



import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


UpperCAmelCase :				Union[str, Any]							=(30, 487)
UpperCAmelCase :				Tuple							=(36, 1024)
UpperCAmelCase :				str							=(100, 100)
UpperCAmelCase :				Dict							=100

UpperCAmelCase :				Any							=datasets.Features(
    {"""text""": ArrayaD(SHAPE_TEST_1, dtype="""float32"""), """image""": ArrayaD(SHAPE_TEST_2, dtype="""float32""")}
)

UpperCAmelCase			,			UpperCAmelCase :				Tuple							=os.path.split(__file__)
UpperCAmelCase :				str							=os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))



@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase):
					with ArrowWriter(features=_lowerCAmelCase ,						path=os.path.join(_lowerCAmelCase ,						"beta.arrow")) as writer:
										for key, record in dummy_data:
															UpperCamelCase_			= my_features.encode_example(_lowerCAmelCase)
															writer.write(_lowerCAmelCase)
										UpperCamelCase_	,     UpperCamelCase_			= writer.finalize()



@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= datasets.Dataset.from_file(
					    filename=os.path.join(_lowerCAmelCase ,						"beta.arrow") ,						info=datasets.DatasetInfo(features=_lowerCAmelCase))
					for _ in dataset:
										pass



@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= datasets.Dataset.from_file(
					    filename=os.path.join(_lowerCAmelCase ,						"beta.arrow") ,						info=datasets.DatasetInfo(features=_lowerCAmelCase))
					dataset.set_format("numpy")
					for _ in dataset:
										pass



@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= 10
					UpperCamelCase_			= datasets.Dataset.from_file(
					    filename=os.path.join(_lowerCAmelCase ,						"beta.arrow") ,						info=datasets.DatasetInfo(features=_lowerCAmelCase))
					for i in range(0 ,						len(_lowerCAmelCase) ,						_lowerCAmelCase):
										UpperCamelCase_			= dataset[i : i + batch_size]



@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= 10
					UpperCamelCase_			= datasets.Dataset.from_file(
					    filename=os.path.join(_lowerCAmelCase ,						"beta.arrow") ,						info=datasets.DatasetInfo(features=_lowerCAmelCase))
					dataset.set_format("numpy")
					for i in range(0 ,						len(_lowerCAmelCase) ,						_lowerCAmelCase):
										UpperCamelCase_			= dataset[i : i + batch_size]



@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= datasets.Dataset.from_file(
					    filename=os.path.join(_lowerCAmelCase ,						"beta.arrow") ,						info=datasets.DatasetInfo(features=_lowerCAmelCase))
					for col in feats:
										UpperCamelCase_			= dataset[col]



@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= datasets.Dataset.from_file(
					    filename=os.path.join(_lowerCAmelCase ,						"beta.arrow") ,						info=datasets.DatasetInfo(features=_lowerCAmelCase))
					dataset.set_format("numpy")
					for col in feats:
										UpperCamelCase_			= dataset[col]



def  _lowerCAmelCase     ():
					UpperCamelCase_			= {}
					UpperCamelCase_			= (
					    read_unformated,
					    read_formatted_as_numpy,
					    read_batch_unformated,
					    read_batch_formatted_as_numpy,
					    read_col_unformated,
					    read_col_formatted_as_numpy,
					)
					with tempfile.TemporaryDirectory() as tmp_dir:
										UpperCamelCase_			= datasets.Features({"image": ArrayaD(_lowerCAmelCase ,						dtype="float32")})
										UpperCamelCase_			= generate_examples(features=_lowerCAmelCase ,						num_examples=_lowerCAmelCase)
										UpperCamelCase_			= write(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase)
										for read_func in read_functions:
															UpperCamelCase_			= read_func(_lowerCAmelCase ,						_lowerCAmelCase)

					with tempfile.TemporaryDirectory() as tmp_dir:
										# don't use fixed length for fair comparison
										# feats = datasets.Features(
										#     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
										# )
										UpperCamelCase_			= datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))})
										UpperCamelCase_			= generate_examples(
										    features=_lowerCAmelCase ,						num_examples=_lowerCAmelCase ,						seq_shapes={"image": SPEED_TEST_SHAPE})
										UpperCamelCase_			= write(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase)
										for read_func in read_functions:
															UpperCamelCase_			= read_func(_lowerCAmelCase ,						_lowerCAmelCase)

					with tempfile.TemporaryDirectory() as tmp_dir:
										# don't use fixed length for fair comparison
										# feats = datasets.Features(
										#     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
										# )
										UpperCamelCase_			= datasets.Features({"image": datasets.Sequence(datasets.Value("float32"))})
										UpperCamelCase_			= generate_examples(
										    features=_lowerCAmelCase ,						num_examples=_lowerCAmelCase ,						seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]} ,						)
										UpperCamelCase_			= write(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase)
										for read_func in read_functions:
															UpperCamelCase_			= read_func(_lowerCAmelCase ,						_lowerCAmelCase)

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


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





'''simple docstring'''


from typing import TYPE_CHECKING

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


lowercase											=      {
    '''configuration_align''': [
        '''ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AlignConfig''',
        '''AlignTextConfig''',
        '''AlignVisionConfig''',
    ],
    '''processing_align''': ['''AlignProcessor'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase											=      [
      '''ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''AlignModel''',
      '''AlignPreTrainedModel''',
      '''AlignTextModel''',
      '''AlignVisionModel''',
  ]

if TYPE_CHECKING:
  from .configuration_align import (
      ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AlignConfig,
      AlignTextConfig,
      AlignVisionConfig,
  )
  from .processing_align import AlignProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_align import (
        ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
        AlignModel,
        AlignPreTrainedModel,
        AlignTextModel,
        AlignVisionModel,
    )

else:
  import sys

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


