
'''simple docstring'''





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


a__		: Dict  							=							logging.get_logger(__name__)

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


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE):
 UpperCAmelCase__     :     Union[str, Any]    					=       'mbart'
 UpperCAmelCase__     :     Union[str, Any]    					=       ['past_key_values']
 UpperCAmelCase__     :     Any    					=       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






 def __init__(      self	:Any						,					_A	:Optional[int]=50_265						,					_A	:Optional[int]=1_024						,					_A	:List[str]=12						,					_A	:int=4_096						,					_A	:int=16						,					_A	:List[Any]=12						,					_A	:Optional[Any]=4_096						,					_A	:List[str]=16						,					_A	:List[Any]=0.0						,					_A	:str=0.0						,					_A	:Tuple=True						,					_A	:int=True						,					_A	:Union[str, Any]="gelu"						,					_A	:str=1_024						,					_A	:Dict=0.1						,					_A	:List[str]=0.0						,					_A	:Dict=0.0						,					_A	:List[Any]=0.02						,					_A	:Union[str, Any]=0.0						,					_A	:int=False						,					_A	:Any=1						,					_A	:List[Any]=0						,					_A	:Dict=2						,					_A	:List[str]=2						,					**_A	:str						,					)					->      str:







       '''simple docstring'''



       __A													=      vocab_size
       __A													=      max_position_embeddings
       __A													=      d_model
       __A													=      encoder_ffn_dim
       __A													=      encoder_layers
       __A													=      encoder_attention_heads
       __A													=      decoder_ffn_dim
       __A													=      decoder_layers
       __A													=      decoder_attention_heads
       __A													=      dropout
       __A													=      attention_dropout
       __A													=      activation_dropout
       __A													=      activation_function
       __A													=      init_std
       __A													=      encoder_layerdrop
       __A													=      decoder_layerdrop
       __A													=      classifier_dropout
       __A													=      use_cache
       __A													=      encoder_layers
       __A													=      scale_embedding  # scale factor will be sqrt(d_model) if True
       super().__init__(
           pad_token_id=_A						,					bos_token_id=_A						,					eos_token_id=_A						,					is_encoder_decoder=_A						,					forced_eos_token_id=_A						,					**_A						,					)


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE):






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







       '''simple docstring'''



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

             if self.use_past:
                   __A													=      {0: 'batch'}
                   __A													=      {0: 'batch', 1: 'past_decoder_sequence + sequence'}
             else:
                   __A													=      {0: 'batch', 1: 'decoder_sequence'}
                   __A													=      {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.
             __A													=      OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                 ]      )
             if self.use_past:
                   __A						,   __A													=      self.num_layers
                   for i in range(_A      ):
                         __A													=      {0: 'batch', 2: 'past_sequence + sequence'}
                         __A													=      {0: 'batch', 2: 'past_sequence + sequence'}
       else:
             __A													=      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 						lowercase_    (      self	:Tuple      )					->      Mapping[str, Mapping[int, str]]:







       '''simple docstring'''



       if self.task in ["default", "seq2seq-lm"]:
             __A													=      super().outputs
       else:
             __A													=      super(_A						,					self      ).outputs
             if self.use_past:
                   __A						,   __A													=      self.num_layers
                   for i in range(_A      ):
                         __A													=      {0: 'batch', 2: 'past_sequence + sequence'}
                         __A													=      {0: 'batch', 2: 'past_sequence + sequence'}
       return common_outputs






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







       '''simple docstring'''



       __A													=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _A						,					_A						,					_A						,					_A						,					_A      )

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

             __A													=      torch.cat(
                 [common_inputs['decoder_attention_mask'], torch.ones(_A						,					_A      )]						,					dim=1      )

             __A													=      []
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             __A						,   __A													=      self.num_layers
             __A													=      min(_A						,					_A      )
             __A													=      max(_A						,					_A      ) - min_num_layers
             __A													=      '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.
             __A													=      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 						lowercase_    (      self	:Optional[int]						,					_A	:PreTrainedTokenizer						,					_A	:int = -1						,					_A	:int = -1						,					_A	:bool = False						,					_A	:Optional[TensorType] = None						,					)					->      Mapping[str, Any]:







       '''simple docstring'''



       __A													=      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
             __A						,   __A													=      common_inputs['input_ids'].shape
             # Not using the same length for past_key_values
             __A													=      seqlen + 2
             __A						,   __A													=      self.num_layers
             __A						,   __A													=      self.num_attention_heads
             __A													=      (
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

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






 def 						lowercase_    (      self	:Tuple						,					_A	:PreTrainedTokenizer						,					_A	:int = -1						,					_A	:int = -1						,					_A	:bool = False						,					_A	:Optional[TensorType] = None						,					)					->      Mapping[str, Any]:







       '''simple docstring'''



       __A													=      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
       __A													=      tokenizer.num_special_tokens_to_add(_A      )
       __A													=      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
       __A													=      [' '.join([tokenizer.unk_token]      ) * seq_length] * batch_size
       __A													=      dict(tokenizer(_A						,					return_tensors=_A      )      )
       return common_inputs






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







       '''simple docstring'''



       if self.task in ["default", "seq2seq-lm"]:
             __A													=      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":
             __A													=      self._generate_dummy_inputs_for_causal_lm(
                 _A						,					batch_size=_A						,					seq_length=_A						,					is_pair=_A						,					framework=_A      )
       else:
             __A													=      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 						lowercase_    (      self	:Tuple						,					_A	:Any						,					_A	:List[str]						,					_A	:Optional[int]						,					_A	:Optional[Any]      )					->      Optional[Any]:







       '''simple docstring'''



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







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


snake_case__						      =			logging.get_logger(__name__)


snake_case__						      =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all MVP models at https://huggingface.co/models?filter=mvp
snake_case__						      =			{
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
}

snake_case__						      =			{
    'RUCAIBox/mvp': 1024,
}
@lru_cache()
def    __magic_name__(	)    ->     List[str]:



      '''simple docstring'''

      _lowerCamelCase           =					(
          list(range(ord('''!'''		)			,    ord('''~'''		) + 1		)		) + list(range(ord('''¡'''		)			,    ord('''¬'''		) + 1		)		) + list(range(ord('''®'''		)			,    ord('''ÿ'''		) + 1		)		)
      )
      _lowerCamelCase           =					bs[:]
      _lowerCamelCase           =					0
      for b in range(2**8		):
            if b not in bs:
                  bs.append(__UpperCAmelCase		)
                  cs.append(2**8 + n		)
                  n += 1
      _lowerCamelCase           =					[chr(__UpperCAmelCase		) for n in cs]
      return dict(zip(__UpperCAmelCase			,    __UpperCAmelCase		)		)
def    __magic_name__(	__UpperCAmelCase		)    ->     List[Any]:



      '''simple docstring'''

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




class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     A_					    =	VOCAB_FILES_NAMES
     A_					    =	PRETRAINED_VOCAB_FILES_MAP
     A_					    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_					    =	['input_ids', 'attention_mask']



     def __init__(       self					,					A_					,					A_					,					A_="replace"					,					A_="<s>"					,					A_="</s>"					,					A_="</s>"					,					A_="<s>"					,					A_="<unk>"					,					A_="<pad>"					,					A_="<mask>"					,					A_=False					,					**A_					,					)			->					Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else bos_token
           _lowerCamelCase           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else eos_token
           _lowerCamelCase           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else sep_token
           _lowerCamelCase           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else cls_token
           _lowerCamelCase           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else unk_token
           _lowerCamelCase           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else pad_token

           # Mask token behave like a normal word, i.e. include the space before it
           _lowerCamelCase           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else mask_token

           super().__init__(
               errors=A_					,					bos_token=A_					,					eos_token=A_					,					unk_token=A_					,					sep_token=A_					,					cls_token=A_					,					pad_token=A_					,					mask_token=A_					,					add_prefix_space=A_					,					**A_					,					)
           with open(A_					,					encoding='''utf-8'''    ) as vocab_handle:
                 _lowerCamelCase           =					json.load(A_    )
           _lowerCamelCase           =					{v: k for k, v in self.encoder.items()}
           _lowerCamelCase           =					errors  # how to handle errors in decoding
           _lowerCamelCase           =					bytes_to_unicode()
           _lowerCamelCase           =					{v: k for k, v in self.byte_encoder.items()}
           with open(A_					,					encoding='''utf-8'''    ) as merges_handle:
                 _lowerCamelCase           =					merges_handle.read().split('''\n'''    )[1:-1]
           _lowerCamelCase           =					[tuple(merge.split()    ) for merge in bpe_merges]
           _lowerCamelCase           =					dict(zip(A_					,					range(len(A_    )    )    )    )
           _lowerCamelCase           =					{}
           _lowerCamelCase           =					add_prefix_space

           # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
           _lowerCamelCase           =					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    )			->					Dict:






           """simple docstring"""
           return len(self.encoder    )



     def 		UpperCamelCase_    (       self    )			->					Tuple:






           """simple docstring"""
           return dict(self.encoder					,					**self.added_tokens_encoder    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					str:






           """simple docstring"""
           if token in self.cache:
                 return self.cache[token]
           _lowerCamelCase           =					tuple(A_    )
           _lowerCamelCase           =					get_pairs(A_    )

           if not pairs:
                 return token

           while True:
                 _lowerCamelCase           =					min(A_					,					key=lambda A_    : self.bpe_ranks.get(A_					,					float('''inf'''    )    )    )
                 if bigram not in self.bpe_ranks:
                       break
                 _lowerCamelCase     ,       _lowerCamelCase           =					bigram
                 _lowerCamelCase           =					[]
                 _lowerCamelCase           =					0
                 while i < len(A_    ):
                       try:
                             _lowerCamelCase           =					word.index(A_					,					A_    )
                       except ValueError:
                             new_word.extend(word[i:]    )
                             break
                       else:
                             new_word.extend(word[i:j]    )
                             _lowerCamelCase           =					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
                 _lowerCamelCase           =					tuple(A_    )
                 _lowerCamelCase           =					new_word
                 if len(A_    ) == 1:
                       break
                 else:
                       _lowerCamelCase           =					get_pairs(A_    )
           _lowerCamelCase           =					''' '''.join(A_    )
           _lowerCamelCase           =					word
           return word



     def 		UpperCamelCase_    (       self					,					A_    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					[]
           for token in re.findall(self.pat					,					A_    ):
                 _lowerCamelCase           =					''''''.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(A_    ).split(''' '''    )    )
           return bpe_tokens



     def 		UpperCamelCase_    (       self					,					A_    )			->					Optional[Any]:






           """simple docstring"""
           return self.encoder.get(A_					,					self.encoder.get(self.unk_token    )    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					Any:






           """simple docstring"""
           return self.decoder.get(A_    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					''''''.join(A_    )
           _lowerCamelCase           =					bytearray([self.byte_decoder[c] for c in text]    ).decode('''utf-8'''					,					errors=self.errors    )
           return text



     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None    )			->					Tuple[str]:






           """simple docstring"""
           if not os.path.isdir(A_    ):
                 logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
                 return
           _lowerCamelCase           =					os.path.join(
               A_					,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
           _lowerCamelCase           =					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'''    )

           _lowerCamelCase           =					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!'''    )
                             _lowerCamelCase           =					token_index
                       writer.write(''' '''.join(A_    ) + '''\n'''    )
                       index += 1

           return vocab_file, merge_file



     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None    )			->					List[int]:






           """simple docstring"""
           if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           _lowerCamelCase           =					[self.cls_token_id]
           _lowerCamelCase           =					[self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep



     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None					,					A_ = False    )			->					List[int]:






           """simple docstring"""
           if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=A_					,					token_ids_a=A_					,					already_has_special_tokens=A_    )

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



     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None    )			->					List[int]:






           """simple docstring"""
           _lowerCamelCase           =					[self.sep_token_id]
           _lowerCamelCase           =					[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 		UpperCamelCase_    (       self					,					A_					,					A_=False					,					**A_    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					kwargs.pop('''add_prefix_space'''					,					self.add_prefix_space    )
           if (is_split_into_words or add_prefix_space) and (len(A_    ) > 0 and not text[0].isspace()):
                 _lowerCamelCase           =					''' ''' + text
           return (text, kwargs)