




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 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase=False					)  ->       Optional[Any]:
   try:
      snake_case				:    Tuple           =	os.environ[key]
   except KeyError:
      # KEY isn't set, default to `default`.
      snake_case				:    List[str]           =	default
   else:
      # KEY is set, convert it to True or False.
      try:
         snake_case				:    Union[str, Any]           =	strtobool(lowercase					)
      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


lowerCamelCase	:	Dict				=   parse_flag_from_env('RUN_SLOW', default=False)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[int]:
   return unittest.skip("""Test was skipped"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[int]:
   return unittest.skipUnless(_run_slow_tests						,"""test is slow"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Tuple:
   return unittest.skipUnless(not torch.cuda.is_available()						,"""test requires only a CPU"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Tuple:
   return unittest.skipUnless(torch.cuda.is_available()						,"""test requires a GPU"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[str]:
   return unittest.skipUnless(is_xpu_available()						,"""test requires a XPU"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[Any]:
   return unittest.skipUnless(is_mps_available()						,"""test requires a `mps` backend support in `torch`"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       str:
   return unittest.skipUnless(
       is_transformers_available() and is_datasets_available()						,"""test requires the Hugging Face suite"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[Any]:
   return unittest.skipUnless(is_bnb_available()						,"""test requires the bitsandbytes library"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[Any]:
   return unittest.skipUnless(is_tpu_available()						,"""test requires TPU"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Dict:
   return unittest.skipUnless(torch.cuda.device_count() == 1						,"""test requires a GPU"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Dict:
   return unittest.skipUnless(torch.xpu.device_count() == 1						,"""test requires a XPU"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Any:
   return unittest.skipUnless(torch.cuda.device_count() > 1						,"""test requires multiple GPUs"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[int]:
   return unittest.skipUnless(torch.xpu.device_count() > 1						,"""test requires multiple XPUs"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Dict:
   return unittest.skipUnless(is_safetensors_available()						,"""test requires safetensors"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[int]:
   return unittest.skipUnless(is_deepspeed_available()						,"""test requires DeepSpeed"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[Any]:
   return unittest.skipUnless(is_torch_version(""">="""						,"""1.12.0"""					)						,"""test requires torch version >= 1.12.0"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase=None						,lowercase=None					)  ->       int:
   if test_case is None:
      return partial(lowercase						,version=lowercase					)
   return unittest.skipUnless(is_torch_version(""">="""						,lowercase					)						,f"""test requires torch version >= {version}"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       int:
   return unittest.skipUnless(is_tensorboard_available()						,"""test requires Tensorboard"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Any:
   return unittest.skipUnless(is_wandb_available()						,"""test requires wandb"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       str:
   return unittest.skipUnless(is_comet_ml_available()						,"""test requires comet_ml"""					)(lowercase					)


lowerCamelCase	:	int				=   (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[Any]:
   return unittest.skipUnless(
       _atleast_one_tracker_available						,"""test requires at least one tracker to be available and for `comet_ml` to not be installed"""						,)(lowercase					)




class 						__lowercase		(unittest.TestCase							):




      """simple docstring"""





      _snake_case												=						True

      @classmethod
      def      UpperCAmelCase				(				cls     )		->    Union[str, Any]:
         snake_case				:    Any           =	tempfile.mkdtemp()

      @classmethod
      def      UpperCAmelCase				(				cls     )		->    str:
         if os.path.exists(cls.tmpdir     ):
            shutil.rmtree(cls.tmpdir     )

      def      UpperCAmelCase				(				self     )		->    Union[str, Any]:
         if self.clear_on_setup:
            for path in Path(self.tmpdir     ).glob("""**/*"""     ):
               if path.is_file():
                  path.unlink()
               elif path.is_dir():
                  shutil.rmtree(A     )




class 						__lowercase		(unittest.TestCase							):




      """simple docstring"""





      def      UpperCAmelCase				(				self     )		->    Tuple:
         super().tearDown()
         # Reset the state of the AcceleratorState singleton.
         AcceleratorState._reset_state()
         PartialState._reset_state()




class 						__lowercase		(unittest.TestCase							):




      """simple docstring"""





      def      UpperCAmelCase				(				self    ,				A     )		->    int:
         snake_case				:    Optional[Any]           =	mocks if isinstance(A    ,				(tuple, list)     ) else [mocks]
         for m in self.mocks:
            m.start()
            self.addCleanup(m.stop     )




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[Any]:
   snake_case				:    Union[str, Any]           =	AcceleratorState()
   snake_case				:    List[str]           =	tensor[None].clone().to(state.device					)
   snake_case				:    Optional[Any]           =	gather(lowercase					).cpu()
   snake_case				:    List[Any]           =	tensor[0].cpu()
   for i in range(tensors.shape[0]					):
      if not torch.equal(tensors[i]						,lowercase					):
         return False
   return True




class 						__lowercase		:




      """simple docstring"""





      def __init__(				self    ,				A    ,				A    ,				A     )		->    Any:
         snake_case				:    Dict           =	returncode
         snake_case				:    str           =	stdout
         snake_case				:    int           =	stderr




async def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Union[str, Any]:
   while True:
      snake_case				:    int           =	await stream.readline()
      if line:
         callback(lowercase					)
      else:
         break




async def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase=None						,lowercase=None						,lowercase=None						,lowercase=False						,lowercase=False					)  ->       _RunOutput:
   if echo:
      print("""\nRunning: """						,""" """.join(lowercase					)					)

   snake_case				:    Any           =	await asyncio.create_subprocess_exec(
       cmd[0]						,*cmd[1:]						,stdin=lowercase						,stdout=asyncio.subprocess.PIPE						,stderr=asyncio.subprocess.PIPE						,env=lowercase						,)

   # 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)

   snake_case				:    Optional[int]           =	[]
   snake_case				:    List[Any]           =	[]

   def tee(lowercase						,lowercase						,lowercase						,lowercase=""					):
      snake_case				:    List[Any]           =	line.decode("""utf-8"""					).rstrip()
      sink.append(lowercase					)
      if not quiet:
         print(lowercase						,lowercase						,file=lowercase					)

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




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase=None						,lowercase=None						,lowercase=180						,lowercase=False						,lowercase=True					)  ->       _RunOutput:
   snake_case				:    Optional[Any]           =	asyncio.get_event_loop()
   snake_case				:    Tuple           =	loop.run_until_complete(
       _stream_subprocess(lowercase						,env=lowercase						,stdin=lowercase						,timeout=lowercase						,quiet=lowercase						,echo=lowercase					)					)

   snake_case				:    List[Any]           =	""" """.join(lowercase					)
   if result.returncode > 0:
      snake_case				:    List[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 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      pass




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase=False					)  ->       List[Any]:
   try:
      snake_case				:    Dict           =	subprocess.check_output(lowercase						,stderr=subprocess.STDOUT					)
      if return_stdout:
         if hasattr(lowercase						,"""decode"""					):
            snake_case				:    Optional[Any]           =	output.decode("""utf-8"""					)
         return output
   except subprocess.CalledProcessError as e:
      raise SubprocessCallException(
          f"""Command `{" ".join(lowercase					)}` 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,
)


lowerCamelCase	:	Optional[Any]				=   {
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Tuple				=   ['RobertaTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	List[str]				=   [
           'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RobertaForCausalLM',
           'RobertaForMaskedLM',
           'RobertaForMultipleChoice',
           'RobertaForQuestionAnswering',
           'RobertaForSequenceClassification',
           'RobertaForTokenClassification',
           'RobertaModel',
           'RobertaPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Dict				=   [
           'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRobertaForCausalLM',
           'TFRobertaForMaskedLM',
           'TFRobertaForMultipleChoice',
           'TFRobertaForQuestionAnswering',
           'TFRobertaForSequenceClassification',
           'TFRobertaForTokenClassification',
           'TFRobertaMainLayer',
           'TFRobertaModel',
           'TFRobertaPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Any				=   [
           'FlaxRobertaForCausalLM',
           'FlaxRobertaForMaskedLM',
           'FlaxRobertaForMultipleChoice',
           'FlaxRobertaForQuestionAnswering',
           'FlaxRobertaForSequenceClassification',
           'FlaxRobertaForTokenClassification',
           'FlaxRobertaModel',
           'FlaxRobertaPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
       from .tokenization_roberta import RobertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roberta_fast import RobertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta import (
                  ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaForCausalLM,
                  RobertaForMaskedLM,
                  RobertaForMultipleChoice,
                  RobertaForQuestionAnswering,
                  RobertaForSequenceClassification,
                  RobertaForTokenClassification,
                  RobertaModel,
                  RobertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta import (
                  TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaForCausalLM,
                  TFRobertaForMaskedLM,
                  TFRobertaForMultipleChoice,
                  TFRobertaForQuestionAnswering,
                  TFRobertaForSequenceClassification,
                  TFRobertaForTokenClassification,
                  TFRobertaMainLayer,
                  TFRobertaModel,
                  TFRobertaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta import (
                  FlaxRobertaForCausalLM,
                  FlaxRobertaForMaskedLM,
                  FlaxRobertaForMultipleChoice,
                  FlaxRobertaForQuestionAnswering,
                  FlaxRobertaForSequenceClassification,
                  FlaxRobertaForTokenClassification,
                  FlaxRobertaModel,
                  FlaxRobertaPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase	:	str				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





