"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


_UpperCamelCase     :		Tuple											=logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
_UpperCamelCase     :		Any											={
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

_UpperCamelCase     :		List[str]											={
    "facebook/bart-base": 10_24,
    "facebook/bart-large": 10_24,
    "facebook/bart-large-mnli": 10_24,
    "facebook/bart-large-cnn": 10_24,
    "facebook/bart-large-xsum": 10_24,
    "yjernite/bart_eli5": 10_24,
}


class   UpperCAmelCase_			(					UpperCamelCase__):
      lowerCamelCase__	:					int										=						VOCAB_FILES_NAMES
      lowerCamelCase__	:					Union[str, Any]										=						PRETRAINED_VOCAB_FILES_MAP
      lowerCamelCase__	:					Dict										=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCamelCase__	:					Optional[int]										=						["input_ids", "attention_mask"]
      lowerCamelCase__	:					Optional[Any]										=						BartTokenizer


      def __init__(     self	,    a=None	,    a=None	,    a=None	,    a="replace"	,    a="<s>"	,    a="</s>"	,    a="</s>"	,    a="<s>"	,    a="<unk>"	,    a="<pad>"	,    a="<mask>"	,    a=False	,    a=True	,    **a	,    ) ->      Tuple:
             super().__init__(
                 _a	,    _a	,    tokenizer_file=_a	,    errors=_a	,    bos_token=_a	,    eos_token=_a	,    sep_token=_a	,    cls_token=_a	,    unk_token=_a	,    pad_token=_a	,    mask_token=_a	,    add_prefix_space=_a	,    trim_offsets=_a	,    **_a	,    )

             lowercase__    :	Dict     					=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
             if pre_tok_state.get('add_prefix_space'	,    _a							) != add_prefix_space:
                    lowercase__    :	Tuple     					=			getattr(_a	,    pre_tok_state.pop('type'							)							)
                    lowercase__    :	Union[str, Any]     					=			add_prefix_space
                    lowercase__    :	List[str]     					=			pre_tok_class(**_a							)

             lowercase__    :	List[Any]     					=			add_prefix_space

             # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
             lowercase__    :	int     					=			"""post_processor"""
             lowercase__    :	Optional[Any]     					=			getattr(self.backend_tokenizer	,    _a	,    _a							)
             if tokenizer_component_instance:
                    lowercase__    :	Dict     					=			json.loads(tokenizer_component_instance.__getstate__()							)

                    # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                    if "sep" in state:
                           lowercase__    :	str     					=			tuple(state['sep']							)
                    if "cls" in state:
                           lowercase__    :	List[str]     					=			tuple(state['cls']							)

                    lowercase__    :	str     					=			False

                    if state.get('add_prefix_space'	,    _a							) != add_prefix_space:
                           lowercase__    :	Any     					=			add_prefix_space
                           lowercase__    :	Dict     					=			True

                    if state.get('trim_offsets'	,    _a							) != trim_offsets:
                           lowercase__    :	List[Any]     					=			trim_offsets
                           lowercase__    :	Union[str, Any]     					=			True

                    if changes_to_apply:
                           lowercase__    :	List[str]     					=			getattr(_a	,    state.pop('type'							)							)
                           lowercase__    :	int     					=			component_class(**_a							)
                           setattr(self.backend_tokenizer	,    _a	,    _a							)


      @property
      def 	_UpperCAmelCase (     self							) ->      str:
             if self._mask_token is None:
                    if self.verbose:
                           logger.error('Using mask_token, but it is not set yet.'							)
                    return None
             return str(self._mask_token							)


      @mask_token.setter
      def 	_UpperCAmelCase (     self	,    a							) ->      Tuple:
             lowercase__    :	Union[str, Any]     					=			AddedToken(_a	,    lstrip=_a	,    rstrip=_a							) if isinstance(_a	,    _a							) else value
             lowercase__    :	Any     					=			value


      def 	_UpperCAmelCase (     self	,    *a	,    **a							) ->      BatchEncoding:
             lowercase__    :	Optional[int]     					=			kwargs.get('is_split_into_words'	,    _a							)

             if is_split_into_words and not self.add_prefix_space:
                    raise ValueError(
                        f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                        'to use it with pretokenized inputs.'							)

             return super()._batch_encode_plus(*_a	,    **_a							)


      def 	_UpperCAmelCase (     self	,    *a	,    **a							) ->      BatchEncoding:
             lowercase__    :	str     					=			kwargs.get('is_split_into_words'	,    _a							)

             if is_split_into_words and not self.add_prefix_space:
                    raise ValueError(
                        f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                        'to use it with pretokenized inputs.'							)

             return super()._encode_plus(*_a	,    **_a							)


      def 	_UpperCAmelCase (     self	,    a	,    a = None							) ->      Tuple[str]:
             lowercase__    :	Union[str, Any]     					=			self._tokenizer.model.save(_a	,    name=_a							)
             return tuple(_a							)


      def 	_UpperCAmelCase (     self	,    a	,    a=None							) ->      Tuple:
             lowercase__    :	Any     					=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
             if token_ids_a is None:
                    return output

             return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


      def 	_UpperCAmelCase (     self	,    a	,    a = None							) ->      List[int]:
             lowercase__    :	List[Any]     					=			[self.sep_token_id]
             lowercase__    :	Tuple     					=			[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]


"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_UpperCamelCase     :		str											=					{
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Tuple											=					[
           "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTNeoForCausalLM",
           "GPTNeoForQuestionAnswering",
           "GPTNeoForSequenceClassification",
           "GPTNeoForTokenClassification",
           "GPTNeoModel",
           "GPTNeoPreTrainedModel",
           "load_tf_weights_in_gpt_neo",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Dict											=					[
           "FlaxGPTNeoForCausalLM",
           "FlaxGPTNeoModel",
           "FlaxGPTNeoPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neo import (
                  GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoForCausalLM,
                  GPTNeoForQuestionAnswering,
                  GPTNeoForSequenceClassification,
                  GPTNeoForTokenClassification,
                  GPTNeoModel,
                  GPTNeoPreTrainedModel,
                  load_tf_weights_in_gpt_neo,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
       import sys

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

