


"""simple docstring"""


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__lowerCamelCase      :Optional[int]	    =		500_000

__lowerCamelCase    ,   __lowerCamelCase      :int	    =		os.path.split(__file__)
__lowerCamelCase      :Any	    =		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def   snake_case		(     UpperCamelCase__       :						datasets.Dataset			,				**UpperCamelCase__       :						Dict    )   ->      List[Any]:
      lowerCamelCase					:					Optional[Any]        =	dataset.map(**UpperCamelCase__    )




@get_duration
def   snake_case		(     UpperCamelCase__       :						datasets.Dataset			,				**UpperCamelCase__       :						Union[str, Any]    )   ->      Union[str, Any]:
      lowerCamelCase					:					Optional[int]        =	dataset.filter(**UpperCamelCase__    )




def   snake_case		(     )   ->      Any:
      lowerCamelCase					:					Tuple        =	{"""num examples""": SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            lowerCamelCase					:					str        =	datasets.Features({"""text""": datasets.Value("""string"""    ), """numbers""": datasets.Value("""float32"""    )}    )
            lowerCamelCase					:					Tuple        =	generate_example_dataset(
                os.path.join(UpperCamelCase__			,				"""dataset.arrow"""    )			,				UpperCamelCase__			,				num_examples=UpperCamelCase__    )

            lowerCamelCase					:					Tuple        =	transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""			,				use_fast=UpperCamelCase__    )

            def tokenize(UpperCamelCase__       :						Dict    ):
                  return tokenizer(examples["""text"""]    )

            lowerCamelCase					:					Any        =	map(UpperCamelCase__    )

            lowerCamelCase					:					Union[str, Any]        =	map(UpperCamelCase__			,				batched=UpperCamelCase__    )

            lowerCamelCase					:					List[str]        =	map(UpperCamelCase__			,				function=lambda UpperCamelCase__    : None			,				batched=UpperCamelCase__    )

            with dataset.formatted_as(type="""numpy"""    ):
                  lowerCamelCase					:					Dict        =	map(UpperCamelCase__			,				function=lambda UpperCamelCase__    : None			,				batched=UpperCamelCase__    )

            with dataset.formatted_as(type="""pandas"""    ):
                  lowerCamelCase					:					List[Any]        =	map(UpperCamelCase__			,				function=lambda UpperCamelCase__    : None			,				batched=UpperCamelCase__    )

            with dataset.formatted_as(type="""torch"""			,				columns="""numbers"""    ):
                  lowerCamelCase					:					str        =	map(UpperCamelCase__			,				function=lambda UpperCamelCase__    : None			,				batched=UpperCamelCase__    )

            with dataset.formatted_as(type="""tensorflow"""			,				columns="""numbers"""    ):
                  lowerCamelCase					:					Tuple        =	map(UpperCamelCase__			,				function=lambda UpperCamelCase__    : None			,				batched=UpperCamelCase__    )

            lowerCamelCase					:					Tuple        =	map(UpperCamelCase__			,				function=UpperCamelCase__			,				batched=UpperCamelCase__    )

            lowerCamelCase					:					List[str]        =	filter(UpperCamelCase__    )

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

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


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








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__lowerCamelCase      :List[str]	    =		{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Optional[int]	    =		['OwlViTFeatureExtractor']
    __lowerCamelCase      :List[str]	    =		['OwlViTImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Optional[Any]	    =		[
        'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'OwlViTModel',
        'OwlViTPreTrainedModel',
        'OwlViTTextModel',
        'OwlViTVisionModel',
        'OwlViTForObjectDetection',
    ]

if TYPE_CHECKING:
    from .configuration_owlvit import (
        OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        OwlViTConfig,
        OwlViTOnnxConfig,
        OwlViTTextConfig,
        OwlViTVisionConfig,
    )
    from .processing_owlvit import OwlViTProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_owlvit import OwlViTFeatureExtractor
        from .image_processing_owlvit import OwlViTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_owlvit import (
            OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            OwlViTForObjectDetection,
            OwlViTModel,
            OwlViTPreTrainedModel,
            OwlViTTextModel,
            OwlViTVisionModel,
        )

else:
    import sys

    __lowerCamelCase      :Dict	    =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




