


from typing import TYPE_CHECKING

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


A   :					Tuple	=      {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A   :					Optional[int]	=      ["RobertaTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A   :					Tuple	=      [
				    "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RobertaForCausalLM",
				    "RobertaForMaskedLM",
				    "RobertaForMultipleChoice",
				    "RobertaForQuestionAnswering",
				    "RobertaForSequenceClassification",
				    "RobertaForTokenClassification",
				    "RobertaModel",
				    "RobertaPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A   :					int	=      [
				    "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRobertaForCausalLM",
				    "TFRobertaForMaskedLM",
				    "TFRobertaForMultipleChoice",
				    "TFRobertaForQuestionAnswering",
				    "TFRobertaForSequenceClassification",
				    "TFRobertaForTokenClassification",
				    "TFRobertaMainLayer",
				    "TFRobertaModel",
				    "TFRobertaPreTrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A   :					str	=      [
				    "FlaxRobertaForCausalLM",
				    "FlaxRobertaForMaskedLM",
				    "FlaxRobertaForMultipleChoice",
				    "FlaxRobertaForQuestionAnswering",
				    "FlaxRobertaForSequenceClassification",
				    "FlaxRobertaForTokenClassification",
				    "FlaxRobertaModel",
				    "FlaxRobertaPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
				from .tokenization_roberta import RobertaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_roberta_fast import RobertaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roberta import (
								    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RobertaForCausalLM,
								    RobertaForMaskedLM,
								    RobertaForMultipleChoice,
								    RobertaForQuestionAnswering,
								    RobertaForSequenceClassification,
								    RobertaForTokenClassification,
								    RobertaModel,
								    RobertaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roberta import (
								    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRobertaForCausalLM,
								    TFRobertaForMaskedLM,
								    TFRobertaForMultipleChoice,
								    TFRobertaForQuestionAnswering,
								    TFRobertaForSequenceClassification,
								    TFRobertaForTokenClassification,
								    TFRobertaMainLayer,
								    TFRobertaModel,
								    TFRobertaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roberta import (
								    FlaxRobertaForCausalLM,
								    FlaxRobertaForMaskedLM,
								    FlaxRobertaForMultipleChoice,
								    FlaxRobertaForQuestionAnswering,
								    FlaxRobertaForSequenceClassification,
								    FlaxRobertaForTokenClassification,
								    FlaxRobertaModel,
								    FlaxRobertaPreTrainedModel,
								)

else:
				import sys

				A   :					Union[str, Any]	=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






from collections import OrderedDict
from typing import Mapping

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


A   :					int	=      logging.get_logger(__name__)

A   :					Optional[int]	=      {
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "xmod"





       def __init__(     self							:    int      ,   __lowerCamelCase							:    Any=30522      ,   __lowerCamelCase							:    Any=768      ,   __lowerCamelCase							:    str=12      ,   __lowerCamelCase							:    Any=12      ,   __lowerCamelCase							:    List[str]=3072      ,   __lowerCamelCase							:    List[Any]="gelu"      ,   __lowerCamelCase							:    Union[str, Any]=0.1      ,   __lowerCamelCase							:    int=0.1      ,   __lowerCamelCase							:    Tuple=512      ,   __lowerCamelCase							:    str=2      ,   __lowerCamelCase							:    List[str]=0.0_2      ,   __lowerCamelCase							:    List[str]=1E-1_2      ,   __lowerCamelCase							:    str=1      ,   __lowerCamelCase							:    Optional[int]=0      ,   __lowerCamelCase							:    Optional[Any]=2      ,   __lowerCamelCase							:    str="absolute"      ,   __lowerCamelCase							:    List[str]=True      ,   __lowerCamelCase							:    Dict=None      ,   __lowerCamelCase							:    Optional[Any]=False      ,   __lowerCamelCase							:    Optional[Any]=2      ,   __lowerCamelCase							:    Tuple=False      ,   __lowerCamelCase							:    Tuple=True      ,   __lowerCamelCase							:    Union[str, Any]=True      ,   __lowerCamelCase							:    str=("en_XX",)      ,   __lowerCamelCase							:    Union[str, Any]=None      ,   **__lowerCamelCase							:    Optional[int]      ,   ):


             '''simple docstring'''



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

             lowerCamelCase__	:       Union[str, Any]					=						vocab_size
             lowerCamelCase__	:       Union[str, Any]					=						hidden_size
             lowerCamelCase__	:       Optional[int]					=						num_hidden_layers
             lowerCamelCase__	:       List[Any]					=						num_attention_heads
             lowerCamelCase__	:       Union[str, Any]					=						hidden_act
             lowerCamelCase__	:       Optional[int]					=						intermediate_size
             lowerCamelCase__	:       Optional[int]					=						hidden_dropout_prob
             lowerCamelCase__	:       List[Any]					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Any					=						max_position_embeddings
             lowerCamelCase__	:       List[Any]					=						type_vocab_size
             lowerCamelCase__	:       int					=						initializer_range
             lowerCamelCase__	:       Tuple					=						layer_norm_eps
             lowerCamelCase__	:       Union[str, Any]					=						position_embedding_type
             lowerCamelCase__	:       str					=						use_cache
             lowerCamelCase__	:       Union[str, Any]					=						classifier_dropout
             lowerCamelCase__	:       Any					=						pre_norm
             lowerCamelCase__	:       Tuple					=						adapter_reduction_factor
             lowerCamelCase__	:       Tuple					=						adapter_layer_norm
             lowerCamelCase__	:       List[Any]					=						adapter_reuse_layer_norm
             lowerCamelCase__	:       Dict					=						ln_before_adapter
             lowerCamelCase__	:       List[Any]					=						list(__lowerCamelCase )
             lowerCamelCase__	:       Optional[Any]					=						default_language







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



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


