






import copy
import random

from transformers import CLIPTokenizer







class  _lowercase   (   _A						):
      def __init__(     self				,			*a				,			**a   ):
       super().__init__(*a				,			**a   )
       snake_case__  :    Optional[Any]							       ={}
      def 						lowercase__					(     self				,			a				,			*a				,			**a   ):
       snake_case__  :    Any							       =super().add_tokens(a				,			*a				,			**a   )
       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 						lowercase__					(     self				,			a				,			*a				,			a=1				,			**a   ):
       snake_case__  :    Optional[int]							       =[]
       if num_vec_per_token == 1:
        self.try_adding_tokens(a				,			*a				,			**a   )
        output.append(a   )
       else:
        snake_case__  :    Tuple							       =[]
        for i in range(a   ):
         snake_case__  :    str							       =placeholder_token + F"_{i}"
         self.try_adding_tokens(a				,			*a				,			**a   )
         output.append(a   )
        # 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"   )
       snake_case__  :    Dict							       =output
      def 						lowercase__					(     self				,			a				,			a=False				,			a=1.0   ):
       if isinstance(a				,			a   ):
        snake_case__  :    Dict							       =[]
        for i in range(len(a   )   ):
         output.append(self.replace_placeholder_tokens_in_text(text[i]				,			vector_shuffle=a   )   )
        return output
       for placeholder_token in self.token_map:
        if placeholder_token in text:
         snake_case__  :    List[str]							       =self.token_map[placeholder_token]
         snake_case__  :    Any							       =tokens[: 1 + int(len(a   ) * prop_tokens_to_load   )]
         if vector_shuffle:
          snake_case__  :    Optional[int]							       =copy.copy(a   )
          random.shuffle(a   )
         snake_case__  :    Dict							       =text.replace(a				,			""" """.join(a   )   )
       return text
      def __call__(     self				,			a				,			*a				,			a=False				,			a=1.0				,			**a   ):
       return super().__call__(
           self.replace_placeholder_tokens_in_text(
               a				,			vector_shuffle=a				,			prop_tokens_to_load=a   )				,			*a				,			**a				,			)



      def 						lowercase__					(     self				,			a				,			*a				,			a=False				,			a=1.0				,			**a   ):
       return super().encode(
           self.replace_placeholder_tokens_in_text(
               a				,			vector_shuffle=a				,			prop_tokens_to_load=a   )				,			*a				,			**a				,			)













from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase	:		Tuple										=   logging.get_logger(__name__)







class  _lowercase   (   _A						):
      _a							:						Optional[int]										=							['pixel_values']
      def __init__(     self				,			a = True				,			a = 3_2				,			a=PILImageResampling.BILINEAR				,			a = True				,			**a				,			):
       snake_case__  :    Any							       =do_resize
       snake_case__  :    Optional[int]							       =do_rescale
       snake_case__  :    Optional[Any]							       =size_divisor
       snake_case__  :    Optional[int]							       =resample
       super().__init__(**a   )
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a = None				,			**a   ):
       snake_case__       ,   snake_case__  :    List[str]							       =get_image_size(a   )
       # Rounds the height and width down to the closest multiple of size_divisor
       snake_case__  :    str							       =height // size_divisor * size_divisor
       snake_case__  :    List[str]							       =width // size_divisor * size_divisor
       snake_case__  :    int							       =resize(a				,			(new_h, new_w)				,			resample=a				,			data_format=a				,			**a   )
       return image
      def 						lowercase__					(     self				,			a				,			a				,			a = None				,			**a   ):
       return rescale(image=a				,			scale=a				,			data_format=a				,			**a   )



      def 						lowercase__					(     self				,			a				,			a = None				,			a = None				,			a=None				,			a = None				,			a = None				,			a = ChannelDimension.FIRST				,			**a				,			):
       snake_case__  :    Optional[Any]							       =do_resize if do_resize is not None else self.do_resize
       snake_case__  :    str							       =do_rescale if do_rescale is not None else self.do_rescale
       snake_case__  :    List[Any]							       =size_divisor if size_divisor is not None else self.size_divisor
       snake_case__  :    Optional[int]							       =resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
        raise ValueError("""size_divisor is required for resizing"""   )

       snake_case__  :    Optional[int]							       =make_list_of_images(a   )

       if not valid_images(a   ):
        raise ValueError("""Invalid image(s)"""   )

       # All transformations expect numpy arrays.
       snake_case__  :    str							       =[to_numpy_array(a   ) for img in images]

       if do_resize:
        snake_case__  :    str							       =[self.resize(a				,			size_divisor=a				,			resample=a   ) for image in images]

       if do_rescale:
        snake_case__  :    Union[str, Any]							       =[self.rescale(a				,			scale=1 / 2_5_5   ) for image in images]

       snake_case__  :    int							       =[to_channel_dimension_format(a				,			a   ) for image in images]

       snake_case__  :    Tuple							       ={"""pixel_values""": images}
       return BatchFeature(data=a				,			tensor_type=a   )





