
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

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


__lowerCAmelCase												=	logging.get_logger(__name__)

__lowerCAmelCase												=	{
    'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
    'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						"xlm-roberta-xl"

							def __init__(self  ,  UpperCAmelCase=250880  ,  UpperCAmelCase=2560  ,  UpperCAmelCase=36  ,  UpperCAmelCase=32  ,  UpperCAmelCase=10240  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=514  ,  UpperCAmelCase=1  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=1e-0_5  ,  UpperCAmelCase=1  ,  UpperCAmelCase=0  ,  UpperCAmelCase=2  ,  UpperCAmelCase="absolute"  ,  UpperCAmelCase=True  ,  UpperCAmelCase=None  ,  **UpperCAmelCase  ,  )					->       Any:
														super().__init__(pad_token_id=UpperCAmelCase  ,  bos_token_id=UpperCAmelCase  ,  eos_token_id=UpperCAmelCase  ,  **UpperCAmelCase  )
														_snake_case       =  vocab_size
														_snake_case       =  hidden_size
														_snake_case       =  num_hidden_layers
														_snake_case       =  num_attention_heads
														_snake_case       =  hidden_act
														_snake_case       =  intermediate_size
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  max_position_embeddings
														_snake_case       =  type_vocab_size
														_snake_case       =  initializer_range
														_snake_case       =  layer_norm_eps
														_snake_case       =  position_embedding_type
														_snake_case       =  use_cache
														_snake_case       =  classifier_dropout


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

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







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__lowerCAmelCase												=	{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'EncodecModel',
				    'EncodecPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_encodec import (
				    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    EncodecConfig,
				)
				from .feature_extraction_encodec import EncodecFeatureExtractor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_encodec import (
								    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EncodecModel,
								    EncodecPreTrainedModel,
								)

else:
				import sys

				__lowerCAmelCase												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)