



"""simple docstring"""
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        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=False	)			->      Tuple:
		try:
				a_     :      List[str] 						=     os.environ[key]
		except KeyError:
				# KEY isn't set, default to `default`.
				a_     :      List[Any] 						=     default
		else:
				# KEY is set, convert it to True or False.
				try:
						a_     :      Dict 						=     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


SCREAMING_SNAKE_CASE_				= parse_flag_from_env("""RUN_SLOW""", default=False)



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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      List[Any]:
		return unittest.skipUnless(_run_slow_tests, "test is slow"	)(SCREAMING_SNAKE_CASE__	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      List[str]:
		return unittest.skipUnless(not torch.cuda.is_available(), "test requires only a CPU"	)(SCREAMING_SNAKE_CASE__	)



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



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



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



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



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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Optional[int]:
		return unittest.skipUnless(is_tpu_available(), "test requires TPU"	)(SCREAMING_SNAKE_CASE__	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      List[str]:
		return unittest.skipUnless(torch.cuda.device_count() == 1, "test requires a GPU"	)(SCREAMING_SNAKE_CASE__	)



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



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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Union[str, Any]:
		return unittest.skipUnless(torch.xpu.device_count() > 1, "test requires multiple XPUs"	)(SCREAMING_SNAKE_CASE__	)



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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
		return unittest.skipUnless(is_deepspeed_available(), "test requires DeepSpeed"	)(SCREAMING_SNAKE_CASE__	)



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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=None	)			->      Any:
		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        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Union[str, Any]:
		return unittest.skipUnless(is_tensorboard_available(), "test requires Tensorboard"	)(SCREAMING_SNAKE_CASE__	)



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



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


SCREAMING_SNAKE_CASE_				= (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Dict:
		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 			snake_case_						(						unittest.TestCase				):
							__lowerCAmelCase    					=					True






							@classmethod
							def snake_case_  (   cls      ):
									a_     :      Optional[int] 						=     tempfile.mkdtemp()






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






							def snake_case_  (   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(a_      )




class 			snake_case_						(						unittest.TestCase				):






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




class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self			,     a_      ):
									a_     :      Union[str, Any] 						=     mocks if isinstance(a_			,     (tuple, list)      ) else [mocks]
									for m in self.mocks:
											m.start()
											self.addCleanup(m.stop      )



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




class 			snake_case_						:






							def __init__(   self			,     a_			,     a_			,     a_      ):
									a_     :      Any 						=     returncode
									a_     :      Optional[Any] 						=     stdout
									a_     :      Optional[Any] 						=     stderr



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



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

		a_     :      Dict 						=     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)

		a_     :      str 						=     []
		a_     :      int 						=     []

		def tee(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=""	):
				a_     :      List[str] 						=     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        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=180, SCREAMING_SNAKE_CASE__=False, SCREAMING_SNAKE_CASE__=True	)			->      _RunOutput:
		a_     :      List[str] 						=     asyncio.get_event_loop()
		a_     :      List[str] 						=     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__	)	)

		a_     :      Any 						=     " ".join(SCREAMING_SNAKE_CASE__	)
		if result.returncode > 0:
				a_     :      Dict 						=     "\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        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=False	)			->      Any:
		try:
				a_     :      Union[str, Any] 						=     subprocess.check_output(SCREAMING_SNAKE_CASE__, stderr=subprocess.STDOUT	)
				if return_stdout:
						if hasattr(SCREAMING_SNAKE_CASE__, "decode"	):
								a_     :      Dict 						=     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 Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"blenderbot-small"
							__lowerCAmelCase    					=					["past_key_values"]
							__lowerCAmelCase    					=					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






							def __init__(   self			,     a_=5_0_2_6_5			,     a_=5_1_2			,     a_=8			,     a_=2_0_4_8			,     a_=1_6			,     a_=8			,     a_=2_0_4_8			,     a_=1_6			,     a_=0.0			,     a_=0.0			,     a_=True			,     a_=True			,     a_="gelu"			,     a_=5_1_2			,     a_=0.1			,     a_=0.0			,     a_=0.0			,     a_=0.02			,     a_=1			,     a_=False			,     a_=0			,     a_=1			,     a_=2			,     a_=2			,     **a_			,     ):
									a_     :      int 						=     vocab_size
									a_     :      Any 						=     max_position_embeddings
									a_     :      Optional[int] 						=     d_model
									a_     :      Tuple 						=     encoder_ffn_dim
									a_     :      List[Any] 						=     encoder_layers
									a_     :      Optional[int] 						=     encoder_attention_heads
									a_     :      Optional[int] 						=     decoder_ffn_dim
									a_     :      List[str] 						=     decoder_layers
									a_     :      Dict 						=     decoder_attention_heads
									a_     :      List[str] 						=     dropout
									a_     :      List[Any] 						=     attention_dropout
									a_     :      List[str] 						=     activation_dropout
									a_     :      Optional[Any] 						=     activation_function
									a_     :      List[Any] 						=     init_std
									a_     :      int 						=     encoder_layerdrop
									a_     :      Optional[int] 						=     decoder_layerdrop
									a_     :      List[str] 						=     use_cache
									a_     :      Optional[int] 						=     encoder_layers
									a_     :      Optional[int] 						=     scale_embedding  # scale factor will be sqrt(d_model) if True

									super().__init__(
									    pad_token_id=a_			,     bos_token_id=a_			,     eos_token_id=a_			,     is_encoder_decoder=a_			,     decoder_start_token_id=a_			,     forced_eos_token_id=a_			,     **a_			,     )




class 			snake_case_						(						a_				):






							@property
							def snake_case_  (   self      ):
									if self.task in ["default", "seq2seq-lm"]:
											a_     :      Optional[int] 						=     OrderedDict(
											    [
											        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
											        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
											    ]      )

											if self.use_past:
													a_     :      Tuple 						=     {0: "batch"}
													a_     :      int 						=     {0: "batch", 1: "past_decoder_sequence + sequence"}
											else:
													a_     :      List[str] 						=     {0: "batch", 1: "decoder_sequence"}
													a_     :      Optional[int] 						=     {0: "batch", 1: "decoder_sequence"}

											if self.use_past:
													self.fill_with_past_key_values_(a_			,     direction="inputs"      )
									elif self.task == "causal-lm":
											# TODO: figure this case out.
											a_     :      Dict 						=     OrderedDict(
											    [
											        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
											        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
											    ]      )
											if self.use_past:
													a_						,				a_     :      Optional[int] 						=     self.num_layers
													for i in range(a_      ):
															a_     :      Union[str, Any] 						=     {0: "batch", 2: "past_sequence + sequence"}
															a_     :      List[Any] 						=     {0: "batch", 2: "past_sequence + sequence"}
									else:
											a_     :      List[str] 						=     OrderedDict(
											    [
											        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
											        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
											        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
											        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
											    ]      )

									return common_inputs






							@property
							def snake_case_  (   self      ):
									if self.task in ["default", "seq2seq-lm"]:
											a_     :      List[Any] 						=     super().outputs
									else:
											a_     :      Tuple 						=     super(a_			,     self      ).outputs
											if self.use_past:
													a_						,				a_     :      Dict 						=     self.num_layers
													for i in range(a_      ):
															a_     :      List[Any] 						=     {0: "batch", 2: "past_sequence + sequence"}
															a_     :      List[Any] 						=     {0: "batch", 2: "past_sequence + sequence"}
									return common_outputs






							def snake_case_  (   self			,     a_			,     a_ = -1			,     a_ = -1			,     a_ = False			,     a_ = None			,     ):
									a_     :      List[str] 						=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    a_			,     a_			,     a_			,     a_			,     a_      )

									# Generate decoder inputs
									a_     :      Optional[int] 						=     seq_length if not self.use_past else 1
									a_     :      str 						=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    a_			,     a_			,     a_			,     a_			,     a_      )
									a_     :      int 						=     {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									a_     :      Tuple 						=     dict(**a_			,     **a_      )

									if self.use_past:
											if not is_torch_available():
													raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."      )
											else:
													import torch
											a_						,				a_     :      Optional[int] 						=     common_inputs["input_ids"].shape
											a_     :      str 						=     common_inputs["decoder_input_ids"].shape[1]
											a_						,				a_     :      Dict 						=     self.num_attention_heads
											a_     :      List[str] 						=     (
											    batch,
											    num_encoder_attention_heads,
											    encoder_seq_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)
											a_     :      Optional[Any] 						=     decoder_seq_length + 3
											a_     :      str 						=     (
											    batch,
											    num_decoder_attention_heads,
											    decoder_past_length,
											    self._config.hidden_size // num_decoder_attention_heads,
											)

											a_     :      Dict 						=     torch.cat(
											    [common_inputs["decoder_attention_mask"], torch.ones(a_			,     a_      )]			,     dim=1      )

											a_     :      Optional[int] 						=     []
											# If the number of encoder and decoder layers are present in the model configuration, both are considered
											a_						,				a_     :      Tuple 						=     self.num_layers
											a_     :      str 						=     min(a_			,     a_      )
											a_     :      Dict 						=     max(a_			,     a_      ) - min_num_layers
											a_     :      Union[str, Any] 						=     "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

											for _ in range(a_      ):
													common_inputs["past_key_values"].append(
													    (
													        torch.zeros(a_      ),
													        torch.zeros(a_      ),
													        torch.zeros(a_      ),
													        torch.zeros(a_      ),
													    )      )
											# TODO: test this.
											a_     :      Optional[int] 						=     encoder_shape if remaining_side_name == "encoder" else decoder_shape
											for _ in range(a_			,     a_      ):
													common_inputs["past_key_values"].append((torch.zeros(a_      ), torch.zeros(a_      ))      )
									return common_inputs






							def snake_case_  (   self			,     a_			,     a_ = -1			,     a_ = -1			,     a_ = False			,     a_ = None			,     ):
									a_     :      Optional[int] 						=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    a_			,     a_			,     a_			,     a_			,     a_      )

									if self.use_past:
											if not is_torch_available():
													raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."      )
											else:
													import torch
											a_						,				a_     :      Dict 						=     common_inputs["input_ids"].shape
											# Not using the same length for past_key_values
											a_     :      int 						=     seqlen + 2
											a_						,				a_     :      Optional[int] 						=     self.num_layers
											a_						,				a_     :      Optional[int] 						=     self.num_attention_heads
											a_     :      Optional[Any] 						=     (
											    batch,
											    num_encoder_attention_heads,
											    past_key_values_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)

											a_     :      str 						=     common_inputs["attention_mask"].dtype
											a_     :      Tuple 						=     torch.cat(
											    [common_inputs["attention_mask"], torch.ones(a_			,     a_			,     dtype=a_      )]			,     dim=1      )
											a_     :      Optional[int] 						=     [
											    (torch.zeros(a_      ), torch.zeros(a_      )) for _ in range(a_      )
											]
									return common_inputs






							def snake_case_  (   self			,     a_			,     a_ = -1			,     a_ = -1			,     a_ = False			,     a_ = None			,     ):
									# Copied from OnnxConfig.generate_dummy_inputs
									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									a_     :      Optional[Any] 						=     compute_effective_axis_dimension(
									    a_			,     fixed_dimension=OnnxConfig.default_fixed_batch			,     num_token_to_add=0      )

									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									a_     :      Tuple 						=     tokenizer.num_special_tokens_to_add(a_      )
									a_     :      Union[str, Any] 						=     compute_effective_axis_dimension(
									    a_			,     fixed_dimension=OnnxConfig.default_fixed_sequence			,     num_token_to_add=a_      )

									# Generate dummy inputs according to compute batch and sequence
									a_     :      Union[str, Any] 						=     [" ".join([tokenizer.unk_token]      ) * seq_length] * batch_size
									a_     :      str 						=     dict(tokenizer(a_			,     return_tensors=a_      )      )
									return common_inputs






							def snake_case_  (   self			,     a_			,     a_ = -1			,     a_ = -1			,     a_ = False			,     a_ = None			,     ):
									if self.task in ["default", "seq2seq-lm"]:
											a_     :      List[str] 						=     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
											    a_			,     batch_size=a_			,     seq_length=a_			,     is_pair=a_			,     framework=a_      )

									elif self.task == "causal-lm":
											a_     :      List[Any] 						=     self._generate_dummy_inputs_for_causal_lm(
											    a_			,     batch_size=a_			,     seq_length=a_			,     is_pair=a_			,     framework=a_      )
									else:
											a_     :      Union[str, Any] 						=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    a_			,     batch_size=a_			,     seq_length=a_			,     is_pair=a_			,     framework=a_      )

									return common_inputs






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_      ):
									if self.task in ["default", "seq2seq-lm"]:
											a_     :      Optional[int] 						=     super()._flatten_past_key_values_(a_			,     a_			,     a_			,     a_      )
									else:
											a_     :      int 						=     super(a_			,     self      )._flatten_past_key_values_(
											    a_			,     a_			,     a_			,     a_      )