
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM


def 						UpperCAmelCase      (     a_						)		->     List[Any]:







				"""simple docstring"""



				if "model" in orig_key:
								__A             =			orig_key.replace("model."		,       ""						)
				if "norm1" in orig_key:
								__A             =			orig_key.replace("norm1"		,       "attention.output.LayerNorm"						)
				if "norm2" in orig_key:
								__A             =			orig_key.replace("norm2"		,       "output.LayerNorm"						)
				if "norm" in orig_key:
								__A             =			orig_key.replace("norm"		,       "LayerNorm"						)
				if "transformer" in orig_key:
								__A             =			orig_key.split("."						)[0].split("_"						)[-1]
								__A             =			orig_key.replace(F'''transformer_{layer_num}'''		,       F'''encoder.layer.{layer_num}'''						)
				if "mha.attn" in orig_key:
								__A             =			orig_key.replace("mha.attn"		,       "attention.self"						)
				if "mha" in orig_key:
								__A             =			orig_key.replace("mha"		,       "attention"						)
				if "W_q" in orig_key:
								__A             =			orig_key.replace("W_q"		,       "self.query"						)
				if "W_k" in orig_key:
								__A             =			orig_key.replace("W_k"		,       "self.key"						)
				if "W_v" in orig_key:
								__A             =			orig_key.replace("W_v"		,       "self.value"						)
				if "ff1" in orig_key:
								__A             =			orig_key.replace("ff1"		,       "intermediate.dense"						)
				if "ff2" in orig_key:
								__A             =			orig_key.replace("ff2"		,       "output.dense"						)
				if "ff" in orig_key:
								__A             =			orig_key.replace("ff"		,       "output.dense"						)
				if "mlm_class" in orig_key:
								__A             =			orig_key.replace("mlm.mlm_class"		,       "cls.predictions.decoder"						)
				if "mlm" in orig_key:
								__A             =			orig_key.replace("mlm"		,       "cls.predictions.transform"						)
				if "cls" not in orig_key:
								__A             =			"yoso." + orig_key

				return orig_key


def 						UpperCAmelCase      (     a_		,       a_						)		->     Optional[Any]:







				"""simple docstring"""



				for key in orig_state_dict.copy().keys():
								__A             =			orig_state_dict.pop(snake_case_						)

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												__A             =			val

				__A             =			orig_state_dict["cls.predictions.decoder.bias"]
				__A             =			torch.arange(snake_case_						).expand((1, -1)						) + 2

				return orig_state_dict


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     Optional[int]:







				"""simple docstring"""



				__A             =			torch.load(snake_case_		,       map_location="cpu"						)["model_state_dict"]
				__A             =			YosoConfig.from_json_file(snake_case_						)
				__A             =			YosoForMaskedLM(snake_case_						)

				__A             =			convert_checkpoint_helper(config.max_position_embeddings		,       snake_case_						)

				print(model.load_state_dict(snake_case_						)						)
				model.eval()
				model.save_pretrained(snake_case_						)

				print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''						)


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE							:int										=    argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
				)
				parser.add_argument(
				    '--config_file',
				    default=None,
				    type=str,
				    required=True,
				    help='The json file for YOSO model config.',
				)
				parser.add_argument(
				    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				SCREAMING_SNAKE_CASE							:List[str]										=    parser.parse_args()
				convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_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]))
