'''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(     _lowerCamelCase   ,_lowerCamelCase   ,unittest.TestCase				):



   """simple docstring"""





   __lowerCamelCase    =  UNetaDModel
   __lowerCamelCase    =  '''sample'''





   @property
   def 			snake_case	(							self:    str	):
    __UpperCAmelCase         =					4
    __UpperCAmelCase         =					14
    __UpperCAmelCase         =					16

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

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





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





   @property
   def 			snake_case	(							self:    Optional[Any]	):
    return (4, 14, 16)





   def 			snake_case	(							self:    int	):
    pass





   def 			snake_case	(							self:    Optional[int]	):
    pass





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





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





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





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





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





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





   def 			snake_case	(							self:    List[Any]	):
    __UpperCAmelCase         =					{
        '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',
    }
    __UpperCAmelCase         =					self.dummy_input
    return init_dict, inputs_dict





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

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

    with torch.no_grad():
     __UpperCAmelCase         =					model(a			,a	).sample.permute(0			,2			,1	)

    __UpperCAmelCase         =					output[0, -3:, -3:].flatten()
    # fmt: off
    __UpperCAmelCase         =					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(a			,a			,rtol=1e-3	)	)





   def 			snake_case	(							self:    str	):
    # Not implemented yet for this UNet
    pass





   @slow
   def 			snake_case	(							self:    List[str]	):
    __UpperCAmelCase         =					'harmonai/maestro-150k'
    __UpperCAmelCase         =					UNetaDModel.from_pretrained(a			,subfolder='unet'	)
    model.to(a	)

    __UpperCAmelCase         =					65536
    __UpperCAmelCase         =					torch.sin(torch.arange(a	)[None, None, :].repeat(1			,2			,1	)	).to(a	)
    __UpperCAmelCase         =					torch.tensor([1]	).to(a	)

    with torch.no_grad():
     __UpperCAmelCase         =					model(a			,a	).sample

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

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




class   _lowercase(     _lowerCamelCase   ,_lowerCamelCase   ,unittest.TestCase				):



   """simple docstring"""





   __lowerCamelCase    =  UNetaDModel
   __lowerCamelCase    =  '''sample'''





   @property
   def 			snake_case	(							self:    str	):
    __UpperCAmelCase         =					4
    __UpperCAmelCase         =					14
    __UpperCAmelCase         =					16

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

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





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





   @property
   def 			snake_case	(							self:    Optional[int]	):
    return (4, 14, 1)





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





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





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





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





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





   @unittest.skipIf(torch_device == 'mps'			,'mish op not supported in MPS'	)
   def 			snake_case	(							self:    Optional[int]	):
    # UNetRL is a value-function is different output shape
    __UpperCAmelCase  ,			__UpperCAmelCase         =					self.prepare_init_args_and_inputs_for_common()
    __UpperCAmelCase         =					self.model_class(**a	)
    model.to(a	)
    model.eval()

    with torch.no_grad():
     __UpperCAmelCase         =					model(**a	)

     if isinstance(a			,a	):
      __UpperCAmelCase         =					output.sample

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





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





   def 			snake_case	(							self:    Any	):
    pass





   def 			snake_case	(							self:    str	):
    __UpperCAmelCase         =					{
        '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',
    }
    __UpperCAmelCase         =					self.dummy_input
    return init_dict, inputs_dict





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

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

    with torch.no_grad():
     __UpperCAmelCase         =					value_function(a			,a	).sample

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





   def 			snake_case	(							self:    Dict	):
    # Not implemented yet for this UNet
    pass




'''simple docstring'''




def 						__snake_case (       lowerCAmelCase       :     list			):

 for insert_index, insert_value in enumerate(collection[1:]			):
  __UpperCAmelCase         =					insert_index
  while insert_index >= 0 and insert_value < collection[insert_index]:
   __UpperCAmelCase         =					collection[insert_index]
   insert_index -= 1
  if insert_index != temp_index:
   __UpperCAmelCase         =					insert_value
 return collection


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     _UpperCamelCase				:		List[Any]       			=		input('Enter numbers separated by a comma:\n').strip()
     _UpperCamelCase				:		List[Any]       			=		[int(item) for item in user_input.split(',')]
     print(f"{insertion_sort(unsorted) = }")



