




'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		:	Optional[Any]       =      logging.get_logger(__name__)

a		:	int       =      {
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}




class      UpperCamelCase       (	snake_case__       ):
  a     :int          =					'yoso'



  def __init__(			self		,     __lowerCAmelCase=5_0265		,     __lowerCAmelCase=768		,     __lowerCAmelCase=12		,     __lowerCAmelCase=12		,     __lowerCAmelCase=3072		,     __lowerCAmelCase="gelu"		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase=4096		,     __lowerCAmelCase=1		,     __lowerCAmelCase=0.02		,     __lowerCAmelCase=1e-12		,     __lowerCAmelCase="absolute"		,     __lowerCAmelCase=True		,     __lowerCAmelCase=9		,     __lowerCAmelCase=64		,     __lowerCAmelCase=None		,     __lowerCAmelCase=True		,     __lowerCAmelCase=True		,     __lowerCAmelCase=1		,     __lowerCAmelCase=0		,     __lowerCAmelCase=2		,     **__lowerCAmelCase		,     ):







      '''simple docstring'''


      super().__init__(pad_token_id=__lowerCAmelCase		,     bos_token_id=__lowerCAmelCase		,     eos_token_id=__lowerCAmelCase		,     **__lowerCAmelCase )

      _a				:							Tuple							       =      vocab_size
      _a				:							List[Any]							       =      max_position_embeddings
      _a				:							int							       =      hidden_size
      _a				:							Optional[int]							       =      num_hidden_layers
      _a				:							Any							       =      num_attention_heads
      _a				:							Any							       =      intermediate_size
      _a				:							Optional[Any]							       =      hidden_act
      _a				:							Union[str, Any]							       =      hidden_dropout_prob
      _a				:							List[str]							       =      attention_probs_dropout_prob
      _a				:							Optional[Any]							       =      initializer_range
      _a				:							Optional[Any]							       =      type_vocab_size
      _a				:							str							       =      layer_norm_eps
      _a				:							str							       =      position_embedding_type
      _a				:							Optional[int]							       =      use_expectation
      _a				:							List[Any]							       =      hash_code_len
      _a				:							Optional[Any]							       =      num_hash
      _a				:							Optional[Any]							       =      conv_window
      _a				:							Optional[Any]							       =      use_fast_hash
      _a				:							Optional[Any]							       =      lsh_backward






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_A									=		50_0000

_A			,	_A									=		os.path.split(__file__)
_A									=		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.select(range(0     ,	len(SCREAMING_SNAKE_CASE__							)     ,	2							)							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.sort('numbers'							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.shuffle()

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.train_test_split(0.1							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset     ,	SCREAMING_SNAKE_CASE__     :   int=10							):
     for shard_id in range(SCREAMING_SNAKE_CASE__							):
          __UpperCamelCase       				=dataset.shard(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     ):
     __UpperCamelCase       				={'num examples': SPEED_TEST_N_EXAMPLES}
     __UpperCamelCase       				=(select, sort, shuffle, train_test_split, shard)
     with tempfile.TemporaryDirectory() as tmp_dir:
          print('generating dataset'							)
          __UpperCamelCase       				=datasets.Features({'text': datasets.Value('string'							), 'numbers': datasets.Value('float32'							)}							)
          __UpperCamelCase       				=generate_example_dataset(
              os.path.join(SCREAMING_SNAKE_CASE__     ,	'dataset.arrow'							)     ,	SCREAMING_SNAKE_CASE__     ,	num_examples=SCREAMING_SNAKE_CASE__							)
          print('Functions'							)
          for func in functions:
               print(func.__name__							)
               __UpperCamelCase       				=func(SCREAMING_SNAKE_CASE__							)

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


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





