

import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


lowerCAmelCase__      		=			'''<<<<<<< This should probably be modified because it mentions: '''

lowerCAmelCase__      		=			'''=======
>>>>>>>
'''

lowerCAmelCase__      		=			[
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

lowerCAmelCase__      		=			[
    # (pattern, replacement)
    # Order is important here for some replacements
    (R'''tfds\.core''', R'''datasets'''),
    (R'''tf\.io\.gfile\.GFile''', R'''open'''),
    (R'''tf\.([\w\d]+)''', R'''datasets.Value(\'\1\')'''),
    (R'''tfds\.features\.Text\(\)''', R'''datasets.Value(\'string\')'''),
    (R'''tfds\.features\.Text\(''', R'''datasets.Value(\'string\'),'''),
    (R'''features\s*=\s*tfds.features.FeaturesDict\(''', R'''features=datasets.Features('''),
    (R'''tfds\.features\.FeaturesDict\(''', R'''dict('''),
    (R'''The TensorFlow Datasets Authors''', R'''The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'''),
    (R'''tfds\.''', R'''datasets.'''),
    (R'''dl_manager\.manual_dir''', R'''self.config.data_dir'''),
    (R'''self\.builder_config''', R'''self.config'''),
]


def 	_A  (					A__   ):






 """simple docstring"""



 return ConvertCommand(args.tfds_path  ,       args.datasets_directory   )






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  @staticmethod
  def       SCREAMING_SNAKE_CASE   (   lowercase__     :  ArgumentParser    ):
   __lowercase													=				parser.add_parser(
       '''convert'''      ,help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'''      ,)
   train_parser.add_argument(
       '''--tfds_path'''      ,type=lowercase__      ,required=lowercase__      ,help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'''      ,)
   train_parser.add_argument(
       '''--datasets_directory'''      ,type=lowercase__      ,required=lowercase__      ,help='''Path to the HuggingFace Datasets folder.'''    )
   train_parser.set_defaults(func=lowercase__    )






  def __init__(   self     :  Optional[Any]      ,lowercase__     :  str      ,lowercase__     :  str      ,*lowercase__     :  Optional[Any]    ):
   __lowercase													=				get_logger('''datasets-cli/converting'''    )

   __lowercase													=				tfds_path
   __lowercase													=				datasets_directory






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   if os.path.isdir(self._tfds_path    ):
    __lowercase													=				os.path.abspath(self._tfds_path    )
   elif os.path.isfile(self._tfds_path    ):
    __lowercase													=				os.path.dirname(self._tfds_path    )
   else:
    raise ValueError('''--tfds_path is neither a directory nor a file. Please check path.'''    )

   __lowercase													=				os.path.abspath(self._datasets_directory    )

   self._logger.info(F"Converting datasets from {abs_tfds_path} to {abs_datasets_path}"    )

   __lowercase													=				[]
   __lowercase													=				[]
   __lowercase													=				{}

   if os.path.isdir(self._tfds_path    ):
    __lowercase													=				os.listdir(lowercase__    )
   else:
    __lowercase													=				[os.path.basename(self._tfds_path    )]

   for f_name in file_names:
    self._logger.info(F"Looking at file {f_name}"    )
    __lowercase													=				os.path.join(lowercase__      ,lowercase__    )
    __lowercase													=				os.path.join(lowercase__      ,lowercase__    )

    if not os.path.isfile(lowercase__    ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
     self._logger.info('''Skipping file'''    )
     continue

    with open(lowercase__      ,encoding='''utf-8'''    ) as f:
     __lowercase													=				f.readlines()

    __lowercase													=				[]
    __lowercase													=				False
    __lowercase													=				False
    __lowercase													=				[]
    for line in lines:
     __lowercase													=				line

     # Convert imports
     if "import tensorflow.compat.v2 as tf" in out_line:
      continue
     elif "@tfds.core" in out_line:
      continue
     elif "builder=self" in out_line:
      continue
     elif "import tensorflow_datasets.public_api as tfds" in out_line:
      __lowercase													=				'''import datasets\n'''
     elif "import tensorflow" in out_line:
      # order is important here
      __lowercase													=				''''''
      continue
     elif "from absl import logging" in out_line:
      __lowercase													=				'''from datasets import logging\n'''
     elif "getLogger" in out_line:
      __lowercase													=				out_line.replace('''getLogger'''      ,'''get_logger'''    )
     elif any(expression in out_line for expression in TO_HIGHLIGHT    ):
      __lowercase													=				True
      __lowercase													=				list(filter(lambda lowercase__    : e in out_line      ,lowercase__    )    )
      out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(lowercase__    ) + '''\n'''    )
      out_lines.append(lowercase__    )
      out_lines.append(lowercase__    )
      continue
     else:
      for pattern, replacement in TO_CONVERT:
       __lowercase													=				re.sub(lowercase__      ,lowercase__      ,lowercase__    )

                # Take care of saving utilities (to later move them together with main script)
     if "tensorflow_datasets" in out_line:
      __lowercase													=				re.match(r'''from\stensorflow_datasets.*import\s([^\.\r\n]+)'''      ,lowercase__    )
      tfds_imports.extend(imp.strip() for imp in match.group(1    ).split(''','''    )    )
      __lowercase													=				'''from . import ''' + match.group(1    )

     # Check we have not forget anything
     if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
      raise ValueError(F"Error converting {out_line.strip()}"    )

     if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
      __lowercase													=				True
     out_lines.append(lowercase__    )

    if is_builder or "wmt" in f_name:
     # We create a new directory for each dataset
     __lowercase													=				f_name.replace('''.py'''      ,''''''    )
     __lowercase													=				os.path.join(lowercase__      ,lowercase__    )
     __lowercase													=				os.path.join(lowercase__      ,lowercase__    )
     os.makedirs(lowercase__      ,exist_ok=lowercase__    )
     self._logger.info(F"Adding directory {output_dir}"    )
     imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}    )
    else:
     # Utilities will be moved at the end
     utils_files.append(lowercase__    )

    if needs_manual_update:
     with_manual_update.append(lowercase__    )

    with open(lowercase__      ,'''w'''      ,encoding='''utf-8'''    ) as f:
     f.writelines(lowercase__    )
    self._logger.info(F"Converted in {output_file}"    )

   for utils_file in utils_files:
    try:
     __lowercase													=				os.path.basename(lowercase__    )
     __lowercase													=				imports_to_builder_map[f_name.replace('''.py'''      ,''''''    )]
     self._logger.info(F"Moving {dest_folder} to {utils_file}"    )
     shutil.copy(lowercase__      ,lowercase__    )
    except KeyError:
     self._logger.error(F"Cannot find destination folder for {utils_file}. Please copy manually."    )

   if with_manual_update:
    for file_path in with_manual_update:
     self._logger.warning(
         F"You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'."    )




'''simple docstring'''






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

import cva
import numpy as np

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


def 	_A  (					):






 """simple docstring"""



 __lowercase						,			__lowercase													=				get_dataset(A__  ,       A__   )
 for index in range(A__   ):
  __lowercase													=				random.sample(range(len(A__   )   )  ,       4   )
  __lowercase						,			__lowercase						,			__lowercase													=				update_image_and_anno(
      A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       filter_scale=A__  ,       )

  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  __lowercase													=				random_chars(32   )
  __lowercase													=				path.split(os.sep   )[-1].rsplit('''.'''  ,       1   )[0]
  __lowercase													=				F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
  cva.imwrite(F"{file_root}.jpg"  ,       A__  ,       [cva.IMWRITE_JPEG_QUALITY, 85]   )
  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(A__   )
  with open(F"{file_root}.txt"  ,       '''w'''   ) as outfile:
   outfile.write('''\n'''.join(line for line in annos_list   )   )


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				[]
 __lowercase													=				[]
 for label_file in glob.glob(os.path.join(A__  ,       '''*.txt'''   )   ):
  __lowercase													=				label_file.split(os.sep   )[-1].rsplit('''.'''  ,       1   )[0]
  with open(A__   ) as in_file:
   __lowercase													=				in_file.readlines()
  __lowercase													=				os.path.join(A__  ,       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(A__   )
  labels.append(A__   )
 return img_paths, labels


def 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__ = 0.0  ,       ):






 """simple docstring"""



 __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(A__   ):
  __lowercase													=				all_img_list[index]
  path_list.append(A__   )
  __lowercase													=				all_annos[index]
  __lowercase													=				cva.imread(A__   )
  if i == 0:  # top-left
   __lowercase													=				cva.resize(A__  ,       (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(A__  ,       (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(A__  ,       (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(
       A__  ,       (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 	_A  (					A__   ):






 """simple docstring"""



 assert number_char > 1, "The number of character should greater than 1"
 __lowercase													=				ascii_lowercase + digits
 return "".join(random.choice(A__   ) for _ in range(A__   )   )


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

