





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase_          =		50_000
lowercase_          =		5_000

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




@get_duration
def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       List[Any]:
  for i in range(_SCREAMING_SNAKE_CASE		):
    lowercase__           =  dataset[i]




@get_duration
def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       str:
  for i in range(0		,						len(_SCREAMING_SNAKE_CASE		)		,						_SCREAMING_SNAKE_CASE		):
    lowercase__           =  dataset[i : i + batch_size]




@get_duration
def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       str:
  with dataset.formatted_as(type=_SCREAMING_SNAKE_CASE		):
    for i in range(_SCREAMING_SNAKE_CASE		):
      lowercase__           =  dataset[i]




@get_duration
def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Optional[Any]:
  with dataset.formatted_as(type=_SCREAMING_SNAKE_CASE		):
    for i in range(0		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		):
      lowercase__           =  dataset[i : i + batch_size]




def       __UpperCamelCase  ()    ->       List[Any]:
  lowercase__           =  {'num examples': SPEED_TEST_N_EXAMPLES}
  lowercase__           =  [
      (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': 100}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
      (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': 1000}),
  ]

  lowercase__           =  [
      (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': 100}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
      (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': 1000}),
  ]
  with tempfile.TemporaryDirectory() as tmp_dir:
    print('generating dataset'		)
    lowercase__           =  datasets.Features(
        {'list': datasets.Sequence(datasets.Value('float32'		)		), 'numbers': datasets.Value('float32'		)}		)
    lowercase__           =  generate_example_dataset(
        os.path.join(_SCREAMING_SNAKE_CASE		,						'dataset.arrow'		)		,						_SCREAMING_SNAKE_CASE		,						num_examples=_SCREAMING_SNAKE_CASE		,						seq_shapes={'list': (100,)}		,						)
    print('first set of iterations'		)
    for func, kwargs in functions:
      print(func.__name__		,						str(_SCREAMING_SNAKE_CASE		)		)
      lowercase__           =  func(_SCREAMING_SNAKE_CASE		,						**_SCREAMING_SNAKE_CASE		)

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

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


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









from typing import TYPE_CHECKING

from ..utils import _LazyModule


lowercase_          =		{
    """config""": [
        """EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
        """OnnxConfig""",
        """OnnxConfigWithPast""",
        """OnnxSeq2SeqConfigWithPast""",
        """PatchingSpec""",
    ],
    """convert""": ["""export""", """validate_model_outputs"""],
    """features""": ["""FeaturesManager"""],
    """utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""],
}


if TYPE_CHECKING:
      from .config import (
          EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
          OnnxConfig,
          OnnxConfigWithPast,
          OnnxSeqaSeqConfigWithPast,
          PatchingSpec,
      )
      from .convert import export, validate_model_outputs
      from .features import FeaturesManager
      from .utils import ParameterFormat, compute_serialized_parameters_size

else:
      import sys

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


