




import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCAmelCase__						:       str							=    500000

UpperCAmelCase__						:       Union[str, Any]							=    os.path.split(__file__)
UpperCAmelCase__						:       Dict							=    os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def 		_lowerCamelCase				(					UpperCamelCase							)			->		List[Any]:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    dataset.select(range(0					,     len(__lowerCamelCase							)					,     2							)							)


@get_duration
def 		_lowerCamelCase				(					UpperCamelCase							)			->		int:





  """simple docstring"""





  _lowercase: str						     =    dataset.sort('numbers'							)


@get_duration
def 		_lowerCamelCase				(					UpperCamelCase							)			->		List[str]:





  """simple docstring"""





  _lowercase: Tuple						     =    dataset.shuffle()


@get_duration
def 		_lowerCamelCase				(					UpperCamelCase							)			->		Dict:





  """simple docstring"""





  _lowercase: Tuple						     =    dataset.train_test_split(0.1							)


@get_duration
def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase=10							)			->		int:





  """simple docstring"""





  for shard_id in range(__lowerCamelCase							):
    _lowercase: str						     =    dataset.shard(__lowerCamelCase					,     __lowerCamelCase							)


def 		_lowerCamelCase				(					)			->		Optional[Any]:





  """simple docstring"""





  _lowercase: List[Any]						     =    {"""num examples""": SPEED_TEST_N_EXAMPLES}
  _lowercase: Dict						     =    (select, sort, shuffle, train_test_split, shard)
  with tempfile.TemporaryDirectory() as tmp_dir:
    print('generating dataset'							)
    _lowercase: Dict						     =    datasets.Features({'text': datasets.Value('string'							), 'numbers': datasets.Value('float32'							)}							)
    _lowercase: Dict						     =    generate_example_dataset(
        os.path.join(__lowerCamelCase					,     'dataset.arrow'							)					,     __lowerCamelCase					,     num_examples=__lowerCamelCase							)
    print('Functions'							)
    for func in functions:
      print(func.__name__							)
      _lowercase: Dict						     =    func(__lowerCamelCase							)

  with open(__lowerCamelCase					,     'wb'							) as f:
    f.write(json.dumps(__lowerCamelCase							).encode('utf-8'							)							)


if __name__ == "__main__":  # useful to run the profiler
   benchmark_indices_mapping()






import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


UpperCAmelCase__						:       Dict							=    logging.get_logger(__name__)

UpperCAmelCase__						:       Optional[Any]							=    {
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							int							=					"vit-hybrid"






   def __init__(	self   :       Optional[int]						,					UpperCamelCase_   :       Any=None						,					UpperCamelCase_   :       str=7_68						,					UpperCamelCase_   :       Optional[int]=12						,					UpperCamelCase_   :       str=12						,					UpperCamelCase_   :       Dict=30_72						,					UpperCamelCase_   :       Dict="gelu"						,					UpperCamelCase_   :       str=0.0						,					UpperCamelCase_   :       List[Any]=0.0						,					UpperCamelCase_   :       List[str]=0.02						,					UpperCamelCase_   :       Tuple=1E-12						,					UpperCamelCase_   :       Union[str, Any]=2_24						,					UpperCamelCase_   :       Any=1						,					UpperCamelCase_   :       Optional[Any]=3						,					UpperCamelCase_   :       Optional[Any]=[1, 10_24, 24, 24]						,					UpperCamelCase_   :       Any=True						,					**UpperCamelCase_   :       int						,					)->			Any:
     super().__init__(**UpperCamelCase_     )

     if backbone_config is None:
       logger.info('`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'     )
       _lowercase: Dict						     =    {
           'global_padding': 'same',
           'layer_type': 'bottleneck',
           'depths': [3, 4, 9],
           'out_features': ['stage3'],
           'embedding_dynamic_padding': True,
       }

     if isinstance(UpperCamelCase_						,					UpperCamelCase_     ):
       if "model_type" in backbone_config:
         _lowercase: int						     =    CONFIG_MAPPING[backbone_config['model_type']]
       else:
         logger.info(
             '`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'     )
         _lowercase: Optional[int]						     =    BitConfig
       _lowercase: List[Any]						     =    backbone_config_class(**UpperCamelCase_     )

     _lowercase: List[Any]						     =    backbone_featmap_shape
     _lowercase: Dict						     =    backbone_config
     _lowercase: Optional[int]						     =    hidden_size
     _lowercase: List[Any]						     =    num_hidden_layers
     _lowercase: Union[str, Any]						     =    num_attention_heads
     _lowercase: str						     =    intermediate_size
     _lowercase: Optional[int]						     =    hidden_act
     _lowercase: int						     =    hidden_dropout_prob
     _lowercase: List[str]						     =    attention_probs_dropout_prob
     _lowercase: Tuple						     =    initializer_range
     _lowercase: Any						     =    layer_norm_eps
     _lowercase: Any						     =    image_size
     _lowercase: List[Any]						     =    patch_size
     _lowercase: Dict						     =    num_channels
     _lowercase: Tuple						     =    qkv_bias






   def  _a						(	self   :       Optional[int]     )->			Dict[str, any]:
     _lowercase: List[str]						     =    copy.deepcopy(self.__dict__     )
     _lowercase: Tuple						     =    self.backbone_config.to_dict()
     _lowercase: int						     =    self.__class__.model_type
     return output
