




"""simple docstring"""




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


a_            =   logging.get_logger(__name__)

a_            =   {
    '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        UpperCAmelCase_  (	snake_case						):
		UpperCamelCase									="layoutlmv2"
		def __init__(		self							,		UpperCamelCase_=3_05_22							,		UpperCamelCase_=7_68							,		UpperCamelCase_=12							,		UpperCamelCase_=12							,		UpperCamelCase_=30_72							,		UpperCamelCase_="gelu"							,		UpperCamelCase_=0.1							,		UpperCamelCase_=0.1							,		UpperCamelCase_=5_12							,		UpperCamelCase_=2							,		UpperCamelCase_=0.0_2							,		UpperCamelCase_=1E-12							,		UpperCamelCase_=0							,		UpperCamelCase_=10_24							,		UpperCamelCase_=1_28							,		UpperCamelCase_=32							,		UpperCamelCase_=True							,		UpperCamelCase_=2_56							,		UpperCamelCase_=64							,		UpperCamelCase_=True							,		UpperCamelCase_=[7, 7, 2_56]							,		UpperCamelCase_=1_28							,		UpperCamelCase_=1_28							,		UpperCamelCase_=True							,		UpperCamelCase_=True							,		UpperCamelCase_=False							,		UpperCamelCase_=None							,		**UpperCamelCase_							,		)		->   Optional[int]:
					super().__init__(
					    vocab_size=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_							,		max_position_embeddings=UpperCamelCase_							,		type_vocab_size=UpperCamelCase_							,		initializer_range=UpperCamelCase_							,		layer_norm_eps=UpperCamelCase_							,		pad_token_id=UpperCamelCase_							,		**UpperCamelCase_							,		)
					__lowercase	:       Dict				=   max_ad_position_embeddings
					__lowercase	:       Union[str, Any]				=   max_rel_pos
					__lowercase	:       str				=   rel_pos_bins
					__lowercase	:       List[str]				=   fast_qkv
					__lowercase	:       Optional[int]				=   max_rel_ad_pos
					__lowercase	:       int				=   rel_ad_pos_bins
					__lowercase	:       Optional[Any]				=   convert_sync_batchnorm
					__lowercase	:       List[str]				=   image_feature_pool_shape
					__lowercase	:       str				=   coordinate_size
					__lowercase	:       str				=   shape_size
					__lowercase	:       Optional[int]				=   has_relative_attention_bias
					__lowercase	:       List[str]				=   has_spatial_attention_bias
					__lowercase	:       Tuple				=   has_visual_segment_embedding
					__lowercase	:       Optional[Any]				=   (
					    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
					)
		@classmethod
		def 					_lowerCamelCase    (		self   )		->   List[str]:
					return {
					    "MODEL.MASK_ON": True,
					    "MODEL.PIXEL_STD": [57.3_75, 57.1_20, 58.3_95],
					    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
					    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
					    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
					    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
					    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 20_00,
					    "MODEL.RPN.PRE_NMS_TOPK_TEST": 10_00,
					    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 10_00,
					    "MODEL.POST_NMS_TOPK_TEST": 10_00,
					    "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": 1_01,
					    "MODEL.RESNETS.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
					    "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   )		->   Optional[int]:
					__lowercase	:       List[Any]				=   detectrona.config.get_cfg()
					for k, v in self.detectrona_config_args.items():
								__lowercase	:       int				=   k.split('''.'''   )
								__lowercase	:       List[Any]				=   detectrona_config
								for attribute in attributes[:-1]:
											__lowercase	:       Optional[int]				=   getattr(UpperCamelCase_							,		UpperCamelCase_   )
								setattr(UpperCamelCase_							,		attributes[-1]							,		UpperCamelCase_   )

					return detectrona_config









"""simple docstring"""




import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
			print('#################### AVAILABLE DEVICES ####################')
			print(jax.devices())
			print('###########################################################')

			# setup for wandb sweep
			a_            =   Args()
			a_            =   wandb.init(project='bigbird-natural-questions', config=args.__dict__)
			a_            =   dict(logger.config)
			del wandb_args["batch_size"]
			a_            =   replace(args, **wandb_args)
			a_            =   args.base_dir + '-' + wandb.run.id
			a_            =   replace(args, base_dir=base_dir)
			print(args)

			a_            =   load_dataset('json', data_files=args.tr_data_path)['train']
			a_            =   load_dataset('json', data_files=args.val_data_path)['train']

			# drop extra batch for now
			a_            =   range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
			a_            =   tr_dataset.shuffle().select(indices)
			a_            =   range(len(val_dataset) - len(val_dataset) % args.batch_size)
			a_            =   val_dataset.shuffle().select(indices)

			if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
						a_            =   tr_dataset.shuffle().select(range(8_0_0_0_0))
						a_            =   val_dataset.shuffle().select(range(8_0_0_0))

			print(tr_dataset)
			print(val_dataset)

			a_            =   FlaxBigBirdForNaturalQuestions.from_pretrained(
			    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
			)
			a_            =   BigBirdTokenizerFast.from_pretrained(args.model_id)
			a_            =   DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

			a_            =   {
			    'lr': args.lr,
			    'init_lr': args.init_lr,
			    'warmup_steps': args.warmup_steps,
			    'num_train_steps': args.max_epochs * (len(tr_dataset) // args.batch_size),
			    'weight_decay': args.weight_decay,
			}
			a_       ,	a_            =   build_tx(**tx_args)

			a_            =   Trainer(
			    args=args,
			    data_collator=data_collator,
			    model_save_fn=model.save_pretrained,
			    train_step_fn=train_step,
			    val_step_fn=val_step,
			    logger=logger,
			    scheduler_fn=lr,
			)

			a_            =   None
			a_            =   trainer.create_state(model, tx, num_train_steps=tx_args['num_train_steps'], ckpt_dir=ckpt_dir)
			try:
						trainer.train(state, tr_dataset, val_dataset)
			except KeyboardInterrupt:
						print('Oooops; TRAINING STOPPED UNFORTUNATELY')

			print('SAVING WEIGHTS IN `final-weights`')
			a_            =   jax_utils.unreplicate(state.params)
			model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)



