




'''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      A       (						_a	,unittest.TestCase       ):
							lowercase_							     =							KandinskyImgaImgPipeline
							lowercase_							     =							['prompt', 'image_embeds', 'negative_image_embeds', 'image']
							lowercase_							     =							[
							    'prompt',
							    'negative_prompt',
							    'image_embeds',
							    'negative_image_embeds',
							    'image',
							]
							lowercase_							     =							[
							    'generator',
							    'height',
							    'width',
							    'strength',
							    'guidance_scale',
							    'negative_prompt',
							    'num_inference_steps',
							    'return_dict',
							    'guidance_scale',
							    'num_images_per_prompt',
							    'output_type',
							    'return_dict',
							]
							lowercase_							     =							False







							@property
							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Tuple:


														"""simple docstring"""


														return 32







							@property
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				str:


														"""simple docstring"""


														return 32







							@property
							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Optional[int]:


														"""simple docstring"""


														return self.time_input_dim







							@property
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				str:


														"""simple docstring"""


														return self.time_input_dim * 4







							@property
							def   __lowerCAmelCase     (    self			:    int   )     ->				Optional[Any]:


														"""simple docstring"""


														return 1_00







							@property
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''   )
														return tokenizer







							@property
							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														torch.manual_seed(0   )
														_a      	=							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=10_05				,     )

														_a      	=							MultilingualCLIP(lowerCAmelCase_   )
														_a      	=							text_encoder.eval()

														return text_encoder







							@property
							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Tuple:


														"""simple docstring"""


														torch.manual_seed(0   )

														_a      	=							{
														    '''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,
														}

														_a      	=							UNetaDConditionModel(**lowerCAmelCase_   )
														return model







							@property
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				int:


														"""simple docstring"""


														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   __lowerCAmelCase     (    self			:    Any   )     ->				str:


														"""simple docstring"""


														torch.manual_seed(0   )
														_a      	=							VQModel(**self.dummy_movq_kwargs   )
														return model







							def   __lowerCAmelCase     (    self			:    int   )     ->				str:


														"""simple docstring"""


														_a      	=							self.dummy_text_encoder
														_a      	=							self.dummy_tokenizer
														_a      	=							self.dummy_unet
														_a      	=							self.dummy_movq

														_a      	=							{
														    '''num_train_timesteps''': 10_00,
														    '''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,
														}

														_a      	=							DDIMScheduler(**lowerCAmelCase_   )

														_a      	=							{
														    '''text_encoder''': text_encoder,
														    '''tokenizer''': tokenizer,
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''movq''': movq,
														}

														return components







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    List[Any]=0   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							floats_tensor((1, self.cross_attention_dim)				,     rng=random.Random(lowerCAmelCase_   )   ).to(lowerCAmelCase_   )
														_a      	=							floats_tensor((1, self.cross_attention_dim)				,     rng=random.Random(seed + 1   )   ).to(lowerCAmelCase_   )
														# create init_image
														_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((2_56, 2_56)   )

														if str(lowerCAmelCase_   ).startswith('''mps'''   ):
																					_a      	=							torch.manual_seed(lowerCAmelCase_   )
														else:
																					_a      	=							torch.Generator(device=lowerCAmelCase_   ).manual_seed(lowerCAmelCase_   )
														_a      	=							{
														    '''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   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				List[str]:


														"""simple docstring"""


														_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.images

														_a      	=							pipe(
														    **self.get_dummy_inputs(lowerCAmelCase_   )				,     return_dict=lowerCAmelCase_				,     )[0]

														_a      	=							image[0, -3:, -3:, -1]
														_a      	=							image_from_tuple[0, -3:, -3:, -1]

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

														_a      	=							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      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Any:


														"""simple docstring"""


														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/kandinsky/kandinsky_img2img_frog.npy'''   )

														_a      	=							load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''   )
														_a      	=							'''A red cartoon frog, 4k'''

														_a      	=							KandinskyPriorPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-1-prior'''				,     torch_dtype=torch.floataa   )
														pipe_prior.to(lowerCAmelCase_   )

														_a      	=							KandinskyImgaImgPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-1'''				,     torch_dtype=torch.floataa   )
														_a      	=							pipeline.to(lowerCAmelCase_   )

														pipeline.set_progress_bar_config(disable=lowerCAmelCase_   )

														_a      	=							torch.Generator(device='''cpu'''   ).manual_seed(0   )
														_a	,				_a      	=							pipe_prior(
														    lowerCAmelCase_				,     generator=lowerCAmelCase_				,     num_inference_steps=5				,     negative_prompt=''''''				,     ).to_tuple()

														_a      	=							pipeline(
														    lowerCAmelCase_				,     image=lowerCAmelCase_				,     image_embeds=lowerCAmelCase_				,     negative_image_embeds=lowerCAmelCase_				,     generator=lowerCAmelCase_				,     num_inference_steps=1_00				,     height=7_68				,     width=7_68				,     strength=0.2				,     output_type='''np'''				,     )

														_a      	=							output.images[0]

														assert image.shape == (7_68, 7_68, 3)

														assert_mean_pixel_difference(lowerCAmelCase_				,     lowerCAmelCase_   )





"""simple docstring"""

from PIL import Image


def lowerCAmelCase_   (					UpperCamelCase__						:			Image					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						(259 * (level + 255)) / (255 * (259 - level))

 def contrast(UpperCamelCase__						:			int			) -> int:
  return int(128 + factor * (c - 128)			)

 return img.point(UpperCamelCase__			)


if __name__ == "__main__":
       # Load image
       with Image.open("image_data/lena.jpg") as img:
              # Change contrast to 170
              UpperCAmelCase__					=change_contrast(img, 170)
              cont_img.save("image_data/lena_high_contrast.png", format="png")
