"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    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(__UpperCAmelCase     ,__UpperCAmelCase			)


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase				,      _lowercase :    Union[str, Any]							=							emb.weight.shape
	_lowercase :    Dict							=							nn.Linear(__UpperCAmelCase     ,__UpperCAmelCase     ,bias=__UpperCAmelCase			)
	_lowercase :    Optional[Any]							=							emb.weight.data
	return lin_layer


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





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

	_lowercase :    str							=							MaMaaaConfig(
	    vocab_size=__UpperCAmelCase     ,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'     ,)

	_lowercase :    Union[str, Any]							=							state_dict['decoder.embed_tokens.weight']
	_lowercase :    Tuple							=							MaMaaaForConditionalGeneration(__UpperCAmelCase			)
	model.model.load_state_dict(__UpperCAmelCase     ,strict=__UpperCAmelCase			)
	_lowercase :    Optional[Any]							=							make_linear_from_emb(model.model.shared			)

	return model


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




"""simple docstring"""







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	return not any(
	    neighbour == 1 and colored_vertices[i] == color
	    for i, neighbour in enumerate(__UpperCAmelCase			)			)


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	if index == len(__UpperCAmelCase			):
		return True

	# Recursive Step
	for i in range(__UpperCAmelCase			):
		if valid_coloring(graph[index]     ,__UpperCAmelCase     ,__UpperCAmelCase			):
			# Color current vertex
			_lowercase :    str							=							i
			# Validate coloring
			if util_color(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,index + 1			):
				return True
			# Backtrack
			_lowercase :    List[str]							=							-1
	return False







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							[-1] * len(__UpperCAmelCase			)

	if util_color(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,0			):
		return colored_vertices

	return []



