'''simple docstring'''
import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


a=  5_0_0_0_0_0

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







@get_duration
def  _UpperCamelCase (     _a	:	datasets.Dataset				,     **_a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  dataset.map(**_a			)







@get_duration
def  _UpperCamelCase (     _a	:	datasets.Dataset				,     **_a	:	int			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  dataset.filter(**_a			)







def  _UpperCamelCase (     ):




    """simple docstring"""



    __UpperCamelCase	:	int		    =  {'num examples': SPEED_TEST_N_EXAMPLES}
    with tempfile.TemporaryDirectory() as tmp_dir:
        __UpperCamelCase	:	Optional[int]		    =  datasets.Features({'text': datasets.Value('string'			), 'numbers': datasets.Value('float32'			)}			)
        __UpperCamelCase	:	Optional[Any]		    =  generate_example_dataset(
            os.path.join(_a				,     'dataset.arrow'			)				,     _a				,     num_examples=_a			)

        __UpperCamelCase	:	Optional[Any]		    =  transformers.AutoTokenizer.from_pretrained('bert-base-cased'				,     use_fast=_a			)

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

        __UpperCamelCase	:	Dict		    =  map(_a			)

        __UpperCamelCase	:	Union[str, Any]		    =  map(_a				,     batched=_a			)

        __UpperCamelCase	:	Any		    =  map(_a				,     function=lambda _a			: None				,     batched=_a			)

        with dataset.formatted_as(type='numpy'			):
            __UpperCamelCase	:	str		    =  map(_a				,     function=lambda _a			: None				,     batched=_a			)

        with dataset.formatted_as(type='pandas'			):
            __UpperCamelCase	:	Tuple		    =  map(_a				,     function=lambda _a			: None				,     batched=_a			)

        with dataset.formatted_as(type='torch'				,     columns='numbers'			):
            __UpperCamelCase	:	Optional[int]		    =  map(_a				,     function=lambda _a			: None				,     batched=_a			)

        with dataset.formatted_as(type='tensorflow'				,     columns='numbers'			):
            __UpperCamelCase	:	Union[str, Any]		    =  map(_a				,     function=lambda _a			: None				,     batched=_a			)

        __UpperCamelCase	:	int		    =  map(_a				,     function=_a				,     batched=_a			)

        __UpperCamelCase	:	List[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()




'''simple docstring'''
a=  '''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
  from .utils import rich



