






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


__UpperCamelCase   :   int      =    logging.get_logger(__name__)

# TODO: upload to AWS
__UpperCamelCase   :   int      =    {
    """yjernite/retribert-base-uncased""": (
        """https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"""
    ),
}


class 							__UpperCamelCase     (  _lowerCAmelCase ):
	__snake_case						:Any =							'retribert'







	def __init__(	self   :				int							,					_lowerCAmelCase   :				Tuple=3_0522							,					_lowerCAmelCase   :				Dict=768							,					_lowerCAmelCase   :				str=8							,					_lowerCAmelCase   :				List[str]=12							,					_lowerCAmelCase   :				Any=3072							,					_lowerCAmelCase   :				Optional[Any]="gelu"							,					_lowerCAmelCase   :				Union[str, Any]=0.1							,					_lowerCAmelCase   :				Optional[Any]=0.1							,					_lowerCAmelCase   :				str=512							,					_lowerCAmelCase   :				Any=2							,					_lowerCAmelCase   :				int=0.02							,					_lowerCAmelCase   :				Tuple=1e-12							,					_lowerCAmelCase   :				Union[str, Any]=True							,					_lowerCAmelCase   :				Any=128							,					_lowerCAmelCase   :				Optional[int]=0							,					**_lowerCAmelCase   :				List[str]							,					)		->			Any:
		"""simple docstring"""







		super().__init__(pad_token_id=_lowerCAmelCase							,					**_lowerCAmelCase      )

		__lowercase   							=	vocab_size
		__lowercase   							=	hidden_size
		__lowercase   							=	num_hidden_layers
		__lowercase   							=	num_attention_heads
		__lowercase   							=	hidden_act
		__lowercase   							=	intermediate_size
		__lowercase   							=	hidden_dropout_prob
		__lowercase   							=	attention_probs_dropout_prob
		__lowercase   							=	max_position_embeddings
		__lowercase   							=	type_vocab_size
		__lowercase   							=	initializer_range
		__lowercase   							=	layer_norm_eps
		__lowercase   							=	share_encoders
		__lowercase   							=	projection_dim




import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_   							=  5_0_0_0_0_0

lowercase_							,		lowercase_   							=  os.path.split(__file__)
lowercase_   							=  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[str]	)       -> Optional[Any]:





      """simple docstring"""



      _lowercase      			=dataset.map(**A__	)
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[Any]	)       -> List[str]:





      """simple docstring"""



      _lowercase      			=dataset.filter(**A__	)
def 	a (     )       -> Union[str, Any]:





      """simple docstring"""



      _lowercase      			={'num examples': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            _lowercase      			=datasets.Features({'text': datasets.Value('string'	), 'numbers': datasets.Value('float32'	)}	)
            _lowercase      			=generate_example_dataset(
                os.path.join(A__ ,  'dataset.arrow'	) ,  A__ ,  num_examples=A__	)

            _lowercase      			=transformers.AutoTokenizer.from_pretrained('bert-base-cased' ,  use_fast=A__	)

            def tokenize(A__      :  int	):
                  return tokenizer(examples['text']	)

            _lowercase      			=map(A__	)

            _lowercase      			=map(A__ ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='numpy'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='pandas'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='torch' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='tensorflow' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=A__ ,  batched=A__	)

            _lowercase      			=filter(A__	)

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

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


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


