

'''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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


_UpperCAmelCase  :		List[str]  =		logging.get_logger(__name__)

class 						__magic_name__   (							_UpperCamelCase ):
 UpperCamelCase__     =       ["""pixel_values"""]







 def __init__(       self  , snake_case_ = True  , snake_case_ = None  , snake_case_ = PILImageResampling.BICUBIC  , snake_case_ = True  , snake_case_ = 1 / 2_55  , snake_case_ = True  , snake_case_ = None  , snake_case_ = None  , snake_case_ = True  , **snake_case_  , ):
   super().__init__(**snake_case_   )
   lowercase									=size if size is not None else {"height": 3_84, "width": 3_84}
   lowercase									=get_size_dict(snake_case_  , default_to_square=snake_case_   )

   lowercase									=do_resize
   lowercase									=size
   lowercase									=resample
   lowercase									=do_rescale
   lowercase									=rescale_factor
   lowercase									=do_normalize
   lowercase									=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   lowercase									=image_std if image_std is not None else OPENAI_CLIP_STD
   lowercase									=do_convert_rgb







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_ = PILImageResampling.BICUBIC  , snake_case_ = None  , **snake_case_  , ):
   lowercase									=get_size_dict(snake_case_  , default_to_square=snake_case_   )
   if "height" not in size or "width" not in size:
     raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'   )
   lowercase									=(size["height"], size["width"])
   return resize(snake_case_  , size=snake_case_  , resample=snake_case_  , data_format=snake_case_  , **snake_case_   )







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_ = None  , **snake_case_  , ):
   return rescale(snake_case_  , scale=snake_case_  , data_format=snake_case_  , **snake_case_   )







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_ = None  , **snake_case_  , ):
   return normalize(snake_case_  , mean=snake_case_  , std=snake_case_  , data_format=snake_case_  , **snake_case_   )







 def 						_A(       self  , snake_case_  , snake_case_ = None  , snake_case_ = None  , snake_case_ = None  , snake_case_ = None  , snake_case_ = None  , snake_case_ = None  , snake_case_ = None  , snake_case_ = None  , snake_case_ = None  , snake_case_ = None  , snake_case_ = ChannelDimension.FIRST  , **snake_case_  , ):
   lowercase									=do_resize if do_resize is not None else self.do_resize
   lowercase									=resample if resample is not None else self.resample
   lowercase									=do_rescale if do_rescale is not None else self.do_rescale
   lowercase									=rescale_factor if rescale_factor is not None else self.rescale_factor
   lowercase									=do_normalize if do_normalize is not None else self.do_normalize
   lowercase									=image_mean if image_mean is not None else self.image_mean
   lowercase									=image_std if image_std is not None else self.image_std
   lowercase									=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   lowercase									=size if size is not None else self.size
   lowercase									=get_size_dict(snake_case_  , default_to_square=snake_case_   )

   lowercase									=make_list_of_images(snake_case_   )

   if not valid_images(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 or resample is None:
     raise ValueError('''Size and resample must be specified if do_resize is True.'''   )

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

   if do_normalize and (image_mean is None or image_std is None):
     raise ValueError('''Image mean and std must be specified if do_normalize is True.'''   )

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
     lowercase									=[convert_to_rgb(snake_case_   ) for image in images]

   # All transformations expect numpy arrays.
   lowercase									=[to_numpy_array(snake_case_   ) for image in images]

   if do_resize:
     lowercase									=[self.resize(image=snake_case_  , size=snake_case_  , resample=snake_case_   ) for image in images]

   if do_rescale:
     lowercase									=[self.rescale(image=snake_case_  , scale=snake_case_   ) for image in images]

   if do_normalize:
     lowercase									=[self.normalize(image=snake_case_  , mean=snake_case_  , std=snake_case_   ) for image in images]

   lowercase									=[to_channel_dimension_format(snake_case_  , snake_case_   ) for image in images]

   lowercase									=BatchFeature(data={'''pixel_values''': images}  , tensor_type=snake_case_   )

   return encoded_outputs







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__							:      Tuple         =    logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__							:      int         =    """▁"""

SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    {"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE__							:      Optional[int]         =    {
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    {
    """google/reformer-crime-and-punishment""": 5_2_4_2_8_8,
}







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    List[Any]  		=					VOCAB_FILES_NAMES
   _UpperCamelCase			:    Any  		=					PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase			:    Any  		=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase			:    Tuple  		=					["""input_ids""", """attention_mask"""]





   def __init__(			self       ,      snake_case       ,      snake_case="</s>"       ,      snake_case="<unk>"       ,      snake_case=[]       ,      snake_case = None       ,      **snake_case       ,      )					->	None:
      """simple docstring"""





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

      super().__init__(
          eos_token=snake_case       ,      unk_token=snake_case       ,      additional_special_tokens=snake_case       ,      sp_model_kwargs=self.sp_model_kwargs       ,      **snake_case       ,      )

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





   @property
   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      return self.sp_model.get_piece_size()





   def 						_snake_case			(			self   )					->	Dict[str, int]:
      """simple docstring"""





      a__						:    Dict   		=	{self.convert_ids_to_tokens(snake_case   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab





   def __getstate__(			self   )					->	Any:
      """simple docstring"""





      a__						:    Tuple   		=	self.__dict__.copy()
      a__						:    Optional[int]   		=	None
      return state





   def __setstate__(			self       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    List[Any]   		=	d

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

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





   def 						_snake_case			(			self       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      return self.sp_model.encode(snake_case       ,      out_type=snake_case   )





   def 						_snake_case			(			self       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





      return self.sp_model.piece_to_id(snake_case   )





   def 						_snake_case			(			self       ,      snake_case   )					->	Any:
      """simple docstring"""





      if index < self.sp_model.get_piece_size():
         a__						:    Optional[Any]   		=	self.sp_model.IdToPiece(snake_case   )
      return token





   def 						_snake_case			(			self       ,      snake_case   )					->	Any:
      """simple docstring"""





      a__						:    int   		=	[]
      a__						:    Dict   		=	""
      for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
            out_string += self.sp_model.decode(snake_case   ) + token
            a__						:    Dict   		=	[]
         else:
            current_sub_tokens.append(snake_case   )
      out_string += self.sp_model.decode(snake_case   )
      return out_string.strip()





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None   )					->	Tuple[str]:
      """simple docstring"""





      if not os.path.isdir(snake_case   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
         return
      a__						:    Tuple   		=	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:
            a__						:    str   		=	self.sp_model.serialized_model_proto()
            fi.write(snake_case   )

      return (out_vocab_file,)

