


import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


__lowerCamelCase   	=  1000


class 	a__						:

       def __init__( self   :  int   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  Optional[int]=1.2   ,				lowerCamelCase_   :  str="g"   ,				lowerCamelCase_   :  Optional[int]=0.5   ,				lowerCamelCase_   :  List[Any]="-"   ,				lowerCamelCase_   :  Any="test_out.jpg"   ,				lowerCamelCase_   :  Tuple=True   ,				lowerCamelCase_   :  Tuple=False   ,				lowerCamelCase_   :  Union[str, Any]=None   ,				lowerCamelCase_   :  str=None   ,				lowerCamelCase_   :  Optional[int]=0.7   ,				):
            if isinstance(UpperCAmelCase__   ,				torch.Tensor     ):
                 a_     :  int				      =     img.numpy().astype("""np.uint8"""     )
            if isinstance(UpperCAmelCase__   ,				UpperCAmelCase__     ):
                 a_     :  Any				      =     img_tensorize(UpperCAmelCase__     )
            assert isinstance(UpperCAmelCase__   ,				np.ndarray     )

            a_      ,      a_     :  Optional[int]				      =     img.shape[1], img.shape[0]
            a_     :  Optional[int]				      =     mplfigure.Figure(frameon=UpperCAmelCase__     )
            a_     :  Optional[Any]				      =     fig.get_dpi()
            a_     :  Tuple				      =     (width * scale + 1E-2) / dpi
            a_     :  Union[str, Any]				      =     (height * scale + 1E-2) / dpi
            fig.set_size_inches(UpperCAmelCase__   ,				UpperCAmelCase__     )
            a_     :  Any				      =     fig.add_axes([0.0, 0.0, 1.0, 1.0]     )
            ax.axis("""off"""     )
            ax.set_xlim(0.0   ,				UpperCAmelCase__     )
            ax.set_ylim(UpperCAmelCase__     )

            a_     :  int				      =     saveas
            a_     :  Optional[int]				      =     rgb
            a_     :  Tuple				      =     pynb
            a_     :  int				      =     img
            a_     :  Dict				      =     edgecolor
            a_     :  List[str]				      =     0.5
            a_     :  Optional[Any]				      =     linestyle
            a_     :  str				      =     int(np.sqrt(min(UpperCAmelCase__   ,				UpperCAmelCase__     )     ) * scale // 3     )
            a_     :  Union[str, Any]				      =     width
            a_     :  List[Any]				      =     height
            a_     :  str				      =     scale
            a_     :  List[Any]				      =     fig
            a_     :  Tuple				      =     ax
            a_     :  Optional[Any]				      =     pad
            a_     :  int				      =     idaobj
            a_     :  List[Any]				      =     idaattr
            a_     :  Tuple				      =     FigureCanvasAgg(UpperCAmelCase__     )

       def     UpperCAmelCase( self   :  List[Any]   ,				lowerCamelCase_   :  Dict   ,				lowerCamelCase_   :  Optional[int]=None     ):
            if color is None:
                 a_     :  int				      =     self.edgecolor
            ((a_)      ,      (a_)      ,      (a_)      ,      (a_))     :  Optional[Any]				      =     box
            a_     :  Union[str, Any]				      =     xa - xa
            a_     :  List[Any]				      =     ya - ya
            self.ax.add_patch(
                mpl.patches.Rectangle(
                    (xa, ya)   ,				UpperCAmelCase__   ,				UpperCAmelCase__   ,				fill=UpperCAmelCase__   ,				edgecolor=UpperCAmelCase__   ,				linewidth=self.font_size // 3   ,				alpha=self.alpha   ,				linestyle=self.linestyle   ,				)     )

       def     UpperCAmelCase( self   :  Dict   ,				lowerCamelCase_   :  Optional[int]   ,				lowerCamelCase_   :  Tuple=None   ,				lowerCamelCase_   :  Optional[int]=None   ,				lowerCamelCase_   :  List[str]=None   ,				lowerCamelCase_   :  Union[str, Any]=None     ):
            if len(boxes.shape     ) > 2:
                 a_     :  Optional[Any]				      =     boxes[0]
            if len(obj_ids.shape     ) > 1:
                 a_     :  Optional[int]				      =     obj_ids[0]
            if len(obj_scores.shape     ) > 1:
                 a_     :  Tuple				      =     obj_scores[0]
            if len(attr_ids.shape     ) > 1:
                 a_     :  Optional[Any]				      =     attr_ids[0]
            if len(attr_scores.shape     ) > 1:
                 a_     :  Tuple				      =     attr_scores[0]
            if isinstance(UpperCAmelCase__   ,				torch.Tensor     ):
                 a_     :  Optional[Any]				      =     boxes.numpy()
            if isinstance(UpperCAmelCase__   ,				UpperCAmelCase__     ):
                 a_     :  Optional[Any]				      =     np.array(UpperCAmelCase__     )
            assert isinstance(UpperCAmelCase__   ,				np.ndarray     )
            a_     :  int				      =     np.prod(boxes[:, 2:] - boxes[:, :2]   ,				axis=1     )
            a_     :  List[str]				      =     np.argsort(-areas     ).tolist()
            a_     :  Optional[int]				      =     boxes[sorted_idxs] if boxes is not None else None
            a_     :  Union[str, Any]				      =     obj_ids[sorted_idxs] if obj_ids is not None else None
            a_     :  Any				      =     obj_scores[sorted_idxs] if obj_scores is not None else None
            a_     :  List[str]				      =     attr_ids[sorted_idxs] if attr_ids is not None else None
            a_     :  Optional[int]				      =     attr_scores[sorted_idxs] if attr_scores is not None else None

            a_     :  Any				      =     [self._random_color(maximum=1     ) for _ in range(len(UpperCAmelCase__     )     )]
            a_     :  int				      =     [assigned_colors[idx] for idx in sorted_idxs]
            if obj_ids is not None:
                 a_     :  Optional[Any]				      =     self._create_text_labels_attr(UpperCAmelCase__   ,				UpperCAmelCase__   ,				UpperCAmelCase__   ,				UpperCAmelCase__     )
                 for i in range(len(UpperCAmelCase__     )     ):
                      a_     :  Dict				      =     assigned_colors[i]
                      self.add_box(boxes[i]   ,				UpperCAmelCase__     )
                      self.draw_labels(labels[i]   ,				boxes[i]   ,				UpperCAmelCase__     )

       def     UpperCAmelCase( self   :  List[Any]   ,				lowerCamelCase_   :  List[Any]   ,				lowerCamelCase_   :  Union[str, Any]   ,				lowerCamelCase_   :  int     ):
            a_      ,      a_      ,      a_      ,      a_     :  str				      =     box
            a_     :  Optional[int]				      =     (xa, ya)
            a_     :  int				      =     (ya - ya) * (xa - xa)
            a_     :  Union[str, Any]				      =     _SMALL_OBJ * self.scale
            if instance_area < small or ya - ya < 4_0 * self.scale:
                 if ya >= self.height - 5:
                      a_     :  Dict				      =     (xa, ya)
                 else:
                      a_     :  Optional[Any]				      =     (xa, ya)

            a_     :  int				      =     (ya - ya) / np.sqrt(self.height * self.width     )
            a_     :  int				      =     self._change_color_brightness(UpperCAmelCase__   ,				brightness_factor=0.7     )
            a_     :  Dict				      =     np.clip((height_ratio - 0.0_2) / 0.0_8 + 1   ,				1.2   ,				2     )
            font_size *= 0.7_5 * self.font_size

            self.draw_text(
                text=UpperCAmelCase__   ,				position=UpperCAmelCase__   ,				color=UpperCAmelCase__   ,				)

       def     UpperCAmelCase( self   :  List[Any]   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  Optional[int]="g"   ,				lowerCamelCase_   :  str="left"   ,				):
            a_     :  int				      =     0
            a_     :  Dict				      =     self.font_size
            a_     :  List[Any]				      =     np.maximum(list(mplc.to_rgb(UpperCAmelCase__     )     )   ,				0.2     )
            a_     :  Optional[Any]				      =     max(0.8   ,				np.max(UpperCAmelCase__     )     )
            a_     :  Optional[int]				      =     {
                """facecolor""": """black""",
                """alpha""": self.alpha,
                """pad""": self.pad,
                """edgecolor""": """none""",
            }
            a_      ,      a_     :  Union[str, Any]				      =     position
            self.ax.text(
                UpperCAmelCase__   ,				UpperCAmelCase__   ,				UpperCAmelCase__   ,				size=font_size * self.scale   ,				family="""sans-serif"""   ,				bbox=UpperCAmelCase__   ,				verticalalignment="""top"""   ,				horizontalalignment=UpperCAmelCase__   ,				color=UpperCAmelCase__   ,				zorder=1_0   ,				rotation=UpperCAmelCase__   ,				)

       def     UpperCAmelCase( self   :  List[Any]   ,				lowerCamelCase_   :  List[str]=None     ):
            if saveas is None:
                 a_     :  Any				      =     self.saveas
            if saveas.lower().endswith(""".jpg"""     ) or saveas.lower().endswith(""".png"""     ):
                 cva.imwrite(
                     UpperCAmelCase__   ,				self._get_buffer()[:, :, ::-1]   ,				)
            else:
                 self.fig.savefig(UpperCAmelCase__     )

       def     UpperCAmelCase( self   :  Union[str, Any]   ,				lowerCamelCase_   :  Optional[int]   ,				lowerCamelCase_   :  Union[str, Any]   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  Any     ):
            a_     :  Dict				      =     [self.idaobj[i] for i in classes]
            a_     :  int				      =     [self.idaattr[i] for i in attr_classes]
            a_     :  str				      =     [
                F'''{label} {score:.2f} {attr} {attr_score:.2f}'''
                for label, score, attr, attr_score in zip(UpperCAmelCase__   ,				UpperCAmelCase__   ,				UpperCAmelCase__   ,				UpperCAmelCase__     )
            ]
            return labels

       def     UpperCAmelCase( self   :  List[Any]   ,				lowerCamelCase_   :  Union[str, Any]   ,				lowerCamelCase_   :  Dict     ):
            a_     :  Any				      =     [self.idaobj[i] for i in classes]
            if scores is not None:
                 if labels is None:
                      a_     :  Optional[Any]				      =     ["""{:.0f}%""".format(s * 1_0_0     ) for s in scores]
                 else:
                      a_     :  Optional[int]				      =     ["""{} {:.0f}%""".format(UpperCAmelCase__   ,				s * 1_0_0     ) for li, s in zip(UpperCAmelCase__   ,				UpperCAmelCase__     )]
            return labels

       def     UpperCAmelCase( self   :  Optional[Any]   ,				lowerCamelCase_   :  Dict=2_5_5     ):
            a_     :  List[str]				      =     np.random.randint(0   ,				len(_COLORS     )     )
            a_     :  int				      =     _COLORS[idx] * maximum
            if not self.rgb:
                 a_     :  List[Any]				      =     ret[::-1]
            return ret

       def     UpperCAmelCase( self   :  List[str]     ):
            if not self.pynb:
                 a_      ,      a_     :  Any				      =     self.canvas.print_to_buffer()
                 if (width, height) != (self.width, self.height):
                      a_     :  List[Any]				      =     cva.resize(self.img   ,				(width, height)     )
                 else:
                      a_     :  List[Any]				      =     self.img
            else:
                 a_     :  int				      =     io.BytesIO()  # works for cairo backend
                 self.canvas.print_rgba(UpperCAmelCase__     )
                 a_      ,      a_     :  Tuple				      =     self.width, self.height
                 a_     :  Dict				      =     buf.getvalue()
                 a_     :  Optional[int]				      =     self.img

            a_     :  Any				      =     np.frombuffer(UpperCAmelCase__   ,				dtype="""uint8"""     )
            a_     :  Union[str, Any]				      =     buffer.reshape(UpperCAmelCase__   ,				UpperCAmelCase__   ,				4     )
            a_      ,      a_     :  Union[str, Any]				      =     np.split(UpperCAmelCase__   ,				[3]   ,				axis=2     )

            try:
                 import numexpr as ne  # fuse them with numexpr

                 a_     :  int				      =     ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""     )
            except ImportError:
                 a_     :  Dict				      =     alpha.astype("""float32"""     ) / 2_5_5.0
                 a_     :  List[str]				      =     img * (1 - alpha) + rgb * alpha

            return visualized_image.astype("""uint8"""     )

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  List[Any]     ):
            assert brightness_factor >= -1.0 and brightness_factor <= 1.0
            a_     :  Tuple				      =     mplc.to_rgb(UpperCAmelCase__     )
            a_     :  Optional[Any]				      =     colorsys.rgb_to_hls(*mplc.to_rgb(UpperCAmelCase__     )     )
            a_     :  str				      =     polygon_color[1] + (brightness_factor * polygon_color[1])
            a_     :  List[str]				      =     0.0 if modified_lightness < 0.0 else modified_lightness
            a_     :  str				      =     1.0 if modified_lightness > 1.0 else modified_lightness
            a_     :  str				      =     colorsys.hls_to_rgb(polygon_color[0]   ,				UpperCAmelCase__   ,				polygon_color[2]     )
            return modified_color

__lowerCamelCase   	=  (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)






"""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]




