





"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 					_lowerCAmelCase   (     a  ,   unittest.TestCase       ):
       """simple docstring"""




       __magic_name__     :Dict		  =					KandinskyVaaImgaImgPipeline
       __magic_name__     :str		  =					["""image_embeds""", """negative_image_embeds""", """image"""]
       __magic_name__     :Union[str, Any]		  =					[
           """image_embeds""",
           """negative_image_embeds""",
           """image""",
       ]
       __magic_name__     :Optional[Any]		  =					[
           """generator""",
           """height""",
           """width""",
           """strength""",
           """guidance_scale""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       __magic_name__     :Any		  =					False






       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

         return 3_2






       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

         return 3_2






       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

         return self.time_input_dim






       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

         return self.time_input_dim * 4






       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

         return 1_0_0






       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

         torch.manual_seed(0      )

         lowerCAmelCase__			:List[Any]											=							{
             'in_channels': 4,
             # Out channels is double in channels because predicts mean and variance
             'out_channels': 8,
             'addition_embed_type': 'image',
             'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
             'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
             'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
             'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
             'layers_per_block': 1,
             'encoder_hid_dim': self.text_embedder_hidden_size,
             'encoder_hid_dim_type': 'image_proj',
             'cross_attention_dim': self.cross_attention_dim,
             'attention_head_dim': 4,
             'resnet_time_scale_shift': 'scale_shift',
             'class_embed_type': None,
         }

         lowerCAmelCase__			:str											=							UNetaDConditionModel(**__UpperCAmelCase      )
         return model






       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

         return {
             "block_out_channels": [3_2, 6_4],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 1_2,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }






       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

         torch.manual_seed(0      )
         lowerCAmelCase__			:Optional[Any]											=							VQModel(**self.dummy_movq_kwargs      )
         return model






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							self.dummy_unet
         lowerCAmelCase__			:Optional[Any]											=							self.dummy_movq

         lowerCAmelCase__			:int											=							{
             'num_train_timesteps': 1_0_0_0,
             'beta_schedule': 'linear',
             'beta_start': 0.0_00_85,
             'beta_end': 0.0_12,
             'clip_sample': False,
             'set_alpha_to_one': False,
             'steps_offset': 0,
             'prediction_type': 'epsilon',
             'thresholding': False,
         }

         lowerCAmelCase__			:Tuple											=							DDIMScheduler(**__UpperCAmelCase      )

         lowerCAmelCase__			:Optional[int]											=							{
             'unet': unet,
             'scheduler': scheduler,
             'movq': movq,
         }

         return components






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase=0      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							floats_tensor((1, self.text_embedder_hidden_size)    , rng=random.Random(__UpperCAmelCase      )      ).to(__UpperCAmelCase      )
         lowerCAmelCase__			:List[str]											=							floats_tensor((1, self.text_embedder_hidden_size)    , rng=random.Random(seed + 1      )      ).to(
             __UpperCAmelCase      )
         # create init_image
         lowerCAmelCase__			:Optional[Any]											=							floats_tensor((1, 3, 6_4, 6_4)    , rng=random.Random(__UpperCAmelCase      )      ).to(__UpperCAmelCase      )
         lowerCAmelCase__			:Dict											=							image.cpu().permute(0    , 2    , 3    , 1      )[0]
         lowerCAmelCase__			:int											=							Image.fromarray(np.uinta(__UpperCAmelCase      )      ).convert('RGB'      ).resize((2_5_6, 2_5_6)      )

         if str(__UpperCAmelCase      ).startswith('mps'      ):
           lowerCAmelCase__			:Any											=							torch.manual_seed(__UpperCAmelCase      )
         else:
           lowerCAmelCase__			:Any											=							torch.Generator(device=__UpperCAmelCase      ).manual_seed(__UpperCAmelCase      )
         lowerCAmelCase__			:List[str]											=							{
             'image': init_image,
             'image_embeds': image_embeds,
             'negative_image_embeds': negative_image_embeds,
             'generator': generator,
             'height': 6_4,
             'width': 6_4,
             'num_inference_steps': 1_0,
             'guidance_scale': 7.0,
             'strength': 0.2,
             'output_type': 'np',
         }
         return inputs






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							'cpu'

         lowerCAmelCase__			:Optional[Any]											=							self.get_dummy_components()

         lowerCAmelCase__			:str											=							self.pipeline_class(**__UpperCAmelCase      )
         lowerCAmelCase__			:Dict											=							pipe.to(__UpperCAmelCase      )

         pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

         lowerCAmelCase__			:int											=							pipe(**self.get_dummy_inputs(__UpperCAmelCase      )      )
         lowerCAmelCase__			:Optional[int]											=							output.images

         lowerCAmelCase__			:Tuple											=							pipe(
             **self.get_dummy_inputs(__UpperCAmelCase      )    , return_dict=__UpperCAmelCase    , )[0]

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

         assert image.shape == (1, 6_4, 6_4, 3)

         lowerCAmelCase__			:Optional[Any]											=							np.array(
             [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63]      )
         assert (
             np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
         ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2
         ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






@slow
@require_torch_gpu
class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinskyv22/kandinskyv22_img2img_frog.npy'      )

         lowerCAmelCase__			:int											=							load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'      )
         lowerCAmelCase__			:List[str]											=							'A red cartoon frog, 4k'

         lowerCAmelCase__			:Dict											=							KandinskyVaaPriorPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-2-prior'    , torch_dtype=torch.floataa      )
         pipe_prior.to(__UpperCAmelCase      )

         lowerCAmelCase__			:List[Any]											=							KandinskyVaaImgaImgPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-2-decoder'    , torch_dtype=torch.floataa      )
         lowerCAmelCase__			:Optional[Any]											=							pipeline.to(__UpperCAmelCase      )

         pipeline.set_progress_bar_config(disable=__UpperCAmelCase      )

         lowerCAmelCase__			:List[Any]											=							torch.Generator(device='cpu'      ).manual_seed(0      )
         lowerCAmelCase__			:Tuple											=							pipe_prior(
             __UpperCAmelCase    , generator=__UpperCAmelCase    , num_inference_steps=5    , negative_prompt=''    , ).to_tuple()

         lowerCAmelCase__			:Dict											=							pipeline(
             image=__UpperCAmelCase    , image_embeds=__UpperCAmelCase    , negative_image_embeds=__UpperCAmelCase    , generator=__UpperCAmelCase    , num_inference_steps=1_0_0    , height=7_6_8    , width=7_6_8    , strength=0.2    , output_type='np'    , )

         lowerCAmelCase__			:int											=							output.images[0]

         assert image.shape == (7_6_8, 7_6_8, 3)

         assert_mean_pixel_difference(__UpperCAmelCase    , __UpperCAmelCase      )


from __future__ import annotations

import math

lowerCamelCase__          =		"""2020.9.26"""
lowerCamelCase__          =		"""xcodz-dot, cclaus, dhruvmanila"""
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		tuple[float, float]:
			if not all(isinstance(SCREAMING_SNAKE_CASE_       ,							(float, int)     ) for val in locals().values()     ):
						lowerCAmelCase__      :      List[str]         =		F'''Input values must either be float or int: {list(locals().values()     )}'''
						raise TypeError(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Tuple         =		((x * distance) / (z + distance)) * scale
			lowerCAmelCase__      :      Optional[int]         =		((y * distance) / (z + distance)) * scale
			return projected_x, projected_y
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		tuple[float, float, float]:
			if not isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     ):
						raise TypeError('Axis must be a str'     )
			lowerCAmelCase__      :      Optional[int]         =		locals()
			del input_variables["axis"]
			if not all(isinstance(SCREAMING_SNAKE_CASE_       ,							(float, int)     ) for val in input_variables.values()     ):
						lowerCAmelCase__      :      List[Any]         =		(
						    'Input values except axis must either be float or int: '
						    F'''{list(input_variables.values()     )}'''
						)
						raise TypeError(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      int         =		(angle % 360) / 450 * 180 / math.pi
			if axis == "z":
						lowerCAmelCase__      :      Tuple         =		x * math.cos(SCREAMING_SNAKE_CASE_     ) - y * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      List[str]         =		y * math.cos(SCREAMING_SNAKE_CASE_     ) + x * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      Optional[int]         =		z
			elif axis == "x":
						lowerCAmelCase__      :      Dict         =		y * math.cos(SCREAMING_SNAKE_CASE_     ) - z * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      List[str]         =		z * math.cos(SCREAMING_SNAKE_CASE_     ) + y * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      Tuple         =		x
			elif axis == "y":
						lowerCAmelCase__      :      str         =		x * math.cos(SCREAMING_SNAKE_CASE_     ) - z * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      Optional[int]         =		z * math.cos(SCREAMING_SNAKE_CASE_     ) + x * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      List[Any]         =		y
			else:
						raise ValueError('not a valid axis, choose one of \'x\', \'y\', \'z\''     )

			return new_x, new_y, new_z


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
					print(F"""{rotate(1.0, 2.0, 3.0, "y", 90.0) = }""")