


import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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


lowerCamelCase			 =	logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class A  (       UpperCamelCase_					):
      UpperCamelCase__							:  torch.FloatTensor
      UpperCamelCase__							:  Optional[torch.FloatTensor]				=None




def      a_				(   SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				Dict=0.9_99		,      SCREAMING_SNAKE_CASE__ :				Optional[Any]="cosine"		,      ):
       '''simple docstring'''





       if alpha_transform_type == "cosine":

              def alpha_bar_fn(SCREAMING_SNAKE_CASE__ :				Dict					):
                     return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(SCREAMING_SNAKE_CASE__ :				Tuple					):
                     return math.exp(t * -12.0					)

       else:
              raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''					)

       _lowerCamelCase	:		Optional[int]          =[]
       for i in range(SCREAMING_SNAKE_CASE__					):
              _lowerCamelCase	:		Tuple          =i / num_diffusion_timesteps
              _lowerCamelCase	:		Optional[Any]          =(i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE__					) / alpha_bar_fn(SCREAMING_SNAKE_CASE__					)		,      SCREAMING_SNAKE_CASE__					)					)
       return torch.tensor(SCREAMING_SNAKE_CASE__		,      dtype=torch.floataa					)




class A  (       UpperCamelCase_   ,							UpperCamelCase_					):
      UpperCamelCase__							:  List[Any]				=[e.name for e in KarrasDiffusionSchedulers]
      UpperCamelCase__							:  Dict				=1






      @register_to_config
      def __init__( self						:					str      ,       lowercase_						:					int = 1000      ,       lowercase_						:					float = 0.0001      ,       lowercase_						:					float = 0.02      ,       lowercase_						:					str = "linear"      ,       lowercase_						:					Optional[Union[np.ndarray, List[float]]] = None      ,       lowercase_						:					str = "epsilon"      ,       lowercase_						:					str = "linspace"      ,       lowercase_						:					int = 0      ,       ) ->						List[Any]:
             """simple docstring"""




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

             _lowerCamelCase	:		List[Any]          =1.0 - self.betas
             _lowerCamelCase	:		List[str]          =torch.cumprod(self.alphas      ,       dim=0			)

             _lowerCamelCase	:		str          =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
             _lowerCamelCase	:		Optional[int]          =np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
             _lowerCamelCase	:		Optional[Any]          =torch.from_numpy(lowercase_			)

             # setable values
             _lowerCamelCase	:		Optional[int]          =None
             _lowerCamelCase	:		int          =np.linspace(0      ,       num_train_timesteps - 1      ,       lowercase_      ,       dtype=lowercase_			)[::-1].copy()
             _lowerCamelCase	:		Optional[Any]          =torch.from_numpy(lowercase_			)
             _lowerCamelCase	:		Tuple          =False






      @property
      def 	lowerCamelCase	( self						:					int			) ->						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 	lowerCamelCase	( self						:					Any      ,       lowercase_						:					torch.FloatTensor      ,       lowercase_						:					Union[float, torch.FloatTensor]			) ->						torch.FloatTensor:
             """simple docstring"""




             if isinstance(lowercase_      ,       torch.Tensor			):
                    _lowerCamelCase	:		Union[str, Any]          =timestep.to(self.timesteps.device			)
             _lowerCamelCase	:		Optional[int]          =(self.timesteps == timestep).nonzero().item()
             _lowerCamelCase	:		List[Any]          =self.sigmas[step_index]
             _lowerCamelCase	:		Dict          =sample / ((sigma**2 + 1) ** 0.5)
             _lowerCamelCase	:		Optional[Any]          =True
             return sample






      def 	lowerCamelCase	( self						:					Tuple      ,       lowercase_						:					int      ,       lowercase_						:					Union[str, torch.device] = None			) ->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =num_inference_steps

             # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
             if self.config.timestep_spacing == "linspace":
                    _lowerCamelCase	:		Tuple          =np.linspace(0      ,       self.config.num_train_timesteps - 1      ,       lowercase_      ,       dtype=lowercase_			)[
                        ::-1
                    ].copy()
             elif self.config.timestep_spacing == "leading":
                    _lowerCamelCase	:		int          =self.config.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
                    _lowerCamelCase	:		List[str]          =(np.arange(0      ,       lowercase_			) * step_ratio).round()[::-1].copy().astype(lowercase_			)
                    timesteps += self.config.steps_offset
             elif self.config.timestep_spacing == "trailing":
                    _lowerCamelCase	:		Union[str, Any]          =self.config.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
                    _lowerCamelCase	:		List[Any]          =(np.arange(self.config.num_train_timesteps      ,       0      ,       -step_ratio			)).round().copy().astype(lowercase_			)
                    timesteps -= 1
             else:
                    raise ValueError(
                        F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''			)

             _lowerCamelCase	:		List[str]          =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
             _lowerCamelCase	:		int          =np.interp(lowercase_      ,       np.arange(0      ,       len(lowercase_			)			)      ,       lowercase_			)
             _lowerCamelCase	:		Optional[int]          =np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
             _lowerCamelCase	:		Dict          =torch.from_numpy(lowercase_			).to(device=lowercase_			)
             if str(lowercase_			).startswith('mps'			):
                    # mps does not support float64
                    _lowerCamelCase	:		Union[str, Any]          =torch.from_numpy(lowercase_			).to(lowercase_      ,       dtype=torch.floataa			)
             else:
                    _lowerCamelCase	:		Any          =torch.from_numpy(lowercase_			).to(device=lowercase_			)






      def 	lowerCamelCase	( self						:					Tuple      ,       lowercase_						:					torch.FloatTensor      ,       lowercase_						:					Union[float, torch.FloatTensor]      ,       lowercase_						:					torch.FloatTensor      ,       lowercase_						:					Optional[torch.Generator] = None      ,       lowercase_						:					bool = True      ,       ) ->						Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:
             """simple docstring"""




             if (
                 isinstance(lowercase_      ,       lowercase_			)
                 or isinstance(lowercase_      ,       torch.IntTensor			)
                 or isinstance(lowercase_      ,       torch.LongTensor			)
             ):
                    raise ValueError(
                        (
                            'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                            ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
                            ' one of the `scheduler.timesteps` as a timestep.'
                        )      ,       )

             if not self.is_scale_input_called:
                    logger.warning(
                        'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
                        'See `StableDiffusionPipeline` for a usage example.'			)

             if isinstance(lowercase_      ,       torch.Tensor			):
                    _lowerCamelCase	:		Tuple          =timestep.to(self.timesteps.device			)

             _lowerCamelCase	:		Optional[Any]          =(self.timesteps == timestep).nonzero().item()
             _lowerCamelCase	:		Optional[int]          =self.sigmas[step_index]

             # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
             if self.config.prediction_type == "epsilon":
                    _lowerCamelCase	:		Any          =sample - sigma * model_output
             elif self.config.prediction_type == "v_prediction":
                    # * c_out + input * c_skip
                    _lowerCamelCase	:		List[Any]          =model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`'''			)

             _lowerCamelCase	:		int          =self.sigmas[step_index]
             _lowerCamelCase	:		Optional[Any]          =self.sigmas[step_index + 1]
             _lowerCamelCase	:		List[Any]          =(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
             _lowerCamelCase	:		Optional[int]          =(sigma_to**2 - sigma_up**2) ** 0.5

             # 2. Convert to an ODE derivative
             _lowerCamelCase	:		Dict          =(sample - pred_original_sample) / sigma

             _lowerCamelCase	:		Optional[int]          =sigma_down - sigma

             _lowerCamelCase	:		List[str]          =sample + derivative * dt

             _lowerCamelCase	:		Any          =model_output.device
             _lowerCamelCase	:		List[Any]          =randn_tensor(model_output.shape      ,       dtype=model_output.dtype      ,       device=lowercase_      ,       generator=lowercase_			)

             _lowerCamelCase	:		List[Any]          =prev_sample + noise * sigma_up

             if not return_dict:
                    return (prev_sample,)

             return EulerAncestralDiscreteSchedulerOutput(
                 prev_sample=lowercase_      ,       pred_original_sample=lowercase_			)






      def 	lowerCamelCase	( self						:					Dict      ,       lowercase_						:					torch.FloatTensor      ,       lowercase_						:					torch.FloatTensor      ,       lowercase_						:					torch.FloatTensor      ,       ) ->						torch.FloatTensor:
             """simple docstring"""




             _lowerCamelCase	:		Dict          =self.sigmas.to(device=original_samples.device      ,       dtype=original_samples.dtype			)
             if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_			):
                    # mps does not support float64
                    _lowerCamelCase	:		str          =self.timesteps.to(original_samples.device      ,       dtype=torch.floataa			)
                    _lowerCamelCase	:		Union[str, Any]          =timesteps.to(original_samples.device      ,       dtype=torch.floataa			)
             else:
                    _lowerCamelCase	:		Any          =self.timesteps.to(original_samples.device			)
                    _lowerCamelCase	:		Tuple          =timesteps.to(original_samples.device			)

             _lowerCamelCase	:		Union[str, Any]          =[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

             _lowerCamelCase	:		int          =original_samples + noise * sigma
             return noisy_samples






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




             return self.config.num_train_timesteps






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


lowerCamelCase						 =	logging.get_logger(__name__)

lowerCamelCase						 =	{
    '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  (       UpperCamelCase_					):
      UpperCamelCase__							:  int				='bigbird_pegasus'
      UpperCamelCase__							:  Tuple				=['past_key_values']
      UpperCamelCase__							:  str				={
          'num_attention_heads': 'encoder_attention_heads',
          'hidden_size': 'd_model',
          'attention_probs_dropout_prob': 'attention_dropout',
      }






      def __init__( self						:					Tuple      ,       lowercase_						:					int=9_6103      ,       lowercase_						:					Optional[Any]=4096      ,       lowercase_						:					Tuple=16      ,       lowercase_						:					int=4096      ,       lowercase_						:					Any=16      ,       lowercase_						:					str=16      ,       lowercase_						:					List[str]=4096      ,       lowercase_						:					Optional[Any]=16      ,       lowercase_						:					Any=0.0      ,       lowercase_						:					List[Any]=0.0      ,       lowercase_						:					Optional[int]=True      ,       lowercase_						:					Tuple=True      ,       lowercase_						:					Dict="gelu_new"      ,       lowercase_						:					List[str]=1024      ,       lowercase_						:					Dict=0.1      ,       lowercase_						:					Union[str, Any]=0.0      ,       lowercase_						:					Any=0.0      ,       lowercase_						:					str=0.02      ,       lowercase_						:					List[str]=2      ,       lowercase_						:					Any=0.0      ,       lowercase_						:					int=True      ,       lowercase_						:					Union[str, Any]=0      ,       lowercase_						:					List[str]=2      ,       lowercase_						:					Optional[Any]=1      ,       lowercase_						:					Any="block_sparse"      ,       lowercase_						:					str=64      ,       lowercase_						:					Dict=3      ,       lowercase_						:					List[str]=False      ,       **lowercase_						:					List[str]      ,       ) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =vocab_size
             _lowerCamelCase	:		Tuple          =max_position_embeddings
             _lowerCamelCase	:		Any          =d_model
             _lowerCamelCase	:		Optional[Any]          =encoder_ffn_dim
             _lowerCamelCase	:		Union[str, Any]          =encoder_layers
             _lowerCamelCase	:		int          =encoder_attention_heads
             _lowerCamelCase	:		Optional[Any]          =decoder_ffn_dim
             _lowerCamelCase	:		int          =decoder_layers
             _lowerCamelCase	:		str          =decoder_attention_heads
             _lowerCamelCase	:		Union[str, Any]          =dropout
             _lowerCamelCase	:		str          =attention_dropout
             _lowerCamelCase	:		int          =activation_dropout
             _lowerCamelCase	:		Union[str, Any]          =activation_function
             _lowerCamelCase	:		Optional[Any]          =init_std
             _lowerCamelCase	:		Optional[Any]          =encoder_layerdrop
             _lowerCamelCase	:		int          =decoder_layerdrop
             _lowerCamelCase	:		Optional[int]          =classifier_dropout
             _lowerCamelCase	:		int          =use_cache
             _lowerCamelCase	:		Dict          =encoder_layers
             _lowerCamelCase	:		List[str]          =scale_embedding  # scale factor will be sqrt(d_model) if True

             # extra config
             _lowerCamelCase	:		Tuple          =attention_type
             _lowerCamelCase	:		str          =block_size
             _lowerCamelCase	:		Optional[int]          =num_random_blocks
             _lowerCamelCase	:		Optional[int]          =use_bias

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




class A  (       UpperCamelCase_					):






      @property
      def 	lowerCamelCase	( self						:					Dict			) ->						Mapping[str, Mapping[int, str]]:
             """simple docstring"""




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

                    if self.use_past:
                           _lowerCamelCase	:		str          ={0: 'batch'}
                           _lowerCamelCase	:		Optional[int]          ={0: 'batch', 1: 'past_decoder_sequence + sequence'}
                    else:
                           _lowerCamelCase	:		Optional[Any]          ={0: 'batch', 1: 'decoder_sequence'}
                           _lowerCamelCase	:		List[Any]          ={0: 'batch', 1: 'decoder_sequence'}

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






      def 	lowerCamelCase	( self						:					Optional[Any]      ,       lowercase_						:					PreTrainedTokenizer      ,       lowercase_						:					int = -1      ,       lowercase_						:					int = -1      ,       lowercase_						:					bool = False      ,       lowercase_						:					Optional[TensorType] = None      ,       ) ->						Mapping[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		int          =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)

             # Generate decoder inputs
             _lowerCamelCase	:		str          =seq_length if not self.use_past else 1
             _lowerCamelCase	:		List[Any]          =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)
             _lowerCamelCase	:		List[Any]          ={F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
             _lowerCamelCase	:		Union[str, Any]          =dict(**lowercase_      ,       **lowercase_			)

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

                    _lowerCamelCase	:		Union[str, Any]          =torch.cat(
                        [common_inputs['decoder_attention_mask'], torch.ones(lowercase_      ,       lowercase_			)]      ,       dim=1			)

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

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






      def 	lowerCamelCase	( self						:					Optional[Any]      ,       lowercase_						:					PreTrainedTokenizer      ,       lowercase_						:					int = -1      ,       lowercase_						:					int = -1      ,       lowercase_						:					bool = False      ,       lowercase_						:					Optional[TensorType] = None      ,       ) ->						Mapping[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		str          =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)

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

                    _lowerCamelCase	:		Tuple          =common_inputs['attention_mask'].dtype
                    _lowerCamelCase	:		Tuple          =torch.cat(
                        [common_inputs['attention_mask'], torch.ones(lowercase_      ,       lowercase_      ,       dtype=lowercase_			)]      ,       dim=1			)
                    _lowerCamelCase	:		Dict          =[
                        (torch.zeros(lowercase_			), torch.zeros(lowercase_			)) for _ in range(lowercase_			)
                    ]
             return common_inputs






      def 	lowerCamelCase	( self						:					List[str]      ,       lowercase_						:					PreTrainedTokenizer      ,       lowercase_						:					int = -1      ,       lowercase_						:					int = -1      ,       lowercase_						:					bool = False      ,       lowercase_						:					Optional[TensorType] = None      ,       ) ->						Mapping[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		Optional[Any]          =compute_effective_axis_dimension(
                 lowercase_      ,       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
             _lowerCamelCase	:		Tuple          =tokenizer.num_special_tokens_to_add(lowercase_			)
             _lowerCamelCase	:		Any          =compute_effective_axis_dimension(
                 lowercase_      ,       fixed_dimension=OnnxConfig.default_fixed_sequence      ,       num_token_to_add=lowercase_			)

             # Generate dummy inputs according to compute batch and sequence
             _lowerCamelCase	:		Any          =[' '.join([tokenizer.unk_token]			) * seq_length] * batch_size
             _lowerCamelCase	:		int          =dict(tokenizer(lowercase_      ,       return_tensors=lowercase_			)			)
             return common_inputs






      def 	lowerCamelCase	( self						:					Any      ,       lowercase_						:					PreTrainedTokenizer      ,       lowercase_						:					int = -1      ,       lowercase_						:					int = -1      ,       lowercase_						:					bool = False      ,       lowercase_						:					Optional[TensorType] = None      ,       ) ->						Mapping[str, Any]:
             """simple docstring"""




             if self.task in ["default", "seq2seq-lm"]:
                    _lowerCamelCase	:		Union[str, Any]          =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                        lowercase_      ,       batch_size=lowercase_      ,       seq_length=lowercase_      ,       is_pair=lowercase_      ,       framework=lowercase_			)

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

             return common_inputs






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					Union[str, Any]      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					Dict      ,       lowercase_						:					Tuple			) ->						Dict:
             """simple docstring"""




             if self.task in ["default", "seq2seq-lm"]:
                    _lowerCamelCase	:		Union[str, Any]          =super()._flatten_past_key_values_(lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)
             else:
                    _lowerCamelCase	:		List[str]          =super(lowercase_      ,       self			)._flatten_past_key_values_(
                        lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)


