

import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

SCREAMING_SNAKE_CASE		:List[str]				  =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:List[Any]				  =    {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

SCREAMING_SNAKE_CASE		:Optional[Any]				  =    {
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
}

SCREAMING_SNAKE_CASE		:Any				  =    {
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}




@lru_cache()
def  lowerCAmelCase(			)->	List[str]:



     """simple docstring"""






     UpperCamelCase_      =	(
         list(range(ord("!" ) ,	ord("~" ) + 1 ) ) + list(range(ord("¡" ) ,	ord("¬" ) + 1 ) ) + list(range(ord("®" ) ,	ord("ÿ" ) + 1 ) )
     )
     UpperCamelCase_      =	bs[:]
     UpperCamelCase_      =	0
     for b in range(2**8 ):
          if b not in bs:
               bs.append(SCREAMING_SNAKE_CASE_ )
               cs.append(2**8 + n )
               n += 1
     UpperCamelCase_      =	[chr(SCREAMING_SNAKE_CASE_ ) for n in cs]
     return dict(zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ) )




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	Tuple:



     """simple docstring"""






     UpperCamelCase_      =	set()
     UpperCamelCase_      =	word[0]
     for char in word[1:]:
          pairs.add((prev_char, char) )
          UpperCamelCase_      =	char
     return pairs






class 	__magic_name__				(       snake_case							):
     UpperCamelCase_  :Optional[int]								=			VOCAB_FILES_NAMES
     UpperCamelCase_  :Any								=			PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_  :Tuple								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_  :Tuple								=			["""input_ids""", """attention_mask"""]






     def __init__(							self		,	_lowercase		,	_lowercase		,	_lowercase="replace"		,	_lowercase="<|endoftext|>"		,	_lowercase="<|endoftext|>"		,	_lowercase="<|endoftext|>"		,	_lowercase=None		,	_lowercase=False		,	_lowercase=False		,	**_lowercase		,	)->    str:
          UpperCamelCase_      =	AddedToken(_lowercase		,	lstrip=_lowercase		,	rstrip=_lowercase  ) if isinstance(_lowercase		,	_lowercase  ) else bos_token
          UpperCamelCase_      =	AddedToken(_lowercase		,	lstrip=_lowercase		,	rstrip=_lowercase  ) if isinstance(_lowercase		,	_lowercase  ) else eos_token
          UpperCamelCase_      =	AddedToken(_lowercase		,	lstrip=_lowercase		,	rstrip=_lowercase  ) if isinstance(_lowercase		,	_lowercase  ) else unk_token
          UpperCamelCase_      =	AddedToken(_lowercase		,	lstrip=_lowercase		,	rstrip=_lowercase  ) if isinstance(_lowercase		,	_lowercase  ) else pad_token
          super().__init__(
              errors=_lowercase		,	unk_token=_lowercase		,	bos_token=_lowercase		,	eos_token=_lowercase		,	pad_token=_lowercase		,	add_prefix_space=_lowercase		,	add_bos_token=_lowercase		,	**_lowercase		,	)
          UpperCamelCase_      =	add_bos_token

          with open(_lowercase		,	encoding="utf-8"  ) as vocab_handle:
               UpperCamelCase_      =	json.load(_lowercase  )
          UpperCamelCase_      =	{v: k for k, v in self.encoder.items()}
          UpperCamelCase_      =	errors  # how to handle errors in decoding
          UpperCamelCase_      =	bytes_to_unicode()
          UpperCamelCase_      =	{v: k for k, v in self.byte_encoder.items()}
          with open(_lowercase		,	encoding="utf-8"  ) as merges_handle:
               UpperCamelCase_      =	merges_handle.read().split("\n"  )[1:-1]
          UpperCamelCase_      =	[tuple(merge.split()  ) for merge in bpe_merges]
          UpperCamelCase_      =	dict(zip(_lowercase		,	range(len(_lowercase  )  )  )  )
          UpperCamelCase_      =	{}
          UpperCamelCase_      =	add_prefix_space

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          UpperCamelCase_      =	re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"  )






     @property
     def   UpperCAmelCase_							(							self  )->    int:
          return len(self.encoder  )






     def   UpperCAmelCase_							(							self  )->    List[str]:
          return dict(self.encoder		,	**self.added_tokens_encoder  )






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    Dict:
          if token in self.cache:
               return self.cache[token]
          UpperCamelCase_      =	tuple(_lowercase  )
          UpperCamelCase_      =	get_pairs(_lowercase  )

          if not pairs:
               return token

          while True:
               UpperCamelCase_      =	min(_lowercase		,	key=lambda _lowercase  : self.bpe_ranks.get(_lowercase		,	float("inf"  )  )  )
               if bigram not in self.bpe_ranks:
                    break
               UpperCamelCase_     ,					UpperCamelCase_      =	bigram
               UpperCamelCase_      =	[]
               UpperCamelCase_      =	0
               while i < len(_lowercase  ):
                    try:
                         UpperCamelCase_      =	word.index(_lowercase		,	_lowercase  )
                    except ValueError:
                         new_word.extend(word[i:]  )
                         break
                    else:
                         new_word.extend(word[i:j]  )
                         UpperCamelCase_      =	j

                    if word[i] == first and i < len(_lowercase  ) - 1 and word[i + 1] == second:
                         new_word.append(first + second  )
                         i += 2
                    else:
                         new_word.append(word[i]  )
                         i += 1
               UpperCamelCase_      =	tuple(_lowercase  )
               UpperCamelCase_      =	new_word
               if len(_lowercase  ) == 1:
                    break
               else:
                    UpperCamelCase_      =	get_pairs(_lowercase  )
          UpperCamelCase_      =	" ".join(_lowercase  )
          UpperCamelCase_      =	word
          return word






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase=None  )->    List[Any]:
          if self.add_bos_token:
               UpperCamelCase_      =	[self.bos_token_id]
          else:
               UpperCamelCase_      =	[]

          UpperCamelCase_      =	bos_token_ids + token_ids_a

          if token_ids_a is None:
               return output

          return output + bos_token_ids + token_ids_a






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = None		,	_lowercase = False  )->    List[int]:
          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=_lowercase		,	token_ids_a=_lowercase		,	already_has_special_tokens=_lowercase  )

          if not self.add_bos_token:
               return super().get_special_tokens_mask(
                   token_ids_a=_lowercase		,	token_ids_a=_lowercase		,	already_has_special_tokens=_lowercase  )

          if token_ids_a is None:
               return [1] + ([0] * len(_lowercase  ))
          return [1] + ([0] * len(_lowercase  )) + [1] + ([0] * len(_lowercase  ))






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    List[Any]:
          UpperCamelCase_      =	[]
          for token in re.findall(self.pat		,	_lowercase  ):
               UpperCamelCase_      =	"".join(
                   self.byte_encoder[b] for b in token.encode("utf-8"  )  )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
               bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowercase  ).split(" "  )  )
          return bpe_tokens






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    str:
          return self.encoder.get(_lowercase		,	self.encoder.get(self.unk_token  )  )






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    Any:
          return self.decoder.get(_lowercase  )






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    List[str]:
          UpperCamelCase_      =	"".join(_lowercase  )
          UpperCamelCase_      =	bytearray([self.byte_decoder[c] for c in text]  ).decode("utf-8"		,	errors=self.errors  )
          return text






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = None  )->    Tuple[str]:
          if not os.path.isdir(_lowercase  ):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
               return
          UpperCamelCase_      =	os.path.join(
              _lowercase		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )
          UpperCamelCase_      =	os.path.join(
              _lowercase		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]  )

          with open(_lowercase		,	"w"		,	encoding="utf-8"  ) as f:
               f.write(json.dumps(self.encoder		,	indent=2		,	sort_keys=_lowercase		,	ensure_ascii=_lowercase  ) + "\n"  )

          UpperCamelCase_      =	0
          with open(_lowercase		,	"w"		,	encoding="utf-8"  ) as writer:
               writer.write("#version: 0.2\n"  )
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,	key=lambda _lowercase  : kv[1]  ):
                    if index != token_index:
                         logger.warning(
                             F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                             " Please check that the tokenizer is not corrupted!"  )
                         UpperCamelCase_      =	token_index
                    writer.write(" ".join(_lowercase  ) + "\n"  )
                    index += 1

          return vocab_file, merge_file






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase=False		,	**_lowercase  )->    Optional[Any]:
          UpperCamelCase_      =	kwargs.pop("add_prefix_space"		,	self.add_prefix_space  )
          if is_split_into_words or add_prefix_space:
               UpperCamelCase_      =	" " + text
          return (text, kwargs)






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    List[int]:
          UpperCamelCase_      =	[]
          for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(_lowercase		,	add_special_tokens=_lowercase  ) + [self.eos_token_id]  )
          if len(_lowercase  ) > self.model_max_length:
               UpperCamelCase_      =	input_ids[-self.model_max_length :]
          return input_ids



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


SCREAMING_SNAKE_CASE		:Any				  =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Dict				  =    {
    """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 	__magic_name__				(       snake_case							):
     UpperCamelCase_  :str								=			"""blenderbot"""
     UpperCamelCase_  :Union[str, Any]								=			["""past_key_values"""]
     UpperCamelCase_  :Any								=			{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






     def __init__(							self		,	_lowercase=8_008		,	_lowercase=128		,	_lowercase=2		,	_lowercase=10_240		,	_lowercase=32		,	_lowercase=24		,	_lowercase=10_240		,	_lowercase=32		,	_lowercase=0.0		,	_lowercase=0.0		,	_lowercase=True		,	_lowercase=True		,	_lowercase="gelu"		,	_lowercase=2_560		,	_lowercase=0.1		,	_lowercase=0.0		,	_lowercase=0.0		,	_lowercase=0.02		,	_lowercase=1		,	_lowercase=False		,	_lowercase=0		,	_lowercase=1		,	_lowercase=2		,	_lowercase=3		,	_lowercase=2		,	**_lowercase		,	)->    List[str]:
         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_      =	use_cache
         UpperCamelCase_      =	encoder_layers
         UpperCamelCase_      =	scale_embedding  # scale factor will be sqrt(d_model) if True

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






class 	__magic_name__				(       snake_case							):






     @property
     def   UpperCAmelCase_							(							self  )->    Mapping[str, Mapping[int, str]]:
         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_(_lowercase		,	direction="inputs"  )
         elif self.task == "causal-lm":
             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(_lowercase  ):
                     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
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def   UpperCAmelCase_							(							self  )->    Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	super().outputs
         else:
             UpperCamelCase_      =	super(_lowercase		,	self  ).outputs
             if self.use_past:
                 UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers
                 for i in range(_lowercase  ):
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
         return common_outputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )
         # Generate decoder inputs
         UpperCamelCase_      =	seq_length if not self.use_past else 1
         UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )
         UpperCamelCase_      =	{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
         UpperCamelCase_      =	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
             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
             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(_lowercase		,	_lowercase  )]		,	dim=1  )
             UpperCamelCase_      =	[]
             UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers

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






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         UpperCamelCase_      =	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
             UpperCamelCase_     ,					UpperCamelCase_      =	common_inputs["input_ids"].shape
             UpperCamelCase_      =	seqlen
             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(_lowercase		,	_lowercase		,	dtype=_lowercase  )]		,	dim=1  )
             UpperCamelCase_      =	[
                 (torch.zeros(_lowercase  ), torch.zeros(_lowercase  )) for _ in range(_lowercase  )
             ]
         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         # 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(
             _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
         UpperCamelCase_      =	tokenizer.num_special_tokens_to_add(_lowercase  )
         UpperCamelCase_      =	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
         UpperCamelCase_      =	[" ".join([tokenizer.unk_token]  ) * seq_length] * batch_size
         UpperCamelCase_      =	dict(tokenizer(_lowercase		,	return_tensors=_lowercase  )  )
         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	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":
             UpperCamelCase_      =	self._generate_dummy_inputs_for_causal_lm(
                 _lowercase		,	batch_size=_lowercase		,	seq_length=_lowercase		,	is_pair=_lowercase		,	framework=_lowercase  )
         else:
             UpperCamelCase_      =	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   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )->    List[Any]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	super()._flatten_past_key_values_(_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )
         else:
             UpperCamelCase_      =	super(_lowercase		,	self  )._flatten_past_key_values_(
                 _lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase  )->    Union[str, Any]:
         if direction not in ["inputs", "outputs"]:
             raise ValueError(F"direction must either be \"inputs\" or \"outputs\", but {direction} was given"  )

         UpperCamelCase_      =	"past_key_values" if direction == "inputs" else "present"
         UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers

         UpperCamelCase_      =	"past_encoder_sequence"
         UpperCamelCase_      =	"past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

         for i in range(_lowercase  ):
             UpperCamelCase_      =	{0: "batch", 2: decoder_sequence}
             UpperCamelCase_      =	{0: "batch", 2: decoder_sequence}
             UpperCamelCase_      =	{0: "batch", 2: encoder_sequence}
             UpperCamelCase_      =	{0: "batch", 2: encoder_sequence}
