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

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

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase   :						Optional[int]      =    [
           "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   :						Optional[Any]      =    [
           "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   :						str      =    [
           "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   :						int      =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__    					=      {
    '''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VivitConfig'''],
}
try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''VivitImageProcessor''']


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''VivitModel''',
      '''VivitPreTrainedModel''',
      '''VivitForVideoClassification''',
  ]


if TYPE_CHECKING:
  from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_vivit import VivitImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vivit import (
        VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        VivitForVideoClassification,
        VivitModel,
        VivitPreTrainedModel,
    )


else:
  import sys

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