




"""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    __lowercase							(      snake_case_    , snake_case_    , unittest.TestCase						):


				'''simple docstring'''


				_lowerCAmelCase						      =UNetaDModel
				_lowerCAmelCase						      ="sample"

				@property
				def    _lowerCamelCase       ( self :  Optional[int]   ):
										snake_case__							:       Union[str, Any]				      =       4
										snake_case__							:       List[Any]				      =       1_4
										snake_case__							:       Tuple				      =       1_6

										snake_case__							:       Dict				      =       floats_tensor((batch_size, num_features, seq_len)   ).to(SCREAMING_SNAKE_CASE_   )
										snake_case__							:       Tuple				      =       torch.tensor([1_0] * batch_size   ).to(SCREAMING_SNAKE_CASE_   )

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

				@property
				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										return (4, 1_4, 1_6)

				@property
				def    _lowerCamelCase       ( self :  List[str]   ):
										return (4, 1_4, 1_6)

				def    _lowerCamelCase       ( self :  List[Any]   ):
										pass

				def    _lowerCamelCase       ( self :  List[str]   ):
										pass

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

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

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

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

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

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

				def    _lowerCamelCase       ( self :  Any   ):
										snake_case__							:       Union[str, Any]				      =       {
										    '''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''',
										}
										snake_case__							:       int				      =       self.dummy_input
										return init_dict, inputs_dict

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

										model.to(SCREAMING_SNAKE_CASE_   )
										snake_case__							:       List[Any]				      =       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    _lowerCamelCase       ( self :  Union[str, Any]   ):
										snake_case__							:       Optional[int]				      =       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__							:       Dict				      =       model.config.in_channels
										snake_case__							:       Optional[int]				      =       1_6
										snake_case__							:       int				      =       torch.randn((1, seq_len, num_features)   ).permute(
										    0   ,2   ,1   )  # match original, we can update values and remove
										snake_case__							:       Optional[Any]				      =       torch.full((num_features,)   ,0   )

										with torch.no_grad():
																snake_case__							:       int				      =       model(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ).sample.permute(0   ,2   ,1   )

										snake_case__							:       Optional[int]				      =       output[0, -3:, -3:].flatten()
										# fmt: off
										snake_case__							:       int				      =       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(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,rtol=1E-3   )   )

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





				@slow
				def    _lowerCamelCase       ( self :  Tuple   ):
										snake_case__							:       List[str]				      =       '''harmonai/maestro-150k'''
										snake_case__							:       Optional[Any]				      =       UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE_   ,subfolder='''unet'''   )
										model.to(SCREAMING_SNAKE_CASE_   )

										snake_case__							:       Dict				      =       6_5_5_3_6
										snake_case__							:       str				      =       torch.sin(torch.arange(SCREAMING_SNAKE_CASE_   )[None, None, :].repeat(1   ,2   ,1   )   ).to(SCREAMING_SNAKE_CASE_   )
										snake_case__							:       int				      =       torch.tensor([1]   ).to(SCREAMING_SNAKE_CASE_   )

										with torch.no_grad():
																snake_case__							:       Tuple				      =       model(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ).sample

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

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







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


				'''simple docstring'''


				_lowerCAmelCase						      =UNetaDModel
				_lowerCAmelCase						      ="sample"

				@property
				def    _lowerCamelCase       ( self :  Any   ):
										snake_case__							:       Optional[Any]				      =       4
										snake_case__							:       Optional[int]				      =       1_4
										snake_case__							:       Any				      =       1_6

										snake_case__							:       Tuple				      =       floats_tensor((batch_size, num_features, seq_len)   ).to(SCREAMING_SNAKE_CASE_   )
										snake_case__							:       Any				      =       torch.tensor([1_0] * batch_size   ).to(SCREAMING_SNAKE_CASE_   )

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

				@property
				def    _lowerCamelCase       ( self :  Union[str, Any]   ):
										return (4, 1_4, 1_6)

				@property
				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										return (4, 1_4, 1)

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

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

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

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

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

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

										with torch.no_grad():
																snake_case__							:       str				      =       model(**SCREAMING_SNAKE_CASE_   )

																if isinstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ):
																						snake_case__							:       int				      =       output.sample

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

				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										pass

				def    _lowerCamelCase       ( self :  List[str]   ):
										pass

				def    _lowerCamelCase       ( self :  List[Any]   ):
										snake_case__							:       Optional[Any]				      =       {
										    '''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''',
										}
										snake_case__							:       int				      =       self.dummy_input
										return init_dict, inputs_dict

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

										value_function.to(SCREAMING_SNAKE_CASE_   )
										snake_case__							:       Any				      =       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    _lowerCamelCase       ( self :  Any   ):
										snake_case__      ,						snake_case__							:       str				      =       UNetaDModel.from_pretrained(
										    '''bglick13/hopper-medium-v2-value-function-hor32'''   ,output_loading_info=SCREAMING_SNAKE_CASE_   ,subfolder='''value_function'''   )
										torch.manual_seed(0   )
										if torch.cuda.is_available():
																torch.cuda.manual_seed_all(0   )

										snake_case__							:       str				      =       value_function.config.in_channels
										snake_case__							:       List[Any]				      =       1_4
										snake_case__							:       List[str]				      =       torch.randn((1, seq_len, num_features)   ).permute(
										    0   ,2   ,1   )  # match original, we can update values and remove
										snake_case__							:       Union[str, Any]				      =       torch.full((num_features,)   ,0   )

										with torch.no_grad():
																snake_case__							:       Dict				      =       value_function(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ).sample

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





				def    _lowerCamelCase       ( self :  List[str]   ):
										# Not implemented yet for this UNet
										pass








"""simple docstring"""





from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
_lowercase						   =     TypeVar("""T""", bound="""Enum""")

@unique
class    __lowercase							(      snake_case_						):


				'''simple docstring'''


				_lowerCAmelCase						      =80
				_lowerCAmelCase						      =70
				_lowerCAmelCase						      =60
				_lowerCAmelCase						      =50
				_lowerCAmelCase						      =40
				_lowerCAmelCase						      =30
				_lowerCAmelCase						      =20
				_lowerCAmelCase						      =10







@unique
class    __lowercase							(      snake_case_						):


				'''simple docstring'''


				_lowerCAmelCase						      =24
				_lowerCAmelCase						      =21
				_lowerCAmelCase						      =18
				_lowerCAmelCase						      =15
				_lowerCAmelCase						      =12
				_lowerCAmelCase						      =9
				_lowerCAmelCase						      =6
				_lowerCAmelCase						      =3
				_lowerCAmelCase						      =2
				_lowerCAmelCase						      =1
				_lowerCAmelCase						      =-1
				_lowerCAmelCase						      =-2
				_lowerCAmelCase						      =-3
				_lowerCAmelCase						      =-6
				_lowerCAmelCase						      =-9
				_lowerCAmelCase						      =-12
				_lowerCAmelCase						      =-15
				_lowerCAmelCase						      =-18
				_lowerCAmelCase						      =-21
				_lowerCAmelCase						      =-24

				@classmethod
				def    _lowerCamelCase       ( cls :  type[T]   ):
										return {unit.name: unit.value for unit in cls if unit.value > 0}





				@classmethod
				def    _lowerCamelCase       ( cls :  type[T]   ):
										return {unit.name: unit.value for unit in cls if unit.value < 0}







def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       List[Any]				      =       SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
						for name_prefix, value_prefix in prefixes.items():
												snake_case__							:       Optional[int]				      =       value / (10**value_prefix)
												if numerical_part > 1:
																		return f'''{numerical_part!s} {name_prefix}'''
						return str(lowerCamelCase		)

def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						for prefix in BinaryUnit:
												snake_case__							:       int				      =       value / (2**prefix.value)
												if numerical_part > 1:
																		return f'''{numerical_part!s} {prefix.name}'''
						return str(lowerCamelCase		)


if __name__ == "__main__":
							import doctest

							doctest.testmod()


