

"""simple docstring"""


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     a__    (    )	->	None:
		__lowerCAmelCase      , __lowerCAmelCase:       Dict          =			get_dataset(UpperCamelCase__					, UpperCamelCase__ )
		for index in range(UpperCamelCase__ ):
				__lowerCAmelCase:       List[str]          =			random.sample(range(len(UpperCamelCase__ ) )					, 4 )
				__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       List[str]          =			update_image_and_anno(
				    UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, filter_scale=UpperCamelCase__					, )

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






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	tuple[list, list]:
		__lowerCAmelCase:       Union[str, Any]          =			[]
		__lowerCAmelCase:       Union[str, Any]          =			[]
		for label_file in glob.glob(os.path.join(UpperCamelCase__					, "*.txt" ) ):
				__lowerCAmelCase:       Optional[int]          =			label_file.split(os.sep )[-1].rsplit("."					, 1 )[0]
				with open(UpperCamelCase__ ) as in_file:
						__lowerCAmelCase:       str          =			in_file.readlines()
				__lowerCAmelCase:       List[str]          =			os.path.join(UpperCamelCase__					, F"{label_name}.jpg" )

				__lowerCAmelCase:       Optional[Any]          =			[]
				for obj_list in obj_lists:
						__lowerCAmelCase:       str          =			obj_list.rstrip("\n" ).split(" " )
						__lowerCAmelCase:       str          =			float(obj[1] ) - float(obj[3] ) / 2
						__lowerCAmelCase:       Optional[int]          =			float(obj[2] ) - float(obj[4] ) / 2
						__lowerCAmelCase:       Optional[int]          =			float(obj[1] ) + float(obj[3] ) / 2
						__lowerCAmelCase:       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(UpperCamelCase__ )
				labels.append(UpperCamelCase__ )
		return img_paths, labels






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE = 0.0					, )	->	tuple[list, list, str]:
		__lowerCAmelCase:       int          =			np.zeros([output_size[0], output_size[1], 3]					, dtype=np.uinta )
		__lowerCAmelCase:       Tuple          =			scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
		__lowerCAmelCase:       Dict          =			scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
		__lowerCAmelCase:       Dict          =			int(scale_x * output_size[1] )
		__lowerCAmelCase:       str          =			int(scale_y * output_size[0] )

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


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









from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A				:    int       =   logging.get_logger(__name__)

__A				:    Tuple       =   {
    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class 	A_       (a_			):
	UpperCAmelCase__					=			'''big_bird'''
	def __init__(    self							,					_A=5_0_3_5_8							,					_A=7_6_8							,					_A=1_2							,					_A=1_2							,					_A=3_0_7_2							,					_A="gelu_new"							,					_A=0.1							,					_A=0.1							,					_A=4_0_9_6							,					_A=2							,					_A=0.02							,					_A=1E-12							,					_A=True							,					_A=0							,					_A=1							,					_A=2							,					_A=6_6							,					_A="block_sparse"							,					_A=True							,					_A=False							,					_A=6_4							,					_A=3							,					_A=None							,					**_A							,					):






				'''simple docstring'''







				super().__init__(
				    pad_token_id=_A							,					bos_token_id=_A							,					eos_token_id=_A							,					sep_token_id=_A							,					**_A							,					)

				UpperCAmelCase   		=				vocab_size
				UpperCAmelCase   		=				max_position_embeddings
				UpperCAmelCase   		=				hidden_size
				UpperCAmelCase   		=				num_hidden_layers
				UpperCAmelCase   		=				num_attention_heads
				UpperCAmelCase   		=				intermediate_size
				UpperCAmelCase   		=				hidden_act
				UpperCAmelCase   		=				hidden_dropout_prob
				UpperCAmelCase   		=				attention_probs_dropout_prob
				UpperCAmelCase   		=				initializer_range
				UpperCAmelCase   		=				type_vocab_size
				UpperCAmelCase   		=				layer_norm_eps
				UpperCAmelCase   		=				use_cache

				UpperCAmelCase   		=				rescale_embeddings
				UpperCAmelCase   		=				attention_type
				UpperCAmelCase   		=				use_bias
				UpperCAmelCase   		=				block_size
				UpperCAmelCase   		=				num_random_blocks
				UpperCAmelCase   		=				classifier_dropout

class 	A_       (a_			):
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				if self.task == "multiple-choice":
							UpperCAmelCase   		=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
							UpperCAmelCase   		=				{0: '''batch''', 1: '''sequence'''}
				return OrderedDict(
				    [
				        ('''input_ids''', dynamic_axis),
				        ('''attention_mask''', dynamic_axis),
				    ]		)



