





'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_pegasus import PegasusTokenizer
else:
      lowercase_					= None


lowercase_					= logging.get_logger(__name__)


lowercase_					= """▁"""

lowercase_					= {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase_					= {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

lowercase_					= {
    """google/pegasus-xsum""": 512,
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = VOCAB_FILES_NAMES
    UpperCamelCase     = PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase     = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase     = PegasusTokenizer
    UpperCamelCase     = ['''input_ids''', '''attention_mask''']


    def __init__(		self ,		A=None ,		A=None ,		A="<pad>" ,		A="</s>" ,		A="<unk>" ,		A="<mask_2>" ,		A="<mask_1>" ,		A=None ,		A=103 ,		**A ,		)		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			offset

        if additional_special_tokens is not None:
            if not isinstance(A ,		A       ):
                raise TypeError(
                    f'additional_special_tokens should be of type {type(A       )}, but is'
                    f' {type(A       )}'       )

            _SCREAMING_SNAKE_CASE									=			(
                ([mask_token_sent] + additional_special_tokens)
                if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                else additional_special_tokens
            )
            # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
            additional_special_tokens_extended += [
                f'<unk_{i}>' for i in range(len(A       ) ,		self.offset - 1       )
            ]

            if len(set(A       )       ) != len(A       ):
                raise ValueError(
                    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
                    f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'       )
            _SCREAMING_SNAKE_CASE									=			additional_special_tokens_extended
        else:
            _SCREAMING_SNAKE_CASE									=			[mask_token_sent] if mask_token_sent is not None else []
            additional_special_tokens += [f'<unk_{i}>' for i in range(2 ,		self.offset       )]

        super().__init__(
            A ,		tokenizer_file=A ,		pad_token=A ,		eos_token=A ,		unk_token=A ,		mask_token=A ,		mask_token_sent=A ,		offset=A ,		additional_special_tokens=A ,		**A ,		)
        _SCREAMING_SNAKE_CASE									=			vocab_file
        _SCREAMING_SNAKE_CASE									=			False if not self.vocab_file else True


    def 			snake_case_(		self ,		A       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			set(self.all_special_ids       )  # call it once instead of inside list comp
        all_special_ids.remove(self.unk_token_id       )  # <unk> is only sometimes special

        if all_special_ids != set(range(len(self.additional_special_tokens       ) + 3       )       ):
            raise ValueError(
                """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
                f' {len(self.additional_special_tokens       )} additional_special_tokens, but got {all_special_ids}'       )

        return [1 if x in all_special_ids else 0 for x in seq]


    def 			snake_case_(		self ,		A ,		A = None ,		A = False       )		->   List[int]:
        if already_has_special_tokens:
            return self._special_token_mask(A       )
        elif token_ids_a is None:
            return self._special_token_mask(A       ) + [1]
        else:
            return self._special_token_mask(token_ids_a + token_ids_a       ) + [1]


    def 			snake_case_(		self ,		A ,		A=None       )		->   List[int]:
        if token_ids_a is None:
            return token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_a + token_ids_a + [self.eos_token_id]





    def 			snake_case_(		self ,		A ,		A = None       )		->   Tuple[str]:
        if not self.can_save_slow_tokenizer:
            raise ValueError(
                """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                """tokenizer."""       )

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

        if os.path.abspath(self.vocab_file       ) != os.path.abspath(A       ):
            copyfile(self.vocab_file ,		A       )

        return (out_vocab_file,)










'''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,
)


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

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

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

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

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

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

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



