


'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
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 						_lowerCamelCase					(    a__    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		Tuple   							=						KandinskyVaaPipeline
 __lowercase				:		str   							=						[
     "image_embeds",
     "negative_image_embeds",
 ]
 __lowercase				:		Tuple   							=						["image_embeds", "negative_image_embeds"]
 __lowercase				:		str   							=						[
     "generator",
     "height",
     "width",
     "latents",
     "guidance_scale",
     "num_inference_steps",
     "return_dict",
     "guidance_scale",
     "num_images_per_prompt",
     "output_type",
     "return_dict",
 ]
 __lowercase				:		Union[str, Any]   							=						False


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  return 32


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  return 32


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  return self.time_input_dim


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  return self.time_input_dim * 4


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  return 100


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)

  __A  :		List[str]       =			{
      '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  :		Union[str, Any]       =			UNetaDConditionModel(**lowerCamelCase_						)
  return model


 @property
 def  snake_case__   (      self						):

  """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  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Optional[Any]       =			VQModel(**self.dummy_movq_kwargs						)
  return model


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			self.dummy_unet
  __A  :		Tuple       =			self.dummy_movq

  __A  :		Tuple       =			DDIMScheduler(
      num_train_timesteps=1_000      ,      beta_schedule='linear'      ,      beta_start=0.0_0_0_8_5      ,      beta_end=0.0_1_2      ,      clip_sample=lowerCamelCase_      ,      set_alpha_to_one=lowerCamelCase_      ,      steps_offset=1      ,      prediction_type='epsilon'      ,      thresholding=lowerCamelCase_      ,      )

  __A  :		Optional[int]       =			{
      'unet': unet,
      'scheduler': scheduler,
      'movq': movq,
  }
  return components


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase=0						):

  """simple docstring"""







  __A  :		Tuple       =			floats_tensor((1, self.text_embedder_hidden_size)      ,      rng=random.Random(lowerCamelCase_						)						).to(lowerCamelCase_						)
  __A  :		Dict       =			floats_tensor((1, self.text_embedder_hidden_size)      ,      rng=random.Random(seed + 1						)						).to(
      lowerCamelCase_						)
  if str(lowerCamelCase_						).startswith('mps'						):
   __A  :		Any       =			torch.manual_seed(lowerCamelCase_						)
  else:
   __A  :		str       =			torch.Generator(device=lowerCamelCase_						).manual_seed(lowerCamelCase_						)
  __A  :		Optional[Any]       =			{
      '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  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Tuple       =			'cpu'

  __A  :		Dict       =			self.get_dummy_components()

  __A  :		int       =			self.pipeline_class(**lowerCamelCase_						)
  __A  :		Union[str, Any]       =			pipe.to(lowerCamelCase_						)

  pipe.set_progress_bar_config(disable=lowerCamelCase_						)

  __A  :		Any       =			pipe(**self.get_dummy_inputs(lowerCamelCase_						)						)
  __A  :		Dict       =			output.images

  __A  :		Union[str, Any]       =			pipe(
      **self.get_dummy_inputs(lowerCamelCase_						)      ,      return_dict=lowerCamelCase_      ,      )[0]

  __A  :		Optional[Any]       =			image[0, -3:, -3:, -1]
  __A  :		List[Any]       =			image_from_tuple[0, -3:, -3:, -1]

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

  __A  :		Union[str, Any]       =			np.array(
      [0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6]						)

  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 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







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







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			load_numpy(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
      '/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'						)

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

  __A  :		Dict       =			KandinskyVaaPipeline.from_pretrained(
      'kandinsky-community/kandinsky-2-2-decoder'      ,      torch_dtype=torch.floataa						)
  __A  :		List[str]       =			pipeline.to(lowerCamelCase_						)
  pipeline.set_progress_bar_config(disable=lowerCamelCase_						)

  __A  :		List[Any]       =			'red cat, 4k photo'

  __A  :		Optional[Any]       =			torch.Generator(device='cuda'						).manual_seed(0						)
  __A     ,__A  :		int       =			pipe_prior(
      lowerCamelCase_      ,      generator=lowerCamelCase_      ,      num_inference_steps=5      ,      negative_prompt=''      ,      ).to_tuple()

  __A  :		int       =			torch.Generator(device='cuda'						).manual_seed(0						)
  __A  :		Optional[Any]       =			pipeline(
      image_embeds=lowerCamelCase_      ,      negative_image_embeds=lowerCamelCase_      ,      generator=lowerCamelCase_      ,      num_inference_steps=100      ,      output_type='np'      ,      )

  __A  :		str       =			output.images[0]

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

  assert_mean_pixel_difference(lowerCamelCase_      ,      lowerCamelCase_						)










'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits

import cva
UpperCAmelCase_ :     int								=      ''
UpperCAmelCase_ :     Union[str, Any]								=      ''
UpperCAmelCase_ :     Any								=      ''
UpperCAmelCase_ :     int								=      1  # (0 is vertical, 1 is horizontal)


def 			_lowercase       (				):
 __A     ,__A  :		Optional[int]       =			get_dataset(UpperCamelCase__,				UpperCamelCase__			)
 print('Processing...'			)
 __A     ,__A     ,__A  :		Any       =			update_image_and_anno(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

 for index, image in enumerate(UpperCamelCase__			):
  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  __A  :		Dict       =			random_chars(32			)
  __A  :		List[Any]       =			paths[index].split(os.sep			)[-1].rsplit('.',				1			)[0]
  __A  :		Dict       =			f"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
  cva.imwrite(f"""/{file_root}.jpg""",				UpperCamelCase__,				[cva.IMWRITE_JPEG_QUALITY, 85]			)
  print(f"""Success {index+1}/{len(UpperCamelCase__			)} with {file_name}"""			)
  __A  :		Tuple       =			[]
  for anno in new_annos[index]:
   __A  :		Any       =			f"""{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"""
   annos_list.append(UpperCamelCase__			)
  with open(f"""/{file_root}.txt""",				'w'			) as outfile:
   outfile.write('\n'.join(line for line in annos_list			)			)


def 			_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					str			):
 __A  :		Tuple       =			[]
 __A  :		int       =			[]
 for label_file in glob.glob(os.path.join(UpperCamelCase__,				'*.txt'			)			):
  __A  :		Optional[int]       =			label_file.split(os.sep			)[-1].rsplit('.',				1			)[0]
  with open(UpperCamelCase__			) as in_file:
   __A  :		Optional[Any]       =			in_file.readlines()
  __A  :		int       =			os.path.join(UpperCamelCase__,				f"""{label_name}.jpg"""			)

  __A  :		Optional[int]       =			[]
  for obj_list in obj_lists:
   __A  :		str       =			obj_list.rstrip('\n'			).split(' '			)
   boxes.append(
       [
           int(obj[0]			),
           float(obj[1]			),
           float(obj[2]			),
           float(obj[3]			),
           float(obj[4]			),
       ]			)
  if not boxes:
   continue
  img_paths.append(UpperCamelCase__			)
  labels.append(UpperCamelCase__			)
 return img_paths, labels


def 			_lowercase       (				UpperCamelCase__       :					list,				UpperCamelCase__       :					list,				UpperCamelCase__       :					int = 1			):
 __A  :		int       =			[]
 __A  :		Optional[Any]       =			[]
 __A  :		str       =			[]
 for idx in range(len(UpperCamelCase__			)			):
  __A  :		List[Any]       =			[]
  __A  :		List[str]       =			img_list[idx]
  path_list.append(UpperCamelCase__			)
  __A  :		Optional[Any]       =			anno_list[idx]
  __A  :		Union[str, Any]       =			cva.imread(UpperCamelCase__			)
  if flip_type == 1:
   __A  :		int       =			cva.flip(UpperCamelCase__,				UpperCamelCase__			)
   for bbox in img_annos:
    __A  :		Union[str, Any]       =			1 - bbox[1]
    new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
  elif flip_type == 0:
   __A  :		Tuple       =			cva.flip(UpperCamelCase__,				UpperCamelCase__			)
   for bbox in img_annos:
    __A  :		Dict       =			1 - bbox[2]
    new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
  new_annos_lists.append(UpperCamelCase__			)
  new_imgs_list.append(UpperCamelCase__			)
 return new_imgs_list, new_annos_lists, path_list


def 			_lowercase       (				UpperCamelCase__       :					int = 32			):
 assert number_char > 1, "The number of character should greater than 1"
 __A  :		int       =			ascii_lowercase + digits
 return "".join(random.choice(UpperCamelCase__			) for _ in range(UpperCamelCase__			)			)


if __name__ == "__main__":
 main()
 print('DONE ✅')






