


'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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    lowercase__  (						_snake_case					,					unittest.TestCase ):





							'''simple docstring'''


							A_							:				Optional[int]				     =  KandinskyPipeline
							A_							:				Dict				     =  [
							    """prompt""",
							    """image_embeds""",
							    """negative_image_embeds""",
							]
							A_							:				Dict				     =  ["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
							A_							:				str				     =  [
							    """generator""",
							    """height""",
							    """width""",
							    """latents""",
							    """guidance_scale""",
							    """negative_prompt""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							A_							:				Union[str, Any]				     =  False






							@property
							def UpperCAmelCase_     (   self  ):
											return 32






							@property
							def UpperCAmelCase_     (   self  ):
											return 32






							@property
							def UpperCAmelCase_     (   self  ):
											return self.time_input_dim






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






							@property
							def UpperCAmelCase_     (   self  ):
											return 100






							@property
							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	int							=				XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""  )
											return tokenizer






							@property
							def UpperCAmelCase_     (   self  ):
											torch.manual_seed(0  )
											_SCREAMING_SNAKE_CASE			:	int							=				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=1005   , )

											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				MultilingualCLIP(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	str							=				text_encoder.eval()

											return text_encoder






							@property
							def UpperCAmelCase_     (   self  ):
											torch.manual_seed(0  )

											_SCREAMING_SNAKE_CASE			:	List[Any]							=				{
											    """in_channels""": 4,
											    # 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,
											}

											_SCREAMING_SNAKE_CASE			:	List[str]							=				UNetaDConditionModel(**__snake_case  )
											return model






							@property
							def UpperCAmelCase_     (   self  ):
											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  ):
											torch.manual_seed(0  )
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				VQModel(**self.dummy_movq_kwargs  )
											return model






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Any							=				self.dummy_text_encoder
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.dummy_tokenizer
											_SCREAMING_SNAKE_CASE			:	Any							=				self.dummy_unet
											_SCREAMING_SNAKE_CASE			:	List[str]							=				self.dummy_movq

											_SCREAMING_SNAKE_CASE			:	Any							=				DDIMScheduler(
											    num_train_timesteps=1000   , beta_schedule="""linear"""   , beta_start=0.00085   , beta_end=0.012   , clip_sample=__snake_case   , set_alpha_to_one=__snake_case   , steps_offset=1   , prediction_type="""epsilon"""   , thresholding=__snake_case   , )

											_SCREAMING_SNAKE_CASE			:	List[str]							=				{
											    """text_encoder""": text_encoder,
											    """tokenizer""": tokenizer,
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """movq""": movq,
											}
											return components






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case=0  ):
											_SCREAMING_SNAKE_CASE			:	int							=				floats_tensor((1, self.cross_attention_dim)   , rng=random.Random(__snake_case  )  ).to(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Tuple							=				floats_tensor((1, self.cross_attention_dim)   , rng=random.Random(seed + 1  )  ).to(__snake_case  )
											if str(__snake_case  ).startswith("""mps"""  ):
															_SCREAMING_SNAKE_CASE			:	int							=				torch.manual_seed(__snake_case  )
											else:
															_SCREAMING_SNAKE_CASE			:	Any							=				torch.Generator(device=__snake_case  ).manual_seed(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				{
											    """prompt""": """horse""",
											    """image_embeds""": image_embeds,
											    """negative_image_embeds""": negative_image_embeds,
											    """generator""": generator,
											    """height""": 64,
											    """width""": 64,
											    """guidance_scale""": 4.0,
											    """num_inference_steps""": 2,
											    """output_type""": """np""",
											}
											return inputs






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[str]							=				"""cpu"""

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

											_SCREAMING_SNAKE_CASE			:	Tuple							=				self.pipeline_class(**__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Tuple							=				pipe.to(__snake_case  )

											pipe.set_progress_bar_config(disable=__snake_case  )

											_SCREAMING_SNAKE_CASE			:	Any							=				pipe(**self.get_dummy_inputs(__snake_case  )  )
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				output.images

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

											_SCREAMING_SNAKE_CASE			:	List[str]							=				image[0, -3:, -3:, -1]
											_SCREAMING_SNAKE_CASE			:	str							=				image_from_tuple[0, -3:, -3:, -1]

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

											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				np.array(
											    [0.328663, 1.0, 0.23216873, 1.0, 0.92717564, 0.4639046, 0.96894777, 0.31713378, 0.6293953]  )

											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    lowercase__  (						unittest.TestCase ):





							'''simple docstring'''


							def UpperCAmelCase_     (   self  ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""  )

											_SCREAMING_SNAKE_CASE			:	List[Any]							=				KandinskyPriorPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-1-prior"""   , torch_dtype=torch.floataa  )
											pipe_prior.to(__snake_case  )

											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""   , torch_dtype=torch.floataa  )
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				pipeline.to(__snake_case  )
											pipeline.set_progress_bar_config(disable=__snake_case  )

											_SCREAMING_SNAKE_CASE			:	Tuple							=				"""red cat, 4k photo"""

											_SCREAMING_SNAKE_CASE			:	List[str]							=				torch.Generator(device="""cuda"""  ).manual_seed(0  )
											_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Dict							=				pipe_prior(
											    __snake_case   , generator=__snake_case   , num_inference_steps=5   , negative_prompt=""""""   , ).to_tuple()

											_SCREAMING_SNAKE_CASE			:	List[str]							=				torch.Generator(device="""cuda"""  ).manual_seed(0  )
											_SCREAMING_SNAKE_CASE			:	int							=				pipeline(
											    __snake_case   , image_embeds=__snake_case   , negative_image_embeds=__snake_case   , generator=__snake_case   , num_inference_steps=100   , output_type="""np"""   , )

											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				output.images[0]

											assert image.shape == (512, 512, 3)

											assert_mean_pixel_difference(__snake_case   , __snake_case  )








'''simple docstring'''


def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Tuple							=				1
				while b > 0:
								if b & 1:
												res *= a

								a *= a
								b >>= 1

				return res







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				1
				while b > 0:
								if b & 1:
												_SCREAMING_SNAKE_CASE			:	Tuple							=				((res % c) * (a % c)) % c

								a *= a
								b >>= 1

				return res




