code: 
  
  
  '''simple docstring'''
  
  
  
  
  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
  __lowercase  :      Tuple      		=				False
  __lowercase  :      List[str]      		=				Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
  __lowercase  :      Tuple      		=				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, 20_48), dtype="""float32"""),
              """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
              """preds_per_image""": datasets.Value(dtype="""int32"""),
          }
      )
  )
  
  
  
  
  
  class _snake_case   :
  							"""simple docstring"""
  
  
  
  
  
  							def __init__(   self			,				__A=sys.argv[1:]	)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[Any]											=					None
  											__lowerCamelCase		:      Optional[Any]											=					None
  											__lowerCamelCase		:      List[Any]											=					None
  											__lowerCamelCase		:      List[Any]											=					1
  											__lowerCamelCase						,					__lowerCamelCase		:      Union[str, Any]											=					getopt.getopt(UpperCamelCase_			,				"i:o:b:s"			,				["inputdir=", "outfile=", "batch_size=", "subset_list="]	)
  											for opt, arg in opts:
  															if opt in ("-i", "--inputdir"):
  																			__lowerCamelCase		:      Dict											=					arg
  															elif opt in ("-o", "--outfile"):
  																			__lowerCamelCase		:      Any											=					arg
  															elif opt in ("-b", "--batch_size"):
  																			__lowerCamelCase		:      Optional[int]											=					int(UpperCamelCase_	)
  															elif opt in ("-s", "--subset_list"):
  																			__lowerCamelCase		:      List[str]											=					arg
  
  											assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
  											assert outputfile is not None and not os.path.isfile(UpperCamelCase_	), f'{outputfile}'
  											if subset_list is not None:
  															with open(os.path.realpath(UpperCamelCase_	)	) as f:
  																			__lowerCamelCase		:      List[Any]											=					{self._vqa_file_split()[0] for x in tryload(UpperCamelCase_	)}
  											else:
  															__lowerCamelCase		:      int											=					None
  
  											__lowerCamelCase		:      Any											=					CONFIG
  											if torch.cuda.is_available():
  															__lowerCamelCase		:      int											=					"cuda"
  											__lowerCamelCase		:      str											=					os.path.realpath(UpperCamelCase_	)
  											__lowerCamelCase		:      str											=					os.path.realpath(UpperCamelCase_	)
  											__lowerCamelCase		:      List[Any]											=					Preprocess(self.config	)
  											__lowerCamelCase		:      str											=					GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"			,				config=self.config	)
  											__lowerCamelCase		:      List[str]											=					batch_size if batch_size != 0 else 1
  											__lowerCamelCase		:      str											=					DEFAULT_SCHEMA
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->Optional[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[Any]											=					int(file.split("."	)[0].split("_"	)[-1]	)
  											__lowerCamelCase		:      List[str]											=					os.path.join(self.inputdir			,				UpperCamelCase_	)
  											return (img_id, filepath)
  
  
  
  
  
  
  
  							@property
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      int											=					[]
  											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(UpperCamelCase_	)	)
  															if len(UpperCamelCase_	) == self.batch:
  																			__lowerCamelCase		:      Optional[int]											=					batch
  																			__lowerCamelCase		:      List[Any]											=					[]
  																			yield list(map(UpperCamelCase_			,				zip(*UpperCamelCase_	)	)	)
  
  											for i in range(1	):
  															yield list(map(UpperCamelCase_			,				zip(*UpperCamelCase_	)	)	)
  
  
  
  
  
  
  
  							def __call__(   self	)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if not TEST:
  															__lowerCamelCase		:      Any											=					datasets.ArrowWriter(features=self.schema			,				path=self.outputfile	)
  											# do file generator
  											for i, (img_ids, filepaths) in enumerate(self.file_generator	):
  															__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      Optional[Any]											=					self.preprocess(UpperCamelCase_	)
  															__lowerCamelCase		:      List[Any]											=					self.model(
  															    UpperCamelCase_			,				UpperCamelCase_			,				scales_yx=UpperCamelCase_			,				padding="max_detections"			,				max_detections=self.config.MAX_DETECTIONS			,				pad_value=0			,				return_tensors="np"			,				location="cpu"			,				)
  															__lowerCamelCase		:      int											=					output_dict.pop("normalized_boxes"	)
  															if not TEST:
  																			__lowerCamelCase		:      int											=					np.array(UpperCamelCase_	)
  																			__lowerCamelCase		:      Union[str, Any]											=					self.schema.encode_batch(UpperCamelCase_	)
  																			writer.write_batch(UpperCamelCase_	)
  															if TEST:
  																			break
  															# finalizer the writer
  											if not TEST:
  															__lowerCamelCase						,					__lowerCamelCase		:      List[Any]											=					writer.finalize()
  															print(f'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'	)
  
  
  
  
  
  def 							a_       (						lowercase    )			->int:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				try:
  								__lowerCamelCase		:      Optional[int]											=					json.load(lowercase    )
  								try:
  												__lowerCamelCase		:      List[str]											=					list(data.keys()    )
  								except Exception:
  												__lowerCamelCase		:      int											=					[d["img_id"] for d in data]
  				except Exception:
  								try:
  												__lowerCamelCase		:      Optional[Any]											=					eval(stream.read()    )
  								except Exception:
  												__lowerCamelCase		:      int											=					stream.read().split("\n"    )
  				return data
  
  
  if __name__ == "__main__":
  						__lowercase  :      Optional[int]      		=				Extract(sys.argv[1:])
  						extract()
  						if not TEST:
  												__lowercase  :      List[str]      		=				datasets.Dataset.from_file(extract.outputfile)
  												# wala!
  												# print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  import unittest
  from functools import partial
  
  import torch
  
  from accelerate import Accelerator, debug_launcher
  from accelerate.state import AcceleratorState, GradientState
  from accelerate.test_utils import require_cpu, require_huggingface_suite
  from accelerate.utils import GradientAccumulationPlugin
  
  
  
  
  
  def 							a_       (						lowercase=2							,						lowercase=True							,						lowercase=False    )			->Optional[Any]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      List[str]											=					Accelerator(step_scheduler_with_optimizer=lowercase							,						split_batches=lowercase    )
  				__lowerCamelCase		:      Union[str, Any]											=					torch.nn.Linear(2							,						4    )
  				__lowerCamelCase		:      str											=					torch.optim.AdamW(model.parameters()							,						lr=1.0    )
  				__lowerCamelCase		:      List[str]											=					torch.optim.lr_scheduler.OneCycleLR(lowercase							,						max_lr=0.0_1							,						steps_per_epoch=2							,						epochs=1    )
  				__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      List[Any]											=					accelerator.prepare(lowercase							,						lowercase							,						lowercase    )
  
  				# Optimizer has stepped
  				scheduler.step()
  				if step_scheduler_with_optimizer or (num_processes == 1):
  								assert (
  								    scheduler.scheduler.last_epoch == num_processes
  								), F'Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'
  				else:
  								assert (
  								    scheduler.scheduler.last_epoch != num_processes
  								), F'Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'
  
  
  
  
  
  def 							a_       (						lowercase=2							,						lowercase=True							,						lowercase=False    )			->Dict:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      Dict											=					Accelerator(step_scheduler_with_optimizer=lowercase							,						split_batches=lowercase    )
  				__lowerCamelCase		:      Tuple											=					torch.nn.Linear(2							,						4    )
  				__lowerCamelCase		:      Optional[Any]											=					torch.optim.AdamW(model.parameters()							,						lr=1.0    )
  				__lowerCamelCase		:      Optional[Any]											=					torch.optim.lr_scheduler.LambdaLR(lowercase							,						lr_lambda=lambda lowercase    : 1 - n / 10    )
  				__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      Optional[int]											=					accelerator.prepare(lowercase							,						lowercase							,						lowercase    )
  
  				# Optimizer has stepped
  				__lowerCamelCase		:      List[Any]											=					False
  				scheduler.step()
  				__lowerCamelCase		:      List[Any]											=					1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
  				assert (
  				    scheduler.get_last_lr()[0] == expected_lr
  				), F'Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'
  
  				# Optimizer has not stepped
  				__lowerCamelCase		:      Any											=					True
  				scheduler.step()
  				if not step_scheduler_with_optimizer:
  								__lowerCamelCase		:      int											=					1 - 2 / 10
  				assert (
  				    scheduler.get_last_lr()[0] == expected_lr
  				), F'Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'
  
  
  
  
  
  def 							a_       (						lowercase = 2    )			->Dict:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				from transformers import get_linear_schedule_with_warmup
  
  				__lowerCamelCase		:      str											=					[1, 2, 4]
  				for num_steps in steps:
  								__lowerCamelCase		:      List[Any]											=					GradientAccumulationPlugin(num_steps=lowercase							,						adjust_scheduler=num_steps > 1    )
  								__lowerCamelCase		:      Dict											=					Accelerator(gradient_accumulation_plugin=lowercase    )
  								__lowerCamelCase		:      Any											=					torch.nn.Linear(2							,						4    )
  								__lowerCamelCase		:      List[Any]											=					torch.optim.AdamW(model.parameters()							,						lr=1_0.0    )
  								__lowerCamelCase		:      Optional[Any]											=					get_linear_schedule_with_warmup(optimizer=lowercase							,						num_warmup_steps=0							,						num_training_steps=20    )
  
  								__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      Optional[int]											=					accelerator.prepare(lowercase							,						lowercase							,						lowercase    )
  
  								for i in range(10 * num_steps    ):
  												with accelerator.accumulate(lowercase    ):
  																optimizer.step()
  																scheduler.step()
  
  												if i == (10 * num_steps - 2):
  																assert (
  																    scheduler.get_last_lr()[0] != 0
  																), F'Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'
  								assert (
  								    scheduler.get_last_lr()[0] == 0
  								), F'Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'
  								GradientState._reset_state()
  
  
  
  
  
  @require_cpu
  class _snake_case   ( unittest.TestCase   ):
  							"""simple docstring"""
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											debug_launcher(partial(__A			,				num_processes=1	)			,				num_processes=1	)
  											debug_launcher(partial(__A			,				num_processes=1			,				split_batches=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											debug_launcher(partial(__A			,				num_processes=1	)			,				num_processes=1	)
  											debug_launcher(partial(__A			,				num_processes=1			,				split_batches=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											debug_launcher(partial(__A			,				num_processes=1			,				step_scheduler_with_optimizer=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											debug_launcher(partial(__A			,				num_processes=1			,				step_scheduler_with_optimizer=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											AcceleratorState._reset_state(__A	)
  											debug_launcher(__A	)
  											debug_launcher(partial(__A			,				num_processes=1			,				split_batches=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Union[str, Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											AcceleratorState._reset_state(__A	)
  											debug_launcher(__A	)
  											debug_launcher(partial(__A			,				num_processes=1			,				split_batches=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											AcceleratorState._reset_state(__A	)
  											debug_launcher(partial(__A			,				step_scheduler_with_optimizer=__A	)	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											AcceleratorState._reset_state(__A	)
  											debug_launcher(partial(__A			,				step_scheduler_with_optimizer=__A	)	)
  
  
  
  
  
  
  
  							@require_huggingface_suite
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											AcceleratorState._reset_state(__A	)
  											debug_launcher(partial(__A			,				num_processes=1	)	)
  											debug_launcher(__A	)
  
  
  
  
  
  