


'''simple docstring'''







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


A      :     Optional[Any]										= 1000


class  __lowerCamelCase :
    """simple docstring"""


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

           _A  , _A     :	Optional[Any]      =					img.shape[1], img.shape[0]
           _A     :	List[str]      =					mplfigure.Figure(frameon=SCREAMING_SNAKE_CASE)
           _A     :	Dict      =					fig.get_dpi()
           _A     :	List[str]      =					(width * scale + 1e-2) / dpi
           _A     :	Dict      =					(height * scale + 1e-2) / dpi
           fig.set_size_inches(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           _A     :	int      =					fig.add_axes([0.0, 0.0, 1.0, 1.0])
           ax.axis('off')
           ax.set_xlim(0.0      ,							SCREAMING_SNAKE_CASE)
           ax.set_ylim(SCREAMING_SNAKE_CASE)

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


    def    A    (  self			:							str      ,							SCREAMING_SNAKE_CASE			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							Any=None):
           if color is None:
                  _A     :	List[Any]      =					self.edgecolor
           ((_A)  , (_A)  , (_A)  , (_A))     :	str      =					box
           _A     :	Tuple      =					xa - xa
           _A     :	int      =					ya - ya
           self.ax.add_patch(
               mpl.patches.Rectangle(
                   (xa, ya)      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							fill=SCREAMING_SNAKE_CASE      ,							edgecolor=SCREAMING_SNAKE_CASE      ,							linewidth=self.font_size // 3      ,							alpha=self.alpha      ,							linestyle=self.linestyle      ,							))


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

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


    def    A    (  self			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							int      ,							SCREAMING_SNAKE_CASE			:							Any      ,							SCREAMING_SNAKE_CASE			:							List[Any]):
           _A  , _A  , _A  , _A     :	Dict      =					box
           _A     :	Union[str, Any]      =					(xa, ya)
           _A     :	str      =					(ya - ya) * (xa - xa)
           _A     :	List[str]      =					_SMALL_OBJ * self.scale
           if instance_area < small or ya - ya < 40 * self.scale:
                  if ya >= self.height - 5:
                         _A     :	List[str]      =					(xa, ya)
                  else:
                         _A     :	int      =					(xa, ya)

           _A     :	Optional[Any]      =					(ya - ya) / np.sqrt(self.height * self.width)
           _A     :	Tuple      =					self._change_color_brightness(SCREAMING_SNAKE_CASE      ,							brightness_factor=0.7)
           _A     :	Tuple      =					np.clip((height_ratio - 0.02) / 0.08 + 1      ,							1.2      ,							2)
           font_size *= 0.75 * self.font_size

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


    def    A    (  self			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]      ,							SCREAMING_SNAKE_CASE			:							str="g"      ,							SCREAMING_SNAKE_CASE			:							Tuple="left"      ,							):
           _A     :	str      =					0
           _A     :	Optional[int]      =					self.font_size
           _A     :	Dict      =					np.maximum(list(mplc.to_rgb(SCREAMING_SNAKE_CASE))      ,							0.2)
           _A     :	Optional[int]      =					max(0.8      ,							np.max(SCREAMING_SNAKE_CASE))
           _A     :	Dict      =					{
               'facecolor': 'black',
               'alpha': self.alpha,
               'pad': self.pad,
               'edgecolor': 'none',
           }
           _A  , _A     :	str      =					position
           self.ax.text(
               SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							size=font_size * self.scale      ,							family='sans-serif'      ,							bbox=SCREAMING_SNAKE_CASE      ,							verticalalignment='top'      ,							horizontalalignment=SCREAMING_SNAKE_CASE      ,							color=SCREAMING_SNAKE_CASE      ,							zorder=10      ,							rotation=SCREAMING_SNAKE_CASE      ,							)


    def    A    (  self			:							int      ,							SCREAMING_SNAKE_CASE			:							Any=None):
           if saveas is None:
                  _A     :	Optional[int]      =					self.saveas
           if saveas.lower().endswith('.jpg') or saveas.lower().endswith('.png'):
                  cva.imwrite(
                      SCREAMING_SNAKE_CASE      ,							self._get_buffer()[:, :, ::-1]      ,							)
           else:
                  self.fig.savefig(SCREAMING_SNAKE_CASE)


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


    def    A    (  self			:							Optional[Any]      ,							SCREAMING_SNAKE_CASE			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							str):
           _A     :	Optional[int]      =					[self.idaobj[i] for i in classes]
           if scores is not None:
                  if labels is None:
                         _A     :	Tuple      =					['{:.0f}%'.format(s * 100) for s in scores]
                  else:
                         _A     :	Optional[int]      =					['{} {:.0f}%'.format(SCREAMING_SNAKE_CASE      ,							s * 100) for li, s in zip(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)]
           return labels


    def    A    (  self			:							Dict      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]=255):
           _A     :	Dict      =					np.random.randint(0      ,							len(_COLORS))
           _A     :	Optional[int]      =					_COLORS[idx] * maximum
           if not self.rgb:
                  _A     :	List[Any]      =					ret[::-1]
           return ret


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

           _A     :	Dict      =					np.frombuffer(SCREAMING_SNAKE_CASE      ,							dtype='uint8')
           _A     :	Tuple      =					buffer.reshape(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							4)
           _A  , _A     :	List[Any]      =					np.split(SCREAMING_SNAKE_CASE      ,							[3]      ,							axis=2)

           try:
                  import numexpr as ne  # fuse them with numexpr

                  _A     :	Union[str, Any]      =					ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)')
           except ImportError:
                  _A     :	Any      =					alpha.astype('float32') / 255.0
                  _A     :	str      =					img * (1 - alpha) + rgb * alpha

           return visualized_image.astype('uint8')


    def    A    (  self			:							Dict      ,							SCREAMING_SNAKE_CASE			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							str):
           assert brightness_factor >= -1.0 and brightness_factor <= 1.0
           _A     :	Any      =					mplc.to_rgb(SCREAMING_SNAKE_CASE)
           _A     :	List[Any]      =					colorsys.rgb_to_hls(*mplc.to_rgb(SCREAMING_SNAKE_CASE))
           _A     :	Union[str, Any]      =					polygon_color[1] + (brightness_factor * polygon_color[1])
           _A     :	Optional[int]      =					0.0 if modified_lightness < 0.0 else modified_lightness
           _A     :	Dict      =					1.0 if modified_lightness > 1.0 else modified_lightness
           _A     :	Optional[Any]      =					colorsys.hls_to_rgb(polygon_color[0]      ,							SCREAMING_SNAKE_CASE      ,							polygon_color[2])
           return modified_color




A      :     int										= (
    np.array(
        [
            0.0_00,
            0.4_47,
            0.7_41,
            0.8_50,
            0.3_25,
            0.0_98,
            0.9_29,
            0.6_94,
            0.1_25,
            0.4_94,
            0.1_84,
            0.5_56,
            0.4_66,
            0.6_74,
            0.1_88,
            0.3_01,
            0.7_45,
            0.9_33,
            0.6_35,
            0.0_78,
            0.1_84,
            0.3_00,
            0.3_00,
            0.3_00,
            0.6_00,
            0.6_00,
            0.6_00,
            1.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.7_49,
            0.7_49,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            0.0_00,
            0.3_33,
            0.6_67,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            0.3_33,
            0.0_00,
            0.6_67,
            0.6_67,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.3_33,
            0.5_00,
            0.0_00,
            0.6_67,
            0.5_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.3_33,
            0.0_00,
            0.5_00,
            0.3_33,
            0.3_33,
            0.5_00,
            0.3_33,
            0.6_67,
            0.5_00,
            0.3_33,
            1.0_00,
            0.5_00,
            0.6_67,
            0.0_00,
            0.5_00,
            0.6_67,
            0.3_33,
            0.5_00,
            0.6_67,
            0.6_67,
            0.5_00,
            0.6_67,
            1.0_00,
            0.5_00,
            1.0_00,
            0.0_00,
            0.5_00,
            1.0_00,
            0.3_33,
            0.5_00,
            1.0_00,
            0.6_67,
            0.5_00,
            1.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            1.0_00,
            0.3_33,
            0.6_67,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.6_67,
            0.3_33,
            1.0_00,
            0.6_67,
            0.6_67,
            1.0_00,
            0.6_67,
            1.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            1.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_43,
            0.1_43,
            0.1_43,
            0.8_57,
            0.8_57,
            0.8_57,
            1.0_00,
            1.0_00,
            1.0_00,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)










'''simple docstring'''







import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

A      :     Optional[int]										= logging.get_logger(__name__)






def       lowerCAmelCase__       (				lowerCamelCase     :			int  ,lowerCamelCase     :			bool = True   ):
       _A     :	Any      =					{}
       for name in model.attn_processors.keys():
              _A     :	Any      =					name.endswith('attn2.processor'   ) and not (
                  name.startswith('transformer_in'   ) or 'temp_attentions' in name.split('.'   )
              )
              _A     :	Dict      =					model.config.cross_attention_dim if has_cross_attention else None
              if name.startswith('mid_block'   ):
                     _A     :	Optional[int]      =					model.config.block_out_channels[-1]
              elif name.startswith('up_blocks'   ):
                     _A     :	str      =					int(name[len('up_blocks.'   )]   )
                     _A     :	Any      =					list(reversed(model.config.block_out_channels   )   )[block_id]
              elif name.startswith('down_blocks'   ):
                     _A     :	str      =					int(name[len('down_blocks.'   )]   )
                     _A     :	int      =					model.config.block_out_channels[block_id]
              elif name.startswith('transformer_in'   ):
                     # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
                     _A     :	Dict      =					8 * model.config.attention_head_dim

              _A     :	int      =					LoRAAttnProcessor(hidden_size=lowerCamelCase  ,cross_attention_dim=lowerCamelCase   )
              _A     :	Tuple      =					lora_attn_procs[name].to(model.device   )

              if mock_weights:
                     # add 1 to weights to mock trained weights
                     with torch.no_grad():
                            lora_attn_procs[name].to_q_lora.up.weight += 1
                            lora_attn_procs[name].to_k_lora.up.weight += 1
                            lora_attn_procs[name].to_v_lora.up.weight += 1
                            lora_attn_procs[name].to_out_lora.up.weight += 1

       return lora_attn_procs


@skip_mps
class  __lowerCamelCase (       a_				,					a_				,					unittest.TestCase       ):
    """simple docstring"""
    a													=    UNetaDConditionModel
    a													=    "sample"


    @property
    def    A    (  self			:							Tuple):
           _A     :	List[Any]      =					4
           _A     :	Tuple      =					4
           _A     :	int      =					4
           _A     :	Dict      =					(32, 32)

           _A     :	Any      =					floats_tensor((batch_size, num_channels, num_frames) + sizes).to(SCREAMING_SNAKE_CASE)
           _A     :	Optional[Any]      =					torch.tensor([10]).to(SCREAMING_SNAKE_CASE)
           _A     :	Optional[Any]      =					floats_tensor((batch_size, 4, 32)).to(SCREAMING_SNAKE_CASE)

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


    @property
    def    A    (  self			:							Tuple):
           return (4, 4, 32, 32)


    @property
    def    A    (  self			:							str):
           return (4, 4, 32, 32)


    def    A    (  self			:							List[Any]):
           _A     :	List[str]      =					{
               'block_out_channels': (32, 64),
               'down_block_types': (
                   'CrossAttnDownBlock3D',
                   'DownBlock3D',
               ),
               'up_block_types': ('UpBlock3D', 'CrossAttnUpBlock3D'),
               'cross_attention_dim': 32,
               'attention_head_dim': 8,
               'out_channels': 4,
               'in_channels': 4,
               'layers_per_block': 1,
               'sample_size': 32,
           }
           _A     :	int      =					self.dummy_input
           return init_dict, inputs_dict


    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()      ,							reason='XFormers attention is only available with CUDA and `xformers` installed'      ,							)
    def    A    (  self			:							Tuple):
           _A  , _A     :	Dict      =					self.prepare_init_args_and_inputs_for_common()
           _A     :	Any      =					self.model_class(**SCREAMING_SNAKE_CASE)

           model.enable_xformers_memory_efficient_attention()

           assert (
               model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
               == "XFormersAttnProcessor"
           ), "xformers is not enabled"


    def    A    (  self			:							str):
           _A  , _A     :	Union[str, Any]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Union[str, Any]      =					32

           _A     :	int      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()

           with torch.no_grad():
                  _A     :	List[str]      =					model(**SCREAMING_SNAKE_CASE)

                  if isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                         _A     :	int      =					output.sample

           self.assertIsNotNone(SCREAMING_SNAKE_CASE)
           _A     :	Optional[int]      =					inputs_dict['sample'].shape
           self.assertEqual(output.shape      ,							SCREAMING_SNAKE_CASE      ,							'Input and output shapes do not match')


    def    A    (  self			:							Dict):
           _A  , _A     :	List[str]      =					self.prepare_init_args_and_inputs_for_common()
           _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()

           with torch.no_grad():
                  # Warmup pass when using mps (see #372)
                  if torch_device == "mps" and isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                         model(**self.dummy_input)

                  _A     :	str      =					model(**SCREAMING_SNAKE_CASE)
                  if isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                         _A     :	Optional[int]      =					first.sample

                  _A     :	Union[str, Any]      =					model(**SCREAMING_SNAKE_CASE)
                  if isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                         _A     :	Dict      =					second.sample

           _A     :	Dict      =					first.cpu().numpy()
           _A     :	Dict      =					second.cpu().numpy()
           _A     :	Optional[int]      =					out_a[~np.isnan(SCREAMING_SNAKE_CASE)]
           _A     :	Dict      =					out_a[~np.isnan(SCREAMING_SNAKE_CASE)]
           _A     :	List[Any]      =					np.amax(np.abs(out_a - out_a))
           self.assertLessEqual(SCREAMING_SNAKE_CASE      ,							1e-5)


    def    A    (  self			:							Optional[int]):
           _A  , _A     :	Union[str, Any]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Optional[int]      =					8

           _A     :	Optional[Any]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()

           model.set_attention_slice('auto')
           with torch.no_grad():
                  _A     :	Any      =					model(**SCREAMING_SNAKE_CASE)
           assert output is not None

           model.set_attention_slice('max')
           with torch.no_grad():
                  _A     :	Optional[Any]      =					model(**SCREAMING_SNAKE_CASE)
           assert output is not None

           model.set_attention_slice(2)
           with torch.no_grad():
                  _A     :	Dict      =					model(**SCREAMING_SNAKE_CASE)
           assert output is not None


    def    A    (  self			:							List[Any]):
           _A  , _A     :	Tuple      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Dict      =					8

           _A     :	Any      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Tuple      =					model(**SCREAMING_SNAKE_CASE).sample

           _A     :	List[str]      =					create_lora_layers(SCREAMING_SNAKE_CASE)

           # make sure we can set a list of attention processors
           model.set_attn_processor(SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           # test that attn processors can be set to itself
           model.set_attn_processor(model.attn_processors)

           with torch.no_grad():
                  _A     :	Union[str, Any]      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.0}).sample
                  _A     :	Dict      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample
                  _A     :	Optional[int]      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample

           assert (samplea - samplea).abs().max() < 3e-3
           assert (samplea - samplea).abs().max() < 3e-3

           # sample 2 and sample 3 should be different
           assert (samplea - samplea).abs().max() > 3e-3


    def    A    (  self			:							str):
           _A  , _A     :	Optional[Any]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Tuple      =					8

           torch.manual_seed(0)
           _A     :	List[str]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Dict      =					model(**SCREAMING_SNAKE_CASE).sample

           _A     :	Optional[int]      =					create_lora_layers(SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Optional[int]      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample

           with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_attn_procs(SCREAMING_SNAKE_CASE)
                  self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE      ,							'pytorch_lora_weights.bin')))
                  torch.manual_seed(0)
                  _A     :	Dict      =					self.model_class(**SCREAMING_SNAKE_CASE)
                  new_model.to(SCREAMING_SNAKE_CASE)
                  new_model.load_attn_procs(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	List[Any]      =					new_model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample

           assert (sample - new_sample).abs().max() < 1e-3

           # LoRA and no LoRA should NOT be the same
           assert (sample - old_sample).abs().max() > 1e-4


    def    A    (  self			:							Union[str, Any]):
           _A  , _A     :	Any      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Any      =					8

           torch.manual_seed(0)
           _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	str      =					model(**SCREAMING_SNAKE_CASE).sample

           _A     :	Any      =					create_lora_layers(SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Tuple      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample

           with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_attn_procs(SCREAMING_SNAKE_CASE      ,							safe_serialization=SCREAMING_SNAKE_CASE)
                  self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE      ,							'pytorch_lora_weights.safetensors')))
                  torch.manual_seed(0)
                  _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
                  new_model.to(SCREAMING_SNAKE_CASE)
                  new_model.load_attn_procs(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Optional[int]      =					new_model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample

           assert (sample - new_sample).abs().max() < 3e-3

           # LoRA and no LoRA should NOT be the same
           assert (sample - old_sample).abs().max() > 1e-4


    def    A    (  self			:							Optional[int]):
           # enable deterministic behavior for gradient checkpointing
           _A  , _A     :	List[Any]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	int      =					8

           torch.manual_seed(0)
           _A     :	List[str]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           _A     :	List[Any]      =					create_lora_layers(SCREAMING_SNAKE_CASE      ,							mock_weights=SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)
           # Saving as torch, properly reloads with directly filename
           with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_attn_procs(SCREAMING_SNAKE_CASE)
                  self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE      ,							'pytorch_lora_weights.bin')))
                  torch.manual_seed(0)
                  _A     :	Dict      =					self.model_class(**SCREAMING_SNAKE_CASE)
                  new_model.to(SCREAMING_SNAKE_CASE)
                  new_model.load_attn_procs(SCREAMING_SNAKE_CASE      ,							weight_name='pytorch_lora_weights.bin')


    def    A    (  self			:							List[Any]):
           _A  , _A     :	Dict      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Optional[Any]      =					8

           torch.manual_seed(0)
           _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           _A     :	Dict      =					create_lora_layers(SCREAMING_SNAKE_CASE      ,							mock_weights=SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)
           # Saving as torch, properly reloads with directly filename
           with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_attn_procs(SCREAMING_SNAKE_CASE)
                  self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE      ,							'pytorch_lora_weights.bin')))
                  torch.manual_seed(0)
                  _A     :	Union[str, Any]      =					self.model_class(**SCREAMING_SNAKE_CASE)
                  new_model.to(SCREAMING_SNAKE_CASE)
                  with self.assertRaises(SCREAMING_SNAKE_CASE) as e:
                         new_model.load_attn_procs(SCREAMING_SNAKE_CASE      ,							use_safetensors=SCREAMING_SNAKE_CASE)
                  self.assertIn('Error no file named pytorch_lora_weights.safetensors'      ,							str(e.exception))


    def    A    (  self			:							Optional[int]):
           _A  , _A     :	str      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	str      =					8

           torch.manual_seed(0)
           _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Optional[int]      =					model(**SCREAMING_SNAKE_CASE).sample

           _A     :	Optional[int]      =					create_lora_layers(SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	str      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.0}).sample

           model.set_attn_processor(AttnProcessor())

           with torch.no_grad():
                  _A     :	List[Any]      =					model(**SCREAMING_SNAKE_CASE).sample

           assert (sample - new_sample).abs().max() < 1e-4
           assert (sample - old_sample).abs().max() < 3e-3


    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()      ,							reason='XFormers attention is only available with CUDA and `xformers` installed'      ,							)
    def    A    (  self			:							List[str]):
           # enable deterministic behavior for gradient checkpointing
           _A  , _A     :	List[str]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	int      =					4

           torch.manual_seed(0)
           _A     :	str      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           _A     :	str      =					create_lora_layers(SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)

           # default
           with torch.no_grad():
                  _A     :	str      =					model(**SCREAMING_SNAKE_CASE).sample

                  model.enable_xformers_memory_efficient_attention()
                  _A     :	Tuple      =					model(**SCREAMING_SNAKE_CASE).sample

                  model.disable_xformers_memory_efficient_attention()
                  _A     :	int      =					model(**SCREAMING_SNAKE_CASE).sample

           assert (sample - on_sample).abs().max() < 1e-4
           assert (sample - off_sample).abs().max() < 1e-4


    def    A    (  self			:							Optional[Any]):
           _A  , _A     :	Dict      =					self.prepare_init_args_and_inputs_for_common()
           _A     :	List[Any]      =					32

           _A     :	Any      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()

           with torch.no_grad():
                  _A     :	Any      =					model(**SCREAMING_SNAKE_CASE)[0]

           model.enable_forward_chunking()
           with torch.no_grad():
                  _A     :	Dict      =					model(**SCREAMING_SNAKE_CASE)[0]

           self.assertEqual(output.shape      ,							output_a.shape      ,							'Shape doesn\'t match')
           assert np.abs(output.cpu() - output_a.cpu()).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.






