
import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     Any:







				"""simple docstring"""



				__A             =			("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

				__A             =			(
				    ("layer.", "layer_"),
				    ("word_embeddings.weight", "word_embeddings"),
				    ("position_embeddings.weight", "position_embeddings"),
				    ("token_type_embeddings.weight", "token_type_embeddings"),
				    (".", "/"),
				    ("LayerNorm/weight", "LayerNorm/gamma"),
				    ("LayerNorm/bias", "LayerNorm/beta"),
				    ("weight", "kernel"),
				)

				if not os.path.isdir(a_						):
								os.makedirs(a_						)

				__A             =			model.state_dict()

				def to_tf_var_name(a_						):
								for patt, repl in iter(a_						):
												__A             =			name.replace(a_		,       a_						)
								return F'''bert/{name}'''

				def create_tf_var(a_		,       a_		,       a_						):
								__A             =			tf.dtypes.as_dtype(tensor.dtype						)
								__A             =			tf.get_variable(dtype=a_		,       shape=tensor.shape		,       name=a_		,       initializer=tf.zeros_initializer()						)
								session.run(tf.variables_initializer([tf_var]						)						)
								session.run(a_						)
								return tf_var

				tf.reset_default_graph()
				with tf.Session() as session:
								for var_name in state_dict:
												__A             =			to_tf_var_name(a_						)
												__A             =			state_dict[var_name].numpy()
												if any(x in var_name for x in tensors_to_transpose						):
																__A             =			torch_tensor.T
												__A             =			create_tf_var(tensor=a_		,       name=a_		,       session=a_						)
												tf.keras.backend.set_value(a_		,       a_						)
												__A             =			session.run(a_						)
												print(F'''Successfully created {tf_name}: {np.allclose(a_		,       a_						)}'''						)

								__A             =			tf.train.Saver(tf.trainable_variables()						)
								saver.save(a_		,       os.path.join(a_		,       model_name.replace("-"		,       "_"						) + ".ckpt"						)						)


def 						UpperCAmelCase      (     a_=None						)		->     List[str]:







				"""simple docstring"""



				__A             =			argparse.ArgumentParser()
				parser.add_argument("--model_name"		,       type=a_		,       required=a_		,       help="model name e.g. bert-base-uncased"						)
				parser.add_argument(
				    "--cache_dir"		,       type=a_		,       default=a_		,       required=a_		,       help="Directory containing pytorch model"						)
				parser.add_argument("--pytorch_model_path"		,       type=a_		,       required=a_		,       help="/path/to/<pytorch-model-name>.bin"						)
				parser.add_argument("--tf_cache_dir"		,       type=a_		,       required=a_		,       help="Directory in which to save tensorflow model"						)
				__A             =			parser.parse_args(a_						)

				__A             =			BertModel.from_pretrained(
				    pretrained_model_name_or_path=args.model_name		,       state_dict=torch.load(args.pytorch_model_path						)		,       cache_dir=args.cache_dir		,       )

				convert_pytorch_checkpoint_to_tf(model=a_		,       ckpt_dir=args.tf_cache_dir		,       model_name=args.model_name						)


if __name__ == "__main__":
					main()


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]))
