





'''simple docstring'''




import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment


class 					SCREAMING_SNAKE_CASE       (unittest.TestCase				):


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					str						=			inspect.getfile(accelerate.test_utils)
          __A		:					Any						=			os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['scripts', 'test_script.py'])
          __A		:					List[str]						=			os.path.sep.join(
              mod_file.split(os.path.sep)[:-1] + ['scripts', 'test_distributed_data_loop.py'])
          __A		:					List[Any]						=			os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['scripts', 'test_ops.py'])


     @require_multi_gpu
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          print(F'Found {torch.cuda.device_count()} devices.')
          __A		:					Optional[Any]						=			['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
          with patch_environment(omp_num_threads=1):
               execute_subprocess_async(_UpperCAmelCase		,      env=os.environ.copy())


     @require_multi_gpu
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          print(F'Found {torch.cuda.device_count()} devices.')
          __A		:					Dict						=			['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
          print(F'Command: {cmd}')
          with patch_environment(omp_num_threads=1):
               execute_subprocess_async(_UpperCAmelCase		,      env=os.environ.copy())


     @require_multi_gpu
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[int]						=			['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__)]
          with patch_environment(omp_num_threads=1):
               execute_subprocess_async(_UpperCAmelCase		,      env=os.environ.copy())


     @require_multi_gpu
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          print(F'Found {torch.cuda.device_count()} devices, using 2 devices only')
          __A		:					Optional[int]						=			['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
          with patch_environment(omp_num_threads=1		,      cuda_visible_devices='0,1'):
               execute_subprocess_async(_UpperCAmelCase		,      env=os.environ.copy())


if __name__ == "__main__":
  lowercase__       : List[Any]         =       Accelerator()
  lowercase__       : Optional[int]         =       (accelerator.state.process_index + 2, 10)
  lowercase__       : Optional[int]         =       torch.randint(0, 10, shape).to(accelerator.device)

  lowercase__       : Optional[Any]         =       ''''''

  lowercase__       : Dict         =       accelerator.pad_across_processes(tensor)
  if tensora.shape[0] != accelerator.state.num_processes + 1:
    error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
  if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
    error_msg += "Tensors have different values."
  if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
    error_msg += "Padding was not done with the right value (0)."

  lowercase__       : Any         =       accelerator.pad_across_processes(tensor, pad_first=True)
  if tensora.shape[0] != accelerator.state.num_processes + 1:
    error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
  lowercase__       : str         =       accelerator.state.num_processes - accelerator.state.process_index - 1
  if not torch.equal(tensora[index:], tensor):
    error_msg += "Tensors have different values."
  if not torch.all(tensora[:index] == 0):
    error_msg += "Padding was not done with the right value (0)."

  # Raise error at the end to make sure we don't stop at the first failure.
  if len(error_msg) > 0:
    raise ValueError(error_msg)



'''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_			:  Dict										=   {
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  List[Any]										=   ['DistilBertTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  int										=   [
     'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DistilBertForMaskedLM',
     'DistilBertForMultipleChoice',
     'DistilBertForQuestionAnswering',
     'DistilBertForSequenceClassification',
     'DistilBertForTokenClassification',
     'DistilBertModel',
     'DistilBertPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[int]										=   [
     'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFDistilBertForMaskedLM',
     'TFDistilBertForMultipleChoice',
     'TFDistilBertForQuestionAnswering',
     'TFDistilBertForSequenceClassification',
     'TFDistilBertForTokenClassification',
     'TFDistilBertMainLayer',
     'TFDistilBertModel',
     'TFDistilBertPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  List[str]										=   [
     'FlaxDistilBertForMaskedLM',
     'FlaxDistilBertForMultipleChoice',
     'FlaxDistilBertForQuestionAnswering',
     'FlaxDistilBertForSequenceClassification',
     'FlaxDistilBertForTokenClassification',
     'FlaxDistilBertModel',
     'FlaxDistilBertPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_distilbert import (
     DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DistilBertConfig,
     DistilBertOnnxConfig,
 )
 from .tokenization_distilbert import DistilBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_distilbert_fast import DistilBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_distilbert import (
      DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DistilBertForMaskedLM,
      DistilBertForMultipleChoice,
      DistilBertForQuestionAnswering,
      DistilBertForSequenceClassification,
      DistilBertForTokenClassification,
      DistilBertModel,
      DistilBertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertMainLayer,
      TFDistilBertModel,
      TFDistilBertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_distilbert import (
      FlaxDistilBertForMaskedLM,
      FlaxDistilBertForMultipleChoice,
      FlaxDistilBertForQuestionAnswering,
      FlaxDistilBertForSequenceClassification,
      FlaxDistilBertForTokenClassification,
      FlaxDistilBertModel,
      FlaxDistilBertPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase_			:  Optional[Any]										=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






