




import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase				:		Optional[Any]     =   50_000
lowerCamelCase				:		Dict     =   5_000

lowerCamelCase    ,    lowerCamelCase				:		Optional[Any]     =   os.path.split(__file__)
lowerCamelCase				:		Dict     =   os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))

@get_duration
def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       datasets.Dataset	,							lowercase	:       Tuple     ):


      '''simple docstring'''



      for i in range(lowercase     ):
            lowerCamelCase_		     =    dataset[i]

@get_duration
def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       datasets.Dataset	,							lowercase	:       Any	,							lowercase	:       Dict     ):


      '''simple docstring'''



      for i in range(0	,							len(lowercase     )	,							lowercase     ):
            lowerCamelCase_		     =    dataset[i : i + batch_size]

@get_duration
def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       datasets.Dataset	,							lowercase	:       Tuple	,							lowercase	:       List[str]     ):


      '''simple docstring'''



      with dataset.formatted_as(type=lowercase     ):
            for i in range(lowercase     ):
                  lowerCamelCase_		     =    dataset[i]

@get_duration
def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       datasets.Dataset	,							lowercase	:       Any	,							lowercase	:       Optional[Any]	,							lowercase	:       List[Any]     ):


      '''simple docstring'''



      with dataset.formatted_as(type=lowercase     ):
            for i in range(0	,							lowercase	,							lowercase     ):
                  lowerCamelCase_		     =    dataset[i : i + batch_size]

def 			_SCREAMING_SNAKE_CASE			(							):


      '''simple docstring'''



      lowerCamelCase_		     =    {'num examples': SPEED_TEST_N_EXAMPLES}
      lowerCamelCase_		     =    [
          (read, {'length': SMALL_TEST}),
          (read, {'length': SPEED_TEST_N_EXAMPLES}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
          (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
          (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
          (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
          (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
          (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
          (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
      ]

      lowerCamelCase_		     =    [
          (read, {'length': SMALL_TEST}),
          (read, {'length': SPEED_TEST_N_EXAMPLES}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
          (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
          (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
          (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
      ]
      with tempfile.TemporaryDirectory() as tmp_dir:
            print('generating dataset'     )
            lowerCamelCase_		     =    datasets.Features(
                {'list': datasets.Sequence(datasets.Value('float32'     )     ), 'numbers': datasets.Value('float32'     )}     )
            lowerCamelCase_		     =    generate_example_dataset(
                os.path.join(lowercase	,							'dataset.arrow'     )	,							lowercase	,							num_examples=lowercase	,							seq_shapes={'list': (1_00,)}	,							)
            print('first set of iterations'     )
            for func, kwargs in functions:
                  print(func.__name__	,							str(lowercase     )     )
                  lowerCamelCase_		     =    func(lowercase	,							**lowercase     )

            print('shuffling dataset'     )
            lowerCamelCase_		     =    dataset.shuffle()
            print('Second set of iterations (after shuffling'     )
            for func, kwargs in functions_shuffled:
                  print('shuffled '	,							func.__name__	,							str(lowercase     )     )
                  lowerCamelCase_		     =    func(
                      lowercase	,							**lowercase     )

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


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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase				:		str     =   {
    "configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		List[str]     =   [
     "NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
     "NezhaForNextSentencePrediction",
     "NezhaForMaskedLM",
     "NezhaForPreTraining",
     "NezhaForMultipleChoice",
     "NezhaForQuestionAnswering",
     "NezhaForSequenceClassification",
     "NezhaForTokenClassification",
     "NezhaModel",
     "NezhaPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nezha import (
      NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
      NezhaForMaskedLM,
      NezhaForMultipleChoice,
      NezhaForNextSentencePrediction,
      NezhaForPreTraining,
      NezhaForQuestionAnswering,
      NezhaForSequenceClassification,
      NezhaForTokenClassification,
      NezhaModel,
      NezhaPreTrainedModel,
  )


else:
 import sys

 lowerCamelCase				:		str     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



