


"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


A     :   Optional[int]			    =			logging.get_logger(__name__)

A     :   Optional[Any]			    =			{
    "caidas/swin2sr-classicalsr-x2-64": (
        "https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"
    ),
}
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    str				      ="""swin2sr"""

							__UpperCAmelCase						:    Union[str, Any]				      ={
							    """hidden_size""": """embed_dim""",
							    """num_attention_heads""": """num_heads""",
							    """num_hidden_layers""": """num_layers""",
							}

							def __init__(     self    ,   __a=64    ,   __a=1    ,   __a=3    ,   __a=1_80    ,   __a=[6, 6, 6, 6, 6, 6]    ,   __a=[6, 6, 6, 6, 6, 6]    ,   __a=8    ,   __a=2.0    ,   __a=True    ,   __a=0.0    ,   __a=0.0    ,   __a=0.1    ,   __a="gelu"    ,   __a=False    ,   __a=0.0_2    ,   __a=1e-5    ,   __a=2    ,   __a=1.0    ,   __a="1conv"    ,   __a="pixelshuffle"    ,   **__a    ,   ):
										super().__init__(**__a	)

										__lowerCAmelCase				    =			image_size
										__lowerCAmelCase				    =			patch_size
										__lowerCAmelCase				    =			num_channels
										__lowerCAmelCase				    =			embed_dim
										__lowerCAmelCase				    =			depths
										__lowerCAmelCase				    =			len(__a	)
										__lowerCAmelCase				    =			num_heads
										__lowerCAmelCase				    =			window_size
										__lowerCAmelCase				    =			mlp_ratio
										__lowerCAmelCase				    =			qkv_bias
										__lowerCAmelCase				    =			hidden_dropout_prob
										__lowerCAmelCase				    =			attention_probs_dropout_prob
										__lowerCAmelCase				    =			drop_path_rate
										__lowerCAmelCase				    =			hidden_act
										__lowerCAmelCase				    =			use_absolute_embeddings
										__lowerCAmelCase				    =			layer_norm_eps
										__lowerCAmelCase				    =			initializer_range
										__lowerCAmelCase				    =			upscale
										__lowerCAmelCase				    =			img_range
										__lowerCAmelCase				    =			resi_connection
										__lowerCAmelCase				    =			upsampler








"""simple docstring"""


import numpy

# List of input, output pairs
A     :   Any			    =			(
    ((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     :   Union[str, Any]			    =			[2, 4, 1, 5]
A     :   int			    =			len(train_data)
A     :   Dict			    =			0.009


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase="train"      ):

			'''simple docstring'''




			return calculate_hypothesis_value(_UpperCamelCase    , _UpperCamelCase      ) - output(
			    _UpperCamelCase    , _UpperCamelCase      )


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			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 _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''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	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''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	(		_UpperCamelCase    , _UpperCamelCase=m      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			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 _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			summation_of_cost_derivative(_UpperCamelCase    , _UpperCamelCase      ) / m
			return cost_derivative_value


def _lowerCamelCase	(		):

			'''simple docstring'''




			global parameter_vector
			# Tune these values to set a tolerance value for predicted output
			__lowerCAmelCase				    =			0.00_00_02
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			0
			while True:
						j += 1
						__lowerCAmelCase				    =			[0, 0, 0, 0]
						for i in range(0    , len(_UpperCamelCase      )      ):
									__lowerCAmelCase				    =			get_cost_derivative(i - 1      )
									__lowerCAmelCase				    =			(
									    parameter_vector[i] - LEARNING_RATE * cost_derivative
									)
						if numpy.allclose(
						    _UpperCamelCase    , _UpperCamelCase    , atol=_UpperCamelCase    , rtol=_UpperCamelCase    , ):
									break
						__lowerCAmelCase				    =			temp_parameter_vector
			print(("Number of iterations:", j)      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			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()




