


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__UpperCAmelCase    				=      500_000

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





@get_duration
def     lowercase__	(  lowerCamelCase     :  Union[str, Any]				)					->     Optional[int]:
  lowerCAmelCase__    :    int								=					dataset.select(range(0				,	len(lowerCamelCase				)				,	2				)				)





@get_duration
def     lowercase__	(  lowerCamelCase     :  List[Any]				)					->     List[Any]:
  lowerCAmelCase__    :    Optional[int]								=					dataset.sort("numbers"				)





@get_duration
def     lowercase__	(  lowerCamelCase     :  Optional[int]				)					->     List[Any]:
  lowerCAmelCase__    :    List[Any]								=					dataset.shuffle()





@get_duration
def     lowercase__	(  lowerCamelCase     :  Dict				)					->     Any:
  lowerCAmelCase__    :    Tuple								=					dataset.train_test_split(0.1				)





@get_duration
def     lowercase__	(  lowerCamelCase     :  Any				,	lowerCamelCase     :  Optional[Any]=1_0				)					->     int:
  for shard_id in range(lowerCamelCase				):
    lowerCAmelCase__    :    Any								=					dataset.shard(lowerCamelCase				,	lowerCamelCase				)





def     lowercase__	(  )					->     Union[str, Any]:
  lowerCAmelCase__    :    Optional[int]								=					{'''num examples''': SPEED_TEST_N_EXAMPLES}
  lowerCAmelCase__    :    Tuple								=					(select, sort, shuffle, train_test_split, shard)
  with tempfile.TemporaryDirectory() as tmp_dir:
    print("generating dataset"				)
    lowerCAmelCase__    :    List[Any]								=					datasets.Features({"text": datasets.Value("string"				), "numbers": datasets.Value("float32"				)}				)
    lowerCAmelCase__    :    str								=					generate_example_dataset(
        os.path.join(lowerCamelCase				,	"dataset.arrow"				)				,	lowerCamelCase				,	num_examples=lowerCamelCase				)
    print("Functions"				)
    for func in functions:
      print(func.__name__				)
      lowerCAmelCase__    :    List[Any]								=					func(lowerCamelCase				)

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


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




"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCAmelCase    				=      {
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase    				=      [
       "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMForMaskedLM",
       "LayoutLMForSequenceClassification",
       "LayoutLMForTokenClassification",
       "LayoutLMForQuestionAnswering",
       "LayoutLMModel",
       "LayoutLMPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase    				=      [
       "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFLayoutLMForMaskedLM",
       "TFLayoutLMForSequenceClassification",
       "TFLayoutLMForTokenClassification",
       "TFLayoutLMForQuestionAnswering",
       "TFLayoutLMMainLayer",
       "TFLayoutLMModel",
       "TFLayoutLMPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )

else:
   import sys

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