
'''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 							_snake_case     (  lowerCAmelCase_						,    unittest.TestCase ):
	"""simple docstring"""





	_UpperCamelCase							:  Dict								=		KandinskyVaaPriorEmbaEmbPipeline
	_UpperCamelCase							:  str								=		['''prompt''', '''image''']
	_UpperCamelCase							:  Optional[Any]								=		['''prompt''', '''image''']
	_UpperCamelCase							:  List[Any]								=		[
	    '''num_images_per_prompt''',
	    '''strength''',
	    '''generator''',
	    '''num_inference_steps''',
	    '''latents''',
	    '''negative_prompt''',
	    '''guidance_scale''',
	    '''output_type''',
	    '''return_dict''',
	]
	_UpperCamelCase							:  str								=		False




	@property
	def 	__A      (						self				: str      ):
			return 32




	@property
	def 	__A      (						self				: List[Any]      ):
			return 32




	@property
	def 	__A      (						self				: Optional[int]      ):
			return self.time_input_dim




	@property
	def 	__A      (						self				: List[str]      ):
			return self.time_input_dim * 4




	@property
	def 	__A      (						self				: List[Any]      ):
			return 100




	@property
	def 	__A      (						self				: Union[str, Any]      ):
			lowerCAmelCase_   :       Any    							=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )
			return tokenizer




	@property
	def 	__A      (						self				: Optional[Any]      ):
			torch.manual_seed(0      )
			lowerCAmelCase_   :       Any    							=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(UpperCamelCase_      )




	@property
	def 	__A      (						self				: Union[str, Any]      ):
			torch.manual_seed(0      )

			lowerCAmelCase_   :       List[Any]    							={
			    '''num_attention_heads''': 2,
			    '''attention_head_dim''': 12,
			    '''embedding_dim''': self.text_embedder_hidden_size,
			    '''num_layers''': 1,
			}

			lowerCAmelCase_   :       List[str]    							=PriorTransformer(**UpperCamelCase_      )
			# 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
			lowerCAmelCase_   :       Any    							=nn.Parameter(torch.ones(model.clip_std.shape      )      )
			return model




	@property
	def 	__A      (						self				: Any      ):
			torch.manual_seed(0      )
			lowerCAmelCase_   :       Any    							=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					,						)

			lowerCAmelCase_   :       str    							=CLIPVisionModelWithProjection(UpperCamelCase_      )
			return model




	@property
	def 	__A      (						self				: Optional[int]      ):
			lowerCAmelCase_   :       Any    							=CLIPImageProcessor(
			    crop_size=224					,						do_center_crop=UpperCamelCase_					,						do_normalize=UpperCamelCase_					,						do_resize=UpperCamelCase_					,						image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]					,						image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]					,						resample=3					,						size=224					,						)

			return image_processor




	def 	__A      (						self				: Dict      ):
			lowerCAmelCase_   :       int    							=self.dummy_prior
			lowerCAmelCase_   :       Union[str, Any]    							=self.dummy_image_encoder
			lowerCAmelCase_   :       Dict    							=self.dummy_text_encoder
			lowerCAmelCase_   :       str    							=self.dummy_tokenizer
			lowerCAmelCase_   :       Union[str, Any]    							=self.dummy_image_processor

			lowerCAmelCase_   :       str    							=UnCLIPScheduler(
			    variance_type='''fixed_small_log'''					,						prediction_type='''sample'''					,						num_train_timesteps=1000					,						clip_sample=UpperCamelCase_					,						clip_sample_range=1_0.0					,						)

			lowerCAmelCase_   :       Optional[Any]    							={
			    '''prior''': prior,
			    '''image_encoder''': image_encoder,
			    '''text_encoder''': text_encoder,
			    '''tokenizer''': tokenizer,
			    '''scheduler''': scheduler,
			    '''image_processor''': image_processor,
			}

			return components




	def 	__A      (						self				: Dict					,						UpperCamelCase_				: Optional[Any]					,						UpperCamelCase_				: Optional[int]=0      ):
			if str(UpperCamelCase_      ).startswith('''mps'''      ):
					lowerCAmelCase_   :       int    							=torch.manual_seed(UpperCamelCase_      )
			else:
					lowerCAmelCase_   :       List[Any]    							=torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )

			lowerCAmelCase_   :       int    							=floats_tensor((1, 3, 64, 64)					,						rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
			lowerCAmelCase_   :       List[Any]    							=image.cpu().permute(0					,						2					,						3					,						1      )[0]
			lowerCAmelCase_   :       Any    							=Image.fromarray(np.uinta(UpperCamelCase_      )      ).convert('''RGB'''      ).resize((256, 256)      )

			lowerCAmelCase_   :       List[Any]    							={
			    '''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				: Optional[Any]      ):
			lowerCAmelCase_   :       int    							='''cpu'''

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

			lowerCAmelCase_   :       Any    							=self.pipeline_class(**UpperCamelCase_      )
			lowerCAmelCase_   :       Any    							=pipe.to(UpperCamelCase_      )

			pipe.set_progress_bar_config(disable=UpperCamelCase_      )

			lowerCAmelCase_   :       Dict    							=pipe(**self.get_dummy_inputs(UpperCamelCase_      )      )
			lowerCAmelCase_   :       List[Any]    							=output.image_embeds

			lowerCAmelCase_   :       List[Any]    							=pipe(
			    **self.get_dummy_inputs(UpperCamelCase_      )					,						return_dict=UpperCamelCase_					,						)[0]

			lowerCAmelCase_   :       Optional[Any]    							=image[0, -10:]
			lowerCAmelCase_   :       List[str]    							=image_from_tuple[0, -10:]

			assert image.shape == (1, 32)

			lowerCAmelCase_   :       List[Any]    							=np.array(
			    [
			        0.1_0_7_1_2_8_4,
			        1.3_3_3_0_2_7_1,
			        0.6_1_2_6_0_2_2_3,
			        -0.6_6_9_1_0_6_5,
			        -0.3_8_4_6_8_5_2,
			        -1.0_3_0_3_6_6_1,
			        0.2_2_7_1_6_1_1_1,
			        0.0_3_3_4_8_9_0_1,
			        0.3_0_0_4_0_6_7_5,
			        -0.2_4_8_0_5_0_2_9,
			    ]      )

			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				: Dict      ):
			lowerCAmelCase_   :       Optional[int]    							=torch_device == '''cpu'''
			lowerCAmelCase_   :       int    							=True
			lowerCAmelCase_   :       List[Any]    							=False

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





	@skip_mps
	def 	__A      (						self				: Union[str, Any]      ):
			lowerCAmelCase_   :       List[str]    							=torch_device == '''cpu'''
			lowerCAmelCase_   :       Dict    							=False

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








'''simple docstring'''






from __future__ import annotations







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		if len(_SCREAMING_SNAKE_CASE  ) == 0:
				raise ValueError('''find_min() arg is an empty sequence'''  )
		if (
		    left >= len(_SCREAMING_SNAKE_CASE  )
		    or left < -len(_SCREAMING_SNAKE_CASE  )
		    or right >= len(_SCREAMING_SNAKE_CASE  )
		    or right < -len(_SCREAMING_SNAKE_CASE  )
		):
				raise IndexError('''list index out of range'''  )
		if left == right:
				return nums[left]
		lowerCAmelCase_   :       Dict    							=(left + right) >> 1  # the middle
		lowerCAmelCase_   :       Optional[int]    							=find_min(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  )  # find min in range[left, mid]
		lowerCAmelCase_   :       Any    							=find_min(_SCREAMING_SNAKE_CASE  ,					mid + 1  ,					_SCREAMING_SNAKE_CASE  )  # find min in range[mid + 1, right]

		return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)






