



import sacrebleu as scb
from packaging import version

import datasets


UpperCamelCase__			=    "\\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

UpperCamelCase__			=    "\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n"

UpperCamelCase__			=    "\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:\n        - `'none'`: no smoothing\n        - `'floor'`: increment zero counts\n        - `'add-k'`: increment num/denom by k for n>1\n        - `'exp'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:\n        - `'none'`: No tokenization.\n        - `'zh'`: Chinese tokenization.\n        - `'13a'`: mimics the `mteval-v13a` script from Moses.\n        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `'char'`: Language-agnostic character-level tokenization.\n        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    'score': BLEU score,\n    'counts': Counts,\n    'totals': Totals,\n    'precisions': Precisions,\n    'bp': Brevity penalty,\n    'sys_len': predictions length,\n    'ref_len': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = [\"hello there general kenobi\",\n        ...                 \"on our way to ankh morpork\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        39.8\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,       _KWARGS_DESCRIPTION      )
class       _lowerCAmelCase (			datasets.Metric      ):






			"""simple docstring"""





			def 		snake_case__      (					self						:							Dict )    ->       str:
									"""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="""https://github.com/mjpost/sacreBLEU"""      ,  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"""]      ,  reference_urls=[
									        """https://github.com/mjpost/sacreBLEU""",
									        """https://en.wikipedia.org/wiki/BLEU""",
									        """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
									    ]      ,  )







			def 		snake_case__      (					self						:							Tuple      ,  lowercase						:							Any      ,  lowercase						:							Optional[Any]      ,  lowercase						:							List[Any]="exp"      ,  lowercase						:							Union[str, Any]=None      ,  lowercase						:							Any=False      ,  lowercase						:							Optional[int]=False      ,  lowercase						:							Dict=None      ,  lowercase						:							Tuple=False      ,  )    ->       Tuple:
									"""simple docstring"""







									__lowercase											=					len(references[0] )
									if any(len(lowercase ) != references_per_prediction for refs in references ):
															raise ValueError("""Sacrebleu requires the same number of references for each prediction""" )
									__lowercase											=					[[refs[i] for refs in references] for i in range(lowercase )]
									__lowercase											=					scb.corpus_bleu(
									    lowercase      ,  lowercase      ,  smooth_method=lowercase      ,  smooth_value=lowercase      ,  force=lowercase      ,  lowercase=lowercase      ,  use_effective_order=lowercase      ,  **({"""tokenize""": tokenize} if tokenize else {})      ,  )
									__lowercase											=					{
									    """score""": output.score,
									    """counts""": output.counts,
									    """totals""": output.totals,
									    """precisions""": output.precisions,
									    """bp""": output.bp,
									    """sys_len""": output.sys_len,
									    """ref_len""": output.ref_len,
									}
									return output_dict










import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
UpperCamelCase__			=    "src/transformers"

# Matches is_xxx_available()
UpperCamelCase__			=    re.compile(R"is\_([a-z_]*)_available()")
# Matches from xxx import bla
UpperCamelCase__			=    re.compile(R"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
UpperCamelCase__			=    re.compile(R"^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)")


UpperCamelCase__			=    "\n{0} = None\n"

UpperCamelCase__			=    "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


UpperCamelCase__			=    "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"






def 			UpperCAmelCase__     (     lowercase__ )			->   List[Any]:
						if _re_test_backend.search(lowercase__ ) is None:
												return None
						__lowercase											=					[b[0] for b in _re_backend.findall(lowercase__ )]
						backends.sort()
						return "_and_".join(lowercase__ )






def 			UpperCAmelCase__     (     )			->   Dict:
						with open(os.path.join(lowercase__    ,							"""__init__.py""" )    ,							"""r"""    ,							encoding="""utf-8"""    ,							newline="""\n""" ) as f:
												__lowercase											=					f.readlines()

						# Get to the point we do the actual imports for type checking
						__lowercase											=					0
						while not lines[line_index].startswith("""if TYPE_CHECKING""" ):
												line_index += 1

						__lowercase											=					{}
						# Go through the end of the file
						while line_index < len(lowercase__ ):
												# If the line is an if is_backend_available, we grab all objects associated.
												__lowercase											=					find_backend(lines[line_index] )
												if backend is not None:
																		while not lines[line_index].startswith("""    else:""" ):
																								line_index += 1
																		line_index += 1

																		__lowercase											=					[]
																		# Until we unindent, add backend objects to the list
																		while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 8 ):
																								__lowercase											=					lines[line_index]
																								__lowercase											=					_re_single_line_import.search(lowercase__ )
																								if single_line_import_search is not None:
																														objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
																								elif line.startswith(""" """ * 12 ):
																														objects.append(line[12:-2] )
																								line_index += 1

																		__lowercase											=					objects
												else:
																		line_index += 1

						return backend_specific_objects






def 			UpperCAmelCase__     (     lowercase__    ,							lowercase__ )			->   int:
						if name.isupper():
												return DUMMY_CONSTANT.format(lowercase__ )
						elif name.islower():
												return DUMMY_FUNCTION.format(lowercase__    ,							lowercase__ )
						else:
												return DUMMY_CLASS.format(lowercase__    ,							lowercase__ )






def 			UpperCAmelCase__     (     lowercase__=None )			->   List[str]:
						if backend_specific_objects is None:
												__lowercase											=					read_init()
						# For special correspondence backend to module name as used in the function requires_modulename
						__lowercase											=					{}

						for backend, objects in backend_specific_objects.items():
												__lowercase											=					"""[""" + """, """.join(F"\"{b}\"" for b in backend.split("""_and_""" ) ) + """]"""
												__lowercase											=					"""# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
												dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
												dummy_file += "\n".join([create_dummy_object(lowercase__    ,							lowercase__ ) for o in objects] )
												__lowercase											=					dummy_file

						return dummy_files






def 			UpperCAmelCase__     (     lowercase__=False )			->   Optional[Any]:
						__lowercase											=					create_dummy_files()
						# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
						__lowercase											=					{"""torch""": """pt"""}

						# Locate actual dummy modules and read their content.
						__lowercase											=					os.path.join(lowercase__    ,							"""utils""" )
						__lowercase											=					{
						    backend: os.path.join(lowercase__    ,							F"dummy_{short_names.get(lowercase__    ,							lowercase__ )}_objects.py" )
						    for backend in dummy_files.keys()
						}

						__lowercase											=					{}
						for backend, file_path in dummy_file_paths.items():
												if os.path.isfile(lowercase__ ):
																		with open(lowercase__    ,							"""r"""    ,							encoding="""utf-8"""    ,							newline="""\n""" ) as f:
																								__lowercase											=					f.read()
												else:
																		__lowercase											=					""""""

						for backend in dummy_files.keys():
												if dummy_files[backend] != actual_dummies[backend]:
																		if overwrite:
																								print(
																								    F"Updating transformers.utils.dummy_{short_names.get(lowercase__    ,							lowercase__ )}_objects.py as the main "
																								    """__init__ has new objects.""" )
																								with open(dummy_file_paths[backend]    ,							"""w"""    ,							encoding="""utf-8"""    ,							newline="""\n""" ) as f:
																														f.write(dummy_files[backend] )
																		else:
																								raise ValueError(
																								    """The main __init__ has objects that are not present in """
																								    F"transformers.utils.dummy_{short_names.get(lowercase__    ,							lowercase__ )}_objects.py. Run `make fix-copies` "
																								    """to fix this.""" )


if __name__ == "__main__":
			UpperCamelCase__			=    argparse.ArgumentParser()
			parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
			UpperCamelCase__			=    parser.parse_args()

			check_dummies(args.fix_and_overwrite)





