



"""simple docstring"""
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Dict:
		a_     :      Union[str, 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_						(							SCREAMING_SNAKE_CASE__	)			->      Optional[Any]:
		a_						,				a_     :      str 						=     emb.weight.shape
		a_     :      Union[str, Any] 						=     nn.Linear(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, bias=SCREAMING_SNAKE_CASE__	)
		a_     :      List[Any] 						=     emb.weight.data
		return lin_layer



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
		a_     :      Optional[int] 						=     torch.load(SCREAMING_SNAKE_CASE__, map_location="cpu"	)
		a_     :      Optional[Any] 						=     mam_aaa["args"] or mam_aaa["cfg"]["model"]
		a_     :      Union[str, Any] 						=     mam_aaa["model"]
		remove_ignore_keys_(SCREAMING_SNAKE_CASE__	)
		a_     :      List[Any] 						=     state_dict["encoder.embed_tokens.weight"].shape[0]

		a_     :      List[Any] 						=     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", )

		a_     :      Tuple 						=     state_dict["decoder.embed_tokens.weight"]
		a_     :      str 						=     MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE__	)
		model.model.load_state_dict(SCREAMING_SNAKE_CASE__, strict=SCREAMING_SNAKE_CASE__	)
		a_     :      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_						(							SCREAMING_SNAKE_CASE__ = 10, SCREAMING_SNAKE_CASE__ = 1_000, SCREAMING_SNAKE_CASE__ = True	)			->      int:
		assert (
		    isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		    and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		    and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		), "Invalid type of value(s) specified to function!"

		if min_val > max_val:
				raise ValueError("Invalid value for min_val or max_val (min_value < max_value)"	)
		return min_val if option else max_val



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      int:
		return int((number_a + number_a) / 2	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      None:
		assert (
		    isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		), 'argument values must be type of "int"'

		if lower > higher:
				raise ValueError("argument value for lower and higher must be(lower > higher)"	)

		if not lower < to_guess < higher:
				raise ValueError(
				    "guess value must be within the range of lower and higher value"	)

		def answer(SCREAMING_SNAKE_CASE__	) -> str:
				if number > to_guess:
						return "high"
				elif number < to_guess:
						return "low"
				else:
						return "same"

		print("started..."	)

		a_     :      List[str] 						=     lower
		a_     :      Dict 						=     higher

		a_     :      str 						=     []

		while True:
				a_     :      List[str] 						=     get_avg(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
				last_numbers.append(SCREAMING_SNAKE_CASE__	)

				if answer(SCREAMING_SNAKE_CASE__	) == "low":
						a_     :      Optional[Any] 						=     number
				elif answer(SCREAMING_SNAKE_CASE__	) == "high":
						a_     :      Union[str, Any] 						=     number
				else:
						break

		print(F"""guess the number : {last_numbers[-1]}"""	)
		print(F"""details : {last_numbers!s}"""	)



def        lowerCAmelCase_						(							)			->      None:
		a_     :      str 						=     int(input("Enter lower value : "	).strip()	)
		a_     :      Dict 						=     int(input("Enter high value : "	).strip()	)
		a_     :      Optional[Any] 						=     int(input("Enter value to guess : "	).strip()	)
		guess_the_number(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
					main()