


"""simple docstring"""






import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_lowerCamelCase  =	16
_lowerCamelCase  =	32


def __lowercase  (   lowerCamelCase_	:   Dict	):
				return int(x / 2**20	)


class       lowerCamelCase_		:





				"""simple docstring"""







				def __enter__(    self  ):
								gc.collect()
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
								SCREAMING_SNAKE_CASE__  				=					torch.cuda.memory_allocated()
								SCREAMING_SNAKE_CASE__  				=					psutil.Process()

								SCREAMING_SNAKE_CASE__  				=					self.cpu_mem_used()
								SCREAMING_SNAKE_CASE__  				=					True
								SCREAMING_SNAKE_CASE__  				=					threading.Thread(target=self.peak_monitor_func  )
								SCREAMING_SNAKE_CASE__  				=					True
								peak_monitor_thread.start()
								return self



				def 	lowerCAmelCase__		(    self  ):
								return self.process.memory_info().rss



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					-1

								while True:
												SCREAMING_SNAKE_CASE__  				=					max(self.cpu_mem_used()							,		self.cpu_peak  )

												# can't sleep or will not catch the peak right (this comment is here on purpose)
												# time.sleep(0.001) # 1msec

												if not self.peak_monitoring:
																break






				def __exit__(    self							,		*UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					False

								gc.collect()
								torch.cuda.empty_cache()
								SCREAMING_SNAKE_CASE__  				=					torch.cuda.memory_allocated()
								SCREAMING_SNAKE_CASE__  				=					torch.cuda.max_memory_allocated()
								SCREAMING_SNAKE_CASE__  				=					bamb(self.end - self.begin  )
								SCREAMING_SNAKE_CASE__  				=					bamb(self.peak - self.begin  )

								SCREAMING_SNAKE_CASE__  				=					self.cpu_mem_used()
								SCREAMING_SNAKE_CASE__  				=					bamb(self.cpu_end - self.cpu_begin  )
								SCREAMING_SNAKE_CASE__  				=					bamb(self.cpu_peak - self.cpu_begin  )
								# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	_lowerCamelCase  =	mocked_dataloaders  # noqa: F811


def __lowercase  (   lowerCamelCase_	:   Optional[Any]    ,       lowerCamelCase_	:   List[str]	):
				# For testing only
				if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,       lowerCamelCase_	) == "1":
								SCREAMING_SNAKE_CASE__  				=					2

				# New Code #
				# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
				SCREAMING_SNAKE_CASE__  				=					FullyShardedDataParallelPlugin(
				    state_dict_config=FullStateDictConfig(offload_to_cpu=lowerCamelCase_    ,       ranka_only=lowerCamelCase_	)    ,       optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowerCamelCase_    ,       ranka_only=lowerCamelCase_	)    ,       )

				# Initialize accelerator
				if args.with_tracking:
								SCREAMING_SNAKE_CASE__  				=					Accelerator(
								    cpu=args.cpu    ,       mixed_precision=args.mixed_precision    ,       log_with="wandb"    ,       project_dir=args.logging_dir    ,       fsdp_plugin=lowerCamelCase_    ,       )
				else:
								SCREAMING_SNAKE_CASE__  				=					Accelerator(fsdp_plugin=lowerCamelCase_	)
				accelerator.print(accelerator.distributed_type	)

				if hasattr(args.checkpointing_steps    ,       "isdigit"	):
								if args.checkpointing_steps == "epoch":
												SCREAMING_SNAKE_CASE__  				=					args.checkpointing_steps
								elif args.checkpointing_steps.isdigit():
												SCREAMING_SNAKE_CASE__  				=					int(args.checkpointing_steps	)
								else:
												raise ValueError(
												    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''	)
				else:
								SCREAMING_SNAKE_CASE__  				=					None
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				SCREAMING_SNAKE_CASE__  				=					config['''lr''']
				SCREAMING_SNAKE_CASE__  				=					int(config["num_epochs"]	)
				SCREAMING_SNAKE_CASE__  				=					int(config["seed"]	)
				SCREAMING_SNAKE_CASE__  				=					int(config["batch_size"]	)

				# We need to initialize the trackers we use, and also store our configuration
				if args.with_tracking:
								SCREAMING_SNAKE_CASE__  				=					vars(lowerCamelCase_	)
								accelerator.init_trackers("fsdp_glue_no_trainer"    ,       lowerCamelCase_	)

				SCREAMING_SNAKE_CASE__  				=					AutoTokenizer.from_pretrained(args.model_name_or_path	)
				SCREAMING_SNAKE_CASE__  				=					load_dataset("glue"    ,       "mrpc"	)
				SCREAMING_SNAKE_CASE__  				=					evaluate.load("glue"    ,       "mrpc"	)

				def tokenize_function(lowerCamelCase_	:   List[str]	):
								# max_length=None => use the model max length (it's actually the default)
								SCREAMING_SNAKE_CASE__  				=					tokenizer(examples["sentence1"]    ,       examples["sentence2"]    ,       truncation=lowerCamelCase_    ,       max_length=lowerCamelCase_	)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								SCREAMING_SNAKE_CASE__  				=					datasets.map(
								    lowerCamelCase_    ,       batched=lowerCamelCase_    ,       remove_columns=["idx", "sentence1", "sentence2"]    ,       )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				SCREAMING_SNAKE_CASE__  				=					tokenized_datasets.rename_column("label"    ,       "labels"	)

				# If the batch size is too big we use gradient accumulation
				SCREAMING_SNAKE_CASE__  				=					1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								SCREAMING_SNAKE_CASE__  				=					batch_size // MAX_GPU_BATCH_SIZE
								SCREAMING_SNAKE_CASE__  				=					MAX_GPU_BATCH_SIZE

				def collate_fn(lowerCamelCase_	:   List[str]	):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								SCREAMING_SNAKE_CASE__  				=					128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												SCREAMING_SNAKE_CASE__  				=					16
								elif accelerator.mixed_precision != "no":
												SCREAMING_SNAKE_CASE__  				=					8
								else:
												SCREAMING_SNAKE_CASE__  				=					None

								return tokenizer.pad(
								    lowerCamelCase_    ,       padding="longest"    ,       max_length=lowerCamelCase_    ,       pad_to_multiple_of=lowerCamelCase_    ,       return_tensors="pt"    ,       )

				# Instantiate dataloaders.
				SCREAMING_SNAKE_CASE__  				=					DataLoader(
				    tokenized_datasets["train"]    ,       shuffle=lowerCamelCase_    ,       collate_fn=lowerCamelCase_    ,       batch_size=lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					DataLoader(
				    tokenized_datasets["validation"]    ,       shuffle=lowerCamelCase_    ,       collate_fn=lowerCamelCase_    ,       batch_size=lowerCamelCase_	)

				set_seed(lowerCamelCase_	)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				SCREAMING_SNAKE_CASE__  				=					AutoModelForSequenceClassification.from_pretrained(
				    args.model_name_or_path    ,       return_dict=lowerCamelCase_    ,       low_cpu_mem_usage=lowerCamelCase_	)

				# New Code #
				# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
				SCREAMING_SNAKE_CASE__  				=					accelerator.prepare(lowerCamelCase_	)
				accelerator.print(lowerCamelCase_	)

				# Instantiate optimizer
				# New Code #
				# For FSDP feature, at present it doesn't support multiple parameter groups,
				# so we need to create a single parameter group for the whole model
				SCREAMING_SNAKE_CASE__  				=					torch.optim.AdamW(params=model.parameters()    ,       lr=lowerCamelCase_    ,       weight_decay=2e-4	)

				# Instantiate scheduler
				SCREAMING_SNAKE_CASE__  				=					get_linear_schedule_with_warmup(
				    optimizer=lowerCamelCase_    ,       num_warmup_steps=10    ,       num_training_steps=(len(lowerCamelCase_	) * num_epochs) // gradient_accumulation_steps    ,       )

				# New Code #
				# For FSDP feature, prepare everything except the model as we have already prepared the model
				# before creating the optimizer
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				SCREAMING_SNAKE_CASE__  				=					accelerator.prepare(
				    lowerCamelCase_    ,       lowerCamelCase_    ,       lowerCamelCase_    ,       lowerCamelCase_	)

				SCREAMING_SNAKE_CASE__  				=					0

				# Potentially load in the weights and states from a previous save
				if args.resume_from_checkpoint:
								if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
												accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''	)
												accelerator.load_state(args.resume_from_checkpoint	)
												SCREAMING_SNAKE_CASE__  				=					os.path.basename(args.resume_from_checkpoint	)
								else:
												# Get the most recent checkpoint
												SCREAMING_SNAKE_CASE__  				=					[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
												dirs.sort(key=os.path.getctime	)
												SCREAMING_SNAKE_CASE__  				=					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
								# Extract `epoch_{i}` or `step_{i}`
								SCREAMING_SNAKE_CASE__  				=					os.path.splitext(lowerCamelCase_	)[0]

								if "epoch" in training_difference:
												num_epochs -= int(training_difference.replace("epoch_"    ,       ""	)	)
												SCREAMING_SNAKE_CASE__  				=					None
								else:
												SCREAMING_SNAKE_CASE__  				=					int(training_difference.replace("step_"    ,       ""	)	)
												num_epochs -= resume_step // len(lowerCamelCase_	)
												# If resuming by step, we also need to know exactly how far into the DataLoader we went
												SCREAMING_SNAKE_CASE__  				=					(num_epochs * len(lowerCamelCase_	)) - resume_step

								# Now we train the model
				for epoch in range(lowerCamelCase_	):
								# New Code #
								# context manager to track the peak memory usage during the training epoch
								with TorchTracemalloc() as tracemalloc:
												model.train()
												if args.with_tracking:
																SCREAMING_SNAKE_CASE__  				=					0
												for step, batch in enumerate(lowerCamelCase_	):
																# We need to skip steps until we reach the resumed step
																if args.resume_from_checkpoint and epoch == 0:
																				if resume_step is not None and step < resume_step:
																								pass
																								    # We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device	)
																SCREAMING_SNAKE_CASE__  				=					model(**lowerCamelCase_	)
																SCREAMING_SNAKE_CASE__  				=					outputs.loss
																SCREAMING_SNAKE_CASE__  				=					loss / gradient_accumulation_steps
																# We keep track of the loss at each epoch
																if args.with_tracking:
																				total_loss += loss.detach().float()
																accelerator.backward(lowerCamelCase_	)
																if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# accelerator.print(lr_scheduler.get_lr())

																overall_step += 1

																if isinstance(lowerCamelCase_    ,       lowerCamelCase_	):
																				SCREAMING_SNAKE_CASE__  				=					F'''step_{overall_step}'''
																				if overall_step % checkpointing_steps == 0:
																								if args.output_dir is not None:
																												SCREAMING_SNAKE_CASE__  				=					os.path.join(args.output_dir    ,       lowerCamelCase_	)
																								accelerator.save_state(lowerCamelCase_	)
																# New Code #
																# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
								accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin	)	)	)
								accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used	)	)
								accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked	)	)
								accelerator.print(
								    "Total Peak Memory consumed during the train (max): {}".format(
								        tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
								# Logging the peak memory usage of the GPU to the tracker
								if args.with_tracking:
												accelerator.log(
												    {
												        "train_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin	),
												    }    ,       step=lowerCamelCase_    ,       )

								# New Code #
								# context manager to track the peak memory usage during the evaluation
								with TorchTracemalloc() as tracemalloc:
												model.eval()
												for step, batch in enumerate(lowerCamelCase_	):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device	)
																with torch.no_grad():
																				SCREAMING_SNAKE_CASE__  				=					model(**lowerCamelCase_	)
																SCREAMING_SNAKE_CASE__  				=					outputs.logits.argmax(dim=-1	)
																SCREAMING_SNAKE_CASE__  				=					accelerator.gather_for_metrics((predictions, batch["labels"])	)
																metric.add_batch(
																    predictions=lowerCamelCase_    ,       references=lowerCamelCase_    ,       )

												SCREAMING_SNAKE_CASE__  				=					metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''    ,       lowerCamelCase_	)
												if args.with_tracking:
																accelerator.log(
																    {
																        "accuracy": eval_metric["accuracy"],
																        "f1": eval_metric["f1"],
																        "train_loss": total_loss.item() / len(lowerCamelCase_	),
																    }    ,       step=lowerCamelCase_    ,       )

												if checkpointing_steps == "epoch":
																SCREAMING_SNAKE_CASE__  				=					F'''epoch_{epoch}'''
																if args.output_dir is not None:
																				SCREAMING_SNAKE_CASE__  				=					os.path.join(args.output_dir    ,       lowerCamelCase_	)
																accelerator.save_state(lowerCamelCase_	)
																# New Code #
																# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
								accelerator.print("Memory before entering the eval : {}".format(bamb(tracemalloc.begin	)	)	)
								accelerator.print("Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used	)	)
								accelerator.print("Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked	)	)
								accelerator.print(
								    "Total Peak Memory consumed during the eval (max): {}".format(tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
								# Logging the peak memory usage of the GPU to the tracker
								if args.with_tracking:
												accelerator.log(
												    {
												        "eval_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin	),
												    }    ,       step=lowerCamelCase_    ,       )

				if args.with_tracking:
								accelerator.end_training()


def __lowercase  (   ):
				SCREAMING_SNAKE_CASE__  				=					argparse.ArgumentParser(description="Simple example of training script."	)
				parser.add_argument(
				    "--mixed_precision"    ,       type=lowerCamelCase_    ,       default=lowerCamelCase_    ,       choices=["no", "fp16", "bf16", "fp8"]    ,       help="Whether to use mixed precision. Choose"
				    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
				    "and an Nvidia Ampere GPU."    ,       )
				parser.add_argument("--cpu"    ,       action="store_true"    ,       help="If passed, will train on the CPU."	)
				parser.add_argument(
				    "--checkpointing_steps"    ,       type=lowerCamelCase_    ,       default=lowerCamelCase_    ,       help="Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch."    ,       )
				parser.add_argument(
				    "--resume_from_checkpoint"    ,       type=lowerCamelCase_    ,       default=lowerCamelCase_    ,       help="If the training should continue from a checkpoint folder."    ,       )
				parser.add_argument(
				    "--with_tracking"    ,       action="store_true"    ,       help="Whether to load in all available experiment trackers from the environment and use them for logging."    ,       )
				parser.add_argument(
				    "--output_dir"    ,       type=lowerCamelCase_    ,       default="."    ,       help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."    ,       )
				parser.add_argument(
				    "--logging_dir"    ,       type=lowerCamelCase_    ,       default="logs"    ,       help="Location on where to store experiment tracking logs`"    ,       )
				parser.add_argument(
				    "--model_name_or_path"    ,       type=lowerCamelCase_    ,       help="Path to pretrained model or model identifier from huggingface.co/models."    ,       required=lowerCamelCase_    ,       )
				SCREAMING_SNAKE_CASE__  				=					parser.parse_args()
				SCREAMING_SNAKE_CASE__  				=					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowerCamelCase_    ,       lowerCamelCase_	)


if __name__ == "__main__":
	main()



import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
_lowerCamelCase							:  Any     =				False
_lowerCamelCase							:  int     =				Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
_lowerCamelCase							:  Union[str, Any]     =				datasets.Features(
    OrderedDict(
        {
            "attr_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "attr_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "boxes": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="float32"),
            "img_id": datasets.Value("int32"),
            "obj_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "obj_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "roi_features": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_0_4_8), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)





class    __UpperCAmelCase	:



   def __init__(				self				:	List[str],						__A				:	int=sys.argv[1:]		):
     UpperCAmelCase			:				List[str]										=					None
     UpperCAmelCase			:				Optional[Any]										=					None
     UpperCAmelCase			:				int										=					None
     UpperCAmelCase			:				Any										=					1
     UpperCAmelCase					,		UpperCAmelCase			:				Optional[int]										=					getopt.getopt(__A,						'''i:o:b:s''',						['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']		)
     for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
         UpperCAmelCase			:				Dict										=					arg
       elif opt in ("-o", "--outfile"):
         UpperCAmelCase			:				Optional[int]										=					arg
       elif opt in ("-b", "--batch_size"):
         UpperCAmelCase			:				Tuple										=					int(__A		)
       elif opt in ("-s", "--subset_list"):
         UpperCAmelCase			:				Optional[Any]										=					arg

     assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
     assert outputfile is not None and not os.path.isfile(__A		), F'''{outputfile}'''
     if subset_list is not None:
       with open(os.path.realpath(__A		)		) as f:
         UpperCAmelCase			:				Optional[Any]										=					{self._vqa_file_split()[0] for x in tryload(__A		)}
     else:
       UpperCAmelCase			:				Tuple										=					None

     UpperCAmelCase			:				Tuple										=					CONFIG
     if torch.cuda.is_available():
       UpperCAmelCase			:				Optional[Any]										=					'''cuda'''
     UpperCAmelCase			:				int										=					os.path.realpath(__A		)
     UpperCAmelCase			:				str										=					os.path.realpath(__A		)
     UpperCAmelCase			:				Union[str, Any]										=					Preprocess(self.config		)
     UpperCAmelCase			:				Optional[Any]										=					GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned''',						config=self.config		)
     UpperCAmelCase			:				Any										=					batch_size if batch_size != 0 else 1
     UpperCAmelCase			:				List[str]										=					DEFAULT_SCHEMA



   def 			__magic_name__       (				self				:	Union[str, Any],						__A				:	int		):
     UpperCAmelCase			:				List[Any]										=					int(file.split('''.'''		)[0].split('''_'''		)[-1]		)
     UpperCAmelCase			:				Optional[Any]										=					os.path.join(self.inputdir,						__A		)
     return (img_id, filepath)



   @property
   def 			__magic_name__       (				self				:	str		):
     UpperCAmelCase			:				Tuple										=					[]
     for i, file in enumerate(os.listdir(self.inputdir		)		):
       if self.subset_list is not None and i not in self.subset_list:
         continue
       batch.append(self._vqa_file_split(__A		)		)
       if len(__A		) == self.batch:
         UpperCAmelCase			:				Tuple										=					batch
         UpperCAmelCase			:				str										=					[]
         yield list(map(__A,						zip(*__A		)		)		)

     for i in range(1		):
       yield list(map(__A,						zip(*__A		)		)		)





   def __call__(				self				:	Optional[Any]		):
     # make writer
     if not TEST:
       UpperCAmelCase			:				int										=					datasets.ArrowWriter(features=self.schema,						path=self.outputfile		)
     # do file generator
     for i, (img_ids, filepaths) in enumerate(self.file_generator		):
       UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase			:				int										=					self.preprocess(__A		)
       UpperCAmelCase			:				Tuple										=					self.model(
           __A,						__A,						scales_yx=__A,						padding='''max_detections''',						max_detections=self.config.MAX_DETECTIONS,						pad_value=0,						return_tensors='''np''',						location='''cpu''',						)
       UpperCAmelCase			:				List[Any]										=					output_dict.pop('''normalized_boxes'''		)
       if not TEST:
         UpperCAmelCase			:				Tuple										=					np.array(__A		)
         UpperCAmelCase			:				Union[str, Any]										=					self.schema.encode_batch(__A		)
         writer.write_batch(__A		)
       if TEST:
         break
       # finalizer the writer
     if not TEST:
       UpperCAmelCase					,		UpperCAmelCase			:				Any										=					writer.finalize()
       print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''		)






def      a__	(						UpperCAmelCase				:     List[Any]     )    ->		int:
  try:
    UpperCAmelCase			:				List[str]										=					json.load(UpperCAmelCase     )
    try:
      UpperCAmelCase			:				Optional[Any]										=					list(data.keys()     )
    except Exception:
      UpperCAmelCase			:				Dict										=					[d['''img_id'''] for d in data]
  except Exception:
    try:
      UpperCAmelCase			:				List[Any]										=					eval(stream.read()     )
    except Exception:
      UpperCAmelCase			:				List[Any]										=					stream.read().split('''\n'''     )
  return data


if __name__ == "__main__":
     _lowerCamelCase							:  Optional[Any]     =				Extract(sys.argv[1:])
     extract()
     if not TEST:
          _lowerCamelCase							:  List[Any]     =				datasets.Dataset.from_file(extract.outputfile)
          # wala!
          # print(np.array(dataset[0:2]["roi_features"]).shape)





