


import copy
import random

from transformers import CLIPTokenizer







class        UpperCamelCase__   (	snake_case_   ):

					def __init__(     self       :   List[Any]							,					*UpperCamelCase__       :   int							,					**UpperCamelCase__       :   List[Any]       ):






										'''simple docstring'''






										super().__init__(*UpperCamelCase__							,					**UpperCamelCase__       )
										lowercase_  							=			{}

					def   UpperCAmelCase__       (     self       :   Optional[Any]							,					UpperCamelCase__       :   int							,					*UpperCamelCase__       :   Any							,					**UpperCamelCase__       :   Tuple       ):






										'''simple docstring'''






										lowercase_  							=			super().add_tokens(UpperCamelCase__							,					*UpperCamelCase__							,					**UpperCamelCase__       )
										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   UpperCAmelCase__       (     self       :   Union[str, Any]							,					UpperCamelCase__       :   Union[str, Any]							,					*UpperCamelCase__       :   List[Any]							,					UpperCamelCase__       :   Optional[Any]=1							,					**UpperCamelCase__       :   Dict       ):






										'''simple docstring'''






										lowercase_  							=			[]
										if num_vec_per_token == 1:
															self.try_adding_tokens(UpperCamelCase__							,					*UpperCamelCase__							,					**UpperCamelCase__       )
															output.append(UpperCamelCase__       )
										else:
															lowercase_  							=			[]
															for i in range(UpperCamelCase__       ):
																				lowercase_  							=			placeholder_token + F'''_{i}'''
																				self.try_adding_tokens(UpperCamelCase__							,					*UpperCamelCase__							,					**UpperCamelCase__       )
																				output.append(UpperCamelCase__       )
        # 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'''       )
										lowercase_  							=			output

					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   Optional[Any]=False							,					UpperCamelCase__       :   Optional[Any]=1.0       ):






										'''simple docstring'''






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

					def __call__(     self       :   Optional[Any]							,					UpperCamelCase__       :   Optional[Any]							,					*UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   Union[str, Any]=False							,					UpperCamelCase__       :   Optional[int]=1.0							,					**UpperCamelCase__       :   List[Any]       ):






										'''simple docstring'''






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







					def   UpperCAmelCase__       (     self       :   str							,					UpperCamelCase__       :   Optional[Any]							,					*UpperCamelCase__       :   int							,					UpperCamelCase__       :   Optional[Any]=False							,					UpperCamelCase__       :   Optional[int]=1.0							,					**UpperCamelCase__       :   int       ):






										'''simple docstring'''






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






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


a					  =     logging.get_logger(__name__)







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  str						=   ['pixel_values']

					def __init__(     self       :   List[Any]							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Union[int, float] = 1 / 255							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   int = 8							,					**UpperCamelCase__       :   Optional[Any]							,					):






										'''simple docstring'''






										super().__init__(**UpperCamelCase__       )

										lowercase_  							=			do_rescale
										lowercase_  							=			rescale_factor
										lowercase_  							=			do_pad
										lowercase_  							=			pad_size

					def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   float							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   Optional[Any]       ):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			get_image_size(UpperCamelCase__       )
										lowercase_  							=			(old_height // size + 1) * size - old_height
										lowercase_  							=			(old_width // size + 1) * size - old_width

										return pad(UpperCamelCase__							,					((0, pad_height), (0, pad_width))							,					mode="""symmetric"""							,					data_format=UpperCamelCase__       )







					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   ImageInput							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[float] = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[int] = None							,					UpperCamelCase__       :   Optional[Union[str, TensorType]] = None							,					UpperCamelCase__       :   Union[str, ChannelDimension] = ChannelDimension.FIRST							,					**UpperCamelCase__       :   Dict							,					):






										'''simple docstring'''






										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_pad if do_pad is not None else self.do_pad
										lowercase_  							=			pad_size if pad_size is not None else self.pad_size

										lowercase_  							=			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_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )

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

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

										if do_pad:
															lowercase_  							=			[self.pad(UpperCamelCase__							,					size=UpperCamelCase__       ) for image in images]

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

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


