

'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 						__lowerCamelCase	(       __snake_case					:		List[Any]	)	->							int:






						"""simple docstring"""





						A__					:       List[str]     =[
						    """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(__snake_case,		__snake_case	)
def 						__lowerCamelCase	(       __snake_case					:		Tuple	)	->							Tuple:






						"""simple docstring"""





						A__   ,						A__					:       List[str]     =emb.weight.shape
						A__					:       int     =nn.Linear(__snake_case,		__snake_case,		bias=__snake_case	)
						A__					:       Optional[int]     =emb.weight.data
						return lin_layer
def 						__lowerCamelCase	(       __snake_case					:		List[Any]	)	->							Union[str, Any]:






						"""simple docstring"""





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

						A__					:       List[str]     =MaMaaaConfig(
						    vocab_size=__snake_case,		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""",		)

						A__					:       List[Any]     =state_dict["""decoder.embed_tokens.weight"""]
						A__					:       Optional[int]     =MaMaaaForConditionalGeneration(__snake_case	)
						model.model.load_state_dict(__snake_case,		strict=__snake_case	)
						A__					:       Optional[int]     =make_linear_from_emb(model.model.shared	)

						return model


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




'''simple docstring'''



from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def 						__lowerCamelCase	(       )	->							List[Any]:






						"""simple docstring"""





						A__					:       List[Any]     =ArgumentParser("""Transformers CLI tool""",		usage="""transformers-cli <command> [<args>]"""	)
						A__					:       List[Any]     =parser.add_subparsers(help="""transformers-cli command helpers"""	)

						# Register commands
						ConvertCommand.register_subcommand(__snake_case	)
						DownloadCommand.register_subcommand(__snake_case	)
						EnvironmentCommand.register_subcommand(__snake_case	)
						RunCommand.register_subcommand(__snake_case	)
						ServeCommand.register_subcommand(__snake_case	)
						UserCommands.register_subcommand(__snake_case	)
						AddNewModelCommand.register_subcommand(__snake_case	)
						AddNewModelLikeCommand.register_subcommand(__snake_case	)
						LfsCommands.register_subcommand(__snake_case	)
						PTtoTFCommand.register_subcommand(__snake_case	)

						# Let's go
						A__					:       List[str]     =parser.parse_args()

						if not hasattr(__snake_case,		"""func"""	):
												parser.print_help()
												exit(1	)

						# Run
						A__					:       Optional[Any]     =args.func(__snake_case	)
						service.run()


if __name__ == "__main__":
		main()

