


"""simple docstring"""




import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__SCREAMING_SNAKE_CASE      		=    50_00_00

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



@get_duration
def     __a   (	a,		**a       ):





       """simple docstring"""







       _a							 =					dataset.map(**a       )



@get_duration
def     __a   (	a,		**a       ):





       """simple docstring"""







       _a							 =					dataset.filter(**a       )



def     __a   (	):





       """simple docstring"""







       _a							 =					{"num examples": SPEED_TEST_N_EXAMPLES}
       with tempfile.TemporaryDirectory() as tmp_dir:
              _a							 =					datasets.Features({"text": datasets.Value("string"       ), "numbers": datasets.Value("float32"       )}       )
              _a							 =					generate_example_dataset(
                  os.path.join(a,		"dataset.arrow"       ),		a,		num_examples=a       )

              _a							 =					transformers.AutoTokenizer.from_pretrained("bert-base-cased",		use_fast=a       )

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

              _a							 =					map(a       )

              _a							 =					map(a,		batched=a       )

              _a							 =					map(a,		function=lambda a       : None,		batched=a       )

              with dataset.formatted_as(type="numpy"       ):
                     _a							 =					map(a,		function=lambda a       : None,		batched=a       )

              with dataset.formatted_as(type="pandas"       ):
                     _a							 =					map(a,		function=lambda a       : None,		batched=a       )

              with dataset.formatted_as(type="torch",		columns="numbers"       ):
                     _a							 =					map(a,		function=lambda a       : None,		batched=a       )

              with dataset.formatted_as(type="tensorflow",		columns="numbers"       ):
                     _a							 =					map(a,		function=lambda a       : None,		batched=a       )

              _a							 =					map(a,		function=a,		batched=a       )

              _a							 =					filter(a       )

              # 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(a,		"wb"       ) as f:
              f.write(json.dumps(a       ).encode("utf-8"       )       )


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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase  :      Union[str, Any]			 ={
    """configuration_clap""": [
        """CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ClapAudioConfig""",
        """ClapConfig""",
        """ClapTextConfig""",
    ],
    """processing_clap""": ["""ClapProcessor"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      Union[str, Any]			 =[
					    """CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ClapModel""",
					    """ClapPreTrainedModel""",
					    """ClapTextModel""",
					    """ClapTextModelWithProjection""",
					    """ClapAudioModel""",
					    """ClapAudioModelWithProjection""",
					]
					_lowerCAmelCase  :      List[str]			 =["""ClapFeatureExtractor"""]

if TYPE_CHECKING:
					from .configuration_clap import (
					    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
					    ClapAudioConfig,
					    ClapConfig,
					    ClapTextConfig,
					)
					from .processing_clap import ClapProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clap import ClapFeatureExtractor
										from .modeling_clap import (
										    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ClapAudioModel,
										    ClapAudioModelWithProjection,
										    ClapModel,
										    ClapPreTrainedModel,
										    ClapTextModel,
										    ClapTextModelWithProjection,
										)


else:
					import sys

					_lowerCAmelCase  :      Optional[int]			 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)