






import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__magic_name__:					Tuple							=     500_000

__magic_name__		,						__magic_name__:					List[Any]							=     os.path.split(__file__)
__magic_name__:					Tuple							=     os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))

@get_duration
def    UpperCamelCase						(	_A,   **_A			):






						"""simple docstring"""







						__magic_name__      :       str       						= dataset.map(**_A			)

@get_duration
def    UpperCamelCase						(	_A,   **_A			):






						"""simple docstring"""







						__magic_name__      :       Tuple       						= dataset.filter(**_A			)

def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       Tuple       						= {"""num examples""": SPEED_TEST_N_EXAMPLES}
						with tempfile.TemporaryDirectory() as tmp_dir:
												__magic_name__      :       List[str]       						= datasets.Features({"""text""": datasets.Value("""string"""			), """numbers""": datasets.Value("""float32"""			)}			)
												__magic_name__      :       str       						= generate_example_dataset(
												    os.path.join(_A,   """dataset.arrow"""			),   _A,   num_examples=_A			)

												__magic_name__      :       Dict       						= transformers.AutoTokenizer.from_pretrained("""bert-base-cased""",   use_fast=_A			)

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

												__magic_name__      :       Optional[Any]       						= map(_A			)

												__magic_name__      :       Optional[int]       						= map(_A,   batched=_A			)

												__magic_name__      :       Tuple       						= map(_A,   function=lambda _A			: None,   batched=_A			)

												with dataset.formatted_as(type="""numpy"""			):
																		__magic_name__      :       Optional[int]       						= map(_A,   function=lambda _A			: None,   batched=_A			)

												with dataset.formatted_as(type="""pandas"""			):
																		__magic_name__      :       int       						= map(_A,   function=lambda _A			: None,   batched=_A			)

												with dataset.formatted_as(type="""torch""",   columns="""numbers"""			):
																		__magic_name__      :       int       						= map(_A,   function=lambda _A			: None,   batched=_A			)

												with dataset.formatted_as(type="""tensorflow""",   columns="""numbers"""			):
																		__magic_name__      :       Optional[Any]       						= map(_A,   function=lambda _A			: None,   batched=_A			)

												__magic_name__      :       Union[str, Any]       						= map(_A,   function=_A,   batched=_A			)

												__magic_name__      :       Any       						= 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_tf_available, is_torch_available


__magic_name__:					Tuple							=     {
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					Dict							=     [
		    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TapasForMaskedLM",
		    "TapasForQuestionAnswering",
		    "TapasForSequenceClassification",
		    "TapasModel",
		    "TapasPreTrainedModel",
		    "load_tf_weights_in_tapas",
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					int							=     [
		    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFTapasForMaskedLM",
		    "TFTapasForQuestionAnswering",
		    "TFTapasForSequenceClassification",
		    "TFTapasModel",
		    "TFTapasPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
		from .tokenization_tapas import TapasTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tapas import (
				    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TapasForMaskedLM,
				    TapasForQuestionAnswering,
				    TapasForSequenceClassification,
				    TapasModel,
				    TapasPreTrainedModel,
				    load_tf_weights_in_tapas,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_tapas import (
				    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTapasForMaskedLM,
				    TFTapasForQuestionAnswering,
				    TFTapasForSequenceClassification,
				    TFTapasModel,
				    TFTapasPreTrainedModel,
				)


else:
		import sys

		__magic_name__:					Tuple							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
