
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 			UpperCamelCase__		(     snake_case_,      snake_case_,      unittest.TestCase ):







	'''simple docstring'''
	_lowerCAmelCase											=					UNetaDModel
	_lowerCAmelCase											=					"sample"





	@property
	def 	__snake_case					(      self		):
					A__	:						Union[str, Any]								=				4
					A__	:						str								=				14
					A__	:						Any								=				16

					A__	:						Optional[Any]								=				floats_tensor((batch_size, num_features, seq_len)		).to(_A		)
					A__	:						Dict								=				torch.tensor([10] * batch_size		).to(_A		)

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





	@property
	def 	__snake_case					(      self		):
					return (4, 14, 16)





	@property
	def 	__snake_case					(      self		):
					return (4, 14, 16)





	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		):
					A__	:						List[Any]								=				{
					    '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',
					}
					A__	:						Tuple								=				self.dummy_input
					return init_dict, inputs_dict





	@unittest.skipIf(torch_device == '''mps'''					,							'''mish op not supported in MPS'''		)
	def 	__snake_case					(      self		):
					A__	:						Optional[int]								=				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		)
					A__	:						Optional[int]								=				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		):
					A__	:						Tuple								=				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		)

					A__	:						str								=				model.config.in_channels
					A__	:						str								=				16
					A__	:						List[Any]								=				torch.randn((1, seq_len, num_features)		).permute(
					    0					,							2					,							1		)  # match original, we can update values and remove
					A__	:						Tuple								=				torch.full((num_features,)					,							0		)

					with torch.no_grad():
									A__	:						Tuple								=				model(_A					,							_A		).sample.permute(0					,							2					,							1		)

					A__	:						List[Any]								=				output[0, -3:, -3:].flatten()
					# fmt: off
					A__	:						str								=				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(_A					,							_A					,							rtol=1e-3		)		)





	def 	__snake_case					(      self		):
					pass





	@slow
	def 	__snake_case					(      self		):
					A__	:						List[Any]								=				'harmonai/maestro-150k'
					A__	:						Dict								=				UNetaDModel.from_pretrained(_A					,							subfolder='''unet'''		)
					model.to(_A		)

					A__	:						int								=				6_5536
					A__	:						Optional[Any]								=				torch.sin(torch.arange(_A		)[None, None, :].repeat(1					,							2					,							1		)		).to(_A		)
					A__	:						Optional[Any]								=				torch.tensor([1]		).to(_A		)

					with torch.no_grad():
									A__	:						Any								=				model(_A					,							_A		).sample

					A__	:						int								=				output.abs().sum()
					A__	:						Optional[Any]								=				output.abs().max()

					assert (output_sum - 224.0896).abs() < 0.5
					assert (output_max - 0.0_6_0_7).abs() < 4e-4







class 			UpperCamelCase__		(     snake_case_,      snake_case_,      unittest.TestCase ):







	'''simple docstring'''
	_lowerCAmelCase											=					UNetaDModel
	_lowerCAmelCase											=					"sample"





	@property
	def 	__snake_case					(      self		):
					A__	:						Dict								=				4
					A__	:						str								=				14
					A__	:						int								=				16

					A__	:						int								=				floats_tensor((batch_size, num_features, seq_len)		).to(_A		)
					A__	:						Tuple								=				torch.tensor([10] * batch_size		).to(_A		)

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





	@property
	def 	__snake_case					(      self		):
					return (4, 14, 16)





	@property
	def 	__snake_case					(      self		):
					return (4, 14, 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		):
					A__	:						List[str]								=				self.prepare_init_args_and_inputs_for_common()
					A__	:						List[str]								=				self.model_class(**_A		)
					model.to(_A		)
					model.eval()

					with torch.no_grad():
									A__	:						Optional[Any]								=				model(**_A		)

									if isinstance(_A					,							_A		):
													A__	:						Any								=				output.sample

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





	def 	__snake_case					(      self		):
					pass





	def 	__snake_case					(      self		):
					pass





	def 	__snake_case					(      self		):
					A__	:						int								=				{
					    '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',
					}
					A__	:						Optional[int]								=				self.dummy_input
					return init_dict, inputs_dict





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

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

					with torch.no_grad():
									A__	:						Tuple								=				value_function(_A					,							_A		).sample

					# fmt: off
					A__	:						str								=				torch.tensor([165.25] * seq_len		)
					# fmt: on
					self.assertTrue(torch.allclose(_A					,							_A					,							rtol=1e-3		)		)





	def 	__snake_case					(      self		):
					pass






def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	if not root or root not in binary_tree_mirror_dictionary:
		return
	snake_case_    ,snake_case_		:      Tuple           =						binary_tree_mirror_dictionary[root][:2]
	snake_case_		:      List[str]           =						[right_child, left_child]
	binary_tree_mirror_dict(__a ,			__a )
	binary_tree_mirror_dict(__a ,			__a )


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a = 1 ):
	if not binary_tree:
		raise ValueError('binary tree cannot be empty' )
	if root not in binary_tree:
		snake_case_		:      Dict           =						f"""root {root} is not present in the binary_tree"""
		raise ValueError(__a )
	snake_case_		:      Any           =						dict(__a )
	binary_tree_mirror_dict(__a ,			__a )
	return binary_tree_mirror_dictionary


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE   						=		{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
						print(F'''Binary tree: {binary_tree}''')
						_SCREAMING_SNAKE_CASE   						=		binary_tree_mirror(binary_tree, 5)
						print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')

