






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    				=		{
    """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  SCREAMING_SNAKE_CASE			(			UpperCamelCase_					):
     '''simple docstring'''


     UpperCamelCase_							:		Dict			=	'''roformer'''


     def __init__(				self       :	Optional[int]					, UpperCAmelCase_       :	Tuple=5_0000					, UpperCAmelCase_       :	Optional[Any]=None					, UpperCAmelCase_       :	Any=768					, UpperCAmelCase_       :	Optional[Any]=12					, UpperCAmelCase_       :	Tuple=12					, UpperCAmelCase_       :	Dict=3072					, UpperCAmelCase_       :	Dict="gelu"					, UpperCAmelCase_       :	Any=0.1					, UpperCAmelCase_       :	Union[str, Any]=0.1					, UpperCAmelCase_       :	str=1536					, UpperCAmelCase_       :	Optional[Any]=2					, UpperCAmelCase_       :	List[Any]=0.02					, UpperCAmelCase_       :	List[Any]=1E-12					, UpperCAmelCase_       :	Tuple=0					, UpperCAmelCase_       :	Optional[Any]=False					, UpperCAmelCase_       :	str=True					, **UpperCAmelCase_       :	Any					, ):
            super().__init__(pad_token_id=__A					, **__A      )

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





class  SCREAMING_SNAKE_CASE			(			UpperCamelCase_					):
     '''simple docstring'''


     @property
     def 				_A       (				self       :	List[Any]      ):
            if self.task == "multiple-choice":
                   SCREAMING_SNAKE_CASE       :      List[Any]							=    {0: "batch", 1: "choice", 2: "sequence"}
            else:
                   SCREAMING_SNAKE_CASE       :      Optional[int]							=    {0: "batch", 1: "sequence"}
            SCREAMING_SNAKE_CASE       :      Optional[int]							=    {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_torch_available


snake_case    				=		{
    """configuration_trajectory_transformer""": [
        """TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TrajectoryTransformerConfig""",
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TrajectoryTransformerModel""",
       """TrajectoryTransformerPreTrainedModel""",
       """load_tf_weights_in_trajectory_transformer""",
   ]


if TYPE_CHECKING:
   from .configuration_trajectory_transformer import (
       TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TrajectoryTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_trajectory_transformer import (
          TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TrajectoryTransformerModel,
          TrajectoryTransformerPreTrainedModel,
          load_tf_weights_in_trajectory_transformer,
      )


else:
   import sys

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






