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

import regex as re

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


lowerCamelCase_  :       str					      =     logging.get_logger(__name__)

lowerCamelCase_  :       str					      =     {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

lowerCamelCase_  :       int					      =     {
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
}

lowerCamelCase_  :       Dict					      =     {
    """roberta-base""": 5_12,
    """roberta-large""": 5_12,
    """roberta-large-mnli""": 5_12,
    """distilroberta-base""": 5_12,
    """roberta-base-openai-detector""": 5_12,
    """roberta-large-openai-detector""": 5_12,
}







@lru_cache()
def 				A__    (  )     ->		Any:
				UpperCamelCase_:							Union[str, Any]		=  (
				    list(range(ord("""!"""  )   ,			ord("""~"""  ) + 1  )  ) + list(range(ord("""¡"""  )   ,			ord("""¬"""  ) + 1  )  ) + list(range(ord("""®"""  )   ,			ord("""ÿ"""  ) + 1  )  )
				)
				UpperCamelCase_:							List[str]		=  bs[:]
				UpperCamelCase_:							Tuple		=  0
				for b in range(2**8  ):
								if b not in bs:
												bs.append(lowerCamelCase  )
												cs.append(2**8 + n  )
												n += 1
				UpperCamelCase_:							str		=  [chr(lowerCamelCase  ) for n in cs]
				return dict(zip(lowerCamelCase   ,			lowerCamelCase  )  )







def 				A__    (  lowerCamelCase  )     ->		Dict:
				UpperCamelCase_:							str		=  set()
				UpperCamelCase_:							str		=  word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)  )
								UpperCamelCase_:							Optional[int]		=  char
				return pairs





class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			__UpperCamelCase					:  int    					=       VOCAB_FILES_NAMES
			__UpperCamelCase					:  Tuple    					=       PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase					:  Dict    					=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase					:  Optional[int]    					=       ["""input_ids""", """attention_mask"""]

			def __init__(				self       :				Tuple  ,		snake_case_       :				Optional[int]  ,		snake_case_       :				Dict  ,		snake_case_       :				Dict="replace"  ,		snake_case_       :				Optional[int]="<s>"  ,		snake_case_       :				str="</s>"  ,		snake_case_       :				Optional[Any]="</s>"  ,		snake_case_       :				int="<s>"  ,		snake_case_       :				int="<unk>"  ,		snake_case_       :				Optional[Any]="<pad>"  ,		snake_case_       :				Optional[Any]="<mask>"  ,		snake_case_       :				List[str]=False  ,		**snake_case_       :				List[Any]  ,		):
							UpperCamelCase_:							List[Any]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_					) if isinstance(snake_case_  ,		snake_case_					) else bos_token
							UpperCamelCase_:							List[str]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_					) if isinstance(snake_case_  ,		snake_case_					) else eos_token
							UpperCamelCase_:							List[str]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_					) if isinstance(snake_case_  ,		snake_case_					) else sep_token
							UpperCamelCase_:							str		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_					) if isinstance(snake_case_  ,		snake_case_					) else cls_token
							UpperCamelCase_:							List[Any]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_					) if isinstance(snake_case_  ,		snake_case_					) else unk_token
							UpperCamelCase_:							Any		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_					) if isinstance(snake_case_  ,		snake_case_					) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							UpperCamelCase_:							List[str]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_					) if isinstance(snake_case_  ,		snake_case_					) else mask_token

							super().__init__(
							    errors=snake_case_  ,		bos_token=snake_case_  ,		eos_token=snake_case_  ,		unk_token=snake_case_  ,		sep_token=snake_case_  ,		cls_token=snake_case_  ,		pad_token=snake_case_  ,		mask_token=snake_case_  ,		add_prefix_space=snake_case_  ,		**snake_case_  ,		)

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

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

			@property
			def 					lowerCAmelCase__		(				self       :				Tuple					):
							return len(self.encoder					)

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]					):
							return dict(self.encoder  ,		**self.added_tokens_encoder					)

			def 					lowerCAmelCase__		(				self       :				str  ,		snake_case_       :				Any					):
							if token in self.cache:
											return self.cache[token]
							UpperCamelCase_:							int		=  tuple(snake_case_					)
							UpperCamelCase_:							int		=  get_pairs(snake_case_					)

							if not pairs:
											return token

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

															if word[i] == first and i < len(snake_case_					) - 1 and word[i + 1] == second:
																			new_word.append(first + second					)
																			i += 2
															else:
																			new_word.append(word[i]					)
																			i += 1
											UpperCamelCase_:							Tuple		=  tuple(snake_case_					)
											UpperCamelCase_:							Optional[int]		=  new_word
											if len(snake_case_					) == 1:
															break
											else:
															UpperCamelCase_:							Optional[Any]		=  get_pairs(snake_case_					)
							UpperCamelCase_:							Tuple		=  """ """.join(snake_case_					)
							UpperCamelCase_:							Union[str, Any]		=  word
							return word

			def 					lowerCAmelCase__		(				self       :				int  ,		snake_case_       :				str					):
							UpperCamelCase_:							Union[str, Any]		=  []
							for token in re.findall(self.pat  ,		snake_case_					):
											UpperCamelCase_:							Dict		=  """""".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(snake_case_					).split(""" """					)					)
							return bpe_tokens

			def 					lowerCAmelCase__		(				self       :				List[str]  ,		snake_case_       :				List[str]					):
							return self.encoder.get(snake_case_  ,		self.encoder.get(self.unk_token					)					)

			def 					lowerCAmelCase__		(				self       :				List[Any]  ,		snake_case_       :				Any					):
							return self.decoder.get(snake_case_					)

			def 					lowerCAmelCase__		(				self       :				Tuple  ,		snake_case_       :				Any					):
							UpperCamelCase_:							List[str]		=  """""".join(snake_case_					)
							UpperCamelCase_:							Dict		=  bytearray([self.byte_decoder[c] for c in text]					).decode("""utf-8"""  ,		errors=self.errors					)
							return text

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]  ,		snake_case_       :				str  ,		snake_case_       :				Optional[str] = None					):
							if not os.path.isdir(snake_case_					):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
											return
							UpperCamelCase_:							List[str]		=  os.path.join(
							    snake_case_  ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
							UpperCamelCase_:							Tuple		=  os.path.join(
							    snake_case_  ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]					)

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

							UpperCamelCase_:							List[Any]		=  0
							with open(snake_case_  ,		"""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 snake_case_					: 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_:							Optional[Any]		=  token_index
															writer.write(""" """.join(snake_case_					) + """\n"""					)
															index += 1

							return vocab_file, merge_file

			def 					lowerCAmelCase__		(				self       :				Tuple  ,		snake_case_       :				List[int]  ,		snake_case_       :				Optional[List[int]] = None					):
							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							UpperCamelCase_:							Tuple		=  [self.cls_token_id]
							UpperCamelCase_:							Optional[int]		=  [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep

			def 					lowerCAmelCase__		(				self       :				Optional[int]  ,		snake_case_       :				List[int]  ,		snake_case_       :				Optional[List[int]] = None  ,		snake_case_       :				bool = False					):
							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=snake_case_  ,		token_ids_a=snake_case_  ,		already_has_special_tokens=snake_case_					)

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

			def 					lowerCAmelCase__		(				self       :				Dict  ,		snake_case_       :				List[int]  ,		snake_case_       :				Optional[List[int]] = None					):
							UpperCamelCase_:							str		=  [self.sep_token_id]
							UpperCamelCase_:							Optional[int]		=  [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep					) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]




			def 					lowerCAmelCase__		(				self       :				Dict  ,		snake_case_       :				List[Any]  ,		snake_case_       :				Union[str, Any]=False  ,		**snake_case_       :				Optional[int]					):
							UpperCamelCase_:							str		=  kwargs.pop("""add_prefix_space"""  ,		self.add_prefix_space					)
							if (is_split_into_words or add_prefix_space) and (len(snake_case_					) > 0 and not text[0].isspace()):
											UpperCamelCase_:							Optional[int]		=  """ """ + text
							return (text, kwargs)












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


_lowerCamelCase			 =						logging.get_logger(__name__)

_lowerCamelCase			 =						{
    '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 			a     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       List[Any]   		=  'blenderbot'
     lowerCAmelCase				:       int   		=  ['past_key_values']
     lowerCAmelCase				:       int   		=  {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
     def __init__(	self   :			int					,    __snake_case   :			Optional[Any]=80_08					,    __snake_case   :			List[str]=1_28					,    __snake_case   :			List[str]=2					,    __snake_case   :			Tuple=1_02_40					,    __snake_case   :			List[Any]=32					,    __snake_case   :			int=24					,    __snake_case   :			str=1_02_40					,    __snake_case   :			Optional[int]=32					,    __snake_case   :			Dict=0.0					,    __snake_case   :			int=0.0					,    __snake_case   :			str=True					,    __snake_case   :			Dict=True					,    __snake_case   :			Dict="gelu"					,    __snake_case   :			List[str]=25_60					,    __snake_case   :			List[Any]=0.1					,    __snake_case   :			str=0.0					,    __snake_case   :			Dict=0.0					,    __snake_case   :			Tuple=0.02					,    __snake_case   :			Any=1					,    __snake_case   :			int=False					,    __snake_case   :			Any=0					,    __snake_case   :			Optional[Any]=1					,    __snake_case   :			Optional[int]=2					,    __snake_case   :			List[Any]=3					,    __snake_case   :			int=2					,    **__snake_case   :			Tuple					,    ):
         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=__snake_case					,    bos_token_id=__snake_case					,    eos_token_id=__snake_case					,    is_encoder_decoder=__snake_case					,    decoder_start_token_id=__snake_case					,    encoder_no_repeat_ngram_size=__snake_case					,    forced_eos_token_id=__snake_case					,    **__snake_case					,    )



class 			a     (   _A			):

     '''simple docstring'''





     @property
     def   lowerCamelCase_   (	self   :			Any				):
         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_(__snake_case					,    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(__snake_case				):
                     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   lowerCamelCase_   (	self   :			Optional[Any]				):
         if self.task in ["default", "seq2seq-lm"]:
             UpperCAmelCase_							 =     super().outputs
         else:
             UpperCAmelCase_							 =     super(__snake_case					,    self				).outputs
             if self.use_past:
                 UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_layers
                 for i in range(__snake_case				):
                     UpperCAmelCase_							 =     {0: '''batch''', 2: '''past_sequence + sequence'''}
                     UpperCAmelCase_							 =     {0: '''batch''', 2: '''past_sequence + sequence'''}
         return common_outputs
     def   lowerCamelCase_   (	self   :			Optional[Any]					,    __snake_case   :			PreTrainedTokenizer					,    __snake_case   :			int = -1					,    __snake_case   :			int = -1					,    __snake_case   :			bool = False					,    __snake_case   :			Optional[TensorType] = None					,    ):
         UpperCAmelCase_							 =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)
         # Generate decoder inputs
         UpperCAmelCase_							 =     seq_length if not self.use_past else 1
         UpperCAmelCase_							 =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)
         UpperCAmelCase_							 =     {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
         UpperCAmelCase_							 =     dict(**__snake_case					,    **__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
             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(__snake_case					,    __snake_case				)]					,    dim=1				)
             UpperCAmelCase_							 =     []
             UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_layers

             for _ in range(__snake_case				):
                 common_inputs["past_key_values"].append(
                     (
                         torch.zeros(__snake_case				),
                         torch.zeros(__snake_case				),
                         torch.zeros(__snake_case				),
                         torch.zeros(__snake_case				),
                     )				)
         return common_inputs
     def   lowerCamelCase_   (	self   :			Optional[Any]					,    __snake_case   :			PreTrainedTokenizer					,    __snake_case   :			int = -1					,    __snake_case   :			int = -1					,    __snake_case   :			bool = False					,    __snake_case   :			Optional[TensorType] = None					,    ):
         UpperCAmelCase_							 =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    __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
             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(__snake_case					,    __snake_case					,    dtype=__snake_case				)]					,    dim=1				)
             UpperCAmelCase_							 =     [
                 (torch.zeros(__snake_case				), torch.zeros(__snake_case				)) for _ in range(__snake_case				)
             ]
         return common_inputs
     def   lowerCamelCase_   (	self   :			Any					,    __snake_case   :			PreTrainedTokenizer					,    __snake_case   :			int = -1					,    __snake_case   :			int = -1					,    __snake_case   :			bool = False					,    __snake_case   :			Optional[TensorType] = 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(
             __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
         UpperCAmelCase_							 =     tokenizer.num_special_tokens_to_add(__snake_case				)
         UpperCAmelCase_							 =     compute_effective_axis_dimension(
             __snake_case					,    fixed_dimension=OnnxConfig.default_fixed_sequence					,    num_token_to_add=__snake_case				)

         # Generate dummy inputs according to compute batch and sequence
         UpperCAmelCase_							 =     [''' '''.join([tokenizer.unk_token]				) * seq_length] * batch_size
         UpperCAmelCase_							 =     dict(tokenizer(__snake_case					,    return_tensors=__snake_case				)				)
         return common_inputs
     def   lowerCamelCase_   (	self   :			List[Any]					,    __snake_case   :			PreTrainedTokenizer					,    __snake_case   :			int = -1					,    __snake_case   :			int = -1					,    __snake_case   :			bool = False					,    __snake_case   :			Optional[TensorType] = None					,    ):
         if self.task in ["default", "seq2seq-lm"]:
             UpperCAmelCase_							 =     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 __snake_case					,    batch_size=__snake_case					,    seq_length=__snake_case					,    is_pair=__snake_case					,    framework=__snake_case				)

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

         return common_inputs
     def   lowerCamelCase_   (	self   :			Dict					,    __snake_case   :			Optional[int]					,    __snake_case   :			int					,    __snake_case   :			str					,    __snake_case   :			Optional[int]				):
         if self.task in ["default", "seq2seq-lm"]:
             UpperCAmelCase_							 =     super()._flatten_past_key_values_(__snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)
         else:
             UpperCAmelCase_							 =     super(__snake_case					,    self				)._flatten_past_key_values_(
                 __snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)




     def   lowerCamelCase_   (	self   :			Dict					,    __snake_case   :			Mapping[str, Mapping[int, str]]					,    __snake_case   :			str				):
         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(__snake_case				):
             UpperCAmelCase_							 =     {0: '''batch''', 2: decoder_sequence}
             UpperCAmelCase_							 =     {0: '''batch''', 2: decoder_sequence}
             UpperCAmelCase_							 =     {0: '''batch''', 2: encoder_sequence}
             UpperCAmelCase_							 =     {0: '''batch''', 2: encoder_sequence}





