
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 						UpperCAmelCase      (     a_						)		->     Dict:







				"""simple docstring"""



				__A             =			[
				    "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(a_		,       a_						)


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A      ,							__A             =			emb.weight.shape
				__A             =			nn.Linear(a_		,       a_		,       bias=a_						)
				__A             =			emb.weight.data
				return lin_layer


def 						UpperCAmelCase      (     a_						)		->     Tuple:







				"""simple docstring"""



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

				__A             =			MaMaaaConfig(
				    vocab_size=a_		,       max_position_embeddings=1_0_2_4		,       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             =			state_dict["decoder.embed_tokens.weight"]
				__A             =			MaMaaaForConditionalGeneration(a_						)
				model.model.load_state_dict(a_		,       strict=a_						)
				__A             =			make_linear_from_emb(model.model.shared						)

				return model


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Optional[Any]										=    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							:Union[str, Any]										=    parser.parse_args()
					SCREAMING_SNAKE_CASE							:List[Any]										=    convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
					model.save_pretrained(args.pytorch_dump_folder_path)


from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

SCREAMING_SNAKE_CASE							:Tuple										=    datasets.load_iris()

SCREAMING_SNAKE_CASE							:Dict										=    np.array(data['data'])
SCREAMING_SNAKE_CASE							:Optional[int]										=    np.array(data['target'])
SCREAMING_SNAKE_CASE							:List[str]										=    data['target_names']

SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE							:List[Any]										=    train_test_split(X, y)


def 						UpperCAmelCase      (     a_		,       a_						)		->     Tuple:







				"""simple docstring"""



				return np.linalg.norm(np.array(a_						) - np.array(a_						)						)


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_=5						)		->     Dict:







				"""simple docstring"""



				__A             =			zip(a_		,       a_						)
				# List of distances of all points from the point to be classified
				__A             =			[]
				for data_point in data:
								__A             =			euclidean_distance(data_point[0]		,       a_						)
								distances.append((distance, data_point[1])						)
				# Choosing 'k' points with the least distances.
				__A             =			[i[1] for i in sorted(a_						)[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				__A             =			Counter(a_						).most_common(1						)[0][0]
				return classes[result]


if __name__ == "__main__":
					print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
