





"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_          =	logging.get_logger(__name__)

lowerCamelCase_          =	{
    "MCG-NJU/videomae-base": "https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json",
}






class       _SCREAMING_SNAKE_CASE(     A						):
       SCREAMING_SNAKE_CASE_     :   Union[str, Any]					=						'''videomae'''




       def __init__(		self	,SCREAMING_SNAKE_CASE__=2_24	,SCREAMING_SNAKE_CASE__=16	,SCREAMING_SNAKE_CASE__=3	,SCREAMING_SNAKE_CASE__=16	,SCREAMING_SNAKE_CASE__=2	,SCREAMING_SNAKE_CASE__=7_68	,SCREAMING_SNAKE_CASE__=12	,SCREAMING_SNAKE_CASE__=12	,SCREAMING_SNAKE_CASE__=30_72	,SCREAMING_SNAKE_CASE__="gelu"	,SCREAMING_SNAKE_CASE__=0.0	,SCREAMING_SNAKE_CASE__=0.0	,SCREAMING_SNAKE_CASE__=0.0_2	,SCREAMING_SNAKE_CASE__=1E-12	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__=6	,SCREAMING_SNAKE_CASE__=3_84	,SCREAMING_SNAKE_CASE__=4	,SCREAMING_SNAKE_CASE__=15_36	,SCREAMING_SNAKE_CASE__=True	,**SCREAMING_SNAKE_CASE__	,)    ->   Union[str, Any]:





             """simple docstring"""






             super().__init__(**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						image_size
             __SCREAMING_SNAKE_CASE     :Dict	       =						patch_size
             __SCREAMING_SNAKE_CASE     :Dict	       =						num_channels
             __SCREAMING_SNAKE_CASE     :str	       =						num_frames
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						tubelet_size

             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						hidden_size
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						num_hidden_layers
             __SCREAMING_SNAKE_CASE     :Any	       =						num_attention_heads
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						intermediate_size
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						hidden_act
             __SCREAMING_SNAKE_CASE     :int	       =						hidden_dropout_prob
             __SCREAMING_SNAKE_CASE     :int	       =						attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						initializer_range
             __SCREAMING_SNAKE_CASE     :Tuple	       =						layer_norm_eps
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						qkv_bias
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						use_mean_pooling

             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						decoder_num_attention_heads
             __SCREAMING_SNAKE_CASE     :Any	       =						decoder_hidden_size
             __SCREAMING_SNAKE_CASE     :Dict	       =						decoder_num_hidden_layers
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						decoder_intermediate_size
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						norm_pix_loss






"""simple docstring"""
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


lowerCamelCase_          =	(3_0, 4_8_7)
lowerCamelCase_          =	(3_6, 1_0_2_4)
lowerCamelCase_          =	(1_0_0, 1_0_0)
lowerCamelCase_          =	1_0_0

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

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



@get_duration
def   __lowerCamelCase     (							a_  :	Optional[Any]				,     a_  :	List[str]				,     a_  :	List[str]							)    ->   List[str]:
      with ArrowWriter(features=a_				,     path=os.path.join(a_				,     '''beta.arrow'''							)							) as writer:
            for key, record in dummy_data:
                  __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						my_features.encode_example(a_							)
                  writer.write(a_							)
            __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						writer.finalize()



@get_duration
def   __lowerCamelCase     (							a_  :	Tuple				,     a_  :	Optional[Any]							)    ->   Dict:
      __SCREAMING_SNAKE_CASE     :Tuple	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      for _ in dataset:
            pass



@get_duration
def   __lowerCamelCase     (							a_  :	List[str]				,     a_  :	List[str]							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      dataset.set_format('''numpy'''							)
      for _ in dataset:
            pass



@get_duration
def   __lowerCamelCase     (							a_  :	Tuple				,     a_  :	Optional[int]							)    ->   Optional[int]:
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						10
      __SCREAMING_SNAKE_CASE     :Dict	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      for i in range(0				,     len(a_							)				,     a_							):
            __SCREAMING_SNAKE_CASE     :Dict	       =						dataset[i : i + batch_size]



@get_duration
def   __lowerCamelCase     (							a_  :	Any				,     a_  :	Optional[Any]							)    ->   Optional[Any]:
      __SCREAMING_SNAKE_CASE     :List[Any]	       =						10
      __SCREAMING_SNAKE_CASE     :List[str]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      dataset.set_format('''numpy'''							)
      for i in range(0				,     len(a_							)				,     a_							):
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						dataset[i : i + batch_size]



@get_duration
def   __lowerCamelCase     (							a_  :	List[Any]				,     a_  :	Optional[Any]							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      for col in feats:
            __SCREAMING_SNAKE_CASE     :Tuple	       =						dataset[col]



@get_duration
def   __lowerCamelCase     (							a_  :	int				,     a_  :	List[str]							)    ->   str:
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      dataset.set_format('''numpy'''							)
      for col in feats:
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						dataset[col]



def   __lowerCamelCase     (							)    ->   Union[str, Any]:
      __SCREAMING_SNAKE_CASE     :Tuple	       =						{}
      __SCREAMING_SNAKE_CASE     :int	       =						(
          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:
            __SCREAMING_SNAKE_CASE     :Dict	       =						datasets.Features({'''image''': ArrayaD(a_				,     dtype='''float32'''							)}							)
            __SCREAMING_SNAKE_CASE     :Any	       =						generate_examples(features=a_				,     num_examples=a_							)
            __SCREAMING_SNAKE_CASE     :Dict	       =						write(a_				,     a_				,     a_							)
            for read_func in read_functions:
                  __SCREAMING_SNAKE_CASE     :str	       =						read_func(a_				,     a_							)

      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])}
            # )
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''							)							)							)}							)
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						generate_examples(
                features=a_				,     num_examples=a_				,     seq_shapes={'''image''': SPEED_TEST_SHAPE}							)
            __SCREAMING_SNAKE_CASE     :Tuple	       =						write(a_				,     a_				,     a_							)
            for read_func in read_functions:
                  __SCREAMING_SNAKE_CASE     :Tuple	       =						read_func(a_				,     a_							)

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

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


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