


"""simple docstring"""







import numpy

# List of input, output pairs
a__    :							int            =					(
    ((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),
)
a__    :							str            =					(((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
a__    :							List[Any]            =					[2, 4, 1, 5]
a__    :							Union[str, Any]            =					len(train_data)
a__    :							Tuple            =					0.0_09




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_="train"       ):




					'''simple docstring'''


					return calculate_hypothesis_value(lowerCAmelCase_  ,						lowerCAmelCase_       ) - output(
					    lowerCAmelCase_  ,						lowerCAmelCase_       )




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    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        UpperCAmelCase__	(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        UpperCAmelCase__	(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        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_=m       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    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        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    summation_of_cost_derivative(lowerCAmelCase_  ,						lowerCAmelCase_       ) / m
					return cost_derivative_value




def        UpperCAmelCase__	():




					'''simple docstring'''


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




def        UpperCAmelCase__	():




					'''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()




"""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

a__    :							Dict            =					TaTokenizer

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

a__    :							Any            =					TaTokenizerFast

a__    :							Tuple            =					{'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

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

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

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Union[str, 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

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