


'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		torch.FloatTensor
 __lowercase				:		torch.FloatTensor
 __lowercase				:		Optional[torch.FloatTensor]    							=						None


class 						_lowerCamelCase					(    snake_case_    ,		snake_case_				):




 '''simple docstring'''
 __lowercase				:		Union[str, Any]    							=						2


 @register_to_config
 def __init__(      self      ,      __lowercase = 0.0_2      ,      __lowercase = 100      ,      __lowercase = 1.0_0_7      ,      __lowercase = 80      ,      __lowercase = 0.0_5      ,      __lowercase = 50      ,      ):

  """simple docstring"""







  __A  :		Optional[Any]       =			sigma_max

  # setable values
  __A  :		int       =			None
  __A  :		np.IntTensor       =			None
  __A  :		torch.FloatTensor       =			None  # sigma(t_i)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None						):

  """simple docstring"""







  return sample


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None						):

  """simple docstring"""







  __A  :		int       =			num_inference_steps
  __A  :		List[str]       =			np.arange(0      ,      self.num_inference_steps						)[::-1].copy()
  __A  :		List[Any]       =			torch.from_numpy(__lowercase						).to(__lowercase						)
  __A  :		Dict       =			[
      (
          self.config.sigma_max**2
          * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
      )
      for i in self.timesteps
  ]
  __A  :		Tuple       =			torch.tensor(__lowercase      ,      dtype=torch.floataa      ,      device=__lowercase						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = None						):

  """simple docstring"""







  if self.config.s_min <= sigma <= self.config.s_max:
   __A  :		Tuple       =			min(self.config.s_churn / self.num_inference_steps      ,      2**0.5 - 1						)
  else:
   __A  :		Optional[int]       =			0

  # sample eps ~ N(0, S_noise^2 * I)
  __A  :		int       =			self.config.s_noise * randn_tensor(sample.shape      ,      generator=__lowercase						).to(sample.device						)
  __A  :		str       =			sigma + gamma * sigma
  __A  :		Optional[int]       =			sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

  return sample_hat, sigma_hat


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

  """simple docstring"""







  __A  :		Dict       =			sample_hat + sigma_hat * model_output
  __A  :		List[Any]       =			(sample_hat - pred_original_sample) / sigma_hat
  __A  :		Union[str, Any]       =			sample_hat + (sigma_prev - sigma_hat) * derivative

  if not return_dict:
   return (sample_prev, derivative)

  return KarrasVeOutput(
      prev_sample=__lowercase      ,      derivative=__lowercase      ,      pred_original_sample=__lowercase						)


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

  """simple docstring"""







  __A  :		Optional[Any]       =			sample_prev + sigma_prev * model_output
  __A  :		List[Any]       =			(sample_prev - pred_original_sample) / sigma_prev
  __A  :		Any       =			sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

  if not return_dict:
   return (sample_prev, derivative)

  return KarrasVeOutput(
      prev_sample=__lowercase      ,      derivative=__lowercase      ,      pred_original_sample=__lowercase						)







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

  """simple docstring"""







  raise NotImplementedError()










'''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 ✅')






