





import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				snake_case_					):
						__magic_name__:			Union[str, Any]       =       1






						@register_to_config
						def __init__(      self      :      Dict		,   _A      :      int = 1000		,   _A      :      Optional[Union[np.ndarray, List[float]]] = None	)							->    Tuple:







							"""simple docstring"""


							self.set_timesteps(_A	)

							# standard deviation of the initial noise distribution
							snake_case_		:			Optional[int]											=	1.0

							# For now we only support F-PNDM, i.e. the runge-kutta method
							# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
							# mainly at formula (9), (12), (13) and the Algorithm 2.
							snake_case_		:			Optional[int]											=	4

							# running values
							snake_case_		:			Dict											=	[]






						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      int		,   _A      :      Union[str, torch.device] = None	)							->    int:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	num_inference_steps
							snake_case_		:			int											=	torch.linspace(1		,   0		,   num_inference_steps + 1	)[:-1]
							snake_case_		:			List[str]											=	torch.cat([steps, torch.tensor([0.0]	)]	)

							if self.config.trained_betas is not None:
								snake_case_		:			Dict											=	torch.tensor(self.config.trained_betas		,   dtype=torch.floataa	)
							else:
								snake_case_		:			Optional[Any]											=	torch.sin(steps * math.pi / 2	) ** 2

							snake_case_		:			Tuple											=	(1.0 - self.betas**2) ** 0.5

							snake_case_		:			Any											=	(torch.atana(self.betas		,   self.alphas	) / math.pi * 2)[:-1]
							snake_case_		:			Any											=	timesteps.to(_A	)

							snake_case_		:			List[str]											=	[]






						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      torch.FloatTensor		,   _A      :      int		,   _A      :      torch.FloatTensor		,   _A      :      bool = True		,   )							->    Union[SchedulerOutput, Tuple]:







							"""simple docstring"""


							if self.num_inference_steps is None:
								raise ValueError(
								    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'	)

							snake_case_		:			Dict											=	(self.timesteps == timestep).nonzero().item()
							snake_case_		:			Tuple											=	timestep_index + 1

							snake_case_		:			Optional[int]											=	sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
							self.ets.append(_A	)

							if len(self.ets	) == 1:
								snake_case_		:			Dict											=	self.ets[-1]
							elif len(self.ets	) == 2:
								snake_case_		:			Union[str, Any]											=	(3 * self.ets[-1] - self.ets[-2]) / 2
							elif len(self.ets	) == 3:
								snake_case_		:			Optional[Any]											=	(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
							else:
								snake_case_		:			Dict											=	(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

							snake_case_		:			Tuple											=	self._get_prev_sample(_A		,   _A		,   _A		,   _A	)

							if not return_dict:
								return (prev_sample,)

							return SchedulerOutput(prev_sample=_A	)






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      torch.FloatTensor		,   *_A      :      Any		,   **_A      :      List[Any]	)							->    torch.FloatTensor:







							"""simple docstring"""


							return sample






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      List[Any]		,   _A      :      List[str]		,   _A      :      Dict		,   _A      :      Tuple	)							->    str:







							"""simple docstring"""


							snake_case_		:			List[str]											=	self.alphas[timestep_index]
							snake_case_		:			List[str]											=	self.betas[timestep_index]

							snake_case_		:			Optional[int]											=	self.alphas[prev_timestep_index]
							snake_case_		:			List[str]											=	self.betas[prev_timestep_index]

							snake_case_		:			Optional[int]											=	(sample - sigma * ets) / max(_A		,   1E-8	)
							snake_case_		:			Optional[Any]											=	next_alpha * pred + ets * next_sigma

							return prev_sample






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







							"""simple docstring"""


							return self.config.num_train_timesteps








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

import cva
import numpy as np

# Parrameters
_SCREAMING_SNAKE_CASE      	=		(7_20, 12_80)  # Height, Width
_SCREAMING_SNAKE_CASE      	=		(0.4, 0.6)  # if height or width lower than this scale, drop it.
_SCREAMING_SNAKE_CASE      	=		1 / 1_00
_SCREAMING_SNAKE_CASE      	=		""""""
_SCREAMING_SNAKE_CASE      	=		""""""
_SCREAMING_SNAKE_CASE      	=		""""""
_SCREAMING_SNAKE_CASE      	=		2_50


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_						,snake_case_		:			Any											=	get_dataset(__a   , __a   )
	for index in range(__a   ):
		snake_case_		:			Dict											=	random.sample(range(len(__a   )   )   , 4   )
		snake_case_						,snake_case_						,snake_case_		:			Optional[int]											=	update_image_and_anno(
		    __a   , __a   , __a   , __a   , __a   , filter_scale=__a   , )

		# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
		snake_case_		:			List[str]											=	random_chars(32   )
		snake_case_		:			Dict											=	path.split(os.sep   )[-1].rsplit('.'   , 1   )[0]
		snake_case_		:			Any											=	f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
		cva.imwrite(f"""{file_root}.jpg"""   , __a   , [cva.IMWRITE_JPEG_QUALITY, 85]   )
		print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""   )
		snake_case_		:			Dict											=	[]
		for anno in new_annos:
			snake_case_		:			int											=	anno[3] - anno[1]
			snake_case_		:			Dict											=	anno[4] - anno[2]
			snake_case_		:			int											=	anno[1] + width / 2
			snake_case_		:			str											=	anno[2] + height / 2
			snake_case_		:			Union[str, Any]											=	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 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			List[Any]											=	[]
	snake_case_		:			Optional[int]											=	[]
	for label_file in glob.glob(os.path.join(__a   , '*.txt'   )   ):
		snake_case_		:			List[str]											=	label_file.split(os.sep   )[-1].rsplit('.'   , 1   )[0]
		with open(__a   ) as in_file:
			snake_case_		:			Dict											=	in_file.readlines()
		snake_case_		:			int											=	os.path.join(__a   , f"""{label_name}.jpg"""   )

		snake_case_		:			int											=	[]
		for obj_list in obj_lists:
			snake_case_		:			str											=	obj_list.rstrip('\n'   ).split(' '   )
			snake_case_		:			Tuple											=	float(obj[1]   ) - float(obj[3]   ) / 2
			snake_case_		:			str											=	float(obj[2]   ) - float(obj[4]   ) / 2
			snake_case_		:			Optional[Any]											=	float(obj[1]   ) + float(obj[3]   ) / 2
			snake_case_		:			List[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 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   , __a   , __a = 0.0   , ):
	snake_case_		:			str											=	np.zeros([output_size[0], output_size[1], 3]   , dtype=np.uinta   )
	snake_case_		:			Tuple											=	scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	snake_case_		:			int											=	scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	snake_case_		:			Optional[Any]											=	int(scale_x * output_size[1]   )
	snake_case_		:			int											=	int(scale_y * output_size[0]   )

	snake_case_		:			str											=	[]
	snake_case_		:			Dict											=	[]
	for i, index in enumerate(__a   ):
		snake_case_		:			Any											=	all_img_list[index]
		path_list.append(__a   )
		snake_case_		:			List[str]											=	all_annos[index]
		snake_case_		:			Any											=	cva.imread(__a   )
		if i == 0:  # top-left
			snake_case_		:			Optional[Any]											=	cva.resize(__a   , (divid_point_x, divid_point_y)   )
			snake_case_		:			Dict											=	img
			for bbox in img_annos:
				snake_case_		:			Dict											=	bbox[1] * scale_x
				snake_case_		:			Tuple											=	bbox[2] * scale_y
				snake_case_		:			Optional[Any]											=	bbox[3] * scale_x
				snake_case_		:			str											=	bbox[4] * scale_y
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
		elif i == 1:  # top-right
			snake_case_		:			Tuple											=	cva.resize(__a   , (output_size[1] - divid_point_x, divid_point_y)   )
			snake_case_		:			Optional[Any]											=	img
			for bbox in img_annos:
				snake_case_		:			int											=	scale_x + bbox[1] * (1 - scale_x)
				snake_case_		:			Dict											=	bbox[2] * scale_y
				snake_case_		:			Union[str, Any]											=	scale_x + bbox[3] * (1 - scale_x)
				snake_case_		:			Union[str, Any]											=	bbox[4] * scale_y
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
		elif i == 2:  # bottom-left
			snake_case_		:			Optional[Any]											=	cva.resize(__a   , (divid_point_x, output_size[0] - divid_point_y)   )
			snake_case_		:			int											=	img
			for bbox in img_annos:
				snake_case_		:			int											=	bbox[1] * scale_x
				snake_case_		:			List[str]											=	scale_y + bbox[2] * (1 - scale_y)
				snake_case_		:			List[str]											=	bbox[3] * scale_x
				snake_case_		:			Union[str, Any]											=	scale_y + bbox[4] * (1 - scale_y)
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
		else:  # bottom-right
			snake_case_		:			str											=	cva.resize(
			    __a   , (output_size[1] - divid_point_x, output_size[0] - divid_point_y)   )
			snake_case_		:			Dict											=	img
			for bbox in img_annos:
				snake_case_		:			Dict											=	scale_x + bbox[1] * (1 - scale_x)
				snake_case_		:			Tuple											=	scale_y + bbox[2] * (1 - scale_y)
				snake_case_		:			Union[str, Any]											=	scale_x + bbox[3] * (1 - scale_x)
				snake_case_		:			List[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:
		snake_case_		:			Dict											=	[
		    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__						(    __a   ):
	assert number_char > 1, "The number of character should greater than 1"
	snake_case_		:			Tuple											=	ascii_lowercase + digits
	return "".join(random.choice(__a   ) for _ in range(__a   )   )


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

