



from __future__ import annotations

import math
import random
from typing import Any

class  __snake_case      :







							'''simple docstring'''






							def __init__(							self		):
											a__				      =		[]
											a__				      =		0
											a__				      =		0


							def _a      (							self		):
											return self.head == self.tail


							def _a      (							self  ,  a_		):
											self.data.append(a_		)
											a__				      =		self.tail + 1


							def _a      (							self		):
											a__				      =		self.data[self.head]
											a__				      =		self.head + 1
											return ret


							def _a      (							self		):
											return self.tail - self.head







							def _a      (							self		):
											print(self.data		)
											print("""**************"""		)
											print(self.data[self.head : self.tail]		)




class  __snake_case      :







							'''simple docstring'''






							def __init__(							self  ,  a_		):
											a__				      =		data
											a__				      =		None
											a__				      =		None
											a__				      =		1


							def _a      (							self		):
											return self.data


							def _a      (							self		):
											return self.left


							def _a      (							self		):
											return self.right


							def _a      (							self		):
											return self.height


							def _a      (							self  ,  a_		):
											a__				      =		data


							def _a      (							self  ,  a_		):
											a__				      =		node


							def _a      (							self  ,  a_		):
											a__				      =		node







							def _a      (							self  ,  a_		):
											a__				      =		height




def 			A_      (	__a     :				Any		):

				"""simple docstring"""
				if node is None:
								return 0
				return node.get_height()

def 			A_      (	__a     :				Dict ,		__a     :				Optional[Any]		):

				"""simple docstring"""
				if a > b:
								return a
				return b

def 			A_      (	__a     :				int		):

				"""simple docstring"""
				print("""left rotation node:""" ,		node.get_data()		)
				a__				      =		node.get_left()
				assert ret is not None
				node.set_left(ret.get_right()		)
				ret.set_right(__SCREAMING_SNAKE_CASE		)
				a__				      =		my_max(get_height(node.get_right()		) ,		get_height(node.get_left()		)		) + 1
				node.set_height(__SCREAMING_SNAKE_CASE		)
				a__				      =		my_max(get_height(ret.get_right()		) ,		get_height(ret.get_left()		)		) + 1
				ret.set_height(__SCREAMING_SNAKE_CASE		)
				return ret

def 			A_      (	__a     :				List[Any]		):

				"""simple docstring"""
				print("""right rotation node:""" ,		node.get_data()		)
				a__				      =		node.get_right()
				assert ret is not None
				node.set_right(ret.get_left()		)
				ret.set_left(__SCREAMING_SNAKE_CASE		)
				a__				      =		my_max(get_height(node.get_right()		) ,		get_height(node.get_left()		)		) + 1
				node.set_height(__SCREAMING_SNAKE_CASE		)
				a__				      =		my_max(get_height(ret.get_right()		) ,		get_height(ret.get_left()		)		) + 1
				ret.set_height(__SCREAMING_SNAKE_CASE		)
				return ret

def 			A_      (	__a     :				Dict		):

				"""simple docstring"""
				a__				      =		node.get_left()
				assert left_child is not None
				node.set_left(left_rotation(__SCREAMING_SNAKE_CASE		)		)
				return right_rotation(__SCREAMING_SNAKE_CASE		)

def 			A_      (	__a     :				List[str]		):

				"""simple docstring"""
				a__				      =		node.get_right()
				assert right_child is not None
				node.set_right(right_rotation(__SCREAMING_SNAKE_CASE		)		)
				return left_rotation(__SCREAMING_SNAKE_CASE		)

def 			A_      (	__a     :				int ,		__a     :				int		):

				"""simple docstring"""
				if node is None:
								return MyNode(__SCREAMING_SNAKE_CASE		)
				if data < node.get_data():
								node.set_left(insert_node(node.get_left() ,		__SCREAMING_SNAKE_CASE		)		)
								if (
								    get_height(node.get_left()		) - get_height(node.get_right()		) == 2
								):  # an unbalance detected
												a__				      =		node.get_left()
												assert left_child is not None
												if (
												    data < left_child.get_data()
												):  # new node is the left child of the left child
																a__				      =		right_rotation(__SCREAMING_SNAKE_CASE		)
												else:
																a__				      =		lr_rotation(__SCREAMING_SNAKE_CASE		)
				else:
								node.set_right(insert_node(node.get_right() ,		__SCREAMING_SNAKE_CASE		)		)
								if get_height(node.get_right()		) - get_height(node.get_left()		) == 2:
												a__				      =		node.get_right()
												assert right_child is not None
												if data < right_child.get_data():
																a__				      =		rl_rotation(__SCREAMING_SNAKE_CASE		)
												else:
																a__				      =		left_rotation(__SCREAMING_SNAKE_CASE		)
				a__				      =		my_max(get_height(node.get_right()		) ,		get_height(node.get_left()		)		) + 1
				node.set_height(__SCREAMING_SNAKE_CASE		)
				return node

def 			A_      (	__a     :				List[str]		):

				"""simple docstring"""
				while True:
								a__				      =		root.get_right()
								if right_child is None:
												break
								a__				      =		right_child
				return root.get_data()

def 			A_      (	__a     :				Tuple		):

				"""simple docstring"""
				while True:
								a__				      =		root.get_left()
								if left_child is None:
												break
								a__				      =		left_child
				return root.get_data()

def 			A_      (	__a     :				Tuple ,		__a     :				Optional[int]		):

				"""simple docstring"""
				a__				      =		root.get_left()
				a__				      =		root.get_right()
				if root.get_data() == data:
								if left_child is not None and right_child is not None:
												a__				      =		get_left_most(__SCREAMING_SNAKE_CASE		)
												root.set_data(__SCREAMING_SNAKE_CASE		)
												root.set_right(del_node(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE		)		)
								elif left_child is not None:
												a__				      =		left_child
								elif right_child is not None:
												a__				      =		right_child
								else:
												return None
				elif root.get_data() > data:
								if left_child is None:
												print("""No such data"""		)
												return root
								else:
												root.set_left(del_node(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE		)		)
				else:  # root.get_data() < data
								if right_child is None:
												return root
								else:
												root.set_right(del_node(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE		)		)

				if get_height(__SCREAMING_SNAKE_CASE		) - get_height(__SCREAMING_SNAKE_CASE		) == 2:
								assert right_child is not None
								if get_height(right_child.get_right()		) > get_height(right_child.get_left()		):
												a__				      =		left_rotation(__SCREAMING_SNAKE_CASE		)
								else:
												a__				      =		rl_rotation(__SCREAMING_SNAKE_CASE		)
				elif get_height(__SCREAMING_SNAKE_CASE		) - get_height(__SCREAMING_SNAKE_CASE		) == -2:
								assert left_child is not None
								if get_height(left_child.get_left()		) > get_height(left_child.get_right()		):
												a__				      =		right_rotation(__SCREAMING_SNAKE_CASE		)
								else:
												a__				      =		lr_rotation(__SCREAMING_SNAKE_CASE		)
				a__				      =		my_max(get_height(root.get_right()		) ,		get_height(root.get_left()		)		) + 1
				root.set_height(__SCREAMING_SNAKE_CASE		)
				return root

class  __snake_case      :







							'''simple docstring'''






							def __init__(							self		):
											a__				      =		None


							def _a      (							self		):
											return get_height(self.root		)


							def _a      (							self  ,  a_		):
											print("""insert:""" + str(a_		)		)
											a__				      =		insert_node(self.root  ,  a_		)


							def _a      (							self  ,  a_		):
											print("""delete:""" + str(a_		)		)
											if self.root is None:
															print("""Tree is empty!"""		)
															return
											a__				      =		del_node(self.root  ,  a_		)







							def __str__(							self  ,  ):  # a level traversale, gives a more intuitive look on the tree
											a__				      =		""""""
											a__				      =		MyQueue()
											q.push(self.root		)
											a__				      =		self.get_height()
											if layer == 0:
															return output
											a__				      =		0
											while not q.is_empty():
															a__				      =		q.pop()
															a__				      =		""" """ * int(math.pow(2  ,  layer - 1		)		)
															output += space
															if node is None:
																			output += "*"
																			q.push(a_		)
																			q.push(a_		)
															else:
																			output += str(node.get_data()		)
																			q.push(node.get_left()		)
																			q.push(node.get_right()		)
															output += space
															a__				      =		cnt + 1
															for i in range(100		):
																			if cnt == math.pow(2  ,  a_		) - 1:
																							a__				      =		layer - 1
																							if layer == 0:
																											output += "\n*************************************"
																											return output
																							output += "\n"
																							break
											output += "\n*************************************"
											return output




def 			A_      (	):

				"""simple docstring"""
				import doctest

				doctest.testmod()


if __name__ == "__main__":
				_test()
				UpperCAmelCase							 =					AVLtree()
				UpperCAmelCase							 =					list(range(10))
				random.shuffle(lst)
				for i in lst:
								t.insert(i)
								print(str(t))
				random.shuffle(lst)
				for i in lst:
								t.del_node(i)
								print(str(t))






import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
	raise RuntimeError("""At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training""")

# TF training parameters
UpperCAmelCase							 =					False
UpperCAmelCase							 =					False

def 			A_      (	__a     :				Namespace		):

				"""simple docstring"""
				return TrainCommand(__a		)




class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							@staticmethod
							def _a      (							a_		):
											a__				      =		parser.add_parser("""train"""  ,  help="""CLI tool to train a model on a task."""		)

											train_parser.add_argument(
											    """--train_data"""  ,  type=a_  ,  required=a_  ,  help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."""  ,  )
											train_parser.add_argument(
											    """--column_label"""  ,  type=a_  ,  default=0  ,  help="""Column of the dataset csv file with example labels."""		)
											train_parser.add_argument(
											    """--column_text"""  ,  type=a_  ,  default=1  ,  help="""Column of the dataset csv file with example texts."""		)
											train_parser.add_argument(
											    """--column_id"""  ,  type=a_  ,  default=2  ,  help="""Column of the dataset csv file with example ids."""		)
											train_parser.add_argument(
											    """--skip_first_row"""  ,  action="""store_true"""  ,  help="""Skip the first row of the csv file (headers)."""		)

											train_parser.add_argument("""--validation_data"""  ,  type=a_  ,  default=""""""  ,  help="""path to validation dataset."""		)
											train_parser.add_argument(
											    """--validation_split"""  ,  type=a_  ,  default=0.1  ,  help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset."""  ,  )

											train_parser.add_argument("""--output"""  ,  type=a_  ,  default="""./"""  ,  help="""path to saved the trained model."""		)

											train_parser.add_argument(
											    """--task"""  ,  type=a_  ,  default="""text_classification"""  ,  help="""Task to train the model on."""		)
											train_parser.add_argument(
											    """--model"""  ,  type=a_  ,  default="""bert-base-uncased"""  ,  help="""Model's name or path to stored model."""		)
											train_parser.add_argument("""--train_batch_size"""  ,  type=a_  ,  default=32  ,  help="""Batch size for training."""		)
											train_parser.add_argument("""--valid_batch_size"""  ,  type=a_  ,  default=64  ,  help="""Batch size for validation."""		)
											train_parser.add_argument("""--learning_rate"""  ,  type=a_  ,  default=3E-5  ,  help="""Learning rate."""		)
											train_parser.add_argument("""--adam_epsilon"""  ,  type=a_  ,  default=1E-0_8  ,  help="""Epsilon for Adam optimizer."""		)
											train_parser.set_defaults(func=a_		)


							def __init__(							self  ,  a_		):
											a__				      =		logging.get_logger("""transformers-cli/training"""		)

											a__				      =		"""tf""" if is_tf_available() else """torch"""

											os.makedirs(args.output  ,  exist_ok=a_		)
											a__				      =		args.output

											a__				      =		args.column_label
											a__				      =		args.column_text
											a__				      =		args.column_id

											self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''		)
											if args.task == "text_classification":
															a__				      =		TextClassificationPipeline.from_pretrained(args.model		)
											elif args.task == "token_classification":
															raise NotImplementedError
											elif args.task == "question_answering":
															raise NotImplementedError

											self.logger.info(F'''Loading dataset from {args.train_data}'''		)
											a__				      =		Processor.create_from_csv(
											    args.train_data  ,  column_label=args.column_label  ,  column_text=args.column_text  ,  column_id=args.column_id  ,  skip_first_row=args.skip_first_row  ,  )
											a__				      =		None
											if args.validation_data:
															self.logger.info(F'''Loading validation dataset from {args.validation_data}'''		)
															a__				      =		Processor.create_from_csv(
															    args.validation_data  ,  column_label=args.column_label  ,  column_text=args.column_text  ,  column_id=args.column_id  ,  skip_first_row=args.skip_first_row  ,  )

											a__				      =		args.validation_split
											a__				      =		args.train_batch_size
											a__				      =		args.valid_batch_size
											a__				      =		args.learning_rate
											a__				      =		args.adam_epsilon


							def _a      (							self		):
											if self.framework == "tf":
															return self.run_tf()
											return self.run_torch()


							def _a      (							self		):
											raise NotImplementedError







							def _a      (							self		):
											self.pipeline.fit(
											    self.train_dataset  ,  validation_data=self.valid_dataset  ,  validation_split=self.validation_split  ,  learning_rate=self.learning_rate  ,  adam_epsilon=self.adam_epsilon  ,  train_batch_size=self.train_batch_size  ,  valid_batch_size=self.valid_batch_size  ,  )

											# Save trained pipeline
											self.pipeline.save_pretrained(self.output		)

