

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 						UpperCamelCase_(			snake_case__:  Union[str, Any]  ,    snake_case__:  List[str]=0.9_9_9  ,    snake_case__:  Dict="cosine"  ,    )       -> Tuple:
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(snake_case__:  Dict   ):
   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(snake_case__:  Optional[int]   ):
   return math.exp(t * -1_2.0   )

 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     lowercase						(       _UpperCamelCase ,				_UpperCamelCase					):



 '''simple docstring'''


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




 @register_to_config
 def __init__(self       ,       __a = 1000       ,       __a = 0.0_00_85       ,       __a = 0.0_12       ,       __a = "linear"       ,       __a = None       ,       __a = "epsilon"       ,       __a = "linspace"       ,       __a = 0       ,       ) ->					Optional[int]:






  """simple docstring"""





  if trained_betas is not None:
   UpperCAmelCase__							=				torch.tensor(__a       ,       dtype=torch.floataa )
  elif beta_schedule == "linear":
   UpperCAmelCase__							=				torch.linspace(__a       ,       __a       ,       __a       ,       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       ,       __a       ,       dtype=torch.floataa ) ** 2
   )
  elif beta_schedule == "squaredcos_cap_v2":
   # Glide cosine schedule
   UpperCAmelCase__							=				betas_for_alpha_bar(__a )
  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(__a       ,       __a       ,       __a )




 def   UpperCamelCase__							(self       ,       __a       ,       __a=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(__a ) > 1 else 0
  else:
   UpperCAmelCase__							=				timestep.cpu().item() if torch.is_tensor(__a ) else timestep
   UpperCAmelCase__							=				self._index_counter[timestep_int]

  return indices[pos].item()




 @property
 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





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

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




 def   UpperCamelCase__							(self       ,       __a       ,       __a       ,       ) ->					torch.FloatTensor:






  """simple docstring"""





  UpperCAmelCase__							=				self.index_for_timestep(__a )

  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   UpperCamelCase__							(self       ,       __a       ,       __a = None       ,       __a = None       ,       ) ->					List[str]:






  """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       ,       __a       ,       dtype=__a )[::-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       ,       __a ) * step_ratio).round()[::-1].copy().astype(__a )
   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(__a       ,       0       ,       -step_ratio )).round().copy().astype(__a )
   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(__a ) ).to(__a )

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

  # 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(__a ).startswith('mps' ):
   # mps does not support float64
   UpperCAmelCase__							=				torch.from_numpy(__a ).to(__a       ,       dtype=torch.floataa )
  else:
   UpperCAmelCase__							=				torch.from_numpy(__a ).to(__a )

  UpperCAmelCase__							=				self.sigma_to_t(__a ).to(__a       ,       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(__a )




 def   UpperCamelCase__							(self       ,       __a ) ->					int:






  """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   UpperCamelCase__							(self ) ->					List[Any]:






  """simple docstring"""





  return self.sample is None




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






  """simple docstring"""





  UpperCAmelCase__							=				self.index_for_timestep(__a )

  # advance index counter by 1
  UpperCAmelCase__							=				timestep.cpu().item() if torch.is_tensor(__a ) 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=__a       ,       generator=__a )

  # 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=__a )




 def   UpperCamelCase__							(self       ,       __a       ,       __a       ,       __a       ,       ) ->					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(__a ):
   # 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(__a       ,       __a ) 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


_UpperCamelCase     			=  logging.get_logger(__name__)

_UpperCamelCase     			=  {
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}







class 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			'''mbart'''
    SCREAMING_SNAKE_CASE		=			['''past_key_values''']
    SCREAMING_SNAKE_CASE		=			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






    def __init__( self	,A=50_265	,A=1_024	,A=12	,A=4_096	,A=16	,A=12	,A=4_096	,A=16	,A=0.0	,A=0.0	,A=True	,A=True	,A="gelu"	,A=1_024	,A=0.1	,A=0.0	,A=0.0	,A=0.02	,A=0.0	,A=False	,A=1	,A=0	,A=2	,A=2	,**A	,):
          UpperCAmelCase							=       vocab_size
          UpperCAmelCase							=       max_position_embeddings
          UpperCAmelCase							=       d_model
          UpperCAmelCase							=       encoder_ffn_dim
          UpperCAmelCase							=       encoder_layers
          UpperCAmelCase							=       encoder_attention_heads
          UpperCAmelCase							=       decoder_ffn_dim
          UpperCAmelCase							=       decoder_layers
          UpperCAmelCase							=       decoder_attention_heads
          UpperCAmelCase							=       dropout
          UpperCAmelCase							=       attention_dropout
          UpperCAmelCase							=       activation_dropout
          UpperCAmelCase							=       activation_function
          UpperCAmelCase							=       init_std
          UpperCAmelCase							=       encoder_layerdrop
          UpperCAmelCase							=       decoder_layerdrop
          UpperCAmelCase							=       classifier_dropout
          UpperCAmelCase							=       use_cache
          UpperCAmelCase							=       encoder_layers
          UpperCAmelCase							=       scale_embedding  # scale factor will be sqrt(d_model) if True
          super().__init__(
              pad_token_id=A	,bos_token_id=A	,eos_token_id=A	,is_encoder_decoder=A	,forced_eos_token_id=A	,**A	,)







class 					lowerCamelCase__					(       snake_case ):






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

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

                if self.use_past:
                      self.fill_with_past_key_values_(A	,direction="""inputs"""					)
          elif self.task == "causal-lm":
                # TODO: figure this case out.
                UpperCAmelCase							=       OrderedDict(
                    [
                        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                    ]					)
                if self.use_past:
                      UpperCAmelCase			,					UpperCAmelCase							=       self.num_layers
                      for i in range(A					):
                            UpperCAmelCase							=       {0: """batch""", 2: """past_sequence + sequence"""}
                            UpperCAmelCase							=       {0: """batch""", 2: """past_sequence + sequence"""}
          else:
                UpperCAmelCase							=       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 				_UpperCamelCase							( self					):
          if self.task in ["default", "seq2seq-lm"]:
                UpperCAmelCase							=       super().outputs
          else:
                UpperCAmelCase							=       super(A	,self					).outputs
                if self.use_past:
                      UpperCAmelCase			,					UpperCAmelCase							=       self.num_layers
                      for i in range(A					):
                            UpperCAmelCase							=       {0: """batch""", 2: """past_sequence + sequence"""}
                            UpperCAmelCase							=       {0: """batch""", 2: """past_sequence + sequence"""}
          return common_outputs






    def 				_UpperCamelCase							( self	,A	,A = -1	,A = -1	,A = False	,A = None	,):
          UpperCAmelCase							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              A	,A	,A	,A	,A					)

          # Generate decoder inputs
          UpperCAmelCase							=       seq_length if not self.use_past else 1
          UpperCAmelCase							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              A	,A	,A	,A	,A					)
          UpperCAmelCase							=       {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
          UpperCAmelCase							=       dict(**A	,**A					)

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

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

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

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






    def 				_UpperCamelCase							( self	,A	,A = -1	,A = -1	,A = False	,A = None	,):
          UpperCAmelCase							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              A	,A	,A	,A	,A					)

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

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






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

          # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
          UpperCAmelCase							=       tokenizer.num_special_tokens_to_add(A					)
          UpperCAmelCase							=       compute_effective_axis_dimension(
              A	,fixed_dimension=OnnxConfig.default_fixed_sequence	,num_token_to_add=A					)

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






    def 				_UpperCamelCase							( self	,A	,A = -1	,A = -1	,A = False	,A = None	,):
          if self.task in ["default", "seq2seq-lm"]:
                UpperCAmelCase							=       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                    A	,batch_size=A	,seq_length=A	,is_pair=A	,framework=A					)

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

          return common_inputs






    def 				_UpperCamelCase							( self	,A	,A	,A	,A					):
          if self.task in ["default", "seq2seq-lm"]:
                UpperCAmelCase							=       super()._flatten_past_key_values_(A	,A	,A	,A					)
          else:
                UpperCAmelCase							=       super(A	,self					)._flatten_past_key_values_(
                    A	,A	,A	,A					)



