

"""simple docstring"""





import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	str,      **UpperCamelCase_					:	Optional[int])     ->						Tuple:
   '''simple docstring'''


   __lowercase								=						AutoConfig.from_pretrained(UpperCamelCase_,      **UpperCamelCase_)
   __lowercase								=						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)







"""simple docstring"""





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_a							=							5_00_00
_a							=							50_00

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




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	List[str])     ->						List[str]:
   '''simple docstring'''


   for i in range(UpperCamelCase_):
      __lowercase								=						dataset[i]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	List[Any],      UpperCamelCase_					:	int)     ->						Dict:
   '''simple docstring'''


   for i in range(0,      len(UpperCamelCase_),      UpperCamelCase_):
      __lowercase								=						dataset[i : i + batch_size]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	Any,      UpperCamelCase_					:	Optional[int])     ->						List[str]:
   '''simple docstring'''


   with dataset.formatted_as(type=UpperCamelCase_):
      for i in range(UpperCamelCase_):
         __lowercase								=						dataset[i]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	str,      UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	Union[str, Any])     ->						Dict:
   '''simple docstring'''


   with dataset.formatted_as(type=UpperCamelCase_):
      for i in range(0,      UpperCamelCase_,      UpperCamelCase_):
         __lowercase								=						dataset[i : i + batch_size]




def   _A  (  )     ->						List[str]:
   '''simple docstring'''


   __lowercase								=						{"num examples": SPEED_TEST_N_EXAMPLES}
   __lowercase								=						[
       (read, {"length": SMALL_TEST}),
       (read, {"length": SPEED_TEST_N_EXAMPLES}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
       (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
       (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
       (read_formatted, {"type": "torch", "length": SMALL_TEST}),
       (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
   ]

   __lowercase								=						[
       (read, {"length": SMALL_TEST}),
       (read, {"length": SPEED_TEST_N_EXAMPLES}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
       (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
   ]
   with tempfile.TemporaryDirectory() as tmp_dir:
      print("generating dataset")
      __lowercase								=						datasets.Features(
          {"list": datasets.Sequence(datasets.Value("float32")), "numbers": datasets.Value("float32")})
      __lowercase								=						generate_example_dataset(
          os.path.join(UpperCamelCase_,      "dataset.arrow"),      UpperCamelCase_,      num_examples=UpperCamelCase_,      seq_shapes={"list": (100,)},      )
      print("first set of iterations")
      for func, kwargs in functions:
         print(func.__name__,      str(UpperCamelCase_))
         __lowercase								=						func(UpperCamelCase_,      **UpperCamelCase_)

      print("shuffling dataset")
      __lowercase								=						dataset.shuffle()
      print("Second set of iterations (after shuffling")
      for func, kwargs in functions_shuffled:
         print("shuffled ",      func.__name__,      str(UpperCamelCase_))
         __lowercase								=						func(
             UpperCamelCase_,      **UpperCamelCase_)

   with open(UpperCamelCase_,      "wb") as f:
      f.write(json.dumps(UpperCamelCase_).encode("utf-8"))


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




