



"""simple docstring"""







import copy
import random

from transformers import CLIPTokenizer






class      UpperCAmelCase__		(      UpperCamelCase     ):



		def __init__( self  :    Tuple      ,							*snake_case  :    Optional[Any]      ,							**snake_case  :    Optional[int]				)			->						Optional[int]:






									'''simple docstring'''

									super().__init__(*snake_case      ,							**snake_case				)
									A         =					{}



		def 					A_		( self  :    int      ,							snake_case  :    str      ,							*snake_case  :    Optional[Any]      ,							**snake_case  :    Tuple				)			->						Optional[int]:






									'''simple docstring'''

									A         =					super().add_tokens(snake_case      ,							*snake_case      ,							**snake_case				)
									if num_added_tokens == 0:
																raise ValueError(
																    f"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
																    ' `placeholder_token` that is not already in the tokenizer.'				)



		def 					A_		( self  :    int      ,							snake_case  :    List[str]      ,							*snake_case  :    List[Any]      ,							snake_case  :    Optional[int]=1      ,							**snake_case  :    Optional[int]				)			->						str:






									'''simple docstring'''

									A         =					[]
									if num_vec_per_token == 1:
																self.try_adding_tokens(snake_case      ,							*snake_case      ,							**snake_case				)
																output.append(snake_case				)
									else:
																A         =					[]
																for i in range(snake_case				):
																							A         =					placeholder_token + f"""_{i}"""
																							self.try_adding_tokens(snake_case      ,							*snake_case      ,							**snake_case				)
																							output.append(snake_case				)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
									for token in self.token_map:
																if token in placeholder_token:
																							raise ValueError(
																							    f"""The tokenizer already has placeholder token {token} that can get confused with"""
																							    f""" {placeholder_token}keep placeholder tokens independent"""				)
									A         =					output



		def 					A_		( self  :    List[Any]      ,							snake_case  :    Tuple      ,							snake_case  :    Any=False      ,							snake_case  :    List[str]=1.0				)			->						List[Any]:






									'''simple docstring'''

									if isinstance(snake_case      ,							snake_case				):
																A         =					[]
																for i in range(len(snake_case				)				):
																							output.append(self.replace_placeholder_tokens_in_text(text[i]      ,							vector_shuffle=snake_case				)				)
																return output
									for placeholder_token in self.token_map:
																if placeholder_token in text:
																							A         =					self.token_map[placeholder_token]
																							A         =					tokens[: 1 + int(len(snake_case				) * prop_tokens_to_load				)]
																							if vector_shuffle:
																														A         =					copy.copy(snake_case				)
																														random.shuffle(snake_case				)
																							A         =					text.replace(snake_case      ,							' '.join(snake_case				)				)
									return text



		def __call__( self  :    int      ,							snake_case  :    Dict      ,							*snake_case  :    Tuple      ,							snake_case  :    List[str]=False      ,							snake_case  :    Optional[int]=1.0      ,							**snake_case  :    Any				)			->						str:






									'''simple docstring'''

									return super().__call__(
									    self.replace_placeholder_tokens_in_text(
									        snake_case      ,							vector_shuffle=snake_case      ,							prop_tokens_to_load=snake_case				)      ,							*snake_case      ,							**snake_case      ,							)



		def 					A_		( self  :    List[str]      ,							snake_case  :    Tuple      ,							*snake_case  :    Tuple      ,							snake_case  :    Optional[int]=False      ,							snake_case  :    List[str]=1.0      ,							**snake_case  :    List[str]				)			->						Optional[Any]:






									'''simple docstring'''

									return super().encode(
									    self.replace_placeholder_tokens_in_text(
									        snake_case      ,							vector_shuffle=snake_case      ,							prop_tokens_to_load=snake_case				)      ,							*snake_case      ,							**snake_case      ,							)








"""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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A    =			logging.get_logger(__name__)






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Tuple 							=     ["""pixel_values"""]



		def __init__( self  :    int      ,							snake_case  :    bool = True      ,							snake_case  :    Optional[Dict[str, int]] = None      ,							snake_case  :    PILImageResampling = PILImageResampling.BICUBIC      ,							snake_case  :    bool = True      ,							snake_case  :    bool = True      ,							snake_case  :    Union[int, float] = 1 / 255      ,							snake_case  :    Dict[str, int] = None      ,							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]      ,							)			->						None:






									'''simple docstring'''

									super().__init__(**snake_case				)
									A         =					size if size is not None else {'height': 224, 'width': 224}
									A         =					get_size_dict(snake_case				)
									A         =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
									A         =					get_size_dict(snake_case      ,							default_to_square=snake_case      ,							param_name='crop_size'				)

									A         =					do_resize
									A         =					do_rescale
									A         =					do_normalize
									A         =					do_center_crop
									A         =					crop_size
									A         =					size
									A         =					resample
									A         =					rescale_factor
									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 					A_		( self  :    Union[str, Any]      ,							snake_case  :    np.ndarray      ,							snake_case  :    Dict[str, int]      ,							snake_case  :    PILImageResampling = PILImageResampling.BILINEAR      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    Dict      ,							)			->						np.ndarray:






									'''simple docstring'''

									A         =					get_size_dict(snake_case				)

									if "shortest_edge" in size:
																A         =					get_resize_output_image_size(snake_case      ,							size=size['shortest_edge']      ,							default_to_square=snake_case				)
																# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
									elif "height" in size and "width" in size:
																A         =					(size['height'], size['width'])
									else:
																raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""				)
									return resize(snake_case      ,							size=snake_case      ,							resample=snake_case      ,							data_format=snake_case      ,							**snake_case				)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    np.ndarray      ,							snake_case  :    Dict[str, int]      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    List[Any]      ,							)			->						np.ndarray:






									'''simple docstring'''

									A         =					get_size_dict(snake_case				)
									if "height" not in size or "width" not in size:
																raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""				)
									return center_crop(snake_case      ,							size=(size['height'], size['width'])      ,							data_format=snake_case      ,							**snake_case				)



		def 					A_		( self  :    Dict      ,							snake_case  :    np.ndarray      ,							snake_case  :    float      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    Dict				)			->						np.ndarray:






									'''simple docstring'''

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



		def 					A_		( self  :    Union[str, Any]      ,							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  :    Union[str, Any]      ,							)			->						np.ndarray:






									'''simple docstring'''

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



		def 					A_		( self  :    int      ,							snake_case  :    ImageInput      ,							snake_case  :    Optional[bool] = None      ,							snake_case  :    Dict[str, int] = None      ,							snake_case  :    PILImageResampling = None      ,							snake_case  :    bool = None      ,							snake_case  :    int = 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  :    Union[str, ChannelDimension] = ChannelDimension.FIRST      ,							**snake_case  :    List[str]      ,							)			->						BatchFeature:






									'''simple docstring'''

									A         =					do_resize if do_resize is not None else self.do_resize
									A         =					do_rescale if do_rescale is not None else self.do_rescale
									A         =					do_normalize if do_normalize is not None else self.do_normalize
									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(snake_case      ,							param_name='crop_size'      ,							default_to_square=snake_case				)
									A         =					resample if resample is not None else self.resample
									A         =					rescale_factor if rescale_factor is not None else self.rescale_factor
									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         =					size if size is not None else self.size
									A         =					get_size_dict(snake_case				)

									if not is_batched(snake_case				):
																A         =					[images]

									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:
																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.'				)

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

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

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

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

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

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

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



