



import numpy

# List of input, output pairs
lowerCAmelCase__					  =  (
    ((5, 2, 3), 1_5),
    ((6, 5, 9), 2_5),
    ((1_1, 1_2, 1_3), 4_1),
    ((1, 1, 1), 8),
    ((1_1, 1_2, 1_3), 4_1),
)
lowerCAmelCase__					  =  (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
lowerCAmelCase__					  =  [2, 4, 1, 5]
lowerCAmelCase__					  =  len(train_data)
lowerCAmelCase__					  =  0.009


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__="train"						):

				"""simple docstring"""


				return calculate_hypothesis_value(lowerCamelCase__					,			lowerCamelCase__						) - output(
				    lowerCamelCase__					,			lowerCamelCase__						)


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   List[Any]													=					0
				for i in range(len(lowerCamelCase__						) - 1						):
								hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
				hyp_val += parameter_vector[0]
				return hyp_val


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				if data_set == "train":
								return train_data[example_no][1]
				elif data_set == "test":
								return test_data[example_no][1]
				return None


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				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  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=m						):

				"""simple docstring"""


				lowercase__					:   int													=					0
				for i in range(lowerCamelCase__						):
								if index == -1:
												summation_value += _error(lowerCamelCase__						)
								else:
												summation_value += _error(lowerCamelCase__						) * train_data[i][0][index]
				return summation_value


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Union[str, Any]													=					summation_of_cost_derivative(lowerCamelCase__					,			lowerCamelCase__						) / m
				return cost_derivative_value


def  __lowerCamelCase (       ):

				"""simple docstring"""


				global parameter_vector
				# Tune these values to set a tolerance value for predicted output
				lowercase__					:   Optional[int]													=					0.000002
				lowercase__					:   List[Any]													=					0
				lowercase__					:   Optional[Any]													=					0
				while True:
								j += 1
								lowercase__					:   Any													=					[0, 0, 0, 0]
								for i in range(0					,			len(lowerCamelCase__						)						):
												lowercase__					:   str													=					get_cost_derivative(i - 1						)
												lowercase__					:   List[Any]													=					(
												    parameter_vector[i] - LEARNING_RATE * cost_derivative
												)
								if numpy.allclose(
								    lowerCamelCase__					,			lowerCamelCase__					,			atol=lowerCamelCase__					,			rtol=lowerCamelCase__					,			):
												break
								lowercase__					:   Union[str, Any]													=					temp_parameter_vector
				print(("Number of iterations:", j)						)


def  __lowerCamelCase (       ):

				"""simple docstring"""


				for i in range(len(lowerCamelCase__						)						):
								print(("Actual output value:", output(lowerCamelCase__					,			"test"						))						)
								print(("Hypothesis output:", calculate_hypothesis_value(lowerCamelCase__					,			"test"						))						)


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





from typing import TYPE_CHECKING

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


lowerCAmelCase__					  =  {
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''DebertaTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''DebertaForMaskedLM''',
				    '''DebertaForQuestionAnswering''',
				    '''DebertaForSequenceClassification''',
				    '''DebertaForTokenClassification''',
				    '''DebertaModel''',
				    '''DebertaPreTrainedModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFDebertaForMaskedLM''',
				    '''TFDebertaForQuestionAnswering''',
				    '''TFDebertaForSequenceClassification''',
				    '''TFDebertaForTokenClassification''',
				    '''TFDebertaModel''',
				    '''TFDebertaPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
				from .tokenization_deberta import DebertaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_deberta_fast import DebertaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deberta import (
								    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DebertaForMaskedLM,
								    DebertaForQuestionAnswering,
								    DebertaForSequenceClassification,
								    DebertaForTokenClassification,
								    DebertaModel,
								    DebertaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_deberta import (
								    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDebertaForMaskedLM,
								    TFDebertaForQuestionAnswering,
								    TFDebertaForSequenceClassification,
								    TFDebertaForTokenClassification,
								    TFDebertaModel,
								    TFDebertaPreTrainedModel,
								)


else:
				import sys

				lowerCAmelCase__					  =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




