




import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_A						)
class 	_UpperCamelCase					( _A						):
      UpperCAmelCase_						=						field(default="""text-classification"""		,				metadata={"""include_in_asdict_even_if_is_default""": True}						)
      UpperCAmelCase_						=						Features({"""text""": Value("""string"""						)}						)
      UpperCAmelCase_						=						Features({"""labels""": ClassLabel}						)
      UpperCAmelCase_						=						"""text"""
      UpperCAmelCase_						=						"""labels"""
      def 		UpperCAmelCase_  (	self      :Tuple       ,					lowerCamelCase      :Dict					)   ->	Dict:






       if self.label_column not in features:
        raise ValueError(f'''Column {self.label_column} is not present in features.'''					)
       if not isinstance(features[self.label_column]       ,					UpperCamelCase__					):
        raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''					)
       UpperCAmelCase__							       =  copy.deepcopy(self					)
       UpperCAmelCase__							       =  self.label_schema.copy()
       UpperCAmelCase__							       =  features[self.label_column]
       UpperCAmelCase__							       =  label_schema
       return task_template







      @property
      def 		UpperCAmelCase_  (	self      :Optional[int]					)   ->	Dict[str, str]:
       return {
           self.text_column: "text",
           self.label_column: "labels",
       }







from typing import TYPE_CHECKING

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


__lowerCAmelCase			:     List[str]           =   {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     str           =   [
        'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'XLMForMultipleChoice',
        'XLMForQuestionAnswering',
        'XLMForQuestionAnsweringSimple',
        'XLMForSequenceClassification',
        'XLMForTokenClassification',
        'XLMModel',
        'XLMPreTrainedModel',
        'XLMWithLMHeadModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Dict           =   [
        'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFXLMForMultipleChoice',
        'TFXLMForQuestionAnsweringSimple',
        'TFXLMForSequenceClassification',
        'TFXLMForTokenClassification',
        'TFXLMMainLayer',
        'TFXLMModel',
        'TFXLMPreTrainedModel',
        'TFXLMWithLMHeadModel',
    ]


if TYPE_CHECKING:
    from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
    from .tokenization_xlm import XLMTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xlm import (
            XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            XLMForMultipleChoice,
            XLMForQuestionAnswering,
            XLMForQuestionAnsweringSimple,
            XLMForSequenceClassification,
            XLMForTokenClassification,
            XLMModel,
            XLMPreTrainedModel,
            XLMWithLMHeadModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_xlm import (
            TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFXLMForMultipleChoice,
            TFXLMForQuestionAnsweringSimple,
            TFXLMForSequenceClassification,
            TFXLMForTokenClassification,
            TFXLMMainLayer,
            TFXLMModel,
            TFXLMPreTrainedModel,
            TFXLMWithLMHeadModel,
        )

else:
    import sys

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

