


from .imports import is_tqdm_available


if is_tqdm_available():
       from tqdm.auto import tqdm as _tqdm

from ..state import PartialState
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__ = True      ,	*SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						):
 if not is_tqdm_available():
  raise ImportError('Accelerate\'s `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.'						)
 __lowerCamelCase		:							Optional[Any]		 =						False
 if main_process_only:
  __lowerCamelCase		:							Dict		 =						PartialState().local_process_index == 0
 return _tqdm(*SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__      ,	disable=SCREAMING_SNAKE_CASE__						)





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase_	   =     5_0_0_0_0
lowercase_	   =     5_0_0_0

lowercase_   ,lowercase_	   =     os.path.split(__file__)
lowercase_	   =     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 for i in range(SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							Tuple		 =						dataset[i]
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 for i in range(0      ,	len(SCREAMING_SNAKE_CASE__						)      ,	SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							Optional[Any]		 =						dataset[i : i + batch_size]
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__						):
  for i in range(SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase		:							str		 =						dataset[i]
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__						):
  for i in range(0      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase		:							int		 =						dataset[i : i + batch_size]
def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							Union[str, Any]		 =						{'num examples': SPEED_TEST_N_EXAMPLES}
 __lowerCamelCase		:							Optional[Any]		 =						[
     (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': 1_000}),
     (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': 1_000}),
 ]

 __lowerCamelCase		:							Any		 =						[
     (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': 1_000}),
     (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': 1_000}),
 ]
 with tempfile.TemporaryDirectory() as tmp_dir:
  print('generating dataset'						)
  __lowerCamelCase		:							Optional[int]		 =						datasets.Features(
      {'list': datasets.Sequence(datasets.Value('float32'						)						), 'numbers': datasets.Value('float32'						)}						)
  __lowerCamelCase		:							str		 =						generate_example_dataset(
      os.path.join(SCREAMING_SNAKE_CASE__      ,	'dataset.arrow'						)      ,	SCREAMING_SNAKE_CASE__      ,	num_examples=SCREAMING_SNAKE_CASE__      ,	seq_shapes={'list': (100,)}      ,	)
  print('first set of iterations'						)
  for func, kwargs in functions:
   print(func.__name__      ,	str(SCREAMING_SNAKE_CASE__						)						)
   __lowerCamelCase		:							Optional[int]		 =						func(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						)

  print('shuffling dataset'						)
  __lowerCamelCase		:							str		 =						dataset.shuffle()
  print('Second set of iterations (after shuffling'						)
  for func, kwargs in functions_shuffled:
   print('shuffled '      ,	func.__name__      ,	str(SCREAMING_SNAKE_CASE__						)						)
   __lowerCamelCase		:							int		 =						func(
       SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						)

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


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

