


"""simple docstring"""





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


snake_case__						:   Dict			 = logging.get_logger(__name__)

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



class 						snake_case_(				a__							,    a__	):
     __UpperCamelCase     =    '''bit'''
     __UpperCamelCase     =    ['''preactivation''', '''bottleneck''']
     __UpperCamelCase     =    ['''SAME''', '''VALID''']






     def __init__(		self     :    int    , UpperCamelCase_     :    Any=3    , UpperCamelCase_     :    str=6_4    , UpperCamelCase_     :    Optional[Any]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]    , UpperCamelCase_     :    Optional[int]=[3, 4, 6, 3]    , UpperCamelCase_     :    Tuple="preactivation"    , UpperCamelCase_     :    List[Any]="relu"    , UpperCamelCase_     :    Tuple=None    , UpperCamelCase_     :    Union[str, Any]=3_2    , UpperCamelCase_     :    List[Any]=0.0    , UpperCamelCase_     :    Dict=False    , UpperCamelCase_     :    str=3_2    , UpperCamelCase_     :    List[str]=1    , UpperCamelCase_     :    Union[str, Any]=None    , UpperCamelCase_     :    Union[str, Any]=None    , **UpperCamelCase_     :    List[str]    , ):
           super().__init__(**_UpperCAmelCase )
           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:
                       lowerCAmelCase    :		Dict				=      global_padding.upper()
                 else:
                       raise ValueError(F'''Padding strategy {global_padding} not supported''' )
           lowerCAmelCase    :		Optional[int]				=      num_channels
           lowerCAmelCase    :		str				=      embedding_size
           lowerCAmelCase    :		List[Any]				=      hidden_sizes
           lowerCAmelCase    :		List[Any]				=      depths
           lowerCAmelCase    :		int				=      layer_type
           lowerCAmelCase    :		Dict				=      hidden_act
           lowerCAmelCase    :		Dict				=      global_padding
           lowerCAmelCase    :		List[Any]				=      num_groups
           lowerCAmelCase    :		List[Any]				=      drop_path_rate
           lowerCAmelCase    :		Dict				=      embedding_dynamic_padding
           lowerCAmelCase    :		int				=      output_stride
           lowerCAmelCase    :		Union[str, Any]				=      width_factor

           lowerCAmelCase    :		Dict				=      ['stem'] + [F'''stage{idx}''' for idx in range(1    , len(_UpperCAmelCase ) + 1 )]
           lowerCAmelCase    :		str				=      get_aligned_output_features_output_indices(
               out_features=_UpperCAmelCase    , out_indices=_UpperCAmelCase    , stage_names=self.stage_names )









'''simple docstring'''




import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast


@dataclass
class  SCREAMING_SNAKE_CASE    (datasets.BuilderConfig							):
     lowerCAmelCase					=       None




class  SCREAMING_SNAKE_CASE    (datasets.ArrowBasedBuilder							):
     lowerCAmelCase					=       PandasConfig


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return datasets.DatasetInfo(features=self.config.features)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          if not self.config.data_files:
               raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}')
          __A						: Dict      =       dl_manager.download_and_extract(self.config.data_files)
          if isinstance(_UpperCAmelCase						,	(str, list, tuple)):
               __A						: Union[str, Any]      =       data_files
               if isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
                    __A						: Optional[int]      =       [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __A						: Optional[Any]      =       [dl_manager.iter_files(_UpperCAmelCase) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,	gen_kwargs={'files': files})]
          __A						: Tuple      =       []
          for split_name, files in data_files.items():
               if isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
                    __A						: Dict      =       [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __A						: Optional[Any]      =       [dl_manager.iter_files(_UpperCAmelCase) for file in files]
               splits.append(datasets.SplitGenerator(name=_UpperCAmelCase						,	gen_kwargs={'files': files}))
          return splits


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          if self.config.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               __A						: List[str]      =       table_cast(_UpperCAmelCase						,	self.config.features.arrow_schema)
          return pa_table


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          for i, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase)):
               with open(_UpperCAmelCase						,	'rb') as f:
                    __A						: Optional[int]      =       pa.Table.from_pandas(pd.read_pickle(_UpperCAmelCase))
                    yield i, self._cast_table(_UpperCAmelCase)