

"""simple docstring"""





import matplotlib.pyplot as plt
import numpy as np




def     _A    (    UpperCamelCase_	:			np.ndarray,     UpperCamelCase_	:			np.ndarray,     UpperCamelCase_	:			float)      ->		np.ndarray:
   '''simple docstring'''


   __lowercase								=		len(UpperCamelCase_)  # Number of training samples
   __lowercase								=		np.eye(UpperCamelCase_)  # Initialize weights as identity matrix
   for j in range(UpperCamelCase_):
      __lowercase								=		point - x_train[j]
      __lowercase								=		np.exp(diff @ diff.T / (-2.0 * tau**2))

   return weights




def     _A    (    UpperCamelCase_	:			np.ndarray,     UpperCamelCase_	:			np.ndarray,     UpperCamelCase_	:			np.ndarray,     UpperCamelCase_	:			float)      ->		np.ndarray:
   '''simple docstring'''


   __lowercase								=		weight_matrix(UpperCamelCase_,     UpperCamelCase_,     UpperCamelCase_)
   __lowercase								=		np.linalg.inv(x_train.T @ weight_mat @ x_train) @ (
       x_train.T @ weight_mat @ y_train.T
   )

   return weight




def     _A    (    UpperCamelCase_	:			np.ndarray,     UpperCamelCase_	:			np.ndarray,     UpperCamelCase_	:			float)      ->		np.ndarray:
   '''simple docstring'''


   __lowercase								=		np.zeros(len(UpperCamelCase_))  # Initialize array of predictions
   for i, item in enumerate(UpperCamelCase_):
      __lowercase								=		item @ local_weight(UpperCamelCase_,     UpperCamelCase_,     UpperCamelCase_,     UpperCamelCase_)

   return y_pred




def     _A    (    UpperCamelCase_	:			str,     UpperCamelCase_	:			str,     UpperCamelCase_	:			str)      ->		tuple[np.ndarray, np.ndarray, np.ndarray]:
   '''simple docstring'''


   import seaborn as sns

   __lowercase								=		sns.load_dataset(UpperCamelCase_)
   __lowercase								=		np.array(data[x_name])
   __lowercase								=		np.array(data[y_name])

   __lowercase								=		np.ones(len(UpperCamelCase_))

   # pairing elements of one and x_data
   __lowercase								=		np.column_stack((one, x_data))

   return x_train, x_data, y_data




def     _A    (    UpperCamelCase_	:			np.ndarray,     UpperCamelCase_	:			np.ndarray,     UpperCamelCase_	:			np.ndarray,     UpperCamelCase_	:			np.ndarray,     UpperCamelCase_	:			str,     UpperCamelCase_	:			str,     )      ->		None:
   '''simple docstring'''


   __lowercase								=		np.sort(UpperCamelCase_,     axis=0)
   plt.scatter(UpperCamelCase_,     UpperCamelCase_,     color="blue")
   plt.plot(
       x_train_sorted[:, 1],     preds[x_train[:, 1].argsort(0)],     color="yellow",     linewidth=5,     )
   plt.title("Local Weighted Regression")
   plt.xlabel(UpperCamelCase_)
   plt.ylabel(UpperCamelCase_)
   plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Demo with a dataset from the seaborn module
   _a ,		_a ,		_a					 =				load_data('tips', 'total_bill', 'tip')
   _a					 =				local_weight_regression(training_data_x, tip, 5)
   plot_preds(training_data_x, predictions, total_bill, tip, 'total_bill', 'tip')







"""simple docstring"""





import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger




class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   Tuple  						=      "config.json"


 @register_to_config
 def __init__(     self				:     List[str],   UpperCAmelCase__				:     Dict=2,   UpperCAmelCase__				:     str=5,   UpperCAmelCase__				:     List[Any]=(2, 5),   UpperCAmelCase__				:     Dict="for diffusion",   UpperCAmelCase__				:     Optional[int]=[1, 3],   ):
    pass







class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   List[str]  						=      "config.json"


 @register_to_config
 def __init__(     self				:     Optional[int],   UpperCAmelCase__				:     List[str]=2,   UpperCAmelCase__				:     List[str]=5,   UpperCAmelCase__				:     List[Any]=(2, 5),   UpperCAmelCase__				:     str="for diffusion",   UpperCAmelCase__				:     str=[1, 3],   ):
    pass







class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   str  						=      "config.json"


 @register_to_config
 def __init__(     self				:     Union[str, Any],   UpperCAmelCase__				:     Union[str, Any]=2,   UpperCAmelCase__				:     str=5,   UpperCAmelCase__				:     Optional[int]=(2, 5),   UpperCAmelCase__				:     str="for diffusion",   UpperCAmelCase__				:     str=[1, 3],   UpperCAmelCase__				:     Dict=[1, 3],   ):
    pass







class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   List[str]  						=      "config.json"


 @register_to_config
 def __init__(     self				:     Union[str, Any],   UpperCAmelCase__				:     Any=2,   UpperCAmelCase__				:     Union[str, Any]=5,   UpperCAmelCase__				:     Optional[Any]=(2, 5),   UpperCAmelCase__				:     List[str]="for diffusion",   UpperCAmelCase__				:     Optional[Any]=[1, 5],   UpperCAmelCase__				:     Dict=[5, 4],   ):
    pass







class    _lowerCAmelCase    (      unittest.TestCase  ):







 """simple docstring"""







 def    _lowercase					(     self				:     Optional[int]      ):
    with self.assertRaises(UpperCAmelCase__      ):
       ConfigMixin.load_config("dummy_path"      )


 def    _lowercase					(     self				:     Optional[int]      ):
    __lowercase								=		SampleObject()
    __lowercase								=		obj.config
    assert config["a"] == 2
    assert config["b"] == 5
    assert config["c"] == (2, 5)
    assert config["d"] == "for diffusion"
    assert config["e"] == [1, 3]

    # init ignore private arguments
    __lowercase								=		SampleObject(_name_or_path="lalala"      )
    __lowercase								=		obj.config
    assert config["a"] == 2
    assert config["b"] == 5
    assert config["c"] == (2, 5)
    assert config["d"] == "for diffusion"
    assert config["e"] == [1, 3]

    # can override default
    __lowercase								=		SampleObject(c=6      )
    __lowercase								=		obj.config
    assert config["a"] == 2
    assert config["b"] == 5
    assert config["c"] == 6
    assert config["d"] == "for diffusion"
    assert config["e"] == [1, 3]

    # can use positional arguments.
    __lowercase								=		SampleObject(1,   c=6      )
    __lowercase								=		obj.config
    assert config["a"] == 1
    assert config["b"] == 5
    assert config["c"] == 6
    assert config["d"] == "for diffusion"
    assert config["e"] == [1, 3]


 def    _lowercase					(     self				:     Tuple      ):
    __lowercase								=		SampleObject()
    __lowercase								=		obj.config

    assert config["a"] == 2
    assert config["b"] == 5
    assert config["c"] == (2, 5)
    assert config["d"] == "for diffusion"
    assert config["e"] == [1, 3]

    with tempfile.TemporaryDirectory() as tmpdirname:
       obj.save_config(UpperCAmelCase__      )
       __lowercase								=		SampleObject.from_config(SampleObject.load_config(UpperCAmelCase__      )      )
       __lowercase								=		new_obj.config

    # unfreeze configs
    __lowercase								=		dict(UpperCAmelCase__      )
    __lowercase								=		dict(UpperCAmelCase__      )

    assert config.pop("c"      ) == (2, 5)  # instantiated as tuple
    assert new_config.pop("c"      ) == [2, 5]  # saved & loaded as list because of json
    config.pop("_use_default_values"      )
    assert config == new_config


 def    _lowercase					(     self				:     Any      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		DDIMScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler"      )

    assert ddim.__class__ == DDIMScheduler
    # no warning should be thrown
    assert cap_logger.out == ""


 def    _lowercase					(     self				:     int      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		EulerDiscreteScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler"      )

    assert euler.__class__ == EulerDiscreteScheduler
    # no warning should be thrown
    assert cap_logger.out == ""


 def    _lowercase					(     self				:     Optional[Any]      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		EulerAncestralDiscreteScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler"      )

    assert euler.__class__ == EulerAncestralDiscreteScheduler
    # no warning should be thrown
    assert cap_logger.out == ""


 def    _lowercase					(     self				:     str      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		PNDMScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler"      )

    assert pndm.__class__ == PNDMScheduler
    # no warning should be thrown
    assert cap_logger.out == ""


 def    _lowercase					(     self				:     Optional[Any]      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		DDPMScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler",   prediction_type="sample",   beta_end=8,   )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger_a:
       __lowercase								=		DDPMScheduler.from_pretrained("google/ddpm-celebahq-256",   beta_start=8_8      )

    assert ddpm.__class__ == DDPMScheduler
    assert ddpm.config.prediction_type == "sample"
    assert ddpm.config.beta_end == 8
    assert ddpm_a.config.beta_start == 8_8

    # no warning should be thrown
    assert cap_logger.out == ""
    assert cap_logger_a.out == ""


 def    _lowercase					(     self				:     List[Any]      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		DPMSolverMultistepScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler"      )

    assert dpm.__class__ == DPMSolverMultistepScheduler
    # no warning should be thrown
    assert cap_logger.out == ""



 def    _lowercase					(     self				:     str      ):
    # let's first save a config that should be in the form
    #    a=2,
    #    b=5,
    #    c=(2, 5),
    #    d="for diffusion",
    #    e=[1, 3],

    __lowercase								=		SampleObject()

    __lowercase								=		{k: v for k, v in config.config.items() if not k.startswith("_"      )}

    # make sure that default config has all keys in `_use_default_values`
    assert set(config_dict.keys()      ) == set(config.config._use_default_values      )

    with tempfile.TemporaryDirectory() as tmpdirname:
       config.save_config(UpperCAmelCase__      )

       # now loading it with SampleObject2 should put f into `_use_default_values`
       __lowercase								=		SampleObjecta.from_config(UpperCAmelCase__      )

       assert "f" in config._use_default_values
       assert config.f == [1, 3]

    # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
    # **BECAUSE** it is part of `config._use_default_values`
    __lowercase								=		SampleObjecta.from_config(config.config      )
    assert new_config.f == [5, 4]

    config.config._use_default_values.pop()
    __lowercase								=		SampleObjecta.from_config(config.config      )
    assert new_config_a.f == [1, 3]

    # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
    assert new_config_a.e == [1, 3]




