



"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


__lowerCAmelCase							:       List[Any]          =       logging.get_logger(__name__)

__lowerCAmelCase							:       Dict          =       {
    '''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     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							'''roformer'''





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







								'''simple docstring'''

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

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




class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






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







								'''simple docstring'''

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


'''simple docstring'''


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case						  =		{"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __snake_case						  =		[
           """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """FocalNetForImageClassification""",
           """FocalNetForMaskedImageModeling""",
           """FocalNetBackbone""",
           """FocalNetModel""",
           """FocalNetPreTrainedModel""",
       ]

if TYPE_CHECKING:
       from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_focalnet import (
                  FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FocalNetBackbone,
                  FocalNetForImageClassification,
                  FocalNetForMaskedImageModeling,
                  FocalNetModel,
                  FocalNetPreTrainedModel,
              )

else:
       import sys

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

