


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							(DPMSolverSinglestepScheduler,)
      UpperCamelCase										=							(('''num_inference_steps''', 25),)





      def 							_lowercase					(		self    :       Optional[int]       ,							**__lowerCamelCase    :       int							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  {
             """num_train_timesteps""": 1_0_0_0,
             """beta_start""": 0.0_001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
             """solver_order""": 2,
             """prediction_type""": """epsilon""",
             """thresholding""": False,
             """sample_max_value""": 1.0,
             """algorithm_type""": """dpmsolver++""",
             """solver_type""": """midpoint""",
             """lambda_min_clipped""": -float("""inf"""							),
             """variance_type""": None,
         }

         config.update(**__lowerCamelCase							)
         return config





      def 							_lowercase					(		self    :       Optional[int]       ,							__lowerCamelCase    :       List[str]=0       ,							**__lowerCamelCase    :       Union[str, Any]							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  dict(self.forward_default_kwargs							)
         UpperCAmelCase      					=  kwargs.pop("""num_inference_steps"""       ,							__lowerCamelCase							)
         UpperCAmelCase      					=  self.dummy_sample
         UpperCAmelCase      					=  0.1 * sample
         UpperCAmelCase      					=  [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
            UpperCAmelCase      					=  self.get_scheduler_config(**__lowerCamelCase							)
            UpperCAmelCase      					=  scheduler_class(**__lowerCamelCase							)
            scheduler.set_timesteps(__lowerCamelCase							)
            # copy over dummy past residuals
            UpperCAmelCase      					=  dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(__lowerCamelCase							)
               UpperCAmelCase      					=  scheduler_class.from_pretrained(__lowerCamelCase							)
               new_scheduler.set_timesteps(__lowerCamelCase							)
               # copy over dummy past residuals
               UpperCAmelCase      					=  dummy_past_residuals[: new_scheduler.config.solver_order]

            UpperCAmelCase					,	UpperCAmelCase      					=  sample, sample
            for t in range(__lowerCamelCase       ,							time_step + scheduler.config.solver_order + 1							):
               UpperCAmelCase      					=  scheduler.step(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase       ,							**__lowerCamelCase							).prev_sample
               UpperCAmelCase      					=  new_scheduler.step(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase       ,							**__lowerCamelCase							).prev_sample

               assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"





      def 							_lowercase					(		self    :       Tuple							)     -> Optional[Any]:






         """simple docstring"""







         pass





      def 							_lowercase					(		self    :       List[Any]       ,							__lowerCamelCase    :       Optional[int]=0       ,							**__lowerCamelCase    :       Tuple							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  dict(self.forward_default_kwargs							)
         UpperCAmelCase      					=  kwargs.pop("""num_inference_steps"""       ,							__lowerCamelCase							)
         UpperCAmelCase      					=  self.dummy_sample
         UpperCAmelCase      					=  0.1 * sample
         UpperCAmelCase      					=  [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
            UpperCAmelCase      					=  self.get_scheduler_config()
            UpperCAmelCase      					=  scheduler_class(**__lowerCamelCase							)
            scheduler.set_timesteps(__lowerCamelCase							)

            # copy over dummy past residuals (must be after setting timesteps)
            UpperCAmelCase      					=  dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(__lowerCamelCase							)
               UpperCAmelCase      					=  scheduler_class.from_pretrained(__lowerCamelCase							)
               # copy over dummy past residuals
               new_scheduler.set_timesteps(__lowerCamelCase							)

               # copy over dummy past residual (must be after setting timesteps)
               UpperCAmelCase      					=  dummy_past_residuals[: new_scheduler.config.solver_order]

            UpperCAmelCase      					=  scheduler.step(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase       ,							**__lowerCamelCase							).prev_sample
            UpperCAmelCase      					=  new_scheduler.step(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase       ,							**__lowerCamelCase							).prev_sample

            assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"





      def 							_lowercase					(		self    :       Any       ,							__lowerCamelCase    :       Dict=None       ,							**__lowerCamelCase    :       Optional[Any]							)     -> str:






         """simple docstring"""







         if scheduler is None:
            UpperCAmelCase      					=  self.scheduler_classes[0]
            UpperCAmelCase      					=  self.get_scheduler_config(**__lowerCamelCase							)
            UpperCAmelCase      					=  scheduler_class(**__lowerCamelCase							)

         UpperCAmelCase      					=  self.scheduler_classes[0]
         UpperCAmelCase      					=  self.get_scheduler_config(**__lowerCamelCase							)
         UpperCAmelCase      					=  scheduler_class(**__lowerCamelCase							)

         UpperCAmelCase      					=  1_0
         UpperCAmelCase      					=  self.dummy_model()
         UpperCAmelCase      					=  self.dummy_sample_deter
         scheduler.set_timesteps(__lowerCamelCase							)

         for i, t in enumerate(scheduler.timesteps							):
            UpperCAmelCase      					=  model(__lowerCamelCase       ,							__lowerCamelCase							)
            UpperCAmelCase      					=  scheduler.step(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							).prev_sample

         return sample





      def 							_lowercase					(		self    :       Dict							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  DPMSolverSinglestepScheduler(**self.get_scheduler_config()							)
         UpperCAmelCase      					=  5_0
         UpperCAmelCase      					=  self.dummy_model()
         UpperCAmelCase      					=  self.dummy_sample_deter
         scheduler.set_timesteps(__lowerCamelCase							)

         # make sure that the first t is uneven
         for i, t in enumerate(scheduler.timesteps[3:]							):
            UpperCAmelCase      					=  model(__lowerCamelCase       ,							__lowerCamelCase							)
            UpperCAmelCase      					=  scheduler.step(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							).prev_sample

         UpperCAmelCase      					=  torch.mean(torch.abs(__lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.2_574							) < 1e-3





      def 							_lowercase					(		self    :       Optional[int]							)     -> List[str]:






         """simple docstring"""







         for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=__lowerCamelCase							)





      def 							_lowercase					(		self    :       Any							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  DPMSolverSinglestepScheduler(**self.get_scheduler_config()							)
         UpperCAmelCase      					=  self.full_loop(scheduler=__lowerCamelCase							)
         UpperCAmelCase      					=  torch.mean(torch.abs(__lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.2_791							) < 1e-3

         UpperCAmelCase      					=  DEISMultistepScheduler.from_config(scheduler.config							)
         UpperCAmelCase      					=  DPMSolverMultistepScheduler.from_config(scheduler.config							)
         UpperCAmelCase      					=  UniPCMultistepScheduler.from_config(scheduler.config							)
         UpperCAmelCase      					=  DPMSolverSinglestepScheduler.from_config(scheduler.config							)

         UpperCAmelCase      					=  self.full_loop(scheduler=__lowerCamelCase							)
         UpperCAmelCase      					=  torch.mean(torch.abs(__lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.2_791							) < 1e-3





      def 							_lowercase					(		self    :       List[str]							)     -> Optional[Any]:






         """simple docstring"""







         self.check_over_configs(thresholding=__lowerCamelCase							)
         for order in [1, 2, 3]:
            for solver_type in ["midpoint", "heun"]:
               for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         thresholding=__lowerCamelCase       ,							prediction_type=__lowerCamelCase       ,							sample_max_value=__lowerCamelCase       ,							algorithm_type="""dpmsolver++"""       ,							solver_order=__lowerCamelCase       ,							solver_type=__lowerCamelCase       ,							)





      def 							_lowercase					(		self    :       Tuple							)     -> int:






         """simple docstring"""







         for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=__lowerCamelCase							)





      def 							_lowercase					(		self    :       List[str]							)     -> Dict:






         """simple docstring"""







         for algorithm_type in ["dpmsolver", "dpmsolver++"]:
            for solver_type in ["midpoint", "heun"]:
               for order in [1, 2, 3]:
                  for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         solver_order=__lowerCamelCase       ,							solver_type=__lowerCamelCase       ,							prediction_type=__lowerCamelCase       ,							algorithm_type=__lowerCamelCase       ,							)
                     UpperCAmelCase      					=  self.full_loop(
                         solver_order=__lowerCamelCase       ,							solver_type=__lowerCamelCase       ,							prediction_type=__lowerCamelCase       ,							algorithm_type=__lowerCamelCase       ,							)
                     assert not torch.isnan(__lowerCamelCase							).any(), "Samples have nan numbers"





      def 							_lowercase					(		self    :       Optional[int]							)     -> Dict:






         """simple docstring"""







         self.check_over_configs(lower_order_final=__lowerCamelCase							)
         self.check_over_configs(lower_order_final=__lowerCamelCase							)





      def 							_lowercase					(		self    :       int							)     -> int:






         """simple docstring"""







         self.check_over_configs(lambda_min_clipped=-float("""inf"""							)							)
         self.check_over_configs(lambda_min_clipped=-5.1							)





      def 							_lowercase					(		self    :       str							)     -> Optional[Any]:






         """simple docstring"""







         self.check_over_configs(variance_type=__lowerCamelCase							)
         self.check_over_configs(variance_type="""learned_range"""							)





      def 							_lowercase					(		self    :       Optional[int]							)     -> Tuple:






         """simple docstring"""







         for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_forward(num_inference_steps=__lowerCamelCase       ,							time_step=0							)





      def 							_lowercase					(		self    :       int							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  self.full_loop()
         UpperCAmelCase      					=  torch.mean(torch.abs(__lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.2_791							) < 1e-3





      def 							_lowercase					(		self    :       Optional[int]							)     -> List[str]:






         """simple docstring"""







         UpperCAmelCase      					=  self.full_loop(use_karras_sigmas=__lowerCamelCase							)
         UpperCAmelCase      					=  torch.mean(torch.abs(__lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.2_248							) < 1e-3





      def 							_lowercase					(		self    :       List[str]							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  self.full_loop(prediction_type="""v_prediction"""							)
         UpperCAmelCase      					=  torch.mean(torch.abs(__lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.1_453							) < 1e-3





      def 							_lowercase					(		self    :       List[Any]							)     -> int:






         """simple docstring"""







         UpperCAmelCase      					=  self.full_loop(prediction_type="""v_prediction"""       ,							use_karras_sigmas=__lowerCamelCase							)
         UpperCAmelCase      					=  torch.mean(torch.abs(__lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.0_649							) < 1e-3





      def 							_lowercase					(		self    :       Dict							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  self.scheduler_classes[0]
         UpperCAmelCase      					=  self.get_scheduler_config(thresholding=__lowerCamelCase       ,							dynamic_thresholding_ratio=0							)
         UpperCAmelCase      					=  scheduler_class(**__lowerCamelCase							)

         UpperCAmelCase      					=  1_0
         UpperCAmelCase      					=  self.dummy_model()
         UpperCAmelCase      					=  self.dummy_sample_deter.half()
         scheduler.set_timesteps(__lowerCamelCase							)

         for i, t in enumerate(scheduler.timesteps							):
            UpperCAmelCase      					=  model(__lowerCamelCase       ,							__lowerCamelCase							)
            UpperCAmelCase      					=  scheduler.step(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							).prev_sample

         assert sample.dtype == torch.floataa











import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__a:     List[Any]   					=					(720, 1280)  # Height, Width
__a:     str   					=					(0.4, 0.6)  # if height or width lower than this scale, drop it.
__a:     Optional[Any]   					=					1 / 100
__a:     Optional[Any]   					=					''''''
__a:     Tuple   					=					''''''
__a:     List[Any]   					=					''''''
__a:     Union[str, Any]   					=					250




def       _SCREAMING_SNAKE_CASE (							)				->						None:
       _UpperCAmelCase	,			_UpperCAmelCase							=    get_dataset(__snake_case   ,      __snake_case )
       for index in range(__snake_case ):
              _UpperCAmelCase							=    random.sample(range(len(__snake_case ) )   ,      4 )
              _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    update_image_and_anno(
                  __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      filter_scale=__snake_case   ,      )

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              _UpperCAmelCase							=    random_chars(3_2 )
              _UpperCAmelCase							=    path.split(os.sep )[-1].rsplit("""."""   ,      1 )[0]
              _UpperCAmelCase							=    f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
              cva.imwrite(f"""{file_root}.jpg"""   ,      __snake_case   ,      [cva.IMWRITE_JPEG_QUALITY, 8_5] )
              print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}""" )
              _UpperCAmelCase							=    []
              for anno in new_annos:
                     _UpperCAmelCase							=    anno[3] - anno[1]
                     _UpperCAmelCase							=    anno[4] - anno[2]
                     _UpperCAmelCase							=    anno[1] + width / 2
                     _UpperCAmelCase							=    anno[2] + height / 2
                     _UpperCAmelCase							=    f"""{anno[0]} {x_center} {y_center} {width} {height}"""
                     annos_list.append(__snake_case )
              with open(f"""{file_root}.txt"""   ,      """w""" ) as outfile:
                     outfile.write("""\n""".join(line for line in annos_list ) )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						tuple[list, list]:
       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    []
       for label_file in glob.glob(os.path.join(__snake_case   ,      """*.txt""" ) ):
              _UpperCAmelCase							=    label_file.split(os.sep )[-1].rsplit("""."""   ,      1 )[0]
              with open(__snake_case ) as in_file:
                     _UpperCAmelCase							=    in_file.readlines()
              _UpperCAmelCase							=    os.path.join(__snake_case   ,      f"""{label_name}.jpg""" )

              _UpperCAmelCase							=    []
              for obj_list in obj_lists:
                     _UpperCAmelCase							=    obj_list.rstrip("""\n""" ).split(""" """ )
                     _UpperCAmelCase							=    float(obj[1] ) - float(obj[3] ) / 2
                     _UpperCAmelCase							=    float(obj[2] ) - float(obj[4] ) / 2
                     _UpperCAmelCase							=    float(obj[1] ) + float(obj[3] ) / 2
                     _UpperCAmelCase							=    float(obj[2] ) + float(obj[4] ) / 2

                     boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
              if not boxes:
                     continue
              img_paths.append(__snake_case )
              labels.append(__snake_case )
       return img_paths, labels




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case = 0.0   ,      )				->						tuple[list, list, str]:
       _UpperCAmelCase							=    np.zeros([output_size[0], output_size[1], 3]   ,      dtype=np.uinta )
       _UpperCAmelCase							=    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       _UpperCAmelCase							=    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       _UpperCAmelCase							=    int(scale_x * output_size[1] )
       _UpperCAmelCase							=    int(scale_y * output_size[0] )

       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    []
       for i, index in enumerate(__snake_case ):
              _UpperCAmelCase							=    all_img_list[index]
              path_list.append(__snake_case )
              _UpperCAmelCase							=    all_annos[index]
              _UpperCAmelCase							=    cva.imread(__snake_case )
              if i == 0:  # top-left
                     _UpperCAmelCase							=    cva.resize(__snake_case   ,      (divid_point_x, divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    bbox[1] * scale_x
                            _UpperCAmelCase							=    bbox[2] * scale_y
                            _UpperCAmelCase							=    bbox[3] * scale_x
                            _UpperCAmelCase							=    bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
              elif i == 1:  # top-right
                     _UpperCAmelCase							=    cva.resize(__snake_case   ,      (output_size[1] - divid_point_x, divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    scale_x + bbox[1] * (1 - scale_x)
                            _UpperCAmelCase							=    bbox[2] * scale_y
                            _UpperCAmelCase							=    scale_x + bbox[3] * (1 - scale_x)
                            _UpperCAmelCase							=    bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
              elif i == 2:  # bottom-left
                     _UpperCAmelCase							=    cva.resize(__snake_case   ,      (divid_point_x, output_size[0] - divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    bbox[1] * scale_x
                            _UpperCAmelCase							=    scale_y + bbox[2] * (1 - scale_y)
                            _UpperCAmelCase							=    bbox[3] * scale_x
                            _UpperCAmelCase							=    scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
              else:  # bottom-right
                     _UpperCAmelCase							=    cva.resize(
                         __snake_case   ,      (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    scale_x + bbox[1] * (1 - scale_x)
                            _UpperCAmelCase							=    scale_y + bbox[2] * (1 - scale_y)
                            _UpperCAmelCase							=    scale_x + bbox[3] * (1 - scale_x)
                            _UpperCAmelCase							=    scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )

    # Remove bounding box small than scale of filter
       if filter_scale > 0:
              _UpperCAmelCase							=    [
                  anno
                  for anno in new_anno
                  if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
              ]

       return output_img, new_anno, path_list[0]




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						str:
       assert number_char > 1, "The number of character should greater than 1"
       _UpperCAmelCase							=    ascii_lowercase + digits
       return "".join(random.choice(__snake_case ) for _ in range(__snake_case ) )


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