





'''simple docstring'''


import random
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 				__UpperCAmelCase	(							A__		,       unittest.TestCase    ):







		'''simple docstring'''







		__lowerCAmelCase								=     KandinskyVaaPriorEmbaEmbPipeline
		__lowerCAmelCase								=     ['''prompt''', '''image''']
		__lowerCAmelCase								=     ['''prompt''', '''image''']
		__lowerCAmelCase								=     [
		    '''num_images_per_prompt''',
		    '''strength''',
		    '''generator''',
		    '''num_inference_steps''',
		    '''latents''',
		    '''negative_prompt''',
		    '''guidance_scale''',
		    '''output_type''',
		    '''return_dict''',
		]
		__lowerCAmelCase								=     False







		@property
		def 							A  (self    :   Union[str, Any]   ):
			return 32







		@property
		def 							A  (self    :   Union[str, Any]   ):
			return 32







		@property
		def 							A  (self    :   Dict   ):
			return self.time_input_dim







		@property
		def 							A  (self    :   Dict   ):
			return self.time_input_dim * 4







		@property
		def 							A  (self    :   Union[str, Any]   ):
			return 100







		@property
		def 							A  (self    :   Optional[int]   ):
			A					=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )
			return tokenizer







		@property
		def 							A  (self    :   Optional[Any]   ):
			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-05   ,   num_attention_heads=4   ,   num_hidden_layers=5   ,   pad_token_id=1   ,   vocab_size=1000   ,   )
			return CLIPTextModelWithProjection(_lowerCAmelCase   )







		@property
		def 							A  (self    :   Tuple   ):
			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(**_lowerCAmelCase   )
			# 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 							A  (self    :   int   ):
			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(_lowerCAmelCase   )
			return model







		@property
		def 							A  (self    :   Optional[Any]   ):
			A					=   CLIPImageProcessor(
			    crop_size=224   ,   do_center_crop=_lowerCAmelCase   ,   do_normalize=_lowerCAmelCase   ,   do_resize=_lowerCAmelCase   ,   image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]   ,   image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]   ,   resample=3   ,   size=224   ,   )

			return image_processor







		def 							A  (self    :   Optional[Any]   ):
			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=_lowerCAmelCase   ,   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 							A  (self    :   Any   ,   _lowerCAmelCase    :   int   ,   _lowerCAmelCase    :   int=0   ):
			if str(_lowerCAmelCase   ).startswith("""mps"""   ):
				A					=   torch.manual_seed(_lowerCAmelCase   )
			else:
				A					=   torch.Generator(device=_lowerCAmelCase   ).manual_seed(_lowerCAmelCase   )

			A					=   floats_tensor((1, 3, 64, 64)   ,   rng=random.Random(_lowerCAmelCase   )   ).to(_lowerCAmelCase   )
			A					=   image.cpu().permute(0   ,   2   ,   3   ,   1   )[0]
			A					=   Image.fromarray(np.uinta(_lowerCAmelCase   )   ).convert("""RGB"""   ).resize((256, 256)   )

			A					=   {
			    """prompt""": """horse""",
			    """image""": init_image,
			    """strength""": 0.5,
			    """generator""": generator,
			    """guidance_scale""": 4.0,
			    """num_inference_steps""": 2,
			    """output_type""": """np""",
			}
			return inputs







		def 							A  (self    :   str   ):
			A					=   """cpu"""

			A					=   self.get_dummy_components()

			A					=   self.pipeline_class(**_lowerCAmelCase   )
			A					=   pipe.to(_lowerCAmelCase   )

			pipe.set_progress_bar_config(disable=_lowerCAmelCase   )

			A					=   pipe(**self.get_dummy_inputs(_lowerCAmelCase   )   )
			A					=   output.image_embeds

			A					=   pipe(
			    **self.get_dummy_inputs(_lowerCAmelCase   )   ,   return_dict=_lowerCAmelCase   ,   )[0]

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

			assert image.shape == (1, 32)

			A					=   np.array(
			    [
			        0.1_071_284,
			        1.3_330_271,
			        0.61_260_223,
			        -0.6_691_065,
			        -0.3_846_852,
			        -1.0_303_661,
			        0.22_716_111,
			        0.03_348_901,
			        0.30_040_675,
			        -0.24_805_029,
			    ]   )

			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 							A  (self    :   int   ):
			A					=   torch_device == """cpu"""
			A					=   True
			A					=   False

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







		@skip_mps
		def 							A  (self    :   Union[str, Any]   ):
			A					=   torch_device == """cpu"""
			A					=   False

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








'''simple docstring'''


from math import pi




def  __a			(  UpperCAmelCase     )   ->float:



	"""simple docstring"""







	return degree / (180 / pi)


if __name__ == "__main__":
				from doctest import testmod

				testmod()

