


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 			SCREAMING_SNAKE_CASE__  (    a_						,   unittest.TestCase      ):
      snake_case__    :					Optional[Any]					       =						KandinskyVaaImgaImgPipeline
      snake_case__    :					int					       =						['''image_embeds''', '''negative_image_embeds''', '''image''']
      snake_case__    :					int					       =						[
          '''image_embeds''',
          '''negative_image_embeds''',
          '''image''',
      ]
      snake_case__    :					Any					       =						[
          '''generator''',
          '''height''',
          '''width''',
          '''strength''',
          '''guidance_scale''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      snake_case__    :					Optional[Any]					       =						False
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      List[str]:
         return 3_2
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      List[str]:
         return 3_2
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Tuple:
         return self.time_input_dim
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Optional[int]:
         return self.time_input_dim * 4
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      List[str]:
         return 1_0_0
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      Union[str, Any]:
         torch.manual_seed(0					)

         a_ :		Tuple							=  {
             '''in_channels''': 4,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 8,
             '''addition_embed_type''': '''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''': '''image_proj''',
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': None,
         }

         a_ :		Optional[Any]							=  UNetaDConditionModel(**lowercase_					)
         return model
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      str:
         return {
             "block_out_channels": [3_2, 6_4],
             "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": 1_2,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Optional[int]:
         torch.manual_seed(0					)
         a_ :		int							=  VQModel(**self.dummy_movq_kwargs					)
         return model
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      List[Any]:
         a_ :		Union[str, Any]							=  self.dummy_unet
         a_ :		str							=  self.dummy_movq

         a_ :		Any							=  {
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_schedule''': '''linear''',
             '''beta_start''': 0.00085,
             '''beta_end''': 0.012,
             '''clip_sample''': False,
             '''set_alpha_to_one''': False,
             '''steps_offset''': 0,
             '''prediction_type''': '''epsilon''',
             '''thresholding''': False,
         }

         a_ :		List[str]							=  DDIMScheduler(**lowercase_					)

         a_ :		Optional[int]							=  {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=0					)		->      str:
         a_ :		Tuple							=  floats_tensor((1, self.text_embedder_hidden_size)					,							rng=random.Random(lowercase_					)					).to(lowercase_					)
         a_ :		List[str]							=  floats_tensor((1, self.text_embedder_hidden_size)					,							rng=random.Random(seed + 1					)					).to(
             lowercase_					)
         # create init_image
         a_ :		List[str]							=  floats_tensor((1, 3, 6_4, 6_4)					,							rng=random.Random(lowercase_					)					).to(lowercase_					)
         a_ :		str							=  image.cpu().permute(0					,							2					,							3					,							1					)[0]
         a_ :		str							=  Image.fromarray(np.uinta(lowercase_					)					).convert('RGB'					).resize((2_5_6, 2_5_6)					)

         if str(lowercase_					).startswith('mps'					):
            a_ :		int							=  torch.manual_seed(lowercase_					)
         else:
            a_ :		Any							=  torch.Generator(device=lowercase_					).manual_seed(lowercase_					)
         a_ :		str							=  {
             '''image''': init_image,
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''generator''': generator,
             '''height''': 6_4,
             '''width''': 6_4,
             '''num_inference_steps''': 1_0,
             '''guidance_scale''': 7.0,
             '''strength''': 0.2,
             '''output_type''': '''np''',
         }
         return inputs

      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      str:
         a_ :		Optional[Any]							=  '''cpu'''

         a_ :		Any							=  self.get_dummy_components()

         a_ :		Any							=  self.pipeline_class(**lowercase_					)
         a_ :		Union[str, Any]							=  pipe.to(lowercase_					)

         pipe.set_progress_bar_config(disable=lowercase_					)

         a_ :		Union[str, Any]							=  pipe(**self.get_dummy_inputs(lowercase_					)					)
         a_ :		Dict							=  output.images

         a_ :		Tuple							=  pipe(
             **self.get_dummy_inputs(lowercase_					)					,							return_dict=lowercase_					,							)[0]

         a_ :		Dict							=  image[0, -3:, -3:, -1]
         a_ :		List[Any]							=  image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         a_ :		str							=  np.array(
             [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263]					)
         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 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      Tuple:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()

      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      Any:
         a_ :		Union[str, Any]							=  load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinskyv22/kandinskyv22_img2img_frog.npy'					)

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

         a_ :		Union[str, Any]							=  KandinskyVaaPriorPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-2-prior'					,							torch_dtype=torch.floataa					)
         pipe_prior.to(lowercase_					)

         a_ :		List[Any]							=  KandinskyVaaImgaImgPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-2-decoder'					,							torch_dtype=torch.floataa					)
         a_ :		List[Any]							=  pipeline.to(lowercase_					)

         pipeline.set_progress_bar_config(disable=lowercase_					)

         a_ :		List[str]							=  torch.Generator(device='cpu'					).manual_seed(0					)
         a_ :		Tuple							=  pipe_prior(
             lowercase_					,							generator=lowercase_					,							num_inference_steps=5					,							negative_prompt=''					,							).to_tuple()

         a_ :		Optional[Any]							=  pipeline(
             image=lowercase_					,							image_embeds=lowercase_					,							negative_image_embeds=lowercase_					,							generator=lowercase_					,							num_inference_steps=1_0_0					,							height=7_6_8					,							width=7_6_8					,							strength=0.2					,							output_type='np'					,							)

         a_ :		int							=  output.images[0]

         assert image.shape == (7_6_8, 7_6_8, 3)

         assert_mean_pixel_difference(lowercase_					,							lowercase_					)






import json
import sys







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[Any]  ,   __A			:      List[str]    )     ->		Tuple:

       """simple docstring"""

       with open(__A  ,   encoding='utf-8'    ) as f:
              a_ :		Union[str, Any]							=  json.load(__A    )

       a_ :		Any							=  ['<details>', '<summary>Show updated benchmarks!</summary>', ' ']

       for benchmark_name in sorted(__A    ):
              a_ :		List[str]							=  results[benchmark_name]

              a_ :		int							=  benchmark_name.split('/'    )[-1]
              output_md.append(F"""### Benchmark: {benchmark_file_name}"""    )

              a_ :		Any							=  '| metric |'
              a_ :		Optional[Any]							=  '|--------|'
              a_ :		int							=  '| new / old (diff) |'
              for metric_name in sorted(__A    ):
                     a_ :		List[Any]							=  benchmark_res[metric_name]
                     a_ :		int							=  metric_vals['new']
                     a_ :		Union[str, Any]							=  metric_vals.get('old'  ,   __A    )
                     a_ :		Optional[int]							=  metric_vals.get('diff'  ,   __A    )

                     a_ :		str							=  F""" {new_val:f}""" if isinstance(__A  ,   (int, float)    ) else 'None'

                     if old_val is not None:
                            val_str += F""" / {old_val:f}""" if isinstance(__A  ,   (int, float)    ) else "None"
                     if dif_val is not None:
                            val_str += F""" ({dif_val:f})""" if isinstance(__A  ,   (int, float)    ) else "None"

                     title += " " + metric_name + " |"
                     lines += "---|"
                     value += val_str + " |"

              output_md += [title, lines, value, " "]

       output_md.append('</details>'    )

       with open(__A  ,   'w'  ,   encoding='utf-8'    ) as f:
              f.writelines('\n'.join(__A    )    )


if __name__ == "__main__":
   UpperCAmelCase_				:			int   =	sys.argv[1]
   UpperCAmelCase_				:			Any   =	sys.argv[2]

   format_json_to_md(input_json_file, output_md_file)


