
'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def A				(__lowerCamelCase				:int		):
						_lowerCAmelCase  						=			[
						    """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(__lowerCamelCase			,     __lowerCamelCase		)


def A				(__lowerCamelCase				:Optional[Any]		):
						_lowerCAmelCase						,   _lowerCAmelCase  						=			emb.weight.shape
						_lowerCAmelCase  						=			nn.Linear(__lowerCamelCase			,     __lowerCamelCase			,     bias=__lowerCamelCase		)
						_lowerCAmelCase  						=			emb.weight.data
						return lin_layer


def A				(__lowerCamelCase				:Tuple		):
						_lowerCAmelCase  						=			torch.load(__lowerCamelCase			,     map_location="""cpu"""		)
						_lowerCAmelCase  						=			mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
						_lowerCAmelCase  						=			mam_aaa["""model"""]
						remove_ignore_keys_(__lowerCamelCase		)
						_lowerCAmelCase  						=			state_dict["""encoder.embed_tokens.weight"""].shape[0]

						_lowerCAmelCase  						=			MaMaaaConfig(
						    vocab_size=__lowerCamelCase			,     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"""			,     )

						_lowerCAmelCase  						=			state_dict["""decoder.embed_tokens.weight"""]
						_lowerCAmelCase  						=			MaMaaaForConditionalGeneration(__lowerCamelCase		)
						model.model.load_state_dict(__lowerCamelCase			,     strict=__lowerCamelCase		)
						_lowerCAmelCase  						=			make_linear_from_emb(model.model.shared		)

						return model


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


'''simple docstring'''



from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowercase    =		10


def A				(__lowerCamelCase				:int			,     __lowerCamelCase				:int			,     __lowerCamelCase				:list[int]			,     __lowerCamelCase				:int		):
						for i in range(__lowerCamelCase			,     __lowerCamelCase		):
												if array[i] == target:
																		return i
						return -1


def A				(__lowerCamelCase				:list[int]			,     __lowerCamelCase				:int		):
						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			len(__lowerCamelCase		)
						while left <= right:
												if right - left < precision:
																		return lin_search(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)

												_lowerCAmelCase  						=			(left + right) // 3 + 1
												_lowerCAmelCase  						=			2 * (left + right) // 3 + 1

												if array[one_third] == target:
																		return one_third
												elif array[two_third] == target:
																		return two_third

												elif target < array[one_third]:
																		_lowerCAmelCase  						=			one_third - 1
												elif array[two_third] < target:
																		_lowerCAmelCase  						=			two_third + 1

												else:
																		_lowerCAmelCase  						=			one_third + 1
																		_lowerCAmelCase  						=			two_third - 1
						else:
												return -1


def A				(__lowerCamelCase				:int			,     __lowerCamelCase				:int			,     __lowerCamelCase				:list[int]			,     __lowerCamelCase				:int		):
						if left < right:
												if right - left < precision:
																		return lin_search(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)
												_lowerCAmelCase  						=			(left + right) // 3 + 1
												_lowerCAmelCase  						=			2 * (left + right) // 3 + 1

												if array[one_third] == target:
																		return one_third
												elif array[two_third] == target:
																		return two_third

												elif target < array[one_third]:
																		return rec_ternary_search(__lowerCamelCase			,     one_third - 1			,     __lowerCamelCase			,     __lowerCamelCase		)
												elif array[two_third] < target:
																		return rec_ternary_search(two_third + 1			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)
												else:
																		return rec_ternary_search(one_third + 1			,     two_third - 1			,     __lowerCamelCase			,     __lowerCamelCase		)
						else:
												return -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_lowercase    =		input("""Enter numbers separated by comma:\n""").strip()
						_lowercase    =		[int(item.strip()) for item in user_input.split(""",""")]
						assert collection == sorted(collection), F"List must be ordered.\n{collection}."
						_lowercase    =		int(input("""Enter the number to be found in the list:\n""").strip())
						_lowercase    =		ite_ternary_search(collection, target)
						_lowercase    =		rec_ternary_search(0, len(collection) - 1, collection, target)
						if resulta != -1:
												print(F"""Iterative search: {target} found at positions: {resulta}""")
												print(F"""Recursive search: {target} found at positions: {resulta}""")
						else:
												print("""Not found""")
