






"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase_	:				int										= logging.get_logger(__name__)

UpperCamelCase_	:				Optional[int]										= {
    '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 (     UpperCamelCase_ ):
      _a   :     Optional[Any]								=   '''camembert'''


      def __init__(    self				:	str							,   a				:	Optional[Any]=3_0_5_2_2							,   a				:	Optional[int]=7_6_8							,   a				:	Dict=1_2							,   a				:	Dict=1_2							,   a				:	Optional[int]=3_0_7_2							,   a				:	int="gelu"							,   a				:	str=0.1							,   a				:	List[Any]=0.1							,   a				:	List[str]=5_1_2							,   a				:	List[str]=2							,   a				:	int=0.0_2							,   a				:	Optional[Any]=1e-12							,   a				:	Any=1							,   a				:	int=0							,   a				:	Tuple=2							,   a				:	List[Any]="absolute"							,   a				:	Union[str, Any]=True							,   a				:	str=None							,   **a				:	Optional[Any]							,   ):





            """simple docstring"""







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

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



class      _lowercase (     UpperCamelCase_ ):


      @property
      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            if self.task == "multiple-choice":
                  __snake_case      :    Optional[int]  			={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
            else:
                  __snake_case      :    Tuple  			={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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_	:				Any										= {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				List[str]										= ["""ReformerTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				List[str]										= ["""ReformerTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				Optional[int]										= [
        """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ReformerAttention""",
        """ReformerForMaskedLM""",
        """ReformerForQuestionAnswering""",
        """ReformerForSequenceClassification""",
        """ReformerLayer""",
        """ReformerModel""",
        """ReformerModelWithLMHead""",
        """ReformerPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_reformer import ReformerTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_reformer_fast import ReformerTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_reformer import (
            REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            ReformerAttention,
            ReformerForMaskedLM,
            ReformerForQuestionAnswering,
            ReformerForSequenceClassification,
            ReformerLayer,
            ReformerModel,
            ReformerModelWithLMHead,
            ReformerPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase_	:				Tuple										= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


