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


_snake_case				:   List[str]     =				parse_flag_from_env("""RUN_SLOW""", default=False)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						return unittest.skip("Test was skipped"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str  ):
						return unittest.skipUnless(_run_slow_tests	,			"test is slow"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]  ):
						return unittest.skipUnless(not torch.cuda.is_available()	,			"test requires only a CPU"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Dict  ):
						return unittest.skipUnless(torch.cuda.is_available()	,			"test requires a GPU"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]  ):
						return unittest.skipUnless(is_xpu_available()	,			"test requires a XPU"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]  ):
						return unittest.skipUnless(is_mps_available()	,			"test requires a `mps` backend support in `torch`"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):
						return unittest.skipUnless(
						    is_transformers_available() and is_datasets_available()	,			"test requires the Hugging Face suite"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):
						return unittest.skipUnless(is_bnb_available()	,			"test requires the bitsandbytes library"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str  ):
						return unittest.skipUnless(is_tpu_available()	,			"test requires TPU"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, Any]  ):
						return unittest.skipUnless(torch.cuda.device_count() == 1	,			"test requires a GPU"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Tuple  ):
						return unittest.skipUnless(torch.xpu.device_count() == 1	,			"test requires a XPU"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[int]  ):
						return unittest.skipUnless(torch.cuda.device_count() > 1	,			"test requires multiple GPUs"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]  ):
						return unittest.skipUnless(torch.xpu.device_count() > 1	,			"test requires multiple XPUs"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						return unittest.skipUnless(is_safetensors_available()	,			"test requires safetensors"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Tuple  ):
						return unittest.skipUnless(is_deepspeed_available()	,			"test requires DeepSpeed"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):
						return unittest.skipUnless(is_torch_version(">="	,			"1.12.0"  )	,			"test requires torch version >= 1.12.0"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]=None	,			_SCREAMING_SNAKE_CASE		:   Any=None  ):
						if test_case is None:
												return partial(_SCREAMING_SNAKE_CASE	,			version=_SCREAMING_SNAKE_CASE  )
						return unittest.skipUnless(is_torch_version(">="	,			_SCREAMING_SNAKE_CASE  )	,			F'test requires torch version >= {version}'  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[int]  ):
						return unittest.skipUnless(is_tensorboard_available()	,			"test requires Tensorboard"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						return unittest.skipUnless(is_wandb_available()	,			"test requires wandb"  )(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Dict  ):
						return unittest.skipUnless(is_comet_ml_available()	,			"test requires comet_ml"  )(_SCREAMING_SNAKE_CASE  )


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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[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"	,			)(_SCREAMING_SNAKE_CASE  )


class 			lowerCAmelCase (				unittest.TestCase			):
		a							:					List[Any]       =					True




		@classmethod
		def     lowercase       (				cls      ):
								_SCREAMING_SNAKE_CASE				= tempfile.mkdtemp()




		@classmethod
		def     lowercase       (				cls      ):
								if os.path.exists(cls.tmpdir      ):
														shutil.rmtree(cls.tmpdir      )




		def     lowercase       (				self      ):
								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     lowercase       (				self      ):
								super().tearDown()
								# Reset the state of the AcceleratorState singleton.
								AcceleratorState._reset_state()
								PartialState._reset_state()


class 			lowerCAmelCase (				unittest.TestCase			):




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= mocks if isinstance(UpperCamelCase					,						(tuple, list)      ) else [mocks]
								for m in self.mocks:
														m.start()
														self.addCleanup(m.stop      )



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


class 			lowerCAmelCase :




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= returncode
								_SCREAMING_SNAKE_CASE				= stdout
								_SCREAMING_SNAKE_CASE				= stderr



async def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[int]	,			_SCREAMING_SNAKE_CASE		:   int  ):
						while True:
												_SCREAMING_SNAKE_CASE				= await stream.readline()
												if line:
																		callback(_SCREAMING_SNAKE_CASE  )
												else:
																		break



async def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   Dict=None	,			_SCREAMING_SNAKE_CASE		:   List[str]=None	,			_SCREAMING_SNAKE_CASE		:   Any=None	,			_SCREAMING_SNAKE_CASE		:   List[str]=False	,			_SCREAMING_SNAKE_CASE		:   Tuple=False  ):
						if echo:
												print("\nRunning: "	,			" ".join(_SCREAMING_SNAKE_CASE  )  )

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

						# 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)

						_SCREAMING_SNAKE_CASE				= []
						_SCREAMING_SNAKE_CASE				= []

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

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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]	,			_SCREAMING_SNAKE_CASE		:   Dict=None	,			_SCREAMING_SNAKE_CASE		:   Dict=None	,			_SCREAMING_SNAKE_CASE		:   List[Any]=180	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]=False	,			_SCREAMING_SNAKE_CASE		:   Dict=True  ):
						_SCREAMING_SNAKE_CASE				= asyncio.get_event_loop()
						_SCREAMING_SNAKE_CASE				= loop.run_until_complete(
						    _stream_subprocess(_SCREAMING_SNAKE_CASE	,			env=_SCREAMING_SNAKE_CASE	,			stdin=_SCREAMING_SNAKE_CASE	,			timeout=_SCREAMING_SNAKE_CASE	,			quiet=_SCREAMING_SNAKE_CASE	,			echo=_SCREAMING_SNAKE_CASE  )  )

						_SCREAMING_SNAKE_CASE				= " ".join(_SCREAMING_SNAKE_CASE  )
						if result.returncode > 0:
												_SCREAMING_SNAKE_CASE				= "\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 (				__UpperCAmelCase			):
		pass



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

'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case				:   List[Any]     =				logging.get_logger(__name__)

_snake_case				:   int     =				{
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Tuple       =					"""convbert"""




		def __init__(				self					,						UpperCamelCase=30_522					,						UpperCamelCase=768					,						UpperCamelCase=12					,						UpperCamelCase=12					,						UpperCamelCase=3_072					,						UpperCamelCase="gelu"					,						UpperCamelCase=0.1					,						UpperCamelCase=0.1					,						UpperCamelCase=512					,						UpperCamelCase=2					,						UpperCamelCase=0.02					,						UpperCamelCase=1e-12					,						UpperCamelCase=1					,						UpperCamelCase=0					,						UpperCamelCase=2					,						UpperCamelCase=768					,						UpperCamelCase=2					,						UpperCamelCase=9					,						UpperCamelCase=1					,						UpperCamelCase=None					,						**UpperCamelCase					,						):
								super().__init__(
								    pad_token_id=UpperCamelCase					,						bos_token_id=UpperCamelCase					,						eos_token_id=UpperCamelCase					,						**UpperCamelCase					,						)

								_SCREAMING_SNAKE_CASE				= vocab_size
								_SCREAMING_SNAKE_CASE				= hidden_size
								_SCREAMING_SNAKE_CASE				= num_hidden_layers
								_SCREAMING_SNAKE_CASE				= num_attention_heads
								_SCREAMING_SNAKE_CASE				= intermediate_size
								_SCREAMING_SNAKE_CASE				= hidden_act
								_SCREAMING_SNAKE_CASE				= hidden_dropout_prob
								_SCREAMING_SNAKE_CASE				= attention_probs_dropout_prob
								_SCREAMING_SNAKE_CASE				= max_position_embeddings
								_SCREAMING_SNAKE_CASE				= type_vocab_size
								_SCREAMING_SNAKE_CASE				= initializer_range
								_SCREAMING_SNAKE_CASE				= layer_norm_eps
								_SCREAMING_SNAKE_CASE				= embedding_size
								_SCREAMING_SNAKE_CASE				= head_ratio
								_SCREAMING_SNAKE_CASE				= conv_kernel_size
								_SCREAMING_SNAKE_CASE				= num_groups
								_SCREAMING_SNAKE_CASE				= classifier_dropout


class 			lowerCAmelCase (				__UpperCAmelCase			):




		@property
		def     lowercase       (				self      ):
								if self.task == "multiple-choice":
														_SCREAMING_SNAKE_CASE				= {0: "batch", 1: "choice", 2: "sequence"}
								else:
														_SCREAMING_SNAKE_CASE				= {0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								        ("token_type_ids", dynamic_axis),
								    ]      )