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
  _A						=     False
  _A						=     Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
  _A						=     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 			lowerCamelCase  :
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase=sys.argv[1:]  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						None
          UpperCAmelCase__ :       Tuple						=						None
          UpperCAmelCase__ :       Optional[Any]						=						None
          UpperCAmelCase__ :       List[str]						=						1
          UpperCAmelCase__				,		UpperCAmelCase__ :       Dict						=						getopt.getopt(_lowerCamelCase       ,					"""i:o:b:s"""       ,					["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]  )
          for opt, arg in opts:
                if opt in ("-i", "--inputdir"):
                      UpperCAmelCase__ :       Optional[int]						=						arg
                elif opt in ("-o", "--outfile"):
                      UpperCAmelCase__ :       Any						=						arg
                elif opt in ("-b", "--batch_size"):
                      UpperCAmelCase__ :       Optional[Any]						=						int(_lowerCamelCase  )
                elif opt in ("-s", "--subset_list"):
                      UpperCAmelCase__ :       Union[str, Any]						=						arg
  
          assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
          assert outputfile is not None and not os.path.isfile(_lowerCamelCase  ), F"""{outputfile}"""
          if subset_list is not None:
                with open(os.path.realpath(_lowerCamelCase  )  ) as f:
                      UpperCAmelCase__ :       int						=						{self._vqa_file_split()[0] for x in tryload(_lowerCamelCase  )}
          else:
                UpperCAmelCase__ :       List[str]						=						None
  
          UpperCAmelCase__ :       Tuple						=						CONFIG
          if torch.cuda.is_available():
                UpperCAmelCase__ :       Union[str, Any]						=						"""cuda"""
          UpperCAmelCase__ :       Optional[Any]						=						os.path.realpath(_lowerCamelCase  )
          UpperCAmelCase__ :       Tuple						=						os.path.realpath(_lowerCamelCase  )
          UpperCAmelCase__ :       Dict						=						Preprocess(self.config  )
          UpperCAmelCase__ :       Optional[int]						=						GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""       ,					config=self.config  )
          UpperCAmelCase__ :       Dict						=						batch_size if batch_size != 0 else 1
          UpperCAmelCase__ :       Tuple						=						DEFAULT_SCHEMA
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						int(file.split("""."""  )[0].split("""_"""  )[-1]  )
          UpperCAmelCase__ :       Optional[int]						=						os.path.join(self.inputdir       ,					_lowerCamelCase  )
          return (img_id, filepath)
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						[]
          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(_lowerCamelCase  )  )
                if len(_lowerCamelCase  ) == self.batch:
                      UpperCAmelCase__ :       Any						=						batch
                      UpperCAmelCase__ :       Tuple						=						[]
                      yield list(map(_lowerCamelCase       ,					zip(*_lowerCamelCase  )  )  )
  
          for i in range(1  ):
                yield list(map(_lowerCamelCase       ,					zip(*_lowerCamelCase  )  )  )
  
  
  
  
    def __call__(self  ):
  
  
          """simple docstring"""
  
          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__ :       Optional[int]						=						self.preprocess(_lowerCamelCase  )
                UpperCAmelCase__ :       Any						=						self.model(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					scales_yx=_lowerCamelCase       ,					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(_lowerCamelCase  )
                      UpperCAmelCase__ :       Optional[int]						=						self.schema.encode_batch(_lowerCamelCase  )
                      writer.write_batch(_lowerCamelCase  )
                if TEST:
                      break
                # finalizer the writer
          if not TEST:
                UpperCAmelCase__				,		UpperCAmelCase__ :       Tuple						=						writer.finalize()
                print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""  )
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   Optional[Any]:
        try:
              UpperCAmelCase__ :       Dict						=						json.load(lowerCAmelCase						)
              try:
                    UpperCAmelCase__ :       Dict						=						list(data.keys()						)
              except Exception:
                    UpperCAmelCase__ :       Optional[Any]						=						[d["""img_id"""] for d in data]
        except Exception:
              try:
                    UpperCAmelCase__ :       Union[str, Any]						=						eval(stream.read()						)
              except Exception:
                    UpperCAmelCase__ :       Any						=						stream.read().split("""\n"""						)
        return data
  
  
  if __name__ == "__main__":
      _A						=     Extract(sys.argv[1:])
      extract()
      if not TEST:
          _A						=     datasets.Dataset.from_file(extract.outputfile)
          # wala!
          # print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  import time
  
  import torch
  import transformers
  from measures_util import end_measure, log_measures, start_measure
  from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer
  
  from accelerate.utils import compute_module_sizes
  
  
  _A						=     {
      """gpt-j-6b""": {"""is_causal""": True, """model""": """sgugger/sharded-gpt-j-6B""", """tokenizer""": """EleutherAI/gpt-j-6B"""},
      """gpt-neox""": {"""is_causal""": True, """model""": """EleutherAI/gpt-neox-20b"""},
      """opt""": {"""is_causal""": True, """model""": """facebook/opt-30b"""},
      """T0pp""": {"""is_causal""": False, """model""": """bigscience/T0pp""", """model_revision""": """sharded"""},
  }
  
  _A						=     [
      """Hello, my name is""",
      """Are unicorns real? Unicorns are""",
      """For the first time in several years,""",
      """My name is Julien and I am""",
      """The goal of life is""",
      """Whenever I'm sad, I like to""",
  ]
  
  
  
  
  def 				a__	(	)  ->   Optional[Any]:
        UpperCAmelCase__ :       int						=						argparse.ArgumentParser(description="""Run and time generations on a big model using Accelerate."""						)
        parser.add_argument("""model_name""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The name of the model to try."""						)
        parser.add_argument(
            """--tokenizer_name""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The name of the tokenizer (if different from the model."""						)
        parser.add_argument("""--is_causal""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""Whether or not the model is causal."""						)
        parser.add_argument(
            """--model_revision""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The revision to use for the model checkpoint."""						)
        parser.add_argument("""--torch_dtype""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The dtype for the model."""						)
        parser.add_argument("""--disk_offload""" ,  action="""store_true"""						)
  
        UpperCAmelCase__ :       Union[str, Any]						=						parser.parse_args()
  
        # Sanitize args
        if args.model_name in DEFAULT_MODELS:
              UpperCAmelCase__ :       Any						=						DEFAULT_MODELS[args.model_name]
              UpperCAmelCase__ :       Tuple						=						defaults["""model"""]
              if args.tokenizer_name is None:
                    UpperCAmelCase__ :       Optional[Any]						=						defaults.get("""tokenizer""" ,  args.model_name						)
              if args.is_causal is None:
                    UpperCAmelCase__ :       List[Any]						=						defaults["""is_causal"""]
              if args.model_revision is None:
                    UpperCAmelCase__ :       Optional[int]						=						defaults.get("""model_revision""" ,  """main"""						)
  
        if args.is_causal is None:
              raise ValueError("""Could not infer the default for `--is_causal`, pass either True or False for it."""						)
        if args.tokenizer_name is None:
              UpperCAmelCase__ :       str						=						args.model_name
        if args.model_revision is None:
              UpperCAmelCase__ :       List[str]						=						"""main"""
  
        return args
  
  
  
  
  def 				a__	(	)  ->   List[Any]:
        transformers.utils.logging.set_verbosity_error()
        UpperCAmelCase__ :       Optional[int]						=						parse_args()
  
        if args.torch_dtype is None:
              UpperCAmelCase__ :       List[str]						=						AutoConfig.from_pretrained(args.model_name						)
              UpperCAmelCase__ :       Optional[Any]						=						getattr(lowerCAmelCase ,  """torch_dtype""" ,  torch.floataa						)
        else:
              UpperCAmelCase__ :       Union[str, Any]						=						getattr(lowerCAmelCase ,  args.torch_dtype						)
        UpperCAmelCase__ :       List[Any]						=						AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
        UpperCAmelCase__ :       List[str]						=						{
            """torch_dtype""": torch_dtype,
            """revision""": args.model_revision,
        }
        if args.disk_offload:
              UpperCAmelCase__ :       List[str]						=						"""tmp_offload"""
              UpperCAmelCase__ :       int						=						True
  
        UpperCAmelCase__ :       Any						=						start_measure()
        UpperCAmelCase__ :       Optional[int]						=						model_cls.from_pretrained(args.model_name ,  device_map="""auto""" ,  **lowerCAmelCase						)
        UpperCAmelCase__ :       Any						=						end_measure(lowerCAmelCase						)
        log_measures(lowerCAmelCase ,  """Model loading"""						)
  
        UpperCAmelCase__ :       List[str]						=						compute_module_sizes(lowerCAmelCase						)
        UpperCAmelCase__ :       Dict						=						{v: 0 for v in model.hf_device_map.values()}
        for module, device in model.hf_device_map.items():
              device_size[device] += module_sizes[module]
        UpperCAmelCase__ :       Optional[Any]						=						"""\n""".join([F"""- {device}: {size // 2**20}MiB""" for device, size in device_size.items()]						)
        print(F"""\nTheoretical use:\n{message}"""						)
  
        UpperCAmelCase__ :       Tuple						=						AutoTokenizer.from_pretrained(args.tokenizer_name						)
  
        UpperCAmelCase__ :       List[str]						=						start_measure()
        UpperCAmelCase__ :       List[Any]						=						[]
        UpperCAmelCase__ :       Tuple						=						[]
        UpperCAmelCase__ :       Optional[Any]						=						[]
        for prompt in PROMPTS:
              UpperCAmelCase__ :       Any						=						tokenizer(lowerCAmelCase ,  return_tensors="""pt"""						).to(0						)
              UpperCAmelCase__ :       List[str]						=						inputs["""input_ids"""][0].tolist()
              UpperCAmelCase__ :       Dict						=						time.time()
              UpperCAmelCase__ :       List[str]						=						model.generate(inputs["""input_ids"""]						)
              UpperCAmelCase__ :       str						=						time.time()
              UpperCAmelCase__ :       str						=						outputs[0].tolist()
              UpperCAmelCase__ :       Optional[Any]						=						len(lowerCAmelCase						) if outputs[: len(lowerCAmelCase						)] != tokens else len(lowerCAmelCase						) - len(lowerCAmelCase						)
              UpperCAmelCase__ :       Tuple						=						after_generate - before_generate
  
              UpperCAmelCase__ :       Optional[Any]						=						tokenizer.decode(lowerCAmelCase ,  skip_special_tokens=lowerCAmelCase						)
              texts_outs.append(lowerCAmelCase						)
              generation_times.append(lowerCAmelCase						)
              gen_tokens.append(lowerCAmelCase						)
              print(F"""Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n"""						)
  
        UpperCAmelCase__ :       int						=						end_measure(lowerCAmelCase						)
        log_measures(lowerCAmelCase ,  """Model generation"""						)
  
        UpperCAmelCase__ :       Any						=						[gen / tok for gen, tok in zip(lowerCAmelCase ,  lowerCAmelCase						)]
        UpperCAmelCase__ :       List[Any]						=						sum(lowerCAmelCase						) / len(lowerCAmelCase						)
        print(F"""Average time of generation per token: {avg_gen:.2f}s"""						)
        print(F"""First generation (avg time per token): {generation_times_per_token[0]:.2f}s"""						)
        UpperCAmelCase__ :       Any						=						sum(generation_times_per_token[1:]						) / (len(lowerCAmelCase						) - 1)
        print(F"""Average time of generation per token (excluding the first): {avg_gen:.2f}s"""						)
  
  
  if __name__ == "__main__":
      main()
  
  