





"""simple docstring"""





import functools
from typing import Any


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		if not isinstance(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ) or len(_SCREAMING_SNAKE_CASE    ) == 0:
				raise ValueError("the string should be not empty string"    )

		if not isinstance(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ) or not all(
		    isinstance(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ) and len(_SCREAMING_SNAKE_CASE    ) > 0 for item in words    ):
				raise ValueError("the words should be a list of non-empty strings"    )

		# Build trie
		UpperCamelCase   =	{}
		UpperCamelCase   =	"WORD_KEEPER"

		for word in words:
				UpperCamelCase   =	trie
				for c in word:
						if c not in trie_node:
								UpperCamelCase   =	{}

						UpperCamelCase   =	trie_node[c]

				UpperCamelCase   =	True

		UpperCamelCase   =	len(_SCREAMING_SNAKE_CASE    )

		# Dynamic programming method
		@functools.cache
		def is_breakable(_SCREAMING_SNAKE_CASE    ) -> bool:
				if index == len_string:
						return True

				UpperCamelCase   =	trie
				for i in range(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):
						UpperCamelCase   =	trie_node.get(string[i]  ,	_SCREAMING_SNAKE_CASE    )

						if trie_node is None:
								return False

						if trie_node.get(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ) and is_breakable(i + 1    ):
								return True

				return False

		return is_breakable(0    )


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)






