






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase      :      Any     						=						{
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCamelCase      :      str     						=						['PerceiverFeatureExtractor']
      __UpperCamelCase      :      Dict     						=						['PerceiverImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCamelCase      :      Optional[Any]     						=						[
          'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'PerceiverForImageClassificationConvProcessing',
          'PerceiverForImageClassificationFourier',
          'PerceiverForImageClassificationLearned',
          'PerceiverForMaskedLM',
          'PerceiverForMultimodalAutoencoding',
          'PerceiverForOpticalFlow',
          'PerceiverForSequenceClassification',
          'PerceiverLayer',
          'PerceiverModel',
          'PerceiverPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
      from .tokenization_perceiver import PerceiverTokenizer

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_perceiver import PerceiverFeatureExtractor
            from .image_processing_perceiver import PerceiverImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_perceiver import (
                PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
                PerceiverForImageClassificationConvProcessing,
                PerceiverForImageClassificationFourier,
                PerceiverForImageClassificationLearned,
                PerceiverForMaskedLM,
                PerceiverForMultimodalAutoencoding,
                PerceiverForOpticalFlow,
                PerceiverForSequenceClassification,
                PerceiverLayer,
                PerceiverModel,
                PerceiverPreTrainedModel,
            )

else:
      import sys

      __UpperCamelCase      :      Tuple     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









# 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_tokenizers_available, is_torch_available


__UpperCamelCase      :      Any     						=						{"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase      :      Dict     						=						[
						    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MraForMaskedLM""",
						    """MraForMultipleChoice""",
						    """MraForQuestionAnswering""",
						    """MraForSequenceClassification""",
						    """MraForTokenClassification""",
						    """MraLayer""",
						    """MraModel""",
						    """MraPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mra import (
												    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MraForMaskedLM,
												    MraForMultipleChoice,
												    MraForQuestionAnswering,
												    MraForSequenceClassification,
												    MraForTokenClassification,
												    MraLayer,
												    MraModel,
												    MraPreTrainedModel,
												)
else:
						import sys

						__UpperCamelCase      :      List[str]     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)

