

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a	 =     logging.get_logger(__name__)

_a	 =     {
    '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json''',
}






class 							A_				(	snake_case__      ):
     _lowercase    :  str          =			'mpnet'


     def __init__(							self    :	List[Any]    ,						UpperCAmelCase    :	Any=3_0_5_2_7    ,						UpperCAmelCase    :	Union[str, Any]=7_6_8    ,						UpperCAmelCase    :	Optional[Any]=1_2    ,						UpperCAmelCase    :	Tuple=1_2    ,						UpperCAmelCase    :	Tuple=3_0_7_2    ,						UpperCAmelCase    :	List[str]="gelu"    ,						UpperCAmelCase    :	List[Any]=0.1    ,						UpperCAmelCase    :	int=0.1    ,						UpperCAmelCase    :	Tuple=5_1_2    ,						UpperCAmelCase    :	Tuple=0.02    ,						UpperCAmelCase    :	str=1E-12    ,						UpperCAmelCase    :	List[Any]=3_2    ,						UpperCAmelCase    :	str=1    ,						UpperCAmelCase    :	Optional[Any]=0    ,						UpperCAmelCase    :	Optional[int]=2    ,						**UpperCAmelCase    :	List[str]    ,						)							->			Tuple:
      super().__init__(pad_token_id=UpperCAmelCase    ,						bos_token_id=UpperCAmelCase    ,						eos_token_id=UpperCAmelCase    ,						**UpperCAmelCase      )

      __lowerCAmelCase:     str     			=   vocab_size
      __lowerCAmelCase:     str     			=   hidden_size
      __lowerCAmelCase:     List[str]     			=   num_hidden_layers
      __lowerCAmelCase:     Tuple     			=   num_attention_heads
      __lowerCAmelCase:     str     			=   hidden_act
      __lowerCAmelCase:     Optional[Any]     			=   intermediate_size
      __lowerCAmelCase:     Union[str, Any]     			=   hidden_dropout_prob
      __lowerCAmelCase:     Dict     			=   attention_probs_dropout_prob
      __lowerCAmelCase:     Any     			=   max_position_embeddings
      __lowerCAmelCase:     Any     			=   initializer_range
      __lowerCAmelCase:     str     			=   layer_norm_eps
      __lowerCAmelCase:     Dict     			=   relative_attention_num_buckets








# 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,
)


_a	 =     {
    '''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:
  _a	 =     [
      '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TvltModel''',
      '''TvltForPreTraining''',
      '''TvltForAudioVisualClassification''',
      '''TvltPreTrainedModel''',
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''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

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





