


from collections import OrderedDict
from typing import Mapping

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


__snake_case   							=						logging.get_logger(__name__)

__snake_case   							=						{
    '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/config.json''',
    '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/config.json''',
    '''xlm-roberta-large-finetuned-conll02-dutch''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll02-spanish''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-english''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-german''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'''
    ),
}



class 	__lowerCamelCase			(_a						):
       _lowercase											=     """xlm-roberta"""







       def __init__(			self: int,A_: Any=3_0522,A_: Any=768,A_: Tuple=12,A_: Any=12,A_: Optional[int]=3072,A_: List[Any]="gelu",A_: Union[str, Any]=0.1,A_: Optional[Any]=0.1,A_: Union[str, Any]=512,A_: Union[str, Any]=2,A_: Any=0.0_2,A_: int=1E-12,A_: List[Any]=1,A_: List[Any]=0,A_: int=2,A_: Any="absolute",A_: int=True,A_: Any=None,**A_: int,):






            '''simple docstring'''




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

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



class 	__lowerCamelCase			(_a						):







       @property
       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




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






from typing import TYPE_CHECKING

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


__snake_case   							=						{'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case   							=						[
        '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''UniSpeechForCTC''',
        '''UniSpeechForPreTraining''',
        '''UniSpeechForSequenceClassification''',
        '''UniSpeechModel''',
        '''UniSpeechPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_unispeech import (
            UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
            UniSpeechForCTC,
            UniSpeechForPreTraining,
            UniSpeechForSequenceClassification,
            UniSpeechModel,
            UniSpeechPreTrainedModel,
        )

else:
    import sys

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


