


"""simple docstring"""


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


A     :   Tuple			    =			logging.get_logger(__name__)

A     :   int			    =			{
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[Any]				      ="""roc_bert"""

							def __init__(     self    ,   __a=3_05_22    ,   __a=7_68    ,   __a=12    ,   __a=12    ,   __a=30_72    ,   __a="gelu"    ,   __a=0.1    ,   __a=0.1    ,   __a=5_12    ,   __a=2    ,   __a=0.0_2    ,   __a=1e-1_2    ,   __a=True    ,   __a=0    ,   __a="absolute"    ,   __a=None    ,   __a=True    ,   __a=True    ,   __a=7_68    ,   __a=9_10    ,   __a=5_12    ,   __a=2_48_58    ,   __a=True    ,   **__a    ,   ):
										__lowerCAmelCase				    =			vocab_size
										__lowerCAmelCase				    =			max_position_embeddings
										__lowerCAmelCase				    =			hidden_size
										__lowerCAmelCase				    =			num_hidden_layers
										__lowerCAmelCase				    =			num_attention_heads
										__lowerCAmelCase				    =			intermediate_size
										__lowerCAmelCase				    =			hidden_act
										__lowerCAmelCase				    =			hidden_dropout_prob
										__lowerCAmelCase				    =			attention_probs_dropout_prob
										__lowerCAmelCase				    =			initializer_range
										__lowerCAmelCase				    =			type_vocab_size
										__lowerCAmelCase				    =			layer_norm_eps
										__lowerCAmelCase				    =			use_cache
										__lowerCAmelCase				    =			enable_pronunciation
										__lowerCAmelCase				    =			enable_shape
										__lowerCAmelCase				    =			pronunciation_embed_dim
										__lowerCAmelCase				    =			pronunciation_vocab_size
										__lowerCAmelCase				    =			shape_embed_dim
										__lowerCAmelCase				    =			shape_vocab_size
										__lowerCAmelCase				    =			concat_input
										__lowerCAmelCase				    =			position_embedding_type
										__lowerCAmelCase				    =			classifier_dropout
										super().__init__(pad_token_id=__a    ,   **__a	)








# 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_efficientnet''': [
        '''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientNetConfig''',
        '''EfficientNetOnnxConfig''',
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							['''EfficientNetImageProcessor''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''EfficientNetForImageClassification''',
      '''EfficientNetModel''',
      '''EfficientNetPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_efficientnet import (
      EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
      EfficientNetConfig,
      EfficientNetOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_efficientnet import EfficientNetImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_efficientnet import (
        EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        EfficientNetForImageClassification,
        EfficientNetModel,
        EfficientNetPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure)

