




from typing import Dict, List, Optional, Tuple

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


a_      						=   logging.get_logger(__name__)

class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       List[Any]			=    ["input_ids", "attention_mask"]







      def __init__(   self    :       int       ,				snake_case    :       Union[str, Any]="[PAD]"       ,				snake_case    :       int="[BOS]"       ,				snake_case    :       int="[EOS]"       ,				snake_case    :       str="[MASK]"       ,				snake_case    :       Dict="[CLS]"       ,				snake_case    :       Any="[SEP]"       ,				snake_case    :       Optional[Any]=2048       ,				**snake_case    :       Union[str, Any]       ,				):
          __UpperCamelCase     						=  AddedToken(snake_case       ,				lstrip=snake_case       ,				rstrip=snake_case ) if isinstance(snake_case       ,				snake_case ) else pad_token
          __UpperCamelCase     						=  AddedToken(snake_case       ,				lstrip=snake_case       ,				rstrip=snake_case ) if isinstance(snake_case       ,				snake_case ) else bos_token
          __UpperCamelCase     						=  AddedToken(snake_case       ,				lstrip=snake_case       ,				rstrip=snake_case ) if isinstance(snake_case       ,				snake_case ) else eos_token
          __UpperCamelCase     						=  AddedToken(snake_case       ,				lstrip=snake_case       ,				rstrip=snake_case ) if isinstance(snake_case       ,				snake_case ) else mask_token
          __UpperCamelCase     						=  AddedToken(snake_case       ,				lstrip=snake_case       ,				rstrip=snake_case ) if isinstance(snake_case       ,				snake_case ) else cls_token
          __UpperCamelCase     						=  AddedToken(snake_case       ,				lstrip=snake_case       ,				rstrip=snake_case ) if isinstance(snake_case       ,				snake_case ) else sep_token

          super().__init__(
              pad_token=snake_case       ,				bos_token=snake_case       ,				eos_token=snake_case       ,				mask_token=snake_case       ,				cls_token=snake_case       ,				sep_token=snake_case       ,				model_max_length=snake_case       ,				**snake_case       ,				)

          __UpperCamelCase     						=  2**8  # utf is 8 bits

          # define special tokens dict
          __UpperCamelCase     						=  {
              self.pad_token: 0,
              self.bos_token: 1,
              self.eos_token: 2,
              self.mask_token: 3,
              self.cls_token: 4,
              self.sep_token: 5,
          }
          __UpperCamelCase     						=  len(self.special_tokens_encoder )
          __UpperCamelCase     						=  {v: k for k, v in self.special_tokens_encoder.items()}







      def 					snake_case		(   self    :       Optional[Any] ):
          __UpperCamelCase     						=  self.special_tokens_encoder.copy()
          vocab.update(self.added_tokens_encoder )
          for i in range(self._utf_vocab_size ):
              __UpperCamelCase     						=  chr(snake_case )
              __UpperCamelCase     						=  i + len(self.special_tokens_encoder )
          return vocab







      @property
      def 					snake_case		(   self    :       List[str] ):
          return self._utf_vocab_size + self._num_special_tokens







      def 					snake_case		(   self    :       Tuple       ,				snake_case    :       List[int]       ,				snake_case    :       Optional[List[int]] = None       ,				snake_case    :       bool = 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 )

          # normal case: some special tokens
          if token_ids_a is None:
              return [1] + [0] * len(snake_case ) + [1]
          return [1] + ([0] * len(snake_case )) + [1] + ([0] * len(snake_case )) + [1]







      def 					snake_case		(   self    :       Dict       ,				snake_case    :       List[int]       ,				snake_case    :       Optional[List[int]] = None ):
          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          else:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]







      def 					snake_case		(   self    :       Optional[Any]       ,				snake_case    :       str ):
          __UpperCamelCase     						=  [chr(snake_case ) for i in text.encode('''utf-8''' )]
          return tokens







      def 					snake_case		(   self    :       Dict       ,				snake_case    :       str ):
          if token in self.special_tokens_encoder:
              __UpperCamelCase     						=  self.special_tokens_encoder[token]
          elif token in self.added_tokens_encoder:
              __UpperCamelCase     						=  self.added_tokens_encoder[token]
          elif len(snake_case ) != 1:
              __UpperCamelCase     						=  self.unk_token_id
          else:
              __UpperCamelCase     						=  ord(snake_case ) + self._num_special_tokens
          return token_id







      def 					snake_case		(   self    :       str       ,				snake_case    :       Dict ):
          if index in self.special_tokens_decoder:
              __UpperCamelCase     						=  self.special_tokens_decoder[index]
          elif index in self.added_tokens_decoder:
              __UpperCamelCase     						=  self.added_tokens_decoder[index]
          else:
              __UpperCamelCase     						=  chr(index - self._num_special_tokens )
          return token







      def 					snake_case		(   self    :       Tuple       ,				snake_case    :       Optional[Any] ):
          __UpperCamelCase     						=  B''''''
          for token in tokens:
              if token in self.special_tokens_decoder:
                  __UpperCamelCase     						=  self.special_tokens_decoder[token].encode('''utf-8''' )
              elif token in self.added_tokens_decoder:
                  __UpperCamelCase     						=  self.special_tokens_decoder[token].encode('''utf-8''' )
              elif token in self.special_tokens_encoder:
                  __UpperCamelCase     						=  token.encode('''utf-8''' )
              elif token in self.added_tokens_encoder:
                  __UpperCamelCase     						=  token.encode('''utf-8''' )
              else:
                  __UpperCamelCase     						=  bytes([ord(snake_case )] )
              bstring += tok_string
          __UpperCamelCase     						=  bstring.decode('''utf-8'''       ,				errors='''replace''' )
          return string







      def 					snake_case		(   self    :       int       ,				snake_case    :       str       ,				snake_case    :       Optional[str] = None ):
          return ()












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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


a_      						=   logging.get_logger(__name__)

class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       Union[str, Any]			=    ["pixel_values"]







      def __init__(   self    :       Union[str, Any]       ,				snake_case    :       bool = True       ,				snake_case    :       Dict[str, int] = None       ,				snake_case    :       bool = True       ,				snake_case    :       Dict[str, int] = None       ,				snake_case    :       PILImageResampling = PILImageResampling.BICUBIC       ,				snake_case    :       bool = True       ,				snake_case    :       Union[int, float] = 1 / 255       ,				snake_case    :       bool = True       ,				snake_case    :       Optional[Union[float, List[float]]] = None       ,				snake_case    :       Optional[Union[float, List[float]]] = None       ,				**snake_case    :       Union[str, Any]       ,				):
          super().__init__(**snake_case )
          __UpperCamelCase     						=  crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
          __UpperCamelCase     						=  get_size_dict(snake_case       ,				param_name='''crop_size''' )
          __UpperCamelCase     						=  size if size is not None else {'''height''': 224, '''width''': 224}
          __UpperCamelCase     						=  get_size_dict(snake_case )

          __UpperCamelCase     						=  do_center_crop
          __UpperCamelCase     						=  crop_size
          __UpperCamelCase     						=  do_resize
          __UpperCamelCase     						=  size
          __UpperCamelCase     						=  resample
          __UpperCamelCase     						=  do_rescale
          __UpperCamelCase     						=  rescale_factor
          __UpperCamelCase     						=  do_normalize
          __UpperCamelCase     						=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          __UpperCamelCase     						=  image_std if image_std is not None else IMAGENET_DEFAULT_STD







      def 					snake_case		(   self    :       Optional[Any]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Dict[str, int]       ,				snake_case    :       Optional[int] = None       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       List[Any]       ,				):
          __UpperCamelCase     						=  self.size if size is None else size
          __UpperCamelCase     						=  get_size_dict(snake_case )
          __UpperCamelCase     						=  get_size_dict(snake_case       ,				param_name='''crop_size''' )

          __UpperCamelCase						,					__UpperCamelCase     						=  get_image_size(snake_case )
          __UpperCamelCase     						=  min(snake_case       ,				snake_case )
          __UpperCamelCase     						=  (size['''height'''] / crop_size['''height''']) * min_dim
          __UpperCamelCase     						=  (size['''width'''] / crop_size['''width''']) * min_dim
          return center_crop(snake_case       ,				size=(cropped_height, cropped_width)       ,				data_format=snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       Union[str, Any]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Dict[str, int]       ,				snake_case    :       PILImageResampling = PIL.Image.BICUBIC       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       List[str]       ,				):
          __UpperCamelCase     						=  get_size_dict(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()}" )
          return resize(
              snake_case       ,				size=(size['''height'''], size['''width'''])       ,				resample=snake_case       ,				data_format=snake_case       ,				**snake_case )







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







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







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

          __UpperCamelCase     						=  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_center_crop and crop_size is None:
              raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.''' )

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

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

          if do_center_crop:
              __UpperCamelCase     						=  [self.center_crop(snake_case       ,				snake_case       ,				size=snake_case ) for image in images]

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

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

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

          __UpperCamelCase     						=  [to_channel_dimension_format(snake_case       ,				snake_case ) for image in images]

          __UpperCamelCase     						=  {'''pixel_values''': images}
          return BatchFeature(data=snake_case       ,				tensor_type=snake_case )






