






"""simple docstring"""



import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_UpperCamelCase						:    int		=						1_0_0_0_0_0_0_0_0_0_0_0
_UpperCamelCase						:    Union[str, Any]		=						1_0_0_0_0

_UpperCamelCase,						_UpperCamelCase						:    Tuple		=						os.path.split(__file__)
_UpperCamelCase						:    List[Any]		=						os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    int						,   __snake_case     :    int       ):





  '''simple docstring'''





  lowercase				       =			pa.Table.from_pydict({'col': [0] * chunk_size}       )
  lowercase				       =			pa.concat_tables([table] * (num_examples // chunk_size)       )
  return datasets.Dataset(__snake_case						,   fingerprint='table_100B'       )

@dataclass
class 			a	:
    UpperCAmelCase_					:	int
    UpperCAmelCase_					:	int
    UpperCAmelCase_					:	int
    UpperCAmelCase_					:	int







    def      UpperCamelCase_ (     self			):
      lowercase				       =			np.random.default_rng(self.seed			)
      lowercase				       =			rng.integers(low=self.low				,						high=self.high				,						size=self.size			).tolist()







    def __iter__(     self			):
      return iter(self._sampled_values			)







    def __len__(     self			):
      return self.size


@get_duration
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    datasets.Dataset       ):





  '''simple docstring'''





  lowercase				       =			dataset[0]
@get_duration
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    datasets.Dataset       ):





  '''simple docstring'''





  lowercase				       =			dataset[-1]
@get_duration
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    datasets.Dataset       ):





  '''simple docstring'''





  lowercase				       =			dataset[range(len(__snake_case       ) // 2						,   len(__snake_case       ) // 2 + 10_24       )]
@get_duration
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    datasets.Dataset       ):





  '''simple docstring'''





  lowercase				       =			dataset[RandIter(0						,   len(__snake_case       )						,   10_24						,   seed=42       )]
def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			{'num examples': SPEED_TEST_N_EXAMPLES}
  lowercase				       =			(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
  print('generating dataset'       )
  lowercase				       =			generate_100B_dataset(num_examples=__snake_case						,   chunk_size=__snake_case       )
  print('Functions'       )
  for func in functions:
    print(func.__name__       )
    lowercase				       =			func(__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_table_100B()










from ...configuration_utils import PretrainedConfig
from ...utils import logging


A										=      logging.get_logger(__name__)

A										=      {
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}


class 				__a				(	__A    ):





		'''simple docstring'''



		UpperCAmelCase__   :					str					     =    """tvlt"""


		def __init__(    self			,     UpperCamelCase__=224			,     UpperCamelCase__=2048			,     UpperCamelCase__=128			,     UpperCamelCase__=[16, 16]			,     UpperCamelCase__=[16, 16]			,     UpperCamelCase__=3			,     UpperCamelCase__=1			,     UpperCamelCase__=8			,     UpperCamelCase__=768			,     UpperCamelCase__=12			,     UpperCamelCase__=12			,     UpperCamelCase__=3072			,     UpperCamelCase__="gelu"			,     UpperCamelCase__=0.0			,     UpperCamelCase__=0.0			,     UpperCamelCase__=0.02			,     UpperCamelCase__=1E-6			,     UpperCamelCase__=True			,     UpperCamelCase__=False			,     UpperCamelCase__=16			,     UpperCamelCase__=512			,     UpperCamelCase__=8			,     UpperCamelCase__=2048			,     UpperCamelCase__=0.75			,     UpperCamelCase__=0.15			,     UpperCamelCase__="frame-level"			,     UpperCamelCase__=True			,     UpperCamelCase__=True			,     UpperCamelCase__="classification"			,     **UpperCamelCase__			,     ):
						super().__init__(**UpperCamelCase__							)

						if audio_mask_type not in ("frame-level", "patch_level"):
										raise ValueError(
										    'audio_mask_type must be one of two acceptable strategies - {\'frame_level\', \'patch-level\') '
										    F'''got {audio_mask_type}'''							)

						SCREAMING_SNAKE_CASE_ :						Tuple    				= image_size
						SCREAMING_SNAKE_CASE_ :						int    				= spectrogram_length
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= frequency_length
						SCREAMING_SNAKE_CASE_ :						Any    				= image_patch_size
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= audio_patch_size
						SCREAMING_SNAKE_CASE_ :						int    				= num_image_channels
						SCREAMING_SNAKE_CASE_ :						Any    				= num_audio_channels
						SCREAMING_SNAKE_CASE_ :						Any    				= num_frames

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

						SCREAMING_SNAKE_CASE_ :						str    				= decoder_num_attention_heads
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= decoder_hidden_size
						SCREAMING_SNAKE_CASE_ :						Any    				= decoder_num_hidden_layers
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= decoder_intermediate_size
						SCREAMING_SNAKE_CASE_ :						List[str]    				= pixel_mask_ratio
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= audio_mask_ratio
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= audio_mask_type

						SCREAMING_SNAKE_CASE_ :						Any    				= task_matching
						SCREAMING_SNAKE_CASE_ :						Any    				= task_mae
						SCREAMING_SNAKE_CASE_ :						int    				= loss_type