



from typing import Dict, List, Optional

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


__lowerCamelCase     :							int  							=				logging.get_logger(__name__)


__lowerCamelCase     :							Optional[int]  							=				{
    "nielsr/canine-s": 2_048,
}

# Unicode defines 1,114,112 total “codepoints”
__lowerCamelCase     :							int  							=				1_114_112

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

__lowerCamelCase     :							int  							=				0xE_0_0_0
__lowerCamelCase     :							List[Any]  							=				0xE_0_0_1
__lowerCamelCase     :							List[Any]  							=				0xE_0_0_2
__lowerCamelCase     :							Optional[Any]  							=				0xE_0_0_3
__lowerCamelCase     :							List[Any]  							=				0xE_0_0_4

# Maps special codepoints to human-readable names.
__lowerCamelCase     :							Tuple  							=				{
    # 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.
__lowerCamelCase     :							Optional[Any]  							=				{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}





class  __magic_name__   (							UpperCAmelCase__						):
		lowercase      :       Tuple          =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






		def __init__(						self      :     Union[str, Any]				,		UpperCamelCase__      :     Any=chr(UpperCamelCase__      )				,		UpperCamelCase__      :     List[str]=chr(UpperCamelCase__      )				,		UpperCamelCase__      :     List[str]=chr(UpperCamelCase__      )				,		UpperCamelCase__      :     Dict=chr(UpperCamelCase__      )				,		UpperCamelCase__      :     Tuple=chr(UpperCamelCase__      )				,		UpperCamelCase__      :     Any=chr(UpperCamelCase__      )				,		UpperCamelCase__      :     Optional[Any]=False				,		UpperCamelCase__      :     Any=20_48				,		**UpperCamelCase__      :     Any				,		)				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     AddedToken(UpperCamelCase__				,		lstrip=UpperCamelCase__				,		rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__				,		UpperCamelCase__      ) else bos_token
								UpperCAmelCase         =     AddedToken(UpperCamelCase__				,		lstrip=UpperCamelCase__				,		rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__				,		UpperCamelCase__      ) else eos_token
								UpperCAmelCase         =     AddedToken(UpperCamelCase__				,		lstrip=UpperCamelCase__				,		rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__				,		UpperCamelCase__      ) else sep_token
								UpperCAmelCase         =     AddedToken(UpperCamelCase__				,		lstrip=UpperCamelCase__				,		rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__				,		UpperCamelCase__      ) else cls_token
								UpperCAmelCase         =     AddedToken(UpperCamelCase__				,		lstrip=UpperCamelCase__				,		rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__				,		UpperCamelCase__      ) else pad_token

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

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

								# Creates a mapping for looking up the IDs of special symbols.
								UpperCAmelCase         =     {}
								for codepoint, name in SPECIAL_CODEPOINTS.items():
														UpperCAmelCase         =     codepoint

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

								UpperCAmelCase         =     UNICODE_VOCAB_SIZE
								UpperCAmelCase         =     len(self._special_codepoints      )






		@property
		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					int:
								'''simple docstring'''







								return self._unicode_vocab_size






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     str      )				->					List[str]:
								'''simple docstring'''







								return list(UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     str      )				->					int:
								'''simple docstring'''







								try:
														return ord(UpperCamelCase__      )
								except TypeError:
														raise ValueError(F'invalid token: \'{token}\''      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]				,		UpperCamelCase__      :     int      )				->					str:
								'''simple docstring'''







								try:
														if index in SPECIAL_CODEPOINTS:
																				return SPECIAL_CODEPOINTS[index]
														return chr(UpperCamelCase__      )
								except TypeError:
														raise ValueError(F'invalid id: {index}'      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     Optional[int]      )				->					Optional[int]:
								'''simple docstring'''







								return "".join(UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     List[int]				,		UpperCamelCase__      :     Optional[List[int]] = None      )				->					List[int]:
								'''simple docstring'''







								UpperCAmelCase         =     [self.sep_token_id]
								UpperCAmelCase         =     [self.cls_token_id]

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     List[int]				,		UpperCamelCase__      :     Optional[List[int]] = None				,		UpperCamelCase__      :     bool = False      )				->					List[int]:
								'''simple docstring'''







								if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=UpperCamelCase__				,		token_ids_a=UpperCamelCase__				,		already_has_special_tokens=UpperCamelCase__      )

								UpperCAmelCase         =     [1] + ([0] * len(UpperCamelCase__      )) + [1]
								if token_ids_a is not None:
														result += ([0] * len(UpperCamelCase__      )) + [1]
								return result






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     List[int]				,		UpperCamelCase__      :     Optional[List[int]] = None      )				->					List[int]:
								'''simple docstring'''







								UpperCAmelCase         =     [self.sep_token_id]
								UpperCAmelCase         =     [self.cls_token_id]

								UpperCAmelCase         =     len(cls + token_ids_a + sep      ) * [0]
								if token_ids_a is not None:
														result += len(token_ids_a + sep      ) * [1]
								return result







		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Optional[str] = None      )				->					List[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,
    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, is_vision_available, logging


if is_vision_available():
		import PIL


__lowerCamelCase     :							List[Any]  							=				logging.get_logger(__name__)





class  __magic_name__   (							A__						):
		lowercase      :       Tuple          =['''pixel_values''']






		def __init__(						self      :     Any				,		UpperCamelCase__      :     bool = True				,		UpperCamelCase__      :     Dict[str, int] = None				,		UpperCamelCase__      :     float = None				,		UpperCamelCase__      :     PILImageResampling = PILImageResampling.BILINEAR				,		UpperCamelCase__      :     bool = True				,		UpperCamelCase__      :     Union[int, float] = 1 / 2_55				,		UpperCamelCase__      :     bool = True				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		**UpperCamelCase__      :     List[str]				,		)				->					None:
								'''simple docstring'''







								super().__init__(**UpperCamelCase__      )
								UpperCAmelCase         =     size if size is not None else {"shortest_edge": 3_84}
								UpperCAmelCase         =     get_size_dict(UpperCamelCase__				,		default_to_square=UpperCamelCase__      )

								UpperCAmelCase         =     do_resize
								UpperCAmelCase         =     size
								# Default value set here for backwards compatibility where the value in config is None
								UpperCAmelCase         =     crop_pct if crop_pct is not None else 2_24 / 2_56
								UpperCAmelCase         =     resample
								UpperCAmelCase         =     do_rescale
								UpperCAmelCase         =     rescale_factor
								UpperCAmelCase         =     do_normalize
								UpperCAmelCase         =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								UpperCAmelCase         =     image_std if image_std is not None else IMAGENET_STANDARD_STD






		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     np.ndarray				,		UpperCamelCase__      :     Dict[str, int]				,		UpperCamelCase__      :     float				,		UpperCamelCase__      :     PILImageResampling = PILImageResampling.BICUBIC				,		UpperCamelCase__      :     Optional[Union[str, ChannelDimension]] = None				,		**UpperCamelCase__      :     Union[str, Any]				,		)				->					np.ndarray:
								'''simple docstring'''







								UpperCAmelCase         =     get_size_dict(UpperCamelCase__				,		default_to_square=UpperCamelCase__      )
								if "shortest_edge" not in size:
														raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'      )
								UpperCAmelCase         =     size["shortest_edge"]

								if shortest_edge < 3_84:
														# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
														UpperCAmelCase         =     int(shortest_edge / crop_pct      )
														UpperCAmelCase         =     get_resize_output_image_size(UpperCamelCase__				,		size=UpperCamelCase__				,		default_to_square=UpperCamelCase__      )
														UpperCAmelCase         =     resize(image=UpperCamelCase__				,		size=UpperCamelCase__				,		resample=UpperCamelCase__				,		data_format=UpperCamelCase__				,		**UpperCamelCase__      )
														# then crop to (shortest_edge, shortest_edge)
														return center_crop(image=UpperCamelCase__				,		size=(shortest_edge, shortest_edge)				,		data_format=UpperCamelCase__				,		**UpperCamelCase__      )
								else:
														# warping (no cropping) when evaluated at 384 or larger
														return resize(
														    UpperCamelCase__				,		size=(shortest_edge, shortest_edge)				,		resample=UpperCamelCase__				,		data_format=UpperCamelCase__				,		**UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     np.ndarray				,		UpperCamelCase__      :     Union[int, float]				,		UpperCamelCase__      :     Optional[Union[str, ChannelDimension]] = None				,		**UpperCamelCase__      :     Optional[Any]				,		)				->					List[str]:
								'''simple docstring'''







								return rescale(UpperCamelCase__				,		scale=UpperCamelCase__				,		data_format=UpperCamelCase__				,		**UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     np.ndarray				,		UpperCamelCase__      :     Union[float, List[float]]				,		UpperCamelCase__      :     Union[float, List[float]]				,		UpperCamelCase__      :     Optional[Union[str, ChannelDimension]] = None				,		**UpperCamelCase__      :     Optional[Any]				,		)				->					np.ndarray:
								'''simple docstring'''







								return normalize(UpperCamelCase__				,		mean=UpperCamelCase__				,		std=UpperCamelCase__				,		data_format=UpperCamelCase__				,		**UpperCamelCase__      )







		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     ImageInput				,		UpperCamelCase__      :     bool = None				,		UpperCamelCase__      :     Dict[str, int] = None				,		UpperCamelCase__      :     float = None				,		UpperCamelCase__      :     PILImageResampling = None				,		UpperCamelCase__      :     bool = None				,		UpperCamelCase__      :     float = None				,		UpperCamelCase__      :     bool = None				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		UpperCamelCase__      :     Optional[Union[str, TensorType]] = None				,		UpperCamelCase__      :     ChannelDimension = ChannelDimension.FIRST				,		**UpperCamelCase__      :     Any				,		)				->					PIL.Image.Image:
								'''simple docstring'''







								UpperCAmelCase         =     do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase         =     crop_pct if crop_pct is not None else self.crop_pct
								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         =     size if size is not None else self.size
								UpperCAmelCase         =     get_size_dict(UpperCamelCase__				,		default_to_square=UpperCamelCase__      )

								UpperCAmelCase         =     make_list_of_images(UpperCamelCase__      )

								if not valid_images(UpperCamelCase__      ):
														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_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
														raise ValueError("crop_pct must be specified if size < 384."      )

								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.
								UpperCAmelCase         =     [to_numpy_array(UpperCamelCase__      ) for image in images]

								if do_resize:
														UpperCAmelCase         =     [self.resize(image=UpperCamelCase__				,		size=UpperCamelCase__				,		crop_pct=UpperCamelCase__				,		resample=UpperCamelCase__      ) for image in images]

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

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

								UpperCAmelCase         =     [to_channel_dimension_format(UpperCamelCase__				,		UpperCamelCase__      ) for image in images]

								UpperCAmelCase         =     {"pixel_values": images}
								return BatchFeature(data=UpperCamelCase__				,		tensor_type=UpperCamelCase__      )



