






"""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


_UpperCamelCase					      =  logging.get_logger(__name__)


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

# See all LED models at https://huggingface.co/models?filter=LED
_UpperCamelCase					      =  {
    """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""",
    },
}

_UpperCamelCase					      =  {
    """allenai/led-base-16384""": 16384,
}

@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def _a			(		):







						"""simple docstring"""






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

def _a			(		_snake_case							):







						"""simple docstring"""






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







class  lowerCamelCase__ (	snake_case			):
				SCREAMING_SNAKE_CASE		=      VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE		=      PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE		=      ['''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     ,):
										UpperCAmelCase          =	AddedToken(A     ,lstrip=A     ,rstrip=A ) if isinstance(A     ,A ) else bos_token
										UpperCAmelCase          =	AddedToken(A     ,lstrip=A     ,rstrip=A ) if isinstance(A     ,A ) else eos_token
										UpperCAmelCase          =	AddedToken(A     ,lstrip=A     ,rstrip=A ) if isinstance(A     ,A ) else sep_token
										UpperCAmelCase          =	AddedToken(A     ,lstrip=A     ,rstrip=A ) if isinstance(A     ,A ) else cls_token
										UpperCAmelCase          =	AddedToken(A     ,lstrip=A     ,rstrip=A ) if isinstance(A     ,A ) else unk_token
										UpperCAmelCase          =	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
										UpperCAmelCase          =	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:
																UpperCAmelCase          =	json.load(A )
										UpperCAmelCase          =	{v: k for k, v in self.encoder.items()}
										UpperCAmelCase          =	errors  # how to handle errors in decoding
										UpperCAmelCase          =	bytes_to_unicode()
										UpperCAmelCase          =	{v: k for k, v in self.byte_encoder.items()}
										with open(A     ,encoding="""utf-8""" ) as merges_handle:
																UpperCAmelCase          =	merges_handle.read().split("""\n""" )[1:-1]
										UpperCAmelCase          =	[tuple(merge.split() ) for merge in bpe_merges]
										UpperCAmelCase          =	dict(zip(A     ,range(len(A ) ) ) )
										UpperCAmelCase          =	{}
										UpperCAmelCase          =	add_prefix_space

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






				@property
				# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
				def     _UpperCamelCase							(			self ):
										return len(self.encoder )






				def     _UpperCamelCase							(			self ):
										return dict(self.encoder     ,**self.added_tokens_encoder )






				def     _UpperCamelCase							(			self     ,A ):
										if token in self.cache:
																return self.cache[token]
										UpperCAmelCase          =	tuple(A )
										UpperCAmelCase          =	get_pairs(A )

										if not pairs:
																return token

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






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






				def     _UpperCamelCase							(			self     ,A ):
										return self.encoder.get(A     ,self.encoder.get(self.unk_token ) )






				def     _UpperCamelCase							(			self     ,A ):
										return self.decoder.get(A )






				def     _UpperCamelCase							(			self     ,A ):
										UpperCAmelCase          =	"""""".join(A )
										UpperCAmelCase          =	bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8"""     ,errors=self.errors )
										return text






				def     _UpperCamelCase							(			self     ,A     ,A = None ):
										if not os.path.isdir(A ):
																logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
																return
										UpperCAmelCase          =	os.path.join(
										    A     ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
										UpperCAmelCase          =	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""" )

										UpperCAmelCase          =	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!""" )
																												UpperCAmelCase          =	token_index
																						writer.write(""" """.join(A ) + """\n""" )
																						index += 1

										return vocab_file, merge_file






				def     _UpperCamelCase							(			self     ,A     ,A = None ):
										if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										UpperCAmelCase          =	[self.cls_token_id]
										UpperCAmelCase          =	[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep






				def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = False ):
										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 ):
										UpperCAmelCase          =	[self.sep_token_id]
										UpperCAmelCase          =	[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 ):
										UpperCAmelCase          =	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()):
																UpperCAmelCase          =	""" """ + text
										return (text, kwargs)






				def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = PaddingStrategy.DO_NOT_PAD     ,A = None     ,A = None     ,):
										UpperCAmelCase          =	super()._pad(
										    encoded_inputs=A     ,max_length=A     ,padding_strategy=A     ,pad_to_multiple_of=A     ,return_attention_mask=A     ,)

										# Load from model defaults
										if return_attention_mask is None:
																UpperCAmelCase          =	"""attention_mask""" in self.model_input_names

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

																if needs_to_be_padded:
																						UpperCAmelCase          =	len(A ) - 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`
																												UpperCAmelCase          =	(
																												    encoded_inputs["""global_attention_mask"""] + [-1] * difference
																												)
																						elif self.padding_side == "left":
																												UpperCAmelCase          =	[-1] * difference + encoded_inputs[
																												    """global_attention_mask"""
																												]
																						else:
																												raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )

										return encoded_inputs











"""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,
)


_UpperCamelCase					      =  {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

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

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

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

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

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """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

  _UpperCamelCase					      =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



