




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

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


__A						:							List[Any]							=  """<<<<<<< This should probably be modified because it mentions: """

__A						:							Tuple							=  """=======
>>>>>>>
"""

__A						:							str							=  [
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

__A						:							Optional[int]							=  [
    # (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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return ConvertCommand(args.tfds_path					,	args.datasets_directory   )




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



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





							def __init__(					self       :	Dict       ,     a       :	str       ,     a       :	str       ,     *a       :	List[str]			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					get_logger("""datasets-cli/converting"""			)

										SCREAMING_SNAKE_CASE           =					tfds_path
										SCREAMING_SNAKE_CASE           =					datasets_directory







							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					int:
										if os.path.isdir(self._tfds_path			):
													SCREAMING_SNAKE_CASE           =					os.path.abspath(self._tfds_path			)
										elif os.path.isfile(self._tfds_path			):
													SCREAMING_SNAKE_CASE           =					os.path.dirname(self._tfds_path			)
										else:
													raise ValueError("""--tfds_path is neither a directory nor a file. Please check path."""			)

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

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

										SCREAMING_SNAKE_CASE           =					[]
										SCREAMING_SNAKE_CASE           =					[]
										SCREAMING_SNAKE_CASE           =					{}

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

										for f_name in file_names:
													self._logger.info(f"""Looking at file {f_name}"""			)
													SCREAMING_SNAKE_CASE           =					os.path.join(a       ,     a			)
													SCREAMING_SNAKE_CASE           =					os.path.join(a       ,     a			)

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

													with open(a       ,     encoding="""utf-8"""			) as f:
																SCREAMING_SNAKE_CASE           =					f.readlines()

													SCREAMING_SNAKE_CASE           =					[]
													SCREAMING_SNAKE_CASE           =					False
													SCREAMING_SNAKE_CASE           =					False
													SCREAMING_SNAKE_CASE           =					[]
													for line in lines:
																SCREAMING_SNAKE_CASE           =					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:
																			SCREAMING_SNAKE_CASE           =					"""import datasets\n"""
																elif "import tensorflow" in out_line:
																			# order is important here
																			SCREAMING_SNAKE_CASE           =					""""""
																			continue
																elif "from absl import logging" in out_line:
																			SCREAMING_SNAKE_CASE           =					"""from datasets import logging\n"""
																elif "getLogger" in out_line:
																			SCREAMING_SNAKE_CASE           =					out_line.replace("""getLogger"""       ,     """get_logger"""			)
																elif any(expression in out_line for expression in TO_HIGHLIGHT			):
																			SCREAMING_SNAKE_CASE           =					True
																			SCREAMING_SNAKE_CASE           =					list(filter(lambda a			: e in out_line       ,     a			)			)
																			out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(a			) + """\n"""			)
																			out_lines.append(a			)
																			out_lines.append(a			)
																			continue
																else:
																			for pattern, replacement in TO_CONVERT:
																						SCREAMING_SNAKE_CASE           =					re.sub(a       ,     a       ,     a			)

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

													if is_builder or "wmt" in f_name:
																# We create a new directory for each dataset
																SCREAMING_SNAKE_CASE           =					f_name.replace(""".py"""       ,     """"""			)
																SCREAMING_SNAKE_CASE           =					os.path.join(a       ,     a			)
																SCREAMING_SNAKE_CASE           =					os.path.join(a       ,     a			)
																os.makedirs(a       ,     exist_ok=a			)
																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(a			)

													if needs_manual_update:
																with_manual_update.append(a			)

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

										for utils_file in utils_files:
													try:
																SCREAMING_SNAKE_CASE           =					os.path.basename(a			)
																SCREAMING_SNAKE_CASE           =					imports_to_builder_map[f_name.replace(""".py"""       ,     """"""			)]
																self._logger.info(f"""Moving {dest_folder} to {utils_file}"""			)
																shutil.copy(a       ,     a			)
													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'."""			)












import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

__A						:							int							=  {
    """User-Agent""": """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
    """ (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"""
}
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE = "dhaka"					,	SCREAMING_SNAKE_CASE = 5   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					min(SCREAMING_SNAKE_CASE					,	50   )  # Prevent abuse!
			SCREAMING_SNAKE_CASE           =					{
			    """q""": query,
			    """tbm""": """isch""",
			    """hl""": """en""",
			    """ijn""": """0""",
			}

			SCREAMING_SNAKE_CASE           =					requests.get("""https://www.google.com/search"""					,	params=SCREAMING_SNAKE_CASE					,	headers=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					BeautifulSoup(html.text					,	"""html.parser"""   )
			SCREAMING_SNAKE_CASE           =					"""""".join(
			    re.findall(r"""AF_initDataCallback\(([^<]+)\);"""					,	str(soup.select("""script"""   )   )   )   )

			SCREAMING_SNAKE_CASE           =					json.dumps(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					json.loads(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					re.findall(
			    r"""\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","""					,	SCREAMING_SNAKE_CASE					,	)
			if not matched_google_image_data:
						return 0

			SCREAMING_SNAKE_CASE           =					re.sub(
			    r"""\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]"""					,	""""""					,	str(SCREAMING_SNAKE_CASE   )					,	)

			SCREAMING_SNAKE_CASE           =					re.findall(
			    r"""(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"""					,	SCREAMING_SNAKE_CASE					,	)
			for index, fixed_full_res_image in enumerate(SCREAMING_SNAKE_CASE   ):
						if index >= max_images:
									return index
						SCREAMING_SNAKE_CASE           =					bytes(SCREAMING_SNAKE_CASE					,	"""ascii"""   ).decode(
						    """unicode-escape"""   )
						SCREAMING_SNAKE_CASE           =					bytes(SCREAMING_SNAKE_CASE					,	"""ascii"""   ).decode(
						    """unicode-escape"""   )
						SCREAMING_SNAKE_CASE           =					urllib.request.build_opener()
						SCREAMING_SNAKE_CASE           =					[
						    (
						        """User-Agent""",
						        """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
						        """ (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582""",
						    )
						]
						urllib.request.install_opener(SCREAMING_SNAKE_CASE   )
						SCREAMING_SNAKE_CASE           =					f"""query_{query.replace(" "					,	"_"   )}"""
						if not os.path.exists(SCREAMING_SNAKE_CASE   ):
									os.makedirs(SCREAMING_SNAKE_CASE   )
						urllib.request.urlretrieve(  # noqa: S310
						    SCREAMING_SNAKE_CASE					,	f"""{path_name}/original_size_img_{index}.jpg"""   )
			return index


if __name__ == "__main__":
		try:
				__A						:							List[Any]							=  download_images_from_google_query(sys.argv[1])
				print(f'''{image_count} images were downloaded to disk.''')
		except IndexError:
				print("""Please provide a search term.""")
				raise






