


'''simple docstring'''


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class 		lowerCAmelCase__	:
			lowerCAmelCase       :		List[Any]				      =   42

			# setable values
			lowerCAmelCase       :		Any				      =   42
			lowerCAmelCase       :		Union[str, Any]				      =   42
			lowerCAmelCase       :		Any				      =   None




			@classmethod
			def 							lowerCAmelCase__			(						cls     :   List[Any]		,     lowerCamelCase__     :   Optional[int]		,     lowerCamelCase__     :   Optional[int]		,     lowerCamelCase__     :   Dict       )      ->Any:



					'''simple docstring'''







					return cls(common=__SCREAMING_SNAKE_CASE		,     init_noise_sigma=__SCREAMING_SNAKE_CASE		,     timesteps=__SCREAMING_SNAKE_CASE       )






@dataclass
class 		lowerCAmelCase__	(	lowerCamelCase_		):
			lowerCAmelCase       :		Dict				      =   42






class 		lowerCAmelCase__	(	lowerCamelCase_			,				lowerCamelCase_		):
			lowerCAmelCase       :		Optional[Any]				      =   [e.name for e in FlaxKarrasDiffusionSchedulers]

			lowerCAmelCase       :		Optional[int]				      =   42




			@property
			def 							lowerCAmelCase__			(						self     :   List[str]       )      ->Tuple:



					'''simple docstring'''







					return True




			@register_to_config
			def __init__(						self     :   int		,     lowerCamelCase__     :   Union[str, Any] = 10_00		,     lowerCamelCase__     :   Any = 0.0_0_0_1		,     lowerCamelCase__     :   Optional[Any] = 0.0_2		,     lowerCamelCase__     :   str = "linear"		,     lowerCamelCase__     :   List[Any] = None		,     lowerCamelCase__     :   int = "fixed_small"		,     lowerCamelCase__     :   Union[str, Any] = True		,     lowerCamelCase__     :   Any = "epsilon"		,     lowerCamelCase__     :   List[str] = jnp.floataa		,     )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					dtype




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   Dict = None       )      ->int:



					'''simple docstring'''







					if common is None:
							_UpperCAmelCase						:				Tuple	    =					CommonSchedulerState.create(self       )

					# standard deviation of the initial noise distribution
					_UpperCAmelCase						:				Union[str, Any]	    =					jnp.array(1.0		,     dtype=self.dtype       )

					_UpperCAmelCase						:				List[Any]	    =					jnp.arange(0		,     self.config.num_train_timesteps       ).round()[::-1]

					return DDPMSchedulerState.create(
					    common=__SCREAMING_SNAKE_CASE		,     init_noise_sigma=__SCREAMING_SNAKE_CASE		,     timesteps=__SCREAMING_SNAKE_CASE		,     )




			def 							lowerCAmelCase__			(						self     :   Optional[int]		,     lowerCamelCase__     :   Tuple		,     lowerCamelCase__     :   List[Any]		,     lowerCamelCase__     :   List[str] = None       )      ->Tuple:



					'''simple docstring'''







					return sample




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   Any		,     lowerCamelCase__     :   Optional[Any]		,     lowerCamelCase__     :   Any = ()       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					self.config.num_train_timesteps // num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# rounding to avoid issues when num_inference_step is power of 3
					_UpperCAmelCase						:				int	    =					(jnp.arange(0		,     __SCREAMING_SNAKE_CASE       ) * step_ratio).round()[::-1]

					return state.replace(
					    num_inference_steps=__SCREAMING_SNAKE_CASE		,     timesteps=__SCREAMING_SNAKE_CASE		,     )




			def 							lowerCAmelCase__			(						self     :   Optional[Any]		,     lowerCamelCase__     :   Any		,     lowerCamelCase__     :   Any		,     lowerCamelCase__     :   Tuple=None		,     lowerCamelCase__     :   Optional[int]=None       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					state.common.alphas_cumprod[t]
					_UpperCAmelCase						:				str	    =					jnp.where(t > 0		,     state.common.alphas_cumprod[t - 1]		,     jnp.array(1.0		,     dtype=self.dtype       )       )

					# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
					# and sample from it to get previous sample
					# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
					_UpperCAmelCase						:				int	    =					(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

					if variance_type is None:
							_UpperCAmelCase						:				str	    =					self.config.variance_type

					# hacks - were probably added for training stability
					if variance_type == "fixed_small":
							_UpperCAmelCase						:				int	    =					jnp.clip(__SCREAMING_SNAKE_CASE		,     a_min=1E-20       )
					# for rl-diffuser https://arxiv.org/abs/2205.09991
					elif variance_type == "fixed_small_log":
							_UpperCAmelCase						:				List[str]	    =					jnp.log(jnp.clip(__SCREAMING_SNAKE_CASE		,     a_min=1E-20       )       )
					elif variance_type == "fixed_large":
							_UpperCAmelCase						:				List[Any]	    =					state.common.betas[t]
					elif variance_type == "fixed_large_log":
							# Glide max_log
							_UpperCAmelCase						:				List[Any]	    =					jnp.log(state.common.betas[t]       )
					elif variance_type == "learned":
							return predicted_variance
					elif variance_type == "learned_range":
							_UpperCAmelCase						:				Optional[Any]	    =					variance
							_UpperCAmelCase						:				Union[str, Any]	    =					state.common.betas[t]
							_UpperCAmelCase						:				Union[str, Any]	    =					(predicted_variance + 1) / 2
							_UpperCAmelCase						:				Any	    =					frac * max_log + (1 - frac) * min_log

					return variance




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   str = None		,     lowerCamelCase__     :   str = True		,     )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					timestep

					if key is None:
							_UpperCAmelCase						:				int	    =					jax.random.PRNGKey(0       )

					if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
							_UpperCAmelCase						:				Optional[Any]	    =					jnp.split(__SCREAMING_SNAKE_CASE		,     sample.shape[1]		,     axis=1       )
					else:
							_UpperCAmelCase						:				int	    =					None

					# 1. compute alphas, betas
					_UpperCAmelCase						:				Any	    =					state.common.alphas_cumprod[t]
					_UpperCAmelCase						:				Optional[int]	    =					jnp.where(t > 0		,     state.common.alphas_cumprod[t - 1]		,     jnp.array(1.0		,     dtype=self.dtype       )       )
					_UpperCAmelCase						:				int	    =					1 - alpha_prod_t
					_UpperCAmelCase						:				str	    =					1 - alpha_prod_t_prev

					# 2. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
					if self.config.prediction_type == "epsilon":
							_UpperCAmelCase						:				Tuple	    =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
					elif self.config.prediction_type == "sample":
							_UpperCAmelCase						:				Any	    =					model_output
					elif self.config.prediction_type == "v_prediction":
							_UpperCAmelCase						:				List[Any]	    =					(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
					else:
							raise ValueError(
							    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
							    " for the FlaxDDPMScheduler."       )

					# 3. Clip "predicted x_0"
					if self.config.clip_sample:
							_UpperCAmelCase						:				Optional[Any]	    =					jnp.clip(__SCREAMING_SNAKE_CASE		,     -1		,     1       )

					# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					_UpperCAmelCase						:				List[Any]	    =					(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
					_UpperCAmelCase						:				Optional[Any]	    =					state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

					# 5. Compute predicted previous sample µ_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					_UpperCAmelCase						:				Optional[Any]	    =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

					# 6. Add noise
					def random_variance():
							_UpperCAmelCase						:				str	    =					jax.random.split(__SCREAMING_SNAKE_CASE		,     num=1       )
							_UpperCAmelCase						:				List[Any]	    =					jax.random.normal(__SCREAMING_SNAKE_CASE		,     shape=model_output.shape		,     dtype=self.dtype       )
							return (self._get_variance(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE		,     predicted_variance=__SCREAMING_SNAKE_CASE       ) ** 0.5) * noise

					_UpperCAmelCase						:				Optional[Any]	    =					jnp.where(t > 0		,     random_variance()		,     jnp.zeros(model_output.shape		,     dtype=self.dtype       )       )

					_UpperCAmelCase						:				Any	    =					pred_prev_sample + variance

					if not return_dict:
							return (pred_prev_sample, state)

					return FlaxDDPMSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE		,     state=__SCREAMING_SNAKE_CASE       )




			def 							lowerCAmelCase__			(						self     :   Tuple		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   Tuple		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   str		,     )      ->str:



					'''simple docstring'''







					return add_noise_common(state.common		,     __SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE       )




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Optional[Any]		,     lowerCamelCase__     :   int		,     )      ->int:



					'''simple docstring'''







					return get_velocity_common(state.common		,     __SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE       )






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



					'''simple docstring'''







					return self.config.num_train_timesteps





'''simple docstring'''







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

import cva
import numpy as np

# Parrameters
_lowercase :  Any    							=       (7_2_0, 1_2_8_0)  # Height, Width
_lowercase :  List[Any]    							=       (0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowercase :  str    							=       1 / 1_0_0
_lowercase :  Any    							=       ""
_lowercase :  Union[str, Any]    							=       ""
_lowercase :  Optional[int]    							=       ""
_lowercase :  List[Any]    							=       2_5_0
def 					snake_case_		(				):



						"""simple docstring"""





						lowercase_						, lowercase_	:     Any													=       get_dataset(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)
						for index in range(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     str													=       random.sample(range(len(__SCREAMING_SNAKE_CASE							)							)      ,      4							)
												lowercase_						, lowercase_						, lowercase_	:     Any													=       update_image_and_anno(
												    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      filter_scale=__SCREAMING_SNAKE_CASE      ,      )

												# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
												lowercase_	:     int													=       random_chars(32							)
												lowercase_	:     str													=       path.split(os.sep							)[-1].rsplit('''.'''      ,      1							)[0]
												lowercase_	:     int													=       F'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
												cva.imwrite(F'''{file_root}.jpg'''      ,      __SCREAMING_SNAKE_CASE      ,      [cva.IMWRITE_JPEG_QUALITY, 85]							)
												print(F'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'''							)
												lowercase_	:     List[Any]													=       []
												for anno in new_annos:
																		lowercase_	:     List[Any]													=       anno[3] - anno[1]
																		lowercase_	:     List[str]													=       anno[4] - anno[2]
																		lowercase_	:     Dict													=       anno[1] + width / 2
																		lowercase_	:     Dict													=       anno[2] + height / 2
																		lowercase_	:     int													=       F'''{anno[0]} {x_center} {y_center} {width} {height}'''
																		annos_list.append(__SCREAMING_SNAKE_CASE							)
												with open(F'''{file_root}.txt'''      ,      '''w'''							) as outfile:
																		outfile.write('''\n'''.join(line for line in annos_list							)							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: str							):



						"""simple docstring"""





						lowercase_	:     Optional[Any]													=       []
						lowercase_	:     Optional[Any]													=       []
						for label_file in glob.glob(os.path.join(__SCREAMING_SNAKE_CASE      ,      '''*.txt'''							)							):
												lowercase_	:     int													=       label_file.split(os.sep							)[-1].rsplit('''.'''      ,      1							)[0]
												with open(__SCREAMING_SNAKE_CASE							) as in_file:
																		lowercase_	:     List[str]													=       in_file.readlines()
												lowercase_	:     Optional[Any]													=       os.path.join(__SCREAMING_SNAKE_CASE      ,      F'''{label_name}.jpg'''							)

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

																		boxes.append([int(obj[0]							), xmin, ymin, xmax, ymax]							)
												if not boxes:
																		continue
												img_paths.append(__SCREAMING_SNAKE_CASE							)
												labels.append(__SCREAMING_SNAKE_CASE							)
						return img_paths, labels
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: list      ,      __SCREAMING_SNAKE_CASE					: list      ,      __SCREAMING_SNAKE_CASE					: list[int]      ,      __SCREAMING_SNAKE_CASE					: tuple[int, int]      ,      __SCREAMING_SNAKE_CASE					: tuple[float, float]      ,      __SCREAMING_SNAKE_CASE					: float = 0.0      ,      ):



						"""simple docstring"""





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

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



						"""simple docstring"""





						assert number_char > 1, "The number of character should greater than 1"
						lowercase_	:     Any													=       ascii_lowercase + digits
						return "".join(random.choice(__SCREAMING_SNAKE_CASE							) for _ in range(__SCREAMING_SNAKE_CASE							)							)


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



