


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


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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 __snake_case				=    42
 __snake_case				=    None






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE=0.9_9_9   ,   SCREAMING_SNAKE_CASE="cosine"   ,   )->    str:




      """simple docstring"""

      if alpha_transform_type == "cosine":

            def alpha_bar_fn(SCREAMING_SNAKE_CASE   ):
                  return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(SCREAMING_SNAKE_CASE   ):
                  return math.exp(t * -1_2.0   )

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

      snake_case_			= []
      for i in range(SCREAMING_SNAKE_CASE   ):
            snake_case_			= i / num_diffusion_timesteps
            snake_case_			= (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 lowerCAmelCase_		(					lowerCamelCase__   ,		lowerCamelCase__			):

 '''simple docstring'''

 __snake_case				=    [e.name for e in KarrasDiffusionSchedulers]
 __snake_case				=    1



 @register_to_config
 def __init__(      self						, _UpperCAmelCase = 10_00						, _UpperCAmelCase = 0.0_001						, _UpperCAmelCase = 0.02						, _UpperCAmelCase = "linear"						, _UpperCAmelCase = None						, _UpperCAmelCase = "epsilon"						, _UpperCAmelCase = "linear"						, _UpperCAmelCase = False						, _UpperCAmelCase = "linspace"						, _UpperCAmelCase = 0						, ):
       if trained_betas is not None:
             snake_case_			= torch.tensor(_UpperCAmelCase						, dtype=torch.floataa   )
       elif beta_schedule == "linear":
             snake_case_			= torch.linspace(_UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, dtype=torch.floataa   )
       elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             snake_case_			= (
                 torch.linspace(beta_start**0.5						, beta_end**0.5						, _UpperCAmelCase						, dtype=torch.floataa   ) ** 2
             )
       elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             snake_case_			= betas_for_alpha_bar(_UpperCAmelCase   )
       else:
             raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''   )

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

       snake_case_			= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
       snake_case_			= np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
       snake_case_			= torch.from_numpy(_UpperCAmelCase   )

       # setable values
       snake_case_			= None
       snake_case_			= np.linspace(0						, num_train_timesteps - 1						, _UpperCAmelCase						, dtype=_UpperCAmelCase   )[::-1].copy()
       snake_case_			= torch.from_numpy(_UpperCAmelCase   )
       snake_case_			= False
       snake_case_			= use_karras_sigmas



 @property
 def 		UpperCamelCase__	(      self   ):
       # standard deviation of the initial noise distribution
       if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()

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



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase   ):
       if isinstance(_UpperCAmelCase						, torch.Tensor   ):
             snake_case_			= timestep.to(self.timesteps.device   )
       snake_case_			= (self.timesteps == timestep).nonzero().item()
       snake_case_			= self.sigmas[step_index]

       snake_case_			= sample / ((sigma**2 + 1) ** 0.5)

       snake_case_			= True
       return sample



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = None   ):
       snake_case_			= 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":
             snake_case_			= np.linspace(0						, self.config.num_train_timesteps - 1						, _UpperCAmelCase						, dtype=_UpperCAmelCase   )[
                 ::-1
             ].copy()
       elif self.config.timestep_spacing == "leading":
             snake_case_			= 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
             snake_case_			= (np.arange(0						, _UpperCAmelCase   ) * step_ratio).round()[::-1].copy().astype(_UpperCAmelCase   )
             timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
             snake_case_			= 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
             snake_case_			= (np.arange(self.config.num_train_timesteps						, 0						, -step_ratio   )).round().copy().astype(_UpperCAmelCase   )
             timesteps -= 1
       else:
             raise ValueError(
                 F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''   )

       snake_case_			= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
       snake_case_			= np.log(_UpperCAmelCase   )

       if self.config.interpolation_type == "linear":
             snake_case_			= np.interp(_UpperCAmelCase						, np.arange(0						, len(_UpperCAmelCase   )   )						, _UpperCAmelCase   )
       elif self.config.interpolation_type == "log_linear":
             snake_case_			= torch.linspace(np.log(sigmas[-1]   )						, np.log(sigmas[0]   )						, num_inference_steps + 1   ).exp()
       else:
             raise ValueError(
                 F'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
                 ''' \'linear\' or \'log_linear\''''   )

       if self.use_karras_sigmas:
             snake_case_			= self._convert_to_karras(in_sigmas=_UpperCAmelCase						, num_inference_steps=self.num_inference_steps   )
             snake_case_			= np.array([self._sigma_to_t(_UpperCAmelCase						, _UpperCAmelCase   ) for sigma in sigmas]   )

       snake_case_			= np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
       snake_case_			= torch.from_numpy(_UpperCAmelCase   ).to(device=_UpperCAmelCase   )
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             # mps does not support float64
             snake_case_			= torch.from_numpy(_UpperCAmelCase   ).to(_UpperCAmelCase						, dtype=torch.floataa   )
       else:
             snake_case_			= torch.from_numpy(_UpperCAmelCase   ).to(device=_UpperCAmelCase   )



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase   ):
       # get log sigma
       snake_case_			= np.log(_UpperCAmelCase   )

       # get distribution
       snake_case_			= log_sigma - log_sigmas[:, np.newaxis]

       # get sigmas range
       snake_case_			= np.cumsum((dists >= 0)						, axis=0   ).argmax(axis=0   ).clip(max=log_sigmas.shape[0] - 2   )
       snake_case_			= low_idx + 1

       snake_case_			= log_sigmas[low_idx]
       snake_case_			= log_sigmas[high_idx]

       # interpolate sigmas
       snake_case_			= (low - log_sigma) / (low - high)
       snake_case_			= np.clip(_UpperCAmelCase						, 0						, 1   )

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



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase   ):
       snake_case_			= in_sigmas[-1].item()
       snake_case_			= in_sigmas[0].item()

       snake_case_			= 7.0  # 7.0 is the value used in the paper
       snake_case_			= np.linspace(0						, 1						, _UpperCAmelCase   )
       snake_case_			= sigma_min ** (1 / rho)
       snake_case_			= sigma_max ** (1 / rho)
       snake_case_			= (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase = 0.0						, _UpperCAmelCase = 0.0						, _UpperCAmelCase = float('''inf'''   )						, _UpperCAmelCase = 1.0						, _UpperCAmelCase = None						, _UpperCAmelCase = True						, ):

       if (
           isinstance(_UpperCAmelCase						, _UpperCAmelCase   )
           or isinstance(_UpperCAmelCase						, torch.IntTensor   )
           or isinstance(_UpperCAmelCase						, 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(_UpperCAmelCase						, torch.Tensor   ):
             snake_case_			= timestep.to(self.timesteps.device   )

       snake_case_			= (self.timesteps == timestep).nonzero().item()
       snake_case_			= self.sigmas[step_index]

       snake_case_			= min(s_churn / (len(self.sigmas   ) - 1)						, 2**0.5 - 1   ) if s_tmin <= sigma <= s_tmax else 0.0

       snake_case_			= randn_tensor(
           model_output.shape						, dtype=model_output.dtype						, device=model_output.device						, generator=_UpperCAmelCase   )

       snake_case_			= noise * s_noise
       snake_case_			= sigma * (gamma + 1)

       if gamma > 0:
             snake_case_			= sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       # NOTE: "original_sample" should not be an expected prediction_type but is left in for
       # backwards compatibility
       if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
             snake_case_			= model_output
       elif self.config.prediction_type == "epsilon":
             snake_case_			= sample - sigma_hat * model_output
       elif self.config.prediction_type == "v_prediction":
             # * c_out + input * c_skip
             snake_case_			= model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
       else:
             raise ValueError(
                 F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''   )

       # 2. Convert to an ODE derivative
       snake_case_			= (sample - pred_original_sample) / sigma_hat

       snake_case_			= self.sigmas[step_index + 1] - sigma_hat

       snake_case_			= sample + derivative * dt

       if not return_dict:
             return (prev_sample,)

       return EulerDiscreteSchedulerOutput(prev_sample=_UpperCAmelCase						, pred_original_sample=_UpperCAmelCase   )



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, ):
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       snake_case_			= self.sigmas.to(device=original_samples.device						, dtype=original_samples.dtype   )
       if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCAmelCase   ):
             # mps does not support float64
             snake_case_			= self.timesteps.to(original_samples.device						, dtype=torch.floataa   )
             snake_case_			= timesteps.to(original_samples.device						, dtype=torch.floataa   )
       else:
             snake_case_			= self.timesteps.to(original_samples.device   )
             snake_case_			= timesteps.to(original_samples.device   )

       snake_case_			= [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

       snake_case_			= original_samples + noise * sigma
       return noisy_samples





 def __len__(      self   ):
       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 ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


UpperCAmelCase     =       logging.get_logger(__name__)

UpperCAmelCase     =       {
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}







class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 __snake_case				=    "blenderbot"
 __snake_case				=    ["past_key_values"]
 __snake_case				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



 def __init__(      self						, _UpperCAmelCase=80_08						, _UpperCAmelCase=1_28						, _UpperCAmelCase=2						, _UpperCAmelCase=1_02_40						, _UpperCAmelCase=32						, _UpperCAmelCase=24						, _UpperCAmelCase=1_02_40						, _UpperCAmelCase=32						, _UpperCAmelCase=0.0						, _UpperCAmelCase=0.0						, _UpperCAmelCase=True						, _UpperCAmelCase=True						, _UpperCAmelCase="gelu"						, _UpperCAmelCase=25_60						, _UpperCAmelCase=0.1						, _UpperCAmelCase=0.0						, _UpperCAmelCase=0.0						, _UpperCAmelCase=0.02						, _UpperCAmelCase=1						, _UpperCAmelCase=False						, _UpperCAmelCase=0						, _UpperCAmelCase=1						, _UpperCAmelCase=2						, _UpperCAmelCase=3						, _UpperCAmelCase=2						, **_UpperCAmelCase						, ):
       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_			= use_cache
       snake_case_			= encoder_layers
       snake_case_			= scale_embedding  # scale factor will be sqrt(d_model) if True

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







class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 @property
 def 		UpperCamelCase__	(      self   ):
       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":
             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
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
 def 		UpperCamelCase__	(      self   ):
       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 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = -1						, _UpperCAmelCase = -1						, _UpperCAmelCase = False						, _UpperCAmelCase = None						, ):
       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
             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_			= []
             snake_case_			,       snake_case_			= self.num_layers

             for _ in range(_UpperCAmelCase   ):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(_UpperCAmelCase   ),
                           torch.zeros(_UpperCAmelCase   ),
                           torch.zeros(_UpperCAmelCase   ),
                           torch.zeros(_UpperCAmelCase   ),
                       )   )
       return common_inputs



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = -1						, _UpperCAmelCase = -1						, _UpperCAmelCase = False						, _UpperCAmelCase = None						, ):
       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
             snake_case_			= seqlen
             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 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = -1						, _UpperCAmelCase = -1						, _UpperCAmelCase = False						, _UpperCAmelCase = 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
       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 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = -1						, _UpperCAmelCase = -1						, _UpperCAmelCase = False						, _UpperCAmelCase = None						, ):
       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 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   ):
       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   )





 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase   ):
       if direction not in ["inputs", "outputs"]:
             raise ValueError(F'''direction must either be "inputs" or "outputs", but {direction} was given'''   )

       snake_case_			= '''past_key_values''' if direction == '''inputs''' else '''present'''
       snake_case_			,       snake_case_			= self.num_layers

       snake_case_			= '''past_encoder_sequence'''
       snake_case_			= '''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

       for i in range(_UpperCAmelCase   ):
             snake_case_			= {0: '''batch''', 2: decoder_sequence}
             snake_case_			= {0: '''batch''', 2: decoder_sequence}
             snake_case_			= {0: '''batch''', 2: encoder_sequence}
             snake_case_			= {0: '''batch''', 2: encoder_sequence}