
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        __UpperCamelCase					(    ):
       __a    ,	__a			:  Optional[Any]		    =  get_dataset(lowerCAmelCase__  ,       lowerCAmelCase__  )
       for index in range(lowerCAmelCase__  ):
              __a			:  Union[str, Any]		    =  random.sample(range(len(lowerCAmelCase__  )  )  ,       4  )
              __a    ,	__a    ,	__a			:  List[str]		    =  update_image_and_anno(
                  lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       filter_scale=lowerCAmelCase__  ,       )

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              __a			:  Union[str, Any]		    =  random_chars(3_2  )
              __a			:  Any		    =  path.split(os.sep  )[-1].rsplit('''.'''  ,       1  )[0]
              __a			:  Dict		    =  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			:  Union[str, Any]		    =  []
              for anno in new_annos:
                     __a			:  Tuple		    =  anno[3] - anno[1]
                     __a			:  int		    =  anno[4] - anno[2]
                     __a			:  Union[str, Any]		    =  anno[1] + width / 2
                     __a			:  Any		    =  anno[2] + height / 2
                     __a			:  Union[str, Any]		    =  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__							:    Dict  ,       lowerCAmelCase__							:    str  ):
       __a			:  Dict		    =  []
       __a			:  Optional[Any]		    =  []
       for label_file in glob.glob(os.path.join(lowerCAmelCase__  ,       '''*.txt'''  )  ):
              __a			:  Optional[int]		    =  label_file.split(os.sep  )[-1].rsplit('''.'''  ,       1  )[0]
              with open(lowerCAmelCase__  ) as in_file:
                     __a			:  List[str]		    =  in_file.readlines()
              __a			:  Optional[Any]		    =  os.path.join(lowerCAmelCase__  ,       f"{label_name}.jpg"  )

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

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


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


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__          ={
    'configuration_efficientnet': [
        'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientNetConfig',
        'EfficientNetOnnxConfig',
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['EfficientNetImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
          'EfficientNetForImageClassification',
          'EfficientNetModel',
          'EfficientNetPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_efficientnet import (
          EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
          EfficientNetConfig,
          EfficientNetOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_efficientnet import EfficientNetImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_efficientnet import (
                EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                EfficientNetForImageClassification,
                EfficientNetModel,
                EfficientNetPreTrainedModel,
            )

else:
      import sys

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure)
