





"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase_									=logging.get_logger(__name__)

UpperCamelCase_									={
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}




class 			_a						(				SCREAMING_SNAKE_CASE__    ):
				UpperCamelCase								=   '''camembert'''






				def __init__(			self		:   int,			lowerCAmelCase__		:   Tuple=3_0_5_2_2,			lowerCAmelCase__		:   Optional[int]=7_6_8,			lowerCAmelCase__		:   List[Any]=1_2,			lowerCAmelCase__		:   List[Any]=1_2,			lowerCAmelCase__		:   Any=3_0_7_2,			lowerCAmelCase__		:   Dict="gelu",			lowerCAmelCase__		:   Tuple=0.1,			lowerCAmelCase__		:   Dict=0.1,			lowerCAmelCase__		:   Optional[Any]=5_1_2,			lowerCAmelCase__		:   Union[str, Any]=2,			lowerCAmelCase__		:   Optional[int]=0.02,			lowerCAmelCase__		:   Tuple=1e-1_2,			lowerCAmelCase__		:   str=1,			lowerCAmelCase__		:   Optional[Any]=0,			lowerCAmelCase__		:   Optional[int]=2,			lowerCAmelCase__		:   str="absolute",			lowerCAmelCase__		:   str=True,			lowerCAmelCase__		:   Optional[Any]=None,			**lowerCAmelCase__		:   Optional[Any],			)     ->				Dict:




										'''simple docstring'''



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

										_UpperCamelCase      : Optional[Any]         =   vocab_size
										_UpperCamelCase      : int         =   hidden_size
										_UpperCamelCase      : str         =   num_hidden_layers
										_UpperCamelCase      : str         =   num_attention_heads
										_UpperCamelCase      : str         =   hidden_act
										_UpperCamelCase      : int         =   intermediate_size
										_UpperCamelCase      : int         =   hidden_dropout_prob
										_UpperCamelCase      : List[Any]         =   attention_probs_dropout_prob
										_UpperCamelCase      : Union[str, Any]         =   max_position_embeddings
										_UpperCamelCase      : Optional[Any]         =   type_vocab_size
										_UpperCamelCase      : List[Any]         =   initializer_range
										_UpperCamelCase      : Dict         =   layer_norm_eps
										_UpperCamelCase      : Any         =   position_embedding_type
										_UpperCamelCase      : Optional[Any]         =   use_cache
										_UpperCamelCase      : int         =   classifier_dropout






class 			_a						(				SCREAMING_SNAKE_CASE__    ):






				@property
				def     snake_case     (			self		:   str							)     ->				List[str]:




										'''simple docstring'''



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










"""simple docstring"""




from ..utils import is_flax_available, is_torch_available


if is_torch_available():
						from .autoencoder_kl import AutoencoderKL
						from .controlnet import ControlNetModel
						from .dual_transformer_ad import DualTransformeraDModel
						from .modeling_utils import ModelMixin
						from .prior_transformer import PriorTransformer
						from .ta_film_transformer import TaFilmDecoder
						from .transformer_ad import TransformeraDModel
						from .unet_ad import UNetaDModel
						from .unet_ad import UNetaDModel
						from .unet_ad_condition import UNetaDConditionModel
						from .unet_ad_condition import UNetaDConditionModel
						from .vq_model import VQModel

if is_flax_available():
						from .controlnet_flax import FlaxControlNetModel
						from .unet_ad_condition_flax import FlaxUNetaDConditionModel
						from .vae_flax import FlaxAutoencoderKL



