





import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__lowercase				    =      (30, 487)
__lowercase				    =      (36, 1_024)
__lowercase				    =      (100, 100)
__lowercase				    =      100

__lowercase				    =      datasets.Features(
    {"""text""": ArrayaD(SHAPE_TEST_1, dtype="""float32"""), """image""": ArrayaD(SHAPE_TEST_2, dtype="""float32""")}
)

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


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any  ,				__lowercase:		Optional[Any]  ,				__lowercase:		List[Any]):



  '''simple docstring'''






  with ArrowWriter(features=__lowercase  ,				path=os.path.join(__lowercase  ,				'''beta.arrow''')) as writer:
    for key, record in dummy_data:
      __lowerCAmelCase								=					my_features.encode_example(__lowercase)
      writer.write(__lowercase)
    __lowerCAmelCase						,    __lowerCAmelCase								=					writer.finalize()


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		str  ,				__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  for _ in dataset:
    pass


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple  ,				__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  dataset.set_format('''numpy''')
  for _ in dataset:
    pass


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		str  ,				__lowercase:		Dict):



  '''simple docstring'''






  __lowerCAmelCase								=					10
  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  for i in range(0  ,				len(__lowercase)  ,				__lowercase):
    __lowerCAmelCase								=					dataset[i : i + batch_size]


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[Any]  ,				__lowercase:		Union[str, Any]):



  '''simple docstring'''






  __lowerCAmelCase								=					10
  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  dataset.set_format('''numpy''')
  for i in range(0  ,				len(__lowercase)  ,				__lowercase):
    __lowerCAmelCase								=					dataset[i : i + batch_size]


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Union[str, Any]  ,				__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  for col in feats:
    __lowerCAmelCase								=					dataset[col]


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Optional[int]  ,				__lowercase:		Optional[Any]):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  dataset.set_format('''numpy''')
  for col in feats:
    __lowerCAmelCase								=					dataset[col]


def        __SCREAMING_SNAKE_CASE	(				):



  '''simple docstring'''






  __lowerCAmelCase								=					{}
  __lowerCAmelCase								=					(
      read_unformated,
      read_formatted_as_numpy,
      read_batch_unformated,
      read_batch_formatted_as_numpy,
      read_col_unformated,
      read_col_formatted_as_numpy,
  )
  with tempfile.TemporaryDirectory() as tmp_dir:
    __lowerCAmelCase								=					datasets.Features({'''image''': ArrayaD(__lowercase  ,				dtype='''float32''')})
    __lowerCAmelCase								=					generate_examples(features=__lowercase  ,				num_examples=__lowercase)
    __lowerCAmelCase								=					write(__lowercase  ,				__lowercase  ,				__lowercase)
    for read_func in read_functions:
      __lowerCAmelCase								=					read_func(__lowercase  ,				__lowercase)

  with tempfile.TemporaryDirectory() as tmp_dir:
    # don't use fixed length for fair comparison
    # feats = datasets.Features(
    #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
    # )
    __lowerCAmelCase								=					datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32''')))})
    __lowerCAmelCase								=					generate_examples(
        features=__lowercase  ,				num_examples=__lowercase  ,				seq_shapes={'''image''': SPEED_TEST_SHAPE})
    __lowerCAmelCase								=					write(__lowercase  ,				__lowercase  ,				__lowercase)
    for read_func in read_functions:
      __lowerCAmelCase								=					read_func(__lowercase  ,				__lowercase)

  with tempfile.TemporaryDirectory() as tmp_dir:
    # don't use fixed length for fair comparison
    # feats = datasets.Features(
    #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
    # )
    __lowerCAmelCase								=					datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''))})
    __lowerCAmelCase								=					generate_examples(
        features=__lowercase  ,				num_examples=__lowercase  ,				seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}  ,				)
    __lowerCAmelCase								=					write(__lowercase  ,				__lowercase  ,				__lowercase)
    for read_func in read_functions:
      __lowerCAmelCase								=					read_func(__lowercase  ,				__lowercase)

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


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







import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
      import torch

      from transformers import GPTaLMHeadModel

@require_torch
@require_sentencepiece
@require_tokenizers
class 	__snake_case					(      unittest.TestCase ):






    '''simple docstring'''





    @slow
    @unittest.skip('''Model is not available.'''				)
    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					'''nvidia/megatron-gpt2-345m/'''
      if "MYDIR" in os.environ:
        __lowerCAmelCase								=					os.path.join(os.environ['''MYDIR''']      ,    snake_case				)
      __lowerCAmelCase								=					GPTaLMHeadModel.from_pretrained(snake_case				)
      model.to(snake_case				)
      model.half()

      __lowerCAmelCase								=					torch.tensor(
          [[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]      ,    device=snake_case      ,    dtype=torch.long      ,    )

      with torch.no_grad():
        __lowerCAmelCase								=					model(snake_case				).logits

      __lowerCAmelCase								=					torch.Size((1, 9, 50_257)				)
      self.assertEqual(output.shape      ,    snake_case				)

      __lowerCAmelCase								=					torch.tensor(
          [
              4.9414,
              -0.2920,
              -1.2148,
              -4.0273,
              -0.5161,
              -5.2109,
              -1.2412,
              -1.8301,
              -1.7734,
              -4.7148,
              -0.2317,
              -1.0811,
              -2.1777,
              0.4141,
              -3.7969,
              -4.0586,
              -2.5332,
              -3.3809,
              4.3867,
          ]      ,    device=snake_case      ,    dtype=torch.half      ,    )

      for i in range(19				):
        __lowerCAmelCase						,    __lowerCAmelCase								=					8 * i // 17, 2_792 * i  # along the diagonal
        __lowerCAmelCase						,    __lowerCAmelCase								=					output[0, r, c], expected_diag[i]
        __lowerCAmelCase								=					f'''row={r} col={c} computed={computed} expected={expected}'''
        self.assertAlmostEqual(snake_case      ,    snake_case      ,    delta=1e-4      ,    msg=snake_case				)
