






"""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, KandinskyImgaImgPipeline, 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      UpperCamelCase_			(__A							,		unittest.TestCase	):
	__magic_name__       =							KandinskyImgaImgPipeline
	__magic_name__       =							['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
	__magic_name__       =							[
	    '''prompt''',
	    '''negative_prompt''',
	    '''image_embeds''',
	    '''negative_image_embeds''',
	    '''image''',
	]
	__magic_name__       =							[
	    '''generator''',
	    '''height''',
	    '''width''',
	    '''strength''',
	    '''guidance_scale''',
	    '''negative_prompt''',
	    '''num_inference_steps''',
	    '''return_dict''',
	    '''guidance_scale''',
	    '''num_images_per_prompt''',
	    '''output_type''',
	    '''return_dict''',
	]
	__magic_name__       =							False





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     Dict:
				return 32





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Tuple:
				return 32





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     str:
				return self.time_input_dim





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     List[str]:
				return self.time_input_dim * 4





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     List[Any]:
				return 100





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     Dict:
				UpperCAmelCase_	:					List[str]     	=   XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"		)
				return tokenizer





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Dict:
				torch.manual_seed(0		)
				UpperCAmelCase_	:					Union[str, Any]     	=   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=1_005      ,      )

				UpperCAmelCase_	:					Optional[int]     	=   MultilingualCLIP(lowerCAmelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   text_encoder.eval()

				return text_encoder





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     List[str]:
				torch.manual_seed(0		)

				UpperCAmelCase_	:					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,
				}

				UpperCAmelCase_	:					Optional[int]     	=   UNetaDConditionModel(**lowerCAmelCase_		)
				return model





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





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

				UpperCAmelCase_	:					int     	=   {
				    "num_train_timesteps": 1_000,
				    "beta_schedule": "linear",
				    "beta_start": 0.0_0_0_8_5,
				    "beta_end": 0.0_1_2,
				    "clip_sample": False,
				    "set_alpha_to_one": False,
				    "steps_offset": 0,
				    "prediction_type": "epsilon",
				    "thresholding": False,
				}

				UpperCAmelCase_	:					int     	=   DDIMScheduler(**lowerCAmelCase_		)

				UpperCAmelCase_	:					int     	=   {
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "unet": unet,
				    "scheduler": scheduler,
				    "movq": movq,
				}

				return components





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					Optional[Any]=0		)		->     Optional[int]:
				UpperCAmelCase_	:					List[str]     	=   floats_tensor((1, self.cross_attention_dim)      ,      rng=random.Random(lowerCAmelCase_		)		).to(lowerCAmelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   floats_tensor((1, self.cross_attention_dim)      ,      rng=random.Random(seed + 1		)		).to(lowerCAmelCase_		)
				# create init_image
				UpperCAmelCase_	:					Optional[Any]     	=   floats_tensor((1, 3, 64, 64)      ,      rng=random.Random(lowerCAmelCase_		)		).to(lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[Any]     	=   image.cpu().permute(0      ,      2      ,      3      ,      1		)[0]
				UpperCAmelCase_	:					Dict     	=   Image.fromarray(np.uinta(lowerCAmelCase_		)		).convert("RGB"		).resize((256, 256)		)

				if str(lowerCAmelCase_		).startswith("mps"		):
							UpperCAmelCase_	:					Optional[int]     	=   torch.manual_seed(lowerCAmelCase_		)
				else:
							UpperCAmelCase_	:					Optional[int]     	=   torch.Generator(device=lowerCAmelCase_		).manual_seed(lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[int]     	=   {
				    "prompt": "horse",
				    "image": init_image,
				    "image_embeds": image_embeds,
				    "negative_image_embeds": negative_image_embeds,
				    "generator": generator,
				    "height": 64,
				    "width": 64,
				    "num_inference_steps": 10,
				    "guidance_scale": 7.0,
				    "strength": 0.2,
				    "output_type": "np",
				}
				return inputs





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Optional[Any]:
				UpperCAmelCase_	:					str     	=   "cpu"

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

				UpperCAmelCase_	:					List[Any]     	=   self.pipeline_class(**lowerCAmelCase_		)
				UpperCAmelCase_	:					str     	=   pipe.to(lowerCAmelCase_		)

				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   pipe(**self.get_dummy_inputs(lowerCAmelCase_		)		)
				UpperCAmelCase_	:					int     	=   output.images

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

				UpperCAmelCase_	:					Tuple     	=   image[0, -3:, -3:, -1]
				UpperCAmelCase_	:					int     	=   image_from_tuple[0, -3:, -3:, -1]

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

				UpperCAmelCase_	:					str     	=   np.array(
				    [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]		)
				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      UpperCamelCase_			(unittest.TestCase	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Dict:
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Union[str, Any]:
				UpperCAmelCase_	:					str     	=   load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/kandinsky/kandinsky_img2img_frog.npy"		)

				UpperCAmelCase_	:					Optional[int]     	=   load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"		)
				UpperCAmelCase_	:					Any     	=   "A red cartoon frog, 4k"

				UpperCAmelCase_	:					str     	=   KandinskyPriorPipeline.from_pretrained(
				    "kandinsky-community/kandinsky-2-1-prior"      ,      torch_dtype=torch.floataa		)
				pipe_prior.to(lowerCAmelCase_		)

				UpperCAmelCase_	:					Dict     	=   KandinskyImgaImgPipeline.from_pretrained(
				    "kandinsky-community/kandinsky-2-1"      ,      torch_dtype=torch.floataa		)
				UpperCAmelCase_	:					Any     	=   pipeline.to(lowerCAmelCase_		)

				pipeline.set_progress_bar_config(disable=lowerCAmelCase_		)

				UpperCAmelCase_	:					Dict     	=   torch.Generator(device="cpu"		).manual_seed(0		)
				UpperCAmelCase_	,      UpperCAmelCase_	:					Dict     	=   pipe_prior(
				    lowerCAmelCase_      ,      generator=lowerCAmelCase_      ,      num_inference_steps=5      ,      negative_prompt=""      ,      ).to_tuple()

				UpperCAmelCase_	:					Any     	=   pipeline(
				    lowerCAmelCase_      ,      image=lowerCAmelCase_      ,      image_embeds=lowerCAmelCase_      ,      negative_image_embeds=lowerCAmelCase_      ,      generator=lowerCAmelCase_      ,      num_inference_steps=100      ,      height=768      ,      width=768      ,      strength=0.2      ,      output_type="np"      ,      )

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

				assert image.shape == (768, 768, 3)

				assert_mean_pixel_difference(lowerCAmelCase_      ,      lowerCAmelCase_		)







"""simple docstring"""

def lowerCAmelCase_   (					UpperCamelCase__						:			list					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int = 0					, UpperCamelCase__						:			int = 0			):






 """simple docstring"""







 __lowercase						=						right or len(UpperCamelCase__			) - 1
 if left > right:
  return -1
 elif list_data[left] == key:
  return left
 elif list_data[right] == key:
  return right
 else:
  return search(UpperCamelCase__					, UpperCamelCase__					, left + 1					, right - 1			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
