

from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						for param, grad_param in zip(model_a.parameters()				,	model_b.parameters()    ):
												if not param.requires_grad:
																		continue
												if not did_step:
																		# Grads should not be in sync
																		assert (
																		    torch.allclose(param.grad				,	grad_param.grad    ) is False
																		), f"Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"
												else:
																		# Grads should be in sync
																		assert (
																		    torch.allclose(param.grad				,	grad_param.grad    ) is True
																		), f"Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=True    ):
						model.train()
						SCREAMING_SNAKE_CASE_: Any  		=       model(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       F.mse_loss(_UpperCAmelCase				,	target.to(output.device    )    )
						if not do_backward:
												loss /= accelerator.gradient_accumulation_steps
												loss.backward()
						else:
												accelerator.backward(_UpperCAmelCase    )







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=False    ):
						set_seed(42    )
						SCREAMING_SNAKE_CASE_: Tuple  		=       RegressionModel()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       deepcopy(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       RegressionDataset(length=80    )
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       DataLoader(_UpperCAmelCase				,	batch_size=16    )
						model.to(accelerator.device    )
						if sched:
												SCREAMING_SNAKE_CASE_: List[Any]  		=       AdamW(params=model.parameters()				,	lr=1e-3    )
												SCREAMING_SNAKE_CASE_: str  		=       AdamW(params=ddp_model.parameters()				,	lr=1e-3    )
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       LambdaLR(_UpperCAmelCase				,	lr_lambda=lambda _UpperCAmelCase    : epoch**0.6_5    )
												SCREAMING_SNAKE_CASE_: List[Any]  		=       LambdaLR(_UpperCAmelCase				,	lr_lambda=lambda _UpperCAmelCase    : epoch**0.6_5    )
						# Make a copy of `model`
						if sched:
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       accelerator.prepare(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
						else:
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       accelerator.prepare(_UpperCAmelCase				,	_UpperCAmelCase    )
						if sched:
												return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
						return model, ddp_model, dataloader







def 			A_      (    _UpperCAmelCase    ):
						# Test when on a single CPU or GPU that the context manager does nothing
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       get_training_setup(_UpperCAmelCase    )
						# Use a single batch
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       next(iter(_UpperCAmelCase    )    ).values()
						for iteration in range(3    ):
												# Gather the distributed inputs and targs for the base model
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       accelerator.gather((ddp_input, ddp_target)    )
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       input.to(accelerator.device    ), target.to(accelerator.device    )
												# Perform our initial ground truth step in non "DDP"
												step_model(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
												# Do "gradient accumulation" (noop)
												if iteration % 2 == 0:
																		# Accumulate grads locally
																		with accelerator.no_sync(_UpperCAmelCase    ):
																								step_model(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
												else:
																		# Sync grads
																		step_model(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )

												# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
												check_model_parameters(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
												for param, ddp_param in zip(model.parameters()				,	ddp_model.parameters()    ):
																		if not param.requires_grad:
																								continue
																		assert torch.allclose(
																		    param.grad				,	ddp_param.grad    ), f"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"

												# Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration    )
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       ddp_input[torch.randperm(len(_UpperCAmelCase    )    )]







def 			A_      (    _UpperCAmelCase    ):
						# Test on distributed setup that context manager behaves properly
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       get_training_setup(_UpperCAmelCase    )
						# Use a single batch
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       next(iter(_UpperCAmelCase    )    ).values()
						for iteration in range(3    ):
												# Gather the distributed inputs and targs for the base model
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       accelerator.gather((ddp_input, ddp_target)    )
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       input.to(accelerator.device    ), target.to(accelerator.device    )
												# Perform our initial ground truth step in non "DDP"
												step_model(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
												# Do "gradient accumulation" (noop)
												if iteration % 2 == 0:
																		# Accumulate grads locally
																		with accelerator.no_sync(_UpperCAmelCase    ):
																								step_model(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
												else:
																		# Sync grads
																		step_model(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )

												# DDP model and model should only be in sync when not (iteration % 2 == 0)
												for param, ddp_param in zip(model.parameters()				,	ddp_model.parameters()    ):
																		if not param.requires_grad:
																								continue
																		if iteration % 2 == 0:
																								# Grads should not be in sync
																								assert (
																								    torch.allclose(param.grad				,	ddp_param.grad    ) is False
																								), f"Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"
																		else:
																								# Grads should be in sync
																								assert (
																								    torch.allclose(param.grad				,	ddp_param.grad    ) is True
																								), f"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"

        # Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration    )
												SCREAMING_SNAKE_CASE_: Any  		=       ddp_input[torch.randperm(len(_UpperCAmelCase    )    )]







def 			A_      (    _UpperCAmelCase=False				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       Accelerator(
						    split_batches=_UpperCAmelCase				,	dispatch_batches=_UpperCAmelCase				,	gradient_accumulation_steps=2    )
						# Test that context manager behaves properly
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       get_training_setup(_UpperCAmelCase    )
						for iteration, batch in enumerate(_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       batch.values()
												# Gather the distributed inputs and targs for the base model
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       accelerator.gather((ddp_input, ddp_target)    )
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       input.to(accelerator.device    ), target.to(accelerator.device    )
												# Perform our initial ground truth step in non "DDP"
												step_model(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
												# Do "gradient accumulation" (noop)
												with accelerator.accumulate(_UpperCAmelCase    ):
																		step_model(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )

												# DDP model and model should only be in sync when not (iteration % 2 == 0)
												for param, ddp_param in zip(model.parameters()				,	ddp_model.parameters()    ):
																		if not param.requires_grad:
																								continue
																		if ((iteration + 1) % 2 == 0) or (iteration == len(_UpperCAmelCase    ) - 1):
																								# Grads should be in sync
																								assert (
																								    torch.allclose(param.grad				,	ddp_param.grad    ) is True
																								), f"Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"
																		else:
																								# Grads should not be in sync
																								assert (
																								    torch.allclose(param.grad				,	ddp_param.grad    ) is False
																								), f"Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"

        # Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration    )
												SCREAMING_SNAKE_CASE_: Dict  		=       ddp_input[torch.randperm(len(_UpperCAmelCase    )    )]
						GradientState._reset_state()







def 			A_      (    _UpperCAmelCase=False				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_: List[Any]  		=       Accelerator(
						    split_batches=_UpperCAmelCase				,	dispatch_batches=_UpperCAmelCase				,	gradient_accumulation_steps=2    )
						# Test that context manager behaves properly
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       get_training_setup(_UpperCAmelCase				,	_UpperCAmelCase    )
						for iteration, batch in enumerate(_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       batch.values()
												# Gather the distributed inputs and targs for the base model
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       accelerator.gather((ddp_input, ddp_target)    )
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       input.to(accelerator.device    ), target.to(accelerator.device    )
												# Perform our initial ground truth step in non "DDP"
												model.train()
												ddp_model.train()
												step_model(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
												opt.step()

												if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_UpperCAmelCase    )):
																		if split_batches:
																								sched.step()
																		else:
																								for _ in range(accelerator.num_processes    ):
																														sched.step()
												opt.zero_grad()
												# Perform gradient accumulation under wrapper
												with accelerator.accumulate(_UpperCAmelCase    ):
																		step_model(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
																		ddp_opt.step()
																		ddp_sched.step()
																		ddp_opt.zero_grad()

												# Learning rates should be the same
												assert (
												    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
												), f"Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_UpperCAmelCase    ))
												if accelerator.num_processes > 1:
																		check_model_parameters(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
												# Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration    )
						GradientState._reset_state()







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: List[Any]  		=       Accelerator()

						SCREAMING_SNAKE_CASE_: Dict  		=       RegressionDataset(length=80    )
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       DataLoader(_UpperCAmelCase				,	batch_size=16    )
						SCREAMING_SNAKE_CASE_: Any  		=       RegressionDataset(length=96    )
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       DataLoader(_UpperCAmelCase				,	batch_size=16    )
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       accelerator.prepare(_UpperCAmelCase				,	_UpperCAmelCase    )
						assert accelerator.gradient_state.active_dataloader is None
						for iteration, _ in enumerate(_UpperCAmelCase    ):
												assert id(accelerator.gradient_state.active_dataloader    ) == id(_UpperCAmelCase    )
												if iteration < len(_UpperCAmelCase    ) - 1:
																		assert not accelerator.gradient_state.end_of_dataloader
																		if iteration == 1:
																								for batch_num, _ in enumerate(_UpperCAmelCase    ):
																														assert id(accelerator.gradient_state.active_dataloader    ) == id(_UpperCAmelCase    )
																														if batch_num < len(_UpperCAmelCase    ) - 1:
																																				assert not accelerator.gradient_state.end_of_dataloader
																														else:
																																				assert accelerator.gradient_state.end_of_dataloader
												else:
																		assert accelerator.gradient_state.end_of_dataloader
						assert accelerator.gradient_state.active_dataloader is None







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       Accelerator()
						SCREAMING_SNAKE_CASE_: int  		=       accelerator.state
						if state.local_process_index == 0:
												print("**Test `accumulate` gradient accumulation with dataloader break**"    )
						test_dataloader_break()
						if state.distributed_type == DistributedType.NO:
												if state.local_process_index == 0:
																		print("**Test NOOP `no_sync` context manager**"    )
												test_noop_sync(_UpperCAmelCase    )
						if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
												if state.local_process_index == 0:
																		print("**Test Distributed `no_sync` context manager**"    )
												test_distributed_sync(_UpperCAmelCase    )
						if state.distributed_type == DistributedType.MULTI_GPU:
												for split_batch in [True, False]:
																		for dispatch_batches in [True, False]:
																								if state.local_process_index == 0:
																														print(
																														    "**Test `accumulate` gradient accumulation, "				,	f"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"				,	)
																								test_gradient_accumulation(_UpperCAmelCase				,	_UpperCAmelCase    )

    # Currently will break on torch 2.0 +, need to investigate why
						if is_torch_version("<"				,	"2.0"    ) or state.distributed_type == DistributedType.NO:
												if state.local_process_index == 0:
																		print(
																		    "**Test `accumulate` gradient accumulation with optimizer and scheduler, "				,	"`split_batches=False`, `dispatch_batches=False`**"				,	)
												test_gradient_accumulation_with_opt_and_scheduler()
												if state.distributed_type == DistributedType.MULTI_GPU:
																		for split_batch in [True, False]:
																								for dispatch_batches in [True, False]:
																														if not split_batch and not dispatch_batches:
																																				continue
																														if state.local_process_index == 0:
																																				print(
																																				    "**Test `accumulate` gradient accumulation with optimizer and scheduler, "				,	f"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"				,	)
																														test_gradient_accumulation_with_opt_and_scheduler(_UpperCAmelCase				,	_UpperCAmelCase    )







def 			A_      (    _UpperCAmelCase    ):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()













"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional

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


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

_snake_case     :		int       						= {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							'''marian'''
					UpperCamelCase      =							['''past_key_values''']
					UpperCamelCase      =							{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


					def __init__(					self			:int     ,       __UpperCamelCase			:Any=5_81_01     ,       __UpperCamelCase			:int=None     ,       __UpperCamelCase			:Union[str, Any]=10_24     ,       __UpperCamelCase			:Union[str, Any]=12     ,       __UpperCamelCase			:str=40_96     ,       __UpperCamelCase			:int=16     ,       __UpperCamelCase			:int=12     ,       __UpperCamelCase			:Optional[Any]=40_96     ,       __UpperCamelCase			:Optional[Any]=16     ,       __UpperCamelCase			:Dict=0.0     ,       __UpperCamelCase			:Dict=0.0     ,       __UpperCamelCase			:str=True     ,       __UpperCamelCase			:Optional[int]=True     ,       __UpperCamelCase			:Any="gelu"     ,       __UpperCamelCase			:Any=10_24     ,       __UpperCamelCase			:List[Any]=0.1     ,       __UpperCamelCase			:Optional[Any]=0.0     ,       __UpperCamelCase			:Union[str, Any]=0.0     ,       __UpperCamelCase			:Tuple=0.02     ,       __UpperCamelCase			:List[str]=5_81_00     ,       __UpperCamelCase			:str=False     ,       __UpperCamelCase			:Optional[int]=5_81_00     ,       __UpperCamelCase			:List[Any]=0     ,       __UpperCamelCase			:List[str]=0     ,       __UpperCamelCase			:Dict=True     ,       **__UpperCamelCase			:Tuple     ,       ):
												A		=  vocab_size
												A		=  decoder_vocab_size or vocab_size
												A		=  max_position_embeddings
												A		=  d_model
												A		=  encoder_ffn_dim
												A		=  encoder_layers
												A		=  encoder_attention_heads
												A		=  decoder_ffn_dim
												A		=  decoder_layers
												A		=  decoder_attention_heads
												A		=  dropout
												A		=  attention_dropout
												A		=  activation_dropout
												A		=  activation_function
												A		=  init_std
												A		=  encoder_layerdrop
												A		=  decoder_layerdrop
												A		=  use_cache
												A		=  encoder_layers
												A		=  scale_embedding  # scale factor will be sqrt(d_model) if True
												A		=  share_encoder_decoder_embeddings
												super().__init__(
												    pad_token_id=__UpperCamelCase     ,       eos_token_id=__UpperCamelCase     ,       is_encoder_decoder=__UpperCamelCase     ,       decoder_start_token_id=__UpperCamelCase     ,       forced_eos_token_id=__UpperCamelCase     ,       **__UpperCamelCase     ,       )







class 					_UpperCAmelCase   (	lowercase_ ):


					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
					def 	lowerCamelCase       (					self			:List[str]		):
												if self.task in ["default", "seq2seq-lm"]:
																			A		=  OrderedDict(
																			    [
																			        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																			        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																			    ]		)

																			if self.use_past:
																										A		=  {0: "batch"}
																										A		=  {0: "batch", 1: "past_decoder_sequence + sequence"}
																			else:
																										A		=  {0: "batch", 1: "decoder_sequence"}
																										A		=  {0: "batch", 1: "decoder_sequence"}

																			if self.use_past:
																										self.fill_with_past_key_values_(__UpperCamelCase     ,       direction="inputs"		)
												elif self.task == "causal-lm":
																			# TODO: figure this case out.
																			A		=  OrderedDict(
																			    [
																			        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																			        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																			    ]		)
																			if self.use_past:
																										A,	A		=  self.num_layers
																										for i in range(__UpperCamelCase		):
																																	A		=  {0: "batch", 2: "past_sequence + sequence"}
																																	A		=  {0: "batch", 2: "past_sequence + sequence"}
												else:
																			A		=  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
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
					def 	lowerCamelCase       (					self			:List[str]		):
												if self.task in ["default", "seq2seq-lm"]:
																			A		=  super().outputs
												else:
																			A		=  super(__UpperCamelCase     ,       self		).outputs
																			if self.use_past:
																										A,	A		=  self.num_layers
																										for i in range(__UpperCamelCase		):
																																	A		=  {0: "batch", 2: "past_sequence + sequence"}
																																	A		=  {0: "batch", 2: "past_sequence + sequence"}
												return common_outputs


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:PreTrainedTokenizer     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:Optional[TensorType] = None     ,       ):
												A		=  self._generate_dummy_inputs_for_encoder_and_decoder(
												    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)

												# Generate decoder inputs
												A		=  seq_length if not self.use_past else 1
												A		=  self._generate_dummy_inputs_for_encoder_and_decoder(
												    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)
												A		=  {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
												A		=  dict(**__UpperCamelCase     ,       **__UpperCamelCase		)

												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		=  common_inputs["input_ids"].shape
																			A		=  common_inputs["decoder_input_ids"].shape[1]
																			A,	A		=  self.num_attention_heads
																			A		=  (
																			    batch,
																			    num_encoder_attention_heads,
																			    encoder_seq_length,
																			    self._config.hidden_size // num_encoder_attention_heads,
																			)
																			A		=  decoder_seq_length + 3
																			A		=  (
																			    batch,
																			    num_decoder_attention_heads,
																			    decoder_past_length,
																			    self._config.hidden_size // num_decoder_attention_heads,
																			)

																			A		=  torch.cat(
																			    [common_inputs["decoder_attention_mask"], torch.ones(__UpperCamelCase     ,       __UpperCamelCase		)]     ,       dim=1		)

																			A		=  []
																			# If the number of encoder and decoder layers are present in the model configuration, both are considered
																			A,	A		=  self.num_layers
																			A		=  min(__UpperCamelCase     ,       __UpperCamelCase		)
																			A		=  max(__UpperCamelCase     ,       __UpperCamelCase		) - min_num_layers
																			A		=  "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:PreTrainedTokenizer     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:Optional[TensorType] = None     ,       ):
												A		=  self._generate_dummy_inputs_for_encoder_and_decoder(
												    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)

												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		=  common_inputs["input_ids"].shape
																			# Not using the same length for past_key_values
																			A		=  seqlen + 2
																			A,	A		=  self.num_layers
																			A,	A		=  self.num_attention_heads
																			A		=  (
																			    batch,
																			    num_encoder_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // num_encoder_attention_heads,
																			)

																			A		=  common_inputs["attention_mask"].dtype
																			A		=  torch.cat(
																			    [common_inputs["attention_mask"], torch.ones(__UpperCamelCase     ,       __UpperCamelCase     ,       dtype=__UpperCamelCase		)]     ,       dim=1		)
																			A		=  [
																			    (torch.zeros(__UpperCamelCase		), torch.zeros(__UpperCamelCase		)) for _ in range(__UpperCamelCase		)
																			]
												return common_inputs


					def 	lowerCamelCase       (					self			:Tuple     ,       __UpperCamelCase			:PreTrainedTokenizer     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:Optional[TensorType] = 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		=  compute_effective_axis_dimension(
												    __UpperCamelCase     ,       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		=  tokenizer.num_special_tokens_to_add(__UpperCamelCase		)
												A		=  compute_effective_axis_dimension(
												    __UpperCamelCase     ,       fixed_dimension=OnnxConfig.default_fixed_sequence     ,       num_token_to_add=__UpperCamelCase		)

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


					def 	lowerCamelCase       (					self			:List[Any]     ,       __UpperCamelCase			:PreTrainedTokenizer     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:Optional[TensorType] = None     ,       ):
												if self.task in ["default", "seq2seq-lm"]:
																			A		=  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																			    __UpperCamelCase     ,       batch_size=__UpperCamelCase     ,       seq_length=__UpperCamelCase     ,       is_pair=__UpperCamelCase     ,       framework=__UpperCamelCase		)

												else:
																			A		=  self._generate_dummy_inputs_for_causal_lm(
																			    __UpperCamelCase     ,       batch_size=__UpperCamelCase     ,       seq_length=__UpperCamelCase     ,       is_pair=__UpperCamelCase     ,       framework=__UpperCamelCase		)

												return common_inputs


					def 	lowerCamelCase       (					self			:List[Any]     ,       __UpperCamelCase			:Tuple     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:str		):
												if self.task in ["default", "seq2seq-lm"]:
																			A		=  super()._flatten_past_key_values_(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)
												else:
																			A		=  super(__UpperCamelCase     ,       self		)._flatten_past_key_values_(
																			    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)


					@property
					def 	lowerCamelCase       (					self			:List[str]		):
												return 1e-4


