





import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)






def       _A  (      lowerCamelCase							, lowerCamelCase=False   ):
   try:
      a__	:						List[str]      		=  os.environ[key]
   except KeyError:
      # KEY isn't set, default to `default`.
      a__	:						Optional[int]      		=  default
   else:
      # KEY is set, convert it to True or False.
      try:
         a__	:						List[str]      		=  strtobool(__A   )
      except ValueError:
         # More values are supported, but let's keep the message simple.
         raise ValueError(F"""If set, {key} must be yes or no."""   )
   return _value


SCREAMING_SNAKE_CASE__       :			str			      =						parse_flag_from_env("""RUN_SLOW""", default=False)






def       _A  (      lowerCamelCase   ):
   return unittest.skip("Test was skipped"   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(_run_slow_tests							, "test is slow"   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(not torch.cuda.is_available()							, "test requires only a CPU"   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(torch.cuda.is_available()							, "test requires a GPU"   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(is_xpu_available()							, "test requires a XPU"   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(is_mps_available()							, "test requires a `mps` backend support in `torch`"   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(
       is_transformers_available() and is_datasets_available()							, "test requires the Hugging Face suite"   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(is_bnb_available()							, "test requires the bitsandbytes library"   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(is_tpu_available()							, "test requires TPU"   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(torch.cuda.device_count() == 1							, "test requires a GPU"   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(torch.xpu.device_count() == 1							, "test requires a XPU"   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(torch.cuda.device_count() > 1							, "test requires multiple GPUs"   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(torch.xpu.device_count() > 1							, "test requires multiple XPUs"   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(is_safetensors_available()							, "test requires safetensors"   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(is_deepspeed_available()							, "test requires DeepSpeed"   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(is_torch_version(">="							, "1.12.0"   )							, "test requires torch version >= 1.12.0"   )(__A   )






def       _A  (      lowerCamelCase=None							, lowerCamelCase=None   ):
   if test_case is None:
      return partial(__A							, version=__A   )
   return unittest.skipUnless(is_torch_version(">="							, __A   )							, F"""test requires torch version >= {version}"""   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(is_tensorboard_available()							, "test requires Tensorboard"   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(is_wandb_available()							, "test requires wandb"   )(__A   )






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(is_comet_ml_available()							, "test requires comet_ml"   )(__A   )


SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)






def       _A  (      lowerCamelCase   ):
   return unittest.skipUnless(
       _atleast_one_tracker_available							, "test requires at least one tracker to be available and for `comet_ml` to not be installed"							, )(__A   )







class   __lowerCAmelCase			(					unittest.TestCase  ):
   _UpperCamelCase :						Dict     		=				True





   @classmethod
   def 	_snake_case ( cls      )				->  List[Any]:
      """simple docstring"""





      a__	:						Optional[int]      		=  tempfile.mkdtemp()





   @classmethod
   def 	_snake_case ( cls      )				->  List[Any]:
      """simple docstring"""





      if os.path.exists(cls.tmpdir      ):
         shutil.rmtree(cls.tmpdir      )





   def 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





      if self.clear_on_setup:
         for path in Path(self.tmpdir      ).glob("**/*"      ):
            if path.is_file():
               path.unlink()
            elif path.is_dir():
               shutil.rmtree(_UpperCamelCase      )







class   __lowerCAmelCase			(					unittest.TestCase  ):





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      super().tearDown()
      # Reset the state of the AcceleratorState singleton.
      AcceleratorState._reset_state()
      PartialState._reset_state()







class   __lowerCAmelCase			(					unittest.TestCase  ):





   def 	_snake_case ( self					,			snake_case      )				->  List[str]:
      """simple docstring"""





      a__	:						Optional[Any]      		=  mocks if isinstance(_UpperCamelCase					,			(tuple, list)      ) else [mocks]
      for m in self.mocks:
         m.start()
         self.addCleanup(m.stop      )







def       _A  (      lowerCamelCase   ):
   a__	:						Tuple      		=  AcceleratorState()
   a__	:						Tuple      		=  tensor[None].clone().to(state.device   )
   a__	:						int      		=  gather(__A   ).cpu()
   a__	:						Optional[int]      		=  tensor[0].cpu()
   for i in range(tensors.shape[0]   ):
      if not torch.equal(tensors[i]							, __A   ):
         return False
   return True







class   __lowerCAmelCase			:





   def __init__( self					,			snake_case					,			snake_case					,			snake_case      )				->  List[str]:
      """simple docstring"""





      a__	:						List[str]      		=  returncode
      a__	:						Union[str, Any]      		=  stdout
      a__	:						List[Any]      		=  stderr







async def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   while True:
      a__	:						List[str]      		=  await stream.readline()
      if line:
         callback(__A   )
      else:
         break






async def       _A  (      lowerCamelCase							, lowerCamelCase=None							, lowerCamelCase=None							, lowerCamelCase=None							, lowerCamelCase=False							, lowerCamelCase=False   ):
   if echo:
      print("\nRunning: "							, " ".join(__A   )   )

   a__	:						Dict      		=  await asyncio.create_subprocess_exec(
       cmd[0]							, *cmd[1:]							, stdin=__A							, stdout=asyncio.subprocess.PIPE							, stderr=asyncio.subprocess.PIPE							, env=__A							, )

   # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
   # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
   #
   # If it starts hanging, will need to switch to the following code. The problem is that no data
   # will be seen until it's done and if it hangs for example there will be no debug info.
   # out, err = await p.communicate()
   # return _RunOutput(p.returncode, out, err)

   a__	:						int      		=  []
   a__	:						int      		=  []

   def tee(lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase=""   ):
      a__	:						Dict      		=  line.decode("utf-8"   ).rstrip()
      sink.append(__A   )
      if not quiet:
         print(__A							, __A							, file=__A   )

    # XXX: the timeout doesn't seem to make any difference here
   await asyncio.wait(
       [
           asyncio.create_task(_read_stream(p.stdout							, lambda lowerCamelCase   : tee(__A							, __A							, sys.stdout							, label="stdout:"   )   )   ),
           asyncio.create_task(_read_stream(p.stderr							, lambda lowerCamelCase   : tee(__A							, __A							, sys.stderr							, label="stderr:"   )   )   ),
       ]							, timeout=__A							, )
   return _RunOutput(await p.wait()							, __A							, __A   )






def       _A  (      lowerCamelCase							, lowerCamelCase=None							, lowerCamelCase=None							, lowerCamelCase=180							, lowerCamelCase=False							, lowerCamelCase=True   ):
   a__	:						List[Any]      		=  asyncio.get_event_loop()
   a__	:						Any      		=  loop.run_until_complete(
       _stream_subprocess(__A							, env=__A							, stdin=__A							, timeout=__A							, quiet=__A							, echo=__A   )   )

   a__	:						Optional[int]      		=  """ """.join(__A   )
   if result.returncode > 0:
      a__	:						Optional[Any]      		=  """\n""".join(result.stderr   )
      raise RuntimeError(
          F"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
          F"""The combined stderr from workers follows:\n{stderr}"""   )

   return result







class   __lowerCAmelCase			(					__lowerCAmelCase  ):
   pass







def       _A  (      lowerCamelCase							, lowerCamelCase=False   ):
   try:
      a__	:						Optional[Any]      		=  subprocess.check_output(__A							, stderr=subprocess.STDOUT   )
      if return_stdout:
         if hasattr(__A							, "decode"   ):
            a__	:						List[str]      		=  output.decode("utf-8"   )
         return output
   except subprocess.CalledProcessError as e:
      raise SubprocessCallException(
          F"""Command `{" ".join(__A   )}` failed with the following error:\n\n{e.output.decode()}"""   ) from e










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


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

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

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

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			List[Any]			      =						[
      """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:
  SCREAMING_SNAKE_CASE__       :			Any			      =						[
      """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

  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

