

"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_							:			List[Any]											= logging.get_logger(__name__)

A_							:			Optional[Any]											= {
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		Optional[Any]							=							'roberta'

			def __init__(					self				:						List[Any]    ,				__UpperCAmelCase				:						Dict=5_0_2_6_5    ,				__UpperCAmelCase				:						List[Any]=7_6_8    ,				__UpperCAmelCase				:						Tuple=1_2    ,				__UpperCAmelCase				:						List[str]=1_2    ,				__UpperCAmelCase				:						Optional[int]=3_0_7_2    ,				__UpperCAmelCase				:						Any="gelu"    ,				__UpperCAmelCase				:						Tuple=0.1    ,				__UpperCAmelCase				:						List[str]=0.1    ,				__UpperCAmelCase				:						Optional[Any]=5_1_2    ,				__UpperCAmelCase				:						Union[str, Any]=2    ,				__UpperCAmelCase				:						str=0.02    ,				__UpperCAmelCase				:						Optional[Any]=1e-12    ,				__UpperCAmelCase				:						int=1    ,				__UpperCAmelCase				:						int=0    ,				__UpperCAmelCase				:						Optional[Any]=2    ,				__UpperCAmelCase				:						Union[str, Any]="absolute"    ,				__UpperCAmelCase				:						Optional[int]=True    ,				__UpperCAmelCase				:						Tuple=None    ,				**__UpperCAmelCase				:						Any    ,				)   ->    Tuple:
						super().__init__(pad_token_id=__UpperCAmelCase    ,				bos_token_id=__UpperCAmelCase    ,				eos_token_id=__UpperCAmelCase    ,				**__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      vocab_size
						SCREAMING_SNAKE_CASE__								=      hidden_size
						SCREAMING_SNAKE_CASE__								=      num_hidden_layers
						SCREAMING_SNAKE_CASE__								=      num_attention_heads
						SCREAMING_SNAKE_CASE__								=      hidden_act
						SCREAMING_SNAKE_CASE__								=      intermediate_size
						SCREAMING_SNAKE_CASE__								=      hidden_dropout_prob
						SCREAMING_SNAKE_CASE__								=      attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__								=      max_position_embeddings
						SCREAMING_SNAKE_CASE__								=      type_vocab_size
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      layer_norm_eps
						SCREAMING_SNAKE_CASE__								=      position_embedding_type
						SCREAMING_SNAKE_CASE__								=      use_cache
						SCREAMING_SNAKE_CASE__								=      classifier_dropout





class    lowerCamelCase						(A__			):

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									SCREAMING_SNAKE_CASE__								=      {0: """batch""", 1: """choice""", 2: """sequence"""}
						else:
									SCREAMING_SNAKE_CASE__								=      {0: """batch""", 1: """sequence"""}
						return OrderedDict(
						    [
						        ("""input_ids""", dynamic_axis),
						        ("""attention_mask""", 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_torch_available, is_vision_available


A_							:			Dict											= {
    "configuration_efficientnet": [
        "EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientNetConfig",
        "EfficientNetOnnxConfig",
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_							:			List[Any]											= ["EfficientNetImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_							:			Dict											= [
						    "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

						A_							:			List[Any]											= _LazyModule(__name__, globals()["__file__"], _import_structure)


