"""simple docstring"""




import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_a				:		int	   =       50_000
_a				:		Dict	   =       5_000

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





@get_duration
def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Union[str, Any]     )	->				List[Any]:
  for i in range(_A     ):
    _lowerCAmelCase						:	List[str]        =					dataset[i]





@get_duration
def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Union[str, Any]      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	Tuple     )	->				Dict:
  for i in range(0      ,len(_A     )      ,_A     ):
    _lowerCAmelCase						:	int        =					dataset[i : i + batch_size]





@get_duration
def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Union[str, Any]      ,_lowerCamelCase						:	List[Any]      ,_lowerCamelCase						:	Dict     )	->				List[Any]:
  with dataset.formatted_as(type=_A     ):
    for i in range(_A     ):
      _lowerCAmelCase						:	Any        =					dataset[i]





@get_duration
def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Union[str, Any]      ,_lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Any      ,_lowerCamelCase						:	Tuple     )	->				Dict:
  with dataset.formatted_as(type=_A     ):
    for i in range(0      ,_A      ,_A     ):
      _lowerCAmelCase						:	str        =					dataset[i : i + batch_size]





def 							SCREAMING_SNAKE_CASE					(      )	->				Optional[int]:
  _lowerCAmelCase						:	int        =					{'num examples': SPEED_TEST_N_EXAMPLES}
  _lowerCAmelCase						:	Dict        =					[
      (read, {'length': SMALL_TEST}),
      (read, {'length': SPEED_TEST_N_EXAMPLES}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
      (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
      (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
      (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
      (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
      (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
      (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
  ]

  _lowerCAmelCase						:	List[Any]        =					[
      (read, {'length': SMALL_TEST}),
      (read, {'length': SPEED_TEST_N_EXAMPLES}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
      (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
      (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
      (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
  ]
  with tempfile.TemporaryDirectory() as tmp_dir:
    print("""generating dataset"""     )
    _lowerCAmelCase						:	Dict        =					datasets.Features(
        {"""list""": datasets.Sequence(datasets.Value("""float32"""     )     ), """numbers""": datasets.Value("""float32"""     )}     )
    _lowerCAmelCase						:	Dict        =					generate_example_dataset(
        os.path.join(_A      ,"""dataset.arrow"""     )      ,_A      ,num_examples=_A      ,seq_shapes={"""list""": (100,)}      ,)
    print("""first set of iterations"""     )
    for func, kwargs in functions:
      print(func.__name__      ,str(_A     )     )
      _lowerCAmelCase						:	Optional[Any]        =					func(_A      ,**_A     )

    print("""shuffling dataset"""     )
    _lowerCAmelCase						:	Optional[Any]        =					dataset.shuffle()
    print("""Second set of iterations (after shuffling"""     )
    for func, kwargs in functions_shuffled:
      print("""shuffled """      ,func.__name__      ,str(_A     )     )
      _lowerCAmelCase						:	List[Any]        =					func(
          _A      ,**_A     )

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


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





"""simple docstring"""




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


_a				:		Any	   =       logging.get_logger(__name__)

_a				:		Any	   =       {
    'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
    'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
    'uclanlp/visualbert-vqa-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
    'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
    'uclanlp/visualbert-vcr-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[Any]		 =		"visual_bert"






     def __init__(		self    ,					a__=30522    ,					a__=768    ,					a__=512    ,					a__=12    ,					a__=12    ,					a__=3072    ,					a__="gelu"    ,					a__=0.1    ,					a__=0.1    ,					a__=512    ,					a__=2    ,					a__=0.0_2    ,					a__=1e-12    ,					a__=False    ,					a__=True    ,					a__=1    ,					a__=0    ,					a__=2    ,					**a__    ,					):
       super().__init__(pad_token_id=a__    ,					bos_token_id=a__    ,					eos_token_id=a__    ,					**a__  )

       _lowerCAmelCase						:	Optional[Any]        =					vocab_size
       _lowerCAmelCase						:	int        =					max_position_embeddings
       _lowerCAmelCase						:	Optional[Any]        =					hidden_size
       _lowerCAmelCase						:	Optional[int]        =					visual_embedding_dim
       _lowerCAmelCase						:	Optional[int]        =					num_hidden_layers
       _lowerCAmelCase						:	List[Any]        =					num_attention_heads
       _lowerCAmelCase						:	Any        =					intermediate_size
       _lowerCAmelCase						:	List[Any]        =					hidden_act
       _lowerCAmelCase						:	int        =					hidden_dropout_prob
       _lowerCAmelCase						:	Optional[Any]        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Any        =					initializer_range
       _lowerCAmelCase						:	List[Any]        =					type_vocab_size
       _lowerCAmelCase						:	Union[str, Any]        =					layer_norm_eps
       _lowerCAmelCase						:	Optional[Any]        =					bypass_transformer
       _lowerCAmelCase						:	List[Any]        =					special_visual_initialize




