


'''simple docstring'''


import copy
import random

from transformers import CLIPTokenizer






class 		lowerCAmelCase__	(	UpperCAmelCase__		):




			def __init__(						self     :   Any		,     *lowerCamelCase__     :   int		,     **lowerCamelCase__     :   Any       )      ->List[Any]:



					'''simple docstring'''







					super().__init__(*lowerCamelCase__		,     **lowerCamelCase__       )
					_UpperCAmelCase						:				str	    =					{}




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   int		,     *lowerCamelCase__     :   Union[str, Any]		,     **lowerCamelCase__     :   Optional[int]       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Tuple	    =					super().add_tokens(lowerCamelCase__		,     *lowerCamelCase__		,     **lowerCamelCase__       )
					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 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   str		,     *lowerCamelCase__     :   Optional[int]		,     lowerCamelCase__     :   Tuple=1		,     **lowerCamelCase__     :   Dict       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase						:				str	    =					[]
					if num_vec_per_token == 1:
							self.try_adding_tokens(lowerCamelCase__		,     *lowerCamelCase__		,     **lowerCamelCase__       )
							output.append(lowerCamelCase__       )
					else:
							_UpperCAmelCase						:				List[Any]	    =					[]
							for i in range(lowerCamelCase__       ):
									_UpperCAmelCase						:				int	    =					placeholder_token + F"""_{i}"""
									self.try_adding_tokens(lowerCamelCase__		,     *lowerCamelCase__		,     **lowerCamelCase__       )
									output.append(lowerCamelCase__       )
        # 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"""       )
					_UpperCAmelCase						:				List[str]	    =					output




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   List[str]		,     lowerCamelCase__     :   Union[str, Any]=False		,     lowerCamelCase__     :   Any=1.0       )      ->Optional[int]:



					'''simple docstring'''







					if isinstance(lowerCamelCase__		,     lowerCamelCase__       ):
							_UpperCAmelCase						:				Dict	    =					[]
							for i in range(len(lowerCamelCase__       )       ):
									output.append(self.replace_placeholder_tokens_in_text(text[i]		,     vector_shuffle=lowerCamelCase__       )       )
							return output
					for placeholder_token in self.token_map:
							if placeholder_token in text:
									_UpperCAmelCase						:				Optional[int]	    =					self.token_map[placeholder_token]
									_UpperCAmelCase						:				Optional[Any]	    =					tokens[: 1 + int(len(lowerCamelCase__       ) * prop_tokens_to_load       )]
									if vector_shuffle:
											_UpperCAmelCase						:				Union[str, Any]	    =					copy.copy(lowerCamelCase__       )
											random.shuffle(lowerCamelCase__       )
									_UpperCAmelCase						:				List[str]	    =					text.replace(lowerCamelCase__		,     " ".join(lowerCamelCase__       )       )
					return text




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



					'''simple docstring'''







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






			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   Dict		,     *lowerCamelCase__     :   Union[str, Any]		,     lowerCamelCase__     :   Union[str, Any]=False		,     lowerCamelCase__     :   Optional[Any]=1.0		,     **lowerCamelCase__     :   int       )      ->Union[str, Any]:



					'''simple docstring'''







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







'''simple docstring'''


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionImg2ImgPipeline` instead.'
)



