



"""simple docstring"""
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


class 							lowerCamelCase_(      A__, A__, unittest.TestCase			):





					'''simple docstring'''



					lowercase__      :				Tuple        =       UNetaDModel
					lowercase__      :				List[str]        =       'sample'

					@property
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = 4
									_lowerCamelCase					     = 1_4
									_lowerCamelCase					     = 1_6

									_lowerCamelCase					     = floats_tensor((batch_size, num_features, seq_len)      ).to(lowerCamelCase__      )
									_lowerCamelCase					     = torch.tensor([1_0] * batch_size      ).to(lowerCamelCase__      )

									return {"sample": noise, "timestep": time_step}

					@property
					def 	snake_case__					(			self      ):
									return (4, 1_4, 1_6)

					@property
					def 	snake_case__					(			self      ):
									return (4, 1_4, 1_6)

					def 	snake_case__					(			self      ):
									pass

					def 	snake_case__					(			self      ):
									pass

					@unittest.skipIf(torch_device == '''mps''' ,				'''mish op not supported in MPS'''      )
					def 	snake_case__					(			self      ):
									super().test_determinism()

					@unittest.skipIf(torch_device == '''mps''' ,				'''mish op not supported in MPS'''      )
					def 	snake_case__					(			self      ):
									super().test_outputs_equivalence()

					@unittest.skipIf(torch_device == '''mps''' ,				'''mish op not supported in MPS'''      )
					def 	snake_case__					(			self      ):
									super().test_from_save_pretrained()

					@unittest.skipIf(torch_device == '''mps''' ,				'''mish op not supported in MPS'''      )
					def 	snake_case__					(			self      ):
									super().test_from_save_pretrained_variant()

					@unittest.skipIf(torch_device == '''mps''' ,				'''mish op not supported in MPS'''      )
					def 	snake_case__					(			self      ):
									super().test_model_from_pretrained()

					@unittest.skipIf(torch_device == '''mps''' ,				'''mish op not supported in MPS'''      )
					def 	snake_case__					(			self      ):
									super().test_output()

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = {
									    '''block_out_channels''': (3_2, 6_4, 1_2_8, 2_5_6),
									    '''in_channels''': 1_4,
									    '''out_channels''': 1_4,
									    '''time_embedding_type''': '''positional''',
									    '''use_timestep_embedding''': True,
									    '''flip_sin_to_cos''': False,
									    '''freq_shift''': 1.0,
									    '''out_block_type''': '''OutConv1DBlock''',
									    '''mid_block_type''': '''MidResTemporalBlock1D''',
									    '''down_block_types''': ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''),
									    '''up_block_types''': ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D'''),
									    '''act_fn''': '''mish''',
									}
									_lowerCamelCase					     = self.dummy_input
									return init_dict, inputs_dict

					@unittest.skipIf(torch_device == '''mps''' ,				'''mish op not supported in MPS'''      )
					def 	snake_case__					(			self      ):
									_lowerCamelCase     ,   _lowerCamelCase					     = UNetaDModel.from_pretrained(
									    '''bglick13/hopper-medium-v2-value-function-hor32''' ,				output_loading_info=lowerCamelCase__ ,				subfolder='''unet'''      )
									self.assertIsNotNone(lowerCamelCase__      )
									self.assertEqual(len(loading_info['''missing_keys''']      ) ,				0      )

									model.to(lowerCamelCase__      )
									_lowerCamelCase					     = model(**self.dummy_input      )

									assert image is not None, "Make sure output is not None"

					@unittest.skipIf(torch_device == '''mps''' ,				'''mish op not supported in MPS'''      )
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = UNetaDModel.from_pretrained('''bglick13/hopper-medium-v2-value-function-hor32''' ,				subfolder='''unet'''      )
									torch.manual_seed(0      )
									if torch.cuda.is_available():
													torch.cuda.manual_seed_all(0      )

									_lowerCamelCase					     = model.config.in_channels
									_lowerCamelCase					     = 1_6
									_lowerCamelCase					     = torch.randn((1, seq_len, num_features)      ).permute(
									    0 ,				2 ,				1      )  # match original, we can update values and remove
									_lowerCamelCase					     = torch.full((num_features,) ,				0      )

									with torch.no_grad():
													_lowerCamelCase					     = model(lowerCamelCase__ ,				lowerCamelCase__      ).sample.permute(0 ,				2 ,				1      )

									_lowerCamelCase					     = output[0, -3:, -3:].flatten()
									# fmt: off
									_lowerCamelCase					     = torch.tensor([-2.1_3_7_1_7_2, 1.1_4_2_6_0_1_6, 0.3_6_8_8_6_8_7, -0.7_6_6_9_2_2, 0.7_3_0_3_1_4_6, 0.1_1_0_3_8_8_6_4, -0.4_7_6_0_6_3_3, 0.1_3_2_7_0_1_7_2, 0.0_2_5_9_1_3_4_8]      )
									# fmt: on
									self.assertTrue(torch.allclose(lowerCamelCase__ ,				lowerCamelCase__ ,				rtol=1e-3      )      )

					def 	snake_case__					(			self      ):
									# Not implemented yet for this UNet
									pass





					@slow
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = '''harmonai/maestro-150k'''
									_lowerCamelCase					     = UNetaDModel.from_pretrained(lowerCamelCase__ ,				subfolder='''unet'''      )
									model.to(lowerCamelCase__      )

									_lowerCamelCase					     = 6_5_5_3_6
									_lowerCamelCase					     = torch.sin(torch.arange(lowerCamelCase__      )[None, None, :].repeat(1 ,				2 ,				1      )      ).to(lowerCamelCase__      )
									_lowerCamelCase					     = torch.tensor([1]      ).to(lowerCamelCase__      )

									with torch.no_grad():
													_lowerCamelCase					     = model(lowerCamelCase__ ,				lowerCamelCase__      ).sample

									_lowerCamelCase					     = output.abs().sum()
									_lowerCamelCase					     = output.abs().max()

									assert (output_sum - 2_2_4.0_8_9_6).abs() < 0.5
									assert (output_max - 0.0_6_0_7).abs() < 4e-4


class 							lowerCamelCase_(      A__, A__, unittest.TestCase			):





					'''simple docstring'''



					lowercase__      :				Any        =       UNetaDModel
					lowercase__      :				Optional[int]        =       'sample'

					@property
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = 4
									_lowerCamelCase					     = 1_4
									_lowerCamelCase					     = 1_6

									_lowerCamelCase					     = floats_tensor((batch_size, num_features, seq_len)      ).to(lowerCamelCase__      )
									_lowerCamelCase					     = torch.tensor([1_0] * batch_size      ).to(lowerCamelCase__      )

									return {"sample": noise, "timestep": time_step}

					@property
					def 	snake_case__					(			self      ):
									return (4, 1_4, 1_6)

					@property
					def 	snake_case__					(			self      ):
									return (4, 1_4, 1)

					@unittest.skipIf(torch_device == '''mps''' ,				'''mish op not supported in MPS'''      )
					def 	snake_case__					(			self      ):
									super().test_determinism()

					@unittest.skipIf(torch_device == '''mps''' ,				'''mish op not supported in MPS'''      )
					def 	snake_case__					(			self      ):
									super().test_outputs_equivalence()

					@unittest.skipIf(torch_device == '''mps''' ,				'''mish op not supported in MPS'''      )
					def 	snake_case__					(			self      ):
									super().test_from_save_pretrained()

					@unittest.skipIf(torch_device == '''mps''' ,				'''mish op not supported in MPS'''      )
					def 	snake_case__					(			self      ):
									super().test_from_save_pretrained_variant()

					@unittest.skipIf(torch_device == '''mps''' ,				'''mish op not supported in MPS'''      )
					def 	snake_case__					(			self      ):
									super().test_model_from_pretrained()

					@unittest.skipIf(torch_device == '''mps''' ,				'''mish op not supported in MPS'''      )
					def 	snake_case__					(			self      ):
									# UNetRL is a value-function is different output shape
									_lowerCamelCase     ,   _lowerCamelCase					     = self.prepare_init_args_and_inputs_for_common()
									_lowerCamelCase					     = self.model_class(**lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()

									with torch.no_grad():
													_lowerCamelCase					     = model(**lowerCamelCase__      )

													if isinstance(lowerCamelCase__ ,				lowerCamelCase__      ):
																	_lowerCamelCase					     = output.sample

									self.assertIsNotNone(lowerCamelCase__      )
									_lowerCamelCase					     = torch.Size((inputs_dict['''sample'''].shape[0], 1)      )
									self.assertEqual(output.shape ,				lowerCamelCase__ ,				'''Input and output shapes do not match'''      )

					def 	snake_case__					(			self      ):
									pass

					def 	snake_case__					(			self      ):
									pass

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = {
									    '''in_channels''': 1_4,
									    '''out_channels''': 1_4,
									    '''down_block_types''': ['''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''],
									    '''up_block_types''': [],
									    '''out_block_type''': '''ValueFunction''',
									    '''mid_block_type''': '''ValueFunctionMidBlock1D''',
									    '''block_out_channels''': [3_2, 6_4, 1_2_8, 2_5_6],
									    '''layers_per_block''': 1,
									    '''downsample_each_block''': True,
									    '''use_timestep_embedding''': True,
									    '''freq_shift''': 1.0,
									    '''flip_sin_to_cos''': False,
									    '''time_embedding_type''': '''positional''',
									    '''act_fn''': '''mish''',
									}
									_lowerCamelCase					     = self.dummy_input
									return init_dict, inputs_dict

					@unittest.skipIf(torch_device == '''mps''' ,				'''mish op not supported in MPS'''      )
					def 	snake_case__					(			self      ):
									_lowerCamelCase     ,   _lowerCamelCase					     = UNetaDModel.from_pretrained(
									    '''bglick13/hopper-medium-v2-value-function-hor32''' ,				output_loading_info=lowerCamelCase__ ,				subfolder='''value_function'''      )
									self.assertIsNotNone(lowerCamelCase__      )
									self.assertEqual(len(vf_loading_info['''missing_keys''']      ) ,				0      )

									value_function.to(lowerCamelCase__      )
									_lowerCamelCase					     = value_function(**self.dummy_input      )

									assert image is not None, "Make sure output is not None"

					@unittest.skipIf(torch_device == '''mps''' ,				'''mish op not supported in MPS'''      )
					def 	snake_case__					(			self      ):
									_lowerCamelCase     ,   _lowerCamelCase					     = UNetaDModel.from_pretrained(
									    '''bglick13/hopper-medium-v2-value-function-hor32''' ,				output_loading_info=lowerCamelCase__ ,				subfolder='''value_function'''      )
									torch.manual_seed(0      )
									if torch.cuda.is_available():
													torch.cuda.manual_seed_all(0      )

									_lowerCamelCase					     = value_function.config.in_channels
									_lowerCamelCase					     = 1_4
									_lowerCamelCase					     = torch.randn((1, seq_len, num_features)      ).permute(
									    0 ,				2 ,				1      )  # match original, we can update values and remove
									_lowerCamelCase					     = torch.full((num_features,) ,				0      )

									with torch.no_grad():
													_lowerCamelCase					     = value_function(lowerCamelCase__ ,				lowerCamelCase__      ).sample

									# fmt: off
									_lowerCamelCase					     = torch.tensor([1_6_5.2_5] * seq_len      )
									# fmt: on
									self.assertTrue(torch.allclose(lowerCamelCase__ ,				lowerCamelCase__ ,				rtol=1e-3      )      )





					def 	snake_case__					(			self      ):
									# Not implemented yet for this UNet
									pass





"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt



def 				lowerCAmelCase_(      lowercase_   :							List[str]							)		->				List[Any]:
				return 1 / (1 + np.exp(-1 * x							))


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				lowerCamelCase__=False      ):
									_lowerCamelCase					     = units
									_lowerCamelCase					     = None
									_lowerCamelCase					     = None
									_lowerCamelCase					     = activation
									if learning_rate is None:
													_lowerCamelCase					     = 0.3
									_lowerCamelCase					     = learning_rate
									_lowerCamelCase					     = is_input_layer

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = np.asmatrix(np.random.normal(0 ,				0.5 ,				(self.units, back_units)      )      )
									_lowerCamelCase					     = np.asmatrix(np.random.normal(0 ,				0.5 ,				self.units      )      ).T
									if self.activation is None:
													_lowerCamelCase					     = sigmoid

					def 	snake_case__					(			self      ):
									# activation function may be sigmoid or linear
									if self.activation == sigmoid:
													_lowerCamelCase					     = np.dot(self.output ,				(1 - self.output).T      )
													_lowerCamelCase					     = np.diag(np.diag(lowerCamelCase__      )      )
									else:
													_lowerCamelCase					     = 1
									return gradient_activation

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = xdata
									if self.is_input_layer:
													# input layer
													_lowerCamelCase					     = xdata
													_lowerCamelCase					     = xdata
													return xdata
									else:
													_lowerCamelCase					     = np.dot(self.weight ,				self.xdata      ) - self.bias
													_lowerCamelCase					     = self.activation(self.wx_plus_b      )
													return self.output





					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = self.cal_gradient()  # i * i 维
									_lowerCamelCase					     = np.asmatrix(np.dot(gradient.T ,				lowerCamelCase__      )      )

									_lowerCamelCase					     = np.asmatrix(self.xdata      )
									_lowerCamelCase					     = -1
									_lowerCamelCase					     = self.weight

									_lowerCamelCase					     = np.dot(gradient.T ,				self._gradient_weight.T      )
									_lowerCamelCase					     = gradient * self._gradient_bias
									_lowerCamelCase					     = np.dot(lowerCamelCase__ ,				self._gradient_x      ).T
									# upgrade: the Negative gradient direction
									_lowerCamelCase					     = self.weight - self.learn_rate * self.gradient_weight
									_lowerCamelCase					     = self.bias - self.learn_rate * self.gradient_bias.T
									# updates the weights and bias according to learning rate (0.3 if undefined)
									return self.gradient


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self      ):
									_lowerCamelCase					     = []
									_lowerCamelCase					     = []
									_lowerCamelCase					     = plt.figure()
									_lowerCamelCase					     = self.fig_loss.add_subplot(1 ,				1 ,				1      )

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									self.layers.append(lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									for i, layer in enumerate(self.layers[:]      ):
													if i < 1:
																	_lowerCamelCase					     = True
													else:
																	layer.initializer(self.layers[i - 1].units      )

					def 	snake_case__					(			self      ):
									for i, layer in enumerate(self.layers[:]      ):
													print(F"""------- layer {i} -------"""      )
													print('''weight.shape ''' ,				np.shape(layer.weight      )      )
													print('''bias.shape ''' ,				np.shape(layer.bias      )      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = train_round
									_lowerCamelCase					     = accuracy

									self.ax_loss.hlines(self.accuracy ,				0 ,				self.train_round * 1.1      )

									_lowerCamelCase					     = np.shape(lowerCamelCase__      )
									for _ in range(lowerCamelCase__      ):
													_lowerCamelCase					     = 0
													for row in range(x_shape[0]      ):
																	_lowerCamelCase					     = np.asmatrix(xdata[row, :]      ).T
																	_lowerCamelCase					     = np.asmatrix(ydata[row, :]      ).T

																	# forward propagation
																	for layer in self.layers:
																					_lowerCamelCase					     = layer.forward_propagation(_xdata      )

																	_lowerCamelCase     ,   _lowerCamelCase					     = self.cal_loss(_ydata ,				_xdata      )
																	_lowerCamelCase					     = all_loss + loss

																	# back propagation: the input_layer does not upgrade
																	for layer in self.layers[:0:-1]:
																					_lowerCamelCase					     = layer.back_propagation(lowerCamelCase__      )

													_lowerCamelCase					     = all_loss / x_shape[0]
													self.train_mse.append(lowerCamelCase__      )

													self.plot_loss()

													if mse < self.accuracy:
																	print('''----达到精度----'''      )
																	return mse
									return None

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = np.sum(np.power((ydata - ydata_) ,				2      )      )
									_lowerCamelCase					     = 2 * (ydata_ - ydata)
									# vector (shape is the same as _ydata.shape)
									return self.loss, self.loss_gradient





					def 	snake_case__					(			self      ):
									if self.ax_loss.lines:
													self.ax_loss.lines.remove(self.ax_loss.lines[0]      )
									self.ax_loss.plot(self.train_mse ,				'''r-'''      )
									plt.ion()
									plt.xlabel('''step'''      )
									plt.ylabel('''loss'''      )
									plt.show()
									plt.pause(0.1      )



def 				lowerCAmelCase_(      )		->				Optional[Any]:
				_lowerCamelCase					     = np.random.randn(10  ,      10							)
				_lowerCamelCase					     = np.asarray(
				    [
				        [0.8, 0.4],
				        [0.4, 0.3],
				        [0.3_4, 0.4_5],
				        [0.6_7, 0.3_2],
				        [0.8_8, 0.6_7],
				        [0.7_8, 0.7_7],
				        [0.5_5, 0.6_6],
				        [0.5_5, 0.4_3],
				        [0.5_4, 0.1],
				        [0.1, 0.5],
				    ]							)
				_lowerCamelCase					     = BPNN()
				for i in (10, 20, 30, 2):
								model.add_layer(DenseLayer(lowercase_							)							)
				model.build()
				model.summary()
				model.train(xdata=lowercase_  ,      ydata=lowercase_  ,      train_round=1_00  ,      accuracy=0.0_1							)


if __name__ == "__main__":
			example()
