


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

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


lowerCamelCase__							:						Tuple							     =   """<<<<<<< This should probably be modified because it mentions: """

lowerCamelCase__							:						str							     =   """=======
>>>>>>>
"""

lowerCamelCase__							:						List[Any]							     =   [
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

lowerCamelCase__							:						Dict							     =   [
    # (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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Tuple:
  return ConvertCommand(args.tfds_path							,   args.datasets_directory				)


class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     @staticmethod
     def 	SCREAMING_SNAKE_CASE__						(					_a:ArgumentParser   ):
       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:Any     ,		_a:str     ,		_a:str     ,		*_a:Any   ):
       snake_case__						= get_logger('''datasets-cli/converting'''   )

       snake_case__						= tfds_path
       snake_case__						= datasets_directory







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

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

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

       snake_case__						= []
       snake_case__						= []
       snake_case__						= {}

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

       for f_name in file_names:
         self._logger.info(F"""Looking at file {f_name}"""   )
         snake_case__						= os.path.join(_a     ,		_a   )
         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:
           snake_case__						= f.readlines()

         snake_case__						= []
         snake_case__						= False
         snake_case__						= False
         snake_case__						= []
         for line in lines:
           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:
             snake_case__						= '''import datasets\n'''
           elif "import tensorflow" in out_line:
             # order is important here
             snake_case__						= ''''''
             continue
           elif "from absl import logging" in out_line:
             snake_case__						= '''from datasets import logging\n'''
           elif "getLogger" in out_line:
             snake_case__						= out_line.replace('''getLogger'''     ,		'''get_logger'''   )
           elif any(expression in out_line for expression in TO_HIGHLIGHT   ):
             snake_case__						= True
             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:
               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:
             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(''','''   )   )
             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:
             snake_case__						= True
           out_lines.append(_a   )

         if is_builder or "wmt" in f_name:
           # We create a new directory for each dataset
           snake_case__						= f_name.replace('''.py'''     ,		''''''   )
           snake_case__						= os.path.join(_a     ,		_a   )
           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:
           snake_case__						= os.path.basename(_a   )
           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 tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase__       		=  50_0000

lowerCamelCase__		,      lowerCamelCase__       		=  os.path.split(__file__)
lowerCamelCase__       		=  os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))






@get_duration
def 	UpperCamelCase	(							snake_case__  : datasets.Dataset						,**snake_case__  : Any	):




     '''simple docstring'''






     __snake_case      :List[str] 							=    dataset.map(**snake_case__	)






@get_duration
def 	UpperCamelCase	(							snake_case__  : datasets.Dataset						,**snake_case__  : Dict	):




     '''simple docstring'''






     __snake_case      :int 							=    dataset.filter(**snake_case__	)






def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :str 							=    {"""num examples""": SPEED_TEST_N_EXAMPLES}
     with tempfile.TemporaryDirectory() as tmp_dir:
          __snake_case      :Dict 							=    datasets.Features({"""text""": datasets.Value("""string"""	), """numbers""": datasets.Value("""float32"""	)}	)
          __snake_case      :Optional[int] 							=    generate_example_dataset(
              os.path.join(snake_case__						,"""dataset.arrow"""	)						,snake_case__						,num_examples=snake_case__	)

          __snake_case      :str 							=    transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""						,use_fast=snake_case__	)

          def tokenize(snake_case__  : Optional[int]	):
               return tokenizer(examples["""text"""]	)

          __snake_case      :Optional[Any] 							=    map(snake_case__	)

          __snake_case      :Optional[Any] 							=    map(snake_case__						,batched=snake_case__	)

          __snake_case      :Optional[Any] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""numpy"""	):
               __snake_case      :int 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""pandas"""	):
               __snake_case      :List[Any] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""torch"""						,columns="""numbers"""	):
               __snake_case      :str 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""tensorflow"""						,columns="""numbers"""	):
               __snake_case      :Optional[int] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          __snake_case      :Dict 							=    map(snake_case__						,function=snake_case__						,batched=snake_case__	)

          __snake_case      :Tuple 							=    filter(snake_case__	)

          # Activate later when tokenizer support batched inputs
          # with dataset.formatted_as(type='numpy'):
          #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

     with open(snake_case__						,"""wb"""	) as f:
          f.write(json.dumps(snake_case__	).encode("""utf-8"""	)	)


if __name__ == "__main__":  # useful to run the profiler
     benchmark_map_filter()


