




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						(__A   :    List[str]   ,				__A   :    Optional[Any]=False       )   ->      List[Any]:







     """simple docstring"""


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


snake_case_ : Any    		=       parse_flag_from_env("RUN_SLOW", default=False)


def    A						(__A   :    Any       )   ->      Tuple:







     """simple docstring"""


     return unittest.skip('''Test was skipped'''       )(__A       )


def    A						(__A   :    int       )   ->      Dict:







     """simple docstring"""


     return unittest.skipUnless(_run_slow_tests   ,				'''test is slow'''       )(__A       )


def    A						(__A   :    Dict       )   ->      int:







     """simple docstring"""


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


def    A						(__A   :    Dict       )   ->      Tuple:







     """simple docstring"""


     return unittest.skipUnless(torch.cuda.is_available()   ,				'''test requires a GPU'''       )(__A       )


def    A						(__A   :    Optional[Any]       )   ->      Any:







     """simple docstring"""


     return unittest.skipUnless(is_xpu_available()   ,				'''test requires a XPU'''       )(__A       )


def    A						(__A   :    List[Any]       )   ->      str:







     """simple docstring"""


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


def    A						(__A   :    int       )   ->      Optional[int]:







     """simple docstring"""


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


def    A						(__A   :    Any       )   ->      List[Any]:







     """simple docstring"""


     return unittest.skipUnless(is_bnb_available()   ,				'''test requires the bitsandbytes library'''       )(__A       )


def    A						(__A   :    List[Any]       )   ->      List[str]:







     """simple docstring"""


     return unittest.skipUnless(is_tpu_available()   ,				'''test requires TPU'''       )(__A       )


def    A						(__A   :    List[Any]       )   ->      Optional[Any]:







     """simple docstring"""


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


def    A						(__A   :    List[str]       )   ->      List[str]:







     """simple docstring"""


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


def    A						(__A   :    Optional[int]       )   ->      Union[str, Any]:







     """simple docstring"""


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


def    A						(__A   :    Optional[Any]       )   ->      Union[str, Any]:







     """simple docstring"""


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


def    A						(__A   :    Union[str, Any]       )   ->      int:







     """simple docstring"""


     return unittest.skipUnless(is_safetensors_available()   ,				'''test requires safetensors'''       )(__A       )


def    A						(__A   :    Dict       )   ->      Optional[int]:







     """simple docstring"""


     return unittest.skipUnless(is_deepspeed_available()   ,				'''test requires DeepSpeed'''       )(__A       )


def    A						(__A   :    Union[str, Any]       )   ->      str:







     """simple docstring"""


     return unittest.skipUnless(is_torch_version('''>='''   ,				'''1.12.0'''       )   ,				'''test requires torch version >= 1.12.0'''       )(__A       )


def    A						(__A   :    Any=None   ,				__A   :    List[Any]=None       )   ->      List[str]:







     """simple docstring"""


     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						(__A   :    Union[str, Any]       )   ->      Any:







     """simple docstring"""


     return unittest.skipUnless(is_tensorboard_available()   ,				'''test requires Tensorboard'''       )(__A       )


def    A						(__A   :    Union[str, Any]       )   ->      str:







     """simple docstring"""


     return unittest.skipUnless(is_wandb_available()   ,				'''test requires wandb'''       )(__A       )


def    A						(__A   :    Any       )   ->      Optional[Any]:







     """simple docstring"""


     return unittest.skipUnless(is_comet_ml_available()   ,				'''test requires comet_ml'''       )(__A       )


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


def    A						(__A   :    Union[str, Any]       )   ->      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'''   ,				)(__A       )







class __snake_case			(			unittest.TestCase		):
   UpperCAmelCase__  :    List[Any]          =  True







   @classmethod
   def lowerCamelCase						(      cls		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							tempfile.mkdtemp()







   @classmethod
   def lowerCamelCase						(      cls		:						Dict):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Tuple):

        """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(_snake_case)







class __snake_case			(			unittest.TestCase		):







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


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







class __snake_case			(			unittest.TestCase		):







   def lowerCamelCase						(      self		:						Optional[Any]					,						_snake_case		:						Union[mock.Mock, List[mock.Mock]]):

        """simple docstring"""


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







def    A						(__A   :    Optional[int]       )   ->      List[str]:







     """simple docstring"""


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







class __snake_case			:







   def __init__(      self		:						List[Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						List[Any]					,						_snake_case		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							returncode
        UpperCAmelCase_          =							stdout
        UpperCAmelCase_          =							stderr







async def    A						(__A   :    List[Any]   ,				__A   :    Union[str, Any]       )   ->      Tuple:







     """simple docstring"""


     while True:
          UpperCAmelCase_          =							await stream.readline()
          if line:
               callback(__A       )
          else:
               break


async def    A						(__A   :    Optional[int]   ,				__A   :    Optional[int]=None   ,				__A   :    Any=None   ,				__A   :    List[Any]=None   ,				__A   :    Any=False   ,				__A   :    Optional[int]=False       )   ->      _RunOutput:







     """simple docstring"""


     if echo:
          print('''\nRunning: '''   ,				''' '''.join(__A       )       )

     UpperCAmelCase_          =							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)

     UpperCAmelCase_          =							[]
     UpperCAmelCase_          =							[]

     def tee(__A   :    Optional[int]   ,				__A   :    List[Any]   ,				__A   :    str   ,				__A   :    Any=""       ):
          UpperCAmelCase_          =							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 __A       : tee(__A   ,				__A   ,				sys.stdout   ,				label='''stdout:'''       )       )       ),
             asyncio.create_task(_read_stream(p.stderr   ,				lambda __A       : tee(__A   ,				__A   ,				sys.stderr   ,				label='''stderr:'''       )       )       ),
         ]   ,				timeout=__A   ,				)
     return _RunOutput(await p.wait()   ,				__A   ,				__A       )


def    A						(__A   :    Dict   ,				__A   :    Any=None   ,				__A   :    Any=None   ,				__A   :    List[Any]=180   ,				__A   :    Optional[int]=False   ,				__A   :    Any=True       )   ->      _RunOutput:







     """simple docstring"""


     UpperCAmelCase_          =							asyncio.get_event_loop()
     UpperCAmelCase_          =							loop.run_until_complete(
         _stream_subprocess(__A   ,				env=__A   ,				stdin=__A   ,				timeout=__A   ,				quiet=__A   ,				echo=__A       )       )

     UpperCAmelCase_          =							''' '''.join(__A       )
     if result.returncode > 0:
          UpperCAmelCase_          =							'''\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 __snake_case			(			a		):
   pass







def    A						(__A   :    List[str]   ,				__A   :    Tuple=False       )   ->      Optional[Any]:







     """simple docstring"""


     try:
          UpperCAmelCase_          =							subprocess.check_output(__A   ,				stderr=subprocess.STDOUT       )
          if return_stdout:
               if hasattr(__A   ,				'''decode'''       ):
                    UpperCAmelCase_          =							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 collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

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


if TYPE_CHECKING:
			from ...onnx.config import PatchingSpec
			from ...tokenization_utils_base import PreTrainedTokenizerBase


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

_lowerCamelCase       :				Union[str, Any]       =			{
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Dict       =					"longformer"



	def __init__(  self	:    Optional[Any]			,  lowercase	:    Union[List[int], int] = 512			,  lowercase	:    int = 2			,  lowercase	:    int = 1			,  lowercase	:    int = 0			,  lowercase	:    int = 2			,  lowercase	:    int = 30_522			,  lowercase	:    int = 768			,  lowercase	:    int = 12			,  lowercase	:    int = 12			,  lowercase	:    int = 3_072			,  lowercase	:    str = "gelu"			,  lowercase	:    float = 0.1			,  lowercase	:    float = 0.1			,  lowercase	:    int = 512			,  lowercase	:    int = 2			,  lowercase	:    float = 0.02			,  lowercase	:    float = 1E-12			,  lowercase	:    bool = False			,  **lowercase	:    Optional[Any]			,  ):


				'''simple docstring'''


				super().__init__(pad_token_id=lowercase			,  **lowercase	)

				_snake_case		=			attention_window
				_snake_case		=			sep_token_id
				_snake_case		=			bos_token_id
				_snake_case		=			eos_token_id
				_snake_case		=			vocab_size
				_snake_case		=			hidden_size
				_snake_case		=			num_hidden_layers
				_snake_case		=			num_attention_heads
				_snake_case		=			hidden_act
				_snake_case		=			intermediate_size
				_snake_case		=			hidden_dropout_prob
				_snake_case		=			attention_probs_dropout_prob
				_snake_case		=			max_position_embeddings
				_snake_case		=			type_vocab_size
				_snake_case		=			initializer_range
				_snake_case		=			layer_norm_eps
				_snake_case		=			onnx_export




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	def __init__(  self	:    int			,  lowercase	:    "PretrainedConfig"			,  lowercase	:    str = "default"			,  lowercase	:    "List[PatchingSpec]" = None	):


				'''simple docstring'''


				super().__init__(lowercase			,  lowercase			,  lowercase	)
				_snake_case		=			True



	@property
	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				if self.task == "multiple-choice":
							_snake_case		=			{0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
							_snake_case		=			{0: 'batch', 1: 'sequence'}
				return OrderedDict(
				    [
				        ('input_ids', dynamic_axis),
				        ('attention_mask', dynamic_axis),
				        ('global_attention_mask', dynamic_axis),
				    ]	)



	@property
	def  A							(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			super().outputs
				if self.task == "default":
							_snake_case		=			{0: 'batch'}
				return outputs



	@property
	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				return 1E-4



	@property
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				return max(super().default_onnx_opset			,  14	)





	def  A							(  self	:    str			,  lowercase	:    "PreTrainedTokenizerBase"			,  lowercase	:    int = -1			,  lowercase	:    int = -1			,  lowercase	:    bool = False			,  lowercase	:    Optional[TensorType] = None			,  ):


				'''simple docstring'''


				_snake_case		=			super().generate_dummy_inputs(
				    preprocessor=lowercase			,  batch_size=lowercase			,  seq_length=lowercase			,  is_pair=lowercase			,  framework=lowercase	)
				import torch

				# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
				# makes the export fail randomly
				_snake_case		=			torch.zeros_like(inputs['input_ids']	)
				# make every second token global
				_snake_case		=			1

				return inputs