
'''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 	UpperCamelCase__					(      SCREAMING_SNAKE_CASE ,       unittest.TestCase):
 UpperCAmelCase__      :      Optional[Any]  			=   KandinskyPipeline
 UpperCAmelCase__      :      List[Any]  			=   [
     'prompt',
     'image_embeds',
     'negative_image_embeds',
 ]
 UpperCAmelCase__      :      int  			=   ['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
 UpperCAmelCase__      :      Optional[int]  			=   [
     'generator',
     'height',
     'width',
     'latents',
     'guidance_scale',
     'negative_prompt',
     'num_inference_steps',
     'return_dict',
     'guidance_scale',
     'num_images_per_prompt',
     'output_type',
     'return_dict',
 ]
 UpperCAmelCase__      :      Tuple  			=   False






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







       '''simple docstring'''



       return 32






 @property
 def 		lowercase_  (     self					:Tuple     )			->     Dict:







       '''simple docstring'''



       return 32






 @property
 def 		lowercase_  (     self					:List[Any]     )			->     Any:







       '''simple docstring'''



       return self.time_input_dim






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







       '''simple docstring'''



       return self.time_input_dim * 4






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







       '''simple docstring'''



       return 100






 @property
 def 		lowercase_  (     self					:Any     )			->     str:







       '''simple docstring'''



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






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







       '''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=1_005		,			)

       __A    		=     MultilingualCLIP(_A     )
       __A    		=     text_encoder.eval()

       return text_encoder






 @property
 def 		lowercase_  (     self					:Dict     )			->     int:







       '''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(**_A     )
       return model






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







       '''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 		lowercase_  (     self					:Dict     )			->     Union[str, Any]:







       '''simple docstring'''



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






 def 		lowercase_  (     self					:str     )			->     Tuple:







       '''simple docstring'''



       __A    		=     self.dummy_text_encoder
       __A    		=     self.dummy_tokenizer
       __A    		=     self.dummy_unet
       __A    		=     self.dummy_movq

       __A    		=     DDIMScheduler(
           num_train_timesteps=1_000		,			beta_schedule='linear'		,			beta_start=0.00_085		,			beta_end=0.012		,			clip_sample=_A		,			set_alpha_to_one=_A		,			steps_offset=1		,			prediction_type='epsilon'		,			thresholding=_A		,			)

       __A    		=     {
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'unet': unet,
           'scheduler': scheduler,
           'movq': movq,
       }
       return components






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







       '''simple docstring'''



       __A    		=     floats_tensor((1, self.cross_attention_dim)		,			rng=random.Random(_A     )     ).to(_A     )
       __A    		=     floats_tensor((1, self.cross_attention_dim)		,			rng=random.Random(seed + 1     )     ).to(_A     )
       if str(_A     ).startswith('mps'     ):
             __A    		=     torch.manual_seed(_A     )
       else:
             __A    		=     torch.Generator(device=_A     ).manual_seed(_A     )
       __A    		=     {
           '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 		lowercase_  (     self					:Tuple     )			->     Tuple:







       '''simple docstring'''



       __A    		=     'cpu'

       __A    		=     self.get_dummy_components()

       __A    		=     self.pipeline_class(**_A     )
       __A    		=     pipe.to(_A     )

       pipe.set_progress_bar_config(disable=_A     )

       __A    		=     pipe(**self.get_dummy_inputs(_A     )     )
       __A    		=     output.images

       __A    		=     pipe(
           **self.get_dummy_inputs(_A     )		,			return_dict=_A		,			)[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(
           [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]     )

       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 		lowercase_  (     self					:List[str]     )			->     List[str]:







       '''simple docstring'''



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






 def 		lowercase_  (     self					:Union[str, Any]     )			->     Any:







       '''simple docstring'''



       __A    		=     load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/kandinsky/kandinsky_text2img_cat_fp16.npy'     )

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

       __A    		=     KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'		,			torch_dtype=torch.floataa     )
       __A    		=     pipeline.to(_A     )
       pipeline.set_progress_bar_config(disable=_A     )

       __A    		=     'red cat, 4k photo'

       __A    		=     torch.Generator(device='cuda'     ).manual_seed(0     )
       __A		, __A    		=     pipe_prior(
           _A		,			generator=_A		,			num_inference_steps=5		,			negative_prompt=''		,			).to_tuple()

       __A    		=     torch.Generator(device='cuda'     ).manual_seed(0     )
       __A    		=     pipeline(
           _A		,			image_embeds=_A		,			negative_image_embeds=_A		,			generator=_A		,			num_inference_steps=100		,			output_type='np'		,			)

       __A    		=     output.images[0]

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

       assert_mean_pixel_difference(_A		,			_A     )








'''simple docstring'''





import math
import os

import cva
import numpy as np

a__						:				List[Any]           =    255.0





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase		)->     float:







      """simple docstring"""
      __A    		=     np.mean((original - contrast) ** 2		)
      if mse == 0:
            return 1_0_0

      return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(UpperCAmelCase		)		)





def     snake_case							(						)->     None:







      """simple docstring"""
      __A    		=     os.path.dirname(os.path.realpath(UpperCAmelCase		)		)
      # Loading images (original image and compressed image)
      __A    		=     cva.imread(os.path.join(UpperCAmelCase ,					'image_data/original_image.png'		)		)
      __A    		=     cva.imread(os.path.join(UpperCAmelCase ,					'image_data/compressed_image.png'		) ,					1		)

      __A    		=     cva.imread(os.path.join(UpperCAmelCase ,					'image_data/PSNR-example-base.png'		)		)
      __A    		=     cva.imread(
          os.path.join(UpperCAmelCase ,					'image_data/PSNR-example-comp-10.jpg'		) ,					1		)

      # Value expected: 29.73dB
      print('-- First Test --'		)
      print(f'PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase ,					UpperCAmelCase		)} dB'		)

      # # Value expected: 31.53dB (Wikipedia Example)
      print('\n-- Second Test --'		)
      print(f'PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase ,					UpperCAmelCase		)} dB'		)


if __name__ == "__main__":
 main()






