






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

import cva
import numpy as np

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

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

						# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
						__a						:  Optional[Any]									=     random_chars(3_2				)
						__a						:  Tuple									=     path.split(os.sep				)[-1].rsplit('.'   ,  1				)[0]
						__a						:  Union[str, Any]									=     f'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
						cva.imwrite(f'''{file_root}.jpg'''   ,  lowerCamelCase_   ,  [cva.IMWRITE_JPEG_QUALITY, 8_5]				)
						print(f'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'''				)
						__a						:  List[Any]									=     []
						for anno in new_annos:
									__a						:  Optional[int]									=     anno[3] - anno[1]
									__a						:  str									=     anno[4] - anno[2]
									__a						:  Tuple									=     anno[1] + width / 2
									__a						:  Optional[int]									=     anno[2] + height / 2
									__a						:  Tuple									=     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        UpperCAmelCase__		(						lowerCamelCase_       :       str   ,  lowerCamelCase_       :       str				):
			__a						:  List[Any]									=     []
			__a						:  int									=     []
			for label_file in glob.glob(os.path.join(lowerCamelCase_   ,  '*.txt'				)				):
						__a						:  Union[str, Any]									=     label_file.split(os.sep				)[-1].rsplit('.'   ,  1				)[0]
						with open(lowerCamelCase_				) as in_file:
									__a						:  Tuple									=     in_file.readlines()
						__a						:  Optional[int]									=     os.path.join(lowerCamelCase_   ,  f'''{label_name}.jpg'''				)

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

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


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










import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCamelCase		       =      logging.get_logger(__name__)


lowerCamelCase		       =      {name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						Dict:
				if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
								raise ValueError(F'Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys() )}.' )

				if tokenizer_name is None:
								a__	:							str      						= TOKENIZER_CLASSES
				else:
								a__	:							int      						= {tokenizer_name: getattr(__UpperCamelCase	,				tokenizer_name + "Fast" )}

				logger.info(F'Loading tokenizer classes: {tokenizer_names}' )

				for tokenizer_name in tokenizer_names:
								a__	:							Any      						= TOKENIZER_CLASSES[tokenizer_name]

								a__	:							Dict      						= True
								if checkpoint_name is None:
												a__	:							Tuple      						= list(tokenizer_class.max_model_input_sizes.keys() )
								else:
												a__	:							List[str]      						= [checkpoint_name]

								logger.info(F'For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}' )

								for checkpoint in checkpoint_names:
												logger.info(F'Loading {tokenizer_class.__class__.__name__} {checkpoint}' )

												# Load tokenizer
												a__	:							List[str]      						= tokenizer_class.from_pretrained(__UpperCamelCase	,				force_download=__UpperCamelCase )

												# Save fast tokenizer
												logger.info(F'Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}' )

												# For organization names we create sub-directories
												if "/" in checkpoint:
																a__						,   a__	:							Dict      						= checkpoint.split("/" )
																a__	:							Any      						= os.path.join(__UpperCamelCase	,				__UpperCamelCase )
												elif add_prefix:
																a__	:							int      						= checkpoint
																a__	:							Optional[Any]      						= dump_path
												else:
																a__	:							Dict      						= None
																a__	:							Optional[Any]      						= dump_path

												logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}' )

												if checkpoint in list(tokenizer.pretrained_vocab_files_map.values() )[0]:
																a__	:							Dict      						= list(tokenizer.pretrained_vocab_files_map.values() )[0][checkpoint]
																a__	:							Optional[Any]      						= file_path.split(__UpperCamelCase )[-1][0]
																if next_char == "/":
																				a__	:							Union[str, Any]      						= os.path.join(__UpperCamelCase	,				__UpperCamelCase )
																				a__	:							Any      						= None

																logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}' )

												a__	:							Any      						= tokenizer.save_pretrained(
												    __UpperCamelCase	,				legacy_format=__UpperCamelCase	,				filename_prefix=__UpperCamelCase )
												logger.info(F'=> File names {file_names}' )

												for file_name in file_names:
																if not file_name.endswith("tokenizer.json" ):
																				os.remove(__UpperCamelCase )
																				logger.info(F'=> removing {file_name}' )


if __name__ == "__main__":
						lowerCamelCase		       =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--dump_path""", default=None, type=str, required=True, help="""Path to output generated fast tokenizer files."""
						)
						parser.add_argument(
						    """--tokenizer_name""",
						    default=None,
						    type=str,
						    help=(
						        F'Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '
						        """download and convert all the checkpoints from AWS."""
						    ),
						)
						parser.add_argument(
						    """--checkpoint_name""",
						    default=None,
						    type=str,
						    help="""Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.""",
						)
						parser.add_argument(
						    """--force_download""",
						    action="""store_true""",
						    help="""Re-download checkpoints.""",
						)
						lowerCamelCase		       =      parser.parse_args()

						convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)


