
import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


lowercase  : List[Any]						   =							"""\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",
    author = \"Lin, Chin-Yew  and
      Och, Franz Josef\",
    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",
    month = \"aug 23{--}aug 27\",
    year = \"2004\",
    address = \"Geneva, Switzerland\",
    publisher = \"COLING\",
    url = \"https://www.aclweb.org/anthology/C04-1072\",
    pages = \"501--507\",
}
"""

lowercase  : List[Any]						   =							"""\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,
the better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""

lowercase  : Union[str, Any]						   =							"""
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    'bleu': bleu score,
    'precisions': geometric mean of n-gram precisions,
    'brevity_penalty': brevity penalty,
    'length_ratio': ratio of lengths,
    'translation_length': translation_length,
    'reference_length': reference_length
Examples:

    >>> predictions = [
    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample
    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)
    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric(\"bleu\")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results[\"bleu\"])
    1.0
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,     _KWARGS_DESCRIPTION					)
class __snake_case		(				datasets.Metric					):
    def     _SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return datasets.MetricInfo(
               description=_DESCRIPTION		,citation=_CITATION		,inputs_description=_KWARGS_DESCRIPTION		,features=datasets.Features(
                   {
                       """predictions""": datasets.Sequence(datasets.Value("""string"""		,id="""token"""				)		,id="""sequence"""				),
                       """references""": datasets.Sequence(
                           datasets.Sequence(datasets.Value("""string"""		,id="""token"""				)		,id="""sequence"""				)		,id="""references"""				),
                   }				)		,codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""]		,reference_urls=[
                   """https://en.wikipedia.org/wiki/BLEU""",
                   """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
               ]		,)
    def     _SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case=4		,snake_case=False				):



           '''simple docstring'''







           lowercase							:					int	     =				compute_bleu(
               reference_corpus=snake_case		,translation_corpus=snake_case		,max_order=snake_case		,smooth=snake_case				)
           (lowercase)							:					Any	     =				score
           return {
               "bleu": bleu,
               "precisions": precisions,
               "brevity_penalty": bp,
               "length_ratio": ratio,
               "translation_length": translation_length,
               "reference_length": reference_length,
           }


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

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


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

lowercase  : Any						   =							"""=======
>>>>>>>
"""

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

lowercase  : Any						   =							[
    # (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 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   List[Any]:
       return ConvertCommand(args.tfds_path ,     args.datasets_directory    )




class __snake_case		(				lowerCAmelCase					):
    @staticmethod
    def 				_SCREAMING_SNAKE_CASE	(  snake_case				):



           '''simple docstring'''







           lowercase							:					str	     =				parser.add_parser(
               """convert"""		,help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."""		,)
           train_parser.add_argument(
               """--tfds_path"""		,type=snake_case		,required=snake_case		,help="""Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."""		,)
           train_parser.add_argument(
               """--datasets_directory"""		,type=snake_case		,required=snake_case		,help="""Path to the HuggingFace Datasets folder."""				)
           train_parser.set_defaults(func=snake_case				)
    def __init__(  self		,snake_case		,snake_case		,*snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				get_logger("""datasets-cli/converting"""				)

           lowercase							:					Optional[int]	     =				tfds_path
           lowercase							:					Dict	     =				datasets_directory
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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

           lowercase							:					Optional[int]	     =				os.path.abspath(self._datasets_directory				)

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

           lowercase							:					List[Any]	     =				[]
           lowercase							:					Optional[int]	     =				[]
           lowercase							:					Dict	     =				{}

           if os.path.isdir(self._tfds_path				):
                  lowercase							:					int	     =				os.listdir(snake_case				)
           else:
                  lowercase							:					List[Any]	     =				[os.path.basename(self._tfds_path				)]

           for f_name in file_names:
                  self._logger.info(f"Looking at file {f_name}"				)
                  lowercase							:					List[Any]	     =				os.path.join(snake_case		,snake_case				)
                  lowercase							:					List[str]	     =				os.path.join(snake_case		,snake_case				)

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

                  with open(snake_case		,encoding="""utf-8"""				) as f:
                         lowercase							:					str	     =				f.readlines()

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

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

                  if is_builder or "wmt" in f_name:
                         # We create a new directory for each dataset
                         lowercase							:					Union[str, Any]	     =				f_name.replace(""".py"""		,""""""				)
                         lowercase							:					Optional[Any]	     =				os.path.join(snake_case		,snake_case				)
                         lowercase							:					List[str]	     =				os.path.join(snake_case		,snake_case				)
                         os.makedirs(snake_case		,exist_ok=snake_case				)
                         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(snake_case				)

                  if needs_manual_update:
                         with_manual_update.append(snake_case				)

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

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