
'''simple docstring'''





from typing import Dict, List, Optional, Tuple

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


SCREAMING_SNAKE_CASE   :		int					 =       logging.get_logger(__name__)






class 					__lowercase		(					A       ):
					__magic_name__						:							Dict     	=	['''input_ids''', '''attention_mask''']


					def __init__(	self       ,       a__="[PAD]"       ,       a__="[BOS]"       ,       a__="[EOS]"       ,       a__="[MASK]"       ,       a__="[CLS]"       ,       a__="[SEP]"       ,       a__=2_0_4_8       ,       **a__       ,       )		->							None:





									'''simple docstring'''
									A_       	=  AddedToken(a__       ,       lstrip=a__       ,       rstrip=a__						) if isinstance(a__       ,       a__						) else pad_token
									A_       	=  AddedToken(a__       ,       lstrip=a__       ,       rstrip=a__						) if isinstance(a__       ,       a__						) else bos_token
									A_       	=  AddedToken(a__       ,       lstrip=a__       ,       rstrip=a__						) if isinstance(a__       ,       a__						) else eos_token
									A_       	=  AddedToken(a__       ,       lstrip=a__       ,       rstrip=a__						) if isinstance(a__       ,       a__						) else mask_token
									A_       	=  AddedToken(a__       ,       lstrip=a__       ,       rstrip=a__						) if isinstance(a__       ,       a__						) else cls_token
									A_       	=  AddedToken(a__       ,       lstrip=a__       ,       rstrip=a__						) if isinstance(a__       ,       a__						) else sep_token

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

									A_       	=  2**8  # utf is 8 bits

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


					def        lowerCAmelCase_    (	self						)		->							Dict[str, int]:





									'''simple docstring'''
									A_       	=  self.special_tokens_encoder.copy()
									vocab.update(self.added_tokens_encoder						)
									for i in range(self._utf_vocab_size						):
													A_       	=  chr(a__						)
													A_       	=  i + len(self.special_tokens_encoder						)
									return vocab


					@property
					def        lowerCAmelCase_    (	self						)		->							List[str]:





									'''simple docstring'''
									return self._utf_vocab_size + self._num_special_tokens


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__ = None       ,       a__ = False						)		->							List[int]:





									'''simple docstring'''
									if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=a__       ,       token_ids_a=a__       ,       already_has_special_tokens=a__						)

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


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__ = None						)		->							List[int]:





									'''simple docstring'''
									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        lowerCAmelCase_    (	self       ,       a__						)		->							List[str]:





									'''simple docstring'''
									A_       	=  [chr(a__						) for i in text.encode('''utf-8'''						)]
									return tokens


					def        lowerCAmelCase_    (	self       ,       a__						)		->							Optional[int]:





									'''simple docstring'''
									if token in self.special_tokens_encoder:
													A_       	=  self.special_tokens_encoder[token]
									elif token in self.added_tokens_encoder:
													A_       	=  self.added_tokens_encoder[token]
									elif len(a__						) != 1:
													A_       	=  self.unk_token_id
									else:
													A_       	=  ord(a__						) + self._num_special_tokens
									return token_id


					def        lowerCAmelCase_    (	self       ,       a__						)		->							Optional[Any]:





									'''simple docstring'''
									if index in self.special_tokens_decoder:
													A_       	=  self.special_tokens_decoder[index]
									elif index in self.added_tokens_decoder:
													A_       	=  self.added_tokens_decoder[index]
									else:
													A_       	=  chr(index - self._num_special_tokens						)
									return token


					def        lowerCAmelCase_    (	self       ,       a__						)		->							str:





									'''simple docstring'''
									A_       	=  b''''''
									for token in tokens:
													if token in self.special_tokens_decoder:
																	A_       	=  self.special_tokens_decoder[token].encode('''utf-8'''						)
													elif token in self.added_tokens_decoder:
																	A_       	=  self.special_tokens_decoder[token].encode('''utf-8'''						)
													elif token in self.special_tokens_encoder:
																	A_       	=  token.encode('''utf-8'''						)
													elif token in self.added_tokens_encoder:
																	A_       	=  token.encode('''utf-8'''						)
													else:
																	A_       	=  bytes([ord(a__						)]						)
													bstring += tok_string
									A_       	=  bstring.decode('''utf-8'''       ,       errors='''replace'''						)
									return string


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__ = None						)		->							Tuple[str]:





									'''simple docstring'''
									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


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






class 					__lowercase		(					A       ):
					__magic_name__						:							int     	=	['''pixel_values''']


					def __init__(	self       ,       a__ = True       ,       a__ = None       ,       a__ = True       ,       a__ = None       ,       a__ = PILImageResampling.BICUBIC       ,       a__ = True       ,       a__ = 1 / 2_5_5       ,       a__ = True       ,       a__ = None       ,       a__ = None       ,       **a__       ,       )		->							None:





									'''simple docstring'''
									super().__init__(**a__						)
									A_       	=  crop_size if crop_size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)
									A_       	=  size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
									A_       	=  get_size_dict(a__						)

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


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = None       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									A_       	=  self.size if size is None else size
									A_       	=  get_size_dict(a__						)
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)

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


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = PIL.Image.BICUBIC       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									A_       	=  get_size_dict(a__						)
									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(
									    a__       ,       size=(size['''height'''], size['''width'''])       ,       resample=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = None       ,       **a__       ,       )		->							Union[str, Any]:





									'''simple docstring'''
									return rescale(a__       ,       scale=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									return normalize(a__       ,       mean=a__       ,       std=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = ChannelDimension.FIRST       ,       **a__       ,       )		->							PIL.Image.Image:





									'''simple docstring'''
									A_       	=  do_center_crop if do_center_crop is not None else self.do_center_crop
									A_       	=  crop_size if crop_size is not None else self.crop_size
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)
									A_       	=  do_resize if do_resize is not None else self.do_resize
									A_       	=  size if size is not None else self.size
									A_       	=  get_size_dict(a__						)
									A_       	=  resample if resample is not None else self.resample
									A_       	=  do_rescale if do_rescale is not None else self.do_rescale
									A_       	=  rescale_factor if rescale_factor is not None else self.rescale_factor
									A_       	=  do_normalize if do_normalize is not None else self.do_normalize
									A_       	=  image_mean if image_mean is not None else self.image_mean
									A_       	=  image_std if image_std is not None else self.image_std

									A_       	=  make_list_of_images(a__						)

									if not valid_images(a__						):
													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.
									A_       	=  [to_numpy_array(a__						) for image in images]

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

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

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

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

									A_       	=  [to_channel_dimension_format(a__       ,       a__						) for image in images]

									A_       	=  {'''pixel_values''': images}
									return BatchFeature(data=a__       ,       tensor_type=a__						)