




"""simple docstring"""






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

import cva
import numpy as np

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







def   _snake_case    (       ):
				_lowerCamelCase,				_lowerCamelCase  :    Optional[Any]					=					get_dataset(lowercase__							,					lowercase__	)
				for index in range(lowercase__	):
								_lowerCamelCase  :    List[Any]					=					random.sample(range(len(lowercase__	)	)							,					4	)
								_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    Union[str, Any]					=					update_image_and_anno(
								    lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					filter_scale=lowercase__							,					)

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







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

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

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







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__ = 0.0							,					):
				_lowerCamelCase  :    Optional[Any]					=					np.zeros([output_size[0], output_size[1], 3]							,					dtype=np.uinta	)
				_lowerCamelCase  :    List[str]					=					scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
				_lowerCamelCase  :    List[Any]					=					scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
				_lowerCamelCase  :    str					=					int(scale_x * output_size[1]	)
				_lowerCamelCase  :    int					=					int(scale_y * output_size[0]	)

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


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





"""simple docstring"""






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """sew-d"""
			def __init__(						self						,   lowercase=32						,   lowercase=768						,   lowercase=12						,   lowercase=12						,   lowercase=3072						,   lowercase=2						,   lowercase=512						,   lowercase=256						,   lowercase=True						,   lowercase=True						,   lowercase=("p2c", "c2p")						,   lowercase="layer_norm"						,   lowercase="gelu_python"						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=0.0						,   lowercase=0.1						,   lowercase=0.02						,   lowercase=1E-7						,   lowercase=1E-5						,   lowercase="group"						,   lowercase="gelu"						,   lowercase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)						,   lowercase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)						,   lowercase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)						,   lowercase=False						,   lowercase=128						,   lowercase=16						,   lowercase=True						,   lowercase=0.05						,   lowercase=10						,   lowercase=2						,   lowercase=0.0						,   lowercase=10						,   lowercase=0						,   lowercase="mean"						,   lowercase=False						,   lowercase=False						,   lowercase=256						,   lowercase=0						,   lowercase=1						,   lowercase=2						,   **lowercase						,   ):
							super().__init__(**lowercase						,   pad_token_id=lowercase						,   bos_token_id=lowercase						,   eos_token_id=lowercase						)
							_lowerCamelCase  :    List[str]					=					hidden_size
							_lowerCamelCase  :    int					=					feat_extract_norm
							_lowerCamelCase  :    Optional[Any]					=					feat_extract_activation
							_lowerCamelCase  :    Optional[Any]					=					list(lowercase						)
							_lowerCamelCase  :    Union[str, Any]					=					list(lowercase						)
							_lowerCamelCase  :    Optional[Any]					=					list(lowercase						)
							_lowerCamelCase  :    List[str]					=					conv_bias
							_lowerCamelCase  :    str					=					num_conv_pos_embeddings
							_lowerCamelCase  :    Optional[Any]					=					num_conv_pos_embedding_groups
							_lowerCamelCase  :    Tuple					=					len(self.conv_dim						)
							_lowerCamelCase  :    Optional[int]					=					num_hidden_layers
							_lowerCamelCase  :    List[str]					=					intermediate_size
							_lowerCamelCase  :    Dict					=					squeeze_factor
							_lowerCamelCase  :    Any					=					max_position_embeddings
							_lowerCamelCase  :    List[str]					=					position_buckets
							_lowerCamelCase  :    Dict					=					share_att_key
							_lowerCamelCase  :    List[str]					=					relative_attention
							_lowerCamelCase  :    Optional[Any]					=					norm_rel_ebd
							_lowerCamelCase  :    List[Any]					=					list(lowercase						)
							_lowerCamelCase  :    List[Any]					=					hidden_act
							_lowerCamelCase  :    Optional[Any]					=					num_attention_heads
							_lowerCamelCase  :    Optional[int]					=					hidden_dropout
							_lowerCamelCase  :    Optional[int]					=					attention_dropout
							_lowerCamelCase  :    Any					=					activation_dropout
							_lowerCamelCase  :    Tuple					=					feat_proj_dropout
							_lowerCamelCase  :    Any					=					final_dropout
							_lowerCamelCase  :    str					=					layer_norm_eps
							_lowerCamelCase  :    Tuple					=					feature_layer_norm_eps
							_lowerCamelCase  :    Any					=					initializer_range
							_lowerCamelCase  :    Dict					=					vocab_size

							if (
							    (len(self.conv_stride						) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel						) != self.num_feat_extract_layers)
							    or (len(self.conv_dim						) != self.num_feat_extract_layers)
							):
											raise ValueError(
											    'Configuration for convolutional layers is incorrect.'
											    'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
											    F'''but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)'''
											    F'''= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'''						)

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							_lowerCamelCase  :    Dict					=					apply_spec_augment
							_lowerCamelCase  :    int					=					mask_time_prob
							_lowerCamelCase  :    Union[str, Any]					=					mask_time_length
							_lowerCamelCase  :    Optional[Any]					=					mask_time_min_masks
							_lowerCamelCase  :    List[Any]					=					mask_feature_prob
							_lowerCamelCase  :    Union[str, Any]					=					mask_feature_length
							_lowerCamelCase  :    List[Any]					=					mask_feature_min_masks

							# ctc loss
							_lowerCamelCase  :    List[str]					=					ctc_loss_reduction
							_lowerCamelCase  :    List[str]					=					ctc_zero_infinity

							# sequence classification
							_lowerCamelCase  :    Dict					=					use_weighted_layer_sum
							_lowerCamelCase  :    List[Any]					=					classifier_proj_size






			@property
			def       A_					(						self						):
							return functools.reduce(operator.mul						,   self.conv_stride						,   1						)