
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_a      :			int					=							logging.get_logger(__name__)

_a      :			int					=							{"""vocab_file""": """sentencepiece.bpe.model"""}

_a      :			List[Any]					=							{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

_a      :			Optional[Any]					=							{
    """moussaKam/mbarthez""": 1_024,
    """moussaKam/barthez""": 1_024,
    """moussaKam/barthez-orangesum-title""": 1_024,
}

_a      :			Union[str, Any]					=							"""▁"""






class      _UpperCAmelCase				(					_snake_case):
				__lowercase							: Union[str, Any]  =					VOCAB_FILES_NAMES
				__lowercase							: Union[str, Any]  =					PRETRAINED_VOCAB_FILES_MAP
				__lowercase							: Any  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowercase							: Union[str, Any]  =					["""input_ids""", """attention_mask"""]
				def __init__(    self						,    snake_case_						,    snake_case_="<s>"						,    snake_case_="</s>"						,    snake_case_="</s>"						,    snake_case_="<s>"						,    snake_case_="<unk>"						,    snake_case_="<pad>"						,    snake_case_="<mask>"						,    snake_case_ = None						,    **snake_case_						,    ):
						# Mask token behave like a normal word, i.e. include the space before it
						_snake_case     :						List[str]						=   AddedToken(snake_case_						,    lstrip=snake_case_						,    rstrip=snake_case_     ) if isinstance(snake_case_						,    snake_case_     ) else mask_token

						_snake_case     :						Optional[Any]						=   {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=snake_case_						,    eos_token=snake_case_						,    unk_token=snake_case_						,    sep_token=snake_case_						,    cls_token=snake_case_						,    pad_token=snake_case_						,    mask_token=snake_case_						,    sp_model_kwargs=self.sp_model_kwargs						,    **snake_case_						,    )

						_snake_case     :						Optional[Any]						=   vocab_file
						_snake_case     :						Tuple						=   spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(str(snake_case_     )     )

						_snake_case     :						Optional[Any]						=   {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

						_snake_case     :						Any						=   len(self.sp_model     ) - 1
						_snake_case     :						int						=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}
				def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_ = None     ):

						if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						_snake_case     :						List[Any]						=   [self.cls_token_id]
						_snake_case     :						Union[str, Any]						=   [self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep
				def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_ = None						,    snake_case_ = False     ):
						if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=snake_case_						,    token_ids_a=snake_case_						,    already_has_special_tokens=snake_case_     )

						if token_ids_a is None:
								return [1] + ([0] * len(snake_case_     )) + [1]
						return [1] + ([0] * len(snake_case_     )) + [1, 1] + ([0] * len(snake_case_     )) + [1]
				def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_ = None     ):
						_snake_case     :						Dict						=   [self.sep_token_id]
						_snake_case     :						List[str]						=   [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]
				@property
				def 					lowerCamelCase__	(    self     ):
						return len(self.sp_model     )
				def 					lowerCamelCase__	(    self     ):
						_snake_case     :						List[str]						=   {self.convert_ids_to_tokens(snake_case_     ): i for i in range(self.vocab_size     )}
						vocab.update(self.added_tokens_encoder     )
						return vocab
				def 					lowerCamelCase__	(    self						,    snake_case_     ):
						return self.sp_model.encode(snake_case_						,    out_type=snake_case_     )
				def 					lowerCamelCase__	(    self						,    snake_case_     ):
						if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
						_snake_case     :						Optional[int]						=   self.sp_model.PieceToId(snake_case_     )

						return spm_id if spm_id else self.unk_token_id
				def 					lowerCamelCase__	(    self						,    snake_case_     ):
						if index in self.fairseq_ids_to_tokens:
								return self.fairseq_ids_to_tokens[index]
						return self.sp_model.IdToPiece(snake_case_     )
				def 					lowerCamelCase__	(    self						,    snake_case_     ):
						_snake_case     :						Optional[Any]						=   []
						_snake_case     :						List[Any]						=   ""
						_snake_case     :						Dict						=   False
						for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
										if not prev_is_special:
												out_string += " "
										out_string += self.sp_model.decode(snake_case_     ) + token
										_snake_case     :						Tuple						=   True
										_snake_case     :						int						=   []
								else:
										current_sub_tokens.append(snake_case_     )
										_snake_case     :						Dict						=   False
						out_string += self.sp_model.decode(snake_case_     )
						return out_string.strip()
				def __getstate__(    self     ):
						_snake_case     :						Dict						=   self.__dict__.copy()
						_snake_case     :						int						=   None
						return state
				def __setstate__(    self						,    snake_case_     ):
						_snake_case     :						str						=   d

						# for backward compatibility
						if not hasattr(self						,    "sp_model_kwargs"     ):
								_snake_case     :						List[str]						=   {}

						_snake_case     :						Any						=   spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(self.vocab_file     )


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

						if os.path.abspath(self.vocab_file     ) != os.path.abspath(snake_case_     ) and os.path.isfile(self.vocab_file     ):
								copyfile(self.vocab_file						,    snake_case_     )
						elif not os.path.isfile(self.vocab_file     ):
								with open(snake_case_						,    "wb"     ) as fi:
										_snake_case     :						Optional[int]						=   self.sp_model.serialized_model_proto()
										fi.write(snake_case_     )

						return (out_vocab_file,)



"""simple docstring"""
from typing import TYPE_CHECKING

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


_a      :			Optional[int]					=							{
    """configuration_pix2struct""": [
        """PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Pix2StructConfig""",
        """Pix2StructTextConfig""",
        """Pix2StructVisionConfig""",
    ],
    """processing_pix2struct""": ["""Pix2StructProcessor"""],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			List[Any]					=							["""Pix2StructImageProcessor"""]


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			Dict					=							[
        """PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """Pix2StructPreTrainedModel""",
        """Pix2StructForConditionalGeneration""",
        """Pix2StructVisionModel""",
        """Pix2StructTextModel""",
    ]

if TYPE_CHECKING:
    from .configuration_pixastruct import (
        PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        PixaStructConfig,
        PixaStructTextConfig,
        PixaStructVisionConfig,
    )
    from .processing_pixastruct import PixaStructProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_pixastruct import PixaStructImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_pixastruct import (
            PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
            PixaStructForConditionalGeneration,
            PixaStructPreTrainedModel,
            PixaStructTextModel,
            PixaStructVisionModel,
        )

else:
    import sys

    _a      :			Optional[int]					=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

