

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     int:
	__lowerCamelCase				:				int					       = [
	    '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(lowerCamelCase__	, lowerCamelCase__			)






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     List[Any]:
	__lowerCamelCase       , __lowerCamelCase				:				List[Any]					       = emb.weight.shape
	__lowerCamelCase				:				Tuple					       = nn.Linear(lowerCamelCase__	, lowerCamelCase__	, bias=lowerCamelCase__			)
	__lowerCamelCase				:				Optional[int]					       = emb.weight.data
	return lin_layer






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     str:
	__lowerCamelCase				:				Optional[Any]					       = torch.load(lowerCamelCase__	, map_location='cpu'			)
	__lowerCamelCase				:				List[str]					       = mam_aaa['args'] or mam_aaa['cfg']['model']
	__lowerCamelCase				:				Tuple					       = mam_aaa['model']
	remove_ignore_keys_(lowerCamelCase__			)
	__lowerCamelCase				:				Any					       = state_dict['encoder.embed_tokens.weight'].shape[0]

	__lowerCamelCase				:				List[Any]					       = MaMaaaConfig(
	    vocab_size=lowerCamelCase__	, max_position_embeddings=1_0_2_4	, 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'	, )

	__lowerCamelCase				:				Any					       = state_dict['decoder.embed_tokens.weight']
	__lowerCamelCase				:				Any					       = MaMaaaForConditionalGeneration(lowerCamelCase__			)
	model.model.load_state_dict(lowerCamelCase__	, strict=lowerCamelCase__			)
	__lowerCamelCase				:				List[Any]					       = make_linear_from_emb(model.model.shared			)

	return model


if __name__ == "__main__":
					a		=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.""")
					a		=parser.parse_args()
					a		=convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
					model.save_pretrained(args.pytorch_dump_folder_path)








'''simple docstring'''


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionImg2ImgPipeline` instead.'
)



