCamelCase_ = tokenizer_r.from_pretrained(snake_case__ )
UpperCamelCase_ = tokenizer_p.from_pretrained(snake_case__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case__ , snake_case__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(snake_case__ )
# Save tokenizer rust, legacy_format=True
UpperCamelCase_ = tempfile.mkdtemp()
UpperCamelCase_ = tokenizer_r.save_pretrained(snake_case__ , legacy_format=snake_case__ )
UpperCamelCase_ = tokenizer_p.save_pretrained(snake_case__ )
# Checks it save with the same files
self.assertSequenceEqual(snake_case__ , snake_case__ )
# Checks everything loads correctly in the same way
UpperCamelCase_ = tokenizer_r.from_pretrained(snake_case__ )
UpperCamelCase_ = tokenizer_p.from_pretrained(snake_case__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case__ , snake_case__ ) )
shutil.rmtree(snake_case__ )
# Save tokenizer rust, legacy_format=False
UpperCamelCase_ = tempfile.mkdtemp()
UpperCamelCase_ = tokenizer_r.save_pretrained(snake_case__ , legacy_format=snake_case__ )
UpperCamelCase_ = tokenizer_p.save_pretrained(snake_case__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
UpperCamelCase_ = tokenizer_r.from_pretrained(snake_case__ )
UpperCamelCase_ = tokenizer_p.from_pretrained(snake_case__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case__ , snake_case__ ) )
shutil.rmtree(snake_case__ )
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowercase (unittest.TestCase ):
'''simple docstring'''
lowercase__ = """facebook/mbart-large-en-ro"""
lowercase__ = [
""" UN Chief Says There Is No Military Solution in Syria""",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
]
lowercase__ = [
"""Şeful ONU declară că nu există o soluţie militară în Siria""",
"""Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
""" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
""" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
]
lowercase__ = [82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]
@classmethod
def _lowerCamelCase ( cls ):
'''simple docstring'''
UpperCamelCase_ = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en_XX" , tgt_lang="ro_RO" )
UpperCamelCase_ = 1
return cls
def _lowerCamelCase ( self ):
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] , 25_0001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] , 25_0004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] , 25_0020 )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , snake_case__ )
def _lowerCamelCase ( self ):
'''simple docstring'''
self.assertIn(snake_case__ , self.tokenizer.all_special_ids )
UpperCamelCase_ = [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
UpperCamelCase_ = self.tokenizer.decode(snake_case__ , skip_special_tokens=snake_case__ )
UpperCamelCase_ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
self.assertNotIn(self.tokenizer.eos_token , snake_case__ )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , snake_case__ )
UpperCamelCase_ = 10
UpperCamelCase_ = self.tokenizer(snake_case__ , max_length=snake_case__ , truncation=snake_case__ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , snake_case__ )
self.assertEqual(len(snake_case__ ) , snake_case__ )
def _lowerCamelCase ( self ):
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] ) , [25_0026, 25_0001] )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = tempfile.mkdtemp()
UpperCamelCase_ = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(snake_case__ )
UpperCamelCase_ = MBartTokenizer.from_pretrained(snake_case__ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , snake_case__ )
@require_torch
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=snake_case__ , return_tensors="pt" )
UpperCamelCase_ = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=snake_case__ , truncation=snake_case__ , max_length=len(self.expected_src_tokens ) , return_tensors="pt" , )
UpperCamelCase_ = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
self.assertIsInstance(snake_case__ , snake_case__ )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
UpperCamelCase_ = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , snake_case__ )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.tokenizer(self.src_text , padding=snake_case__ , truncation=snake_case__ , max_length=3 , return_tensors="pt" )
UpperCamelCase_ = self.tokenizer(
text_target=self.tgt_text , padding=snake_case__ , truncation=snake_case__ , max_length=10 , return_tensors="pt" )
UpperCamelCase_ = targets["input_ids"]
UpperCamelCase_ = shift_tokens_right(snake_case__ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="en_XX" , tgt_lang="ar_AR" )
self.assertEqual(
nested_simplify(snake_case__ ) , {
# A, test, EOS, en_XX
"input_ids": [[62, 3034, 2, 25_0004]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 25_0001,
} , )
| 504 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE : Dict = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Any = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
SCREAMING_SNAKE_CASE : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 257 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : List[Any] = {
"configuration_longformer": [
"LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"LongformerConfig",
"LongformerOnnxConfig",
],
"tokenization_longformer": ["LongformerTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Optional[int] = ["LongformerTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Dict = [
"LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"LongformerForMaskedLM",
"LongformerForMultipleChoice",
"LongformerForQuestionAnswering",
"LongformerForSequenceClassification",
"LongformerForTokenClassification",
"LongformerModel",
"LongformerPreTrainedModel",
"LongformerSelfAttention",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Dict = [
"TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFLongformerForMaskedLM",
"TFLongformerForMultipleChoice",
"TFLongformerForQuestionAnswering",
"TFLongformerForSequenceClassification",
"TFLongformerForTokenClassification",
"TFLongformerModel",
"TFLongformerPreTrainedModel",
"TFLongformerSelfAttention",
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
SCREAMING_SNAKE_CASE : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 257 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class lowerCamelCase__ :
a : Union[str, Any] = PegasusConfig
a : List[Any] = {}
a : Optional[int] = """gelu"""
def __init__( self : List[Any] , A_ : Dict , A_ : Any=1_3 , A_ : Union[str, Any]=7 , A_ : Any=True , A_ : Optional[Any]=False , A_ : Any=9_9 , A_ : List[str]=3_2 , A_ : List[str]=2 , A_ : str=4 , A_ : List[Any]=3_7 , A_ : List[Any]=0.1 , A_ : List[Any]=0.1 , A_ : Optional[Any]=4_0 , A_ : Any=2 , A_ : str=1 , A_ : Tuple=0 , ):
'''simple docstring'''
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = is_training
__lowercase = use_labels
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = eos_token_id
__lowercase = pad_token_id
__lowercase = bos_token_id
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
'''simple docstring'''
__lowercase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__lowercase = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__lowercase = tf.concat([input_ids, eos_tensor] , axis=1 )
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowercase = self.config_cls(
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 , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
__lowercase = prepare_pegasus_inputs_dict(A_ , A_ , A_ )
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , A_ : List[Any] , A_ : Union[str, Any] ):
'''simple docstring'''
__lowercase = TFPegasusModel(config=A_ ).get_decoder()
__lowercase = inputs_dict["""input_ids"""]
__lowercase = input_ids[:1, :]
__lowercase = inputs_dict["""attention_mask"""][:1, :]
__lowercase = inputs_dict["""head_mask"""]
__lowercase = 1
# first forward pass
__lowercase = model(A_ , attention_mask=A_ , head_mask=A_ , use_cache=A_ )
__lowercase , __lowercase = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__lowercase = ids_tensor((self.batch_size, 3) , config.vocab_size )
__lowercase = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
__lowercase = tf.concat([input_ids, next_tokens] , axis=-1 )
__lowercase = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
__lowercase = model(A_ , attention_mask=A_ )[0]
__lowercase = model(A_ , attention_mask=A_ , past_key_values=A_ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__lowercase = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
__lowercase = output_from_no_past[:, -3:, random_slice_idx]
__lowercase = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(A_ , A_ , rtol=1e-3 )
def lowerCAmelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[str]=None , UpperCamelCase__ : List[Any]=None , UpperCamelCase__ : Dict=None , UpperCamelCase__ : Optional[int]=None , UpperCamelCase__ : Optional[Any]=None , ):
"""simple docstring"""
if attention_mask is None:
__lowercase = tf.cast(tf.math.not_equal(UpperCamelCase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__lowercase = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__lowercase = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__lowercase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__lowercase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class lowerCamelCase__ ( _a , _a , unittest.TestCase ):
a : Tuple = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
a : int = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
a : Optional[int] = (
{
"""conversational""": TFPegasusForConditionalGeneration,
"""feature-extraction""": TFPegasusModel,
"""summarization""": TFPegasusForConditionalGeneration,
"""text2text-generation""": TFPegasusForConditionalGeneration,
"""translation""": TFPegasusForConditionalGeneration,
}
if is_tf_available()
else {}
)
a : Optional[Any] = True
a : Any = False
a : int = False
def SCREAMING_SNAKE_CASE_ ( self : str ):
'''simple docstring'''
__lowercase = TFPegasusModelTester(self )
__lowercase = ConfigTester(self , config_class=A_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*A_ )
@require_sentencepiece
@require_tokenizers
@require_tf
class lowerCamelCase__ ( unittest.TestCase ):
a : int = [
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
]
a : Union[str, Any] = [
"""California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"""
""" reduce the risk of wildfires.""",
"""N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.""",
] # differs slightly from pytorch, likely due to numerical differences in linear layers
a : Optional[Any] = """google/pegasus-xsum"""
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
'''simple docstring'''
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , **A_ : Union[str, Any] ):
'''simple docstring'''
__lowercase = self.translate_src_text(**A_ )
assert self.expected_text == generated_words
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , **A_ : int ):
'''simple docstring'''
__lowercase = self.tokenizer(self.src_text , **A_ , padding=A_ , return_tensors="""tf""" )
__lowercase = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=A_ , )
__lowercase = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=A_ )
return generated_words
@slow
def SCREAMING_SNAKE_CASE_ ( self : str ):
'''simple docstring'''
self._assert_generated_batch_equal_expected()
| 442 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCAmelCase_ ( UpperCamelCase__ : Callable , UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float ):
"""simple docstring"""
__lowercase = int(np.ceil((x_end - xa) / step_size ) )
__lowercase = np.zeros((n + 1,) )
__lowercase = ya
__lowercase = xa
for k in range(UpperCamelCase__ ):
__lowercase = y[k] + step_size * ode_func(UpperCamelCase__ , y[k] )
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 442 | 1 |
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
__lowercase : Union[str, Any] =logging.get_logger(__name__)
if is_vision_available():
import PIL
class A ( __lowercase ):
_snake_case =['''pixel_values''']
def __init__( self: Union[str, Any] , _lowerCAmelCase: bool = True , _lowerCAmelCase: Dict[str, int] = None , _lowerCAmelCase: PILImageResampling = PILImageResampling.BICUBIC , _lowerCAmelCase: bool = True , _lowerCAmelCase: Dict[str, int] = None , _lowerCAmelCase: bool = True , _lowerCAmelCase: Union[int, float] = 1 / 255 , _lowerCAmelCase: bool = True , _lowerCAmelCase: Optional[Union[float, List[float]]] = None , _lowerCAmelCase: Optional[Union[float, List[float]]] = None , _lowerCAmelCase: bool = True , **_lowerCAmelCase: str , ) -> None:
'''simple docstring'''
super().__init__(**_lowerCAmelCase )
UpperCAmelCase_ =size if size is not None else {"shortest_edge": 224}
UpperCAmelCase_ =get_size_dict(_lowerCAmelCase , default_to_square=_lowerCAmelCase )
UpperCAmelCase_ =crop_size if crop_size is not None else {"height": 224, "width": 224}
UpperCAmelCase_ =get_size_dict(_lowerCAmelCase , default_to_square=_lowerCAmelCase , param_name="crop_size" )
UpperCAmelCase_ =do_resize
UpperCAmelCase_ =size
UpperCAmelCase_ =resample
UpperCAmelCase_ =do_center_crop
UpperCAmelCase_ =crop_size
UpperCAmelCase_ =do_rescale
UpperCAmelCase_ =rescale_factor
UpperCAmelCase_ =do_normalize
UpperCAmelCase_ =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
UpperCAmelCase_ =image_std if image_std is not None else OPENAI_CLIP_STD
UpperCAmelCase_ =do_convert_rgb
def lowerCAmelCase__ ( self: Dict , _lowerCAmelCase: np.ndarray , _lowerCAmelCase: Dict[str, int] , _lowerCAmelCase: PILImageResampling = PILImageResampling.BICUBIC , _lowerCAmelCase: Optional[Union[str, ChannelDimension]] = None , **_lowerCAmelCase: Union[str, Any] , ) -> np.ndarray:
'''simple docstring'''
UpperCAmelCase_ =get_size_dict(_lowerCAmelCase , default_to_square=_lowerCAmelCase )
if "shortest_edge" not in size:
raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
UpperCAmelCase_ =get_resize_output_image_size(_lowerCAmelCase , size=size["shortest_edge"] , default_to_square=_lowerCAmelCase )
return resize(_lowerCAmelCase , size=_lowerCAmelCase , resample=_lowerCAmelCase , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def lowerCAmelCase__ ( self: Optional[int] , _lowerCAmelCase: np.ndarray , _lowerCAmelCase: Dict[str, int] , _lowerCAmelCase: Optional[Union[str, ChannelDimension]] = None , **_lowerCAmelCase: Optional[int] , ) -> np.ndarray:
'''simple docstring'''
UpperCAmelCase_ =get_size_dict(_lowerCAmelCase )
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(_lowerCAmelCase , size=(size["height"], size["width"]) , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def lowerCAmelCase__ ( self: Union[str, Any] , _lowerCAmelCase: np.ndarray , _lowerCAmelCase: Union[int, float] , _lowerCAmelCase: Optional[Union[str, ChannelDimension]] = None , **_lowerCAmelCase: Optional[Any] , ) -> Optional[int]:
'''simple docstring'''
return rescale(_lowerCAmelCase , scale=_lowerCAmelCase , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def lowerCAmelCase__ ( self: int , _lowerCAmelCase: np.ndarray , _lowerCAmelCase: Union[float, List[float]] , _lowerCAmelCase: Union[float, List[float]] , _lowerCAmelCase: Optional[Union[str, ChannelDimension]] = None , **_lowerCAmelCase: Optional[int] , ) -> np.ndarray:
'''simple docstring'''
return normalize(_lowerCAmelCase , mean=_lowerCAmelCase , std=_lowerCAmelCase , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def lowerCAmelCase__ ( self: List[str] , _lowerCAmelCase: ImageInput , _lowerCAmelCase: bool = None , _lowerCAmelCase: Dict[str, int] = None , _lowerCAmelCase: PILImageResampling = None , _lowerCAmelCase: bool = None , _lowerCAmelCase: int = None , _lowerCAmelCase: bool = None , _lowerCAmelCase: float = None , _lowerCAmelCase: bool = None , _lowerCAmelCase: Optional[Union[float, List[float]]] = None , _lowerCAmelCase: Optional[Union[float, List[float]]] = None , _lowerCAmelCase: bool = None , _lowerCAmelCase: Optional[Union[str, TensorType]] = None , _lowerCAmelCase: Optional[ChannelDimension] = ChannelDimension.FIRST , **_lowerCAmelCase: Tuple , ) -> PIL.Image.Image:
'''simple docstring'''
UpperCAmelCase_ =do_resize if do_resize is not None else self.do_resize
UpperCAmelCase_ =size if size is not None else self.size
UpperCAmelCase_ =get_size_dict(_lowerCAmelCase , param_name="size" , default_to_square=_lowerCAmelCase )
UpperCAmelCase_ =resample if resample is not None else self.resample
UpperCAmelCase_ =do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase_ =crop_size if crop_size is not None else self.crop_size
UpperCAmelCase_ =get_size_dict(_lowerCAmelCase , param_name="crop_size" , default_to_square=_lowerCAmelCase )
UpperCAmelCase_ =do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase_ =rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase_ =do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase_ =image_mean if image_mean is not None else self.image_mean
UpperCAmelCase_ =image_std if image_std is not None else self.image_std
UpperCAmelCase_ =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
UpperCAmelCase_ =make_list_of_images(_lowerCAmelCase )
if not valid_images(_lowerCAmelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_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:
UpperCAmelCase_ =[convert_to_rgb(_lowerCAmelCase ) for image in images]
# All transformations expect numpy arrays.
UpperCAmelCase_ =[to_numpy_array(_lowerCAmelCase ) for image in images]
if do_resize:
UpperCAmelCase_ =[self.resize(image=_lowerCAmelCase , size=_lowerCAmelCase , resample=_lowerCAmelCase ) for image in images]
if do_center_crop:
UpperCAmelCase_ =[self.center_crop(image=_lowerCAmelCase , size=_lowerCAmelCase ) for image in images]
if do_rescale:
UpperCAmelCase_ =[self.rescale(image=_lowerCAmelCase , scale=_lowerCAmelCase ) for image in images]
if do_normalize:
UpperCAmelCase_ =[self.normalize(image=_lowerCAmelCase , mean=_lowerCAmelCase , std=_lowerCAmelCase ) for image in images]
UpperCAmelCase_ =[to_channel_dimension_format(_lowerCAmelCase , _lowerCAmelCase ) for image in images]
UpperCAmelCase_ ={"pixel_values": images}
return BatchFeature(data=_lowerCAmelCase , tensor_type=_lowerCAmelCase )
| 54 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__A = logging.get_logger(__name__)
class A ( __UpperCAmelCase ):
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> None:
'''simple docstring'''
warnings.warn(
"""The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use GLPNImageProcessor instead.""" , lowerCamelCase__ , )
super().__init__(*lowerCamelCase__ , **lowerCamelCase__ )
| 325 | 0 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
BertTokenizer,
ProcessorMixin,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available
sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
from test_module.custom_processing import CustomProcessor # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
_UpperCAmelCase = get_tests_dir('fixtures/dummy_feature_extractor_config.json')
_UpperCAmelCase = get_tests_dir('fixtures/vocab.json')
_UpperCAmelCase = get_tests_dir('fixtures')
class _UpperCamelCase ( unittest.TestCase ):
_UpperCamelCase : Tuple = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']
def lowercase ( self: Union[str, Any] ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ = 0
def lowercase ( self: Dict ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h" )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def lowercase ( self: Any ) -> Dict:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase_ = WavaVecaConfig()
UpperCamelCase_ = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h" )
# save in new folder
model_config.save_pretrained(_SCREAMING_SNAKE_CASE )
processor.save_pretrained(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = AutoProcessor.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def lowercase ( self: int ) -> Optional[Any]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
# copy relevant files
copyfile(_SCREAMING_SNAKE_CASE , os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
copyfile(_SCREAMING_SNAKE_CASE , os.path.join(_SCREAMING_SNAKE_CASE , "vocab.json" ) )
UpperCamelCase_ = AutoProcessor.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def lowercase ( self: Any ) -> str:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase_ = WavaVecaFeatureExtractor()
UpperCamelCase_ = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h" )
UpperCamelCase_ = WavaVecaProcessor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# save in new folder
processor.save_pretrained(_SCREAMING_SNAKE_CASE )
# drop `processor_class` in tokenizer
with open(os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , "r" ) as f:
UpperCamelCase_ = json.load(_SCREAMING_SNAKE_CASE )
config_dict.pop("processor_class" )
with open(os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , "w" ) as f:
f.write(json.dumps(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase_ = AutoProcessor.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def lowercase ( self: int ) -> Any:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase_ = WavaVecaFeatureExtractor()
UpperCamelCase_ = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h" )
UpperCamelCase_ = WavaVecaProcessor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# save in new folder
processor.save_pretrained(_SCREAMING_SNAKE_CASE )
# drop `processor_class` in feature extractor
with open(os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , "r" ) as f:
UpperCamelCase_ = json.load(_SCREAMING_SNAKE_CASE )
config_dict.pop("processor_class" )
with open(os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , "w" ) as f:
f.write(json.dumps(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase_ = AutoProcessor.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def lowercase ( self: Optional[Any] ) -> List[Any]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase_ = WavaVecaConfig(processor_class="Wav2Vec2Processor" )
model_config.save_pretrained(_SCREAMING_SNAKE_CASE )
# copy relevant files
copyfile(_SCREAMING_SNAKE_CASE , os.path.join(_SCREAMING_SNAKE_CASE , "vocab.json" ) )
# create emtpy sample processor
with open(os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , "w" ) as f:
f.write("{}" )
UpperCamelCase_ = AutoProcessor.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def lowercase ( self: Optional[int] ) -> Dict:
"""simple docstring"""
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
UpperCamelCase_ = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
UpperCamelCase_ = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor" , trust_remote_code=_SCREAMING_SNAKE_CASE )
self.assertTrue(processor.special_attribute_present )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
UpperCamelCase_ = processor.feature_extractor
self.assertTrue(feature_extractor.special_attribute_present )
self.assertEqual(feature_extractor.__class__.__name__ , "NewFeatureExtractor" )
UpperCamelCase_ = processor.tokenizer
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" )
# Test we can also load the slow version
UpperCamelCase_ = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=_SCREAMING_SNAKE_CASE , use_fast=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = new_processor.tokenizer
self.assertTrue(new_tokenizer.special_attribute_present )
self.assertEqual(new_tokenizer.__class__.__name__ , "NewTokenizer" )
else:
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
def lowercase ( self: Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
try:
AutoConfig.register("custom" , _SCREAMING_SNAKE_CASE )
AutoFeatureExtractor.register(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
AutoTokenizer.register(_SCREAMING_SNAKE_CASE , slow_tokenizer_class=_SCREAMING_SNAKE_CASE )
AutoProcessor.register(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
AutoProcessor.register(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Now that the config is registered, it can be used as any other config with the auto-API
UpperCamelCase_ = CustomFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase_ = os.path.join(_SCREAMING_SNAKE_CASE , "vocab.txt" )
with open(_SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
UpperCamelCase_ = CustomTokenizer(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = CustomProcessor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = AutoProcessor.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def lowercase ( self: str ) -> List[str]:
"""simple docstring"""
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : List[Any] = False
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : Union[str, Any] = False
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : List[str] = '''AutoFeatureExtractor'''
_UpperCamelCase : Dict = '''AutoTokenizer'''
_UpperCamelCase : Optional[Any] = False
try:
AutoConfig.register("custom" , _SCREAMING_SNAKE_CASE )
AutoFeatureExtractor.register(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
AutoTokenizer.register(_SCREAMING_SNAKE_CASE , slow_tokenizer_class=_SCREAMING_SNAKE_CASE )
AutoProcessor.register(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# If remote code is not set, the default is to use local classes.
UpperCamelCase_ = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor" )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote code is disabled, we load the local ones.
UpperCamelCase_ = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=_SCREAMING_SNAKE_CASE )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub.
UpperCamelCase_ = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=_SCREAMING_SNAKE_CASE )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
self.assertTrue(processor.special_attribute_present )
self.assertTrue(processor.feature_extractor.special_attribute_present )
self.assertTrue(processor.tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def lowercase ( self: Any ) -> Dict:
"""simple docstring"""
UpperCamelCase_ = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert" )
self.assertEqual(processor.__class__.__name__ , "BertTokenizerFast" )
def lowercase ( self: int ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext" )
self.assertEqual(processor.__class__.__name__ , "ConvNextImageProcessor" )
@is_staging_test
class _UpperCamelCase ( unittest.TestCase ):
_UpperCamelCase : Dict = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']
@classmethod
def lowercase ( cls: List[Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = TOKEN
HfFolder.save_token(_SCREAMING_SNAKE_CASE )
@classmethod
def lowercase ( cls: Tuple ) -> List[str]:
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id="test-processor" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-processor-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-processor" )
except HTTPError:
pass
def lowercase ( self: Optional[int] ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = WavaVecaProcessor.from_pretrained(_SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(_SCREAMING_SNAKE_CASE , "test-processor" ) , push_to_hub=_SCREAMING_SNAKE_CASE , use_auth_token=self._token )
UpperCamelCase_ = WavaVecaProcessor.from_pretrained(f'''{USER}/test-processor''' )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(_SCREAMING_SNAKE_CASE , getattr(new_processor.feature_extractor , _SCREAMING_SNAKE_CASE ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def lowercase ( self: str ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = WavaVecaProcessor.from_pretrained(_SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(_SCREAMING_SNAKE_CASE , "test-processor-org" ) , push_to_hub=_SCREAMING_SNAKE_CASE , use_auth_token=self._token , organization="valid_org" , )
UpperCamelCase_ = WavaVecaProcessor.from_pretrained("valid_org/test-processor-org" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(_SCREAMING_SNAKE_CASE , getattr(new_processor.feature_extractor , _SCREAMING_SNAKE_CASE ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def lowercase ( self: List[Any] ) -> List[str]:
"""simple docstring"""
CustomFeatureExtractor.register_for_auto_class()
CustomTokenizer.register_for_auto_class()
CustomProcessor.register_for_auto_class()
UpperCamelCase_ = CustomFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase_ = os.path.join(_SCREAMING_SNAKE_CASE , "vocab.txt" )
with open(_SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
UpperCamelCase_ = CustomTokenizer(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = CustomProcessor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmp_dir:
create_repo(f'''{USER}/test-dynamic-processor''' , token=self._token )
UpperCamelCase_ = Repository(_SCREAMING_SNAKE_CASE , clone_from=f'''{USER}/test-dynamic-processor''' , token=self._token )
processor.save_pretrained(_SCREAMING_SNAKE_CASE )
# This has added the proper auto_map field to the feature extractor config
self.assertDictEqual(
processor.feature_extractor.auto_map , {
"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor",
"AutoProcessor": "custom_processing.CustomProcessor",
} , )
# This has added the proper auto_map field to the tokenizer config
with open(os.path.join(_SCREAMING_SNAKE_CASE , "tokenizer_config.json" ) ) as f:
UpperCamelCase_ = json.load(_SCREAMING_SNAKE_CASE )
self.assertDictEqual(
tokenizer_config["auto_map"] , {
"AutoTokenizer": ["custom_tokenization.CustomTokenizer", None],
"AutoProcessor": "custom_processing.CustomProcessor",
} , )
# The code has been copied from fixtures
self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE , "custom_feature_extraction.py" ) ) )
self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE , "custom_tokenization.py" ) ) )
self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE , "custom_processing.py" ) ) )
repo.push_to_hub()
UpperCamelCase_ = AutoProcessor.from_pretrained(f'''{USER}/test-dynamic-processor''' , trust_remote_code=_SCREAMING_SNAKE_CASE )
# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
self.assertEqual(new_processor.__class__.__name__ , "CustomProcessor" )
| 371 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Optional[int]:
return x + 2
class _UpperCamelCase ( unittest.TestCase ):
def lowercase ( self: Optional[int] ) -> str:
"""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 lowercase ( self: List[str] ) -> Dict:
"""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 lowercase ( self: Union[str, Any] ) -> str:
"""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 lowercase ( self: Dict ) -> Any:
"""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 lowercase ( self: List[str] ) -> Union[str, 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 lowercase ( self: List[Any] ) -> int:
"""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 lowercase ( self: int ) -> Union[str, Any]:
"""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 lowercase ( self: str ) -> Optional[Any]:
"""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 lowercase ( self: List[str] ) -> int:
"""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 lowercase ( self: str ) -> int:
"""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 lowercase ( self: Dict ) -> Optional[int]:
"""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} )
| 371 | 1 |
"""simple docstring"""
def UpperCamelCase ( _lowerCAmelCase : int = 1000 ) -> Any:
_UpperCAmelCase : Union[str, Any] = -1
_UpperCAmelCase : Optional[Any] = 0
for a in range(1, n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_UpperCAmelCase : int = (n * n - 2 * a * n) // (2 * n - 2 * a)
_UpperCAmelCase : Union[str, Any] = n - a - b
if c * c == (a * a + b * b):
_UpperCAmelCase : Dict = a * b * c
if candidate >= product:
_UpperCAmelCase : List[str] = candidate
return product
if __name__ == "__main__":
print(F'''{solution() = }''')
| 238 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase__ ( lowerCamelCase : list ):
if not nums:
raise ValueError('List is empty' )
return sum(lowerCamelCase ) / len(lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 128 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__UpperCamelCase = abspath(join(dirname(__file__), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def UpperCamelCase_( _A :str )-> int:
config.addinivalue_line(
"markers" , "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested" )
config.addinivalue_line(
"markers" , "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested" )
config.addinivalue_line("markers" , "is_pipeline_test: mark test to run only when pipelines are tested" )
config.addinivalue_line("markers" , "is_staging_test: mark test to run only in the staging environment" )
config.addinivalue_line("markers" , "accelerate_tests: mark test that require accelerate" )
config.addinivalue_line("markers" , "tool_tests: mark the tool tests that are run on their specific schedule" )
def UpperCamelCase_( _A :str )-> Optional[Any]:
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(_A )
def UpperCamelCase_( _A :List[Any] )-> Optional[Any]:
from transformers.testing_utils import pytest_terminal_summary_main
UpperCamelCase__ = terminalreporter.config.getoption("--make-reports" )
if make_reports:
pytest_terminal_summary_main(_A , id=_A )
def UpperCamelCase_( _A :List[str] , _A :Optional[int] )-> Union[str, Any]:
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
UpperCamelCase__ = 0
# Doctest custom flag to ignore output.
__UpperCamelCase = doctest.register_optionflag('IGNORE_RESULT')
__UpperCamelCase = doctest.OutputChecker
class lowerCamelCase__ ( UpperCAmelCase ):
"""simple docstring"""
def snake_case__ ( self , snake_case , snake_case , snake_case ):
'''simple docstring'''
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , snake_case , snake_case , snake_case )
__UpperCamelCase = CustomOutputChecker
__UpperCamelCase = HfDoctestModule
__UpperCamelCase = HfDocTestParser
| 185 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__UpperCamelCase = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'processing_trocr': ['TrOCRProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase = [
'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 = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 185 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
lowercase : Optional[int] = ["""small""", """medium""", """large"""]
lowercase : List[Any] = """lm_head.decoder.weight"""
lowercase : Tuple = """lm_head.weight"""
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> str:
lowercase : Tuple = torch.load(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = d.pop(SCREAMING_SNAKE_CASE__ )
os.makedirs(SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ )
torch.save(SCREAMING_SNAKE_CASE__ , os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
if __name__ == "__main__":
lowercase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
lowercase : Union[str, Any] = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
lowercase : int = os.path.join(args.dialogpt_path, F'''{MODEL}_ft.pkl''')
lowercase : Tuple = F'''./DialoGPT-{MODEL}'''
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 336 |
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,
)
lowercase : List[str] = {
"""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:
lowercase : List[str] = ["""CLIPTokenizerFast"""]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Union[str, Any] = ["""CLIPFeatureExtractor"""]
lowercase : Tuple = ["""CLIPImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Union[str, Any] = [
"""CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CLIPModel""",
"""CLIPPreTrainedModel""",
"""CLIPTextModel""",
"""CLIPTextModelWithProjection""",
"""CLIPVisionModel""",
"""CLIPVisionModelWithProjection""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Any = [
"""TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFCLIPModel""",
"""TFCLIPPreTrainedModel""",
"""TFCLIPTextModel""",
"""TFCLIPVisionModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : List[str] = [
"""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
lowercase : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 336 | 1 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
_UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
def UpperCamelCase ( lowercase_ : Any , lowercase_ : List[Any] , lowercase_ : Union[str, Any] ) -> List[str]:
'''simple docstring'''
return [
int(1_0_0_0 * (box[0] / width) ),
int(1_0_0_0 * (box[1] / height) ),
int(1_0_0_0 * (box[2] / width) ),
int(1_0_0_0 * (box[3] / height) ),
]
def UpperCamelCase ( lowercase_ : np.ndarray , lowercase_ : Optional[str] , lowercase_ : Optional[str] = None ) -> Tuple:
'''simple docstring'''
lowercase =tesseract_config if tesseract_config is not None else ''''''
# apply OCR
lowercase =to_pil_image(lowercase_ )
lowercase , lowercase =pil_image.size
lowercase =pytesseract.image_to_data(lowercase_ , lang=lowercase_ , output_type='''dict''' , config=lowercase_ )
lowercase , lowercase , lowercase , lowercase , lowercase =data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']
# filter empty words and corresponding coordinates
lowercase =[idx for idx, word in enumerate(lowercase_ ) if not word.strip()]
lowercase =[word for idx, word in enumerate(lowercase_ ) if idx not in irrelevant_indices]
lowercase =[coord for idx, coord in enumerate(lowercase_ ) if idx not in irrelevant_indices]
lowercase =[coord for idx, coord in enumerate(lowercase_ ) if idx not in irrelevant_indices]
lowercase =[coord for idx, coord in enumerate(lowercase_ ) if idx not in irrelevant_indices]
lowercase =[coord for idx, coord in enumerate(lowercase_ ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
lowercase =[]
for x, y, w, h in zip(lowercase_ , lowercase_ , lowercase_ , lowercase_ ):
lowercase =[x, y, x + w, y + h]
actual_boxes.append(lowercase_ )
# finally, normalize the bounding boxes
lowercase =[]
for box in actual_boxes:
normalized_boxes.append(normalize_box(lowercase_ , lowercase_ , lowercase_ ) )
assert len(lowercase_ ) == len(lowercase_ ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = ['pixel_values']
def __init__( self , snake_case_ = True , snake_case_ = None , snake_case_ = PILImageResampling.BILINEAR , snake_case_ = True , snake_case_ = None , snake_case_ = "" , **snake_case_ , ):
super().__init__(**snake_case_ )
lowercase =size if size is not None else {'''height''': 2_24, '''width''': 2_24}
lowercase =get_size_dict(snake_case_ )
lowercase =do_resize
lowercase =size
lowercase =resample
lowercase =apply_ocr
lowercase =ocr_lang
lowercase =tesseract_config
def _A( self , snake_case_ , snake_case_ , snake_case_ = PILImageResampling.BILINEAR , snake_case_ = None , **snake_case_ , ):
lowercase =get_size_dict(snake_case_ )
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 =(size['''height'''], size['''width'''])
return resize(snake_case_ , size=snake_case_ , resample=snake_case_ , data_format=snake_case_ , **snake_case_ )
def _A( self , snake_case_ , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = ChannelDimension.FIRST , **snake_case_ , ):
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(snake_case_ )
lowercase =resample if resample is not None else self.resample
lowercase =apply_ocr if apply_ocr is not None else self.apply_ocr
lowercase =ocr_lang if ocr_lang is not None else self.ocr_lang
lowercase =tesseract_config if tesseract_config is not None else self.tesseract_config
lowercase =make_list_of_images(snake_case_ )
if not valid_images(snake_case_ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
# All transformations expect numpy arrays.
lowercase =[to_numpy_array(snake_case_ ) for image in images]
if apply_ocr:
requires_backends(self , '''pytesseract''' )
lowercase =[]
lowercase =[]
for image in images:
lowercase , lowercase =apply_tesseract(snake_case_ , snake_case_ , snake_case_ )
words_batch.append(snake_case_ )
boxes_batch.append(snake_case_ )
if do_resize:
lowercase =[self.resize(image=snake_case_ , size=snake_case_ , resample=snake_case_ ) for image in images]
# flip color channels from RGB to BGR (as Detectron2 requires this)
lowercase =[flip_channel_order(snake_case_ ) for image in images]
lowercase =[to_channel_dimension_format(snake_case_ , snake_case_ ) for image in images]
lowercase =BatchFeature(data={'''pixel_values''': images} , tensor_type=snake_case_ )
if apply_ocr:
lowercase =words_batch
lowercase =boxes_batch
return data
| 145 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : Any = {
'''configuration_blenderbot_small''': [
'''BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlenderbotSmallConfig''',
'''BlenderbotSmallOnnxConfig''',
],
'''tokenization_blenderbot_small''': ['''BlenderbotSmallTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : int = ['''BlenderbotSmallTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Optional[Any] = [
'''BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlenderbotSmallForCausalLM''',
'''BlenderbotSmallForConditionalGeneration''',
'''BlenderbotSmallModel''',
'''BlenderbotSmallPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Any = [
'''TFBlenderbotSmallForConditionalGeneration''',
'''TFBlenderbotSmallModel''',
'''TFBlenderbotSmallPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Optional[Any] = [
'''FlaxBlenderbotSmallForConditionalGeneration''',
'''FlaxBlenderbotSmallModel''',
'''FlaxBlenderbotSmallPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotSmallConfig,
BlenderbotSmallOnnxConfig,
)
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotSmallForCausalLM,
BlenderbotSmallForConditionalGeneration,
BlenderbotSmallModel,
BlenderbotSmallPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot_small import (
TFBlenderbotSmallForConditionalGeneration,
TFBlenderbotSmallModel,
TFBlenderbotSmallPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallPreTrainedModel,
)
else:
import sys
_UpperCAmelCase : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 145 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : List[Any] , a__ : Optional[Any] , a__ : Any=13 , a__ : str=32 , a__ : Optional[int]=3 , a__ : Tuple=4 , a__ : Any=[10, 20, 30, 40] , a__ : Any=[2, 2, 3, 2] , a__ : List[Any]=True , a__ : List[str]=True , a__ : Union[str, Any]=37 , a__ : Tuple="gelu" , a__ : Any=10 , a__ : List[str]=0.02 , a__ : List[Any]=["stage2", "stage3", "stage4"] , a__ : Any=[2, 3, 4] , a__ : int=None , ):
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = image_size
UpperCAmelCase = num_channels
UpperCAmelCase = num_stages
UpperCAmelCase = hidden_sizes
UpperCAmelCase = depths
UpperCAmelCase = is_training
UpperCAmelCase = use_labels
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = num_labels
UpperCAmelCase = initializer_range
UpperCAmelCase = out_features
UpperCAmelCase = out_indices
UpperCAmelCase = scope
def __snake_case ( self : Dict ):
UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def __snake_case ( self : Dict ):
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=a__ , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def __snake_case ( self : Dict , a__ : Tuple , a__ : Union[str, Any] , a__ : List[Any] ):
UpperCAmelCase = ConvNextVaModel(config=a__ )
model.to(a__ )
model.eval()
UpperCAmelCase = model(a__ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def __snake_case ( self : Dict , a__ : Any , a__ : Dict , a__ : List[Any] ):
UpperCAmelCase = ConvNextVaForImageClassification(a__ )
model.to(a__ )
model.eval()
UpperCAmelCase = model(a__ , labels=a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __snake_case ( self : Any , a__ : Optional[Any] , a__ : List[Any] , a__ : Dict ):
UpperCAmelCase = ConvNextVaBackbone(config=a__ )
model.to(a__ )
model.eval()
UpperCAmelCase = model(a__ )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
UpperCAmelCase = None
UpperCAmelCase = ConvNextVaBackbone(config=a__ )
model.to(a__ )
model.eval()
UpperCAmelCase = model(a__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def __snake_case ( self : Any ):
UpperCAmelCase = self.prepare_config_and_inputs()
UpperCAmelCase, UpperCAmelCase, UpperCAmelCase = config_and_inputs
UpperCAmelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
def __snake_case ( self : List[str] ):
UpperCAmelCase = self.prepare_config_and_inputs()
UpperCAmelCase, UpperCAmelCase, UpperCAmelCase = config_and_inputs
UpperCAmelCase = {'''pixel_values''': pixel_values, '''labels''': labels}
return config, inputs_dict
@require_torch
class lowerCAmelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_lowerCamelCase =(
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
_lowerCamelCase =(
{"feature-extraction": ConvNextVaModel, "image-classification": ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
_lowerCamelCase =False
_lowerCamelCase =False
_lowerCamelCase =False
_lowerCamelCase =False
_lowerCamelCase =False
def __snake_case ( self : List[Any] ):
UpperCAmelCase = ConvNextVaModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=a__ , has_text_modality=a__ , hidden_size=37 )
def __snake_case ( self : Tuple ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __snake_case ( self : Optional[Any] ):
return
@unittest.skip(reason='''ConvNextV2 does not use inputs_embeds''' )
def __snake_case ( self : List[str] ):
pass
@unittest.skip(reason='''ConvNextV2 does not support input and output embeddings''' )
def __snake_case ( self : Union[str, Any] ):
pass
@unittest.skip(reason='''ConvNextV2 does not use feedforward chunking''' )
def __snake_case ( self : str ):
pass
def __snake_case ( self : Optional[Any] ):
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
UpperCAmelCase, UpperCAmelCase = self.model_tester.prepare_config_and_inputs_with_labels()
UpperCAmelCase = True
if model_class.__name__ in [
*get_values(a__ ),
*get_values(a__ ),
]:
continue
UpperCAmelCase = model_class(a__ )
model.to(a__ )
model.train()
UpperCAmelCase = self._prepare_for_class(a__ , a__ , return_labels=a__ )
UpperCAmelCase = model(**a__ ).loss
loss.backward()
def __snake_case ( self : Union[str, Any] ):
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
UpperCAmelCase, UpperCAmelCase = self.model_tester.prepare_config_and_inputs_with_labels()
UpperCAmelCase = False
UpperCAmelCase = True
if (
model_class.__name__
in [*get_values(a__ ), *get_values(a__ )]
or not model_class.supports_gradient_checkpointing
):
continue
UpperCAmelCase = model_class(a__ )
model.to(a__ )
model.gradient_checkpointing_enable()
model.train()
UpperCAmelCase = self._prepare_for_class(a__ , a__ , return_labels=a__ )
UpperCAmelCase = model(**a__ ).loss
loss.backward()
def __snake_case ( self : str ):
UpperCAmelCase, UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(a__ )
UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase = [*signature.parameters.keys()]
UpperCAmelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , a__ )
def __snake_case ( self : Any ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a__ )
def __snake_case ( self : Any ):
def check_hidden_states_output(a__ : Dict , a__ : str , a__ : Dict ):
UpperCAmelCase = model_class(a__ )
model.to(a__ )
model.eval()
with torch.no_grad():
UpperCAmelCase = model(**self._prepare_for_class(a__ , a__ ) )
UpperCAmelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
UpperCAmelCase = self.model_tester.num_stages
self.assertEqual(len(a__ ) , expected_num_stages + 1 )
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
UpperCAmelCase, UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = True
check_hidden_states_output(a__ , a__ , a__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase = True
check_hidden_states_output(a__ , a__ , a__ )
def __snake_case ( self : Union[str, Any] ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a__ )
@slow
def __snake_case ( self : Tuple ):
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase = ConvNextVaModel.from_pretrained(a__ )
self.assertIsNotNone(a__ )
def __snake_case ( ) -> int:
"""simple docstring"""
UpperCAmelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __snake_case ( self : Dict ):
return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ) if is_vision_available() else None
@slow
def __snake_case ( self : Dict ):
UpperCAmelCase = ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ).to(a__ )
UpperCAmelCase = self.default_image_processor
UpperCAmelCase = prepare_img()
UpperCAmelCase = preprocessor(images=a__ , return_tensors='''pt''' ).to(a__ )
# forward pass
with torch.no_grad():
UpperCAmelCase = model(**a__ )
# verify the logits
UpperCAmelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , a__ )
UpperCAmelCase = torch.tensor([0.9_996, 0.1_966, -0.4_386] ).to(a__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a__ , atol=1e-4 ) )
| 51 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCAmelCase: List[str] = logging.get_logger()
def _lowercase( __a : int , __a : str , __a : LevitConfig , __a : Path , __a : bool = True ):
print(f"""Converting {name}...""" )
with torch.no_grad():
if hidden_sizes == 128:
if name[-1] == "S":
a__ =timm.create_model('levit_128s' , pretrained=__a )
else:
a__ =timm.create_model('levit_128' , pretrained=__a )
if hidden_sizes == 192:
a__ =timm.create_model('levit_192' , pretrained=__a )
if hidden_sizes == 256:
a__ =timm.create_model('levit_256' , pretrained=__a )
if hidden_sizes == 384:
a__ =timm.create_model('levit_384' , pretrained=__a )
from_model.eval()
a__ =LevitForImageClassificationWithTeacher(__a ).eval()
a__ =OrderedDict()
a__ =from_model.state_dict()
a__ =list(from_model.state_dict().keys() )
a__ =list(our_model.state_dict().keys() )
print(len(__a ) , len(__a ) )
for i in range(len(__a ) ):
a__ =weights[og_keys[i]]
our_model.load_state_dict(__a )
a__ =torch.randn((2, 3, 224, 224) )
a__ =from_model(__a )
a__ =our_model(__a ).logits
assert torch.allclose(__a , __a ), "The model logits don't match the original one."
a__ =name
print(__a )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
a__ =LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(f"""Pushed {checkpoint_name}""" )
def _lowercase( __a : Path , __a : str = None , __a : bool = True ):
a__ ='imagenet-1k-id2label.json'
a__ =1000
a__ =(1, num_labels)
a__ ='huggingface/label-files'
a__ =num_labels
a__ =json.load(open(hf_hub_download(__a , __a , repo_type='dataset' ) , 'r' ) )
a__ ={int(__a ): v for k, v in idalabel.items()}
a__ =idalabel
a__ ={v: k for k, v in idalabel.items()}
a__ =partial(__a , num_labels=__a , idalabel=__a , labelaid=__a )
a__ ={
'levit-128S': 128,
'levit-128': 128,
'levit-192': 192,
'levit-256': 256,
'levit-384': 384,
}
a__ ={
'levit-128S': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 6, 8] , depths=[2, 3, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'levit-128': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 8, 12] , depths=[4, 4, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'levit-192': ImageNetPreTrainedConfig(
hidden_sizes=[192, 288, 384] , num_attention_heads=[3, 5, 6] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'levit-256': ImageNetPreTrainedConfig(
hidden_sizes=[256, 384, 512] , num_attention_heads=[4, 6, 8] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'levit-384': ImageNetPreTrainedConfig(
hidden_sizes=[384, 512, 768] , num_attention_heads=[6, 9, 12] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0.1 , ),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name] , __a , names_to_config[model_name] , __a , __a )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name] , __a , __a , __a , __a )
return config, expected_shape
if __name__ == "__main__":
_lowerCAmelCase: Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default=None,
type=str,
help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='levit-dump-folder/',
type=Path,
required=False,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
parser.add_argument(
'--no-push_to_hub',
dest='push_to_hub',
action='store_false',
help='Do not push model and image processor to the hub',
)
_lowerCAmelCase: Union[str, Any] = parser.parse_args()
_lowerCAmelCase: Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 20 | 0 |
'''simple docstring'''
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
lowerCAmelCase : Any = '\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n'
lowerCAmelCase : int = '\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'
lowerCAmelCase : List[str] = '\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: "c" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric(\'mauve\')\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class SCREAMING_SNAKE_CASE__ ( datasets.Metric):
def UpperCAmelCase_ ( self )-> Union[str, Any]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='https://github.com/krishnap25/mauve' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/krishnap25/mauve'] , reference_urls=[
'https://arxiv.org/abs/2102.01454',
'https://github.com/krishnap25/mauve',
] , )
def UpperCAmelCase_ ( self , A_ , A_ , A_=None , A_=None , A_=None , A_=None , A_="auto" , A_=-1 , A_=0.9 , A_=5 , A_=500 , A_="gpt2-large" , A_=-1 , A_=1024 , A_=25 , A_=5 , A_=True , A_=25 , )-> Optional[int]:
'''simple docstring'''
UpperCamelCase = compute_mauve(
p_text=A_ , q_text=A_ , p_features=A_ , q_features=A_ , p_tokens=A_ , q_tokens=A_ , num_buckets=A_ , pca_max_data=A_ , kmeans_explained_var=A_ , kmeans_num_redo=A_ , kmeans_max_iter=A_ , featurize_model_name=A_ , device_id=A_ , max_text_length=A_ , divergence_curve_discretization_size=A_ , mauve_scaling_factor=A_ , verbose=A_ , seed=A_ , )
return out
| 432 |
'''simple docstring'''
import argparse
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
########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowerCAmelCase : Union[str, Any] = 16
lowerCAmelCase : Any = 32
def A_( A : Accelerator , A : int = 16):
UpperCamelCase = AutoTokenizer.from_pretrained('bert-base-cased')
UpperCamelCase = load_dataset('glue' , 'mrpc')
def tokenize_function(A : Dict):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=A , max_length=A)
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCamelCase = datasets.map(
A , batched=A , remove_columns=['idx', 'sentence1', 'sentence2'] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase = tokenized_datasets.rename_column('label' , 'labels')
def collate_fn(A : int):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCamelCase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCamelCase = 16
elif accelerator.mixed_precision != "no":
UpperCamelCase = 8
else:
UpperCamelCase = None
return tokenizer.pad(
A , padding='longest' , max_length=A , pad_to_multiple_of=A , return_tensors='pt' , )
# Instantiate dataloaders.
UpperCamelCase = DataLoader(
tokenized_datasets['train'] , shuffle=A , collate_fn=A , batch_size=A)
UpperCamelCase = DataLoader(
tokenized_datasets['validation'] , shuffle=A , collate_fn=A , batch_size=A)
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
lowerCAmelCase : int = mocked_dataloaders # noqa: F811
def A_( A : List[str] , A : Dict):
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS' , A) == "1":
UpperCamelCase = 2
# New Code #
UpperCamelCase = int(args.gradient_accumulation_steps)
# Initialize accelerator
UpperCamelCase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=A)
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`')
# 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 = evaluate.load('glue' , 'mrpc')
set_seed(A)
UpperCamelCase , UpperCamelCase = get_dataloaders(A , A)
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=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).
UpperCamelCase = model.to(accelerator.device)
# Instantiate optimizer
UpperCamelCase = AdamW(params=model.parameters() , lr=A)
# Instantiate scheduler
UpperCamelCase = get_linear_schedule_with_warmup(
optimizer=A , num_warmup_steps=100 , num_training_steps=(len(A) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = accelerator.prepare(
A , A , A , A , A)
# Now we train the model
for epoch in range(A):
model.train()
for step, batch in enumerate(A):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device)
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(A):
UpperCamelCase = model(**A)
UpperCamelCase = output.loss
accelerator.backward(A)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(A):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device)
with torch.no_grad():
UpperCamelCase = model(**A)
UpperCamelCase = outputs.logits.argmax(dim=-1)
UpperCamelCase , UpperCamelCase = accelerator.gather_for_metrics((predictions, batch['labels']))
metric.add_batch(
predictions=A , references=A , )
UpperCamelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''' , A)
def A_( ):
UpperCamelCase = argparse.ArgumentParser(description='Simple example of training script.')
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.' , )
# New Code #
parser.add_argument(
'--gradient_accumulation_steps' , type=A , default=1 , help='The number of minibatches to be ran before gradients are accumulated.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.')
UpperCamelCase = parser.parse_args()
UpperCamelCase = {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(A , A)
if __name__ == "__main__":
main()
| 432 | 1 |
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