






"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_UpperCamelCase    :   List[str]   			=						logging.get_logger(__name__)

_UpperCamelCase    :   int   			=						{
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}






class 						snake_case  (  UpperCAmelCase       ,			UpperCAmelCase	):
    __magic_name__   					=					'''bit'''
    __magic_name__   					=					['''preactivation''', '''bottleneck''']
    __magic_name__   					=					['''SAME''', '''VALID''']





    def __init__(							self     : List[str]    ,		A     : Dict=3    ,		A     : str=6_4    ,		A     : Optional[int]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]    ,		A     : Dict=[3, 4, 6, 3]    ,		A     : List[str]="preactivation"    ,		A     : List[str]="relu"    ,		A     : str=None    ,		A     : Tuple=3_2    ,		A     : List[str]=0.0    ,		A     : Optional[int]=False    ,		A     : Union[str, Any]=3_2    ,		A     : Dict=1    ,		A     : Dict=None    ,		A     : List[str]=None    ,		**A     : List[Any]    ,		):


      '''simple docstring'''




      super().__init__(**A      )
      if layer_type not in self.layer_types:
        raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types      )}'''      )
      if global_padding is not None:
        if global_padding.upper() in self.supported_padding:
          a					:      Optional[Any]    =       global_padding.upper()
        else:
          raise ValueError(F'''Padding strategy {global_padding} not supported'''      )
      a					:      List[Any]    =       num_channels
      a					:      Union[str, Any]    =       embedding_size
      a					:      str    =       hidden_sizes
      a					:      List[str]    =       depths
      a					:      Dict    =       layer_type
      a					:      Any    =       hidden_act
      a					:      Any    =       global_padding
      a					:      int    =       num_groups
      a					:      Union[str, Any]    =       drop_path_rate
      a					:      Dict    =       embedding_dynamic_padding
      a					:      List[Any]    =       output_stride
      a					:      str    =       width_factor

      a					:      Union[str, Any]    =       ['stem'] + [F'''stage{idx}''' for idx in range(1    ,		len(A      ) + 1      )]
      a,					a					:      str    =       get_aligned_output_features_output_indices(
          out_features=A    ,		out_indices=A    ,		stage_names=self.stage_names      )












"""simple docstring"""


import os
import sys

_UpperCamelCase    :   str   			=						os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_UpperCamelCase    :   int   			=						[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]




@add_start_docstrings(AutoConfig.__doc__				)
def       snake_case  (*A_				:Optional[int]     ,      **A_				:int				):







  '''simple docstring'''

  return AutoConfig.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoTokenizer.__doc__				)
def       snake_case  (*A_				:Optional[int]     ,      **A_				:List[Any]				):







  '''simple docstring'''

  return AutoTokenizer.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModel.__doc__				)
def       snake_case  (*A_				:Optional[Any]     ,      **A_				:Tuple				):







  '''simple docstring'''

  return AutoModel.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModelForCausalLM.__doc__				)
def       snake_case  (*A_				:str     ,      **A_				:Optional[int]				):







  '''simple docstring'''

  return AutoModelForCausalLM.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModelForMaskedLM.__doc__				)
def       snake_case  (*A_				:Optional[Any]     ,      **A_				:Dict				):







  '''simple docstring'''

  return AutoModelForMaskedLM.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModelForSequenceClassification.__doc__				)
def       snake_case  (*A_				:Dict     ,      **A_				:str				):







  '''simple docstring'''

  return AutoModelForSequenceClassification.from_pretrained(*A_     ,      **A_				)






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__				)
def       snake_case  (*A_				:Dict     ,      **A_				:List[Any]				):







  '''simple docstring'''

  return AutoModelForQuestionAnswering.from_pretrained(*A_     ,      **A_				)




