






'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE						=	{'configuration_mbart': ['MBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MBartConfig', 'MBartOnnxConfig']}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	['MBartTokenizer']

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	['MBartTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'MBART_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MBartForCausalLM',
          'MBartForConditionalGeneration',
          'MBartForQuestionAnswering',
          'MBartForSequenceClassification',
          'MBartModel',
          'MBartPreTrainedModel',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'TFMBartForConditionalGeneration',
          'TFMBartModel',
          'TFMBartPreTrainedModel',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'FlaxMBartForConditionalGeneration',
          'FlaxMBartForQuestionAnswering',
          'FlaxMBartForSequenceClassification',
          'FlaxMBartModel',
          'FlaxMBartPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart import MBartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart_fast import MBartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mbart import (
                MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                MBartForCausalLM,
                MBartForConditionalGeneration,
                MBartForQuestionAnswering,
                MBartForSequenceClassification,
                MBartModel,
                MBartPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mbart import (
                FlaxMBartForConditionalGeneration,
                FlaxMBartForQuestionAnswering,
                FlaxMBartForSequenceClassification,
                FlaxMBartModel,
                FlaxMBartPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











'''simple docstring'''



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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)


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

# See all LED models at https://huggingface.co/models?filter=LED
SCREAMING_SNAKE_CASE						=	{
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE						=	{
    'allenai/led-base-16384': 16_384,
}


@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 							lowercase_				(    )       ->      Any:



 """simple docstring"""




 lowercase					:   int				  =(
     list(range(ord('''!'''			)					,      ord('''~'''			) + 1			)			) + list(range(ord('''¡'''			)					,      ord('''¬'''			) + 1			)			) + list(range(ord('''®'''			)					,      ord('''ÿ'''			) + 1			)			)
 )
 lowercase					:   Union[str, Any]				  =bs[:]
 lowercase					:   Tuple				  =0
 for b in range(2**8			):
  if b not in bs:
   bs.append(__A			)
   cs.append(2**8 + n			)
   n += 1
 lowercase					:   Optional[Any]				  =[chr(__A			) for n in cs]
 return dict(zip(__A					,      __A			)			)


def 							lowercase_				(    __A :       str			)       ->      List[Any]:



 """simple docstring"""




 lowercase					:   Optional[Any]				  =set()
 lowercase					:   Tuple				  =word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)			)
  lowercase					:   List[str]				  =char
 return pairs





class UpperCAmelCase_					(    __A				):




      """simple docstring"""




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






      def __init__( self			:		Tuple     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		str="replace"     ,      UpperCAmelCase			:		int="<s>"     ,      UpperCAmelCase			:		Optional[int]="</s>"     ,      UpperCAmelCase			:		Optional[int]="</s>"     ,      UpperCAmelCase			:		List[Any]="<s>"     ,      UpperCAmelCase			:		str="<unk>"     ,      UpperCAmelCase			:		Dict="<pad>"     ,      UpperCAmelCase			:		Union[str, Any]="<mask>"     ,      UpperCAmelCase			:		str=False     ,      **UpperCAmelCase			:		int     ,      )  ->  Dict:


       '''simple docstring'''

       lowercase					:   int				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else bos_token
       lowercase					:   Union[str, Any]				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else eos_token
       lowercase					:   str				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else sep_token
       lowercase					:   Optional[int]				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else cls_token
       lowercase					:   Union[str, Any]				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else unk_token
       lowercase					:   List[Any]				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else pad_token

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

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

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

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






      @property
      # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
      def 				A__      ( self			:		Union[str, Any]		)  ->  Optional[int]:


       '''simple docstring'''

       return len(self.encoder		)






      def 				A__      ( self			:		Union[str, Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       return dict(self.encoder     ,      **self.added_tokens_encoder		)






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		str		)  ->  Optional[Any]:


       '''simple docstring'''

       if token in self.cache:
        return self.cache[token]
       lowercase					:   List[str]				  =tuple(UpperCAmelCase		)
       lowercase					:   List[str]				  =get_pairs(UpperCAmelCase		)

       if not pairs:
        return token

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

         if word[i] == first and i < len(UpperCAmelCase		) - 1 and word[i + 1] == second:
          new_word.append(first + second		)
          i += 2
         else:
          new_word.append(word[i]		)
          i += 1
        lowercase					:   List[str]				  =tuple(UpperCAmelCase		)
        lowercase					:   str				  =new_word
        if len(UpperCAmelCase		) == 1:
         break
        else:
         lowercase					:   Optional[Any]				  =get_pairs(UpperCAmelCase		)
       lowercase					:   Optional[Any]				  =''' '''.join(UpperCAmelCase		)
       lowercase					:   Union[str, Any]				  =word
       return word






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		Optional[Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   Dict				  =[]
       for token in re.findall(self.pat     ,      UpperCAmelCase		):
        lowercase					:   Optional[int]				  =''''''.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(UpperCAmelCase		).split(''' '''		)		)
       return bpe_tokens






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		Union[str, Any]		)  ->  List[str]:


       '''simple docstring'''

       return self.encoder.get(UpperCAmelCase     ,      self.encoder.get(self.unk_token		)		)






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		Optional[int]		)  ->  Any:


       '''simple docstring'''

       return self.decoder.get(UpperCAmelCase		)






      def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		List[Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   str				  =''''''.join(UpperCAmelCase		)
       lowercase					:   Dict				  =bytearray([self.byte_decoder[c] for c in text]		).decode('''utf-8'''     ,      errors=self.errors		)
       return text






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Optional[str] = None		)  ->  Tuple[str]:


       '''simple docstring'''

       if not os.path.isdir(UpperCAmelCase		):
        logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
        return
       lowercase					:   Optional[Any]				  =os.path.join(
           UpperCAmelCase     ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
       lowercase					:   List[Any]				  =os.path.join(
           UpperCAmelCase     ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']		)

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

       lowercase					:   List[str]				  =0
       with open(UpperCAmelCase     ,      '''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 UpperCAmelCase		: 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!'''		)
          lowercase					:   Any				  =token_index
         writer.write(''' '''.join(UpperCAmelCase		) + '''\n'''		)
         index += 1

       return vocab_file, merge_file






      def 				A__      ( self			:		str     ,      UpperCAmelCase			:		List[int]     ,      UpperCAmelCase			:		Optional[List[int]] = None		)  ->  List[int]:


       '''simple docstring'''

       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       lowercase					:   Optional[int]				  =[self.cls_token_id]
       lowercase					:   List[Any]				  =[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep






      def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		List[int]     ,      UpperCAmelCase			:		Optional[List[int]] = None     ,      UpperCAmelCase			:		bool = False		)  ->  List[int]:


       '''simple docstring'''

       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=UpperCAmelCase     ,      token_ids_a=UpperCAmelCase     ,      already_has_special_tokens=UpperCAmelCase		)

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






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		List[int]     ,      UpperCAmelCase			:		Optional[List[int]] = None		)  ->  List[int]:


       '''simple docstring'''

       lowercase					:   Dict				  =[self.sep_token_id]
       lowercase					:   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 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Optional[int]=False     ,      **UpperCAmelCase			:		Optional[Any]		)  ->  str:


       '''simple docstring'''

       lowercase					:   Tuple				  =kwargs.pop('''add_prefix_space'''     ,      self.add_prefix_space		)
       if (is_split_into_words or add_prefix_space) and (len(UpperCAmelCase		) > 0 and not text[0].isspace()):
        lowercase					:   Union[str, Any]				  =''' ''' + text
       return (text, kwargs)






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		Union[Dict[str, EncodedInput], BatchEncoding]     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		Optional[bool] = None     ,      )  ->  dict:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =super()._pad(
           encoded_inputs=UpperCAmelCase     ,      max_length=UpperCAmelCase     ,      padding_strategy=UpperCAmelCase     ,      pad_to_multiple_of=UpperCAmelCase     ,      return_attention_mask=UpperCAmelCase     ,      )

       # Load from model defaults
       if return_attention_mask is None:
        lowercase					:   Tuple				  ='''attention_mask''' in self.model_input_names

       if return_attention_mask and "global_attention_mask" in encoded_inputs:
        lowercase					:   Optional[Any]				  =encoded_inputs[self.model_input_names[0]]
        # `global_attention_mask` need to have the same length as other (sequential) inputs.
        lowercase					:   str				  =len(encoded_inputs['''global_attention_mask''']		) != len(UpperCAmelCase		)

        if needs_to_be_padded:
         lowercase					:   Tuple				  =len(UpperCAmelCase		) - len(encoded_inputs['''global_attention_mask''']		)

         if self.padding_side == "right":
          # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
          lowercase					:   List[str]				  =(
              encoded_inputs['''global_attention_mask'''] + [-1] * difference
          )
         elif self.padding_side == "left":
          lowercase					:   Any				  =[-1] * difference + encoded_inputs[
              '''global_attention_mask'''
          ]
         else:
          raise ValueError('''Invalid padding strategy:''' + str(self.padding_side		)		)

       return encoded_inputs



