
import re
import string

import numpy as np

import datasets


A     :	Union[str, Any]    = '\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'

A     :	int    = '\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'

A     :	List[str]    = '\n'







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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[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		,     _snake_case		,     _snake_case		,     _snake_case=None		,     _snake_case=False		,     _snake_case=False		,     _snake_case=False		,     )    ->      List[Any]:


								'''simple docstring'''




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

								if ignore_case:
												__a										=    np.char.lower(_snake_case				)
												__a										=    np.char.lower(_snake_case				)

								if ignore_punctuation:
												__a										=    string.punctuation.maketrans(''''''		,     ''''''		,     string.punctuation				)
												__a										=    np.char.translate(_snake_case		,     table=_snake_case				)
												__a										=    np.char.translate(_snake_case		,     table=_snake_case				)

								if ignore_numbers:
												__a										=    string.digits.maketrans(''''''		,     ''''''		,     string.digits				)
												__a										=    np.char.translate(_snake_case		,     table=_snake_case				)
												__a										=    np.char.translate(_snake_case		,     table=_snake_case				)

								__a										=    predictions == references

								return {"exact_match": np.mean(_snake_case				) * 100}

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :	str    = {
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Optional[Any]    = ['BlenderbotTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Optional[Any]    = [
					    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'BlenderbotForCausalLM',
					    'BlenderbotForConditionalGeneration',
					    'BlenderbotModel',
					    'BlenderbotPreTrainedModel',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Dict    = [
					    'TFBlenderbotForConditionalGeneration',
					    'TFBlenderbotModel',
					    'TFBlenderbotPreTrainedModel',
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Dict    = [
					    'FlaxBlenderbotForConditionalGeneration',
					    'FlaxBlenderbotModel',
					    'FlaxBlenderbotPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_blenderbot import (
					    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BlenderbotConfig,
					    BlenderbotOnnxConfig,
					)
					from .tokenization_blenderbot import BlenderbotTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blenderbot import (
										    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlenderbotForCausalLM,
										    BlenderbotForConditionalGeneration,
										    BlenderbotModel,
										    BlenderbotPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_blenderbot import (
										    TFBlenderbotForConditionalGeneration,
										    TFBlenderbotModel,
										    TFBlenderbotPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_blenderbot import (
										    FlaxBlenderbotForConditionalGeneration,
										    FlaxBlenderbotModel,
										    FlaxBlenderbotPreTrainedModel,
										)

else:
					import sys

					A     :	List[Any]    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)