






'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			int:
	_snake_case     		=	[
	    '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__			(     __A			) ->			List[Any]:
	_snake_case     		=	emb.weight.shape
	_snake_case     		=	nn.Linear(lowerCamelCase_    ,       lowerCamelCase_    ,       bias=lowerCamelCase_			)
	_snake_case     		=	emb.weight.data
	return lin_layer







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			int:
	_snake_case     		=	torch.load(lowerCamelCase_    ,       map_location='cpu'			)
	_snake_case     		=	mam_aaa['args'] or mam_aaa['cfg']['model']
	_snake_case     		=	mam_aaa['model']
	remove_ignore_keys_(lowerCamelCase_			)
	_snake_case     		=	state_dict['encoder.embed_tokens.weight'].shape[0]

	_snake_case     		=	MaMaaaConfig(
	    vocab_size=lowerCamelCase_    ,       max_position_embeddings=1_024    ,       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'    ,       )

	_snake_case     		=	state_dict['decoder.embed_tokens.weight']
	_snake_case     		=	MaMaaaForConditionalGeneration(lowerCamelCase_			)
	model.model.load_state_dict(lowerCamelCase_    ,       strict=lowerCamelCase_			)
	_snake_case     		=	make_linear_from_emb(model.model.shared			)

	return model


if __name__ == "__main__":
					lowercase					:							Optional[Any]   =      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.")
					lowercase					:							List[str]   =      parser.parse_args()
					lowercase					:							Dict   =      convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
					model.save_pretrained(args.pytorch_dump_folder_path)



'''simple docstring'''







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
					from .pipeline_unclip import UnCLIPPipeline
					from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
					from .text_proj import UnCLIPTextProjModel

