



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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_:List[Any]					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:str					=						"""▁"""

SCREAMING_SNAKE_CASE_:Dict					=						{"""vocab_file""": """sentencepiece.bpe.model"""}

SCREAMING_SNAKE_CASE_:Union[str, Any]					=						{
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

SCREAMING_SNAKE_CASE_:str					=						{
    """facebook/xglm-564M""": 2_048,
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):
    __lowerCamelCase      :			int =				VOCAB_FILES_NAMES
    __lowerCamelCase      :			int =				PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase      :			Dict =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase      :			int =				["input_ids", "attention_mask"]






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__="<s>",		lowerCamelCase__="</s>",		lowerCamelCase__="</s>",		lowerCamelCase__="<s>",		lowerCamelCase__="<unk>",		lowerCamelCase__="<pad>",		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A			:	Optional[Any]										=       {} if sp_model_kwargs is None else sp_model_kwargs

        # Compatibility with the original tokenizer
        A			:	Optional[Any]										=       7
        A			:	Optional[int]										=       [f'''<madeupword{i}>''' for i in range(self.num_madeup_words		)]

        A			:	str										=       kwargs.get("""additional_special_tokens""",		[]		)
        kwargs["additional_special_tokens"] += [
            word for word in madeup_words if word not in kwargs["additional_special_tokens"]
        ]

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

        A			:	Optional[int]										=       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(str(lowerCamelCase__		)		)
        A			:	Union[str, Any]										=       vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
        A			:	Any										=       1

        # Mimic fairseq token-to-id alignment for the first 4 token
        A			:	int										=       {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

        A			:	Any										=       len(self.sp_model		)
        A			:	Dict										=       {f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words		)}
        self.fairseq_tokens_to_ids.update(lowerCamelCase__		)

        A			:	List[Any]										=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}






    def __getstate__(	self		):
        A			:	str										=       self.__dict__.copy()
        A			:	Optional[int]										=       None
        A			:	Optional[Any]										=       self.sp_model.serialized_model_proto()
        return state






    def __setstate__(	self,		lowerCamelCase__		):
        A			:	Tuple										=       d

        # for backward compatibility
        if not hasattr(self,		"""sp_model_kwargs"""		):
            A			:	Optional[int]										=       {}

        A			:	Union[str, Any]										=       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None		):

        if token_ids_a is None:
            return [self.sep_token_id] + token_ids_a
        A			:	Tuple										=       [self.sep_token_id]
        return sep + token_ids_a + sep + sep + token_ids_a






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = False		):

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

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






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        A			:	Dict										=       [self.sep_token_id]

        if token_ids_a is None:
            return len(sep + token_ids_a		) * [0]
        return len(sep + token_ids_a + sep + sep + token_ids_a		) * [0]






    @property
    def       _lowerCAmelCase (	self		):
        return len(self.sp_model		) + self.fairseq_offset + self.num_madeup_words






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       {self.convert_ids_to_tokens(lowerCamelCase__		): i for i in range(self.vocab_size		)}
        vocab.update(self.added_tokens_encoder		)
        return vocab






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        return self.sp_model.encode(lowerCamelCase__,		out_type=lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
        A			:	List[str]										=       self.sp_model.PieceToId(lowerCamelCase__		)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	Dict										=       """""".join(lowerCamelCase__		).replace(lowerCamelCase__,		""" """		).strip()
        return out_string






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

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

        return (out_vocab_file,)







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_:Any					=						{
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:List[Any]					=						["""MobileNetV2FeatureExtractor"""]
     SCREAMING_SNAKE_CASE_:Tuple					=						["""MobileNetV2ImageProcessor"""]


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Any					=						[
         """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MobileNetV2ForImageClassification""",
         """MobileNetV2ForSemanticSegmentation""",
         """MobileNetV2Model""",
         """MobileNetV2PreTrainedModel""",
         """load_tf_weights_in_mobilenet_v2""",
     ]


if TYPE_CHECKING:
     from .configuration_mobilenet_va import (
         MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileNetVaConfig,
         MobileNetVaOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
          from .image_processing_mobilenet_va import MobileNetVaImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilenet_va import (
              MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileNetVaForImageClassification,
              MobileNetVaForSemanticSegmentation,
              MobileNetVaModel,
              MobileNetVaPreTrainedModel,
              load_tf_weights_in_mobilenet_va,
          )


else:
     import sys

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


