from collections import OrderedDict
from typing import Mapping

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


__SCREAMING_SNAKE_CASE						:   Any         =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:   List[Any]         =			{
    '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        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'camembert'



							def __init__(						self				,    lowercase_=30_522				,    lowercase_=768				,    lowercase_=12				,    lowercase_=12				,    lowercase_=3_072				,    lowercase_="gelu"				,    lowercase_=0.1				,    lowercase_=0.1				,    lowercase_=512				,    lowercase_=2				,    lowercase_=0.02				,    lowercase_=1e-12				,    lowercase_=1				,    lowercase_=0				,    lowercase_=2				,    lowercase_="absolute"				,    lowercase_=True				,    lowercase_=None				,    **lowercase_				,    ):
													super().__init__(pad_token_id=lowercase_				,    bos_token_id=lowercase_				,    eos_token_id=lowercase_				,    **lowercase_    )

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




class        lowercase_   (					__snake_case		):



							@property
							def 	UpperCamelCase	(						self    ):
													if self.task == "multiple-choice":
																			_snake_case						:					Tuple    =   {0: "batch", 1: "choice", 2: "sequence"}
													else:
																			_snake_case						:					List[Any]    =   {0: "batch", 1: "sequence"}
													return OrderedDict(
													    [
													        ("input_ids", dynamic_axis),
													        ("attention_mask", dynamic_axis),
													    ]    )
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			[
				    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'AutoformerForPrediction',
				    'AutoformerModel',
				    'AutoformerPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_autoformer import (
				    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AutoformerConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_autoformer import (
								    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AutoformerForPrediction,
								    AutoformerModel,
								    AutoformerPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)