





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 SCREAMING_SNAKE_CASE_    (       snake_case_							,					snake_case_							,					unittest.TestCase       ):
						__magic_name__:      int			   =							UNetaDModel
						__magic_name__:      List[Any]			   =							"sample"






						@property
						def UpperCAmelCase_					(   self   :   List[str]						)				->     int:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						4
							snake_case_		:      str           =						14
							snake_case_		:      Any           =						16

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

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






						@property
						def UpperCAmelCase_					(   self   :   str						)				->     List[str]:







							"""simple docstring"""


							return (4, 14, 16)






						@property
						def UpperCAmelCase_					(   self   :   Union[str, Any]						)				->     Dict:







							"""simple docstring"""


							return (4, 14, 16)






						def UpperCAmelCase_					(   self   :   Any						)				->     Optional[Any]:







							"""simple docstring"""


							pass






						def UpperCAmelCase_					(   self   :   int						)				->     Union[str, Any]:







							"""simple docstring"""


							pass






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







							"""simple docstring"""


							super().test_determinism()






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







							"""simple docstring"""


							super().test_outputs_equivalence()






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







							"""simple docstring"""


							super().test_from_save_pretrained()






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







							"""simple docstring"""


							super().test_from_save_pretrained_variant()






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







							"""simple docstring"""


							super().test_model_from_pretrained()






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







							"""simple docstring"""


							super().test_output()






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







							"""simple docstring"""


							snake_case_		:      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',
							}
							snake_case_		:      Tuple           =						self.dummy_input
							return init_dict, inputs_dict






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







							"""simple docstring"""


							snake_case_    ,snake_case_		:      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						)
							snake_case_		:      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 UpperCAmelCase_					(   self   :   Tuple						)				->     List[Any]:







							"""simple docstring"""


							snake_case_		:      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						)

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

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

							snake_case_		:      List[Any]           =						output[0, -3:, -3:].flatten()
							# fmt: off
							snake_case_		:      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 UpperCAmelCase_					(   self   :   List[Any]						)				->     Optional[int]:







							"""simple docstring"""


							pass






						@slow
						def UpperCAmelCase_					(   self   :   int						)				->     Optional[Any]:







							"""simple docstring"""


							snake_case_		:      List[Any]           =						'harmonai/maestro-150k'
							snake_case_		:      Dict           =						UNetaDModel.from_pretrained(_A		, subfolder='unet'						)
							model.to(_A						)

							snake_case_		:      int           =						65536
							snake_case_		:      Optional[Any]           =						torch.sin(torch.arange(_A						)[None, None, :].repeat(1		, 2		, 1						)						).to(_A						)
							snake_case_		:      Optional[Any]           =						torch.tensor([1]						).to(_A						)

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

							snake_case_		:      int           =						output.abs().sum()
							snake_case_		:      Optional[Any]           =						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 SCREAMING_SNAKE_CASE_    (       snake_case_							,					snake_case_							,					unittest.TestCase       ):
						__magic_name__:      str			   =							UNetaDModel
						__magic_name__:      str			   =							"sample"






						@property
						def UpperCAmelCase_					(   self   :   Any						)				->     Dict:







							"""simple docstring"""


							snake_case_		:      Dict           =						4
							snake_case_		:      str           =						14
							snake_case_		:      int           =						16

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

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






						@property
						def UpperCAmelCase_					(   self   :   List[Any]						)				->     Optional[int]:







							"""simple docstring"""


							return (4, 14, 16)






						@property
						def UpperCAmelCase_					(   self   :   Tuple						)				->     Tuple:







							"""simple docstring"""


							return (4, 14, 1)






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







							"""simple docstring"""


							super().test_determinism()






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







							"""simple docstring"""


							super().test_outputs_equivalence()






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







							"""simple docstring"""


							super().test_from_save_pretrained()






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







							"""simple docstring"""


							super().test_from_save_pretrained_variant()






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







							"""simple docstring"""


							super().test_model_from_pretrained()






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







							"""simple docstring"""


							snake_case_    ,snake_case_		:      List[str]           =						self.prepare_init_args_and_inputs_for_common()
							snake_case_		:      List[str]           =						self.model_class(**_A						)
							model.to(_A						)
							model.eval()

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

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

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






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







							"""simple docstring"""


							pass






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







							"""simple docstring"""


							pass






						def UpperCAmelCase_					(   self   :   Union[str, Any]						)				->     Dict:







							"""simple docstring"""


							snake_case_		:      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',
							}
							snake_case_		:      Optional[int]           =						self.dummy_input
							return init_dict, inputs_dict






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







							"""simple docstring"""


							snake_case_    ,snake_case_		:      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						)
							snake_case_		:      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 UpperCAmelCase_					(   self   :   str						)				->     Optional[int]:







							"""simple docstring"""


							snake_case_    ,snake_case_		:      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						)

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

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

							# fmt: off
							snake_case_		:      str           =						torch.tensor([1_6_5.2_5] * seq_len						)
							# fmt: on
							self.assertTrue(torch.allclose(_A		, _A		, rtol=1E-3						)						)






						def UpperCAmelCase_					(   self   :   str						)				->     Optional[int]:







							"""simple docstring"""


							pass




from __future__ import annotations






def        lowerCamelCase_  (				UpperCAmelCase_				:  int						)    ->					list[int]:




	'''simple docstring'''




	_UpperCamelCase :		Any								=    [0] * max_one

	for i in range(0				,							UpperCAmelCase_						):
		_UpperCamelCase :		List[Any]								=    i

	for i in range(2				,							UpperCAmelCase_						):
		if totients[i] == i:
			for j in range(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_						):
				totients[j] -= totients[j] // i

	return totients






def        lowerCamelCase_  (				UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  int						)    ->					bool:




	'''simple docstring'''




	return sorted(str(UpperCAmelCase_						)						) == sorted(str(UpperCAmelCase_						)						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  int = 1_0_0_0_0_0_0_0						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		List[Any]								=    1  # i
	_UpperCamelCase :		List[str]								=    0  # φ(i)
	_UpperCamelCase :		str								=    get_totients(max_n + 1						)

	for i in range(2				,							max_n + 1						):
		_UpperCamelCase :		List[str]								=    totients[i]

		if i * min_denominator < min_numerator * t and has_same_digits(UpperCAmelCase_				,							UpperCAmelCase_						):
			_UpperCamelCase :		Optional[int]								=    i
			_UpperCamelCase :		Tuple								=    t

	return min_numerator


if __name__ == "__main__":
							print(f'{solution() = }')




