
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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__a         =   logging.get_logger(__name__)
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Any       	=      ['pixel_values']
	def __init__(			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Optional[Dict[str, int]] = None			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = PILImageResampling.BILINEAR			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int] = None			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Union[int, float] = 1 / 2_5_5			,				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_    :     Tuple			,				)			->	None:
					super().__init__(**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   size if size is not None else {'''shortest_edge''': 2_5_6}
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			,				default_to_square=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   do_resize
					lowercase_	    =   size
					lowercase_	    =   resample
					lowercase_	    =   do_center_crop
					lowercase_	    =   crop_size
					lowercase_	    =   do_rescale
					lowercase_	    =   rescale_factor
					lowercase_	    =   do_normalize
					lowercase_	    =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					lowercase_	    =   image_std if image_std is not None else IMAGENET_STANDARD_STD
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int]			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = PILImageResampling.BICUBIC			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     List[str]			,				)			->	np.ndarray:
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			,				default_to_square=SCREAMING_SNAKE_CASE_			)
					if "shortest_edge" not in size:
									raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''			)
					lowercase_	    =   get_resize_output_image_size(SCREAMING_SNAKE_CASE_			,				size=size['''shortest_edge''']			,				default_to_square=SCREAMING_SNAKE_CASE_			)
					return resize(SCREAMING_SNAKE_CASE_			,				size=SCREAMING_SNAKE_CASE_			,				resample=SCREAMING_SNAKE_CASE_			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int]			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     Tuple			,				)			->	np.ndarray:
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			)
					return center_crop(SCREAMING_SNAKE_CASE_			,				size=(size['''height'''], size['''width'''])			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     float			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	np.ndarray:
					return rescale(SCREAMING_SNAKE_CASE_			,				scale=SCREAMING_SNAKE_CASE_			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Union[str, 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_    :     Any			,				)			->	np.ndarray:
					return normalize(SCREAMING_SNAKE_CASE_			,				mean=SCREAMING_SNAKE_CASE_			,				std=SCREAMING_SNAKE_CASE_			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)




	def 		_lowercase    (			self    :     int			,				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_    :     Dict[str, 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]			,				)			->	str:
					lowercase_	    =   do_resize if do_resize is not None else self.do_resize
					lowercase_	    =   size if size is not None else self.size
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			,				default_to_square=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   resample if resample is not None else self.resample
					lowercase_	    =   do_center_crop if do_center_crop is not None else self.do_center_crop
					lowercase_	    =   crop_size if crop_size is not None else self.crop_size
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			)
					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_	    =   make_list_of_images(SCREAMING_SNAKE_CASE_			)

					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.'''			)

					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.'''			)

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

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

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

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

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

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

					lowercase_	    =   {'''pixel_values''': images}
					return BatchFeature(data=SCREAMING_SNAKE_CASE_			,				tensor_type=SCREAMING_SNAKE_CASE_			)









"""simple docstring"""

from typing import Dict, List, Optional

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


A_						:    Tuple				=      logging.get_logger(__name__)


A_						:    int				=      {
    'nielsr/canine-s': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
A_						:    List[Any]				=      111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
A_						:    Optional[Any]				=      0

A_						:    int				=      0xE000
A_						:    Any				=      0xE001
A_						:    List[Any]				=      0xE002
A_						:    Optional[Any]				=      0xE003
A_						:    int				=      0xE004

# Maps special codepoints to human-readable names.
A_						:    Dict[int, str]				=      {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
A_						:    Dict[str, int]				=      {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[Any]  						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





	def __init__(      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       Any=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       int=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       Tuple=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       int=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       Any=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=False     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=2_048     ,       **_SCREAMING_SNAKE_CASE				:       Tuple     ,       )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else bos_token
					SCREAMING_SNAKE_CASE						:							Tuple							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else eos_token
					SCREAMING_SNAKE_CASE						:							Any							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else sep_token
					SCREAMING_SNAKE_CASE						:							int							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else cls_token
					SCREAMING_SNAKE_CASE						:							Dict							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else pad_token

					# Mask token behave like a normal word, i.e. include the space before it
					SCREAMING_SNAKE_CASE						:							int							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else mask_token

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

					# Creates a mapping for looking up the IDs of special symbols.
					SCREAMING_SNAKE_CASE						:							Dict[str, int]							=     {}
					for codepoint, name in SPECIAL_CODEPOINTS.items():
									SCREAMING_SNAKE_CASE						:							str							=     codepoint

					# Creates a mapping for looking up the string forms of special symbol IDs.
					SCREAMING_SNAKE_CASE						:							Dict[int, str]							=     {
					    codepoint: name for name, codepoint in self._special_codepoints.items()
					}

					SCREAMING_SNAKE_CASE						:							Dict							=     UNICODE_VOCAB_SIZE
					SCREAMING_SNAKE_CASE						:							List[str]							=     len(self._special_codepoints    )





	@property
	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	int:





					"""simple docstring"""


					return self._unicode_vocab_size





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	List[str]:





					"""simple docstring"""


					return list(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	int:





					"""simple docstring"""


					try:
									return ord(_SCREAMING_SNAKE_CASE    )
					except TypeError:
									raise ValueError(f"""invalid token: '{token}'"""    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       int    )						->	str:





					"""simple docstring"""


					try:
									if index in SPECIAL_CODEPOINTS:
													return SPECIAL_CODEPOINTS[index]
									return chr(_SCREAMING_SNAKE_CASE    )
					except TypeError:
									raise ValueError(f"""invalid id: {index}"""    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Tuple    )						->	List[str]:





					"""simple docstring"""


					return "".join(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None    )						->	List[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     [self.sep_token_id]
					SCREAMING_SNAKE_CASE						:							Tuple							=     [self.cls_token_id]

					SCREAMING_SNAKE_CASE						:							Any							=     cls + token_ids_a + sep
					if token_ids_a is not None:
									result += token_ids_a + sep
					return result





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None     ,       _SCREAMING_SNAKE_CASE				:       bool = False    )						->	List[int]:





					"""simple docstring"""


					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    )

					SCREAMING_SNAKE_CASE						:							int							=     [1] + ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1]
					if token_ids_a is not None:
									result += ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1]
					return result





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None    )						->	List[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     [self.sep_token_id]
					SCREAMING_SNAKE_CASE						:							Dict							=     [self.cls_token_id]

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     len(cls + token_ids_a + sep    ) * [0]
					if token_ids_a is not None:
									result += len(token_ids_a + sep    ) * [1]
					return result







	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Optional[str] = None    )						->	Any:





					"""simple docstring"""


					return ()





