



'''simple docstring'''





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,
)


_UpperCamelCase											=						{
    'configuration_xlm_roberta': [
        'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaConfig',
        'XLMRobertaOnnxConfig',
    ],
}

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

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						[
       '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:
   _UpperCamelCase											=						[
       '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:
   _UpperCamelCase											=						[
       '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

   _UpperCamelCase											=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











'''simple docstring'''





import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version

def        a_     (							*_lowerCAmelCase       ,_lowerCAmelCase = None       ,_lowerCAmelCase=True       ,_lowerCAmelCase=2       )      ->      List[str]:
  from .. import __version__

  __lowerCamelCase       :       Any           = take_from
  __lowerCamelCase       :       Optional[int]           = ()
  if not isinstance(args[0]       ,_lowerCAmelCase       ):
    __lowerCamelCase       :       Optional[Any]           = (args,)

  for attribute, version_name, message in args:
    if version.parse(version.parse(_lowerCAmelCase       ).base_version       ) >= version.parse(_lowerCAmelCase       ):
      raise ValueError(
          F'The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''
          F' version {__version__} is >= {version_name}'       )

    __lowerCamelCase       :       Union[str, Any]           = None
    if isinstance(_lowerCAmelCase       ,_lowerCAmelCase       ) and attribute in deprecated_kwargs:
      values += (deprecated_kwargs.pop(_lowerCAmelCase       ),)
      __lowerCamelCase       :       Optional[Any]           = F'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
    elif hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ):
      values += (getattr(_lowerCAmelCase       ,_lowerCAmelCase       ),)
      __lowerCamelCase       :       List[str]           = F'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
    elif deprecated_kwargs is None:
      __lowerCamelCase       :       Optional[Any]           = F'`{attribute}` is deprecated and will be removed in version {version_name}.'

    if warning is not None:
      __lowerCamelCase       :       Optional[int]           = warning + ' ' if standard_warn else ''
      warnings.warn(warning + message       ,_lowerCAmelCase       ,stacklevel=_lowerCAmelCase       )

  if isinstance(_lowerCAmelCase       ,_lowerCAmelCase       ) and len(_lowerCAmelCase       ) > 0:
    __lowerCamelCase       :       Optional[Any]           = inspect.getouterframes(inspect.currentframe()       )[1]
    __lowerCamelCase       :       List[str]           = call_frame.filename
    __lowerCamelCase       :       int           = call_frame.lineno
    __lowerCamelCase       :       Union[str, Any]           = call_frame.function
    __lowerCamelCase       ,__lowerCamelCase       :       Union[str, Any]           = next(iter(deprecated_kwargs.items()       )       )
    raise TypeError(F'{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'       )

  if len(_lowerCAmelCase       ) == 0:
    return
  elif len(_lowerCAmelCase       ) == 1:
    return values[0]
  return values






