






'''simple docstring'''

import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


SCREAMING_SNAKE_CASE__     =       5_0_0_0_0_0

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







@get_duration
def    lowercase__			(				__UpperCamelCase		,						**__UpperCamelCase   )->			Dict:
      UpperCamelCase      = dataset.map(**__UpperCamelCase   )







@get_duration
def    lowercase__			(				__UpperCamelCase		,						**__UpperCamelCase   )->			Union[str, Any]:
      UpperCamelCase      = dataset.filter(**__UpperCamelCase   )







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

            UpperCamelCase      = transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""		,						use_fast=__UpperCamelCase   )

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

            UpperCamelCase      = map(__UpperCamelCase   )

            UpperCamelCase      = map(__UpperCamelCase		,						batched=__UpperCamelCase   )

            UpperCamelCase      = map(__UpperCamelCase		,						function=lambda __UpperCamelCase   : None		,						batched=__UpperCamelCase   )

            with dataset.formatted_as(type="""numpy"""   ):
                  UpperCamelCase      = map(__UpperCamelCase		,						function=lambda __UpperCamelCase   : None		,						batched=__UpperCamelCase   )

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

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

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

            UpperCamelCase      = map(__UpperCamelCase		,						function=__UpperCamelCase		,						batched=__UpperCamelCase   )

            UpperCamelCase      = 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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__     =       {
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       ['MobileBertTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MobileBertForMaskedLM',
         'MobileBertForMultipleChoice',
         'MobileBertForNextSentencePrediction',
         'MobileBertForPreTraining',
         'MobileBertForQuestionAnswering',
         'MobileBertForSequenceClassification',
         'MobileBertForTokenClassification',
         'MobileBertLayer',
         'MobileBertModel',
         'MobileBertPreTrainedModel',
         'load_tf_weights_in_mobilebert',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFMobileBertForMaskedLM',
         'TFMobileBertForMultipleChoice',
         'TFMobileBertForNextSentencePrediction',
         'TFMobileBertForPreTraining',
         'TFMobileBertForQuestionAnswering',
         'TFMobileBertForSequenceClassification',
         'TFMobileBertForTokenClassification',
         'TFMobileBertMainLayer',
         'TFMobileBertModel',
         'TFMobileBertPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mobilebert import (
         MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileBertConfig,
         MobileBertOnnxConfig,
     )
     from .tokenization_mobilebert import MobileBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mobilebert_fast import MobileBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilebert import (
              MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileBertForMaskedLM,
              MobileBertForMultipleChoice,
              MobileBertForNextSentencePrediction,
              MobileBertForPreTraining,
              MobileBertForQuestionAnswering,
              MobileBertForSequenceClassification,
              MobileBertForTokenClassification,
              MobileBertLayer,
              MobileBertModel,
              MobileBertPreTrainedModel,
              load_tf_weights_in_mobilebert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mobilebert import (
              TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMobileBertForMaskedLM,
              TFMobileBertForMultipleChoice,
              TFMobileBertForNextSentencePrediction,
              TFMobileBertForPreTraining,
              TFMobileBertForQuestionAnswering,
              TFMobileBertForSequenceClassification,
              TFMobileBertForTokenClassification,
              TFMobileBertMainLayer,
              TFMobileBertModel,
              TFMobileBertPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE__     =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
