


'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def  __lowerCAmelCase (__lowerCAmelCase   ):
		_UpperCAmelCase		:       List[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(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_   )






def  __lowerCAmelCase (__lowerCAmelCase   ):
		_UpperCAmelCase					,    _UpperCAmelCase		:       Any								=      emb.weight.shape
		_UpperCAmelCase		:       List[Any]								=      nn.Linear(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , bias=SCREAMING_SNAKE_CASE_   )
		_UpperCAmelCase		:       Tuple								=      emb.weight.data
		return lin_layer






def  __lowerCAmelCase (__lowerCAmelCase   ):
		_UpperCAmelCase		:       List[Any]								=      torch.load(SCREAMING_SNAKE_CASE_      , map_location="cpu"   )
		_UpperCAmelCase		:       Tuple								=      mam_aaa["args"] or mam_aaa["cfg"]["model"]
		_UpperCAmelCase		:       Optional[Any]								=      mam_aaa["model"]
		remove_ignore_keys_(SCREAMING_SNAKE_CASE_   )
		_UpperCAmelCase		:       List[str]								=      state_dict["encoder.embed_tokens.weight"].shape[0]

		_UpperCAmelCase		:       List[str]								=      MaMaaaConfig(
		    vocab_size=SCREAMING_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"      , )

		_UpperCAmelCase		:       Tuple								=      state_dict["decoder.embed_tokens.weight"]
		_UpperCAmelCase		:       str								=      MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_   )
		model.model.load_state_dict(SCREAMING_SNAKE_CASE_      , strict=SCREAMING_SNAKE_CASE_   )
		_UpperCAmelCase		:       Optional[int]								=      make_linear_from_emb(model.model.shared   )

		return model


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







'''simple docstring'''


from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase = None   ):
		if version.parse(hfh.__version__   ).release < version.parse("0.11.0"   ).release:
				# old versions of hfh don't url-encode the file path
				_UpperCAmelCase		:       str								=      quote(__lowerCAmelCase   )
		return hfh.hf_hub_url(__lowerCAmelCase      , __lowerCAmelCase      , repo_type="dataset"      , revision=__lowerCAmelCase   )



