



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def snake_case__	(      __SCREAMING_SNAKE_CASE			)		->				int:
		UpperCAmelCase_						       =					[
		    "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 snake_case__	(      __SCREAMING_SNAKE_CASE			)		->				int:
		UpperCAmelCase_       ,    UpperCAmelCase_						       =					emb.weight.shape
		UpperCAmelCase_						       =					nn.Linear(UpperCAmelCase_      ,  UpperCAmelCase_      ,  bias=UpperCAmelCase_			)
		UpperCAmelCase_						       =					emb.weight.data
		return lin_layer







def snake_case__	(      __SCREAMING_SNAKE_CASE			)		->				List[Any]:
		UpperCAmelCase_						       =					torch.load(UpperCAmelCase_      ,  map_location="cpu"			)
		UpperCAmelCase_						       =					mam_aaa["args"] or mam_aaa["cfg"]["model"]
		UpperCAmelCase_						       =					mam_aaa["model"]
		remove_ignore_keys_(UpperCAmelCase_			)
		UpperCAmelCase_						       =					state_dict["encoder.embed_tokens.weight"].shape[0]

		UpperCAmelCase_						       =					MaMaaaConfig(
		    vocab_size=UpperCAmelCase_      ,  max_position_embeddings=1024      ,  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_						       =					state_dict["decoder.embed_tokens.weight"]
		UpperCAmelCase_						       =					MaMaaaForConditionalGeneration(UpperCAmelCase_			)
		model.model.load_state_dict(UpperCAmelCase_      ,  strict=UpperCAmelCase_			)
		UpperCAmelCase_						       =					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'''




from collections import Counter
from timeit import timeit




def 						__lowerCamelCase			(	UpperCAmelCase_ = ""					,				)   ->bool:
	return sum(c % 2 for c in Counter(input_str.replace(' '					,				''							).lower()							).values()							) < 2




def 						__lowerCamelCase			(	UpperCAmelCase_ = ""							)   ->bool:
	if len(UpperCAmelCase_							) == 0:
		return True
	snake_case__	       =   input_str.replace(' '					,				''							).lower()
	# character_freq_dict: Stores the frequency of every character in the input string
	snake_case__	       =   {}

	for character in lower_case_input_str:
		snake_case__	       =   character_freq_dict.get(UpperCAmelCase_					,				0							) + 1
	snake_case__	       =   0

	for character_count in character_freq_dict.values():
		if character_count % 2:
			odd_char += 1
	if odd_char > 1:
		return False
	return True




def 						__lowerCamelCase			(	UpperCAmelCase_ = ""							)   ->None:
	print('\nFor string = '					,				UpperCAmelCase_					,				':'							)
	print(
	    '> can_string_be_rearranged_as_palindrome_counter()'					,				'\tans ='					,				can_string_be_rearranged_as_palindrome_counter(UpperCAmelCase_							)					,				'\ttime ='					,				timeit(
	        'z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'					,				setup='import __main__ as z'					,				)					,				'seconds'					,				)
	print(
	    '> can_string_be_rearranged_as_palindrome()'					,				'\tans ='					,				can_string_be_rearranged_as_palindrome(UpperCAmelCase_							)					,				'\ttime ='					,				timeit(
	        'z.can_string_be_rearranged_as_palindrome(z.check_str)'					,				setup='import __main__ as z'					,				)					,				'seconds'					,				)


if __name__ == "__main__":
			a__					:			str          =     input(
			    '''Enter string to determine if it can be rearranged as a palindrome or not: '''
			).strip()
			benchmark(check_str)
			a__					:			List[Any]          =     can_string_be_rearranged_as_palindrome_counter(check_str)
			print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")



