


"""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 _lowerCAmelCase       (   unittest.TestCase			):







   def 				lowerCamelCase	(			self		)			->	List[Any]:

       '''simple docstring'''




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







   @require_multi_gpu
   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       print(F'Found {torch.cuda.device_count()} devices.'		)
       snake_case					:   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 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       print(F'Found {torch.cuda.device_count()} devices.'		)
       snake_case					:   Optional[int]         =					["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 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   List[str]         =					["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 				lowerCamelCase	(			self		)			->	List[Any]:

       '''simple docstring'''




       print(F'Found {torch.cuda.device_count()} devices, using 2 devices only'		)
       snake_case					:   Union[str, Any]         =					["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__":
  __snake_case         =					Accelerator()
  __snake_case         =					(accelerator.state.process_index + 2, 10)
  __snake_case         =					torch.randint(0, 10, shape).to(accelerator.device)

  __snake_case         =					""""""

  __snake_case         =					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)."

  __snake_case         =					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."
  __snake_case         =					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,
)


__snake_case         =					{
    """configuration_distilbert""": [
        """DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DistilBertConfig""",
        """DistilBertOnnxConfig""",
    ],
    """tokenization_distilbert""": ["""DistilBertTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case         =					["""DistilBertTokenizerFast"""]

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

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case         =					[
      """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:
  __snake_case         =					[
      """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

  __snake_case         =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





