






'''simple docstring'''
import numpy

# List of input, output pairs
snake_case_			:     Tuple  	=  (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
snake_case_			:     Optional[Any]  	=  (((515, 22, 13), 555), ((61, 35, 49), 150))
snake_case_			:     List[Any]  	=  [2, 4, 1, 5]
snake_case_			:     int  	=  len(train_data)
snake_case_			:     Union[str, Any]  	=  0.0_09






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_="train"							):
						return calculate_hypothesis_value(UpperCAmelCase_  ,  UpperCAmelCase_							) - output(
						    UpperCAmelCase_  ,  UpperCAmelCase_							)






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		Dict			    =       0
						for i in range(len(UpperCAmelCase_							) - 1							):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val






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






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						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__   (					UpperCAmelCase_  ,  UpperCAmelCase_=m							):
						_UpperCamelCase :		Union[str, Any]			    =       0
						for i in range(UpperCAmelCase_							):
												if index == -1:
																		summation_value += _error(UpperCAmelCase_							)
												else:
																		summation_value += _error(UpperCAmelCase_							) * train_data[i][0][index]
						return summation_value






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		Union[str, Any]			    =       summation_of_cost_derivative(UpperCAmelCase_  ,  UpperCAmelCase_							) / m
						return cost_derivative_value






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






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


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







'''simple docstring'''
import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
					import torch






@require_torch
@require_optimum
@slow
class 							lowercase__     (							unittest.TestCase       ):





	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       'hf-internal-testing/tiny-random-t5'
							_UpperCamelCase :		str			    =       AutoTokenizer.from_pretrained(lowerCamelCase__			)
							_UpperCamelCase :		int			    =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__			)

							_UpperCamelCase :		List[str]			    =       tokenizer('This is me'			,return_tensors='pt'			)

							_UpperCamelCase :		str			    =       model.to_bettertransformer()

							self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

							_UpperCamelCase :		Optional[Any]			    =       model.generate(**lowerCamelCase__			)

							_UpperCamelCase :		Union[str, Any]			    =       model.reverse_bettertransformer()

							self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

							with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(lowerCamelCase__			)

													_UpperCamelCase :		List[Any]			    =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__			)

													self.assertFalse(
													    any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

													_UpperCamelCase :		Optional[Any]			    =       model_reloaded.generate(**lowerCamelCase__			)
													self.assertTrue(torch.allclose(lowerCamelCase__			,lowerCamelCase__			)			)





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       'hf-internal-testing/tiny-random-t5'
							_UpperCamelCase :		List[Any]			    =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__			)

							_UpperCamelCase :		List[str]			    =       model.to_bettertransformer()

							with tempfile.TemporaryDirectory() as tmpdirname:
													with self.assertRaises(lowerCamelCase__			):
																			model.save_pretrained(lowerCamelCase__			)

													_UpperCamelCase :		str			    =       model.reverse_bettertransformer()
													model.save_pretrained(lowerCamelCase__			)


