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													=		(30, 487)
UpperCamelCase													=		(36, 1024)
UpperCamelCase													=		(100, 100)
UpperCamelCase													=		100

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

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

@get_duration
def     lowercase_       (    _lowerCamelCase							: str	,			_lowerCamelCase							: str	,			_lowerCamelCase							: List[str]):
    with ArrowWriter(features=_lowerCamelCase	,			path=os.path.join(_lowerCamelCase	,			"beta.arrow")) as writer:
        for key, record in dummy_data:
            lowercase__				:     str					      =       my_features.encode_example(_lowerCamelCase)
            writer.write(_lowerCamelCase)
        lowercase__      ,     lowercase__				:     Any					      =       writer.finalize()

@get_duration
def     lowercase_       (    _lowerCamelCase							: Optional[int]	,			_lowerCamelCase							: int):
    lowercase__				:     Dict					      =       datasets.Dataset.from_file(
        filename=os.path.join(_lowerCamelCase	,			"beta.arrow")	,			info=datasets.DatasetInfo(features=_lowerCamelCase))
    for _ in dataset:
        pass

@get_duration
def     lowercase_       (    _lowerCamelCase							: Any	,			_lowerCamelCase							: List[str]):
    lowercase__				:     Tuple					      =       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     lowercase_       (    _lowerCamelCase							: str	,			_lowerCamelCase							: List[str]):
    lowercase__				:     Tuple					      =       10
    lowercase__				:     Optional[int]					      =       datasets.Dataset.from_file(
        filename=os.path.join(_lowerCamelCase	,			"beta.arrow")	,			info=datasets.DatasetInfo(features=_lowerCamelCase))
    for i in range(0	,			len(_lowerCamelCase)	,			_lowerCamelCase):
        lowercase__				:     Union[str, Any]					      =       dataset[i : i + batch_size]

@get_duration
def     lowercase_       (    _lowerCamelCase							: Union[str, Any]	,			_lowerCamelCase							: int):
    lowercase__				:     str					      =       10
    lowercase__				:     int					      =       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):
        lowercase__				:     List[str]					      =       dataset[i : i + batch_size]

@get_duration
def     lowercase_       (    _lowerCamelCase							: Dict	,			_lowerCamelCase							: str):
    lowercase__				:     str					      =       datasets.Dataset.from_file(
        filename=os.path.join(_lowerCamelCase	,			"beta.arrow")	,			info=datasets.DatasetInfo(features=_lowerCamelCase))
    for col in feats:
        lowercase__				:     int					      =       dataset[col]

@get_duration
def     lowercase_       (    _lowerCamelCase							: int	,			_lowerCamelCase							: Optional[Any]):
    lowercase__				:     Optional[Any]					      =       datasets.Dataset.from_file(
        filename=os.path.join(_lowerCamelCase	,			"beta.arrow")	,			info=datasets.DatasetInfo(features=_lowerCamelCase))
    dataset.set_format("numpy")
    for col in feats:
        lowercase__				:     Union[str, Any]					      =       dataset[col]

def     lowercase_       (    ):
    lowercase__				:     List[str]					      =       {}
    lowercase__				:     List[Any]					      =       (
        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:
        lowercase__				:     str					      =       datasets.Features({"image": ArrayaD(_lowerCamelCase	,			dtype="float32")})
        lowercase__				:     List[str]					      =       generate_examples(features=_lowerCamelCase	,			num_examples=_lowerCamelCase)
        lowercase__				:     int					      =       write(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase)
        for read_func in read_functions:
            lowercase__				:     Optional[int]					      =       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])}
        # )
        lowercase__				:     Union[str, Any]					      =       datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))})
        lowercase__				:     List[Any]					      =       generate_examples(
            features=_lowerCamelCase	,			num_examples=_lowerCamelCase	,			seq_shapes={"image": SPEED_TEST_SHAPE})
        lowercase__				:     Any					      =       write(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase)
        for read_func in read_functions:
            lowercase__				:     str					      =       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])}
        # )
        lowercase__				:     Optional[Any]					      =       datasets.Features({"image": datasets.Sequence(datasets.Value("float32"))})
        lowercase__				:     int					      =       generate_examples(
            features=_lowerCamelCase	,			num_examples=_lowerCamelCase	,			seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}	,			)
        lowercase__				:     Dict					      =       write(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase)
        for read_func in read_functions:
            lowercase__				:     Dict					      =       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()




from typing import TYPE_CHECKING

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


UpperCamelCase													=		{
    '''configuration_mobilenet_v1''': [
        '''MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV1Config''',
        '''MobileNetV1OnnxConfig''',
    ],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		['''MobileNetV1FeatureExtractor''']
      UpperCamelCase													=		['''MobileNetV1ImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		[
          '''MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''MobileNetV1ForImageClassification''',
          '''MobileNetV1Model''',
          '''MobileNetV1PreTrainedModel''',
          '''load_tf_weights_in_mobilenet_v1''',
      ]


if TYPE_CHECKING:
      from .configuration_mobilenet_va import (
          MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MobileNetVaConfig,
          MobileNetVaOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
            from .image_processing_mobilenet_va import MobileNetVaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilenet_va import (
                MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileNetVaForImageClassification,
                MobileNetVaModel,
                MobileNetVaPreTrainedModel,
                load_tf_weights_in_mobilenet_va,
            )


else:
      import sys

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



