

"""simple docstring"""

import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
_UpperCamelCase		    =						logging.getLogger(__name__)



def 			SCREAMING_SNAKE_CASE       (   ) ->	str:
 lowerCAmelCase__      :							Any    =							argparse.ArgumentParser(description="PyTorch Transformer Language Model"      )
 parser.add_argument("--model_name"				,				type=lowercase__				,				default="transfo-xl-wt103"				,				help="pretrained model name"      )
 parser.add_argument(
     "--split"				,				type=lowercase__				,				default="test"				,				choices=["all", "valid", "test"]				,				help="which split to evaluate"      )
 parser.add_argument("--batch_size"				,				type=lowercase__				,				default=1_0				,				help="batch size"      )
 parser.add_argument("--tgt_len"				,				type=lowercase__				,				default=1_2_8				,				help="number of tokens to predict"      )
 parser.add_argument("--ext_len"				,				type=lowercase__				,				default=0				,				help="length of the extended context"      )
 parser.add_argument("--mem_len"				,				type=lowercase__				,				default=1_6_0_0				,				help="length of the retained previous heads"      )
 parser.add_argument("--clamp_len"				,				type=lowercase__				,				default=1_0_0_0				,				help="max positional embedding index"      )
 parser.add_argument("--no_cuda"				,				action="store_true"				,				help="Do not use CUDA even though CUA is available"      )
 parser.add_argument("--work_dir"				,				type=lowercase__				,				required=lowercase__				,				help="path to the work_dir"      )
 parser.add_argument("--no_log"				,				action="store_true"				,				help="do not log the eval result"      )
 parser.add_argument("--same_length"				,				action="store_true"				,				help="set same length attention with masking"      )
 parser.add_argument("--server_ip"				,				type=lowercase__				,				default=""				,				help="Can be used for distant debugging."      )
 parser.add_argument("--server_port"				,				type=lowercase__				,				default=""				,				help="Can be used for distant debugging."      )
 lowerCAmelCase__      :							Optional[Any]    =							parser.parse_args()
 assert args.ext_len >= 0, "extended context length must be non-negative"

 if args.server_ip and args.server_port:
  # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
  import ptvsd

  print("Waiting for debugger attach"      )
  ptvsd.enable_attach(address=(args.server_ip, args.server_port)				,				redirect_output=lowercase__      )
  ptvsd.wait_for_attach()

 lowerCAmelCase__      :							Union[str, Any]    =							torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"      )
 logger.info("device: {}".format(lowercase__      )      )

 # Load a pre-processed dataset
 # You can also build the corpus yourself using TransfoXLCorpus methods
 # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
 # and tokenizing the dataset
 # The pre-processed corpus is a convertion (using the conversion script )
 lowerCAmelCase__      :							Optional[Any]    =							TransfoXLCorpus.from_pretrained(args.model_name      )

 lowerCAmelCase__      :							Any    =							corpus.get_iterator("valid"				,				args.batch_size				,				args.tgt_len				,				device=lowercase__				,				ext_len=args.ext_len      )
 lowerCAmelCase__      :							int    =							corpus.get_iterator("test"				,				args.batch_size				,				args.tgt_len				,				device=lowercase__				,				ext_len=args.ext_len      )

 # Load a pre-trained model
 lowerCAmelCase__      :							Dict    =							TransfoXLLMHeadModel.from_pretrained(args.model_name      )
 model.to(lowercase__      )

 logger.info(
     "Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}".format(
         args.batch_size				,				args.tgt_len				,				args.ext_len				,				args.mem_len				,				args.clamp_len      )      )

 model.reset_memory_length(args.mem_len      )
 if args.clamp_len > 0:
  lowerCAmelCase__      :							List[Any]    =							args.clamp_len
 if args.same_length:
  lowerCAmelCase__      :							List[str]    =							True

 ###############################################################################
 # Evaluation code
 ###############################################################################
 def evaluate(lowercase__      ):
  # Turn on evaluation mode which disables dropout.
  model.eval()
  lowerCAmelCase__ ,	lowerCAmelCase__      :							str    =							0, 0.0
  lowerCAmelCase__      :							Optional[Any]    =							time.time()
  with torch.no_grad():
   lowerCAmelCase__      :							str    =							None
   for idx, (data, target, seq_len) in enumerate(lowercase__      ):
    lowerCAmelCase__      :							Tuple    =							model(lowercase__				,				lm_labels=lowercase__				,				mems=lowercase__      )
    lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							str    =							ret
    lowerCAmelCase__      :							Optional[Any]    =							loss.mean()
    total_loss += seq_len * loss.item()
    total_len += seq_len
   lowerCAmelCase__      :							Any    =							time.time() - start_time
  logger.info("Time : {:.2f}s, {:.2f}ms/segment".format(lowercase__				,				1_0_0_0 * total_time / (idx + 1)      )      )
  return total_loss / total_len

 # Run on test data.
 if args.split == "all":
  lowerCAmelCase__      :							Dict    =							evaluate(lowercase__      )
  lowerCAmelCase__      :							Union[str, Any]    =							evaluate(lowercase__      )
 elif args.split == "valid":
  lowerCAmelCase__      :							int    =							evaluate(lowercase__      )
  lowerCAmelCase__      :							Union[str, Any]    =							None
 elif args.split == "test":
  lowerCAmelCase__      :							Dict    =							evaluate(lowercase__      )
  lowerCAmelCase__      :							Optional[Any]    =							None

 def format_log(lowercase__				,				lowercase__      ):
  lowerCAmelCase__      :							int    =							"| {0} loss {1:5.2f} | {0} ppl {2:9.3f} ".format(lowercase__				,				lowercase__				,				math.exp(lowercase__      )      )
  return log_str

 lowerCAmelCase__      :							Any    =							""
 if valid_loss is not None:
  log_str += format_log(lowercase__				,				"valid"      )
 if test_loss is not None:
  log_str += format_log(lowercase__				,				"test"      )

 logger.info("=" * 1_0_0      )
 logger.info(lowercase__      )
 logger.info("=" * 1_0_0      )


if __name__ == "__main__":
    main()









"""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
_UpperCamelCase		    =						False
_UpperCamelCase		    =						Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
_UpperCamelCase		    =						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  __a				:





       """simple docstring"""


       def __init__(      self ,				snake_case=sys.argv[1:]      ):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							None
        lowerCAmelCase__      :							str    =							None
        lowerCAmelCase__      :							int    =							None
        lowerCAmelCase__      :							Any    =							1
        lowerCAmelCase__ ,	lowerCAmelCase__      :							List[Any]    =							getopt.getopt(snake_case ,				"i:o:b:s" ,				["inputdir=", "outfile=", "batch_size=", "subset_list="]      )
        for opt, arg in opts:
         if opt in ("-i", "--inputdir"):
          lowerCAmelCase__      :							List[str]    =							arg
         elif opt in ("-o", "--outfile"):
          lowerCAmelCase__      :							Optional[int]    =							arg
         elif opt in ("-b", "--batch_size"):
          lowerCAmelCase__      :							Optional[int]    =							int(snake_case      )
         elif opt in ("-s", "--subset_list"):
          lowerCAmelCase__      :							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(snake_case      ), F"""{outputfile}"""
        if subset_list is not None:
         with open(os.path.realpath(snake_case      )      ) as f:
          lowerCAmelCase__      :							Union[str, Any]    =							{self._vqa_file_split()[0] for x in tryload(snake_case      )}
        else:
         lowerCAmelCase__      :							Optional[int]    =							None

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							int(file.split("."      )[0].split("_"      )[-1]      )
        lowerCAmelCase__      :							str    =							os.path.join(self.inputdir ,				snake_case      )
        return (img_id, filepath)






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							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(snake_case      )      )
         if len(snake_case      ) == self.batch:
          lowerCAmelCase__      :							str    =							batch
          lowerCAmelCase__      :							str    =							[]
          yield list(map(snake_case ,				zip(*snake_case      )      )      )

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






       def __call__(      self      ):





        """simple docstring"""

        if not TEST:
         lowerCAmelCase__      :							Tuple    =							datasets.ArrowWriter(features=self.schema ,				path=self.outputfile      )
        # do file generator
        for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
         lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Union[str, Any]    =							self.preprocess(snake_case      )
         lowerCAmelCase__      :							List[Any]    =							self.model(
             snake_case ,				snake_case ,				scales_yx=snake_case ,				padding="max_detections" ,				max_detections=self.config.MAX_DETECTIONS ,				pad_value=0 ,				return_tensors="np" ,				location="cpu" ,				)
         lowerCAmelCase__      :							Any    =							output_dict.pop("normalized_boxes"      )
         if not TEST:
          lowerCAmelCase__      :							Optional[int]    =							np.array(snake_case      )
          lowerCAmelCase__      :							Optional[int]    =							self.schema.encode_batch(snake_case      )
          writer.write_batch(snake_case      )
         if TEST:
          break
         # finalizer the writer
        if not TEST:
         lowerCAmelCase__ ,	lowerCAmelCase__      :							Any    =							writer.finalize()
         print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""      )




def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	int:
 try:
  lowerCAmelCase__      :							Optional[int]    =							json.load(lowercase__      )
  try:
   lowerCAmelCase__      :							str    =							list(data.keys()      )
  except Exception:
   lowerCAmelCase__      :							str    =							[d["img_id"] for d in data]
 except Exception:
  try:
   lowerCAmelCase__      :							int    =							eval(stream.read()      )
  except Exception:
   lowerCAmelCase__      :							List[str]    =							stream.read().split("\n"      )
 return data


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






