



'''simple docstring'''





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowerCAmelCase      						=	50_0000

_lowerCAmelCase						,			_lowerCAmelCase      						=	os.path.split(__file__)
_lowerCAmelCase      						=	os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    **UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					dataset.map(**UpperCamelCase			)
@get_duration
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    **UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  List[Any]							      =					dataset.filter(**UpperCamelCase			)
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    lowerCAmelCase__ :  Tuple							      =					{"""num examples""": SPEED_TEST_N_EXAMPLES}
    with tempfile.TemporaryDirectory() as tmp_dir:
        lowerCAmelCase__ :  List[str]							      =					datasets.Features({"""text""": datasets.Value("""string"""			), """numbers""": datasets.Value("""float32"""			)}			)
        lowerCAmelCase__ :  Union[str, Any]							      =					generate_example_dataset(
            os.path.join(UpperCamelCase							,    """dataset.arrow"""			)							,    UpperCamelCase							,    num_examples=UpperCamelCase			)

        lowerCAmelCase__ :  Optional[Any]							      =					transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""							,    use_fast=UpperCamelCase			)

        def tokenize(UpperCamelCase			):
            return tokenizer(examples["""text"""]			)

        lowerCAmelCase__ :  int							      =					map(UpperCamelCase			)

        lowerCAmelCase__ :  List[Any]							      =					map(UpperCamelCase							,    batched=UpperCamelCase			)

        lowerCAmelCase__ :  int							      =					map(UpperCamelCase							,    function=lambda UpperCamelCase			: None							,    batched=UpperCamelCase			)

        with dataset.formatted_as(type="""numpy"""			):
            lowerCAmelCase__ :  Optional[Any]							      =					map(UpperCamelCase							,    function=lambda UpperCamelCase			: None							,    batched=UpperCamelCase			)

        with dataset.formatted_as(type="""pandas"""			):
            lowerCAmelCase__ :  Dict							      =					map(UpperCamelCase							,    function=lambda UpperCamelCase			: None							,    batched=UpperCamelCase			)

        with dataset.formatted_as(type="""torch"""							,    columns="""numbers"""			):
            lowerCAmelCase__ :  Any							      =					map(UpperCamelCase							,    function=lambda UpperCamelCase			: None							,    batched=UpperCamelCase			)

        with dataset.formatted_as(type="""tensorflow"""							,    columns="""numbers"""			):
            lowerCAmelCase__ :  Dict							      =					map(UpperCamelCase							,    function=lambda UpperCamelCase			: None							,    batched=UpperCamelCase			)

        lowerCAmelCase__ :  Optional[int]							      =					map(UpperCamelCase							,    function=UpperCamelCase							,    batched=UpperCamelCase			)

        lowerCAmelCase__ :  Any							      =					filter(UpperCamelCase			)

        # 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(UpperCamelCase							,    """wb"""			) as f:
        f.write(json.dumps(UpperCamelCase			).encode("""utf-8"""			)			)


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







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase      						=	logging.get_logger(__name__)

_lowerCAmelCase      						=	{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      int				=      '''xlm-roberta-xl'''




  def __init__(    self  ,__UpperCAmelCase=25_0880  ,__UpperCAmelCase=2560  ,__UpperCAmelCase=36  ,__UpperCAmelCase=32  ,__UpperCAmelCase=1_0240  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=514  ,__UpperCAmelCase=1  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-05  ,__UpperCAmelCase=1  ,__UpperCAmelCase=0  ,__UpperCAmelCase=2  ,__UpperCAmelCase="absolute"  ,__UpperCAmelCase=True  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)    ->		str:
      super().__init__(pad_token_id=__UpperCAmelCase  ,bos_token_id=__UpperCAmelCase  ,eos_token_id=__UpperCAmelCase  ,**__UpperCAmelCase      )
      lowerCAmelCase__ :  List[Any]							      =					vocab_size
      lowerCAmelCase__ :  int							      =					hidden_size
      lowerCAmelCase__ :  int							      =					num_hidden_layers
      lowerCAmelCase__ :  str							      =					num_attention_heads
      lowerCAmelCase__ :  int							      =					hidden_act
      lowerCAmelCase__ :  Dict							      =					intermediate_size
      lowerCAmelCase__ :  List[Any]							      =					hidden_dropout_prob
      lowerCAmelCase__ :  str							      =					attention_probs_dropout_prob
      lowerCAmelCase__ :  Optional[int]							      =					max_position_embeddings
      lowerCAmelCase__ :  List[str]							      =					type_vocab_size
      lowerCAmelCase__ :  List[Any]							      =					initializer_range
      lowerCAmelCase__ :  Tuple							      =					layer_norm_eps
      lowerCAmelCase__ :  int							      =					position_embedding_type
      lowerCAmelCase__ :  Optional[Any]							      =					use_cache
      lowerCAmelCase__ :  Optional[Any]							      =					classifier_dropout







class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  @property
  def 							UpperCAmelCase_	(    self      )    ->		Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
          lowerCAmelCase__ :  Dict							      =					{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
          lowerCAmelCase__ :  Any							      =					{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]      )


