



'''simple docstring'''




import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase							:Dict       = 5_0_0_0_0_0

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



@get_duration
def      a      (			lowerCamelCase__						,				**lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Any     =					dataset.map(**lowerCamelCase__   )



@get_duration
def      a      (			lowerCamelCase__						,				**lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Tuple     =					dataset.filter(**lowerCamelCase__   )



def      a      (			):
			'''simple docstring'''





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

						A_     :						List[Any]     =					transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""						,				use_fast=lowerCamelCase__   )

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

						A_     :						List[str]     =					map(lowerCamelCase__   )

						A_     :						int     =					map(lowerCamelCase__						,				batched=lowerCamelCase__   )

						A_     :						List[Any]     =					map(lowerCamelCase__						,				function=lambda lowerCamelCase__   : None						,				batched=lowerCamelCase__   )

						with dataset.formatted_as(type="""numpy"""   ):
									A_     :						Tuple     =					map(lowerCamelCase__						,				function=lambda lowerCamelCase__   : None						,				batched=lowerCamelCase__   )

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

						with dataset.formatted_as(type="""torch"""						,				columns="""numbers"""   ):
									A_     :						Tuple     =					map(lowerCamelCase__						,				function=lambda lowerCamelCase__   : None						,				batched=lowerCamelCase__   )

						with dataset.formatted_as(type="""tensorflow"""						,				columns="""numbers"""   ):
									A_     :						Dict     =					map(lowerCamelCase__						,				function=lambda lowerCamelCase__   : None						,				batched=lowerCamelCase__   )

						A_     :						int     =					map(lowerCamelCase__						,				function=lowerCamelCase__						,				batched=lowerCamelCase__   )

						A_     :						str     =					filter(lowerCamelCase__   )

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


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




'''simple docstring'''




from typing import Dict

from .base import GenericTensor, Pipeline







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def    _a     (self	,				lowercase=None	,				lowercase=None	,				lowercase=None	,				**lowercase							):
				if tokenize_kwargs is None:
							A_     :						Optional[Any]     =					{}

				if truncation is not None:
							if "truncation" in tokenize_kwargs:
										raise ValueError(
										    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""							)
							A_     :						str     =					truncation

				A_     :						List[str]     =					tokenize_kwargs

				A_     :						Dict     =					{}
				if return_tensors is not None:
							A_     :						List[Any]     =					return_tensors

				return preprocess_params, {}, postprocess_params


	def    _a     (self	,				lowercase	,				**lowercase							):
				A_     :						Optional[int]     =					self.framework
				A_     :						str     =					self.tokenizer(lowercase	,				return_tensors=lowercase	,				**lowercase							)
				return model_inputs


	def    _a     (self	,				lowercase							):
				A_     :						str     =					self.model(**lowercase							)
				return model_outputs


	def    _a     (self	,				lowercase	,				lowercase=False							):
				# [0] is the first available tensor, logits or last_hidden_state.
				if return_tensors:
							return model_outputs[0]
				if self.framework == "pt":
							return model_outputs[0].tolist()
				elif self.framework == "tf":
							return model_outputs[0].numpy().tolist()


	def __call__(self	,				*lowercase	,				**lowercase							):
				return super().__call__(*lowercase	,				**lowercase							)