


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration





def     _lowerCamelCase		(				SCREAMING_SNAKE_CASE_					:			List[str]   ):






							"""simple docstring"""



							a_							: Optional[Any]								=						[
							    '''encoder.version''',
							    '''decoder.version''',
							    '''model.encoder.version''',
							    '''model.decoder.version''',
							    '''decoder.output_projection.weight''',
							    '''_float_tensor''',
							    '''encoder.embed_positions._float_tensor''',
							    '''decoder.embed_positions._float_tensor''',
							]
							for k in ignore_keys:
														state_dict.pop(__a						,						__a   )





def     _lowerCamelCase		(				SCREAMING_SNAKE_CASE_					:			Tuple   ):






							"""simple docstring"""



							a_							: Optional[Any]								=						emb.weight.shape
							a_							: int								=						nn.Linear(__a						,						__a						,						bias=__a   )
							a_							: List[Any]								=						emb.weight.data
							return lin_layer





def     _lowerCamelCase		(				SCREAMING_SNAKE_CASE_					:			Any   ):






							"""simple docstring"""



							a_							: str								=						torch.load(__a						,						map_location="""cpu"""   )
							a_							: int								=						mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
							a_							: List[str]								=						mam_aaa['''model''']
							remove_ignore_keys_(__a   )
							a_							: Union[str, Any]								=						state_dict['''encoder.embed_tokens.weight'''].shape[0]

							a_							: int								=						MaMaaaConfig(
							    vocab_size=__a						,						max_position_embeddings=10_24						,						encoder_layers=args.encoder_layers						,						decoder_layers=args.decoder_layers						,						encoder_attention_heads=args.encoder_attention_heads						,						decoder_attention_heads=args.decoder_attention_heads						,						encoder_ffn_dim=args.encoder_ffn_embed_dim						,						decoder_ffn_dim=args.decoder_ffn_embed_dim						,						d_model=args.encoder_embed_dim						,						encoder_layerdrop=args.encoder_layerdrop						,						decoder_layerdrop=args.decoder_layerdrop						,						dropout=args.dropout						,						attention_dropout=args.attention_dropout						,						activation_dropout=args.activation_dropout						,						activation_function="""relu"""						,						)

							a_							: Dict								=						state_dict['''decoder.embed_tokens.weight''']
							a_							: int								=						MaMaaaForConditionalGeneration(__a   )
							model.model.load_state_dict(__a						,						strict=__a   )
							a_							: Any								=						make_linear_from_emb(model.model.shared   )

							return model


if __name__ == "__main__":
	SCREAMING_SNAKE_CASE			:			Tuple        =     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
	parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
	SCREAMING_SNAKE_CASE			:			List[str]        =     parser.parse_args()
	SCREAMING_SNAKE_CASE			:			Optional[int]        =     convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
	model.save_pretrained(args.pytorch_dump_folder_path)






'''simple docstring'''
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

		__snake_case				 =	parser.parse_args()

		__snake_case				 =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

		__snake_case				 =	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)