


'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def      a (     __a  )    ->      Any:



					'''simple docstring'''






					UpperCamelCase__  :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      a (     __a  )    ->      List[str]:



					'''simple docstring'''






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


def      a (     __a  )    ->      Dict:



					'''simple docstring'''






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

					UpperCamelCase__  :int				   =					MaMaaaConfig(
					    vocab_size=__a					,   max_position_embeddings=1024					,   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'''					,   )

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

					return model


if __name__ == "__main__":
		__snake_case				 =	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.''')
		__snake_case				 =	parser.parse_args()
		__snake_case				 =	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)