
import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


_A     =  {
    "cola": 2,
    "mnli": 3,
    "mrpc": 2,
    "sst-2": 2,
    "sts-b": 1,
    "qqp": 2,
    "qnli": 2,
    "rte": 2,
    "wnli": 2,
}


logging.set_verbosity_info()






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[str]					,							__lowerCAmelCase						:						Any					,							__lowerCAmelCase						:						Any					,							__lowerCAmelCase						:						str=None       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			XLNetConfig.from_json_file(__lowerCAmelCase       )

			lowerCAmelCase_      			=			finetuning_task.lower() if finetuning_task is not None else ""
			if finetuning_task in GLUE_TASKS_NUM_LABELS:
						print(F"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}"""       )
						lowerCAmelCase_      			=			finetuning_task
						lowerCAmelCase_      			=			GLUE_TASKS_NUM_LABELS[finetuning_task]
						lowerCAmelCase_      			=			XLNetForSequenceClassification(__lowerCAmelCase       )
			elif "squad" in finetuning_task:
						lowerCAmelCase_      			=			finetuning_task
						lowerCAmelCase_      			=			XLNetForQuestionAnswering(__lowerCAmelCase       )
			else:
						lowerCAmelCase_      			=			XLNetLMHeadModel(__lowerCAmelCase       )

			# Load weights from tf checkpoint
			load_tf_weights_in_xlnet(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase       )

			# Save pytorch-model
			lowerCAmelCase_      			=			os.path.join(__lowerCAmelCase					,							__lowerCAmelCase       )
			lowerCAmelCase_      			=			os.path.join(__lowerCAmelCase					,							__lowerCAmelCase       )
			print(F"""Save PyTorch model to {os.path.abspath(__lowerCAmelCase       )}"""       )
			torch.save(model.state_dict()					,							__lowerCAmelCase       )
			print(F"""Save configuration file to {os.path.abspath(__lowerCAmelCase       )}"""       )
			with open(__lowerCAmelCase					,							"w"					,							encoding="utf-8"       ) as f:
						f.write(config.to_json_string()       )


if __name__ == "__main__":
			_A     =  argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
			)
			parser.add_argument(
			    "--xlnet_config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained XLNet model. \n"
			        "This specifies the model architecture."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path",
			    default=None,
			    type=str,
			    required=True,
			    help="Path to the folder to store the PyTorch model or dataset/vocab.",
			)
			parser.add_argument(
			    "--finetuning_task",
			    default=None,
			    type=str,
			    help="Name of a task on which the XLNet TensorFlow model was fine-tuned",
			)
			_A     =  parser.parse_args()
			print(args)

			convert_xlnet_checkpoint_to_pytorch(
			    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
			)





from collections import Counter

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

_A     =  datasets.load_iris()

_A     =  np.array(data["data"])
_A     =  np.array(data["target"])
_A     =  data["target_names"]

_A,   _A,   _A,   _A     =  train_test_split(X, y)






def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


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






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Any=5       ):




			"""simple docstring"""


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



