
"""simple docstring"""





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


SCREAMING_SNAKE_CASE_												= 500000

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







@get_duration
def   A__ ( A__      ,      **A__		)		->			str:





   '''simple docstring'''




   _UpperCAmelCase         =      dataset.map(**UpperCAmelCase__		)







@get_duration
def   A__ ( A__      ,      **A__		)		->			Optional[int]:





   '''simple docstring'''




   _UpperCAmelCase         =      dataset.filter(**UpperCAmelCase__		)







def   A__ ( )		->			List[Any]:





   '''simple docstring'''




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

      _UpperCAmelCase         =      transformers.AutoTokenizer.from_pretrained("bert-base-cased"      ,      use_fast=UpperCAmelCase__		)

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

      _UpperCAmelCase         =      map(UpperCAmelCase__		)

      _UpperCAmelCase         =      map(UpperCAmelCase__      ,      batched=UpperCAmelCase__		)

      _UpperCAmelCase         =      map(UpperCAmelCase__      ,      function=lambda A__		: None      ,      batched=UpperCAmelCase__		)

      with dataset.formatted_as(type="numpy"		):
         _UpperCAmelCase         =      map(UpperCAmelCase__      ,      function=lambda A__		: None      ,      batched=UpperCAmelCase__		)

      with dataset.formatted_as(type="pandas"		):
         _UpperCAmelCase         =      map(UpperCAmelCase__      ,      function=lambda A__		: None      ,      batched=UpperCAmelCase__		)

      with dataset.formatted_as(type="torch"      ,      columns="numbers"		):
         _UpperCAmelCase         =      map(UpperCAmelCase__      ,      function=lambda A__		: None      ,      batched=UpperCAmelCase__		)

      with dataset.formatted_as(type="tensorflow"      ,      columns="numbers"		):
         _UpperCAmelCase         =      map(UpperCAmelCase__      ,      function=lambda A__		: None      ,      batched=UpperCAmelCase__		)

      _UpperCAmelCase         =      map(UpperCAmelCase__      ,      function=UpperCAmelCase__      ,      batched=UpperCAmelCase__		)

      _UpperCAmelCase         =      filter(UpperCAmelCase__		)

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


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



import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   str      ,					**UpperCAmelCase__					:   List[str]							)	->     Tuple:
							lowerCamelCase_					=       AutoConfig.from_pretrained(UpperCAmelCase__      ,					**UpperCAmelCase__							)
							lowerCamelCase_					=       AutoModelForSeqaSeqLM.from_config(UpperCAmelCase__							)
							model.save_pretrained(UpperCAmelCase__							)
							AutoTokenizer.from_pretrained(UpperCAmelCase__							).save_pretrained(UpperCAmelCase__							)
							return model


if __name__ == "__main__":
		fire.Fire(save_randomly_initialized_version)





