
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      ={
    "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
    "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json",
    "junnyu/roformer_chinese_char_small": (
        "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"
    ),
    "junnyu/roformer_chinese_char_base": (
        "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"
    ),
    "junnyu/roformer_small_discriminator": (
        "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"
    ),
    "junnyu/roformer_small_generator": (
        "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}




class 				_lowerCamelCase (      _UpperCamelCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  "roformer"






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=5_0_0_0_0						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=7_6_8						,						__SCREAMING_SNAKE_CASE=1_2						,						__SCREAMING_SNAKE_CASE=1_2						,						__SCREAMING_SNAKE_CASE=3_0_7_2						,						__SCREAMING_SNAKE_CASE="gelu"						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=1_5_3_6						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=0.02						,						__SCREAMING_SNAKE_CASE=1e-12						,						__SCREAMING_SNAKE_CASE=0						,						__SCREAMING_SNAKE_CASE=False						,						__SCREAMING_SNAKE_CASE=True						,						**__SCREAMING_SNAKE_CASE						,						)  ->   Optional[int]:




           """simple docstring"""







           super().__init__(pad_token_id=__a						,						**__a  )

           UpperCamelCase__    :   Any					=				vocab_size
           UpperCamelCase__    :   Optional[Any]					=				hidden_size if embedding_size is None else embedding_size
           UpperCamelCase__    :   str					=				hidden_size
           UpperCamelCase__    :   Optional[Any]					=				num_hidden_layers
           UpperCamelCase__    :   List[str]					=				num_attention_heads
           UpperCamelCase__    :   Optional[Any]					=				hidden_act
           UpperCamelCase__    :   Union[str, Any]					=				intermediate_size
           UpperCamelCase__    :   List[str]					=				hidden_dropout_prob
           UpperCamelCase__    :   Tuple					=				attention_probs_dropout_prob
           UpperCamelCase__    :   Any					=				max_position_embeddings
           UpperCamelCase__    :   Optional[Any]					=				type_vocab_size
           UpperCamelCase__    :   List[str]					=				initializer_range
           UpperCamelCase__    :   int					=				layer_norm_eps
           UpperCamelCase__    :   Optional[int]					=				rotary_value
           UpperCamelCase__    :   int					=				use_cache




class 				_lowerCamelCase (      _UpperCamelCase ):





     """simple docstring"""







     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Mapping[str, Mapping[int, str]]:




           """simple docstring"""







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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase      ={"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SwinForImageClassification",
        "SwinForMaskedImageModeling",
        "SwinModel",
        "SwinPreTrainedModel",
        "SwinBackbone",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFSwinForImageClassification",
        "TFSwinForMaskedImageModeling",
        "TFSwinModel",
        "TFSwinPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_swin import (
            SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
            SwinBackbone,
            SwinForImageClassification,
            SwinForMaskedImageModeling,
            SwinModel,
            SwinPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_swin import (
            TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFSwinForImageClassification,
            TFSwinForMaskedImageModeling,
            TFSwinModel,
            TFSwinPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


