




"""simple docstring"""

import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def 						_snake_case     (					_snake_case							:					str							)    ->					Optional[int]:






      '''simple docstring'''


      def wrapper(*_snake_case							:					Optional[Any]				,  **_snake_case							:					Any							):
            _A					   =   timeit.default_timer()
            _A					   =   func(*__a				,  **__a							)
            _A					   =   timeit.default_timer() - starttime
            return delta

      _A					   =   func.__name__

      return wrapper






def 						_snake_case     (					_snake_case							:					dict				,  _snake_case							:					Dict=1_00				,  _snake_case							:					Optional[Any]=None							)    ->					Union[str, Any]:






      '''simple docstring'''


      _A					   =   []
      _A					   =   seq_shapes or {}
      for i in range(__a							):
            _A					   =   {}
            for col_id, (k, v) in enumerate(features.items()							):
                  if isinstance(__a				,  _ArrayXD							):
                        _A					   =   np.random.rand(*v.shape							).astype(v.dtype							)
                  elif isinstance(__a				,  datasets.Value							):
                        if v.dtype == "string":
                              _A					   =   '''The small grey turtle was surprisingly fast when challenged.'''
                        else:
                              _A					   =   np.random.randint(10				,  size=1							).astype(v.dtype							).item()
                  elif isinstance(__a				,  datasets.Sequence							):
                        while isinstance(__a				,  datasets.Sequence							):
                              _A					   =   v.feature
                        _A					   =   seq_shapes[k]
                        _A					   =   np.random.rand(*__a							).astype(v.dtype							)
                  _A					   =   data

            dummy_data.append((i, example)							)

      return dummy_data







def 						_snake_case     (					_snake_case							:					Tuple				,  _snake_case							:					List[Any]				,  _snake_case							:					Tuple=1_00				,  _snake_case							:					Tuple=None							)    ->					Any:






      '''simple docstring'''


      _A					   =   generate_examples(__a				,  num_examples=__a				,  seq_shapes=__a							)

      with ArrowWriter(features=__a				,  path=__a							) as writer:
            for key, record in dummy_data:
                  _A					   =   features.encode_example(__a							)
                  writer.write(__a							)

            _A					   =   writer.finalize()

      if not num_final_examples == num_examples:
            raise ValueError(
                F'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'''							)

      _A					   =   datasets.Dataset.from_file(filename=__a				,  info=datasets.DatasetInfo(features=__a							)							)

      return dataset




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


a__				=		logging.get_logger(__name__)

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

class 				UpperCAmelCase_		(			__lowercase				,       __lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "bit"
							UpperCAmelCase__       :						Optional[int]	    =    ["preactivation", "bottleneck"]
							UpperCAmelCase__       :						Optional[Any]	    =    ["SAME", "VALID"]


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

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



