





"""simple docstring"""





from math import pow


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	):




		"""simple docstring"""


		if current_sum == needed_sum:
				# If the sum of the powers is equal to needed_sum, then we have a solution.
				solutions_count += 1
				return current_sum, solutions_count

		UpperCamelCase   =	int(pow(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )    )
		if current_sum + i_to_n <= needed_sum:
				# If the sum of the powers is less than needed_sum, then continue adding powers.
				current_sum += i_to_n
				UpperCamelCase   ,				UpperCamelCase   =	backtrack(
				    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	current_number + 1  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
				current_sum -= i_to_n
		if i_to_n < needed_sum:
				# If the power of i is less than needed_sum, then try with the next power.
				UpperCamelCase   ,				UpperCamelCase   =	backtrack(
				    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	current_number + 1  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
		return current_sum, solutions_count


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		if not (1 <= needed_sum <= 1_000 and 2 <= power <= 10):
				raise ValueError(
				    "Invalid input\n"
				    "needed_sum must be between 1 and 1000, power between 2 and 10."    )

		return backtrack(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	1  ,	0  ,	0    )[1]  # Return the solutions_count


if __name__ == "__main__":
						import doctest

						doctest.testmod()













"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		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(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   ,				UpperCamelCase   =	emb.weight.shape
		UpperCamelCase   =	nn.Linear(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	bias=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	emb.weight.data
		return lin_layer


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	torch.load(_SCREAMING_SNAKE_CASE  ,	map_location="cpu"    )
		UpperCamelCase   =	mam_aaa["args"] or mam_aaa["cfg"]["model"]
		UpperCamelCase   =	mam_aaa["model"]
		remove_ignore_keys_(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	state_dict["encoder.embed_tokens.weight"].shape[0]

		UpperCamelCase   =	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   =	state_dict["decoder.embed_tokens.weight"]
		UpperCamelCase   =	MaMaaaForConditionalGeneration(_SCREAMING_SNAKE_CASE    )
		model.model.load_state_dict(_SCREAMING_SNAKE_CASE  ,	strict=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	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)






