
'''simple docstring'''
import numpy

# List of input, output pairs
lowerCAmelCase					:    Dict           = (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowerCAmelCase					:    int           = (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
lowerCAmelCase					:    str           = [2, 4, 1, 5]
lowerCAmelCase					:    int           = len(train_data)
lowerCAmelCase					:    Union[str, Any]           = 0.009







def 		A_(     A						:		List[str]		,     A						:		Any="train"):
							return calculate_hypothesis_value(A		,     A) - output(
							    A		,     A)







def 		A_(     A						:		Dict):
							UpperCamelCase   			=     0
							for i in range(len(A) - 1):
														hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
							hyp_val += parameter_vector[0]
							return hyp_val







def 		A_(     A						:		int		,     A						:		int):
							if data_set == "train":
														return train_data[example_no][1]
							elif data_set == "test":
														return test_data[example_no][1]
							return None







def 		A_(     A						:		str		,     A						:		Optional[Any]):
							if data_set == "train":
														return _hypothesis_value(train_data[example_no][0])
							elif data_set == "test":
														return _hypothesis_value(test_data[example_no][0])
							return None







def 		A_(     A						:		List[Any]		,     A						:		Optional[Any]=m):
							UpperCamelCase   			=     0
							for i in range(A):
														if index == -1:
																					summation_value += _error(A)
														else:
																					summation_value += _error(A) * train_data[i][0][index]
							return summation_value







def 		A_(     A						:		Dict):
							UpperCamelCase   			=     summation_of_cost_derivative(A		,     A) / m
							return cost_derivative_value







def 		A_(     ):
							global parameter_vector
							# Tune these values to set a tolerance value for predicted output
							UpperCamelCase   			=     0.000_002
							UpperCamelCase   			=     0
							UpperCamelCase   			=     0
							while True:
														j += 1
														UpperCamelCase   			=     [0, 0, 0, 0]
														for i in range(0		,     len(A)):
																					UpperCamelCase   			=     get_cost_derivative(i - 1)
																					UpperCamelCase   			=     (
																					    parameter_vector[i] - LEARNING_RATE * cost_derivative
																					)
														if numpy.allclose(
														    A		,     A		,     atol=A		,     rtol=A		,     ):
																					break
														UpperCamelCase   			=     temp_parameter_vector
							print(('Number of iterations:', j))







def 		A_(     ):
							for i in range(len(A)):
														print(('Actual output value:', output(A		,     'test')))
														print(('Hypothesis output:', calculate_hypothesis_value(A		,     'test')))


if __name__ == "__main__":
						run_gradient_descent()
						print('\nTesting gradient descent for a linear hypothesis function.\n')
						test_gradient_descent()






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
_snake_case      :							Any     	=    False
_snake_case      :							int     	=    False

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				return TrainCommand(__magic_name__						)





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						@staticmethod
						def        SCREAMING_SNAKE_CASE(			lowerCAmelCase__							:ArgumentParser     )  ->str:
										lowercase         =      parser.add_parser("train"		,    help="CLI tool to train a model on a task."     )

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

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





						def __init__(			self							:Optional[int]		,    lowerCAmelCase__							:Namespace     )  ->str:
										lowercase         =      logging.get_logger("transformers-cli/training"     )

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

										os.makedirs(args.output		,    exist_ok=lowerCAmelCase__     )
										lowercase         =      args.output

										lowercase         =      args.column_label
										lowercase         =      args.column_text
										lowercase         =      args.column_id

										self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''     )
										if args.task == "text_classification":
														lowercase         =      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}'''     )
										lowercase         =      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		,    )
										lowercase         =      None
										if args.validation_data:
														self.logger.info(F'''Loading validation dataset from {args.validation_data}'''     )
														lowercase         =      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		,    )

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





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->List[str]:
										if self.framework == "tf":
														return self.run_tf()
										return self.run_torch()





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Dict:
										raise NotImplementedError





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->str:
										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     )
