






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

import cva
import numpy as np

# Parrameters
A        =			(720, 1280)  # Height, Width
A        =			(0.4, 0.6)  # if height or width lower than this scale, drop it.
A        =			1 / 100
A        =			''
A        =			''
A        =			''
A        =			250



def 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[int]													=   get_dataset(lowerCAmelCase__    ,    lowerCAmelCase__      )
				for index in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	List[str]													=   random.sample(range(len(lowerCAmelCase__      )      )    ,    4      )
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   update_image_and_anno(
								    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    filter_scale=lowerCAmelCase__    ,    )

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



def 					_lowerCamelCase(			lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		str      ):





				'''simple docstring'''







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

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

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



def 					_lowerCamelCase(			lowerCAmelCase__				:		list    ,    lowerCAmelCase__				:		list    ,    lowerCAmelCase__				:		list[int]    ,    lowerCAmelCase__				:		tuple[int, int]    ,    lowerCAmelCase__				:		tuple[float, float]    ,    lowerCAmelCase__				:		float = 0.0    ,    ):





				'''simple docstring'''







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

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





				'''simple docstring'''







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


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







import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm
				import torch_xla.debug.metrics as met


class 							__a							(  __A						):





		'''simple docstring'''



		def __init__(						self ,    *UpperCamelCase__ ,    UpperCamelCase__=None ,    UpperCamelCase__=None ,    **UpperCamelCase__       ):
						super().__init__(*UpperCamelCase__ ,    **UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   eval_examples
						SCREAMING_SNAKE_CASE_   :	int													=   post_process_function


		def   __snake_case			(						self ,    UpperCamelCase__ = None ,    UpperCamelCase__=None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = "eval" ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	int													=   gen_kwargs.copy()
						SCREAMING_SNAKE_CASE_   :	Tuple													=   (
						    gen_kwargs['max_length'] if gen_kwargs.get('max_length'       ) is not None else self.args.generation_max_length
						)
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   (
						    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'       ) is not None else self.args.generation_num_beams
						)
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   gen_kwargs

						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.eval_dataset if eval_dataset is None else eval_dataset
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_eval_dataloader(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   self.eval_examples if eval_examples is None else eval_examples

						# Temporarily disable metric computation, we will do it in the loop here.
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.compute_metrics
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   None
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   time.time()
						SCREAMING_SNAKE_CASE_   :	int													=   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
						try:
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   eval_loop(
										    UpperCamelCase__ ,    description='Evaluation' ,    prediction_loss_only=True if compute_metrics is None else None ,    ignore_keys=UpperCamelCase__ ,    metric_key_prefix=UpperCamelCase__ ,    )
						finally:
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   compute_metrics
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.args.eval_batch_size * self.args.world_size
						if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
										start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
						output.metrics.update(
						    speed_metrics(
						        UpperCamelCase__ ,    UpperCamelCase__ ,    num_samples=output.num_samples ,    num_steps=math.ceil(output.num_samples / total_batch_size       ) ,    )       )

						if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
										# Only the main node write the results by default
										SCREAMING_SNAKE_CASE_   :	List[str]													=   self.post_process_function(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.compute_metrics(UpperCamelCase__       )

										# Prefix all keys with metric_key_prefix + '_'
										for key in list(metrics.keys()       ):
														if not key.startswith(F'''{metric_key_prefix}_'''       ):
																		SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   metrics.pop(UpperCamelCase__       )

										metrics.update(output.metrics       )
						else:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   output.metrics

						if self.args.should_log:
										# Only the main node log the results by default
										self.log(UpperCamelCase__       )

						if self.args.tpu_metrics_debug or self.args.debug:
										# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
										xm.master_print(met.metrics_report()       )

						SCREAMING_SNAKE_CASE_   :	Dict													=   self.callback_handler.on_evaluate(self.args ,    self.state ,    self.control ,    UpperCamelCase__       )
						return metrics






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__=None ,    UpperCamelCase__ = "test" ,    **UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   gen_kwargs.copy()

						SCREAMING_SNAKE_CASE_   :	Any													=   self.get_test_dataloader(UpperCamelCase__       )

						# Temporarily disable metric computation, we will do it in the loop here.
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.compute_metrics
						SCREAMING_SNAKE_CASE_   :	str													=   None
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   time.time()
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
						try:
										SCREAMING_SNAKE_CASE_   :	List[str]													=   eval_loop(
										    UpperCamelCase__ ,    description='Prediction' ,    prediction_loss_only=True if compute_metrics is None else None ,    ignore_keys=UpperCamelCase__ ,    metric_key_prefix=UpperCamelCase__ ,    )
						finally:
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   compute_metrics

						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.args.eval_batch_size * self.args.world_size
						if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
										start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
						output.metrics.update(
						    speed_metrics(
						        UpperCamelCase__ ,    UpperCamelCase__ ,    num_samples=output.num_samples ,    num_steps=math.ceil(output.num_samples / total_batch_size       ) ,    )       )
						if self.post_process_function is None or self.compute_metrics is None:
										return output

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.post_process_function(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    'predict'       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.compute_metrics(UpperCamelCase__       )

						# Prefix all keys with metric_key_prefix + '_'
						for key in list(metrics.keys()       ):
										if not key.startswith(F'''{metric_key_prefix}_'''       ):
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   metrics.pop(UpperCamelCase__       )
						metrics.update(output.metrics       )
						return PredictionOutput(predictions=predictions.predictions ,    label_ids=predictions.label_ids ,    metrics=UpperCamelCase__       )