


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   A__							(						UpperCamelCase__      ,   UpperCamelCase__      ,   unittest.TestCase     ):
						UpperCAmelCase									=  UNetaDModel
						UpperCAmelCase									=  "sample"




						@property
						def 							__UpperCamelCase (				self    :					Any     )							->   str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =4
										_SCREAMING_SNAKE_CASE	     =14
										_SCREAMING_SNAKE_CASE	     =16

										_SCREAMING_SNAKE_CASE	     =floats_tensor((batch_size, num_features, seq_len)     ).to(_a     )
										_SCREAMING_SNAKE_CASE	     =torch.tensor([10] * batch_size     ).to(_a     )

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




						@property
						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   Dict:


										"""simple docstring"""





										return (4, 14, 16)




						@property
						def 							__UpperCamelCase (				self    :					int     )							->   Union[str, Any]:


										"""simple docstring"""





										return (4, 14, 16)




						def 							__UpperCamelCase (				self    :					Dict     )							->   List[str]:


										"""simple docstring"""





										pass




						def 							__UpperCamelCase (				self    :					Tuple     )							->   List[str]:


										"""simple docstring"""





										pass




						@unittest.skipIf(torch_device == '''mps''' ,			'''mish op not supported in MPS'''     )
						def 							__UpperCamelCase (				self    :					int     )							->   List[Any]:


										"""simple docstring"""





										super().test_determinism()




						@unittest.skipIf(torch_device == '''mps''' ,			'''mish op not supported in MPS'''     )
						def 							__UpperCamelCase (				self    :					List[str]     )							->   str:


										"""simple docstring"""





										super().test_outputs_equivalence()




						@unittest.skipIf(torch_device == '''mps''' ,			'''mish op not supported in MPS'''     )
						def 							__UpperCamelCase (				self    :					Dict     )							->   Tuple:


										"""simple docstring"""





										super().test_from_save_pretrained()




						@unittest.skipIf(torch_device == '''mps''' ,			'''mish op not supported in MPS'''     )
						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   Any:


										"""simple docstring"""





										super().test_from_save_pretrained_variant()




						@unittest.skipIf(torch_device == '''mps''' ,			'''mish op not supported in MPS'''     )
						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   Optional[int]:


										"""simple docstring"""





										super().test_model_from_pretrained()




						@unittest.skipIf(torch_device == '''mps''' ,			'''mish op not supported in MPS'''     )
						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   List[Any]:


										"""simple docstring"""





										super().test_output()




						def 							__UpperCamelCase (				self    :					Any     )							->   List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     ={
										    '''block_out_channels''': (32, 64, 128, 256),
										    '''in_channels''': 14,
										    '''out_channels''': 14,
										    '''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''',
										}
										_SCREAMING_SNAKE_CASE	     =self.dummy_input
										return init_dict, inputs_dict




						@unittest.skipIf(torch_device == '''mps''' ,			'''mish op not supported in MPS'''     )
						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =UNetaDModel.from_pretrained(
										    '''bglick13/hopper-medium-v2-value-function-hor32''' ,			output_loading_info=_a ,			subfolder='''unet'''     )
										self.assertIsNotNone(_a     )
										self.assertEqual(len(loading_info['''missing_keys''']     ) ,			0     )

										model.to(_a     )
										_SCREAMING_SNAKE_CASE	     =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 							__UpperCamelCase (				self    :					int     )							->   Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =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     )

										_SCREAMING_SNAKE_CASE	     =model.config.in_channels
										_SCREAMING_SNAKE_CASE	     =16
										_SCREAMING_SNAKE_CASE	     =torch.randn((1, seq_len, num_features)     ).permute(
										    0 ,			2 ,			1     )  # match original, we can update values and remove
										_SCREAMING_SNAKE_CASE	     =torch.full((num_features,) ,			0     )

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE	     =model(_a ,			_a     ).sample.permute(0 ,			2 ,			1     )

										_SCREAMING_SNAKE_CASE	     =output[0, -3:, -3:].flatten()
										# fmt: off
										_SCREAMING_SNAKE_CASE	     =torch.tensor([-2.13_71_72, 1.1_42_60_16, 0.3_68_86_87, -0.76_69_22, 0.7_30_31_46, 0.11_03_88_64, -0.4_76_06_33, 0.13_27_01_72, 0.02_59_13_48]     )
										# fmt: on
										self.assertTrue(torch.allclose(_a ,			_a ,			rtol=1E-3     )     )




						def 							__UpperCamelCase (				self    :					int     )							->   Dict:


										"""simple docstring"""





										pass




						@slow
						def 							__UpperCamelCase (				self    :					Tuple     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     ='''harmonai/maestro-150k'''
										_SCREAMING_SNAKE_CASE	     =UNetaDModel.from_pretrained(_a ,			subfolder='''unet'''     )
										model.to(_a     )

										_SCREAMING_SNAKE_CASE	     =6_5536
										_SCREAMING_SNAKE_CASE	     =torch.sin(torch.arange(_a     )[None, None, :].repeat(1 ,			2 ,			1     )     ).to(_a     )
										_SCREAMING_SNAKE_CASE	     =torch.tensor([1]     ).to(_a     )

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE	     =model(_a ,			_a     ).sample

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

										assert (output_sum - 2_24.08_96).abs() < 0.5
										assert (output_max - 0.06_07).abs() < 4E-4

class   A__							(						UpperCamelCase__      ,   UpperCamelCase__      ,   unittest.TestCase     ):
						UpperCAmelCase									=  UNetaDModel
						UpperCAmelCase									=  "sample"




						@property
						def 							__UpperCamelCase (				self    :					int     )							->   str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =4
										_SCREAMING_SNAKE_CASE	     =14
										_SCREAMING_SNAKE_CASE	     =16

										_SCREAMING_SNAKE_CASE	     =floats_tensor((batch_size, num_features, seq_len)     ).to(_a     )
										_SCREAMING_SNAKE_CASE	     =torch.tensor([10] * batch_size     ).to(_a     )

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




						@property
						def 							__UpperCamelCase (				self    :					str     )							->   str:


										"""simple docstring"""





										return (4, 14, 16)




						@property
						def 							__UpperCamelCase (				self    :					List[str]     )							->   Optional[Any]:


										"""simple docstring"""





										return (4, 14, 1)




						@unittest.skipIf(torch_device == '''mps''' ,			'''mish op not supported in MPS'''     )
						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   Optional[Any]:


										"""simple docstring"""





										super().test_determinism()




						@unittest.skipIf(torch_device == '''mps''' ,			'''mish op not supported in MPS'''     )
						def 							__UpperCamelCase (				self    :					List[str]     )							->   Tuple:


										"""simple docstring"""





										super().test_outputs_equivalence()




						@unittest.skipIf(torch_device == '''mps''' ,			'''mish op not supported in MPS'''     )
						def 							__UpperCamelCase (				self    :					Dict     )							->   Union[str, Any]:


										"""simple docstring"""





										super().test_from_save_pretrained()




						@unittest.skipIf(torch_device == '''mps''' ,			'''mish op not supported in MPS'''     )
						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   int:


										"""simple docstring"""





										super().test_from_save_pretrained_variant()




						@unittest.skipIf(torch_device == '''mps''' ,			'''mish op not supported in MPS'''     )
						def 							__UpperCamelCase (				self    :					List[Any]     )							->   str:


										"""simple docstring"""





										super().test_model_from_pretrained()




						@unittest.skipIf(torch_device == '''mps''' ,			'''mish op not supported in MPS'''     )
						def 							__UpperCamelCase (				self    :					Tuple     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.prepare_init_args_and_inputs_for_common()
										_SCREAMING_SNAKE_CASE	     =self.model_class(**_a     )
										model.to(_a     )
										model.eval()

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE	     =model(**_a     )

														if isinstance(_a ,			_a     ):
																		_SCREAMING_SNAKE_CASE	     =output.sample

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




						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   str:


										"""simple docstring"""





										pass




						def 							__UpperCamelCase (				self    :					Dict     )							->   Any:


										"""simple docstring"""





										pass




						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     ={
										    '''in_channels''': 14,
										    '''out_channels''': 14,
										    '''down_block_types''': ['''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''],
										    '''up_block_types''': [],
										    '''out_block_type''': '''ValueFunction''',
										    '''mid_block_type''': '''ValueFunctionMidBlock1D''',
										    '''block_out_channels''': [32, 64, 128, 256],
										    '''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''',
										}
										_SCREAMING_SNAKE_CASE	     =self.dummy_input
										return init_dict, inputs_dict




						@unittest.skipIf(torch_device == '''mps''' ,			'''mish op not supported in MPS'''     )
						def 							__UpperCamelCase (				self    :					List[str]     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =UNetaDModel.from_pretrained(
										    '''bglick13/hopper-medium-v2-value-function-hor32''' ,			output_loading_info=_a ,			subfolder='''value_function'''     )
										self.assertIsNotNone(_a     )
										self.assertEqual(len(vf_loading_info['''missing_keys''']     ) ,			0     )

										value_function.to(_a     )
										_SCREAMING_SNAKE_CASE	     =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 							__UpperCamelCase (				self    :					Optional[int]     )							->   List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =UNetaDModel.from_pretrained(
										    '''bglick13/hopper-medium-v2-value-function-hor32''' ,			output_loading_info=_a ,			subfolder='''value_function'''     )
										torch.manual_seed(0     )
										if torch.cuda.is_available():
														torch.cuda.manual_seed_all(0     )

										_SCREAMING_SNAKE_CASE	     =value_function.config.in_channels
										_SCREAMING_SNAKE_CASE	     =14
										_SCREAMING_SNAKE_CASE	     =torch.randn((1, seq_len, num_features)     ).permute(
										    0 ,			2 ,			1     )  # match original, we can update values and remove
										_SCREAMING_SNAKE_CASE	     =torch.full((num_features,) ,			0     )

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE	     =value_function(_a ,			_a     ).sample

										# fmt: off
										_SCREAMING_SNAKE_CASE	     =torch.tensor([1_65.25] * seq_len     )
										# fmt: on
										self.assertTrue(torch.allclose(_a ,			_a ,			rtol=1E-3     )     )




						def 							__UpperCamelCase (				self    :					List[str]     )							->   int:


										"""simple docstring"""





										pass



import math as m







def   lowerCamelCase(      a__		,a__		,a__=0.001):
				return (f(a + h) - f(a - h)) / (2 * h)







def   lowerCamelCase(      a__		,a__=0		,a__=100		,a__=0.0001		,a__=1e-6		,a__=False):
				_SCREAMING_SNAKE_CASE	     =xa  # set the initial guess
				_SCREAMING_SNAKE_CASE	     =[a]
				_SCREAMING_SNAKE_CASE	     =abs(f(a__))
				_SCREAMING_SNAKE_CASE	     =lambda a__: calc_derivative(a__		,a__		,h=a__)  # noqa: E731  Derivative of f(x)
				for _ in range(a__):
								if fa(a__) == 0:
												raise ValueError('''No converging solution found''')
								_SCREAMING_SNAKE_CASE	     =a - f(a__) / fa(a__)  # Calculate the next estimate
								if logsteps:
												steps.append(a__)
								if error < maxerror:
												break
				else:
								raise ValueError('''Iteration limit reached, no converging solution found''')
				if logsteps:
								# If logstep is true, then log intermediate steps
								return a, error, steps
				return a, error


if __name__ == "__main__":
							from matplotlib import pyplot as plt

							snake_case_  :	Tuple							  =							lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
							snake_case_  , snake_case_  , snake_case_  :	str							  =							newton_raphson(
							    f, xa=10, maxiter=10_00, step=1e-6, logsteps=True
							)
							plt.plot([abs(f(x)) for x in steps])
							plt.xlabel('''step''')
							plt.ylabel('''error''')
							plt.show()
							print(f"""solution = {{{solution:f}}}, error = {{{error:f}}}""")