



'''simple docstring'''


import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


lowercase_      :				Any   					=    importlib.util.find_spec('''s3fs''') is not None

if _has_safs:
						from .safilesystem import SaFileSystem  # noqa: F401

lowercase_      :				List[compression.BaseCompressedFileFileSystem]   					=    [
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
						if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
												warnings.warn(f'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
						fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						if "://" in dataset_path:
												lowercase        =							dataset_path.split("""://"""						)[1]
						return dataset_path







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      fsspec.AbstractFileSystem						):
						if fs is not None and fs.protocol != "file":
												return True
						else:
												return False







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      fsspec.AbstractFileSystem ,  lowercase_    :      str ,  lowercase_    :      str						):
						lowercase        =							not is_remote_filesystem(lowercase_						)
						if is_local:
												# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
												shutil.move(fs._strip_protocol(lowercase_						) ,  fs._strip_protocol(lowercase_						)						)
						else:
												fs.mv(lowercase_ ,  lowercase_ ,  recursive=lowercase_						)







def      SCREAMING_SNAKE_CASE				(				):
						if hasattr(fsspec.asyn ,  """reset_lock"""						):
												# for future fsspec>2022.05.0
												fsspec.asyn.reset_lock()
						else:
												lowercase        =							None
												lowercase        =							None
												lowercase        =							threading.Lock()







'''simple docstring'''


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_      :				int   					=    50_0000

lowercase_					,       lowercase_      :				Union[str, Any]   					=    os.path.split(__file__)
lowercase_      :				Optional[Any]   					=    os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))







@get_duration
def      SCREAMING_SNAKE_CASE				(				lowercase_    :      datasets.Dataset ,  **lowercase_    :      Dict						):
						lowercase        =							dataset.map(**lowercase_						)







@get_duration
def      SCREAMING_SNAKE_CASE				(				lowercase_    :      datasets.Dataset ,  **lowercase_    :      Optional[int]						):
						lowercase        =							dataset.filter(**lowercase_						)







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

												lowercase        =							transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" ,  use_fast=lowercase_						)

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

												lowercase        =							map(lowercase_						)

												lowercase        =							map(lowercase_ ,  batched=lowercase_						)

												lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

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

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

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

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

												lowercase        =							map(lowercase_ ,  function=lowercase_ ,  batched=lowercase_						)

												lowercase        =							filter(lowercase_						)

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


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


