
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def 						A_			(				snake_case				: Optional[int]			,   snake_case				: Optional[Any]=0.999			,   snake_case				: List[str]="cosine"			,   )   ->     Optional[int]:



						'''simple docstring'''

						if alpha_transform_type == "cosine":

												def alpha_bar_fn(snake_case				: Tuple			):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(snake_case				: Dict			):
																		return math.exp(t * -12.0			)

						else:
												raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"			)

						__UpperCamelCase  	=      []
						for i in range(snake_case			):
												__UpperCamelCase  	=      i / num_diffusion_timesteps
												__UpperCamelCase  	=      (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(snake_case			) / alpha_bar_fn(snake_case			)			,   snake_case			)			)
						return torch.tensor(snake_case			,   dtype=torch.floataa			)






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	[e.name for e in KarrasDiffusionSchedulers]
							_snake_case  		=	2



							@register_to_config
							def __init__(	self  ,						SCREAMING_SNAKE_CASE_ = 1000  ,						SCREAMING_SNAKE_CASE_ = 0.0_0_0_8_5  ,						SCREAMING_SNAKE_CASE_ = 0.0_1_2  ,						SCREAMING_SNAKE_CASE_ = "linear"  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = "epsilon"  ,						SCREAMING_SNAKE_CASE_ = "linspace"  ,						SCREAMING_SNAKE_CASE_ = 0  ,						)-> List[Any]:





													'''simple docstring'''






													if trained_betas is not None:
																			__UpperCamelCase  	=      torch.tensor(SCREAMING_SNAKE_CASE_  ,						dtype=torch.floataa      )
													elif beta_schedule == "linear":
																			__UpperCamelCase  	=      torch.linspace(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						dtype=torch.floataa      )
													elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			__UpperCamelCase  	=      (
																			    torch.linspace(beta_start**0.5  ,						beta_end**0.5  ,						SCREAMING_SNAKE_CASE_  ,						dtype=torch.floataa      ) ** 2
																			)
													elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			__UpperCamelCase  	=      betas_for_alpha_bar(SCREAMING_SNAKE_CASE_      )
													else:
																			raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"      )

													__UpperCamelCase  	=      1.0 - self.betas
													__UpperCamelCase  	=      torch.cumprod(self.alphas  ,						dim=0      )

													#  set all values
													self.set_timesteps(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=None      )-> List[str]:





													'''simple docstring'''






													if schedule_timesteps is None:
																			__UpperCamelCase  	=      self.timesteps

													__UpperCamelCase  	=      (schedule_timesteps == timestep).nonzero()

													# The sigma index that is taken for the **very** first `step`
													# is always the second index (or the last index if there is only 1)
													# This way we can ensure we don't accidentally skip a sigma in
													# case we start in the middle of the denoising schedule (e.g. for image-to-image)
													if len(self._index_counter      ) == 0:
																			__UpperCamelCase  	=      1 if len(SCREAMING_SNAKE_CASE_      ) > 1 else 0
													else:
																			__UpperCamelCase  	=      timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_      ) else timestep
																			__UpperCamelCase  	=      self._index_counter[timestep_int]

													return indices[pos].item()



							@property
							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													if self.config.timestep_spacing in ["linspace", "trailing"]:
																			return self.sigmas.max()

													return (self.sigmas.max() ** 2 + 1) ** 0.5



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> torch.FloatTensor:





													'''simple docstring'''






													__UpperCamelCase  	=      self.index_for_timestep(SCREAMING_SNAKE_CASE_      )

													if self.state_in_first_order:
																			__UpperCamelCase  	=      self.sigmas[step_index]
													else:
																			__UpperCamelCase  	=      self.sigmas_interpol[step_index - 1]

													__UpperCamelCase  	=      sample / ((sigma**2 + 1) ** 0.5)
													return sample



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						)-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      num_inference_steps

													__UpperCamelCase  	=      num_train_timesteps or self.config.num_train_timesteps

													# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
													if self.config.timestep_spacing == "linspace":
																			__UpperCamelCase  	=      np.linspace(0  ,						num_train_timesteps - 1  ,						SCREAMING_SNAKE_CASE_  ,						dtype=SCREAMING_SNAKE_CASE_      )[::-1].copy()
													elif self.config.timestep_spacing == "leading":
																			__UpperCamelCase  	=      num_train_timesteps // self.num_inference_steps
																			# creates integer timesteps by multiplying by ratio
																			# casting to int to avoid issues when num_inference_step is power of 3
																			__UpperCamelCase  	=      (np.arange(0  ,						SCREAMING_SNAKE_CASE_      ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_      )
																			timesteps += self.config.steps_offset
													elif self.config.timestep_spacing == "trailing":
																			__UpperCamelCase  	=      num_train_timesteps / self.num_inference_steps
																			# creates integer timesteps by multiplying by ratio
																			# casting to int to avoid issues when num_inference_step is power of 3
																			__UpperCamelCase  	=      (np.arange(SCREAMING_SNAKE_CASE_  ,						0  ,						-step_ratio      )).round().copy().astype(SCREAMING_SNAKE_CASE_      )
																			timesteps -= 1
													else:
																			raise ValueError(
																			    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."      )

													__UpperCamelCase  	=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
													__UpperCamelCase  	=      torch.from_numpy(np.log(SCREAMING_SNAKE_CASE_      )      ).to(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      np.interp(SCREAMING_SNAKE_CASE_  ,						np.arange(0  ,						len(SCREAMING_SNAKE_CASE_      )      )  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
													__UpperCamelCase  	=      torch.from_numpy(SCREAMING_SNAKE_CASE_      ).to(device=SCREAMING_SNAKE_CASE_      )

													# compute up and down sigmas
													__UpperCamelCase  	=      sigmas.roll(-1      )
													__UpperCamelCase  	=      0.0
													__UpperCamelCase  	=      (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
													__UpperCamelCase  	=      (sigmas_next**2 - sigmas_up**2) ** 0.5
													__UpperCamelCase  	=      0.0

													# compute interpolated sigmas
													__UpperCamelCase  	=      sigmas.log().lerp(sigmas_down.log()  ,						0.5      ).exp()
													__UpperCamelCase  	=      0.0

													# set sigmas
													__UpperCamelCase  	=      torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
													__UpperCamelCase  	=      torch.cat(
													    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )
													__UpperCamelCase  	=      torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2      ), sigmas_up[-1:]]      )
													__UpperCamelCase  	=      torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2      ), sigmas_down[-1:]]      )

													if str(SCREAMING_SNAKE_CASE_      ).startswith('''mps'''      ):
																			# mps does not support float64
																			__UpperCamelCase  	=      torch.from_numpy(SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_  ,						dtype=torch.floataa      )
													else:
																			__UpperCamelCase  	=      torch.from_numpy(SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      self.sigma_to_t(SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_  ,						dtype=timesteps.dtype      )
													__UpperCamelCase  	=      torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])  ,						dim=-1      ).flatten()

													__UpperCamelCase  	=      torch.cat([timesteps[:1], interleaved_timesteps]      )

													__UpperCamelCase  	=      None

													# for exp beta schedules, such as the one for `pipeline_shap_e.py`
													# we need an index counter
													__UpperCamelCase  	=      defaultdict(SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      sigma.log()

													# get distribution
													__UpperCamelCase  	=      log_sigma - self.log_sigmas[:, None]

													# get sigmas range
													__UpperCamelCase  	=      dists.ge(0      ).cumsum(dim=0      ).argmax(dim=0      ).clamp(max=self.log_sigmas.shape[0] - 2      )
													__UpperCamelCase  	=      low_idx + 1

													__UpperCamelCase  	=      self.log_sigmas[low_idx]
													__UpperCamelCase  	=      self.log_sigmas[high_idx]

													# interpolate sigmas
													__UpperCamelCase  	=      (low - log_sigma) / (low - high)
													__UpperCamelCase  	=      w.clamp(0  ,						1      )

													# transform interpolation to time range
													__UpperCamelCase  	=      (1 - w) * low_idx + w * high_idx
													__UpperCamelCase  	=      t.view(sigma.shape      )
													return t



							@property
							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													return self.sample is None



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = True  ,						)-> Union[SchedulerOutput, Tuple]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.index_for_timestep(SCREAMING_SNAKE_CASE_      )

													# advance index counter by 1
													__UpperCamelCase  	=      timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_      ) else timestep
													self._index_counter[timestep_int] += 1

													if self.state_in_first_order:
																			__UpperCamelCase  	=      self.sigmas[step_index]
																			__UpperCamelCase  	=      self.sigmas_interpol[step_index]
																			__UpperCamelCase  	=      self.sigmas_up[step_index]
																			__UpperCamelCase  	=      self.sigmas_down[step_index - 1]
													else:
																			# 2nd order / KPDM2's method
																			__UpperCamelCase  	=      self.sigmas[step_index - 1]
																			__UpperCamelCase  	=      self.sigmas_interpol[step_index - 1]
																			__UpperCamelCase  	=      self.sigmas_up[step_index - 1]
																			__UpperCamelCase  	=      self.sigmas_down[step_index - 1]

													# currently only gamma=0 is supported. This usually works best anyways.
													# We can support gamma in the future but then need to scale the timestep before
													# passing it to the model which requires a change in API
													__UpperCamelCase  	=      0
													__UpperCamelCase  	=      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

													__UpperCamelCase  	=      model_output.device
													__UpperCamelCase  	=      randn_tensor(model_output.shape  ,						dtype=model_output.dtype  ,						device=SCREAMING_SNAKE_CASE_  ,						generator=SCREAMING_SNAKE_CASE_      )

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																			__UpperCamelCase  	=      sigma_hat if self.state_in_first_order else sigma_interpol
																			__UpperCamelCase  	=      sample - sigma_input * model_output
													elif self.config.prediction_type == "v_prediction":
																			__UpperCamelCase  	=      sigma_hat if self.state_in_first_order else sigma_interpol
																			__UpperCamelCase  	=      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																			    sample / (sigma_input**2 + 1)
																			)
													elif self.config.prediction_type == "sample":
																			raise NotImplementedError('''prediction_type not implemented yet: sample'''      )
													else:
																			raise ValueError(
																			    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"      )

													if self.state_in_first_order:
																			# 2. Convert to an ODE derivative for 1st order
																			__UpperCamelCase  	=      (sample - pred_original_sample) / sigma_hat
																			# 3. delta timestep
																			__UpperCamelCase  	=      sigma_interpol - sigma_hat

																			# store for 2nd order step
																			__UpperCamelCase  	=      sample
																			__UpperCamelCase  	=      dt
																			__UpperCamelCase  	=      sample + derivative * dt
													else:
																			# DPM-Solver-2
																			# 2. Convert to an ODE derivative for 2nd order
																			__UpperCamelCase  	=      (sample - pred_original_sample) / sigma_interpol
																			# 3. delta timestep
																			__UpperCamelCase  	=      sigma_down - sigma_hat

																			__UpperCamelCase  	=      self.sample
																			__UpperCamelCase  	=      None

																			__UpperCamelCase  	=      sample + derivative * dt
																			__UpperCamelCase  	=      prev_sample + noise * sigma_up

													if not return_dict:
																			return (prev_sample,)

													return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> torch.FloatTensor:





													'''simple docstring'''






													__UpperCamelCase  	=      self.sigmas.to(device=original_samples.device  ,						dtype=original_samples.dtype      )
													if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_      ):
																			# mps does not support float64
																			__UpperCamelCase  	=      self.timesteps.to(original_samples.device  ,						dtype=torch.floataa      )
																			__UpperCamelCase  	=      timesteps.to(original_samples.device  ,						dtype=torch.floataa      )
													else:
																			__UpperCamelCase  	=      self.timesteps.to(original_samples.device      )
																			__UpperCamelCase  	=      timesteps.to(original_samples.device      )

													__UpperCamelCase  	=      [self.index_for_timestep(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) for t in timesteps]

													__UpperCamelCase  	=      sigmas[step_indices].flatten()
													while len(sigma.shape      ) < len(original_samples.shape      ):
																			__UpperCamelCase  	=      sigma.unsqueeze(-1      )

													__UpperCamelCase  	=      original_samples + noise * sigma
													return noisy_samples



							def __len__(	self      )-> Optional[int]:





													'''simple docstring'''






													return self.config.num_train_timesteps








"""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


__SCREAMING_SNAKE_CASE				:     int		=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE				:     str		=     {
    'google/bigbird-pegasus-large-arxiv': (
        'https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'
    ),
    'google/bigbird-pegasus-large-pubmed': (
        'https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'
    ),
    'google/bigbird-pegasus-large-bigpatent': (
        'https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}





class   __A  (snake_case__):





							'''simple docstring'''

							__lowercase:		str				   =     """bigbird_pegasus"""
							__lowercase:		Any				   =     ["""past_key_values"""]
							__lowercase:		Optional[Any]				   =     {
							    """num_attention_heads""": """encoder_attention_heads""",
							    """hidden_size""": """d_model""",
							    """attention_probs_dropout_prob""": """attention_dropout""",
							}



							def __init__(					self				:   Optional[int]		,							UpperCAmelCase_				:   List[Any]=96_103		,							UpperCAmelCase_				:   List[str]=4_096		,							UpperCAmelCase_				:   Optional[Any]=16		,							UpperCAmelCase_				:   str=4_096		,							UpperCAmelCase_				:   Optional[int]=16		,							UpperCAmelCase_				:   List[Any]=16		,							UpperCAmelCase_				:   Optional[int]=4_096		,							UpperCAmelCase_				:   List[str]=16		,							UpperCAmelCase_				:   Tuple=0.0		,							UpperCAmelCase_				:   Union[str, Any]=0.0		,							UpperCAmelCase_				:   str=True		,							UpperCAmelCase_				:   Dict=True		,							UpperCAmelCase_				:   List[str]="gelu_new"		,							UpperCAmelCase_				:   Tuple=1_024		,							UpperCAmelCase_				:   int=0.1		,							UpperCAmelCase_				:   Union[str, Any]=0.0		,							UpperCAmelCase_				:   str=0.0		,							UpperCAmelCase_				:   str=0.02		,							UpperCAmelCase_				:   Tuple=2		,							UpperCAmelCase_				:   int=0.0		,							UpperCAmelCase_				:   str=True		,							UpperCAmelCase_				:   Any=0		,							UpperCAmelCase_				:   Dict=2		,							UpperCAmelCase_				:   List[str]=1		,							UpperCAmelCase_				:   Optional[int]="block_sparse"		,							UpperCAmelCase_				:   Tuple=64		,							UpperCAmelCase_				:   List[str]=3		,							UpperCAmelCase_				:   Optional[int]=False		,							**UpperCAmelCase_				:   str		,							)       ->List[str]:



														"""simple docstring"""



														snake_case_     							=		vocab_size
														snake_case_     							=		max_position_embeddings
														snake_case_     							=		d_model
														snake_case_     							=		encoder_ffn_dim
														snake_case_     							=		encoder_layers
														snake_case_     							=		encoder_attention_heads
														snake_case_     							=		decoder_ffn_dim
														snake_case_     							=		decoder_layers
														snake_case_     							=		decoder_attention_heads
														snake_case_     							=		dropout
														snake_case_     							=		attention_dropout
														snake_case_     							=		activation_dropout
														snake_case_     							=		activation_function
														snake_case_     							=		init_std
														snake_case_     							=		encoder_layerdrop
														snake_case_     							=		decoder_layerdrop
														snake_case_     							=		classifier_dropout
														snake_case_     							=		use_cache
														snake_case_     							=		encoder_layers
														snake_case_     							=		scale_embedding  # scale factor will be sqrt(d_model) if True

														# extra config
														snake_case_     							=		attention_type
														snake_case_     							=		block_size
														snake_case_     							=		num_random_blocks
														snake_case_     							=		use_bias

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





class   __A  (snake_case__):





							'''simple docstring'''

							@property
							def 		lowerCAmelCase							(					self				:   List[str]							)       ->Mapping[str, Mapping[int, str]]:



														"""simple docstring"""



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

																					if self.use_past:
																												snake_case_     							=		{0: """batch"""}
																												snake_case_     							=		{0: """batch""", 1: """past_decoder_sequence + sequence"""}
																					else:
																												snake_case_     							=		{0: """batch""", 1: """decoder_sequence"""}
																												snake_case_     							=		{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.
																					snake_case_     							=		OrderedDict(
																					    [
																					        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																					        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																					    ]							)
																					if self.use_past:
																												snake_case_							,    snake_case_     							=		self.num_layers
																												for i in range(UpperCAmelCase_							):
																																			snake_case_     							=		{0: """batch""", 2: """past_sequence + sequence"""}
																																			snake_case_     							=		{0: """batch""", 2: """past_sequence + sequence"""}
														else:
																					snake_case_     							=		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 		lowerCAmelCase							(					self				:   Dict							)       ->Mapping[str, Mapping[int, str]]:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   int		,							UpperCAmelCase_				:   PreTrainedTokenizer		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[TensorType] = None		,							)       ->Mapping[str, Any]:



														"""simple docstring"""



														snake_case_     							=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_							)

														# Generate decoder inputs
														snake_case_     							=		seq_length if not self.use_past else 1
														snake_case_     							=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_							)
														snake_case_     							=		{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
														snake_case_     							=		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
																					snake_case_							,    snake_case_     							=		common_inputs["""input_ids"""].shape
																					snake_case_     							=		common_inputs["""decoder_input_ids"""].shape[1]
																					snake_case_							,    snake_case_     							=		self.num_attention_heads
																					snake_case_     							=		(
																					    batch,
																					    num_encoder_attention_heads,
																					    encoder_seq_length,
																					    self._config.hidden_size // num_encoder_attention_heads,
																					)
																					snake_case_     							=		decoder_seq_length + 3
																					snake_case_     							=		(
																					    batch,
																					    num_decoder_attention_heads,
																					    decoder_past_length,
																					    self._config.hidden_size // num_decoder_attention_heads,
																					)

																					snake_case_     							=		torch.cat(
																					    [common_inputs["""decoder_attention_mask"""], torch.ones(UpperCAmelCase_		,							UpperCAmelCase_							)]		,							dim=1							)

																					snake_case_     							=		[]
																					# If the number of encoder and decoder layers are present in the model configuration, both are considered
																					snake_case_							,    snake_case_     							=		self.num_layers
																					snake_case_     							=		min(UpperCAmelCase_		,							UpperCAmelCase_							)
																					snake_case_     							=		max(UpperCAmelCase_		,							UpperCAmelCase_							) - min_num_layers
																					snake_case_     							=		"""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.
																					snake_case_     							=		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				:   Union[str, Any]		,							UpperCAmelCase_				:   PreTrainedTokenizer		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[TensorType] = None		,							)       ->Mapping[str, Any]:



														"""simple docstring"""



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

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



							def 		lowerCAmelCase							(					self				:   Dict		,							UpperCAmelCase_				:   PreTrainedTokenizer		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[TensorType] = None		,							)       ->Mapping[str, Any]:



														"""simple docstring"""



														snake_case_     							=		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
														snake_case_     							=		tokenizer.num_special_tokens_to_add(UpperCAmelCase_							)
														snake_case_     							=		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
														snake_case_     							=		[""" """.join([tokenizer.unk_token]							) * seq_length] * batch_size
														snake_case_     							=		dict(tokenizer(UpperCAmelCase_		,							return_tensors=UpperCAmelCase_							)							)
														return common_inputs



							def 		lowerCAmelCase							(					self				:   str		,							UpperCAmelCase_				:   PreTrainedTokenizer		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[TensorType] = None		,							)       ->Mapping[str, Any]:



														"""simple docstring"""



														if self.task in ["default", "seq2seq-lm"]:
																					snake_case_     							=		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																					    UpperCAmelCase_		,							batch_size=UpperCAmelCase_		,							seq_length=UpperCAmelCase_		,							is_pair=UpperCAmelCase_		,							framework=UpperCAmelCase_							)

														elif self.task == "causal-lm":
																					snake_case_     							=		self._generate_dummy_inputs_for_causal_lm(
																					    UpperCAmelCase_		,							batch_size=UpperCAmelCase_		,							seq_length=UpperCAmelCase_		,							is_pair=UpperCAmelCase_		,							framework=UpperCAmelCase_							)
														else:
																					snake_case_     							=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																					    UpperCAmelCase_		,							batch_size=UpperCAmelCase_		,							seq_length=UpperCAmelCase_		,							is_pair=UpperCAmelCase_		,							framework=UpperCAmelCase_							)

														return common_inputs



							def 		lowerCAmelCase							(					self				:   Union[str, Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Union[str, Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Any							)       ->Tuple:



														"""simple docstring"""



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



