
'''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()






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
						from ..ta.tokenization_ta import TaTokenizer
else:
						from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCAmelCase					:    Tuple           = TaTokenizer

if is_tokenizers_available():
						from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
						from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCAmelCase					:    Optional[int]           = TaTokenizerFast

lowerCAmelCase					:    Any           = {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    Optional[int]           = [
						    'MT5EncoderModel',
						    'MT5ForConditionalGeneration',
						    'MT5ForQuestionAnswering',
						    'MT5Model',
						    'MT5PreTrainedModel',
						    'MT5Stack',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    Dict           = ['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    Optional[Any]           = ['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
						from .configuration_mta import MTaConfig, MTaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mta import (
												    MTaEncoderModel,
												    MTaForConditionalGeneration,
												    MTaForQuestionAnswering,
												    MTaModel,
												    MTaPreTrainedModel,
												    MTaStack,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
						import sys

						lowerCAmelCase					:    Tuple           = _LazyModule(
						    __name__,
						    globals()['__file__'],
						    _import_structure,
						    extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
						    module_spec=__spec__,
						)




