




'''simple docstring'''



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


SCREAMING_SNAKE_CASE						=	500_000

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_				(    __A :       datasets.Dataset					,      **__A :       Dict			)       ->      List[Any]:



 """simple docstring"""




 lowercase					:   Optional[Any]				  =dataset.map(**UpperCamelCase__			)


@get_duration
def 							lowercase_				(    __A :       datasets.Dataset					,      **__A :       Union[str, Any]			)       ->      Union[str, Any]:



 """simple docstring"""




 lowercase					:   Optional[int]				  =dataset.filter(**UpperCamelCase__			)


def 							lowercase_				(    )       ->      Any:



 """simple docstring"""




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

  lowercase					:   Tuple				  =transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''					,      use_fast=UpperCamelCase__			)

  def tokenize(__A :       Dict			):
   return tokenizer(examples['''text''']			)

  lowercase					:   Any				  =map(UpperCamelCase__			)

  lowercase					:   Union[str, Any]				  =map(UpperCamelCase__					,      batched=UpperCamelCase__			)

  lowercase					:   List[str]				  =map(UpperCamelCase__					,      function=lambda __A			: None					,      batched=UpperCamelCase__			)

  with dataset.formatted_as(type='''numpy'''			):
   lowercase					:   Dict				  =map(UpperCamelCase__					,      function=lambda __A			: None					,      batched=UpperCamelCase__			)

  with dataset.formatted_as(type='''pandas'''			):
   lowercase					:   List[Any]				  =map(UpperCamelCase__					,      function=lambda __A			: None					,      batched=UpperCamelCase__			)

  with dataset.formatted_as(type='''torch'''					,      columns='''numbers'''			):
   lowercase					:   str				  =map(UpperCamelCase__					,      function=lambda __A			: None					,      batched=UpperCamelCase__			)

  with dataset.formatted_as(type='''tensorflow'''					,      columns='''numbers'''			):
   lowercase					:   Tuple				  =map(UpperCamelCase__					,      function=lambda __A			: None					,      batched=UpperCamelCase__			)

  lowercase					:   Tuple				  =map(UpperCamelCase__					,      function=UpperCamelCase__					,      batched=UpperCamelCase__			)

  lowercase					:   List[str]				  =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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE						=	{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	['RemBertTokenizer']

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RemBertForCausalLM',
          'RemBertForMaskedLM',
          'RemBertForMultipleChoice',
          'RemBertForQuestionAnswering',
          'RemBertForSequenceClassification',
          'RemBertForTokenClassification',
          'RemBertLayer',
          'RemBertModel',
          'RemBertPreTrainedModel',
          'load_tf_weights_in_rembert',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRemBertForCausalLM',
          'TFRemBertForMaskedLM',
          'TFRemBertForMultipleChoice',
          'TFRemBertForQuestionAnswering',
          'TFRemBertForSequenceClassification',
          'TFRemBertForTokenClassification',
          'TFRemBertLayer',
          'TFRemBertModel',
          'TFRemBertPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert import RemBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert_fast import RemBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rembert import (
                REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RemBertForCausalLM,
                RemBertForMaskedLM,
                RemBertForMultipleChoice,
                RemBertForQuestionAnswering,
                RemBertForSequenceClassification,
                RemBertForTokenClassification,
                RemBertLayer,
                RemBertModel,
                RemBertPreTrainedModel,
                load_tf_weights_in_rembert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rembert import (
                TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRemBertForCausalLM,
                TFRemBertForMaskedLM,
                TFRemBertForMultipleChoice,
                TFRemBertForQuestionAnswering,
                TFRemBertForSequenceClassification,
                TFRemBertForTokenClassification,
                TFRemBertLayer,
                TFRemBertModel,
                TFRemBertPreTrainedModel,
            )


else:
      import sys

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



