

'''simple docstring'''





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


A_	= logging.get_logger(__name__)

A_	= {
    "uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
    "uclanlp/visualbert-vqa-pre": "https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json",
    "uclanlp/visualbert-vqa-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-vcr": "https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json",
    "uclanlp/visualbert-vcr-pre": "https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json",
    "uclanlp/visualbert-vcr-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-nlvr2": "https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-pre": "https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					'visual_bert'
		def __init__(					self   ,			SCREAMING_SNAKE_CASE_=30522   ,			SCREAMING_SNAKE_CASE_=768   ,			SCREAMING_SNAKE_CASE_=512   ,			SCREAMING_SNAKE_CASE_=12   ,			SCREAMING_SNAKE_CASE_=12   ,			SCREAMING_SNAKE_CASE_=3072   ,			SCREAMING_SNAKE_CASE_="gelu"   ,			SCREAMING_SNAKE_CASE_=0.1   ,			SCREAMING_SNAKE_CASE_=0.1   ,			SCREAMING_SNAKE_CASE_=512   ,			SCREAMING_SNAKE_CASE_=2   ,			SCREAMING_SNAKE_CASE_=0.02   ,			SCREAMING_SNAKE_CASE_=1E-12   ,			SCREAMING_SNAKE_CASE_=False   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=1   ,			SCREAMING_SNAKE_CASE_=0   ,			SCREAMING_SNAKE_CASE_=2   ,			**SCREAMING_SNAKE_CASE_   ,			)  ->     Dict:






							'''simple docstring'''



							super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_   ,			bos_token_id=SCREAMING_SNAKE_CASE_   ,			eos_token_id=SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      vocab_size
							lowerCamelCase_  				=      max_position_embeddings
							lowerCamelCase_  				=      hidden_size
							lowerCamelCase_  				=      visual_embedding_dim
							lowerCamelCase_  				=      num_hidden_layers
							lowerCamelCase_  				=      num_attention_heads
							lowerCamelCase_  				=      intermediate_size
							lowerCamelCase_  				=      hidden_act
							lowerCamelCase_  				=      hidden_dropout_prob
							lowerCamelCase_  				=      attention_probs_dropout_prob
							lowerCamelCase_  				=      initializer_range
							lowerCamelCase_  				=      type_vocab_size
							lowerCamelCase_  				=      layer_norm_eps
							lowerCamelCase_  				=      bypass_transformer
							lowerCamelCase_  				=      special_visual_initialize





'''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)
