


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

import cva
import numpy as np

# Parrameters
a       	=  (7_2_0, 1_2_8_0)  # Height, Width
a       	=  (0.4, 0.6)  # if height or width lower than this scale, drop it.
a       	=  1 / 1_0_0
a       	=  ''
a       	=  ''
a       	=  ''
a       	=  2_5_0


def       UpperCAmelCase_					(   ):
					lowercase_				,       lowercase_					=      get_dataset(UpperCAmelCase__    ,				UpperCAmelCase__	)
					for index in range(UpperCAmelCase__	):
										lowercase_					=      random.sample(range(len(UpperCAmelCase__	)	)    ,				4	)
										lowercase_				,       lowercase_				,       lowercase_					=      update_image_and_anno(
										    UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__    ,				filter_scale=UpperCAmelCase__    ,				)

										# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
										lowercase_					=      random_chars(3_2	)
										lowercase_					=      path.split(os.sep	)[-1].rsplit("""."""    ,				1	)[0]
										lowercase_					=      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}'''	)
										lowercase_					=      []
										for anno in new_annos:
															lowercase_					=      anno[3] - anno[1]
															lowercase_					=      anno[4] - anno[2]
															lowercase_					=      anno[1] + width / 2
															lowercase_					=      anno[2] + height / 2
															lowercase_					=      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       UpperCAmelCase_					(   UpperCAmelCase__    ,				UpperCAmelCase__	):
					lowercase_					=      []
					lowercase_					=      []
					for label_file in glob.glob(os.path.join(UpperCAmelCase__    ,				"""*.txt"""	)	):
										lowercase_					=      label_file.split(os.sep	)[-1].rsplit("""."""    ,				1	)[0]
										with open(UpperCAmelCase__	) as in_file:
															lowercase_					=      in_file.readlines()
										lowercase_					=      os.path.join(UpperCAmelCase__    ,				F'''{label_name}.jpg'''	)

										lowercase_					=      []
										for obj_list in obj_lists:
															lowercase_					=      obj_list.rstrip("""\n"""	).split(""" """	)
															lowercase_					=      float(obj[1]	) - float(obj[3]	) / 2
															lowercase_					=      float(obj[2]	) - float(obj[4]	) / 2
															lowercase_					=      float(obj[1]	) + float(obj[3]	) / 2
															lowercase_					=      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       UpperCAmelCase_					(   UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__ = 0.0    ,				):
					lowercase_					=      np.zeros([output_size[0], output_size[1], 3]    ,				dtype=np.uinta	)
					lowercase_					=      scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					lowercase_					=      scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					lowercase_					=      int(scale_x * output_size[1]	)
					lowercase_					=      int(scale_y * output_size[0]	)

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


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






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:  Tuple    		=      {
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  List[str]    		=      [
     'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DistilBertForMaskedLM',
     'DistilBertForMultipleChoice',
     'DistilBertForQuestionAnswering',
     'DistilBertForSequenceClassification',
     'DistilBertForTokenClassification',
     'DistilBertModel',
     'DistilBertPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  Union[str, Any]    		=      [
     'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFDistilBertForMaskedLM',
     'TFDistilBertForMultipleChoice',
     'TFDistilBertForQuestionAnswering',
     'TFDistilBertForSequenceClassification',
     'TFDistilBertForTokenClassification',
     'TFDistilBertMainLayer',
     'TFDistilBertModel',
     'TFDistilBertPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  str    		=      [
     'FlaxDistilBertForMaskedLM',
     'FlaxDistilBertForMultipleChoice',
     'FlaxDistilBertForQuestionAnswering',
     'FlaxDistilBertForSequenceClassification',
     'FlaxDistilBertForTokenClassification',
     'FlaxDistilBertModel',
     'FlaxDistilBertPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_distilbert import (
     DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DistilBertConfig,
     DistilBertOnnxConfig,
 )
 from .tokenization_distilbert import DistilBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_distilbert_fast import DistilBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_distilbert import (
      DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DistilBertForMaskedLM,
      DistilBertForMultipleChoice,
      DistilBertForQuestionAnswering,
      DistilBertForSequenceClassification,
      DistilBertForTokenClassification,
      DistilBertModel,
      DistilBertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertMainLayer,
      TFDistilBertModel,
      TFDistilBertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_distilbert import (
      FlaxDistilBertForMaskedLM,
      FlaxDistilBertForMultipleChoice,
      FlaxDistilBertForQuestionAnswering,
      FlaxDistilBertForSequenceClassification,
      FlaxDistilBertForTokenClassification,
      FlaxDistilBertModel,
      FlaxDistilBertPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase				:  int    		=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




