
from typing import TYPE_CHECKING

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


__lowerCamelCase							=       {
    """configuration_xlm_roberta""": [
        """XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaConfig""",
        """XLMRobertaOnnxConfig""",
    ],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""XLMRobertaTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""XLMRobertaTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """XLMRobertaForCausalLM""",
       """XLMRobertaForMaskedLM""",
       """XLMRobertaForMultipleChoice""",
       """XLMRobertaForQuestionAnswering""",
       """XLMRobertaForSequenceClassification""",
       """XLMRobertaForTokenClassification""",
       """XLMRobertaModel""",
       """XLMRobertaPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFXLMRobertaForCausalLM""",
       """TFXLMRobertaForMaskedLM""",
       """TFXLMRobertaForMultipleChoice""",
       """TFXLMRobertaForQuestionAnswering""",
       """TFXLMRobertaForSequenceClassification""",
       """TFXLMRobertaForTokenClassification""",
       """TFXLMRobertaModel""",
       """TFXLMRobertaPreTrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """FlaxXLMRobertaForMaskedLM""",
       """FlaxXLMRobertaForCausalLM""",
       """FlaxXLMRobertaForMultipleChoice""",
       """FlaxXLMRobertaForQuestionAnswering""",
       """FlaxXLMRobertaForSequenceClassification""",
       """FlaxXLMRobertaForTokenClassification""",
       """FlaxXLMRobertaModel""",
       """FlaxXLMRobertaPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_xlm_roberta import (
       XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
       XLMRobertaConfig,
       XLMRobertaOnnxConfig,
   )

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlm_roberta import XLMRobertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm_roberta import (
          XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMRobertaForCausalLM,
          XLMRobertaForMaskedLM,
          XLMRobertaForMultipleChoice,
          XLMRobertaForQuestionAnswering,
          XLMRobertaForSequenceClassification,
          XLMRobertaForTokenClassification,
          XLMRobertaModel,
          XLMRobertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlm_roberta import (
          TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMRobertaForCausalLM,
          TFXLMRobertaForMaskedLM,
          TFXLMRobertaForMultipleChoice,
          TFXLMRobertaForQuestionAnswering,
          TFXLMRobertaForSequenceClassification,
          TFXLMRobertaForTokenClassification,
          TFXLMRobertaModel,
          TFXLMRobertaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_xlm_roberta import (
          FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaxXLMRobertaForCausalLM,
          FlaxXLMRobertaForMaskedLM,
          FlaxXLMRobertaForMultipleChoice,
          FlaxXLMRobertaForQuestionAnswering,
          FlaxXLMRobertaForSequenceClassification,
          FlaxXLMRobertaForTokenClassification,
          FlaxXLMRobertaModel,
          FlaxXLMRobertaPreTrainedModel,
      )

else:
   import sys

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







import pytest

from datasets import inspect_metric, list_metrics, load_metric


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]    ):
       monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"				,					set()    )


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : str    ):






       class   UpperCAmelCase		:



          def __init__(self		:					Optional[int]    ,						snake_case__		:					str )		->	Any:


                 '''simple docstring'''


                 snake_case       :   List[str]							=      metric_id






       class   UpperCAmelCase		:
          A__					:		List[str]						     =     [MetricMock(A_ ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]



          def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	List[str]:


                 '''simple docstring'''


                 return self._metrics



       monkeypatch.setattr("datasets.inspect.huggingface_hub"				,					HfhMock()    )




@pytest.mark.parametrize(
    "func, args"				,					[(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))]    )
def 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]				,					__lowerCamelCase     : Any				,					__lowerCamelCase     : Any				,					__lowerCamelCase     : int				,					__lowerCamelCase     : Any    ):
       if "tmp_path" in args:
              snake_case       :   str							=      tuple(arg if arg != "tmp_path" else tmp_path for arg in args    )
       with pytest.warns(__lowerCamelCase				,					match="https://huggingface.co/docs/evaluate"    ):
              func(*__lowerCamelCase    )





