






"""simple docstring"""




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

import cva
import numpy as np

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

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  _a  :       Dict             =	get_dataset(snake_case__     ,   snake_case__				)
  for index in range(snake_case__				):
    _a  :       Tuple             =	random.sample(range(len(snake_case__				)				)     ,   4				)
    _a  :       List[str]             =	update_image_and_anno(
        snake_case__     ,   snake_case__     ,   snake_case__     ,   snake_case__     ,   snake_case__     ,   filter_scale=snake_case__     ,   )

    # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
    _a  :       Union[str, Any]             =	random_chars(3_2				)
    _a  :       Any             =	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"""     ,   snake_case__     ,   [cva.IMWRITE_JPEG_QUALITY, 8_5]				)
    print(F"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""				)
    _a  :       Dict             =	[]
    for anno in new_annos:
      _a  :       Optional[int]             =	anno[3] - anno[1]
      _a  :       int             =	anno[4] - anno[2]
      _a  :       str             =	anno[1] + width / 2
      _a  :       List[str]             =	anno[2] + height / 2
      _a  :       Dict             =	F"""{anno[0]} {x_center} {y_center} {width} {height}"""
      annos_list.append(snake_case__				)
    with open(F"""{file_root}.txt"""     ,   """w"""				) as outfile:
      outfile.write("""\n""".join(line for line in annos_list				)				)

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



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

    _a  :       Tuple             =	[]
    for obj_list in obj_lists:
      _a  :       List[Any]             =	obj_list.rstrip("""\n"""				).split(""" """				)
      _a  :       str             =	float(obj[1]				) - float(obj[3]				) / 2
      _a  :       List[Any]             =	float(obj[2]				) - float(obj[4]				) / 2
      _a  :       int             =	float(obj[1]				) + float(obj[3]				) / 2
      _a  :       Optional[Any]             =	float(obj[2]				) + float(obj[4]				) / 2

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

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__ = 0.0     ,   ):



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

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



  '''simple docstring'''
  assert number_char > 1, "The number of character should greater than 1"
  _a  :       int             =	ascii_lowercase + digits
  return "".join(random.choice(snake_case__				) for _ in range(snake_case__				)				)


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



'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


lowercase			:				Union[str, Any]    		=			logging.get_logger(__name__)

lowercase			:				str    		=			{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
    'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
    'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
    'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
    'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
    'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
    'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
    'bert-large-uncased-whole-word-masking': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking': (
        'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
    'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
    'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-cased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-uncased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
    ),
    'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
    # See all BERT models at https://huggingface.co/models?filter=bert
}





class  A				(							__snake_case						):
     __magic_name__						=		'''bert'''





     def __init__(							self						,			SCREAMING_SNAKE_CASE=30522						,			SCREAMING_SNAKE_CASE=768						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=3072						,			SCREAMING_SNAKE_CASE="gelu"						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=512						,			SCREAMING_SNAKE_CASE=2						,			SCREAMING_SNAKE_CASE=0.02						,			SCREAMING_SNAKE_CASE=1e-12						,			SCREAMING_SNAKE_CASE=0						,			SCREAMING_SNAKE_CASE="absolute"						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=None						,			**SCREAMING_SNAKE_CASE						,			) ->				Optional[int]:






         """simple docstring"""






         super().__init__(pad_token_id=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)

         A       :   Optional[int]					=						vocab_size
         A       :   Optional[Any]					=						hidden_size
         A       :   List[Any]					=						num_hidden_layers
         A       :   List[str]					=						num_attention_heads
         A       :   Dict					=						hidden_act
         A       :   Optional[Any]					=						intermediate_size
         A       :   List[Any]					=						hidden_dropout_prob
         A       :   List[Any]					=						attention_probs_dropout_prob
         A       :   Optional[Any]					=						max_position_embeddings
         A       :   List[str]					=						type_vocab_size
         A       :   Dict					=						initializer_range
         A       :   str					=						layer_norm_eps
         A       :   int					=						position_embedding_type
         A       :   Dict					=						use_cache
         A       :   str					=						classifier_dropout





class  A				(							__snake_case						):





     @property
     def 				__lowerCAmelCase							(							self	) ->				Mapping[str, Mapping[int, str]]:






         """simple docstring"""






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




