


import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def   _lowerCAmelCase				(   UpperCamelCase__:     List[str]						) ->   str:





	"""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   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Optional[Any]						) ->   Optional[int]:





	"""simple docstring"""







	for key in orig_state_dict.copy().keys():
		A								=				orig_state_dict.pop(UpperCamelCase__						)

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

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

	return orig_state_dict





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     Any							,					UpperCamelCase__:     List[Any]						) ->   List[Any]:





	"""simple docstring"""







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

	A								=				convert_checkpoint_helper(config.max_position_embeddings							,					UpperCamelCase__						)

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

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


if __name__ == "__main__":
				_lowercase       :      str	=	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."
				)
				_lowercase       :      Tuple	=	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

_lowercase       :      Any	=	datasets.load_iris()

_lowercase       :      Tuple	=	np.array(data["data"])
_lowercase       :      int	=	np.array(data["target"])
_lowercase       :      int	=	data["target_names"]

_lowercase	,							_lowercase	,							_lowercase	,							_lowercase       :      Any	=	train_test_split(X, y)





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Any						) ->   List[Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     int							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     int=5						) ->   Tuple:





	"""simple docstring"""







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



