






'''simple docstring'''

from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__     =       OrderedDict(
    [
        # Base model mapping
        ('albert', 'FlaxAlbertModel'),
        ('bart', 'FlaxBartModel'),
        ('beit', 'FlaxBeitModel'),
        ('bert', 'FlaxBertModel'),
        ('big_bird', 'FlaxBigBirdModel'),
        ('blenderbot', 'FlaxBlenderbotModel'),
        ('blenderbot-small', 'FlaxBlenderbotSmallModel'),
        ('clip', 'FlaxCLIPModel'),
        ('distilbert', 'FlaxDistilBertModel'),
        ('electra', 'FlaxElectraModel'),
        ('gpt-sw3', 'FlaxGPT2Model'),
        ('gpt2', 'FlaxGPT2Model'),
        ('gpt_neo', 'FlaxGPTNeoModel'),
        ('gptj', 'FlaxGPTJModel'),
        ('longt5', 'FlaxLongT5Model'),
        ('marian', 'FlaxMarianModel'),
        ('mbart', 'FlaxMBartModel'),
        ('mt5', 'FlaxMT5Model'),
        ('opt', 'FlaxOPTModel'),
        ('pegasus', 'FlaxPegasusModel'),
        ('regnet', 'FlaxRegNetModel'),
        ('resnet', 'FlaxResNetModel'),
        ('roberta', 'FlaxRobertaModel'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormModel'),
        ('roformer', 'FlaxRoFormerModel'),
        ('t5', 'FlaxT5Model'),
        ('vision-text-dual-encoder', 'FlaxVisionTextDualEncoderModel'),
        ('vit', 'FlaxViTModel'),
        ('wav2vec2', 'FlaxWav2Vec2Model'),
        ('whisper', 'FlaxWhisperModel'),
        ('xglm', 'FlaxXGLMModel'),
        ('xlm-roberta', 'FlaxXLMRobertaModel'),
    ]
)

SCREAMING_SNAKE_CASE__     =       OrderedDict(
    [
        # Model for pre-training mapping
        ('albert', 'FlaxAlbertForPreTraining'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForPreTraining'),
        ('big_bird', 'FlaxBigBirdForPreTraining'),
        ('electra', 'FlaxElectraForPreTraining'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
        ('wav2vec2', 'FlaxWav2Vec2ForPreTraining'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

SCREAMING_SNAKE_CASE__     =       OrderedDict(
    [
        # Model for Masked LM mapping
        ('albert', 'FlaxAlbertForMaskedLM'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForMaskedLM'),
        ('big_bird', 'FlaxBigBirdForMaskedLM'),
        ('distilbert', 'FlaxDistilBertForMaskedLM'),
        ('electra', 'FlaxElectraForMaskedLM'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

SCREAMING_SNAKE_CASE__     =       OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('blenderbot', 'FlaxBlenderbotForConditionalGeneration'),
        ('blenderbot-small', 'FlaxBlenderbotSmallForConditionalGeneration'),
        ('encoder-decoder', 'FlaxEncoderDecoderModel'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('marian', 'FlaxMarianMTModel'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('pegasus', 'FlaxPegasusForConditionalGeneration'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
    ]
)

SCREAMING_SNAKE_CASE__     =       OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

SCREAMING_SNAKE_CASE__     =       OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

SCREAMING_SNAKE_CASE__     =       OrderedDict(
    [
        # Model for Causal LM mapping
        ('bart', 'FlaxBartForCausalLM'),
        ('bert', 'FlaxBertForCausalLM'),
        ('big_bird', 'FlaxBigBirdForCausalLM'),
        ('electra', 'FlaxElectraForCausalLM'),
        ('gpt-sw3', 'FlaxGPT2LMHeadModel'),
        ('gpt2', 'FlaxGPT2LMHeadModel'),
        ('gpt_neo', 'FlaxGPTNeoForCausalLM'),
        ('gptj', 'FlaxGPTJForCausalLM'),
        ('opt', 'FlaxOPTForCausalLM'),
        ('roberta', 'FlaxRobertaForCausalLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForCausalLM'),
        ('xglm', 'FlaxXGLMForCausalLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForCausalLM'),
    ]
)

SCREAMING_SNAKE_CASE__     =       OrderedDict(
    [
        # Model for Sequence Classification mapping
        ('albert', 'FlaxAlbertForSequenceClassification'),
        ('bart', 'FlaxBartForSequenceClassification'),
        ('bert', 'FlaxBertForSequenceClassification'),
        ('big_bird', 'FlaxBigBirdForSequenceClassification'),
        ('distilbert', 'FlaxDistilBertForSequenceClassification'),
        ('electra', 'FlaxElectraForSequenceClassification'),
        ('mbart', 'FlaxMBartForSequenceClassification'),
        ('roberta', 'FlaxRobertaForSequenceClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForSequenceClassification'),
        ('roformer', 'FlaxRoFormerForSequenceClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForSequenceClassification'),
    ]
)

SCREAMING_SNAKE_CASE__     =       OrderedDict(
    [
        # Model for Question Answering mapping
        ('albert', 'FlaxAlbertForQuestionAnswering'),
        ('bart', 'FlaxBartForQuestionAnswering'),
        ('bert', 'FlaxBertForQuestionAnswering'),
        ('big_bird', 'FlaxBigBirdForQuestionAnswering'),
        ('distilbert', 'FlaxDistilBertForQuestionAnswering'),
        ('electra', 'FlaxElectraForQuestionAnswering'),
        ('mbart', 'FlaxMBartForQuestionAnswering'),
        ('roberta', 'FlaxRobertaForQuestionAnswering'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForQuestionAnswering'),
        ('roformer', 'FlaxRoFormerForQuestionAnswering'),
        ('xlm-roberta', 'FlaxXLMRobertaForQuestionAnswering'),
    ]
)

SCREAMING_SNAKE_CASE__     =       OrderedDict(
    [
        # Model for Token Classification mapping
        ('albert', 'FlaxAlbertForTokenClassification'),
        ('bert', 'FlaxBertForTokenClassification'),
        ('big_bird', 'FlaxBigBirdForTokenClassification'),
        ('distilbert', 'FlaxDistilBertForTokenClassification'),
        ('electra', 'FlaxElectraForTokenClassification'),
        ('roberta', 'FlaxRobertaForTokenClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForTokenClassification'),
        ('roformer', 'FlaxRoFormerForTokenClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForTokenClassification'),
    ]
)

SCREAMING_SNAKE_CASE__     =       OrderedDict(
    [
        # Model for Multiple Choice mapping
        ('albert', 'FlaxAlbertForMultipleChoice'),
        ('bert', 'FlaxBertForMultipleChoice'),
        ('big_bird', 'FlaxBigBirdForMultipleChoice'),
        ('distilbert', 'FlaxDistilBertForMultipleChoice'),
        ('electra', 'FlaxElectraForMultipleChoice'),
        ('roberta', 'FlaxRobertaForMultipleChoice'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMultipleChoice'),
        ('roformer', 'FlaxRoFormerForMultipleChoice'),
        ('xlm-roberta', 'FlaxXLMRobertaForMultipleChoice'),
    ]
)

SCREAMING_SNAKE_CASE__     =       OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

SCREAMING_SNAKE_CASE__     =       OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

SCREAMING_SNAKE_CASE__     =       OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

SCREAMING_SNAKE_CASE__     =       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
SCREAMING_SNAKE_CASE__     =       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
SCREAMING_SNAKE_CASE__     =       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE__     =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE__     =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE__     =       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
SCREAMING_SNAKE_CASE__     =       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE__     =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE__     =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE__     =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE__     =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE__     =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE__     =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE__     =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)






class     a_						(      _BaseAutoModelClass    ):
  lowercase							=				FLAX_MODEL_MAPPING


SCREAMING_SNAKE_CASE__     =       auto_class_update(FlaxAutoModel)






class     a_						(      _BaseAutoModelClass    ):
  lowercase							=				FLAX_MODEL_FOR_PRETRAINING_MAPPING


SCREAMING_SNAKE_CASE__     =       auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')






class     a_						(      _BaseAutoModelClass    ):
  lowercase							=				FLAX_MODEL_FOR_CAUSAL_LM_MAPPING


SCREAMING_SNAKE_CASE__     =       auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')






class     a_						(      _BaseAutoModelClass    ):
  lowercase							=				FLAX_MODEL_FOR_MASKED_LM_MAPPING


SCREAMING_SNAKE_CASE__     =       auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')






class     a_						(      _BaseAutoModelClass    ):
  lowercase							=				FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


SCREAMING_SNAKE_CASE__     =       auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)






class     a_						(      _BaseAutoModelClass    ):
  lowercase							=				FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


SCREAMING_SNAKE_CASE__     =       auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)






class     a_						(      _BaseAutoModelClass    ):
  lowercase							=				FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING


SCREAMING_SNAKE_CASE__     =       auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')






class     a_						(      _BaseAutoModelClass    ):
  lowercase							=				FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING


SCREAMING_SNAKE_CASE__     =       auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)






class     a_						(      _BaseAutoModelClass    ):
  lowercase							=				FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING


SCREAMING_SNAKE_CASE__     =       auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')






class     a_						(      _BaseAutoModelClass    ):
  lowercase							=				FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING


SCREAMING_SNAKE_CASE__     =       auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)






class     a_						(      _BaseAutoModelClass    ):
  lowercase							=				FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


SCREAMING_SNAKE_CASE__     =       auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)






class     a_						(      _BaseAutoModelClass    ):
  lowercase							=				FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING


SCREAMING_SNAKE_CASE__     =       auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')






class     a_						(      _BaseAutoModelClass    ):
  lowercase							=				FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING


SCREAMING_SNAKE_CASE__     =       auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)








'''simple docstring'''

import re
import string

import numpy as np

import datasets


SCREAMING_SNAKE_CASE__     =       '\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'

SCREAMING_SNAKE_CASE__     =       '\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'

SCREAMING_SNAKE_CASE__     =       '\n'






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

  def   A__						(				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   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=False      ,		)  ->				List[Any]:



        """simple docstring"""






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

        if ignore_case:
              UpperCamelCase      = np.char.lower(_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = np.char.lower(_SCREAMING_SNAKE_CASE				)

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

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

        UpperCamelCase      = predictions == references

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