# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


a_    					=       get_logger(__name__)





class 		_lowercase						:
 lowercase				     =   'dummy_data'
 lowercase				     =   'datasets'
 lowercase				     =   False

 def __init__( self	:							Tuple   ,      snake_case	:							str   ,      snake_case	:							str   ,      snake_case	:							Union[Version, str]   ,      snake_case	:							Optional[str] = None   ,      snake_case	:							bool = False   ,      snake_case	:							bool = True   ,      snake_case	:							Optional[List[Callable]] = None   ,      )   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					0
  UpperCamelCase_					:						int =					dataset_name
  UpperCamelCase_					:						Tuple =					cache_dir
  UpperCamelCase_					:						List[str] =					use_local_dummy_data
  UpperCamelCase_					:						Dict =					config
  # download_callbacks take a single url as input
  UpperCamelCase_					:						List[Callable] =					download_callbacks or []
  # if False, it doesn't load existing files and it returns the paths of the dummy files relative
  # to the dummy_data zip file root
  UpperCamelCase_					:						Optional[int] =					load_existing_dummy_data

  # TODO(PVP, QL) might need to make this more general
  UpperCamelCase_					:						Optional[Any] =					str(snake_case			)
  # to be downloaded
  UpperCamelCase_					:						str =					None
  UpperCamelCase_					:						Optional[int] =					None

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Optional[Any]:

  """simple docstring"""





  if self._dummy_file is None:
   UpperCamelCase_					:						Tuple =					self.download_dummy_data()
  return self._dummy_file

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						Union[str, Any]:

  """simple docstring"""





  if self.config is not None:
   # structure is dummy / config_name / version_name
   return os.path.join('dummy'   ,      self.config.name   ,      self.version_name			)
  # structure is dummy / version_name
  return os.path.join('dummy'   ,      self.version_name			)

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Optional[Any]:

  """simple docstring"""





  return os.path.join(self.dummy_data_folder   ,      'dummy_data.zip'			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					(
      self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
  )

  UpperCamelCase_					:						Union[str, Any] =					cached_path(
      snake_case   ,      cache_dir=self.cache_dir   ,      extract_compressed_file=snake_case   ,      force_extract=snake_case			)

  return os.path.join(snake_case   ,      self.dummy_file_name			)

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						Optional[Any]:

  """simple docstring"""





  return os.path.join(self.datasets_scripts_dir   ,      self.dataset_name   ,      self.dummy_zip_file			)

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						str:

  """simple docstring"""





  if self._bucket_url is None:
   UpperCamelCase_					:						int =					hf_github_url(self.dataset_name   ,      self.dummy_zip_file.replace(os.sep   ,      '/'			)			)
  return self._bucket_url

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						List[Any]:

  """simple docstring"""





  if os.path.isdir(self.dummy_file			):
   return self.dummy_file
  # else cut off path to file -> example `xsum`.
  return "/".join(self.dummy_file.replace(os.sep   ,      '/'			).split('/'			)[:-1]			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							Optional[Any]   ,      *snake_case	:							Optional[Any]			)   ->						Optional[int]:

  """simple docstring"""





  if self.load_existing_dummy_data:
   # dummy data is downloaded and tested
   UpperCamelCase_					:						Optional[Any] =					self.dummy_file
  else:
   # dummy data cannot be downloaded and only the path to dummy file is returned
   UpperCamelCase_					:						List[Any] =					self.dummy_file_name

  # special case when data_url is a dict
  if isinstance(snake_case   ,      snake_case			):
   return self.create_dummy_data_dict(snake_case   ,      snake_case			)
  elif isinstance(snake_case   ,      (list, tuple)			):
   return self.create_dummy_data_list(snake_case   ,      snake_case			)
  else:
   return self.create_dummy_data_single(snake_case   ,      snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple   ,      snake_case	:							Optional[int]   ,      *snake_case	:							Optional[Any]			)   ->						int:

  """simple docstring"""





  return self.download_and_extract(snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							Optional[Any]   ,      snake_case	:							str			)   ->						List[Any]:

  """simple docstring"""





  return self.download_and_extract(snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							Any   ,      *snake_case	:							Dict   ,      **snake_case	:							Optional[int]			)   ->						List[Any]:

  """simple docstring"""





  return path

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Union[str, Any]:

  """simple docstring"""





  return {}

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							str   ,      snake_case	:							List[str]			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					{}
  for key, single_urls in data_url.items():
   for download_callback in self.download_callbacks:
    if isinstance(snake_case   ,      snake_case			):
     for single_url in single_urls:
      download_callback(snake_case			)
    else:
     UpperCamelCase_					:						str =					single_urls
     download_callback(snake_case			)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
   if isinstance(snake_case   ,      snake_case			):
    UpperCamelCase_					:						Any =					[os.path.join(snake_case   ,      urllib.parse.quote_plus(Path(snake_case			).name			)			) for x in single_urls]
   else:
    UpperCamelCase_					:						str =					single_urls
    UpperCamelCase_					:						List[str] =					os.path.join(snake_case   ,      urllib.parse.quote_plus(Path(snake_case			).name			)			)
   UpperCamelCase_					:						Optional[int] =					value

  # make sure that values are unique
  if all(isinstance(snake_case   ,      snake_case			) for i in dummy_data_dict.values()			) and len(set(dummy_data_dict.values()			)			) < len(
      dummy_data_dict.values()			):
   # append key to value to make its name unique
   UpperCamelCase_					:						Union[str, Any] =					{key: value + key for key, value in dummy_data_dict.items()}

  return dummy_data_dict

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							str			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					[]
  # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
  UpperCamelCase_					:						Dict =					all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}'   ,      snake_case			)			) for url in data_url			)
  UpperCamelCase_					:						List[Any] =					all(
      url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'			) for url in data_url			)
  if data_url and (is_tf_records or is_pubmed_records):
   UpperCamelCase_					:						Any =					[data_url[0]] * len(snake_case			)
  for single_url in data_url:
   for download_callback in self.download_callbacks:
    download_callback(snake_case			)
   # we force the name of each key to be the last file / folder name of the url path
   # if the url has arguments, we need to encode them with urllib.parse.quote_plus
   UpperCamelCase_					:						int =					os.path.join(snake_case   ,      urllib.parse.quote_plus(single_url.split('/'			)[-1]			)			)
   dummy_data_list.append(snake_case			)
  return dummy_data_list

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							List[Any]   ,      snake_case	:							List[Any]			)   ->						Tuple:

  """simple docstring"""





  for download_callback in self.download_callbacks:
   download_callback(snake_case			)
  # we force the name of each key to be the last file / folder name of the url path
  # if the url has arguments, we need to encode them with urllib.parse.quote_plus
  UpperCamelCase_					:						Tuple =					os.path.join(snake_case   ,      urllib.parse.quote_plus(data_url.split('/'			)[-1]			)			)
  if os.path.exists(snake_case			) or not self.load_existing_dummy_data:
   return value
  else:
   # Backward compatibility, maybe deprecate at one point.
   # For many datasets with single url calls to dl_manager.download_and_extract,
   # the dummy_data.zip file is actually the zipped downloaded file
   # while now we expected the dummy_data.zip file to be a directory containing
   # the downloaded file.
   return path_to_dummy_data

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						Optional[Any]:

  """simple docstring"""





  pass

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Any:

  """simple docstring"""





  pass

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							Any			)   ->						Union[str, Any]:

  """simple docstring"""





  def _iter_archive_members(snake_case	:							Dict			):
   # this preserves the order of the members inside the ZIP archive
   UpperCamelCase_					:						Optional[int] =					Path(self.dummy_file			).parent
   UpperCamelCase_					:						Optional[Any] =					path.relative_to(snake_case			)
   with ZipFile(self.local_path_to_dummy_data			) as zip_file:
    UpperCamelCase_					:						Tuple =					zip_file.namelist()
   for member in members:
    if member.startswith(relative_path.as_posix()			):
     yield dummy_parent_path.joinpath(snake_case			)

  UpperCamelCase_					:						List[str] =					Path(snake_case			)
  UpperCamelCase_					:						Dict =					_iter_archive_members(snake_case			) if self.use_local_dummy_data else path.rglob('*'			)
  for file_path in file_paths:
   if file_path.is_file() and not file_path.name.startswith(('.', '__')			):
    yield file_path.relative_to(snake_case			).as_posix(), file_path.open('rb'			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Any			)   ->						Any:

  """simple docstring"""





  if not isinstance(snake_case   ,      snake_case			):
   UpperCamelCase_					:						Tuple =					[paths]
  for path in paths:
   if os.path.isfile(snake_case			):
    if os.path.basename(snake_case			).startswith(('.', '__')			):
     return
    yield path
   else:
    for dirpath, dirnames, filenames in os.walk(snake_case			):
     if os.path.basename(snake_case			).startswith(('.', '__')			):
      continue
     dirnames.sort()
     for filename in sorted(snake_case			):
      if filename.startswith(('.', '__')			):
       continue
      yield os.path.join(snake_case   ,      snake_case			)




from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def    __lowercase     (						):
 UpperCamelCase_					:						Optional[Any] =					HfArgumentParser(lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					parser.parse_args_into_dataclasses()[0]
 UpperCamelCase_					:						Dict =					TensorFlowBenchmark(args=lowerCamelCase	)
 try:
  UpperCamelCase_					:						Any =					parser.parse_args_into_dataclasses()[0]
 except ValueError as e:
  UpperCamelCase_					:						Any =					'Arg --no_{0} is no longer used, please use --no-{0} instead.'
  UpperCamelCase_					:						Optional[int] =					' '.join(str(lowerCamelCase	).split(' '	)[:-1]	)
  UpperCamelCase_					:						Any =					''
  UpperCamelCase_					:						Any =					eval(str(lowerCamelCase	).split(' '	)[-1]	)
  UpperCamelCase_					:						List[Any] =					[]
  for arg in depreciated_args:
   # arg[2:] removes '--'
   if arg[2:] in TensorFlowBenchmark.deprecated_args:
    # arg[5:] removes '--no_'
    full_error_msg += arg_error_msg.format(arg[5:]	)
   else:
    wrong_args.append(lowerCamelCase	)
  if len(lowerCamelCase	) > 0:
   UpperCamelCase_					:						List[str] =					full_error_msg + begin_error_msg + str(lowerCamelCase	)
  raise ValueError(lowerCamelCase	)
 benchmark.run()


if __name__ == "__main__":
   main()



