'''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


lowerCAmelCase_ : Optional[int]						  =      logging.get_logger(__name__)

lowerCAmelCase_ : List[str]						  =      {"vocab_file": "sentencepiece.bpe.model"}

lowerCAmelCase_ : Optional[int]						  =      {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

lowerCAmelCase_ : Tuple						  =      {
    "camembert-base": 512,
}

lowerCAmelCase_ : Tuple						  =      "▁"





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=VOCAB_FILES_NAMES
   lowerCamelCase_    					=PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_    					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_    					=['input_ids', 'attention_mask']


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

         lowercase_     =      {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=__lowerCAmelCase   ,		eos_token=__lowerCAmelCase   ,		unk_token=__lowerCAmelCase   ,		sep_token=__lowerCAmelCase   ,		cls_token=__lowerCAmelCase   ,		pad_token=__lowerCAmelCase   ,		mask_token=__lowerCAmelCase   ,		additional_special_tokens=__lowerCAmelCase   ,		sp_model_kwargs=self.sp_model_kwargs   ,		**__lowerCAmelCase   ,		)
         lowercase_     =      spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(str(__lowerCAmelCase))
         lowercase_     =      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>
         lowercase_     =      {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
         lowercase_     =      len(self.fairseq_tokens_to_ids)
         lowercase_     =      len(self.sp_model) + len(self.fairseq_tokens_to_ids)
         lowercase_     =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	List[int]   ,		__lowerCAmelCase				:	Optional[List[int]] = None)      ->					List[int]:

         if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         lowercase_     =      [self.cls_token_id]
         lowercase_     =      [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep


   def __UpperCAmelCase       (   self				:	Dict   ,		__lowerCAmelCase				:	List[int]   ,		__lowerCAmelCase				:	Optional[List[int]] = None   ,		__lowerCAmelCase				:	bool = False)      ->					List[int]:
         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)) + [1]
         return [1] + ([0] * len(__lowerCAmelCase)) + [1, 1] + ([0] * len(__lowerCAmelCase)) + [1]


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	List[int]   ,		__lowerCAmelCase				:	Optional[List[int]] = None)      ->					List[int]:
         lowercase_     =      [self.sep_token_id]
         lowercase_     =      [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 __UpperCAmelCase       (   self				:	Union[str, Any])      ->					str:
         return len(self.fairseq_tokens_to_ids) + len(self.sp_model)


   def __UpperCAmelCase       (   self				:	List[str])      ->					Optional[Any]:
         lowercase_     =      {self.convert_ids_to_tokens(__lowerCAmelCase): i for i in range(self.vocab_size)}
         vocab.update(self.added_tokens_encoder)
         return vocab


   def __UpperCAmelCase       (   self				:	Dict   ,		__lowerCAmelCase				:	str)      ->					List[str]:
         return self.sp_model.encode(__lowerCAmelCase   ,		out_type=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	List[Any])      ->					List[str]:
         if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
         elif self.sp_model.PieceToId(__lowerCAmelCase) == 0:
               # Convert sentence piece unk token to fairseq unk token index
               return self.unk_token_id
         return self.fairseq_offset + self.sp_model.PieceToId(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	List[str])      ->					List[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 __UpperCAmelCase       (   self				:	List[str]   ,		__lowerCAmelCase				:	Any)      ->					List[str]:
         lowercase_     =      []
         lowercase_     =      ""
         lowercase_     =      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(__lowerCAmelCase) + token
                     lowercase_     =      True
                     lowercase_     =      []
               else:
                     current_sub_tokens.append(__lowerCAmelCase)
                     lowercase_     =      False
         out_string += self.sp_model.decode(__lowerCAmelCase)
         return out_string.strip()


   def __getstate__(   self				:	str)      ->					Tuple:
         lowercase_     =      self.__dict__.copy()
         lowercase_     =      None
         return state


   def __setstate__(   self				:	Dict   ,		__lowerCAmelCase				:	Dict)      ->					Any:
         lowercase_     =      d

         # for backward compatibility
         if not hasattr(self   ,		"sp_model_kwargs"):
               lowercase_     =      {}

         lowercase_     =      spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(self.vocab_file)


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Optional[str] = None)      ->					Tuple[str]:
         if not os.path.isdir(__lowerCAmelCase):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
         lowercase_     =      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:
                     lowercase_     =      self.sp_model.serialized_model_proto()
                     fi.write(__lowerCAmelCase)

         return (out_vocab_file,)







'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase_ : Any						  =      logging.get_logger(__name__)





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=['pixel_values']


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	int = 32   ,		__lowerCAmelCase				:	List[str]=PILImageResampling.BILINEAR   ,		__lowerCAmelCase				:	bool = True   ,		**__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					None:
         lowercase_     =      do_resize
         lowercase_     =      do_rescale
         lowercase_     =      size_divisor
         lowercase_     =      resample
         super().__init__(**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[str]   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	Optional[ChannelDimension] = None   ,		**__lowerCAmelCase				:	Any)      ->					np.ndarray:
         lowercase_ ,							lowercase_     =      get_image_size(__lowerCAmelCase)
         # Rounds the height and width down to the closest multiple of size_divisor
         lowercase_     =      height // size_divisor * size_divisor
         lowercase_     =      width // size_divisor * size_divisor
         lowercase_     =      resize(__lowerCAmelCase   ,		(new_h, new_w)   ,		resample=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)
         return image


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	float   ,		__lowerCAmelCase				:	Optional[ChannelDimension] = None   ,		**__lowerCAmelCase				:	int)      ->					np.ndarray:
         return rescale(image=__lowerCAmelCase   ,		scale=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[int] = None   ,		__lowerCAmelCase				:	str=None   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[Union[TensorType, str]] = None   ,		__lowerCAmelCase				:	ChannelDimension = ChannelDimension.FIRST   ,		**__lowerCAmelCase				:	List[str]   ,		)      ->					BatchFeature:
         lowercase_     =      do_resize if do_resize is not None else self.do_resize
         lowercase_     =      do_rescale if do_rescale is not None else self.do_rescale
         lowercase_     =      size_divisor if size_divisor is not None else self.size_divisor
         lowercase_     =      resample if resample is not None else self.resample

         if do_resize and size_divisor is None:
               raise ValueError("size_divisor is required for resizing")

         lowercase_     =      make_list_of_images(__lowerCAmelCase)

         if not valid_images(__lowerCAmelCase):
               raise ValueError("Invalid image(s)")

         # All transformations expect numpy arrays.
         lowercase_     =      [to_numpy_array(__lowerCAmelCase) for img in images]

         if do_resize:
               lowercase_     =      [self.resize(__lowerCAmelCase   ,		size_divisor=__lowerCAmelCase   ,		resample=__lowerCAmelCase) for image in images]

         if do_rescale:
               lowercase_     =      [self.rescale(__lowerCAmelCase   ,		scale=1 / 255) for image in images]

         lowercase_     =      [to_channel_dimension_format(__lowerCAmelCase   ,		__lowerCAmelCase) for image in images]

         lowercase_     =      {"pixel_values": images}
         return BatchFeature(data=__lowerCAmelCase   ,		tensor_type=__lowerCAmelCase)






