'''simple docstring'''


from __future__ import annotations

import math
import random
from typing import Any






class     A :





							def __init__(   self					:				Optional[Any]						)				->						None:
								__UpperCAmelCase      					=      []
								__UpperCAmelCase      					=      0
								__UpperCAmelCase      					=      0





							def    snake_case__    (   self					:				List[Any]						)				->						bool:
								return self.head == self.tail





							def    snake_case__    (   self					:				Optional[int]  ,		__a					:				str						)				->						None:
								self.data.append(__SCREAMING_SNAKE_CASE						)
								__UpperCAmelCase      					=      self.tail + 1





							def    snake_case__    (   self					:				List[Any]						)				->						Any:
								__UpperCAmelCase      					=      self.data[self.head]
								__UpperCAmelCase      					=      self.head + 1
								return ret





							def    snake_case__    (   self					:				Optional[int]						)				->						int:
								return self.tail - self.head






							def    snake_case__    (   self					:				Tuple						)				->						None:
								print(self.data						)
								print('''**************'''						)
								print(self.data[self.head : self.tail]						)






class     A :





							def __init__(   self					:				int  ,		__a					:				str						)				->						None:
								__UpperCAmelCase      					=      data
								__UpperCAmelCase      					=      None
								__UpperCAmelCase      					=      None
								__UpperCAmelCase      					=      1





							def    snake_case__    (   self					:				str						)				->						Any:
								return self.data





							def    snake_case__    (   self					:				List[str]						)				->						MyNode | None:
								return self.left





							def    snake_case__    (   self					:				List[Any]						)				->						MyNode | None:
								return self.right





							def    snake_case__    (   self					:				List[Any]						)				->						int:
								return self.height





							def    snake_case__    (   self					:				Optional[Any]  ,		__a					:				Optional[int]						)				->						None:
								__UpperCAmelCase      					=      data





							def    snake_case__    (   self					:				Any  ,		__a					:				str						)				->						None:
								__UpperCAmelCase      					=      node





							def    snake_case__    (   self					:				int  ,		__a					:				List[Any]						)				->						None:
								__UpperCAmelCase      					=      node






							def    snake_case__    (   self					:				Tuple  ,		__a					:				Any						)				->						None:
								__UpperCAmelCase      					=      height





def    lowerCAmelCase       (		UpperCamelCase__					: MyNode | None							):


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





def    lowerCAmelCase       (		UpperCamelCase__					: int     ,       UpperCamelCase__					: int							):


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





def    lowerCAmelCase       (		UpperCamelCase__					: MyNode							):


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





def    lowerCAmelCase       (		UpperCamelCase__					: MyNode							):


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





def    lowerCAmelCase       (		UpperCamelCase__					: MyNode							):


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





def    lowerCAmelCase       (		UpperCamelCase__					: MyNode							):


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





def    lowerCAmelCase       (		UpperCamelCase__					: MyNode | None     ,       UpperCamelCase__					: Any							):


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





def    lowerCAmelCase       (		UpperCamelCase__					: MyNode							):


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





def    lowerCAmelCase       (		UpperCamelCase__					: MyNode							):


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





def    lowerCAmelCase       (		UpperCamelCase__					: MyNode     ,       UpperCamelCase__					: Any							):


	"""simple docstring"""
	__UpperCAmelCase      					=      root.get_left()
	__UpperCAmelCase      					=      root.get_right()
	if root.get_data() == data:
		if left_child is not None and right_child is not None:
			__UpperCAmelCase      					=      get_left_most(UpperCamelCase__							)
			root.set_data(UpperCamelCase__							)
			root.set_right(del_node(UpperCamelCase__     ,       UpperCamelCase__							)							)
		elif left_child is not None:
			__UpperCAmelCase      					=      left_child
		elif right_child is not None:
			__UpperCAmelCase      					=      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(UpperCamelCase__     ,       UpperCamelCase__							)							)
	else:  # root.get_data() < data
		if right_child is None:
			return root
		else:
			root.set_right(del_node(UpperCamelCase__     ,       UpperCamelCase__							)							)

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






class     A :





							def __init__(   self					:				Dict						)				->						None:
								__UpperCAmelCase      					=      None





							def    snake_case__    (   self					:				Optional[int]						)				->						int:
								return get_height(self.root						)





							def    snake_case__    (   self					:				Dict  ,		__a					:				str						)				->						None:
								print('''insert:''' + str(__SCREAMING_SNAKE_CASE						)						)
								__UpperCAmelCase      					=      insert_node(self.root  ,		__SCREAMING_SNAKE_CASE						)





							def    snake_case__    (   self					:				Tuple  ,		__a					:				int						)				->						None:
								print('''delete:''' + str(__SCREAMING_SNAKE_CASE						)						)
								if self.root is None:
									print('''Tree is empty!'''						)
									return
								__UpperCAmelCase      					=      del_node(self.root  ,		__SCREAMING_SNAKE_CASE						)






							def __str__(   self					:				List[Any]  ,		)				->						str:  # a level traversale, gives a more intuitive look on the tree
								__UpperCAmelCase      					=      ''''''
								__UpperCAmelCase      					=      MyQueue()
								q.push(self.root						)
								__UpperCAmelCase      					=      self.get_height()
								if layer == 0:
									return output
								__UpperCAmelCase      					=      0
								while not q.is_empty():
									__UpperCAmelCase      					=      q.pop()
									__UpperCAmelCase      					=      ''' ''' * int(math.pow(2  ,		layer - 1						)						)
									output += space
									if node is None:
										output += "*"
										q.push(__SCREAMING_SNAKE_CASE						)
										q.push(__SCREAMING_SNAKE_CASE						)
									else:
										output += str(node.get_data()						)
										q.push(node.get_left()						)
										q.push(node.get_right()						)
									output += space
									__UpperCAmelCase      					=      cnt + 1
									for i in range(1_0_0						):
										if cnt == math.pow(2  ,		__SCREAMING_SNAKE_CASE						) - 1:
											__UpperCAmelCase      					=      layer - 1
											if layer == 0:
												output += "\n*************************************"
												return output
											output += "\n"
											break
								output += "\n*************************************"
								return output





def    lowerCAmelCase       (		):


	"""simple docstring"""
	import doctest

	doctest.testmod()


if __name__ == "__main__":
					_test()
					__lowerCAmelCase						:							Tuple    							=						AVLtree()
					__lowerCAmelCase						:							Optional[Any]    							=						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))


"""simple docstring"""




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
a		:       Optional[Any]     						=       False
a		:       str     						=       False



def     __magic_name__		(					UpperCamelCase     :		Namespace     )     ->						Optional[int]:
    return TrainCommand(UpperCamelCase     )







class     lowercase(_lowercase       ):



     @staticmethod
     def 			lowercase__  (							__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			parser.add_parser('train'     ,				help='CLI tool to train a model on a task.'   )

         train_parser.add_argument(
             '--train_data'     ,				type=__SCREAMING_SNAKE_CASE     ,				required=__SCREAMING_SNAKE_CASE     ,				help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'     ,				)
         train_parser.add_argument(
             '--column_label'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=0     ,				help='Column of the dataset csv file with example labels.'   )
         train_parser.add_argument(
             '--column_text'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=1     ,				help='Column of the dataset csv file with example texts.'   )
         train_parser.add_argument(
             '--column_id'     ,				type=__SCREAMING_SNAKE_CASE     ,				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=__SCREAMING_SNAKE_CASE     ,				default=''     ,				help='path to validation dataset.'   )
         train_parser.add_argument(
             '--validation_split'     ,				type=__SCREAMING_SNAKE_CASE     ,				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=__SCREAMING_SNAKE_CASE     ,				default='./'     ,				help='path to saved the trained model.'   )

         train_parser.add_argument(
             '--task'     ,				type=__SCREAMING_SNAKE_CASE     ,				default='text_classification'     ,				help='Task to train the model on.'   )
         train_parser.add_argument(
             '--model'     ,				type=__SCREAMING_SNAKE_CASE     ,				default='bert-base-uncased'     ,				help='Model\'s name or path to stored model.'   )
         train_parser.add_argument('--train_batch_size'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=3_2     ,				help='Batch size for training.'   )
         train_parser.add_argument('--valid_batch_size'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=6_4     ,				help='Batch size for validation.'   )
         train_parser.add_argument('--learning_rate'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=3e-5     ,				help='Learning rate.'   )
         train_parser.add_argument('--adam_epsilon'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=1e-08     ,				help='Epsilon for Adam optimizer.'   )
         train_parser.set_defaults(func=__SCREAMING_SNAKE_CASE   )



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			logging.get_logger('transformers-cli/training'   )

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

         os.makedirs(args.output     ,				exist_ok=__SCREAMING_SNAKE_CASE   )
         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 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



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



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         raise NotImplementedError




     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         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   )


