



'''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  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       unittest.TestCase					):
  """simple docstring"""




  a_							 =UNetaDModel
  a_							 ="""sample"""




  @property
  def 						_lowercase					(			self				:     Optional[int]       ) -> List[Any]:
    __lowerCamelCase	:	Optional[int]   	=			4
    __lowerCamelCase	:	List[str]   	=			14
    __lowerCamelCase	:	Union[str, Any]   	=			16

    __lowerCamelCase	:	List[str]   	=			floats_tensor((batch_size, num_features, seq_len)       ).to(_a       )
    __lowerCamelCase	:	List[Any]   	=			torch.tensor([10] * batch_size       ).to(_a       )

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




  @property
  def 						_lowercase					(			self				:     Tuple       ) -> Union[str, Any]:
    return (4, 14, 16)




  @property
  def 						_lowercase					(			self				:     int       ) -> str:
    return (4, 14, 16)




  def 						_lowercase					(			self				:     List[Any]       ) -> Dict:
    pass




  def 						_lowercase					(			self				:     List[str]       ) -> Dict:
    pass




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




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




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




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




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




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




  def 						_lowercase					(			self				:     Tuple       ) -> Dict:
    __lowerCamelCase	:	Tuple   	=			{
        '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',
    }
    __lowerCamelCase	:	Tuple   	=			self.dummy_input
    return init_dict, inputs_dict




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

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

    with torch.no_grad():
      __lowerCamelCase	:	Optional[Any]   	=			model(_a      ,     _a       ).sample.permute(0      ,     2      ,     1       )

    __lowerCamelCase	:	Union[str, Any]   	=			output[0, -3:, -3:].flatten()
    # fmt: off
    __lowerCamelCase	:	List[str]   	=			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 						_lowercase					(			self				:     Optional[int]       ) -> Optional[int]:
    # Not implemented yet for this UNet
    pass




  @slow
  def 						_lowercase					(			self				:     int       ) -> Optional[Any]:
    __lowerCamelCase	:	int   	=			'harmonai/maestro-150k'
    __lowerCamelCase	:	Union[str, Any]   	=			UNetaDModel.from_pretrained(_a      ,     subfolder='unet'       )
    model.to(_a       )

    __lowerCamelCase	:	int   	=			6_5536
    __lowerCamelCase	:	Dict   	=			torch.sin(torch.arange(_a       )[None, None, :].repeat(1      ,     2      ,     1       )       ).to(_a       )
    __lowerCamelCase	:	List[str]   	=			torch.tensor([1]       ).to(_a       )

    with torch.no_grad():
      __lowerCamelCase	:	List[str]   	=			model(_a      ,     _a       ).sample

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

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






class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       unittest.TestCase					):
  """simple docstring"""




  a_							 =UNetaDModel
  a_							 ="""sample"""




  @property
  def 						_lowercase					(			self				:     Any       ) -> Optional[int]:
    __lowerCamelCase	:	Optional[int]   	=			4
    __lowerCamelCase	:	List[str]   	=			14
    __lowerCamelCase	:	List[Any]   	=			16

    __lowerCamelCase	:	Tuple   	=			floats_tensor((batch_size, num_features, seq_len)       ).to(_a       )
    __lowerCamelCase	:	Optional[Any]   	=			torch.tensor([10] * batch_size       ).to(_a       )

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




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




  @property
  def 						_lowercase					(			self				:     Dict       ) -> List[str]:
    return (4, 14, 1)




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




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




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




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




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




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

    with torch.no_grad():
      __lowerCamelCase	:	Tuple   	=			model(**_a       )

      if isinstance(_a      ,     _a       ):
        __lowerCamelCase	:	Optional[int]   	=			output.sample

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




  def 						_lowercase					(			self				:     Optional[int]       ) -> int:
    pass




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




  def 						_lowercase					(			self				:     Any       ) -> List[Any]:
    __lowerCamelCase	:	Tuple   	=			{
        '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',
    }
    __lowerCamelCase	:	int   	=			self.dummy_input
    return init_dict, inputs_dict




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

    __lowerCamelCase	:	Dict   	=			value_function.config.in_channels
    __lowerCamelCase	:	Dict   	=			14
    __lowerCamelCase	:	str   	=			torch.randn((1, seq_len, num_features)       ).permute(
        0      ,     2      ,     1       )  # match original, we can update values and remove
    __lowerCamelCase	:	List[Any]   	=			torch.full((num_features,)      ,     0       )

    with torch.no_grad():
      __lowerCamelCase	:	Any   	=			value_function(_a      ,     _a       ).sample

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




  def 						_lowercase					(			self				:     Optional[Any]       ) -> int:
    # Not implemented yet for this UNet
    pass











'''simple docstring'''





from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
_UpperCamelCase							    =						load_iris()
iris.keys()


print(f'''Target names: \n {iris.target_names} ''')
print(f'''\n Features: \n {iris.feature_names}''')

# Train set e Test set
_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase							    =						train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

_UpperCamelCase							    =						KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
_UpperCamelCase							    =						[[1, 2, 1, 4], [2, 3, 4, 5]]

_UpperCamelCase							    =						knn.predict(X_new)

print(
    f'''\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'''
    f''' {iris["target_names"][prediction]}'''
)






