
'''simple docstring'''
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


lowercase__						=			(3_0, 4_8_7)
lowercase__						=			(3_6, 1_0_2_4)
lowercase__						=			(1_0_0, 1_0_0)
lowercase__						=			1_0_0

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

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__   ,					SCREAMING_SNAKE_CASE__ )     ->       List[str]:


  '''simple docstring'''


  with ArrowWriter(features=_UpperCamelCase   ,					path=os.path.join(_UpperCamelCase   ,					'''beta.arrow''' ) ) as writer:
    for key, record in dummy_data:
      snake_case     :					List[str]     =	my_features.encode_example(_UpperCamelCase )
      writer.write(_UpperCamelCase )
    snake_case ,snake_case     :					Any     =	writer.finalize()




@get_duration
def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:


  '''simple docstring'''


  snake_case     :					int     =	datasets.Dataset.from_file(
      filename=os.path.join(_UpperCamelCase   ,					'''beta.arrow''' )   ,					info=datasets.DatasetInfo(features=_UpperCamelCase ) )
  for _ in dataset:
    pass




@get_duration
def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Optional[int]:


  '''simple docstring'''


  snake_case     :					Tuple     =	datasets.Dataset.from_file(
      filename=os.path.join(_UpperCamelCase   ,					'''beta.arrow''' )   ,					info=datasets.DatasetInfo(features=_UpperCamelCase ) )
  dataset.set_format('''numpy''' )
  for _ in dataset:
    pass




@get_duration
def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       List[Any]:


  '''simple docstring'''


  snake_case     :					Optional[Any]     =	10
  snake_case     :					Optional[Any]     =	datasets.Dataset.from_file(
      filename=os.path.join(_UpperCamelCase   ,					'''beta.arrow''' )   ,					info=datasets.DatasetInfo(features=_UpperCamelCase ) )
  for i in range(0   ,					len(_UpperCamelCase )   ,					_UpperCamelCase ):
    snake_case     :					Dict     =	dataset[i : i + batch_size]




@get_duration
def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Any:


  '''simple docstring'''


  snake_case     :					Tuple     =	10
  snake_case     :					Union[str, Any]     =	datasets.Dataset.from_file(
      filename=os.path.join(_UpperCamelCase   ,					'''beta.arrow''' )   ,					info=datasets.DatasetInfo(features=_UpperCamelCase ) )
  dataset.set_format('''numpy''' )
  for i in range(0   ,					len(_UpperCamelCase )   ,					_UpperCamelCase ):
    snake_case     :					str     =	dataset[i : i + batch_size]




@get_duration
def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       List[Any]:


  '''simple docstring'''


  snake_case     :					Optional[int]     =	datasets.Dataset.from_file(
      filename=os.path.join(_UpperCamelCase   ,					'''beta.arrow''' )   ,					info=datasets.DatasetInfo(features=_UpperCamelCase ) )
  for col in feats:
    snake_case     :					Tuple     =	dataset[col]




@get_duration
def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Tuple:


  '''simple docstring'''


  snake_case     :					List[str]     =	datasets.Dataset.from_file(
      filename=os.path.join(_UpperCamelCase   ,					'''beta.arrow''' )   ,					info=datasets.DatasetInfo(features=_UpperCamelCase ) )
  dataset.set_format('''numpy''' )
  for col in feats:
    snake_case     :					List[Any]     =	dataset[col]




def       _UpperCamelCase	(					)     ->       Tuple:


  '''simple docstring'''


  snake_case     :					int     =	{}
  snake_case     :					Tuple     =	(
      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:
    snake_case     :					Union[str, Any]     =	datasets.Features({'''image''': ArrayaD(_UpperCamelCase   ,					dtype='''float32''' )} )
    snake_case     :					Optional[Any]     =	generate_examples(features=_UpperCamelCase   ,					num_examples=_UpperCamelCase )
    snake_case     :					List[str]     =	write(_UpperCamelCase   ,					_UpperCamelCase   ,					_UpperCamelCase )
    for read_func in read_functions:
      snake_case     :					Dict     =	read_func(_UpperCamelCase   ,					_UpperCamelCase )

  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])}
    # )
    snake_case     :					Any     =	datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32''' ) ) )} )
    snake_case     :					Any     =	generate_examples(
        features=_UpperCamelCase   ,					num_examples=_UpperCamelCase   ,					seq_shapes={'''image''': SPEED_TEST_SHAPE} )
    snake_case     :					Tuple     =	write(_UpperCamelCase   ,					_UpperCamelCase   ,					_UpperCamelCase )
    for read_func in read_functions:
      snake_case     :					List[Any]     =	read_func(_UpperCamelCase   ,					_UpperCamelCase )

  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])}
    # )
    snake_case     :					Optional[Any]     =	datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32''' ) )} )
    snake_case     :					Any     =	generate_examples(
        features=_UpperCamelCase   ,					num_examples=_UpperCamelCase   ,					seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}   ,					)
    snake_case     :					List[str]     =	write(_UpperCamelCase   ,					_UpperCamelCase   ,					_UpperCamelCase )
    for read_func in read_functions:
      snake_case     :					Union[str, Any]     =	read_func(_UpperCamelCase   ,					_UpperCamelCase )

  with open(_UpperCamelCase   ,					'''wb''' ) as f:
    f.write(json.dumps(_UpperCamelCase ).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, is_vision_available


lowercase__						=			{
    "configuration_flava": [
        "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "FlavaConfig",
        "FlavaImageCodebookConfig",
        "FlavaImageConfig",
        "FlavaMultimodalConfig",
        "FlavaTextConfig",
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						=			["FlavaFeatureExtractor"]
						lowercase__						=			["FlavaImageProcessor"]
						lowercase__						=			["FlavaProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						=			[
						    "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "FlavaForPreTraining",
						    "FlavaImageCodebook",
						    "FlavaImageModel",
						    "FlavaModel",
						    "FlavaMultimodalModel",
						    "FlavaPreTrainedModel",
						    "FlavaTextModel",
						]

if TYPE_CHECKING:
						from .configuration_flava import (
						    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    FlavaConfig,
						    FlavaImageCodebookConfig,
						    FlavaImageConfig,
						    FlavaMultimodalConfig,
						    FlavaTextConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_flava import FlavaFeatureExtractor
												from .image_processing_flava import FlavaImageProcessor
												from .processing_flava import FlavaProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flava import (
												    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlavaForPreTraining,
												    FlavaImageCodebook,
												    FlavaImageModel,
												    FlavaModel,
												    FlavaMultimodalModel,
												    FlavaPreTrainedModel,
												    FlavaTextModel,
												)

else:
						import sys

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


