



"""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


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

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

lowercase__		:    str				   =     {
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

lowercase__		:    str				   =     {
    """camembert-base""": 5_1_2,
}

lowercase__		:    str				   =     """▁"""







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _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    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]="<s>"   ,      SCREAMING_SNAKE_CASE_    :				Dict="</s>"   ,      SCREAMING_SNAKE_CASE_    :				List[Any]="</s>"   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]="<s>"   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]="<unk>"   ,      SCREAMING_SNAKE_CASE_    :				str="<pad>"   ,      SCREAMING_SNAKE_CASE_    :				List[Any]="<mask>"   ,      SCREAMING_SNAKE_CASE_    :				Tuple=["<s>NOTUSED", "</s>NOTUSED"]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Dict[str, Any]] = None   ,      **SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      ):
        # Mask token behave like a normal word, i.e. include the space before it
        lowerCAmelCase_       :    str								=			AddedToken(SCREAMING_SNAKE_CASE_   ,      lstrip=SCREAMING_SNAKE_CASE_   ,      rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	) else mask_token

        lowerCAmelCase_       :    Optional[int]								=			{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=SCREAMING_SNAKE_CASE_   ,      eos_token=SCREAMING_SNAKE_CASE_   ,      unk_token=SCREAMING_SNAKE_CASE_   ,      sep_token=SCREAMING_SNAKE_CASE_   ,      cls_token=SCREAMING_SNAKE_CASE_   ,      pad_token=SCREAMING_SNAKE_CASE_   ,      mask_token=SCREAMING_SNAKE_CASE_   ,      additional_special_tokens=SCREAMING_SNAKE_CASE_   ,      sp_model_kwargs=self.sp_model_kwargs   ,      **SCREAMING_SNAKE_CASE_   ,      )
        lowerCAmelCase_       :    Optional[Any]								=			spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(str(SCREAMING_SNAKE_CASE_	)	)
        lowerCAmelCase_       :    Any								=			vocab_file
        # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
        # sentencepiece vocabulary (this is the case for <s> and </s>
        lowerCAmelCase_       :    Optional[int]								=			{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
        lowerCAmelCase_       :    int								=			len(self.fairseq_tokens_to_ids	)
        lowerCAmelCase_       :    Tuple								=			len(self.sp_model	) + len(self.fairseq_tokens_to_ids	)
        lowerCAmelCase_       :    Any								=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				List[int]   ,      SCREAMING_SNAKE_CASE_    :				Optional[List[int]] = None	):

        if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        lowerCAmelCase_       :    str								=			[self.cls_token_id]
        lowerCAmelCase_       :    Optional[int]								=			[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				List[int]   ,      SCREAMING_SNAKE_CASE_    :				Optional[List[int]] = None   ,      SCREAMING_SNAKE_CASE_    :				bool = False	):
        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=SCREAMING_SNAKE_CASE_   ,      token_ids_a=SCREAMING_SNAKE_CASE_   ,      already_has_special_tokens=SCREAMING_SNAKE_CASE_	)

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



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[int]   ,      SCREAMING_SNAKE_CASE_    :				Optional[List[int]] = None	):
        lowerCAmelCase_       :    Optional[Any]								=			[self.sep_token_id]
        lowerCAmelCase_       :    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 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        return len(self.fairseq_tokens_to_ids	) + len(self.sp_model	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    Optional[int]								=			{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	): i for i in range(self.vocab_size	)}
        vocab.update(self.added_tokens_encoder	)
        return vocab



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				str	):
        return self.sp_model.encode(SCREAMING_SNAKE_CASE_   ,      out_type=SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				int	):
        if token in self.fairseq_tokens_to_ids:
          return self.fairseq_tokens_to_ids[token]
        elif self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_	) == 0:
          # Convert sentence piece unk token to fairseq unk token index
          return self.unk_token_id
        return self.fairseq_offset + self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str   ,      SCREAMING_SNAKE_CASE_    :				Any	):
        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 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Dict	):
        lowerCAmelCase_       :    int								=			[]
        lowerCAmelCase_       :    str								=			''
        lowerCAmelCase_       :    Union[str, Any]								=			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(SCREAMING_SNAKE_CASE_	) + token
            lowerCAmelCase_       :    Dict								=			True
            lowerCAmelCase_       :    List[Any]								=			[]
          else:
            current_sub_tokens.append(SCREAMING_SNAKE_CASE_	)
            lowerCAmelCase_       :    str								=			False
        out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	)
        return out_string.strip()



      def __getstate__(				self    :				List[Any]	):
        lowerCAmelCase_       :    Tuple								=			self.__dict__.copy()
        lowerCAmelCase_       :    str								=			None
        return state



      def __setstate__(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Any	):
        lowerCAmelCase_       :    Tuple								=			d

        # for backward compatibility
        if not hasattr(self   ,      'sp_model_kwargs'	):
          lowerCAmelCase_       :    List[Any]								=			{}

        lowerCAmelCase_       :    int								=			spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(self.vocab_file	)





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

        if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE_	) and os.path.isfile(self.vocab_file	):
          copyfile(self.vocab_file   ,      SCREAMING_SNAKE_CASE_	)
        elif not os.path.isfile(self.vocab_file	):
          with open(SCREAMING_SNAKE_CASE_   ,      'wb'	) as fi:
            lowerCAmelCase_       :    Dict								=			self.sp_model.serialized_model_proto()
            fi.write(SCREAMING_SNAKE_CASE_	)

        return (out_vocab_file,)










"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase__		:    Tuple				   =     logging.get_logger(__name__)







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					["""pixel_values"""]



      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Dict[str, int]] = None   ,      SCREAMING_SNAKE_CASE_    :				PILImageResampling = PILImageResampling.BICUBIC   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				Union[int, float] = 1 / 2_5_5   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int] = None   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      **SCREAMING_SNAKE_CASE_    :				int   ,      ):
        super().__init__(**SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Dict								=			size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
        lowerCAmelCase_       :    List[Any]								=			get_size_dict(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
        lowerCAmelCase_       :    List[str]								=			get_size_dict(SCREAMING_SNAKE_CASE_   ,      default_to_square=SCREAMING_SNAKE_CASE_   ,      param_name='crop_size'	)

        lowerCAmelCase_       :    List[Any]								=			do_resize
        lowerCAmelCase_       :    Any								=			do_rescale
        lowerCAmelCase_       :    int								=			do_normalize
        lowerCAmelCase_       :    List[str]								=			do_center_crop
        lowerCAmelCase_       :    Dict								=			crop_size
        lowerCAmelCase_       :    Optional[Any]								=			size
        lowerCAmelCase_       :    Tuple								=			resample
        lowerCAmelCase_       :    Optional[int]								=			rescale_factor
        lowerCAmelCase_       :    Optional[Any]								=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowerCAmelCase_       :    List[str]								=			image_std if image_std is not None else IMAGENET_DEFAULT_STD



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int]   ,      SCREAMING_SNAKE_CASE_    :				PILImageResampling = PILImageResampling.BILINEAR   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				str   ,      ):
        lowerCAmelCase_       :    str								=			get_size_dict(SCREAMING_SNAKE_CASE_	)

        if "shortest_edge" in size:
          lowerCAmelCase_       :    Any								=			get_resize_output_image_size(SCREAMING_SNAKE_CASE_   ,      size=size['shortest_edge']   ,      default_to_square=SCREAMING_SNAKE_CASE_	)
          # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
          lowerCAmelCase_       :    Union[str, Any]								=			(size['height'], size['width'])
        else:
          raise ValueError(F"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"	)
        return resize(SCREAMING_SNAKE_CASE_   ,      size=SCREAMING_SNAKE_CASE_   ,      resample=SCREAMING_SNAKE_CASE_   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				int   ,      ):
        lowerCAmelCase_       :    Any								=			get_size_dict(SCREAMING_SNAKE_CASE_	)
        if "height" not in size or "width" not in size:
          raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}"	)
        return center_crop(SCREAMING_SNAKE_CASE_   ,      size=(size['height'], size['width'])   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				float   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				Optional[int]	):
        return rescale(SCREAMING_SNAKE_CASE_   ,      scale=SCREAMING_SNAKE_CASE_   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				Union[float, List[float]]   ,      SCREAMING_SNAKE_CASE_    :				Union[float, List[float]]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				str   ,      ):
        return normalize(SCREAMING_SNAKE_CASE_   ,      mean=SCREAMING_SNAKE_CASE_   ,      std=SCREAMING_SNAKE_CASE_   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				ImageInput   ,      SCREAMING_SNAKE_CASE_    :				Optional[bool] = None   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int] = None   ,      SCREAMING_SNAKE_CASE_    :				PILImageResampling = None   ,      SCREAMING_SNAKE_CASE_    :				bool = None   ,      SCREAMING_SNAKE_CASE_    :				int = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[bool] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[float] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[bool] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, TensorType]] = None   ,      SCREAMING_SNAKE_CASE_    :				Union[str, ChannelDimension] = ChannelDimension.FIRST   ,      **SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      ):
        lowerCAmelCase_       :    Optional[int]								=			do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase_       :    Optional[int]								=			do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase_       :    Optional[Any]								=			do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase_       :    Dict								=			do_center_crop if do_center_crop is not None else self.do_center_crop
        lowerCAmelCase_       :    Dict								=			crop_size if crop_size is not None else self.crop_size
        lowerCAmelCase_       :    Optional[Any]								=			get_size_dict(SCREAMING_SNAKE_CASE_   ,      param_name='crop_size'   ,      default_to_square=SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Union[str, Any]								=			resample if resample is not None else self.resample
        lowerCAmelCase_       :    Optional[Any]								=			rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase_       :    str								=			image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase_       :    List[str]								=			image_std if image_std is not None else self.image_std

        lowerCAmelCase_       :    Tuple								=			size if size is not None else self.size
        lowerCAmelCase_       :    str								=			get_size_dict(SCREAMING_SNAKE_CASE_	)

        if not is_batched(SCREAMING_SNAKE_CASE_	):
          lowerCAmelCase_       :    List[Any]								=			[images]

        if not valid_images(SCREAMING_SNAKE_CASE_	):
          raise ValueError(
              'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
              'torch.Tensor, tf.Tensor or jax.ndarray.'	)

        if do_resize and size is None:
          raise ValueError('Size must be specified if do_resize is True.'	)

        if do_center_crop and crop_size is None:
          raise ValueError('Crop size must be specified if do_center_crop is True.'	)

        if do_rescale and rescale_factor is None:
          raise ValueError('Rescale factor must be specified if do_rescale is True.'	)

        # All transformations expect numpy arrays.
        lowerCAmelCase_       :    str								=			[to_numpy_array(SCREAMING_SNAKE_CASE_	) for image in images]

        if do_resize:
          lowerCAmelCase_       :    Any								=			[self.resize(image=SCREAMING_SNAKE_CASE_   ,      size=SCREAMING_SNAKE_CASE_   ,      resample=SCREAMING_SNAKE_CASE_	) for image in images]

        if do_center_crop:
          lowerCAmelCase_       :    Optional[int]								=			[self.center_crop(image=SCREAMING_SNAKE_CASE_   ,      size=SCREAMING_SNAKE_CASE_	) for image in images]

        if do_rescale:
          lowerCAmelCase_       :    Tuple								=			[self.rescale(image=SCREAMING_SNAKE_CASE_   ,      scale=SCREAMING_SNAKE_CASE_	) for image in images]

        if do_normalize:
          lowerCAmelCase_       :    str								=			[self.normalize(image=SCREAMING_SNAKE_CASE_   ,      mean=SCREAMING_SNAKE_CASE_   ,      std=SCREAMING_SNAKE_CASE_	) for image in images]

        lowerCAmelCase_       :    Any								=			[to_channel_dimension_format(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	) for image in images]

        lowerCAmelCase_       :    Optional[Any]								=			{'pixel_values': images}
        return BatchFeature(data=SCREAMING_SNAKE_CASE_   ,      tensor_type=SCREAMING_SNAKE_CASE_	)





