






import re
import tempfile
from pathlib import Path

import pytest
import yaml

from datasets.utils.readme import ReadMe


# @pytest.fixture
# def example_yaml_structure():

__snake_case      :      str             =       yaml.safe_load(
    """\\nname: \"\"\nallow_empty: false\nallow_empty_text: true\nsubsections:\n  - name: \"Dataset Card for X\" # First-level markdown heading\n    allow_empty: false\n    allow_empty_text: true\n    subsections:\n      - name: \"Table of Contents\"\n        allow_empty: false\n        allow_empty_text: false\n        subsections: null\n      - name: \"Dataset Description\"\n        allow_empty: false\n        allow_empty_text: false\n        subsections:\n          - name: \"Dataset Summary\"\n            allow_empty: false\n            allow_empty_text: false\n            subsections: null\n          - name: \"Supported Tasks and Leaderboards\"\n            allow_empty: true\n            allow_empty_text: true\n            subsections: null\n          - name: Languages\n            allow_empty: false\n            allow_empty_text: true\n            subsections: null\n"""
)


__snake_case      :      Union[str, Any]             =       {
    """name""": """root""",
    """text""": """""",
    """is_empty_text""": True,
    """subsections""": [
        {
            """name""": """Dataset Card for My Dataset""",
            """text""": """""",
            """is_empty_text""": True,
            """subsections""": [
                {"""name""": """Table of Contents""", """text""": """Some text here.""", """is_empty_text""": False, """subsections""": []},
                {
                    """name""": """Dataset Description""",
                    """text""": """Some text here.""",
                    """is_empty_text""": False,
                    """subsections""": [
                        {
                            """name""": """Dataset Summary""",
                            """text""": """Some text here.""",
                            """is_empty_text""": False,
                            """subsections""": [],
                        },
                        {
                            """name""": """Supported Tasks and Leaderboards""",
                            """text""": """""",
                            """is_empty_text""": True,
                            """subsections""": [],
                        },
                        {"""name""": """Languages""", """text""": """Language Text""", """is_empty_text""": False, """subsections""": []},
                    ],
                },
            ],
        }
    ],
}


__snake_case      :      Dict             =       """\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"""


__snake_case      :      str             =       """\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n#### Extra Ignored Subsection\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"""

__snake_case      :      Any             =       {
    """name""": """root""",
    """text""": """""",
    """is_empty_text""": True,
    """subsections""": [
        {
            """name""": """Dataset Card for My Dataset""",
            """text""": """""",
            """is_empty_text""": True,
            """subsections""": [
                {"""name""": """Table of Contents""", """text""": """Some text here.""", """is_empty_text""": False, """subsections""": []},
                {
                    """name""": """Dataset Description""",
                    """text""": """Some text here.""",
                    """is_empty_text""": False,
                    """subsections""": [
                        {
                            """name""": """Dataset Summary""",
                            """text""": """Some text here.""",
                            """is_empty_text""": False,
                            """subsections""": [
                                {
                                    """name""": """Extra Ignored Subsection""",
                                    """text""": """""",
                                    """is_empty_text""": True,
                                    """subsections""": [],
                                }
                            ],
                        },
                        {
                            """name""": """Supported Tasks and Leaderboards""",
                            """text""": """""",
                            """is_empty_text""": True,
                            """subsections""": [],
                        },
                        {"""name""": """Languages""", """text""": """Language Text""", """is_empty_text""": False, """subsections""": []},
                    ],
                },
            ],
        }
    ],
}

__snake_case      :      Union[str, Any]             =       """\\n---\n---\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"""

__snake_case      :      List[str]             =       (
    """The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README."""
)

__snake_case      :      Tuple             =       """\\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"""

__snake_case      :      Any             =       (
    """The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README."""
)

__snake_case      :      Union[str, Any]             =       """\\n---\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"""

__snake_case      :      Dict             =       """The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README."""

__snake_case      :      Any             =       """\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"""
__snake_case      :      Union[str, Any]             =       """The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored)."""


__snake_case      :      Optional[Any]             =       """\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n"""
__snake_case      :      Tuple             =       """The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found \'None\'."""

__snake_case      :      Tuple             =       """\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Languages\nLanguage Text\n"""

__snake_case      :      Tuple             =       """The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`."""


__snake_case      :      Optional[Any]             =       """\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\n"""

__snake_case      :      Any             =       """The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty."""

__snake_case      :      List[str]             =       """\\n---\nlanguage:\n- zh\n- en\n---\n\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"""
__snake_case      :      Any             =       """The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README."""

__snake_case      :      int             =       """\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n# Dataset Card My Dataset\n"""

__snake_case      :      Dict             =       """The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README."""

__snake_case      :      int             =       """\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"""

__snake_case      :      Union[str, Any]             =       """The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README."""

__snake_case      :      Tuple             =       """"""

__snake_case      :      Optional[Any]             =       """The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README."""

__snake_case      :      Tuple             =       """\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"""

__snake_case      :      Dict             =       """The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections."""







@pytest.mark.parametrize(
    'readme_md, expected_dict'      ,    [
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ]      ,    )
def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Dict						)					->				int:

      """simple docstring"""



      assert ReadMe.from_string(UpperCamelCase_      ,    UpperCamelCase_						).to_dict() == expected_dict







@pytest.mark.parametrize(
    'readme_md, expected_error'      ,    [
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ]      ,    )
def 		_UpperCamelCase				(      UpperCamelCase_    :   Union[str, Any]      ,    UpperCamelCase_    :   List[Any]						)					->				Optional[Any]:

      """simple docstring"""



      with pytest.raises(UpperCamelCase_      ,    match=re.escape(expected_error.format(path='root'						)						)						):
            lowerCAmelCase__        =  ReadMe.from_string(UpperCamelCase_      ,    UpperCamelCase_						)
            readme.validate()







@pytest.mark.parametrize(
    'readme_md, expected_error'      ,    [
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]      ,    )
def 		_UpperCamelCase				(      UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   int						)					->				int:

      """simple docstring"""



      with pytest.raises(UpperCamelCase_      ,    match=re.escape(expected_error.format(path='root'						)						)						):
            ReadMe.from_string(UpperCamelCase_      ,    UpperCamelCase_						)







@pytest.mark.parametrize(
    'readme_md,'      ,    [
        (README_MULTIPLE_SAME_HEADING_1),
    ]      ,    )
def 		_UpperCamelCase				(      UpperCamelCase_    :   int						)					->				Any:

      """simple docstring"""



      ReadMe.from_string(UpperCamelCase_      ,    UpperCamelCase_      ,    suppress_parsing_errors=UpperCamelCase_						)







@pytest.mark.parametrize(
    'readme_md, expected_dict'      ,    [
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ]      ,    )
def 		_UpperCamelCase				(      UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   int						)					->				Optional[int]:

      """simple docstring"""



      with tempfile.TemporaryDirectory() as tmp_dir:
            lowerCAmelCase__        =  Path(UpperCamelCase_						) / 'README.md'
            with open(UpperCamelCase_      ,    'w+'						) as readme_file:
                  readme_file.write(UpperCamelCase_						)
            lowerCAmelCase__        =  ReadMe.from_readme(UpperCamelCase_      ,    UpperCamelCase_						).to_dict()
            assert out["name"] == path
            assert out["text"] == ""
            assert out["is_empty_text"]
            assert out["subsections"] == expected_dict["subsections"]







@pytest.mark.parametrize(
    'readme_md, expected_error'      ,    [
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ]      ,    )
def 		_UpperCamelCase				(      UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   Any						)					->				Union[str, Any]:

      """simple docstring"""



      with tempfile.TemporaryDirectory() as tmp_dir:
            lowerCAmelCase__        =  Path(UpperCamelCase_						) / 'README.md'
            with open(UpperCamelCase_      ,    'w+'						) as readme_file:
                  readme_file.write(UpperCamelCase_						)
            lowerCAmelCase__        =  expected_error.format(path=UpperCamelCase_						)
            with pytest.raises(UpperCamelCase_      ,    match=re.escape(UpperCamelCase_						)						):
                  lowerCAmelCase__        =  ReadMe.from_readme(UpperCamelCase_      ,    UpperCamelCase_						)
                  readme.validate()







@pytest.mark.parametrize(
    'readme_md, expected_error'      ,    [
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]      ,    )
def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   Optional[Any]						)					->				Optional[int]:

      """simple docstring"""



      with tempfile.TemporaryDirectory() as tmp_dir:
            lowerCAmelCase__        =  Path(UpperCamelCase_						) / 'README.md'
            with open(UpperCamelCase_      ,    'w+'						) as readme_file:
                  readme_file.write(UpperCamelCase_						)
            lowerCAmelCase__        =  expected_error.format(path=UpperCamelCase_						)
            with pytest.raises(UpperCamelCase_      ,    match=re.escape(UpperCamelCase_						)						):
                  ReadMe.from_readme(UpperCamelCase_      ,    UpperCamelCase_						)







@pytest.mark.parametrize(
    'readme_md,'      ,    [
        (README_MULTIPLE_SAME_HEADING_1),
    ]      ,    )
def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict						)					->				int:

      """simple docstring"""



      with tempfile.TemporaryDirectory() as tmp_dir:
            lowerCAmelCase__        =  Path(UpperCamelCase_						) / 'README.md'
            with open(UpperCamelCase_      ,    'w+'						) as readme_file:
                  readme_file.write(UpperCamelCase_						)
            ReadMe.from_readme(UpperCamelCase_      ,    UpperCamelCase_      ,    suppress_parsing_errors=UpperCamelCase_						)













import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


__snake_case      :      Optional[int]             =       """\
@inproceedings{snover-etal-2006-study,
    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
    author = \"Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John\",
    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",
    month = aug # \" 8-12\",
    year = \"2006\",
    address = \"Cambridge, Massachusetts, USA\",
    publisher = \"Association for Machine Translation in the Americas\",
    url = \"https://aclanthology.org/2006.amta-papers.25\",
    pages = \"223--231\",
}
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

__snake_case      :      List[str]             =       """\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
"""

__snake_case      :      Dict             =       """
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    'score' (float): TER score (num_edits / sum_ref_lengths * 100)
    'num_edits' (int): The cumulative number of edits
    'ref_length' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\",
        ...                     \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}

    Example 2:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}

    Example 3:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}

    Example 4:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}

    Example 5:
        >>> predictions = [\"does this sentence match??\",
        ...                    \"what about this sentence?\",
        ...                    \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,   _KWARGS_DESCRIPTION)
class       __SCREAMING_SNAKE_CASE   (				datasets.Metric):






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												if version.parse(scb.__version__				) < version.parse('1.4.12'				):
																		raise ImportWarning(
																		    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
																		    'You can install it with `pip install "sacrebleu>=1.4.12"`.'				)
												return datasets.MetricInfo(
												    description=_DESCRIPTION       ,  citation=_CITATION       ,  homepage='http://www.cs.umd.edu/~snover/tercom/'       ,  inputs_description=_KWARGS_DESCRIPTION       ,  features=datasets.Features(
												        {
												            'predictions': datasets.Value('string'       ,  id='sequence'				),
												            'references': datasets.Sequence(datasets.Value('string'       ,  id='sequence'				)       ,  id='references'				),
												        }				)       ,  codebase_urls=['https://github.com/mjpost/sacreBLEU#ter']       ,  reference_urls=[
												        'https://github.com/jhclark/tercom',
												    ]       ,  )







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  len(references[0]				)
												if any(len(_UpperCamelCase				) != references_per_prediction for refs in references				):
																		raise ValueError('Sacrebleu requires the same number of references for each prediction'				)
												lowerCAmelCase__        =  [[refs[i] for refs in references] for i in range(_UpperCamelCase				)]

												lowerCAmelCase__        =  TER(
												    normalized=_UpperCamelCase       ,  no_punct=_UpperCamelCase       ,  asian_support=_UpperCamelCase       ,  case_sensitive=_UpperCamelCase       ,  )
												lowerCAmelCase__        =  sb_ter.corpus_score(_UpperCamelCase       ,  _UpperCamelCase				)

												return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}





