






import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


a__      =		set()
a__      =		get_logger(__name__)



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Union[str, Any] = None			)					->       Any:





 def decorator(SCREAMING_SNAKE_CASE__    :		Tuple			):
  global _emitted_deprecation_warnings

  if inspect.isclass(_A			):
   _snake_case      :					Optional[int]					=   deprecated_class_or_function.__init__
   _snake_case      :					Any					=   deprecated_class_or_function.__name__
  else:
   _snake_case      :					Optional[int]					=   deprecated_class_or_function
   _snake_case      :					Optional[int]					=   deprecated_function.__name__
   # Support deprecating __init__ class method: class name instead
   _snake_case      :					Optional[int]					=   name if name != """__init__""" else deprecated_function.__qualname__.split("""."""			)[-2]

  _snake_case      :					Union[str, Any]					=   (
      F'''{name} is deprecated and will be removed in the next major version of datasets.''' + F''' {help_message}'''
      if help_message
      else """"""
  )

  @wraps(_A			)
  def wrapper(*SCREAMING_SNAKE_CASE__    :		Any			,    **SCREAMING_SNAKE_CASE__    :		int			):
   _snake_case      :					Optional[Any]					=   hash(_A			)
   if func_hash not in _emitted_deprecation_warnings:
    warnings.warn(_A			,    category=_A			,    stacklevel=2			)
    _emitted_deprecation_warnings.add(_A			)
   return deprecated_function(*_A			,    **_A			)

  _snake_case      :					Optional[Any]					=   """deprecated"""

  if inspect.isclass(_A			):
   _snake_case      :					List[Any]					=   wrapper
   return deprecated_class_or_function
  else:
   return wrapper

 return decorator







class 						snake_case  (		enum.EnumMeta					):



   '''simple docstring'''






   def __getattribute__(   cls							:     Any			,						lowerCAmelCase							:     Optional[Any])			->			str:

    """simple docstring"""







    _snake_case      :					Union[str, Any]					=   super().__getattribute__(__lowercase)
    if isinstance(__lowercase			,						enum.Enum) and obj._on_access:
     obj._on_access()
    return obj

   def __getitem__(   cls							:     Union[str, Any]			,						lowerCAmelCase							:     Tuple)			->			Union[str, Any]:

    """simple docstring"""







    _snake_case      :					Union[str, Any]					=   super().__getitem__(__lowercase)
    if member._on_access:
     member._on_access()
    return member


   def __call__(   cls							:     Dict			,						lowerCAmelCase							:     Any			,						lowerCAmelCase							:     Optional[Any]=None			,						*, lowerCAmelCase							:     Optional[int]=None			,						lowerCAmelCase							:     Tuple=None			,						lowerCAmelCase							:     Dict=None			,						lowerCAmelCase							:     Any=1)			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					List[Any]					=   super().__call__(__lowercase			,						__lowercase			,						module=__lowercase			,						qualname=__lowercase			,						type=__lowercase			,						start=__lowercase)
    if isinstance(__lowercase			,						enum.Enum) and obj._on_access:
     obj._on_access()
    return obj







class 						snake_case  (		enum.Enum   ,metaclass=UpperCAmelCase__					):



   '''simple docstring'''






   def __new__(   cls							:     List[Any]			,						lowerCAmelCase							:     Any)			->			Any:

    """simple docstring"""







    _snake_case      :					Union[str, Any]					=   object.__new__(cls)
    _snake_case      :					int					=   value
    _snake_case      :					Any					=   member.deprecate
    return member

   @property
   def        UpperCamelCase_					(   self							:     Optional[Any])			->			Any:

    """simple docstring"""







    return ""


   def        UpperCamelCase_					(   self							:     Optional[Any])			->			int:

    """simple docstring"""







    _snake_case      :					Any					=   F''' {self.help_message}''' if self.help_message else """"""
    warnings.warn(
        F'''\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'''
        + help_message			,						__lowercase			,						stacklevel=3			,						)





import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()







class 						snake_case  (		unittest.TestCase					):



   '''simple docstring'''






   @property
   def        UpperCamelCase_					(   self							:     Union[str, Any])			->			int:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case      :					Any					=   UNetaDModel(
        block_out_channels=(32, 64)			,						layers_per_block=2			,						sample_size=32			,						in_channels=3			,						out_channels=3			,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")			,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")			,						)
    return model

   def        UpperCamelCase_					(   self							:     List[Any])			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					List[str]					=   """cpu"""
    _snake_case      :					Union[str, Any]					=   self.dummy_uncond_unet
    _snake_case      :					Union[str, Any]					=   DDPMScheduler()

    _snake_case      :					Dict					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Optional[Any]					=   torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case      :					Optional[int]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""").images

    _snake_case      :					Optional[int]					=   torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case      :					Union[str, Any]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy"""			,						return_dict=lowerCAmelCase)[0]

    _snake_case      :					Any					=   image[0, -3:, -3:, -1]
    _snake_case      :					str					=   image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Optional[int]					=   np.array(
        [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04])

    assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2


   def        UpperCamelCase_					(   self							:     List[Any])			->			Optional[int]:

    """simple docstring"""







    _snake_case      :					int					=   self.dummy_uncond_unet
    _snake_case      :					int					=   DDPMScheduler(prediction_type="""sample""")

    _snake_case      :					Union[str, Any]					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Tuple					=   torch.manual_seed(0)
    _snake_case      :					Union[str, Any]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""").images

    _snake_case      :					Any					=   torch.manual_seed(0)
    _snake_case      :					Dict					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""")[0]

    _snake_case      :					str					=   image[0, -3:, -3:, -1]
    _snake_case      :					List[Any]					=   image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Any					=   1E-2 if torch_device != """mps""" else 3E-2
    assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance







@slow
@require_torch_gpu
class 						snake_case  (		unittest.TestCase					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Dict)			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					Dict					=   """google/ddpm-cifar10-32"""

    _snake_case      :					List[Any]					=   UNetaDModel.from_pretrained(lowerCAmelCase)
    _snake_case      :					Optional[Any]					=   DDPMScheduler.from_pretrained(lowerCAmelCase)

    _snake_case      :					Dict					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Optional[Any]					=   torch.manual_seed(0)
    _snake_case      :					Any					=   ddpm(generator=lowerCAmelCase			,						output_type="""numpy""").images

    _snake_case      :					Optional[Any]					=   image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Union[str, Any]					=   np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385])
    assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
