


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

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort

class 						_lowerCamelCase					(    snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		int    							=						'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


 def  snake_case__   (      self      ,      __lowercase=0						):

  """simple docstring"""







  __A  :		Tuple       =			floats_tensor((1, 3, 128, 128)      ,      rng=random.Random(__lowercase						)						)
  __A  :		Optional[int]       =			np.random.RandomState(__lowercase						)
  __A  :		str       =			{
      'prompt': 'A painting of a squirrel eating a burger',
      'image': image,
      'generator': generator,
      'num_inference_steps': 3,
      'strength': 0.7_5,
      'guidance_scale': 7.5,
      'output_type': 'numpy',
  }
  return inputs


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,      provider='CPUExecutionProvider'						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		Tuple       =			self.get_dummy_inputs()
  __A  :		List[Any]       =			pipe(**__lowercase						).images
  __A  :		Optional[int]       =			image[0, -3:, -3:, -1].flatten()

  assert image.shape == (1, 128, 128, 3)
  __A  :		str       =			np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]						)
  assert np.abs(image_slice - expected_slice						).max() < 1E-1


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,      provider='CPUExecutionProvider'						)
  __A  :		Dict       =			PNDMScheduler.from_config(pipe.scheduler.config      ,      skip_prk_steps=__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		Tuple       =			self.get_dummy_inputs()
  __A  :		str       =			pipe(**__lowercase						).images
  __A  :		Union[str, Any]       =			image[0, -3:, -3:, -1]

  assert image.shape == (1, 128, 128, 3)
  __A  :		Union[str, Any]       =			np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-1


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,      provider='CPUExecutionProvider'						)
  __A  :		Tuple       =			LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  # warmup pass to apply optimizations
  __A  :		Optional[int]       =			pipe(**self.get_dummy_inputs()						)

  __A  :		Dict       =			self.get_dummy_inputs()
  __A  :		str       =			pipe(**__lowercase						).images
  __A  :		List[str]       =			image[0, -3:, -3:, -1]

  assert image.shape == (1, 128, 128, 3)
  __A  :		Any       =			np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-1


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,      provider='CPUExecutionProvider'						)
  __A  :		List[Any]       =			EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		int       =			self.get_dummy_inputs()
  __A  :		Dict       =			pipe(**__lowercase						).images
  __A  :		Optional[Any]       =			image[0, -3:, -3:, -1]

  assert image.shape == (1, 128, 128, 3)
  __A  :		List[Any]       =			np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-1


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,      provider='CPUExecutionProvider'						)
  __A  :		Optional[int]       =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		List[str]       =			self.get_dummy_inputs()
  __A  :		List[str]       =			pipe(**__lowercase						).images
  __A  :		List[str]       =			image[0, -3:, -3:, -1]

  assert image.shape == (1, 128, 128, 3)
  __A  :		Optional[int]       =			np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-1







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,      provider='CPUExecutionProvider'						)
  __A  :		List[str]       =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		List[str]       =			self.get_dummy_inputs()
  __A  :		Union[str, Any]       =			pipe(**__lowercase						).images
  __A  :		Optional[Any]       =			image[0, -3:, -3:, -1]

  assert image.shape == (1, 128, 128, 3)
  __A  :		Any       =			np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-1


@nightly
@require_onnxruntime
@require_torch_gpu
class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  return (
      "CUDAExecutionProvider",
      {
          "gpu_mem_limit": "15000000000",  # 15GB
          "arena_extend_strategy": "kSameAsRequested",
      },
  )


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			ort.SessionOptions()
  __A  :		Tuple       =			False
  return options


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			load_image(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
      '/img2img/sketch-mountains-input.jpg'						)
  __A  :		Any       =			init_image.resize((768, 512)						)
  # using the PNDM scheduler by default
  __A  :		Union[str, Any]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
      'CompVis/stable-diffusion-v1-4'      ,      revision='onnx'      ,      safety_checker=__lowercase      ,      feature_extractor=__lowercase      ,      provider=self.gpu_provider      ,      sess_options=self.gpu_options      ,      )
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		List[str]       =			'A fantasy landscape, trending on artstation'

  __A  :		str       =			np.random.RandomState(0						)
  __A  :		Union[str, Any]       =			pipe(
      prompt=__lowercase      ,      image=__lowercase      ,      strength=0.7_5      ,      guidance_scale=7.5      ,      num_inference_steps=10      ,      generator=__lowercase      ,      output_type='np'      ,      )
  __A  :		List[str]       =			output.images
  __A  :		Union[str, Any]       =			images[0, 255:258, 383:386, -1]

  assert images.shape == (1, 512, 768, 3)
  __A  :		int       =			np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]						)
  # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

  assert np.abs(image_slice.flatten() - expected_slice						).max() < 2E-2







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			load_image(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
      '/img2img/sketch-mountains-input.jpg'						)
  __A  :		str       =			init_image.resize((768, 512)						)
  __A  :		Tuple       =			LMSDiscreteScheduler.from_pretrained(
      'runwayml/stable-diffusion-v1-5'      ,      subfolder='scheduler'      ,      revision='onnx'						)
  __A  :		List[str]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
      'runwayml/stable-diffusion-v1-5'      ,      revision='onnx'      ,      scheduler=__lowercase      ,      safety_checker=__lowercase      ,      feature_extractor=__lowercase      ,      provider=self.gpu_provider      ,      sess_options=self.gpu_options      ,      )
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		Tuple       =			'A fantasy landscape, trending on artstation'

  __A  :		Union[str, Any]       =			np.random.RandomState(0						)
  __A  :		List[str]       =			pipe(
      prompt=__lowercase      ,      image=__lowercase      ,      strength=0.7_5      ,      guidance_scale=7.5      ,      num_inference_steps=20      ,      generator=__lowercase      ,      output_type='np'      ,      )
  __A  :		Optional[int]       =			output.images
  __A  :		Dict       =			images[0, 255:258, 383:386, -1]

  assert images.shape == (1, 512, 768, 3)
  __A  :		Tuple       =			np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]						)
  # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

  assert np.abs(image_slice.flatten() - expected_slice						).max() < 2E-2










'''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 ✅')






