


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

import cva
import numpy as np

# Parrameters
lowerCAmelCase__				    =       (720, 1_280)  # Height, Width
lowerCAmelCase__				    =       (0.4, 0.6)  # if height or width lower than this scale, drop it.
lowerCAmelCase__				    =       1 / 100
lowerCAmelCase__				    =       ''''''
lowerCAmelCase__				    =       ''''''
lowerCAmelCase__				    =       ''''''
lowerCAmelCase__				    =       250
def  a__      (    ):


  '''simple docstring'''







  lowerCAmelCase						,  lowerCAmelCase					:					Any								=							get_dataset(A__    ,				A__    )
  for index in range(A__    ):
    lowerCAmelCase					:					List[Any]								=							random.sample(range(len(A__    )    )    ,				4    )
    lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase					:					List[Any]								=							update_image_and_anno(
        A__    ,				A__    ,				A__    ,				A__    ,				A__    ,				filter_scale=A__    ,				)

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


  '''simple docstring'''







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

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

      boxes.append([int(obj[0]    ), xmin, ymin, xmax, ymax]    )
    if not boxes:
      continue
    img_paths.append(A__    )
    labels.append(A__    )
  return img_paths, labels
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   Any    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   int = 0.0    ,				):


  '''simple docstring'''







  lowerCAmelCase					:					Union[str, Any]								=							np.zeros([output_size[0], output_size[1], 3]    ,				dtype=np.uinta    )
  lowerCAmelCase					:					Union[str, Any]								=							scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
  lowerCAmelCase					:					Optional[Any]								=							scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
  lowerCAmelCase					:					Union[str, Any]								=							int(scale_x * output_size[1]    )
  lowerCAmelCase					:					List[Any]								=							int(scale_y * output_size[0]    )

  lowerCAmelCase					:					Dict								=							[]
  lowerCAmelCase					:					List[str]								=							[]
  for i, index in enumerate(A__    ):
    lowerCAmelCase					:					List[str]								=							all_img_list[index]
    path_list.append(A__    )
    lowerCAmelCase					:					List[str]								=							all_annos[index]
    lowerCAmelCase					:					int								=							cva.imread(A__    )
    if i == 0:  # top-left
      lowerCAmelCase					:					Optional[Any]								=							cva.resize(A__    ,				(divid_point_x, divid_point_y)    )
      lowerCAmelCase					:					List[str]								=							img
      for bbox in img_annos:
        lowerCAmelCase					:					List[str]								=							bbox[1] * scale_x
        lowerCAmelCase					:					int								=							bbox[2] * scale_y
        lowerCAmelCase					:					Optional[int]								=							bbox[3] * scale_x
        lowerCAmelCase					:					Dict								=							bbox[4] * scale_y
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]    )
    elif i == 1:  # top-right
      lowerCAmelCase					:					Union[str, Any]								=							cva.resize(A__    ,				(output_size[1] - divid_point_x, divid_point_y)    )
      lowerCAmelCase					:					Any								=							img
      for bbox in img_annos:
        lowerCAmelCase					:					Union[str, Any]								=							scale_x + bbox[1] * (1 - scale_x)
        lowerCAmelCase					:					Dict								=							bbox[2] * scale_y
        lowerCAmelCase					:					Optional[int]								=							scale_x + bbox[3] * (1 - scale_x)
        lowerCAmelCase					:					List[Any]								=							bbox[4] * scale_y
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]    )
    elif i == 2:  # bottom-left
      lowerCAmelCase					:					Union[str, Any]								=							cva.resize(A__    ,				(divid_point_x, output_size[0] - divid_point_y)    )
      lowerCAmelCase					:					Any								=							img
      for bbox in img_annos:
        lowerCAmelCase					:					int								=							bbox[1] * scale_x
        lowerCAmelCase					:					List[Any]								=							scale_y + bbox[2] * (1 - scale_y)
        lowerCAmelCase					:					Optional[Any]								=							bbox[3] * scale_x
        lowerCAmelCase					:					List[str]								=							scale_y + bbox[4] * (1 - scale_y)
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]    )
    else:  # bottom-right
      lowerCAmelCase					:					str								=							cva.resize(
          A__    ,				(output_size[1] - divid_point_x, output_size[0] - divid_point_y)    )
      lowerCAmelCase					:					Dict								=							img
      for bbox in img_annos:
        lowerCAmelCase					:					int								=							scale_x + bbox[1] * (1 - scale_x)
        lowerCAmelCase					:					Union[str, Any]								=							scale_y + bbox[2] * (1 - scale_y)
        lowerCAmelCase					:					Union[str, Any]								=							scale_x + bbox[3] * (1 - scale_x)
        lowerCAmelCase					:					Union[str, Any]								=							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:
    lowerCAmelCase					:					str								=							[
        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  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







  assert number_char > 1, "The number of character should greater than 1"
  lowerCAmelCase					:					Optional[int]								=							ascii_lowercase + digits
  return "".join(random.choice(A__    ) for _ in range(A__    )    )


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









"""simple docstring"""







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    Optional[int]       =(DEISMultistepScheduler,)
       a  :    str       =(("num_inference_steps", 25),)

       def 	lowercase__							(						self		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							{
             "num_train_timesteps": 1_000,
             "beta_start": 0.0001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "solver_order": 2,
         }

         config.update(**snake_case__						)
         return config

       def 	lowercase__							(						self		,					snake_case__=0		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							dict(self.forward_default_kwargs						)
         lowerCAmelCase					:					List[str]								=							kwargs.pop("num_inference_steps"		,					snake_case__						)
         lowerCAmelCase					:					List[str]								=							self.dummy_sample
         lowerCAmelCase					:					int								=							0.1 * sample
         lowerCAmelCase					:					Tuple								=							[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase					:					Any								=							self.get_scheduler_config(**snake_case__						)
           lowerCAmelCase					:					List[str]								=							scheduler_class(**snake_case__						)
           scheduler.set_timesteps(snake_case__						)
           # copy over dummy past residuals
           lowerCAmelCase					:					str								=							dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(snake_case__						)
             lowerCAmelCase					:					List[str]								=							scheduler_class.from_pretrained(snake_case__						)
             new_scheduler.set_timesteps(snake_case__						)
             # copy over dummy past residuals
             lowerCAmelCase					:					Optional[int]								=							dummy_past_residuals[: new_scheduler.config.solver_order]

           lowerCAmelCase						,  lowerCAmelCase					:					Union[str, Any]								=							sample, sample
           for t in range(snake_case__		,					time_step + scheduler.config.solver_order + 1						):
             lowerCAmelCase					:					Any								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample
             lowerCAmelCase					:					str								=							new_scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample

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

       def 	lowercase__							(						self						):






         """simple docstring"""





         pass

       def 	lowercase__							(						self		,					snake_case__=0		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							dict(self.forward_default_kwargs						)
         lowerCAmelCase					:					List[str]								=							kwargs.pop("num_inference_steps"		,					snake_case__						)
         lowerCAmelCase					:					List[str]								=							self.dummy_sample
         lowerCAmelCase					:					Optional[int]								=							0.1 * sample
         lowerCAmelCase					:					List[Any]								=							[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase					:					int								=							self.get_scheduler_config()
           lowerCAmelCase					:					Any								=							scheduler_class(**snake_case__						)
           scheduler.set_timesteps(snake_case__						)

           # copy over dummy past residuals (must be after setting timesteps)
           lowerCAmelCase					:					Union[str, Any]								=							dummy_past_residuals[: scheduler.config.solver_order]

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

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

           lowerCAmelCase					:					Union[str, Any]								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample
           lowerCAmelCase					:					int								=							new_scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample

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

       def 	lowercase__							(						self		,					snake_case__=None		,					**snake_case__						):






         """simple docstring"""





         if scheduler is None:
           lowerCAmelCase					:					List[str]								=							self.scheduler_classes[0]
           lowerCAmelCase					:					Optional[int]								=							self.get_scheduler_config(**snake_case__						)
           lowerCAmelCase					:					Any								=							scheduler_class(**snake_case__						)

         lowerCAmelCase					:					List[str]								=							self.scheduler_classes[0]
         lowerCAmelCase					:					Tuple								=							self.get_scheduler_config(**snake_case__						)
         lowerCAmelCase					:					List[str]								=							scheduler_class(**snake_case__						)

         lowerCAmelCase					:					int								=							10
         lowerCAmelCase					:					List[str]								=							self.dummy_model()
         lowerCAmelCase					:					List[Any]								=							self.dummy_sample_deter
         scheduler.set_timesteps(snake_case__						)

         for i, t in enumerate(scheduler.timesteps						):
           lowerCAmelCase					:					Any								=							model(snake_case__		,					snake_case__						)
           lowerCAmelCase					:					List[Any]								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__						).prev_sample

         return sample

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							dict(self.forward_default_kwargs						)

         lowerCAmelCase					:					Optional[Any]								=							kwargs.pop("num_inference_steps"		,					snake_case__						)

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase					:					Dict								=							self.get_scheduler_config()
           lowerCAmelCase					:					List[Any]								=							scheduler_class(**snake_case__						)

           lowerCAmelCase					:					int								=							self.dummy_sample
           lowerCAmelCase					:					int								=							0.1 * sample

           if num_inference_steps is not None and hasattr(snake_case__		,					"set_timesteps"						):
             scheduler.set_timesteps(snake_case__						)
           elif num_inference_steps is not None and not hasattr(snake_case__		,					"set_timesteps"						):
             lowerCAmelCase					:					List[str]								=							num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           lowerCAmelCase					:					int								=							[residual + 0.2, residual + 0.15, residual + 0.10]
           lowerCAmelCase					:					Tuple								=							dummy_past_residuals[: scheduler.config.solver_order]

           lowerCAmelCase					:					int								=							scheduler.timesteps[5]
           lowerCAmelCase					:					str								=							scheduler.timesteps[6]

           lowerCAmelCase					:					str								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample
           lowerCAmelCase					:					Union[str, Any]								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample

           self.assertEqual(output_a.shape		,					sample.shape						)
           self.assertEqual(output_a.shape		,					output_a.shape						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							DEISMultistepScheduler(**self.get_scheduler_config()						)
         lowerCAmelCase					:					Dict								=							self.full_loop(scheduler=snake_case__						)
         lowerCAmelCase					:					Optional[Any]								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.23916						) < 1e-3

         lowerCAmelCase					:					List[str]								=							DPMSolverSinglestepScheduler.from_config(scheduler.config						)
         lowerCAmelCase					:					Any								=							DPMSolverMultistepScheduler.from_config(scheduler.config						)
         lowerCAmelCase					:					str								=							UniPCMultistepScheduler.from_config(scheduler.config						)
         lowerCAmelCase					:					Union[str, Any]								=							DEISMultistepScheduler.from_config(scheduler.config						)

         lowerCAmelCase					:					Union[str, Any]								=							self.full_loop(scheduler=snake_case__						)
         lowerCAmelCase					:					str								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.23916						) < 1e-3

       def 	lowercase__							(						self						):






         """simple docstring"""





         for timesteps in [25, 50, 100, 999, 1_000]:
           self.check_over_configs(num_train_timesteps=snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         self.check_over_configs(thresholding=snake_case__						)
         for order in [1, 2, 3]:
           for solver_type in ["logrho"]:
             for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     thresholding=snake_case__		,					prediction_type=snake_case__		,					sample_max_value=snake_case__		,					algorithm_type="deis"		,					solver_order=snake_case__		,					solver_type=snake_case__		,					)

       def 	lowercase__							(						self						):






         """simple docstring"""





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

       def 	lowercase__							(						self						):






         """simple docstring"""





         for algorithm_type in ["deis"]:
           for solver_type in ["logrho"]:
             for order in [1, 2, 3]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=snake_case__		,					solver_type=snake_case__		,					prediction_type=snake_case__		,					algorithm_type=snake_case__		,					)
                 lowerCAmelCase					:					Any								=							self.full_loop(
                     solver_order=snake_case__		,					solver_type=snake_case__		,					prediction_type=snake_case__		,					algorithm_type=snake_case__		,					)
                 assert not torch.isnan(snake_case__						).any(), "Samples have nan numbers"

       def 	lowercase__							(						self						):






         """simple docstring"""





         self.check_over_configs(lower_order_final=snake_case__						)
         self.check_over_configs(lower_order_final=snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
           self.check_over_forward(num_inference_steps=snake_case__		,					time_step=0						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							self.full_loop()
         lowerCAmelCase					:					Dict								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.23916						) < 1e-3

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							self.full_loop(prediction_type="v_prediction"						)
         lowerCAmelCase					:					List[Any]								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.091						) < 1e-3







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							self.scheduler_classes[0]
         lowerCAmelCase					:					str								=							self.get_scheduler_config(thresholding=snake_case__		,					dynamic_thresholding_ratio=0						)
         lowerCAmelCase					:					Optional[Any]								=							scheduler_class(**snake_case__						)

         lowerCAmelCase					:					Optional[Any]								=							10
         lowerCAmelCase					:					Tuple								=							self.dummy_model()
         lowerCAmelCase					:					List[str]								=							self.dummy_sample_deter.half()
         scheduler.set_timesteps(snake_case__						)

         for i, t in enumerate(scheduler.timesteps						):
           lowerCAmelCase					:					Union[str, Any]								=							model(snake_case__		,					snake_case__						)
           lowerCAmelCase					:					Tuple								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__						).prev_sample

         assert sample.dtype == torch.floataa





