


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__UpperCAmelCase					       =							500_000

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


@get_duration
def       _snake_case     (  A				,					**A )		->					int:
							lowerCAmelCase__      =			dataset.map(**A )


@get_duration
def       _snake_case     (  A				,					**A )		->					Union[str, Any]:
							lowerCAmelCase__      =			dataset.filter(**A )


def       _snake_case     (  )		->					Any:
							lowerCAmelCase__      =			{'''num examples''': SPEED_TEST_N_EXAMPLES}
							with tempfile.TemporaryDirectory() as tmp_dir:
														lowerCAmelCase__      =			datasets.Features({'''text''': datasets.Value('''string''' ), '''numbers''': datasets.Value('''float32''' )} )
														lowerCAmelCase__      =			generate_example_dataset(
														    os.path.join(A				,					'''dataset.arrow''' )				,					A				,					num_examples=A )

														lowerCAmelCase__      =			transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''				,					use_fast=A )

														def tokenize(A ):
																					return tokenizer(examples['''text'''] )

														lowerCAmelCase__      =			map(A )

														lowerCAmelCase__      =			map(A				,					batched=A )

														lowerCAmelCase__      =			map(A				,					function=lambda A : None				,					batched=A )

														with dataset.formatted_as(type='''numpy''' ):
																					lowerCAmelCase__      =			map(A				,					function=lambda A : None				,					batched=A )

														with dataset.formatted_as(type='''pandas''' ):
																					lowerCAmelCase__      =			map(A				,					function=lambda A : None				,					batched=A )

														with dataset.formatted_as(type='''torch'''				,					columns='''numbers''' ):
																					lowerCAmelCase__      =			map(A				,					function=lambda A : None				,					batched=A )

														with dataset.formatted_as(type='''tensorflow'''				,					columns='''numbers''' ):
																					lowerCAmelCase__      =			map(A				,					function=lambda A : None				,					batched=A )

														lowerCAmelCase__      =			map(A				,					function=A				,					batched=A )

														lowerCAmelCase__      =			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()



'''simple docstring'''






import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							'''T5Config'''


def       _snake_case     (  A				,					A				,					A )		->					jnp.ndarray:
							lowerCAmelCase__      =			jnp.zeros_like(A )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 0].set(A )

							lowerCAmelCase__      =			jnp.where(shifted_input_ids == -100				,					A				,					A )
							return shifted_input_ids




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Dict					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Any					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Union[str, Any]					=   "mt5"
							lowercase__    :     Tuple					=   MTaConfig