

"""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)




"""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()

