
"""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 					=    10_00_00_00_00_00
__UpperCAmelCase 					=    1_00_00

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






def 		_snake_case			(      lowercase__						:    int ,	lowercase__						:    int						)    ->							datasets.Dataset:


			'''simple docstring'''





			lowerCAmelCase_       :Optional[Any]     	=    pa.Table.from_pydict({"""col""": [0] * chunk_size}						)
			lowerCAmelCase_       :str     	=    pa.concat_tables([table] * (num_examples // chunk_size)						)
			return datasets.Dataset(lowercase__ ,	fingerprint="""table_100B"""						)




@dataclass
class      _SCREAMING_SNAKE_CASE   :
			UpperCAmelCase_    :int
			UpperCAmelCase_    :int
			UpperCAmelCase_    :int
			UpperCAmelCase_    :int






			def 					__lowerCAmelCase					(   self			)     ->	Union[str, Any]:
						lowerCAmelCase_       :Optional[Any]     	=    np.random.default_rng(self.seed			)
						lowerCAmelCase_       :Dict     	=    rng.integers(low=self.low			,  high=self.high			,  size=self.size			).tolist()






			def __iter__(   self			)     ->	Optional[Any]:
						return iter(self._sampled_values			)






			def __len__(   self			)     ->	int:
						return self.size






@get_duration
def 		_snake_case			(      lowercase__						:    datasets.Dataset						)    ->							List[Any]:


			'''simple docstring'''





			lowerCAmelCase_       :Any     	=    dataset[0]






@get_duration
def 		_snake_case			(      lowercase__						:    datasets.Dataset						)    ->							Optional[int]:


			'''simple docstring'''





			lowerCAmelCase_       :List[Any]     	=    dataset[-1]






@get_duration
def 		_snake_case			(      lowercase__						:    datasets.Dataset						)    ->							Any:


			'''simple docstring'''





			lowerCAmelCase_       :Optional[int]     	=    dataset[range(len(lowercase__						) // 2 ,	len(lowercase__						) // 2 + 1_0_2_4						)]






@get_duration
def 		_snake_case			(      lowercase__						:    datasets.Dataset						)    ->							List[Any]:


			'''simple docstring'''





			lowerCAmelCase_       :List[Any]     	=    dataset[RandIter(0 ,	len(lowercase__						) ,	1_0_2_4 ,	seed=4_2						)]






def 		_snake_case			(      )    ->							Optional[int]:


			'''simple docstring'''





			lowerCAmelCase_       :Any     	=    {"""num examples""": SPEED_TEST_N_EXAMPLES}
			lowerCAmelCase_       :int     	=    (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
			print("""generating dataset"""						)
			lowerCAmelCase_       :List[str]     	=    generate_100B_dataset(num_examples=lowercase__ ,	chunk_size=lowercase__						)
			print("""Functions"""						)
			for func in functions:
						print(func.__name__						)
						lowerCAmelCase_       :List[str]     	=    func(lowercase__						)

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


if __name__ == "__main__":  # useful to run the profiler
				benchmark_table_100B()








"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


__UpperCAmelCase 					=    logging.get_logger(__name__)

__UpperCAmelCase 					=    {
    'layoutlmv2-base-uncased': 'https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json',
    'layoutlmv2-large-uncased': 'https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
				import detectrona




class      _SCREAMING_SNAKE_CASE   (     A__				):
			UpperCAmelCase_    :Optional[int]												=		"layoutlmv2"






			def __init__(   self			,  __A=3_0522			,  __A=768			,  __A=12			,  __A=12			,  __A=3072			,  __A="gelu"			,  __A=0.1			,  __A=0.1			,  __A=512			,  __A=2			,  __A=0.0_2			,  __A=1E-12			,  __A=0			,  __A=1024			,  __A=128			,  __A=32			,  __A=True			,  __A=256			,  __A=64			,  __A=True			,  __A=[7, 7, 256]			,  __A=128			,  __A=128			,  __A=True			,  __A=True			,  __A=False			,  __A=None			,  **__A			,  )     ->	List[str]:
						super().__init__(
						    vocab_size=__A			,  hidden_size=__A			,  num_hidden_layers=__A			,  num_attention_heads=__A			,  intermediate_size=__A			,  hidden_act=__A			,  hidden_dropout_prob=__A			,  attention_probs_dropout_prob=__A			,  max_position_embeddings=__A			,  type_vocab_size=__A			,  initializer_range=__A			,  layer_norm_eps=__A			,  pad_token_id=__A			,  **__A			,  )
						lowerCAmelCase_       :Optional[Any]     	=    max_ad_position_embeddings
						lowerCAmelCase_       :Dict     	=    max_rel_pos
						lowerCAmelCase_       :str     	=    rel_pos_bins
						lowerCAmelCase_       :List[str]     	=    fast_qkv
						lowerCAmelCase_       :Union[str, Any]     	=    max_rel_ad_pos
						lowerCAmelCase_       :List[str]     	=    rel_ad_pos_bins
						lowerCAmelCase_       :Any     	=    convert_sync_batchnorm
						lowerCAmelCase_       :Dict     	=    image_feature_pool_shape
						lowerCAmelCase_       :str     	=    coordinate_size
						lowerCAmelCase_       :Tuple     	=    shape_size
						lowerCAmelCase_       :Tuple     	=    has_relative_attention_bias
						lowerCAmelCase_       :Union[str, Any]     	=    has_spatial_attention_bias
						lowerCAmelCase_       :int     	=    has_visual_segment_embedding
						lowerCAmelCase_       :Any     	=    (
						    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
						)






			@classmethod
			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						return {
						    "MODEL.MASK_ON": True,
						    "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
						    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
						    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
						    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
						    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
						    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
						    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
						    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
						    "MODEL.POST_NMS_TOPK_TEST": 1000,
						    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
						    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
						    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
						    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
						    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
						    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
						    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
						    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
						    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
						    "MODEL.RESNETS.DEPTH": 101,
						    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
						    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
						    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
						    "MODEL.RESNETS.NUM_GROUPS": 32,
						    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
						    "MODEL.RESNETS.STRIDE_IN_1X1": False,
						}






			def 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						lowerCAmelCase_       :Any     	=    detectrona.config.get_cfg()
						for k, v in self.detectrona_config_args.items():
									lowerCAmelCase_       :int     	=    k.split("""."""			)
									lowerCAmelCase_       :List[Any]     	=    detectrona_config
									for attribute in attributes[:-1]:
												lowerCAmelCase_       :List[Any]     	=    getattr(__A			,  __A			)
									setattr(__A			,  attributes[-1]			,  __A			)

						return detectrona_config






