




import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


a_   		=     50_0000

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




@get_duration
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     **lowercase_      )       ->	Optional[int]:

    """simple docstring"""


    __UpperCamelCase  		=						dataset.map(**lowercase_      )




@get_duration
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     **lowercase_      )       ->	Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase  		=						dataset.filter(**lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  )       ->	List[str]:

    """simple docstring"""


    __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(lowercase_					,     '''dataset.arrow'''      )					,     lowercase_					,     num_examples=lowercase_      )

        __UpperCamelCase  		=						transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''					,     use_fast=lowercase_      )

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

        __UpperCamelCase  		=						map(lowercase_      )

        __UpperCamelCase  		=						map(lowercase_					,     batched=lowercase_      )

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

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

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

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

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

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

        __UpperCamelCase  		=						filter(lowercase_      )

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


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












from typing import TYPE_CHECKING

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


a_   		=     {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     ["LongformerTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     [
     "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LongformerForMaskedLM",
     "LongformerForMultipleChoice",
     "LongformerForQuestionAnswering",
     "LongformerForSequenceClassification",
     "LongformerForTokenClassification",
     "LongformerModel",
     "LongformerPreTrainedModel",
     "LongformerSelfAttention",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     [
     "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFLongformerForMaskedLM",
     "TFLongformerForMultipleChoice",
     "TFLongformerForQuestionAnswering",
     "TFLongformerForSequenceClassification",
     "TFLongformerForTokenClassification",
     "TFLongformerModel",
     "TFLongformerPreTrainedModel",
     "TFLongformerSelfAttention",
 ]


if TYPE_CHECKING:
 from .configuration_longformer import (
     LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LongformerConfig,
     LongformerOnnxConfig,
 )
 from .tokenization_longformer import LongformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_longformer_fast import LongformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longformer import (
      LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongformerForMaskedLM,
      LongformerForMultipleChoice,
      LongformerForQuestionAnswering,
      LongformerForSequenceClassification,
      LongformerForTokenClassification,
      LongformerModel,
      LongformerPreTrainedModel,
      LongformerSelfAttention,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_longformer import (
      TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLongformerForMaskedLM,
      TFLongformerForMultipleChoice,
      TFLongformerForQuestionAnswering,
      TFLongformerForSequenceClassification,
      TFLongformerForTokenClassification,
      TFLongformerModel,
      TFLongformerPreTrainedModel,
      TFLongformerSelfAttention,
  )

else:
 import sys

 a_   		=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






