import os
import re
import shutil
import sys
import tempfile
import unittest

import black


SCREAMING_SNAKE_CASE		:     Optional[int]         =	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
SCREAMING_SNAKE_CASE		:     Dict         =	"    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"

class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self						)				->				List[str]:
												UpperCamelCase_		:							Any  =		tempfile.mkdtemp()
												os.makedirs(os.path.join(self.transformer_dir			,     """models/bert/"""						)						)
												UpperCamelCase_		:							List[Any]  =		self.transformer_dir
												shutil.copy(
												    os.path.join(__UpperCamelCase			,     """src/transformers/models/bert/modeling_bert.py"""						)			,     os.path.join(self.transformer_dir			,     """models/bert/modeling_bert.py"""						)			,     )





						def        A_							(self						)				->				int:
												UpperCamelCase_		:							Optional[Any]  =		"""src/transformers"""
												shutil.rmtree(self.transformer_dir						)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase=None						)				->				str:
												UpperCamelCase_		:							Any  =		comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
												if overwrite_result is not None:
																		UpperCamelCase_		:							Tuple  =		comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
												UpperCamelCase_		:							List[str]  =		black.Mode(target_versions={black.TargetVersion.PYaa}			,     line_length=119						)
												UpperCamelCase_		:							str  =		black.format_str(__UpperCamelCase			,     mode=__UpperCamelCase						)
												UpperCamelCase_		:							Optional[int]  =		os.path.join(self.transformer_dir			,     """new_code.py"""						)
												with open(__UpperCamelCase			,     """w"""			,     newline="""\n"""						) as f:
																		f.write(__UpperCamelCase						)
												if overwrite_result is None:
																		self.assertTrue(len(check_copies.is_copy_consistent(__UpperCamelCase						)						) == 0						)
												else:
																		check_copies.is_copy_consistent(f.name			,     overwrite=__UpperCamelCase						)
																		with open(__UpperCamelCase			,     """r"""						) as f:
																								self.assertTrue(f.read()			,     __UpperCamelCase						)





						def        A_							(self						)				->				int:
												UpperCamelCase_		:							List[str]  =		check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""						)
												self.assertEqual(__UpperCamelCase			,     __UpperCamelCase						)





						def        A_							(self						)				->				int:
												# Base copy consistency
												self.check_copy_consistency(
												    """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"""			,     """BertLMPredictionHead"""			,     REFERENCE_CODE + """\n"""			,     )

												# With no empty line at the end
												self.check_copy_consistency(
												    """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"""			,     """BertLMPredictionHead"""			,     __UpperCamelCase			,     )

												# Copy consistency with rename
												self.check_copy_consistency(
												    """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"""			,     """TestModelLMPredictionHead"""			,     re.sub("""Bert"""			,     """TestModel"""			,     __UpperCamelCase						)			,     )

												# Copy consistency with a really long name
												UpperCamelCase_		:							Optional[int]  =		"""TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"""
												self.check_copy_consistency(
												    f'''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'''			,     f'''{long_class_name}LMPredictionHead'''			,     re.sub("""Bert"""			,     __UpperCamelCase			,     __UpperCamelCase						)			,     )

												# Copy consistency with overwrite
												self.check_copy_consistency(
												    """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"""			,     """TestModelLMPredictionHead"""			,     __UpperCamelCase			,     overwrite_result=re.sub("""Bert"""			,     """TestModel"""			,     __UpperCamelCase						)			,     )





						def        A_							(self						)				->				List[Any]:
												UpperCamelCase_		:							Dict  =		check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

												UpperCamelCase_		:							int  =		(
												    """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"""
												    """ Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"""
												    """ Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"""
												    """ Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."""
												    """ **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"""
												    """ released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"""
												    """ lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"""
												    """ method has been applied to compress GPT2 into"""
												    """ [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"""
												    """ [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"""
												    """ Multilingual BERT into"""
												    """ [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"""
												    """ version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"""
												    """ (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"""
												    """ as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"""
												    """ Luong, Quoc V. Le, Christopher D. Manning."""
												)
												UpperCamelCase_		:							Optional[Any]  =		(
												    """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
												    """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
												    """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
												    """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
												)
												UpperCamelCase_		:							Any  =		(
												    """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
												    """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
												    """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
												    """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."""
												    """ **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"""
												    """ [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"""
												    """ lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"""
												    """ method has been applied to compress GPT2 into"""
												    """ [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"""
												    """ [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"""
												    """ Multilingual BERT into"""
												    """ [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"""
												    """ version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"""
												    """ Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"""
												    """ than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"""
												    """ Christopher D. Manning 发布。\n"""
												)

												UpperCamelCase_,UpperCamelCase_		:							Any  =		check_copies.convert_to_localized_md(
												    __UpperCamelCase			,     __UpperCamelCase			,     localized_readme["""format_model_list"""]						)

												self.assertFalse(__UpperCamelCase						)
												self.assertEqual(__UpperCamelCase			,     __UpperCamelCase						)

												UpperCamelCase_,UpperCamelCase_		:							Union[str, Any]  =		check_copies.convert_to_localized_md(
												    __UpperCamelCase			,     __UpperCamelCase			,     localized_readme["""format_model_list"""]						)

												# Check whether the number of models is equal to README.md after conversion.
												self.assertTrue(__UpperCamelCase						)

												UpperCamelCase_		:							int  =		(
												    """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"""
												    """ Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"""
												    """ Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"""
												    """ Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."""
												)
												UpperCamelCase_		:							int  =		(
												    """1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"""
												    """ the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
												    """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
												    """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
												)
												UpperCamelCase_		:							List[Any]  =		(
												    """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
												    """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
												    """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
												    """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
												)

												UpperCamelCase_,UpperCamelCase_		:							Tuple  =		check_copies.convert_to_localized_md(
												    __UpperCamelCase			,     __UpperCamelCase			,     localized_readme["""format_model_list"""]						)

												# Check if the model link is synchronized.
												self.assertEqual(__UpperCamelCase			,     __UpperCamelCase						)







import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


SCREAMING_SNAKE_CASE		:     Tuple         =	"\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\n}\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"

SCREAMING_SNAKE_CASE		:     Tuple         =	"\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

SCREAMING_SNAKE_CASE		:     Optional[int]         =	"\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"

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





						def        A_							(self						)				->				Dict:
												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#chrf--chrf"""			,     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#chrf--chrf"""]			,     reference_urls=[
												        """https://github.com/m-popovic/chrF""",
												    ]			,     )





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase = CHRF.CHAR_ORDER			,     __UpperCamelCase = CHRF.WORD_ORDER			,     __UpperCamelCase = CHRF.BETA			,     __UpperCamelCase = False			,     __UpperCamelCase = False			,     __UpperCamelCase = False			,     )				->				List[Any]:
												UpperCamelCase_		:							List[str]  =		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"""						)
												UpperCamelCase_		:							Optional[Any]  =		[[refs[i] for refs in references] for i in range(__UpperCamelCase						)]

												UpperCamelCase_		:							List[Any]  =		CHRF(__UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)
												UpperCamelCase_		:							int  =		sb_chrf.corpus_score(__UpperCamelCase			,     __UpperCamelCase						)

												return {
												    "score": output.score,
												    "char_order": output.char_order,
												    "word_order": output.word_order,
												    "beta": output.beta,
												}






