


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 					UpperCamelCase   (      __magic_name__	:				Dict       ,       __magic_name__	:				Union[str, Any]=False	)  ->    Dict:
		"""simple docstring"""




		try:
				lowercase__	     =						os.environ[key]
		except KeyError:
				# KEY isn't set, default to `default`.
				lowercase__	     =						default
		else:
				# KEY is set, convert it to True or False.
				try:
						lowercase__	     =						strtobool(__magic_name__	)
				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


A			:						int        =  parse_flag_from_env('RUN_SLOW', default=False)






def 					UpperCamelCase   (      __magic_name__	:				Tuple	)  ->    Dict:
		"""simple docstring"""




		return unittest.skip("""Test was skipped"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				str	)  ->    int:
		"""simple docstring"""




		return unittest.skipUnless(_run_slow_tests       ,       """test is slow"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				List[str]	)  ->    Union[str, Any]:
		"""simple docstring"""




		return unittest.skipUnless(not torch.cuda.is_available()       ,       """test requires only a CPU"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				Any	)  ->    str:
		"""simple docstring"""




		return unittest.skipUnless(torch.cuda.is_available()       ,       """test requires a GPU"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		return unittest.skipUnless(is_xpu_available()       ,       """test requires a XPU"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				Any	)  ->    str:
		"""simple docstring"""




		return unittest.skipUnless(is_mps_available()       ,       """test requires a `mps` backend support in `torch`"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]	)  ->    Union[str, Any]:
		"""simple docstring"""




		return unittest.skipUnless(
		    is_transformers_available() and is_datasets_available()       ,       """test requires the Hugging Face suite"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				List[Any]	)  ->    str:
		"""simple docstring"""




		return unittest.skipUnless(is_bnb_available()       ,       """test requires the bitsandbytes library"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				str	)  ->    Union[str, Any]:
		"""simple docstring"""




		return unittest.skipUnless(is_tpu_available()       ,       """test requires TPU"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				List[Any]	)  ->    Tuple:
		"""simple docstring"""




		return unittest.skipUnless(torch.cuda.device_count() == 1       ,       """test requires a GPU"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				Optional[int]	)  ->    Optional[int]:
		"""simple docstring"""




		return unittest.skipUnless(torch.xpu.device_count() == 1       ,       """test requires a XPU"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    Optional[Any]:
		"""simple docstring"""




		return unittest.skipUnless(torch.cuda.device_count() > 1       ,       """test requires multiple GPUs"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				Optional[int]	)  ->    Tuple:
		"""simple docstring"""




		return unittest.skipUnless(torch.xpu.device_count() > 1       ,       """test requires multiple XPUs"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				List[Any]	)  ->    Optional[Any]:
		"""simple docstring"""




		return unittest.skipUnless(is_safetensors_available()       ,       """test requires safetensors"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				List[Any]	)  ->    Union[str, Any]:
		"""simple docstring"""




		return unittest.skipUnless(is_deepspeed_available()       ,       """test requires DeepSpeed"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				Tuple	)  ->    Tuple:
		"""simple docstring"""




		return unittest.skipUnless(is_torch_version(""">="""       ,       """1.12.0"""	)       ,       """test requires torch version >= 1.12.0"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]=None       ,       __magic_name__	:				Optional[Any]=None	)  ->    Optional[int]:
		"""simple docstring"""




		if test_case is None:
				return partial(__magic_name__       ,       version=__magic_name__	)
		return unittest.skipUnless(is_torch_version(""">="""       ,       __magic_name__	)       ,       f'''test requires torch version >= {version}'''	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    str:
		"""simple docstring"""




		return unittest.skipUnless(is_tensorboard_available()       ,       """test requires Tensorboard"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    List[Any]:
		"""simple docstring"""




		return unittest.skipUnless(is_wandb_available()       ,       """test requires wandb"""	)(__magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				Dict	)  ->    str:
		"""simple docstring"""




		return unittest.skipUnless(is_comet_ml_available()       ,       """test requires comet_ml"""	)(__magic_name__	)


A			:						Any        =  (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)






def 					UpperCamelCase   (      __magic_name__	:				Dict	)  ->    Tuple:
		"""simple docstring"""




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



class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					A__     =					True

					@classmethod
					def     lowerCamelCase__	(cls       :		Dict						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						tempfile.mkdtemp()

					@classmethod
					def     lowerCamelCase__	(cls       :		str						)		->  Optional[Any]:




							"""simple docstring"""


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


					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  Dict:




							"""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 				A       (			unittest.TestCase				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		Dict						)		->  List[Any]:




							"""simple docstring"""


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






class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		Union[str, Any] ,   _UpperCAmelCase       :		Union[mock.Mock, List[mock.Mock]]						)		->  int:




							"""simple docstring"""


							lowercase__	     =						mocks if isinstance(_UpperCAmelCase ,   (tuple, list)						) else [mocks]
							for m in self.mocks:
									m.start()
									self.addCleanup(m.stop						)






def 					UpperCamelCase   (      __magic_name__	:				Dict	)  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						AcceleratorState()
		lowercase__	     =						tensor[None].clone().to(state.device	)
		lowercase__	     =						gather(__magic_name__	).cpu()
		lowercase__	     =						tensor[0].cpu()
		for i in range(tensors.shape[0]	):
				if not torch.equal(tensors[i]       ,       __magic_name__	):
						return False
		return True



class 				A       :
					'''simple docstring'''






					def __init__(self       :		Any ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Optional[Any]						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						returncode
							lowercase__	     =						stdout
							lowercase__	     =						stderr






async def 					UpperCamelCase   (      __magic_name__	:				List[str]       ,       __magic_name__	:				Optional[Any]	)  ->    Optional[int]:
		"""simple docstring"""




		while True:
				lowercase__	     =						await stream.readline()
				if line:
						callback(__magic_name__	)
				else:
						break






async def 					UpperCamelCase   (      __magic_name__	:				Dict       ,       __magic_name__	:				Dict=None       ,       __magic_name__	:				List[Any]=None       ,       __magic_name__	:				Tuple=None       ,       __magic_name__	:				int=False       ,       __magic_name__	:				Any=False	)  ->    _RunOutput:
		"""simple docstring"""




		if echo:
				print("""\nRunning: """       ,       """ """.join(__magic_name__	)	)

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

		# 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)

		lowercase__	     =						[]
		lowercase__	     =						[]

		def tee(__magic_name__	:				Union[str, Any]       ,       __magic_name__	:				int       ,       __magic_name__	:				List[str]       ,       __magic_name__	:				Any=""	):
				lowercase__	     =						line.decode("""utf-8"""	).rstrip()
				sink.append(__magic_name__	)
				if not quiet:
						print(__magic_name__       ,       __magic_name__       ,       file=__magic_name__	)

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






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]       ,       __magic_name__	:				str=None       ,       __magic_name__	:				Dict=None       ,       __magic_name__	:				Tuple=180       ,       __magic_name__	:				str=False       ,       __magic_name__	:				Any=True	)  ->    _RunOutput:
		"""simple docstring"""




		lowercase__	     =						asyncio.get_event_loop()
		lowercase__	     =						loop.run_until_complete(
		    _stream_subprocess(__magic_name__       ,       env=__magic_name__       ,       stdin=__magic_name__       ,       timeout=__magic_name__       ,       quiet=__magic_name__       ,       echo=__magic_name__	)	)

		lowercase__	     =						""" """.join(__magic_name__	)
		if result.returncode > 0:
				lowercase__	     =						"""\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 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					pass






def 					UpperCamelCase   (      __magic_name__	:				List[str]       ,       __magic_name__	:				Optional[int]=False	)  ->    Dict:
		"""simple docstring"""




		try:
				lowercase__	     =						subprocess.check_output(__magic_name__       ,       stderr=subprocess.STDOUT	)
				if return_stdout:
						if hasattr(__magic_name__       ,       """decode"""	):
								lowercase__	     =						output.decode("""utf-8"""	)
						return output
		except subprocess.CalledProcessError as e:
				raise SubprocessCallException(
				    f'''Command `{" ".join(__magic_name__	)}` failed with the following error:\n\n{e.output.decode()}'''	) from e



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A_    							=					{
    """configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """GPTNeoForCausalLM""",
						    """GPTNeoForQuestionAnswering""",
						    """GPTNeoForSequenceClassification""",
						    """GPTNeoForTokenClassification""",
						    """GPTNeoModel""",
						    """GPTNeoPreTrainedModel""",
						    """load_tf_weights_in_gpt_neo""",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """FlaxGPTNeoForCausalLM""",
						    """FlaxGPTNeoModel""",
						    """FlaxGPTNeoPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gpt_neo import (
												    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTNeoForCausalLM,
												    GPTNeoForQuestionAnswering,
												    GPTNeoForSequenceClassification,
												    GPTNeoForTokenClassification,
												    GPTNeoModel,
												    GPTNeoPreTrainedModel,
												    load_tf_weights_in_gpt_neo,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
						import sys

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



