





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 				_lowerCAmelCase			(	UpperCAmelCase_							,			UpperCAmelCase_							,			unittest.TestCase							):


  '''simple docstring'''
  a_							:				str					    =UNetaDModel
  a_							:				Union[str, Any]					    ="""sample"""







  @property
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    _snake_case      : Union[str, Any]														=		4
    _snake_case      : List[Any]														=		14
    _snake_case      : str														=		16

    _snake_case      : int														=		floats_tensor((batch_size, num_features, seq_len)							).to(UpperCamelCase							)
    _snake_case      : Union[str, Any]														=		torch.tensor([10] * batch_size							).to(UpperCamelCase							)

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







  @property
  def UpperCamelCase_       (					self					:    Optional[int]							):


    '''simple docstring'''





    return (4, 14, 16)







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


    '''simple docstring'''





    return (4, 14, 16)







  def UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





    pass







  def UpperCamelCase_       (					self					:    Optional[Any]							):


    '''simple docstring'''





    pass







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


    '''simple docstring'''





    super().test_determinism()







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


    '''simple docstring'''





    super().test_outputs_equivalence()







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


    '''simple docstring'''





    super().test_from_save_pretrained()







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


    '''simple docstring'''





    super().test_from_save_pretrained_variant()







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


    '''simple docstring'''





    super().test_model_from_pretrained()







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


    '''simple docstring'''





    super().test_output()







  def UpperCamelCase_       (					self					:    Optional[int]							):


    '''simple docstring'''





    _snake_case      : Dict														=		{
        'block_out_channels': (32, 64, 1_28, 2_56),
        '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      : int														=		self.dummy_input
    return init_dict, inputs_dict







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


    '''simple docstring'''





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

    model.to(UpperCamelCase							)
    _snake_case      : Optional[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 UpperCamelCase_       (					self					:    List[str]							):


    '''simple docstring'''





    _snake_case      : 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      : Optional[Any]														=		model.config.in_channels
    _snake_case      : List[str]														=		16
    _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      : List[Any]														=		model(UpperCamelCase					, UpperCamelCase							).sample.permute(0					, 2					, 1							)

    _snake_case      : Tuple														=		output[0, -3:, -3:].flatten()
    # fmt: off
    _snake_case      : Tuple														=		torch.tensor([-2.13_71_72, 1.1_42_60_16, 0.3_68_86_87, -0.76_69_22, 0.7_30_31_46, 0.11_03_88_64, -0.4_76_06_33, 0.13_27_01_72, 0.02_59_13_48]							)
    # fmt: on
    self.assertTrue(torch.allclose(UpperCamelCase					, UpperCamelCase					, rtol=1e-3							)							)







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


    '''simple docstring'''





    pass







  @slow
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    _snake_case      : Tuple														=		'harmonai/maestro-150k'
    _snake_case      : Dict														=		UNetaDModel.from_pretrained(UpperCamelCase					, subfolder='unet'							)
    model.to(UpperCamelCase							)

    _snake_case      : str														=		6_55_36
    _snake_case      : int														=		torch.sin(torch.arange(UpperCamelCase							)[None, None, :].repeat(1					, 2					, 1							)							).to(UpperCamelCase							)
    _snake_case      : str														=		torch.tensor([1]							).to(UpperCamelCase							)

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

    _snake_case      : str														=		output.abs().sum()
    _snake_case      : Tuple														=		output.abs().max()

    assert (output_sum - 2_24.08_96).abs() < 0.5
    assert (output_max - 0.06_07).abs() < 4e-4




class 				_lowerCAmelCase			(	UpperCAmelCase_							,			UpperCAmelCase_							,			unittest.TestCase							):


  '''simple docstring'''
  a_							:				Dict					    =UNetaDModel
  a_							:				str					    ="""sample"""







  @property
  def UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





    _snake_case      : Tuple														=		4
    _snake_case      : Optional[int]														=		14
    _snake_case      : Optional[int]														=		16

    _snake_case      : Union[str, Any]														=		floats_tensor((batch_size, num_features, seq_len)							).to(UpperCamelCase							)
    _snake_case      : str														=		torch.tensor([10] * batch_size							).to(UpperCamelCase							)

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







  @property
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    return (4, 14, 16)







  @property
  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





    return (4, 14, 1)







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


    '''simple docstring'''





    super().test_determinism()







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


    '''simple docstring'''





    super().test_outputs_equivalence()







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


    '''simple docstring'''





    super().test_from_save_pretrained()







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


    '''simple docstring'''





    super().test_model_from_pretrained()







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


    '''simple docstring'''





    _snake_case							,    _snake_case      : Optional[int]														=		self.prepare_init_args_and_inputs_for_common()
    _snake_case      : Tuple														=		self.model_class(**UpperCamelCase							)
    model.to(UpperCamelCase							)
    model.eval()

    with torch.no_grad():
      _snake_case      : List[Any]														=		model(**UpperCamelCase							)

      if isinstance(UpperCamelCase					, UpperCamelCase							):
        _snake_case      : Any														=		output.sample

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







  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    pass







  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    pass







  def UpperCamelCase_       (					self					:    int							):


    '''simple docstring'''





    _snake_case      : Any														=		{
        '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, 1_28, 2_56],
        '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      : Any														=		self.dummy_input
    return init_dict, inputs_dict







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


    '''simple docstring'''





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

    value_function.to(UpperCamelCase							)
    _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 UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : int														=		UNetaDModel.from_pretrained(
        'bglick13/hopper-medium-v2-value-function-hor32'					, output_loading_info=UpperCamelCase					, 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      : Any														=		14
    _snake_case      : Any														=		torch.randn((1, seq_len, num_features)							).permute(
        0					, 2					, 1							)  # match original, we can update values and remove
    _snake_case      : List[Any]														=		torch.full((num_features,)					, 0							)

    with torch.no_grad():
      _snake_case      : List[str]														=		value_function(UpperCamelCase					, UpperCamelCase							).sample

    # fmt: off
    _snake_case      : Union[str, Any]														=		torch.tensor([1_65.25] * seq_len							)
    # fmt: on
    self.assertTrue(torch.allclose(UpperCamelCase					, UpperCamelCase					, rtol=1e-3							)							)







  def UpperCamelCase_       (					self					:    List[str]							):


    '''simple docstring'''





    pass











def 			lowerCamelCase_		(					lowerCAmelCase:		list			,							lowerCAmelCase:		list )->						list:
  _snake_case      : int														=		len(lowerCAmelCase )
  _snake_case      : int														=		len(lowerCAmelCase )
  _snake_case      : int														=		(
      first_input_list_length
      if first_input_list_length > second_input_list_length
      else second_input_list_length
  )
  _snake_case      : list														=		[]
  for char_count in range(lowerCAmelCase ):
    if char_count < first_input_list_length:
      output_result_list.append(first_input_list[char_count] )
    if char_count < second_input_list_length:
      output_result_list.append(second_input_list[char_count] )

  return output_result_list


if __name__ == "__main__":
      print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)




