

'''simple docstring'''



import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


UpperCAmelCase					:			Dict       =	[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]







def   a__			(    a__		):




     """simple docstring"""


     for pegasus_name, hf_name in PATTERNS:
          __SCREAMING_SNAKE_CASE      = k.replace(a__ ,       a__		)
     return k







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = DEFAULTS.copy()
     cfg_kwargs.update(a__		)
     __SCREAMING_SNAKE_CASE      = PegasusConfig(**a__		)
     __SCREAMING_SNAKE_CASE      = PegasusForConditionalGeneration(a__		)
     __SCREAMING_SNAKE_CASE      = torch_model.model.state_dict()
     __SCREAMING_SNAKE_CASE      = {}
     for k, v in tf_weights.items():
          __SCREAMING_SNAKE_CASE      = rename_state_dict_key(a__		)
          if new_k not in sd:
               raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'		)

          if "dense" in k or "proj" in new_k:
               __SCREAMING_SNAKE_CASE      = v.T
          __SCREAMING_SNAKE_CASE      = torch.tensor(a__ ,       dtype=sd[new_k].dtype		)
          assert v.shape == sd[new_k].shape, F'{new_k}, {k}, {v.shape}, {sd[new_k].shape}'
     # make sure embedding.padding_idx is respected
     __SCREAMING_SNAKE_CASE      = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]		)
     __SCREAMING_SNAKE_CASE      = mapping["""shared.weight"""]
     __SCREAMING_SNAKE_CASE      = mapping["""shared.weight"""]
     __SCREAMING_SNAKE_CASE      = {k: torch.zeros_like(a__		) for k, v in sd.items() if k.endswith("""bias"""		) and k not in mapping}
     mapping.update(**a__		)
     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = torch_model.model.load_state_dict(a__ ,       strict=a__		)
     __SCREAMING_SNAKE_CASE      = [
         k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
     ]
     assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
     assert extra == [], F'no matches found for the following tf keys {extra}'
     return torch_model







def   a__			(    a__="./ckpt/aeslc/model.ckpt-32000"		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = tf.train.list_variables(a__		)
     __SCREAMING_SNAKE_CASE      = {}
     __SCREAMING_SNAKE_CASE      = ["""Adafactor""", """global_step"""]
     for name, shape in tqdm(a__ ,       desc="""converting tf checkpoint to dict"""		):
          __SCREAMING_SNAKE_CASE      = any(pat in name for pat in ignore_name		)
          if skip_key:
               continue
          __SCREAMING_SNAKE_CASE      = tf.train.load_variable(a__ ,       a__		)
          __SCREAMING_SNAKE_CASE      = array
     return tf_weights







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = Path(a__		).parent.name
     __SCREAMING_SNAKE_CASE      = task_specific_params[F'summarization_{dataset}']["""max_position_embeddings"""]
     __SCREAMING_SNAKE_CASE      = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" ,       model_max_length=a__		)
     assert tok.model_max_length == desired_max_model_length
     tok.save_pretrained(a__		)

     # convert model
     __SCREAMING_SNAKE_CASE      = get_tf_weights_as_numpy(a__		)
     __SCREAMING_SNAKE_CASE      = task_specific_params[F'summarization_{dataset}']
     if dataset == "large":
          __SCREAMING_SNAKE_CASE      = task_specific_params
     __SCREAMING_SNAKE_CASE      = convert_pegasus(a__ ,       a__		)
     torch_model.save_pretrained(a__		)
     __SCREAMING_SNAKE_CASE      = torch_model.state_dict()
     sd.pop("""model.decoder.embed_positions.weight"""		)
     sd.pop("""model.encoder.embed_positions.weight"""		)
     torch.save(a__ ,       Path(a__		) / """pytorch_model.bin"""		)


if __name__ == "__main__":
  UpperCAmelCase					:			Optional[Any]       =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
  parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
  UpperCAmelCase					:			Tuple       =	parser.parse_args()
  if args.save_dir is None:
    UpperCAmelCase					:			List[str]       =	Path(args.tf_ckpt_path).parent.name
    UpperCAmelCase					:			List[Any]       =	os.path.join('pegasus', dataset)
  convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)









'''simple docstring'''



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					:			str       =	logging.get_logger(__name__)

UpperCAmelCase					:			Optional[Any]       =	{
    'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "blenderbot-small"
 lowerCAmelCase__       = ["past_key_values"]
 lowerCAmelCase__       = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
 def __init__(						self   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=50_265   ,				__SCREAMING_SNAKE_CASE   :			Tuple=512   ,				__SCREAMING_SNAKE_CASE   :			Tuple=8   ,				__SCREAMING_SNAKE_CASE   :			str=2_048   ,				__SCREAMING_SNAKE_CASE   :			Tuple=16   ,				__SCREAMING_SNAKE_CASE   :			Dict=8   ,				__SCREAMING_SNAKE_CASE   :			Dict=2_048   ,				__SCREAMING_SNAKE_CASE   :			str=16   ,				__SCREAMING_SNAKE_CASE   :			Tuple=0.0   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.0   ,				__SCREAMING_SNAKE_CASE   :			Any=True   ,				__SCREAMING_SNAKE_CASE   :			Any=True   ,				__SCREAMING_SNAKE_CASE   :			List[Any]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			Tuple=512   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.0   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=0.0   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.02   ,				__SCREAMING_SNAKE_CASE   :			Any=1   ,				__SCREAMING_SNAKE_CASE   :			Any=False   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=0   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=2   ,				__SCREAMING_SNAKE_CASE   :			Dict=2   ,				**__SCREAMING_SNAKE_CASE   :			Any   ,				)    ->					Union[str, Any]:


      """simple docstring"""




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

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







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



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


      """simple docstring"""




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

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

           if self.use_past:
                self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE   ,				direction="""inputs"""     )
      elif self.task == "causal-lm":
           # TODO: figure this case out.
           __SCREAMING_SNAKE_CASE      = OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
               ]     )
           if self.use_past:
                __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_layers
                for i in range(__SCREAMING_SNAKE_CASE     ):
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
      else:
           __SCREAMING_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  UpperCAmelCase__						(						self   :			Dict     )    ->					Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      if self.task in ["default", "seq2seq-lm"]:
           __SCREAMING_SNAKE_CASE      = super().outputs
      else:
           __SCREAMING_SNAKE_CASE      = super(__SCREAMING_SNAKE_CASE   ,				self     ).outputs
           if self.use_past:
                __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_layers
                for i in range(__SCREAMING_SNAKE_CASE     ):
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
      return common_outputs
 def  UpperCAmelCase__						(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

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

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

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

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

           for _ in range(__SCREAMING_SNAKE_CASE     ):
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                    )     )
           # TODO: test this.
           __SCREAMING_SNAKE_CASE      = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
           for _ in range(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
                common_inputs["past_key_values"].append((torch.zeros(__SCREAMING_SNAKE_CASE     ), torch.zeros(__SCREAMING_SNAKE_CASE     ))     )
      return common_inputs
 def  UpperCAmelCase__						(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

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

           __SCREAMING_SNAKE_CASE      = common_inputs["""attention_mask"""].dtype
           __SCREAMING_SNAKE_CASE      = torch.cat(
               [common_inputs["""attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				dtype=__SCREAMING_SNAKE_CASE     )]   ,				dim=1     )
           __SCREAMING_SNAKE_CASE      = [
               (torch.zeros(__SCREAMING_SNAKE_CASE     ), torch.zeros(__SCREAMING_SNAKE_CASE     )) for _ in range(__SCREAMING_SNAKE_CASE     )
           ]
      return common_inputs
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = compute_effective_axis_dimension(
          __SCREAMING_SNAKE_CASE   ,				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
      __SCREAMING_SNAKE_CASE      = tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = compute_effective_axis_dimension(
          __SCREAMING_SNAKE_CASE   ,				fixed_dimension=OnnxConfig.default_fixed_sequence   ,				num_token_to_add=__SCREAMING_SNAKE_CASE     )

      # Generate dummy inputs according to compute batch and sequence
      __SCREAMING_SNAKE_CASE      = [""" """.join([tokenizer.unk_token]     ) * seq_length] * batch_size
      __SCREAMING_SNAKE_CASE      = dict(tokenizer(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE     )     )
      return common_inputs
 def  UpperCAmelCase__						(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




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

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

      return common_inputs


 def  UpperCAmelCase__						(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]     )    ->					Optional[Any]:


      """simple docstring"""




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






