


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


lowerCamelCase__    	=     logging.get_logger(__name__)

lowerCamelCase__    	=     {
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	Union[str, Any]          =     "biogpt"




			def __init__(		self  :     Dict	,  lowerCamelCase__  :     Union[str, Any]=4_23_84	,  lowerCamelCase__  :     Any=10_24	,  lowerCamelCase__  :     List[str]=24	,  lowerCamelCase__  :     Dict=16	,  lowerCamelCase__  :     Optional[Any]=40_96	,  lowerCamelCase__  :     Optional[Any]="gelu"	,  lowerCamelCase__  :     Any=0.1	,  lowerCamelCase__  :     Union[str, Any]=0.1	,  lowerCamelCase__  :     List[str]=10_24	,  lowerCamelCase__  :     List[str]=0.0_2	,  lowerCamelCase__  :     Any=1E-12	,  lowerCamelCase__  :     Tuple=True	,  lowerCamelCase__  :     Optional[Any]=True	,  lowerCamelCase__  :     Union[str, Any]=0.0	,  lowerCamelCase__  :     Optional[Any]=0.0	,  lowerCamelCase__  :     str=1	,  lowerCamelCase__  :     Any=0	,  lowerCamelCase__  :     List[str]=2	,  **lowerCamelCase__  :     List[Any]	,  )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      vocab_size
					_UpperCAmelCase		:       Optional[int]								=      max_position_embeddings
					_UpperCAmelCase		:       List[str]								=      hidden_size
					_UpperCAmelCase		:       List[str]								=      num_hidden_layers
					_UpperCAmelCase		:       List[Any]								=      num_attention_heads
					_UpperCAmelCase		:       int								=      intermediate_size
					_UpperCAmelCase		:       Tuple								=      hidden_act
					_UpperCAmelCase		:       str								=      hidden_dropout_prob
					_UpperCAmelCase		:       Tuple								=      attention_probs_dropout_prob
					_UpperCAmelCase		:       List[Any]								=      initializer_range
					_UpperCAmelCase		:       Optional[int]								=      layer_norm_eps
					_UpperCAmelCase		:       List[Any]								=      scale_embedding
					_UpperCAmelCase		:       List[str]								=      use_cache
					_UpperCAmelCase		:       Optional[int]								=      layerdrop
					_UpperCAmelCase		:       Any								=      activation_dropout
					super().__init__(pad_token_id=lowerCamelCase__	,  bos_token_id=lowerCamelCase__	,  eos_token_id=lowerCamelCase__	,  **lowerCamelCase__ )







'''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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase__    	=     {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     [
						    '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

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



