



"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 				A_      (					SCREAMING_SNAKE_CASE_     ,       unittest.TestCase    ):



						"""simple docstring"""
						__UpperCamelCase		    =							KandinskyInpaintPipeline
						__UpperCamelCase		    =							["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
						__UpperCamelCase		    =							[
						    """prompt""",
						    """negative_prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						    """mask_image""",
						]
						__UpperCamelCase		    =							[
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """negative_prompt""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						__UpperCamelCase		    =							False






						@property
						def      UpperCAmelCase__					( self			:Any )    ->      Tuple:
												return 32






						@property
						def      UpperCAmelCase__					( self			:str )    ->      List[str]:
												return 32






						@property
						def      UpperCAmelCase__					( self			:List[str] )    ->      Union[str, Any]:
												return self.time_input_dim






						@property
						def      UpperCAmelCase__					( self			:int )    ->      Optional[Any]:
												return self.time_input_dim * 4






						@property
						def      UpperCAmelCase__					( self			:List[Any] )    ->      Optional[Any]:
												return 1_00






						@property
						def      UpperCAmelCase__					( self			:str )    ->      Union[str, Any]:
												UpperCAmelCase					=			XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base' )
												return tokenizer






						@property
						def      UpperCAmelCase__					( self			:Any )    ->      List[str]:
												torch.manual_seed(0 )
												UpperCAmelCase					=			MCLIPConfig(
												    numDims=self.cross_attention_dim       ,  transformerDimensions=self.text_embedder_hidden_size       ,  hidden_size=self.text_embedder_hidden_size       ,  intermediate_size=37       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  vocab_size=10_05       ,  )

												UpperCAmelCase					=			MultilingualCLIP(lowercase_ )
												UpperCAmelCase					=			text_encoder.eval()

												return text_encoder






						@property
						def      UpperCAmelCase__					( self			:str )    ->      Optional[int]:
												torch.manual_seed(0 )

												UpperCAmelCase					=			{
												    'in_channels': 9,
												    # Out channels is double in channels because predicts mean and variance
												    'out_channels': 8,
												    'addition_embed_type': 'text_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': 'text_image_proj',
												    'cross_attention_dim': self.cross_attention_dim,
												    'attention_head_dim': 4,
												    'resnet_time_scale_shift': 'scale_shift',
												    'class_embed_type': None,
												}

												UpperCAmelCase					=			UNetaDConditionModel(**lowercase_ )
												return model






						@property
						def      UpperCAmelCase__					( self			:str )    ->      Tuple:
												return {
												    "block_out_channels": [32, 64],
												    "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": 12,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}






						@property
						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Optional[int]:
												torch.manual_seed(0 )
												UpperCAmelCase					=			VQModel(**self.dummy_movq_kwargs )
												return model






						def      UpperCAmelCase__					( self			:List[Any] )    ->      List[Any]:
												UpperCAmelCase					=			self.dummy_text_encoder
												UpperCAmelCase					=			self.dummy_tokenizer
												UpperCAmelCase					=			self.dummy_unet
												UpperCAmelCase					=			self.dummy_movq

												UpperCAmelCase					=			DDIMScheduler(
												    num_train_timesteps=10_00       ,  beta_schedule='linear'       ,  beta_start=0.0_0085       ,  beta_end=0.012       ,  clip_sample=lowercase_       ,  set_alpha_to_one=lowercase_       ,  steps_offset=1       ,  prediction_type='epsilon'       ,  thresholding=lowercase_       ,  )

												UpperCAmelCase					=			{
												    'text_encoder': text_encoder,
												    'tokenizer': tokenizer,
												    'unet': unet,
												    'scheduler': scheduler,
												    'movq': movq,
												}

												return components






						def      UpperCAmelCase__					( self			:List[str]       ,  lowercase_			:List[str]       ,  lowercase_			:List[Any]=0 )    ->      Dict:
												UpperCAmelCase					=			floats_tensor((1, self.cross_attention_dim)       ,  rng=random.Random(lowercase_ ) ).to(lowercase_ )
												UpperCAmelCase					=			floats_tensor((1, self.cross_attention_dim)       ,  rng=random.Random(seed + 1 ) ).to(lowercase_ )
												# create init_image
												UpperCAmelCase					=			floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(lowercase_ ) ).to(lowercase_ )
												UpperCAmelCase					=			image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												UpperCAmelCase					=			Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((2_56, 2_56) )
												# create mask
												UpperCAmelCase					=			np.ones((64, 64)       ,  dtype=np.floataa )
												UpperCAmelCase					=			0

												if str(lowercase_ ).startswith('mps' ):
																		UpperCAmelCase					=			torch.manual_seed(lowercase_ )
												else:
																		UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
												UpperCAmelCase					=			{
												    'prompt': 'horse',
												    'image': init_image,
												    'mask_image': mask,
												    'image_embeds': image_embeds,
												    'negative_image_embeds': negative_image_embeds,
												    'generator': generator,
												    'height': 64,
												    'width': 64,
												    'num_inference_steps': 2,
												    'guidance_scale': 4.0,
												    'output_type': 'np',
												}
												return inputs






						def      UpperCAmelCase__					( self			:List[str] )    ->      List[str]:
												UpperCAmelCase					=			'cpu'

												UpperCAmelCase					=			self.get_dummy_components()

												UpperCAmelCase					=			self.pipeline_class(**lowercase_ )
												UpperCAmelCase					=			pipe.to(lowercase_ )

												pipe.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			pipe(**self.get_dummy_inputs(lowercase_ ) )
												UpperCAmelCase					=			output.images

												UpperCAmelCase					=			pipe(
												    **self.get_dummy_inputs(lowercase_ )       ,  return_dict=lowercase_       ,  )[0]

												UpperCAmelCase					=			image[0, -3:, -3:, -1]
												UpperCAmelCase					=			image_from_tuple[0, -3:, -3:, -1]

												print(f"""image.shape {image.shape}""" )

												assert image.shape == (1, 64, 64, 3)

												UpperCAmelCase					=			np.array(
												    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786] )

												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()}"""






						def      UpperCAmelCase__					( self			:Tuple )    ->      Union[str, Any]:
												super().test_inference_batch_single_identical(expected_max_diff=3E-3 )







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



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:int )    ->      str:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Optional[int]:
												UpperCAmelCase					=			load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy' )

												UpperCAmelCase					=			load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
												UpperCAmelCase					=			np.ones((7_68, 7_68)       ,  dtype=np.floataa )
												UpperCAmelCase					=			0

												UpperCAmelCase					=			'a hat'

												UpperCAmelCase					=			KandinskyPriorPipeline.from_pretrained(
												    'kandinsky-community/kandinsky-2-1-prior'       ,  torch_dtype=torch.floataa )
												pipe_prior.to(lowercase_ )

												UpperCAmelCase					=			KandinskyInpaintPipeline.from_pretrained(
												    'kandinsky-community/kandinsky-2-1-inpaint'       ,  torch_dtype=torch.floataa )
												UpperCAmelCase					=			pipeline.to(lowercase_ )
												pipeline.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			torch.Generator(device='cpu' ).manual_seed(0 )
												UpperCAmelCase				,   UpperCAmelCase					=			pipe_prior(
												    lowercase_       ,  generator=lowercase_       ,  num_inference_steps=5       ,  negative_prompt=''       ,  ).to_tuple()

												UpperCAmelCase					=			pipeline(
												    lowercase_       ,  image=lowercase_       ,  mask_image=lowercase_       ,  image_embeds=lowercase_       ,  negative_image_embeds=lowercase_       ,  generator=lowercase_       ,  num_inference_steps=1_00       ,  height=7_68       ,  width=7_68       ,  output_type='np'       ,  )

												UpperCAmelCase					=			output.images[0]

												assert image.shape == (7_68, 7_68, 3)

												assert_mean_pixel_difference(lowercase_       ,  lowercase_ )










"""simple docstring"""


from math import factorial, radians

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_ = 18		,     lowercase_ = 10						):
						UpperCAmelCase					=			angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0)

						# Converting from degrees to radians
						UpperCAmelCase					=			radians(lowercase_						)

						UpperCAmelCase					=			angle_in_radians
						UpperCAmelCase					=			3
						UpperCAmelCase					=			-1

						for _ in range(lowercase_						):
												result += (b * (angle_in_radians**a)) / factorial(lowercase_						)

												UpperCAmelCase					=			-b  # One positive term and the next will be negative and so on...
												a += 2  # Increased by 2 for every term.

						return round(lowercase_		,     lowercase_						)


if __name__ == "__main__":
				__import__("""doctest""").testmod()





