
"""simple docstring"""




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


_A     :			Dict  						=			logging.get_logger(__name__)

_A     :			Any  						=			{
    """studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
    """studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}

class 						a__		( a_		):
   __lowerCAmelCase				=							"""luke"""
   def __init__(     self      ,       _a=50_267      ,       _a=500_000      ,       _a=768      ,       _a=256      ,       _a=12      ,       _a=12      ,       _a=3_072      ,       _a="gelu"      ,       _a=0.1      ,       _a=0.1      ,       _a=512      ,       _a=2      ,       _a=0.0_2      ,       _a=1E-12      ,       _a=True      ,       _a=None      ,       _a=1      ,       _a=0      ,       _a=2      ,       **_a      ,       ):
          super().__init__(pad_token_id=_a      ,       bos_token_id=_a      ,       eos_token_id=_a      ,       **_a							)

          lowercase       :							Optional[Any]									=				vocab_size
          lowercase       :							List[str]									=				entity_vocab_size
          lowercase       :							Union[str, Any]									=				hidden_size
          lowercase       :							Tuple									=				entity_emb_size
          lowercase       :							List[str]									=				num_hidden_layers
          lowercase       :							Union[str, Any]									=				num_attention_heads
          lowercase       :							Union[str, Any]									=				hidden_act
          lowercase       :							str									=				intermediate_size
          lowercase       :							Any									=				hidden_dropout_prob
          lowercase       :							List[Any]									=				attention_probs_dropout_prob
          lowercase       :							List[str]									=				max_position_embeddings
          lowercase       :							List[Any]									=				type_vocab_size
          lowercase       :							int									=				initializer_range
          lowercase       :							Tuple									=				layer_norm_eps
          lowercase       :							Union[str, Any]									=				use_entity_aware_attention
          lowercase       :							Any									=				classifier_dropout




"""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


_A     :			Optional[int]  						=			{
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			Any  						=			[
       """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """MgpstrModel""",
       """MgpstrPreTrainedModel""",
       """MgpstrForSceneTextRecognition""",
   ]

if TYPE_CHECKING:
   from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
   from .processing_mgp_str import MgpstrProcessor
   from .tokenization_mgp_str import MgpstrTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mgp_str import (
          MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
          MgpstrForSceneTextRecognition,
          MgpstrModel,
          MgpstrPreTrainedModel,
      )
else:
   import sys

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


