




import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 						__lowerCAmelCase      (      __magic_name__ ,			unittest.TestCase					):
							UpperCamelCase__   	=    KandinskyVaaPriorPipeline
							UpperCamelCase__   	=    ['''prompt''']
							UpperCamelCase__   	=    ['''prompt''', '''negative_prompt''']
							UpperCamelCase__   	=    [
							    '''num_images_per_prompt''',
							    '''generator''',
							    '''num_inference_steps''',
							    '''latents''',
							    '''negative_prompt''',
							    '''guidance_scale''',
							    '''output_type''',
							    '''return_dict''',
							]
							UpperCamelCase__   	=    False
							@property
							def  lowerCamelCase__				(		self		:List[Any]							):







								'''simple docstring'''







								return 32
							@property
							def  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







								return 32
							@property
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								return self.time_input_dim
							@property
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								return self.time_input_dim * 4
							@property
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								return 100
							@property
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								a									=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
								return tokenizer
							@property
							def  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







								torch.manual_seed(0							)
								a									=				CLIPTextConfig(
								    bos_token_id=0						, eos_token_id=2						, hidden_size=self.text_embedder_hidden_size						, projection_dim=self.text_embedder_hidden_size						, intermediate_size=37						, layer_norm_eps=1E-0_5						, num_attention_heads=4						, num_hidden_layers=5						, pad_token_id=1						, vocab_size=1000						, )
								return CLIPTextModelWithProjection(__magic_name__							)
							@property
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								torch.manual_seed(0							)

								a									=				{
								    """num_attention_heads""": 2,
								    """attention_head_dim""": 12,
								    """embedding_dim""": self.text_embedder_hidden_size,
								    """num_layers""": 1,
								}

								a									=				PriorTransformer(**__magic_name__							)
								# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
								a									=				nn.Parameter(torch.ones(model.clip_std.shape							)							)
								return model
							@property
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								torch.manual_seed(0							)
								a									=				CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size						, image_size=224						, projection_dim=self.text_embedder_hidden_size						, intermediate_size=37						, num_attention_heads=4						, num_channels=3						, num_hidden_layers=5						, patch_size=14						, )

								a									=				CLIPVisionModelWithProjection(__magic_name__							)
								return model
							@property
							def  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								a									=				CLIPImageProcessor(
								    crop_size=224						, do_center_crop=__magic_name__						, do_normalize=__magic_name__						, do_resize=__magic_name__						, image_mean=[0.48145466, 0.4578275, 0.40821073]						, image_std=[0.26862954, 0.26130258, 0.27577711]						, resample=3						, size=224						, )

								return image_processor
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								a									=				self.dummy_prior
								a									=				self.dummy_image_encoder
								a									=				self.dummy_text_encoder
								a									=				self.dummy_tokenizer
								a									=				self.dummy_image_processor

								a									=				UnCLIPScheduler(
								    variance_type="""fixed_small_log"""						, prediction_type="""sample"""						, num_train_timesteps=1000						, clip_sample=__magic_name__						, clip_sample_range=10.0						, )

								a									=				{
								    """prior""": prior,
								    """image_encoder""": image_encoder,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """scheduler""": scheduler,
								    """image_processor""": image_processor,
								}

								return components
							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:Any						, __magic_name__		:Any=0							):







								'''simple docstring'''







								if str(__magic_name__							).startswith("""mps"""							):
									a									=				torch.manual_seed(__magic_name__							)
								else:
									a									=				torch.Generator(device=__magic_name__							).manual_seed(__magic_name__							)
								a									=				{
								    """prompt""": """horse""",
								    """generator""": generator,
								    """guidance_scale""": 4.0,
								    """num_inference_steps""": 2,
								    """output_type""": """np""",
								}
								return inputs
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								a									=				"""cpu"""

								a									=				self.get_dummy_components()

								a									=				self.pipeline_class(**__magic_name__							)
								a									=				pipe.to(__magic_name__							)

								pipe.set_progress_bar_config(disable=__magic_name__							)

								a									=				pipe(**self.get_dummy_inputs(__magic_name__							)							)
								a									=				output.image_embeds

								a									=				pipe(
								    **self.get_dummy_inputs(__magic_name__							)						, return_dict=__magic_name__						, )[0]

								a									=				image[0, -10:]
								a									=				image_from_tuple[0, -10:]

								assert image.shape == (1, 32)

								a									=				np.array(
								    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]							)

								assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
							@skip_mps
							def  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







								a									=				torch_device == """cpu"""
								a									=				True
								a									=				False

								self._test_inference_batch_single_identical(
								    test_max_difference=__magic_name__						, relax_max_difference=__magic_name__						, test_mean_pixel_difference=__magic_name__						, )





							@skip_mps
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								a									=				torch_device == """cpu"""
								a									=				False

								self._test_attention_slicing_forward_pass(
								    test_max_difference=__magic_name__						, test_mean_pixel_difference=__magic_name__						, )







from math import factorial, radians





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase = 18				,     __lowerCamelCase = 10       ) ->			float:
	a									=				angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

	# Converting from degrees to radians
	a									=				radians(__lowerCamelCase       )

	a									=				angle_in_radians
	a									=				3
	a									=				-1

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

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

	return round(__lowerCamelCase				,     __lowerCamelCase       )


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

