



import re
import string

import numpy as np

import datasets


__lowerCamelCase     :							Optional[int]											=				"\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"

__lowerCamelCase     :							Tuple											=				"\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    33.3\n\n"

__lowerCamelCase     :							str											=				"\n"





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






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Union[str, Any]:
								'''simple docstring'''







								return datasets.MetricInfo(
								    description=_DESCRIPTION				,		citation=_CITATION				,		inputs_description=_KWARGS_DESCRIPTION				,		features=datasets.Features(
								        {
								            "predictions": datasets.Value("string"				,		id="sequence"      ),
								            "references": datasets.Value("string"				,		id="sequence"      ),
								        }      )				,		reference_urls=[]				,		)







		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Optional[Any]=None				,		UpperCamelCase__      :     str=False				,		UpperCamelCase__      :     Optional[int]=False				,		UpperCamelCase__      :     Optional[Any]=False				,		)				->					Any:
								'''simple docstring'''







								if regexes_to_ignore is not None:
														for s in regexes_to_ignore:
																				UpperCAmelCase         =     np.array([re.sub(_lowercase				,		""				,		_lowercase      ) for x in predictions]      )
																				UpperCAmelCase         =     np.array([re.sub(_lowercase				,		""				,		_lowercase      ) for x in references]      )
								else:
														UpperCAmelCase         =     np.asarray(_lowercase      )
														UpperCAmelCase         =     np.asarray(_lowercase      )

								if ignore_case:
														UpperCAmelCase         =     np.char.lower(_lowercase      )
														UpperCAmelCase         =     np.char.lower(_lowercase      )

								if ignore_punctuation:
														UpperCAmelCase         =     string.punctuation.maketrans(""				,		""				,		string.punctuation      )
														UpperCAmelCase         =     np.char.translate(_lowercase				,		table=_lowercase      )
														UpperCAmelCase         =     np.char.translate(_lowercase				,		table=_lowercase      )

								if ignore_numbers:
														UpperCAmelCase         =     string.digits.maketrans(""				,		""				,		string.digits      )
														UpperCAmelCase         =     np.char.translate(_lowercase				,		table=_lowercase      )
														UpperCAmelCase         =     np.char.translate(_lowercase				,		table=_lowercase      )

								UpperCAmelCase         =     predictions == references

								return {"exact_match": np.mean(_lowercase      ) * 1_00}








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCamelCase     :							Optional[int]  							=				{
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase     :							List[Any]  							=				[
		    "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RoCBertForCausalLM",
		    "RoCBertForMaskedLM",
		    "RoCBertForMultipleChoice",
		    "RoCBertForPreTraining",
		    "RoCBertForQuestionAnswering",
		    "RoCBertForSequenceClassification",
		    "RoCBertForTokenClassification",
		    "RoCBertLayer",
		    "RoCBertModel",
		    "RoCBertPreTrainedModel",
		    "load_tf_weights_in_roc_bert",
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		__lowerCamelCase     :							Optional[int]  							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



