
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__  (			UpperCAmelCase							,UpperCAmelCase							,unittest.TestCase      ):





 '''simple docstring'''



 _UpperCAmelCase :	List[Any]					     =      UNetaDModel
 _UpperCAmelCase :	Optional[Any]					     =      "sample"



 @property
 def 				A							(						self			:					Dict			):


    '''simple docstring'''


    _snake_case						=    4
    _snake_case						=    14
    _snake_case						=    16

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

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



 @property
 def 				A							(						self			:					List[str]			):


    '''simple docstring'''


    return (4, 14, 16)



 @property
 def 				A							(						self			:					Any			):


    '''simple docstring'''


    return (4, 14, 16)



 def 				A							(						self			:					Any			):


    '''simple docstring'''


    pass



 def 				A							(						self			:					Tuple			):


    '''simple docstring'''


    pass



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


    '''simple docstring'''


    super().test_determinism()



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


    '''simple docstring'''


    super().test_outputs_equivalence()



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


    '''simple docstring'''


    super().test_from_save_pretrained()



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


    '''simple docstring'''


    super().test_from_save_pretrained_variant()



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


    '''simple docstring'''


    super().test_model_from_pretrained()



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


    '''simple docstring'''


    super().test_output()



 def 				A							(						self			:					Union[str, Any]			):


    '''simple docstring'''


    _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',
    }
    _snake_case						=    self.dummy_input
    return init_dict, inputs_dict



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


    '''simple docstring'''


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

    model.to(lowercase			)
    _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 				A							(						self			:					str			):


    '''simple docstring'''


    _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			)

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

    with torch.no_grad():
       _snake_case						=    model(lowercase    ,						lowercase			).sample.permute(0    ,						2    ,						1			)

    _snake_case						=    output[0, -3:, -3:].flatten()
    # fmt: off
    _snake_case						=    torch.tensor([-2.137172, 1.1426016, 0.3688687, -0.766922, 0.7303146, 0.11038864, -0.4760633, 0.13270172, 0.02591348]			)
    # fmt: on
    self.assertTrue(torch.allclose(lowercase    ,						lowercase    ,						rtol=1E-3			)			)



 def 				A							(						self			:					Optional[Any]			):


    '''simple docstring'''


    pass





 @slow
 def 				A							(						self			:					int			):


    '''simple docstring'''


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

    _snake_case						=    65_536
    _snake_case						=    torch.sin(torch.arange(lowercase			)[None, None, :].repeat(1    ,						2    ,						1			)			).to(lowercase			)
    _snake_case						=    torch.tensor([1]			).to(lowercase			)

    with torch.no_grad():
       _snake_case						=    model(lowercase    ,						lowercase			).sample

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

    assert (output_sum - 224.0896).abs() < 0.5
    assert (output_max - 0.0607).abs() < 4E-4




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase							,UpperCAmelCase							,unittest.TestCase      ):





 '''simple docstring'''



 _UpperCAmelCase :	Optional[Any]					     =      UNetaDModel
 _UpperCAmelCase :	Dict					     =      "sample"



 @property
 def 				A							(						self			:					Optional[Any]			):


    '''simple docstring'''


    _snake_case						=    4
    _snake_case						=    14
    _snake_case						=    16

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

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



 @property
 def 				A							(						self			:					Tuple			):


    '''simple docstring'''


    return (4, 14, 16)



 @property
 def 				A							(						self			:					Union[str, Any]			):


    '''simple docstring'''


    return (4, 14, 1)



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


    '''simple docstring'''


    super().test_determinism()



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


    '''simple docstring'''


    super().test_outputs_equivalence()



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


    '''simple docstring'''


    super().test_from_save_pretrained()



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


    '''simple docstring'''


    super().test_from_save_pretrained_variant()



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


    '''simple docstring'''


    super().test_model_from_pretrained()



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


    '''simple docstring'''


    _snake_case			,      _snake_case						=    self.prepare_init_args_and_inputs_for_common()
    _snake_case						=    self.model_class(**lowercase			)
    model.to(lowercase			)
    model.eval()

    with torch.no_grad():
       _snake_case						=    model(**lowercase			)

       if isinstance(lowercase    ,						lowercase			):
          _snake_case						=    output.sample

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



 def 				A							(						self			:					List[str]			):


    '''simple docstring'''


    pass



 def 				A							(						self			:					int			):


    '''simple docstring'''


    pass



 def 				A							(						self			:					Optional[int]			):


    '''simple docstring'''


    _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',
    }
    _snake_case						=    self.dummy_input
    return init_dict, inputs_dict



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


    '''simple docstring'''


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

    value_function.to(lowercase			)
    _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 				A							(						self			:					int			):


    '''simple docstring'''


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

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

    with torch.no_grad():
       _snake_case						=    value_function(lowercase    ,						lowercase			).sample

    # fmt: off
    _snake_case						=    torch.tensor([165.25] * seq_len			)
    # fmt: on
    self.assertTrue(torch.allclose(lowercase    ,						lowercase    ,						rtol=1E-3			)			)





 def 				A							(						self			:					Optional[int]			):


    '''simple docstring'''


    pass

_lowerCamelCase :       Union[str, Any]     					=    {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

_lowerCamelCase :       Optional[Any]     					=    [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_lowerCamelCase :       Dict     					=    [0 for x in range(len(graph))]
_lowerCamelCase :       Dict     					=    []






def  a_	( __lowercase     :			Optional[int] ,      __lowercase     :			Dict				)				->						Optional[int]:
			_snake_case						=    1
			while stack:
						_snake_case						=    stack.pop()
						print(__lowercase ,      clothes[current_clothing]				)
						order += 1






def  a_	( __lowercase     :			Union[str, Any] ,      __lowercase     :			Dict ,      __lowercase     :			Union[str, Any]				)				->						List[Any]:
			_snake_case						=    1
			for v in graph[u]:
						if not visited[v]:
									depth_first_search(__lowercase ,      __lowercase ,      __lowercase				)

			stack.append(__lowercase				)






def  a_	( __lowercase     :			Dict ,      __lowercase     :			int				)				->						List[str]:
			for v in range(len(__lowercase				)				):
						if not visited[v]:
									depth_first_search(__lowercase ,      __lowercase ,      __lowercase				)


if __name__ == "__main__":
			topological_sort(graph, visited)
			print(stack)
			print_stack(stack, clothes)