


"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


UpperCAmelCase    :     str   						=		{
    'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
    'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
    'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
    'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
    'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
    'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
    'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
    'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							"""albert"""






					def __init__(       self    : Tuple ,       UpperCamelCase    : Union[str, Any]=30_000 ,       UpperCamelCase    : int=128 ,       UpperCamelCase    : Optional[Any]=4_096 ,       UpperCamelCase    : Optional[Any]=12 ,       UpperCamelCase    : Optional[Any]=1 ,       UpperCamelCase    : Optional[int]=64 ,       UpperCamelCase    : Dict=16_384 ,       UpperCamelCase    : Any=1 ,       UpperCamelCase    : List[Any]="gelu_new" ,       UpperCamelCase    : str=0 ,       UpperCamelCase    : List[str]=0 ,       UpperCamelCase    : Optional[int]=512 ,       UpperCamelCase    : Optional[Any]=2 ,       UpperCamelCase    : str=0.02 ,       UpperCamelCase    : Dict=1e-1_2 ,       UpperCamelCase    : Any=0.1 ,       UpperCamelCase    : Union[str, Any]="absolute" ,       UpperCamelCase    : Tuple=0 ,       UpperCamelCase    : Dict=2 ,       UpperCamelCase    : str=3 ,       **UpperCamelCase    : Any ,       ):
												'''simple docstring'''






												super().__init__(pad_token_id=UpperCamelCase ,       bos_token_id=UpperCamelCase ,       eos_token_id=UpperCamelCase ,       **UpperCamelCase  )

												__UpperCAmelCase   :						List[Any]      							=					vocab_size
												__UpperCAmelCase   :						str      							=					embedding_size
												__UpperCAmelCase   :						Any      							=					hidden_size
												__UpperCAmelCase   :						int      							=					num_hidden_layers
												__UpperCAmelCase   :						Optional[int]      							=					num_hidden_groups
												__UpperCAmelCase   :						Dict      							=					num_attention_heads
												__UpperCAmelCase   :						Any      							=					inner_group_num
												__UpperCAmelCase   :						Union[str, Any]      							=					hidden_act
												__UpperCAmelCase   :						Union[str, Any]      							=					intermediate_size
												__UpperCAmelCase   :						Union[str, Any]      							=					hidden_dropout_prob
												__UpperCAmelCase   :						Optional[int]      							=					attention_probs_dropout_prob
												__UpperCAmelCase   :						Dict      							=					max_position_embeddings
												__UpperCAmelCase   :						Optional[Any]      							=					type_vocab_size
												__UpperCAmelCase   :						List[Any]      							=					initializer_range
												__UpperCAmelCase   :						Tuple      							=					layer_norm_eps
												__UpperCAmelCase   :						Union[str, Any]      							=					classifier_dropout_prob
												__UpperCAmelCase   :						Any      							=					position_embedding_type






class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					@property
					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												if self.task == "multiple-choice":
																			__UpperCAmelCase   :						List[Any]      							=					{0: """batch""", 1: """choice""", 2: """sequence"""}
												else:
																			__UpperCAmelCase   :						Optional[int]      							=					{0: """batch""", 1: """sequence"""}
												return OrderedDict(
												    [
												        ("""input_ids""", dynamic_axis),
												        ("""attention_mask""", dynamic_axis),
												        ("""token_type_ids""", dynamic_axis),
												    ]  )




"""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


UpperCAmelCase    :     Union[str, Any]   						=		{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

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

							UpperCAmelCase    :     Tuple   						=		_LazyModule(__name__, globals()['__file__'], _import_structure)
