






'''simple docstring'''

import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
     import jax

     from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
     from transformers.models.bert.modeling_flax_bert import FlaxBertModel
     from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel






@require_flax
class     a_						(      unittest.TestCase    ):

  @slow
  def   A__						(				self				)  ->				Optional[int]:



        """simple docstring"""






        for model_name in ["bert-base-cased", "bert-large-uncased"]:
              with self.subTest(_SCREAMING_SNAKE_CASE				):
                    UpperCamelCase      = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
                    self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)
                    self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

                    UpperCamelCase      = FlaxAutoModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
                    self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)
                    self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

  @slow
  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        for model_name in ["roberta-base", "roberta-large"]:
              with self.subTest(_SCREAMING_SNAKE_CASE				):
                    UpperCamelCase      = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
                    self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)
                    self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

                    UpperCamelCase      = FlaxAutoModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
                    self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)
                    self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

  @slow
  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        for model_name in ["bert-base-cased", "bert-large-uncased"]:
              UpperCamelCase      = AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = tokenizer("""Do you support jax jitted function?"""      ,		return_tensors=TensorType.JAX				)

              @jax.jit
              def eval(**_SCREAMING_SNAKE_CASE				):
                    return model(**_SCREAMING_SNAKE_CASE				)

              eval(**_SCREAMING_SNAKE_CASE				).block_until_ready()

  @slow
  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        for model_name in ["roberta-base", "roberta-large"]:
              UpperCamelCase      = AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = FlaxRobertaModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = tokenizer("""Do you support jax jitted function?"""      ,		return_tensors=TensorType.JAX				)

              @jax.jit
              def eval(**_SCREAMING_SNAKE_CASE				):
                    return model(**_SCREAMING_SNAKE_CASE				)

              eval(**_SCREAMING_SNAKE_CASE				).block_until_ready()

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        with self.assertRaisesRegex(
            _SCREAMING_SNAKE_CASE      ,		"""bert-base is not a local folder and is not a valid model identifier"""				):
              UpperCamelCase      = FlaxAutoModel.from_pretrained("""bert-base"""				)

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        with self.assertRaisesRegex(
            _SCREAMING_SNAKE_CASE      ,		R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""				):
              UpperCamelCase      = FlaxAutoModel.from_pretrained(_SCREAMING_SNAKE_CASE      ,		revision="""aaaaaa"""				)

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        with self.assertRaisesRegex(
            _SCREAMING_SNAKE_CASE      ,		"""hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack"""      ,		):
              UpperCamelCase      = FlaxAutoModel.from_pretrained("""hf-internal-testing/config-no-model"""				)


  def   A__						(				self				)  ->				int:



        """simple docstring"""






        with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE      ,		"""Use `from_pt=True` to load this model"""				):
              UpperCamelCase      = FlaxAutoModel.from_pretrained("""hf-internal-testing/tiny-bert-pt-only"""				)








'''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}
