

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


_UpperCamelCase			       =			(30, 487)
_UpperCamelCase			       =			(36, 1024)
_UpperCamelCase			       =			(100, 100)
_UpperCamelCase			       =			100

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

_UpperCamelCase       ,				_UpperCamelCase			       =			os.path.split(__file__)
_UpperCamelCase			       =			os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def 						UpperCamelCase_(			snake_case__:  List[Any]  ,    snake_case__:  Dict  ,    snake_case__:  Dict   )       -> Optional[Any]:
 with ArrowWriter(features=snake_case__  ,    path=os.path.join(snake_case__  ,    'beta.arrow'   )   ) as writer:
  for key, record in dummy_data:
   UpperCAmelCase__							=				my_features.encode_example(snake_case__   )
   writer.write(snake_case__   )
  UpperCAmelCase__				,    UpperCAmelCase__							=				writer.finalize()
@get_duration
def 						UpperCamelCase_(			snake_case__:  List[Any]  ,    snake_case__:  str   )       -> Union[str, Any]:
 UpperCAmelCase__							=				datasets.Dataset.from_file(
     filename=os.path.join(snake_case__  ,    'beta.arrow'   )  ,    info=datasets.DatasetInfo(features=snake_case__   )   )
 for _ in dataset:
  pass
@get_duration
def 						UpperCamelCase_(			snake_case__:  Tuple  ,    snake_case__:  Union[str, Any]   )       -> Any:
 UpperCAmelCase__							=				datasets.Dataset.from_file(
     filename=os.path.join(snake_case__  ,    'beta.arrow'   )  ,    info=datasets.DatasetInfo(features=snake_case__   )   )
 dataset.set_format('numpy'   )
 for _ in dataset:
  pass
@get_duration
def 						UpperCamelCase_(			snake_case__:  List[Any]  ,    snake_case__:  List[Any]   )       -> Union[str, Any]:
 UpperCAmelCase__							=				10
 UpperCAmelCase__							=				datasets.Dataset.from_file(
     filename=os.path.join(snake_case__  ,    'beta.arrow'   )  ,    info=datasets.DatasetInfo(features=snake_case__   )   )
 for i in range(0  ,    len(snake_case__   )  ,    snake_case__   ):
  UpperCAmelCase__							=				dataset[i : i + batch_size]
@get_duration
def 						UpperCamelCase_(			snake_case__:  List[Any]  ,    snake_case__:  Dict   )       -> Any:
 UpperCAmelCase__							=				10
 UpperCAmelCase__							=				datasets.Dataset.from_file(
     filename=os.path.join(snake_case__  ,    'beta.arrow'   )  ,    info=datasets.DatasetInfo(features=snake_case__   )   )
 dataset.set_format('numpy'   )
 for i in range(0  ,    len(snake_case__   )  ,    snake_case__   ):
  UpperCAmelCase__							=				dataset[i : i + batch_size]
@get_duration
def 						UpperCamelCase_(			snake_case__:  Tuple  ,    snake_case__:  List[str]   )       -> Union[str, Any]:
 UpperCAmelCase__							=				datasets.Dataset.from_file(
     filename=os.path.join(snake_case__  ,    'beta.arrow'   )  ,    info=datasets.DatasetInfo(features=snake_case__   )   )
 for col in feats:
  UpperCAmelCase__							=				dataset[col]
@get_duration
def 						UpperCamelCase_(			snake_case__:  List[Any]  ,    snake_case__:  Tuple   )       -> Dict:
 UpperCAmelCase__							=				datasets.Dataset.from_file(
     filename=os.path.join(snake_case__  ,    'beta.arrow'   )  ,    info=datasets.DatasetInfo(features=snake_case__   )   )
 dataset.set_format('numpy'   )
 for col in feats:
  UpperCAmelCase__							=				dataset[col]
def 						UpperCamelCase_(			)       -> Dict:
 UpperCAmelCase__							=				{}
 UpperCAmelCase__							=				(
     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:
  UpperCAmelCase__							=				datasets.Features({'image': ArrayaD(snake_case__  ,    dtype='float32'   )}   )
  UpperCAmelCase__							=				generate_examples(features=snake_case__  ,    num_examples=snake_case__   )
  UpperCAmelCase__							=				write(snake_case__  ,    snake_case__  ,    snake_case__   )
  for read_func in read_functions:
   UpperCAmelCase__							=				read_func(snake_case__  ,    snake_case__   )

 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])}
  # )
  UpperCAmelCase__							=				datasets.Features({'image': datasets.Sequence(datasets.Sequence(datasets.Value('float32'   )   )   )}   )
  UpperCAmelCase__							=				generate_examples(
      features=snake_case__  ,    num_examples=snake_case__  ,    seq_shapes={'image': SPEED_TEST_SHAPE}   )
  UpperCAmelCase__							=				write(snake_case__  ,    snake_case__  ,    snake_case__   )
  for read_func in read_functions:
   UpperCAmelCase__							=				read_func(snake_case__  ,    snake_case__   )

 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])}
  # )
  UpperCAmelCase__							=				datasets.Features({'image': datasets.Sequence(datasets.Value('float32'   )   )}   )
  UpperCAmelCase__							=				generate_examples(
      features=snake_case__  ,    num_examples=snake_case__  ,    seq_shapes={'image': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}  ,    )
  UpperCAmelCase__							=				write(snake_case__  ,    snake_case__  ,    snake_case__   )
  for read_func in read_functions:
   UpperCAmelCase__							=				read_func(snake_case__  ,    snake_case__   )

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


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






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase			       =			logging.get_logger(__name__)

_UpperCamelCase			       =			{
    '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/config.json''',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}



class     lowercase						(       _UpperCamelCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				"""rembert"""




 def __init__(self       ,       __a=250300       ,       __a=1152       ,       __a=32       ,       __a=18       ,       __a=256       ,       __a=1664       ,       __a=4608       ,       __a="gelu"       ,       __a=0.0       ,       __a=0.0       ,       __a=0.1       ,       __a=512       ,       __a=2       ,       __a=0.02       ,       __a=1E-1_2       ,       __a=True       ,       __a=0       ,       __a=312       ,       __a=313       ,       **__a       ,       ) ->					str:






  """simple docstring"""





  super().__init__(pad_token_id=__a       ,       bos_token_id=__a       ,       eos_token_id=__a       ,       **__a )

  UpperCAmelCase__							=				vocab_size
  UpperCAmelCase__							=				input_embedding_size
  UpperCAmelCase__							=				output_embedding_size
  UpperCAmelCase__							=				max_position_embeddings
  UpperCAmelCase__							=				hidden_size
  UpperCAmelCase__							=				num_hidden_layers
  UpperCAmelCase__							=				num_attention_heads
  UpperCAmelCase__							=				intermediate_size
  UpperCAmelCase__							=				hidden_act
  UpperCAmelCase__							=				hidden_dropout_prob
  UpperCAmelCase__							=				attention_probs_dropout_prob
  UpperCAmelCase__							=				classifier_dropout_prob
  UpperCAmelCase__							=				initializer_range
  UpperCAmelCase__							=				type_vocab_size
  UpperCAmelCase__							=				layer_norm_eps
  UpperCAmelCase__							=				use_cache
  UpperCAmelCase__							=				False







class     lowercase						(       _UpperCamelCase					):



 '''simple docstring'''


 @property
 def   UpperCamelCase__							(self ) ->					Mapping[str, Mapping[int, str]]:






  """simple docstring"""





  if self.task == "multiple-choice":
   UpperCAmelCase__							=				{0: 'batch', 1: 'choice', 2: 'sequence'}
  else:
   UpperCAmelCase__							=				{0: 'batch', 1: 'sequence'}
  return OrderedDict(
      [
          ('input_ids', dynamic_axis),
          ('attention_mask', dynamic_axis),
          ('token_type_ids', dynamic_axis),
      ] )




 @property
 def   UpperCamelCase__							(self ) ->					float:






  """simple docstring"""





  return 1E-4



