
import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


_A  					= logging.get_logger(__name__)

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

_A  					= {
    "vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
    "merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
}

_A  					= {
    "openai-gpt": 5_12,
}



def 						lowercase_			(   A__		)							->							Optional[int]:
    """simple docstring"""

    snake_case      =					text.strip()
    if not text:
        return []
    snake_case      =					text.split()
    return tokens







class      lowerCamelCase		(  A_  ):





     def __init__(self      :    int				,    _A      :    List[str]=True				,    _A      :    str=None				,    _A      :    int=True				,    _A      :    List[Any]=None				,    _A      :    Any=True				,    )						->						Any:
         if never_split is None:
             snake_case      =					[]
         snake_case      =					do_lower_case
         snake_case      =					set(_A   )
         snake_case      =					tokenize_chinese_chars
         snake_case      =					strip_accents
         snake_case      =					do_split_on_punc





     def 		UpperCAmelCase(self      :    Tuple				,    _A      :    List[Any]				,    _A      :    List[Any]=None   )						->						Union[str, Any]:
         snake_case      =					self.never_split.union(set(_A   )   ) if never_split else self.never_split
         snake_case      =					self._clean_text(_A   )

         # This was added on November 1st, 2018 for the multilingual and Chinese
         # models. This is also applied to the English models now, but it doesn't
         # matter since the English models were not trained on any Chinese data
         # and generally don't have any Chinese data in them (there are Chinese
         # characters in the vocabulary because Wikipedia does have some Chinese
         # words in the English Wikipedia.).
         if self.tokenize_chinese_chars:
             snake_case      =					self._tokenize_chinese_chars(_A   )
         # prevents treating the same character with different unicode codepoints as different characters
         snake_case      =					unicodedata.normalize("NFC"				,    _A   )
         snake_case      =					whitespace_tokenize(_A   )
         snake_case      =					[]
         for token in orig_tokens:
             if token not in never_split:
                 if self.do_lower_case:
                     snake_case      =					token.lower()
                     if self.strip_accents is not False:
                         snake_case      =					self._run_strip_accents(_A   )
                 elif self.strip_accents:
                     snake_case      =					self._run_strip_accents(_A   )
             split_tokens.extend(self._run_split_on_punc(_A				,    _A   )   )

         snake_case      =					whitespace_tokenize(" ".join(_A   )   )
         return output_tokens





     def 		UpperCAmelCase(self      :    Any				,    _A      :    str   )						->						str:
         snake_case      =					unicodedata.normalize("NFD"				,    _A   )
         snake_case      =					[]
         for char in text:
             snake_case      =					unicodedata.category(_A   )
             if cat == "Mn":
                 continue
             output.append(_A   )
         return "".join(_A   )





     def 		UpperCAmelCase(self      :    Any				,    _A      :    str				,    _A      :    str=None   )						->						List[str]:
         if not self.do_split_on_punc or (never_split is not None and text in never_split):
             return [text]
         snake_case      =					list(_A   )
         snake_case      =					0
         snake_case      =					True
         snake_case      =					[]
         while i < len(_A   ):
             snake_case      =					chars[i]
             if _is_punctuation(_A   ):
                 output.append([char]   )
                 snake_case      =					True
             else:
                 if start_new_word:
                     output.append([]   )
                 snake_case      =					False
                 output[-1].append(_A   )
             i += 1

         return ["".join(_A   ) for x in output]





     def 		UpperCAmelCase(self      :    int				,    _A      :    Optional[int]   )						->						Tuple:
         snake_case      =					[]
         for char in text:
             snake_case      =					ord(_A   )
             if self._is_chinese_char(_A   ):
                 output.append(" "   )
                 output.append(_A   )
                 output.append(" "   )
             else:
                 output.append(_A   )
         return "".join(_A   )





     def 		UpperCAmelCase(self      :    int				,    _A      :    Union[str, Any]   )						->						Union[str, Any]:
         # This defines a "chinese character" as anything in the CJK Unicode block:
         #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
         #
         # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
         # despite its name. The modern Korean Hangul alphabet is a different block,
         # as is Japanese Hiragana and Katakana. Those alphabets are used to write
         # space-separated words, so they are not treated specially and handled
         # like the all of the other languages.
         if (
             (cp >= 0X4e_00 and cp <= 0X9f_ff)
             or (cp >= 0X34_00 and cp <= 0X4d_bf)  #
             or (cp >= 0X2_00_00 and cp <= 0X2_a6_df)  #
             or (cp >= 0X2_a7_00 and cp <= 0X2_b7_3f)  #
             or (cp >= 0X2_b7_40 and cp <= 0X2_b8_1f)  #
             or (cp >= 0X2_b8_20 and cp <= 0X2_ce_af)  #
             or (cp >= 0Xf9_00 and cp <= 0Xfa_ff)
             or (cp >= 0X2_f8_00 and cp <= 0X2_fa_1f)  #
         ):  #
             return True

         return False





     def 		UpperCAmelCase(self      :    Optional[Any]				,    _A      :    Optional[int]   )						->						List[Any]:
         snake_case      =					[]
         for char in text:
             snake_case      =					ord(_A   )
             if cp == 0 or cp == 0Xff_fd or _is_control(_A   ):
                 continue
             if _is_whitespace(_A   ):
                 output.append(" "   )
             else:
                 output.append(_A   )
         return "".join(_A   )



def 						lowercase_			(   A__		)							->							Optional[Any]:
    """simple docstring"""

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



def 						lowercase_			(   A__		)							->							int:
    """simple docstring"""

    snake_case      =					text.replace("—" ,		"-"		)
    snake_case      =					text.replace("–" ,		"-"		)
    snake_case      =					text.replace("―" ,		"-"		)
    snake_case      =					text.replace("…" ,		"..."		)
    snake_case      =					text.replace("´" ,		"'"		)
    snake_case      =					re.sub(r"(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)" ,		r" \1 " ,		A__		)
    snake_case      =					re.sub(r"\s*\n\s*" ,		" \n " ,		A__		)
    snake_case      =					re.sub(r"[^\S\n]+" ,		" " ,		A__		)
    return text.strip()







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Optional[int]           =		VOCAB_FILES_NAMES
     UpperCAmelCase__				:							List[Any]           =		PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__				:							str           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__				:							Any           =		["input_ids", "attention_mask"]





     def __init__(self      :    Union[str, Any]				,    _A      :    Union[str, Any]				,    _A      :    str				,    _A      :    Tuple="<unk>"				,    **_A      :    List[Any]   )						->						Any:
         super().__init__(unk_token=_A				,    **_A   )

         try:
             import ftfy
             from spacy.lang.en import English

             snake_case      =					English()
             snake_case      =					_nlp.tokenizer
             snake_case      =					ftfy.fix_text
         except ImportError:
             logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."   )
             snake_case      =					BasicTokenizer(do_lower_case=_A   )
             snake_case      =					None

         with open(_A				,    encoding="utf-8"   ) as vocab_handle:
             snake_case      =					json.load(_A   )
         snake_case      =					{v: k for k, v in self.encoder.items()}
         with open(_A				,    encoding="utf-8"   ) as merges_handle:
             snake_case      =					merges_handle.read().split("\n"   )[1:-1]
         snake_case      =					[tuple(merge.split()   ) for merge in merges]
         snake_case      =					dict(zip(_A				,    range(len(_A   )   )   )   )
         snake_case      =					{}





     @property
     def 		UpperCAmelCase(self      :    int   )						->						Tuple:
         return True





     @property
     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						str:
         return len(self.encoder   )





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





     def 		UpperCAmelCase(self      :    str				,    _A      :    Union[str, Any]   )						->						List[str]:
         snake_case      =					tuple(token[:-1]   ) + (token[-1] + "</w>",)
         if token in self.cache:
             return self.cache[token]
         snake_case      =					get_pairs(_A   )

         if not pairs:
             return token + "</w>"

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

                 if word[i] == first and i < len(_A   ) - 1 and word[i + 1] == second:
                     new_word.append(first + second   )
                     i += 2
                 else:
                     new_word.append(word[i]   )
                     i += 1
             snake_case      =					tuple(_A   )
             snake_case      =					new_word
             if len(_A   ) == 1:
                 break
             else:
                 snake_case      =					get_pairs(_A   )
         snake_case      =					" ".join(_A   )
         if word == "\n  </w>":
             snake_case      =					"\n</w>"
         snake_case      =					word
         return word





     def 		UpperCAmelCase(self      :    List[Any]				,    _A      :    Any   )						->						Any:
         snake_case      =					[]
         if self.fix_text is None:
             # Using BERT's BasicTokenizer
             snake_case      =					self.nlp.tokenize(_A   )
             for token in text:
                 split_tokens.extend(list(self.bpe(_A   ).split(" "   )   )   )
         else:
             # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
             snake_case      =					self.nlp(text_standardize(self.fix_text(_A   )   )   )
             for token in text:
                 split_tokens.extend(list(self.bpe(token.text.lower()   ).split(" "   )   )   )
         return split_tokens





     def 		UpperCAmelCase(self      :    Dict				,    _A      :    Any   )						->						List[Any]:
         return self.encoder.get(_A				,    self.encoder.get(self.unk_token   )   )





     def 		UpperCAmelCase(self      :    List[str]				,    _A      :    Any   )						->						Optional[Any]:
         return self.decoder.get(_A				,    self.unk_token   )





     def 		UpperCAmelCase(self      :    Tuple				,    _A      :    Tuple   )						->						Tuple:
         snake_case      =					"".join(_A   ).replace("</w>"				,    " "   ).strip()
         return out_string





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

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

         snake_case      =					0
         with open(_A				,    "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 _A   : 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!"   )
                     snake_case      =					token_index
                 writer.write(" ".join(_A   ) + "\n"   )
                 index += 1

         return vocab_file, merge_file



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


_A  					= logging.get_logger(__name__)

_A  					= {
    "google/bigbird-pegasus-large-arxiv": (
        "https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-pubmed": (
        "https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-bigpatent": (
        "https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							List[Any]           =		"bigbird_pegasus"
     UpperCAmelCase__				:							Tuple           =		["past_key_values"]
     UpperCAmelCase__				:							Optional[Any]           =		{
         "num_attention_heads": "encoder_attention_heads",
         "hidden_size": "d_model",
         "attention_probs_dropout_prob": "attention_dropout",
     }





     def __init__(self      :    Any				,    _A      :    List[str]=9_6_1_0_3				,    _A      :    List[Any]=4_0_9_6				,    _A      :    str=1_6				,    _A      :    Tuple=4_0_9_6				,    _A      :    List[str]=1_6				,    _A      :    Optional[int]=1_6				,    _A      :    List[Any]=4_0_9_6				,    _A      :    Dict=1_6				,    _A      :    Tuple=0.0				,    _A      :    List[str]=0.0				,    _A      :    Dict=True				,    _A      :    int=True				,    _A      :    List[Any]="gelu_new"				,    _A      :    int=1_0_2_4				,    _A      :    int=0.1				,    _A      :    List[Any]=0.0				,    _A      :    Optional[int]=0.0				,    _A      :    Any=0.02				,    _A      :    Dict=2				,    _A      :    List[Any]=0.0				,    _A      :    Any=True				,    _A      :    str=0				,    _A      :    Dict=2				,    _A      :    Any=1				,    _A      :    List[str]="block_sparse"				,    _A      :    int=6_4				,    _A      :    Optional[int]=3				,    _A      :    List[Any]=False				,    **_A      :    Tuple				,    )						->						Any:
         snake_case      =					vocab_size
         snake_case      =					max_position_embeddings
         snake_case      =					d_model
         snake_case      =					encoder_ffn_dim
         snake_case      =					encoder_layers
         snake_case      =					encoder_attention_heads
         snake_case      =					decoder_ffn_dim
         snake_case      =					decoder_layers
         snake_case      =					decoder_attention_heads
         snake_case      =					dropout
         snake_case      =					attention_dropout
         snake_case      =					activation_dropout
         snake_case      =					activation_function
         snake_case      =					init_std
         snake_case      =					encoder_layerdrop
         snake_case      =					decoder_layerdrop
         snake_case      =					classifier_dropout
         snake_case      =					use_cache
         snake_case      =					encoder_layers
         snake_case      =					scale_embedding  # scale factor will be sqrt(d_model) if True

         # extra config
         snake_case      =					attention_type
         snake_case      =					block_size
         snake_case      =					num_random_blocks
         snake_case      =					use_bias

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







class      lowerCamelCase		(  A_  ):





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

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

             if self.use_past:
                 self.fill_with_past_key_values_(_A				,    direction="inputs"   )
         elif self.task == "causal-lm":
             # TODO: figure this case out.
             snake_case      =					OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                 ]   )
             if self.use_past:
                 snake_case     ,							snake_case      =					self.num_layers
                 for i in range(_A   ):
                     snake_case      =					{0: "batch", 2: "past_sequence + sequence"}
                     snake_case      =					{0: "batch", 2: "past_sequence + sequence"}
         else:
             snake_case      =					OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                     ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                     ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
                 ]   )

         return common_inputs





     @property
     def 		UpperCAmelCase(self      :    str   )						->						Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
             snake_case      =					super().outputs
         else:
             snake_case      =					super(_A				,    self   ).outputs
             if self.use_past:
                 snake_case     ,							snake_case      =					self.num_layers
                 for i in range(_A   ):
                     snake_case      =					{0: "batch", 2: "past_sequence + sequence"}
                     snake_case      =					{0: "batch", 2: "past_sequence + sequence"}
         return common_outputs





     def 		UpperCAmelCase(self      :    Any				,    _A      :    PreTrainedTokenizer				,    _A      :    int = -1				,    _A      :    int = -1				,    _A      :    bool = False				,    _A      :    Optional[TensorType] = None				,    )						->						Mapping[str, Any]:
         snake_case      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _A				,    _A				,    _A				,    _A				,    _A   )

         # Generate decoder inputs
         snake_case      =					seq_length if not self.use_past else 1
         snake_case      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _A				,    _A				,    _A				,    _A				,    _A   )
         snake_case      =					{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
         snake_case      =					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
             snake_case     ,							snake_case      =					common_inputs["input_ids"].shape
             snake_case      =					common_inputs["decoder_input_ids"].shape[1]
             snake_case     ,							snake_case      =					self.num_attention_heads
             snake_case      =					(
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             snake_case      =					decoder_seq_length + 3
             snake_case      =					(
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )

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

             snake_case      =					[]
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             snake_case     ,							snake_case      =					self.num_layers
             snake_case      =					min(_A				,    _A   )
             snake_case      =					max(_A				,    _A   ) - min_num_layers
             snake_case      =					"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.
             snake_case      =					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      :    Tuple				,    _A      :    PreTrainedTokenizer				,    _A      :    int = -1				,    _A      :    int = -1				,    _A      :    bool = False				,    _A      :    Optional[TensorType] = None				,    )						->						Mapping[str, Any]:
         snake_case      =					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
             snake_case     ,							snake_case      =					common_inputs["input_ids"].shape
             # Not using the same length for past_key_values
             snake_case      =					seqlen + 2
             snake_case     ,							snake_case      =					self.num_layers
             snake_case     ,							snake_case      =					self.num_attention_heads
             snake_case      =					(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

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





     def 		UpperCAmelCase(self      :    Dict				,    _A      :    PreTrainedTokenizer				,    _A      :    int = -1				,    _A      :    int = -1				,    _A      :    bool = False				,    _A      :    Optional[TensorType] = 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
         snake_case      =					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
         snake_case      =					tokenizer.num_special_tokens_to_add(_A   )
         snake_case      =					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
         snake_case      =					[" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
         snake_case      =					dict(tokenizer(_A				,    return_tensors=_A   )   )
         return common_inputs





     def 		UpperCAmelCase(self      :    Any				,    _A      :    PreTrainedTokenizer				,    _A      :    int = -1				,    _A      :    int = -1				,    _A      :    bool = False				,    _A      :    Optional[TensorType] = None				,    )						->						Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
             snake_case      =					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":
             snake_case      =					self._generate_dummy_inputs_for_causal_lm(
                 _A				,    batch_size=_A				,    seq_length=_A				,    is_pair=_A				,    framework=_A   )
         else:
             snake_case      =					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      :    List[Any]				,    _A      :    str				,    _A      :    Union[str, Any]				,    _A      :    Tuple				,    _A      :    List[Any]   )						->						Any:
         if self.task in ["default", "seq2seq-lm"]:
             snake_case      =					super()._flatten_past_key_values_(_A				,    _A				,    _A				,    _A   )
         else:
             snake_case      =					super(_A				,    self   )._flatten_past_key_values_(
                 _A				,    _A				,    _A				,    _A   )

