
'''simple docstring'''
import copy
import os
from typing import Union

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


lowercase__											=logging.get_logger(__name__)

lowercase__											={
    'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json',
    'Salesforce/blip-vqa-capfit-large': (
        'https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-base': (
        'https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-large': (
        'https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'
    ),
    'Salesforce/blip-itm-base-coco': 'https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json',
    'Salesforce/blip-itm-large-coco': 'https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json',
    'Salesforce/blip-itm-base-flikr': 'https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json',
    'Salesforce/blip-itm-large-flikr': (
        'https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'
    ),
}







class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			Dict       =    'blip_text_model'




			def __init__(       self					,   UpperCAmelCase=3_05_24					,   UpperCAmelCase=7_68					,   UpperCAmelCase=7_68					,   UpperCAmelCase=30_72					,   UpperCAmelCase=7_68					,   UpperCAmelCase=12					,   UpperCAmelCase=8					,   UpperCAmelCase=5_12					,   UpperCAmelCase="gelu"					,   UpperCAmelCase=1e-12					,   UpperCAmelCase=0.0					,   UpperCAmelCase=0.0					,   UpperCAmelCase=0.02					,   UpperCAmelCase=3_05_22					,   UpperCAmelCase=2					,   UpperCAmelCase=0					,   UpperCAmelCase=1_02					,   UpperCAmelCase=True					,   UpperCAmelCase=True					,   **UpperCAmelCase					,   ):
				super().__init__(
				    pad_token_id=UpperCAmelCase					,   bos_token_id=UpperCAmelCase					,   eos_token_id=UpperCAmelCase					,   sep_token_id=UpperCAmelCase					,   **UpperCAmelCase					,   )

				a_      				=						vocab_size
				a_      				=						hidden_size
				a_      				=						encoder_hidden_size
				a_      				=						intermediate_size
				a_      				=						projection_dim
				a_      				=						hidden_dropout_prob
				a_      				=						num_hidden_layers
				a_      				=						num_attention_heads
				a_      				=						max_position_embeddings
				a_      				=						layer_norm_eps
				a_      				=						hidden_act
				a_      				=						initializer_range
				a_      				=						attention_probs_dropout_prob
				a_      				=						is_decoder
				a_      				=						use_cache






			@classmethod
			def 					lowerCAmelCase__					(       cls					,   UpperCAmelCase					,   **UpperCAmelCase	):
				cls._set_token_in_kwargs(UpperCAmelCase	)

				a_				,  a_      				=						cls.get_config_dict(UpperCAmelCase					,   **UpperCAmelCase	)

				# get the text config dict if we are loading from BlipConfig
				if config_dict.get("""model_type"""	) == "blip":
					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(UpperCAmelCase					,   **UpperCAmelCase	)







class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			Optional[Any]       =    'blip_vision_model'




			def __init__(       self					,   UpperCAmelCase=7_68					,   UpperCAmelCase=30_72					,   UpperCAmelCase=5_12					,   UpperCAmelCase=12					,   UpperCAmelCase=12					,   UpperCAmelCase=3_84					,   UpperCAmelCase=16					,   UpperCAmelCase="gelu"					,   UpperCAmelCase=1e-5					,   UpperCAmelCase=0.0					,   UpperCAmelCase=1e-10					,   **UpperCAmelCase					,   ):
				super().__init__(**UpperCAmelCase	)

				a_      				=						hidden_size
				a_      				=						intermediate_size
				a_      				=						projection_dim
				a_      				=						num_hidden_layers
				a_      				=						num_attention_heads
				a_      				=						patch_size
				a_      				=						image_size
				a_      				=						initializer_range
				a_      				=						attention_dropout
				a_      				=						layer_norm_eps
				a_      				=						hidden_act






			@classmethod
			def 					lowerCAmelCase__					(       cls					,   UpperCAmelCase					,   **UpperCAmelCase	):
				cls._set_token_in_kwargs(UpperCAmelCase	)

				a_				,  a_      				=						cls.get_config_dict(UpperCAmelCase					,   **UpperCAmelCase	)

				# get the vision config dict if we are loading from BlipConfig
				if config_dict.get("""model_type"""	) == "blip":
					a_      				=						config_dict["""vision_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(UpperCAmelCase					,   **UpperCAmelCase	)







class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			Tuple       =    'blip'
			lowerCamelCase__	:			Any       =    True




			def __init__(       self					,   UpperCAmelCase=None					,   UpperCAmelCase=None					,   UpperCAmelCase=5_12					,   UpperCAmelCase=2.65_92					,   UpperCAmelCase=2_56					,   **UpperCAmelCase					,   ):
				super().__init__(**UpperCAmelCase	)

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

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

				a_      				=						BlipTextConfig(**UpperCAmelCase	)
				a_      				=						BlipVisionConfig(**UpperCAmelCase	)

				a_      				=						self.vision_config.hidden_size

				a_      				=						projection_dim
				a_      				=						logit_scale_init_value
				a_      				=						1.0
				a_      				=						0.02
				a_      				=						image_text_hidden_size




			@classmethod
			def 					lowerCAmelCase__					(       cls					,   UpperCAmelCase					,   UpperCAmelCase					,   **UpperCAmelCase	):
				return cls(text_config=text_config.to_dict()					,   vision_config=vision_config.to_dict()					,   **UpperCAmelCase	)






			def 					lowerCAmelCase__					(       self	):
				a_      				=						copy.deepcopy(self.__dict__	)
				a_      				=						self.text_config.to_dict()
				a_      				=						self.vision_config.to_dict()
				a_      				=						self.__class__.model_type
				return output


'''simple docstring'''
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase__											=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.',
		)

		lowercase__											=parser.parse_args()

		lowercase__											=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase__											=CLIPImageProcessor()
		lowercase__											=CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase__											=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)
