





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A       :   int					= logging.get_logger(__name__)

A       :   Dict					= {
    '''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json''',
    '''BridgeTower/bridgetower-base-itm-mlm''': (
        '''https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'''
    ),
}


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			int							     =       '''bridgetower_vision_model'''






			def __init__(     self  :       List[str]				,      __lowerCAmelCase  :       Tuple=7_68				,      __lowerCAmelCase  :       int=12				,      __lowerCAmelCase  :       Tuple=3				,      __lowerCAmelCase  :       Any=16				,      __lowerCAmelCase  :       str=2_88				,      __lowerCAmelCase  :       int=1				,      __lowerCAmelCase  :       Union[str, Any]=1e-05				,      __lowerCAmelCase  :       Tuple=False				,      __lowerCAmelCase  :       Optional[Any]=True				,      __lowerCAmelCase  :       Optional[Any]=False				,      **__lowerCAmelCase  :       Any				,      )      ->      Tuple:







					"""simple docstring"""






					super().__init__(**__lowerCAmelCase  )
					A__     =  hidden_size
					A__     =  num_hidden_layers
					A__     =  num_channels
					A__     =  patch_size
					A__     =  image_size
					A__     =  initializer_factor
					A__     =  layer_norm_eps
					A__     =  stop_gradient
					A__     =  share_layernorm
					A__     =  remove_last_layer







			@classmethod
			def   a_			(     cls  :       Optional[int]				,      __lowerCAmelCase  :       Union[str, os.PathLike]				,      **__lowerCAmelCase  :       int  )      ->      "PretrainedConfig":







					"""simple docstring"""






					A__ ,     A__     =  cls.get_config_dict(__lowerCAmelCase				,      **__lowerCAmelCase  )

					if config_dict.get("""model_type"""  ) == "bridgetower":
							A__     =  config_dict["""text_config"""]

					if "model_type" in config_dict and hasattr(cls				,      """model_type"""  ) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
							    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'  )

					return cls.from_dict(__lowerCAmelCase				,      **__lowerCAmelCase  )


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			List[Any]							     =       '''bridgetower_text_model'''






			def __init__(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       Any=5_02_65				,      __lowerCAmelCase  :       Union[str, Any]=7_68				,      __lowerCAmelCase  :       List[Any]=12				,      __lowerCAmelCase  :       int=12				,      __lowerCAmelCase  :       str=1				,      __lowerCAmelCase  :       int=30_72				,      __lowerCAmelCase  :       List[Any]="gelu"				,      __lowerCAmelCase  :       Any=0.1				,      __lowerCAmelCase  :       Dict=0.1				,      __lowerCAmelCase  :       Tuple=5_14				,      __lowerCAmelCase  :       Optional[Any]=1				,      __lowerCAmelCase  :       Optional[Any]=1e-05				,      __lowerCAmelCase  :       Optional[Any]=1				,      __lowerCAmelCase  :       Optional[int]=0				,      __lowerCAmelCase  :       Dict=2				,      __lowerCAmelCase  :       List[Any]="absolute"				,      __lowerCAmelCase  :       Optional[Any]=True				,      **__lowerCAmelCase  :       Union[str, Any]				,      )      ->      Optional[int]:







					"""simple docstring"""






					super().__init__(**__lowerCAmelCase  )

					A__     =  vocab_size
					A__     =  hidden_size
					A__     =  num_hidden_layers
					A__     =  num_attention_heads
					A__     =  hidden_act
					A__     =  initializer_factor
					A__     =  intermediate_size
					A__     =  hidden_dropout_prob
					A__     =  attention_probs_dropout_prob
					A__     =  max_position_embeddings
					A__     =  type_vocab_size
					A__     =  layer_norm_eps
					A__     =  position_embedding_type
					A__     =  use_cache
					A__     =  pad_token_id
					A__     =  bos_token_id
					A__     =  eos_token_id







			@classmethod
			def   a_			(     cls  :       List[Any]				,      __lowerCAmelCase  :       Union[str, os.PathLike]				,      **__lowerCAmelCase  :       str  )      ->      "PretrainedConfig":







					"""simple docstring"""






					A__ ,     A__     =  cls.get_config_dict(__lowerCAmelCase				,      **__lowerCAmelCase  )

					if config_dict.get("""model_type"""  ) == "bridgetower":
							A__     =  config_dict["""text_config"""]

					if "model_type" in config_dict and hasattr(cls				,      """model_type"""  ) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
							    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'  )

					return cls.from_dict(__lowerCAmelCase				,      **__lowerCAmelCase  )


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Union[str, Any]							     =       '''bridgetower'''






			def __init__(     self  :       Optional[int]				,      __lowerCAmelCase  :       Dict=True				,      __lowerCAmelCase  :       Dict="gelu"				,      __lowerCAmelCase  :       Optional[Any]=7_68				,      __lowerCAmelCase  :       List[Any]=1				,      __lowerCAmelCase  :       Tuple=1e-05				,      __lowerCAmelCase  :       Union[str, Any]=False				,      __lowerCAmelCase  :       Any="add"				,      __lowerCAmelCase  :       Dict=12				,      __lowerCAmelCase  :       List[str]=6				,      __lowerCAmelCase  :       Tuple=False				,      __lowerCAmelCase  :       Optional[Any]=False				,      __lowerCAmelCase  :       List[str]=None				,      __lowerCAmelCase  :       List[str]=None				,      **__lowerCAmelCase  :       Optional[int]				,      )      ->      List[str]:







					"""simple docstring"""






					A__     =  kwargs.pop("""text_config_dict"""				,      __lowerCAmelCase  )
					A__     =  kwargs.pop("""vision_config_dict"""				,      __lowerCAmelCase  )

					super().__init__(**__lowerCAmelCase  )
					A__     =  share_cross_modal_transformer_layers
					A__     =  hidden_act
					A__     =  hidden_size
					A__     =  initializer_factor
					A__     =  layer_norm_eps
					A__     =  share_link_tower_layers
					A__     =  link_tower_type
					A__     =  num_attention_heads
					A__     =  num_hidden_layers
					A__     =  tie_word_embeddings
					A__     =  init_layernorm_from_vision_encoder

					if text_config is None:
							A__     =  {}
							logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."""  )

					if vision_config is None:
							A__     =  {}
							logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."""  )

					A__     =  BridgeTowerTextConfig(**__lowerCAmelCase  )
					A__     =  BridgeTowerVisionConfig(**__lowerCAmelCase  )






			@classmethod
			def   a_			(     cls  :       Optional[Any]				,      __lowerCAmelCase  :       BridgeTowerTextConfig				,      __lowerCAmelCase  :       BridgeTowerVisionConfig				,      **__lowerCAmelCase  :       Dict  )      ->      List[Any]:







					"""simple docstring"""






					return cls(text_config=text_config.to_dict()				,      vision_config=vision_config.to_dict()				,      **__lowerCAmelCase  )







			def   a_			(     self  :       List[str]  )      ->      Optional[int]:







					"""simple docstring"""






					A__     =  copy.deepcopy(self.__dict__  )
					A__     =  self.text_config.to_dict()
					A__     =  self.vision_config.to_dict()
					A__     =  self.__class__.model_type
					return output










import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
					A       :   List[Any]					= argparse.ArgumentParser()

					parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

					parser.add_argument(
					    '''--txt2img_unclip''',
					    default='''kakaobrain/karlo-v1-alpha''',
					    type=str,
					    required=False,
					    help='''The pretrained txt2img unclip.''',
					)

					A       :   List[Any]					= parser.parse_args()

					A       :   int					= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

					A       :   Tuple					= CLIPImageProcessor()
					A       :   int					= CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

					A       :   Tuple					= UnCLIPImageVariationPipeline(
					    decoder=txtaimg.decoder,
					    text_encoder=txtaimg.text_encoder,
					    tokenizer=txtaimg.tokenizer,
					    text_proj=txtaimg.text_proj,
					    feature_extractor=feature_extractor,
					    image_encoder=image_encoder,
					    super_res_first=txtaimg.super_res_first,
					    super_res_last=txtaimg.super_res_last,
					    decoder_scheduler=txtaimg.decoder_scheduler,
					    super_res_scheduler=txtaimg.super_res_scheduler,
					)

					imgaimg.save_pretrained(args.dump_path)



