





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

import cva
import numpy as np

# Parrameters
__lowerCamelCase       :     Tuple   =     (720, 1280)  # Height, Width
__lowerCamelCase       :     int   =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
__lowerCamelCase       :     int   =     1 / 100
__lowerCamelCase       :     Any   =     """"""
__lowerCamelCase       :     List[str]   =     """"""
__lowerCamelCase       :     List[Any]   =     """"""
__lowerCamelCase       :     Tuple   =     250



def 		A_				(     )						-> None:
 UpperCamelCase	,    UpperCamelCase	:					Tuple       	=   get_dataset(_lowerCAmelCase							,   _lowerCAmelCase     )
 for index in range(_lowerCAmelCase     ):
  UpperCamelCase	:					Union[str, Any]       	=   random.sample(range(len(_lowerCAmelCase     )     )							,   4     )
  UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	:					str       	=   update_image_and_anno(
      _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   filter_scale=_lowerCAmelCase							,   )

  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  UpperCamelCase	:					Union[str, Any]       	=   random_chars(32     )
  UpperCamelCase	:					Optional[Any]       	=   path.split(os.sep     )[-1].rsplit("."							,   1     )[0]
  UpperCamelCase	:					Optional[Any]       	=   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}"""     )
  UpperCamelCase	:					int       	=   []
  for anno in new_annos:
   UpperCamelCase	:					Dict       	=   anno[3] - anno[1]
   UpperCamelCase	:					Union[str, Any]       	=   anno[4] - anno[2]
   UpperCamelCase	:					Optional[int]       	=   anno[1] + width / 2
   UpperCamelCase	:					Tuple       	=   anno[2] + height / 2
   UpperCamelCase	:					Dict       	=   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 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> tuple[list, list]:
 UpperCamelCase	:					int       	=   []
 UpperCamelCase	:					Tuple       	=   []
 for label_file in glob.glob(os.path.join(_lowerCAmelCase							,   "*.txt"     )     ):
  UpperCamelCase	:					Union[str, Any]       	=   label_file.split(os.sep     )[-1].rsplit("."							,   1     )[0]
  with open(_lowerCAmelCase     ) as in_file:
   UpperCamelCase	:					int       	=   in_file.readlines()
  UpperCamelCase	:					Optional[Any]       	=   os.path.join(_lowerCAmelCase							,   F"""{label_name}.jpg"""     )

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

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


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






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a  :				Tuple									=  {
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				Optional[Any]									=  ['LlamaTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				str									=  ['LlamaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				Optional[Any]									=  [
							    'LlamaForCausalLM',
							    'LlamaModel',
							    'LlamaPreTrainedModel',
							    'LlamaForSequenceClassification',
							]


if TYPE_CHECKING:
							from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama import LlamaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama_fast import LlamaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
							import sys

							a  :				str									=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

