



'''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

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


SCREAMING_SNAKE_CASE__     : Dict				=   {
    """configuration_tvlt""": ["""TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TvltConfig"""],
    """feature_extraction_tvlt""": ["""TvltFeatureExtractor"""],
    """processing_tvlt""": ["""TvltProcessor"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__     : Tuple				=   [
							    """TVLT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TvltModel""",
							    """TvltForPreTraining""",
							    """TvltForAudioVisualClassification""",
							    """TvltPreTrainedModel""",
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__     : List[str]				=   ["""TvltImageProcessor"""]


if TYPE_CHECKING:
							from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
							from .processing_tvlt import TvltProcessor
							from .feature_extraction_tvlt import TvltFeatureExtractor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tvlt import (
														    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TvltForAudioVisualClassification,
														    TvltForPreTraining,
														    TvltModel,
														    TvltPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_tvlt import TvltImageProcessor


else:
							import sys

							SCREAMING_SNAKE_CASE__     : Any				=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





'''simple docstring'''







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__     : int				=   {"""configuration_bert_generation""": ["""BertGenerationConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__     : List[Any]				=   ["""BertGenerationTokenizer"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__     : Optional[Any]				=   [
							    """BertGenerationDecoder""",
							    """BertGenerationEncoder""",
							    """BertGenerationPreTrainedModel""",
							    """load_tf_weights_in_bert_generation""",
							]


if TYPE_CHECKING:
							from .configuration_bert_generation import BertGenerationConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_generation import BertGenerationTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bert_generation import (
														    BertGenerationDecoder,
														    BertGenerationEncoder,
														    BertGenerationPreTrainedModel,
														    load_tf_weights_in_bert_generation,
														)

else:
							import sys

							SCREAMING_SNAKE_CASE__     : Any				=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
