

import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_a			=       getLogger(__name__)


_a			=       '''cuda''' if torch.cuda.is_available() else '''cpu'''







def _a			(	SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      int = 8   , SCREAMING_SNAKE_CASE		:      str = DEFAULT_DEVICE   , SCREAMING_SNAKE_CASE		:      Union[str, Any]=False   , SCREAMING_SNAKE_CASE		:      Tuple="summarization"   , SCREAMING_SNAKE_CASE		:      int=None   , **SCREAMING_SNAKE_CASE		:      Optional[Any]   , )   ->   Dict:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				Path(SCREAMING_SNAKE_CASE		).open('w'   , encoding='utf-8'		)
 __lowerCAmelCase:       Tuple            =				str(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[int]            =				AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE		).to(SCREAMING_SNAKE_CASE		)
 if fpaa:
  __lowerCAmelCase:       List[Any]            =				model.half()

 __lowerCAmelCase:       Dict            =				AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE		)
 logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''		)  # if this is wrong, check config.model_type.

 __lowerCAmelCase:       Any            =				time.time()
 # update config with task specific params
 use_task_specific_params(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
 if prefix is None:
  __lowerCAmelCase:       Optional[Any]            =				prefix or getattr(model.config   , 'prefix'   , ''		) or ''
 for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)		)		):
  __lowerCAmelCase:       str            =				[prefix + text for text in examples_chunk]
  __lowerCAmelCase:       Optional[Any]            =				tokenizer(SCREAMING_SNAKE_CASE   , return_tensors='pt'   , truncation=SCREAMING_SNAKE_CASE   , padding='longest'		).to(SCREAMING_SNAKE_CASE		)
  __lowerCAmelCase:       List[str]            =				model.generate(
      input_ids=batch.input_ids   , attention_mask=batch.attention_mask   , **SCREAMING_SNAKE_CASE   , )
  __lowerCAmelCase:       Dict            =				tokenizer.batch_decode(SCREAMING_SNAKE_CASE   , skip_special_tokens=SCREAMING_SNAKE_CASE   , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE		)
  for hypothesis in dec:
   fout.write(hypothesis + '\n'		)
   fout.flush()
 fout.close()
 __lowerCAmelCase:       str            =				int(time.time() - start_time		)  # seconds
 __lowerCAmelCase:       Tuple            =				len(SCREAMING_SNAKE_CASE		)
 return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs   , 4		)}







def _a			(	)   ->   Dict:

 """simple docstring"""






 return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'		)







def _a			(	SCREAMING_SNAKE_CASE		:      Tuple=True		)   ->   int:

 """simple docstring"""






 __lowerCAmelCase:       str            =				argparse.ArgumentParser()
 parser.add_argument('model_name'   , type=SCREAMING_SNAKE_CASE   , help='like facebook/bart-large-cnn,t5-base, etc.'		)
 parser.add_argument('input_path'   , type=SCREAMING_SNAKE_CASE   , help='like cnn_dm/test.source'		)
 parser.add_argument('save_path'   , type=SCREAMING_SNAKE_CASE   , help='where to save summaries'		)
 parser.add_argument('--reference_path'   , type=SCREAMING_SNAKE_CASE   , required=SCREAMING_SNAKE_CASE   , help='like cnn_dm/test.target'		)
 parser.add_argument('--score_path'   , type=SCREAMING_SNAKE_CASE   , required=SCREAMING_SNAKE_CASE   , default='metrics.json'   , help='where to save metrics'		)
 parser.add_argument('--device'   , type=SCREAMING_SNAKE_CASE   , required=SCREAMING_SNAKE_CASE   , default=SCREAMING_SNAKE_CASE   , help='cuda, cuda:1, cpu etc.'		)
 parser.add_argument(
     '--prefix'   , type=SCREAMING_SNAKE_CASE   , required=SCREAMING_SNAKE_CASE   , default=SCREAMING_SNAKE_CASE   , help='will be added to the begininng of src examples'		)
 parser.add_argument('--task'   , type=SCREAMING_SNAKE_CASE   , default='summarization'   , help='used for task_specific_params + metrics'		)
 parser.add_argument('--bs'   , type=SCREAMING_SNAKE_CASE   , default=8   , required=SCREAMING_SNAKE_CASE   , help='batch size'		)
 parser.add_argument(
     '--n_obs'   , type=SCREAMING_SNAKE_CASE   , default=-1   , required=SCREAMING_SNAKE_CASE   , help='How many observations. Defaults to all.'		)
 parser.add_argument('--fp16'   , action='store_true'		)
 parser.add_argument('--dump-args'   , action='store_true'   , help='print the custom hparams with the results'		)
 parser.add_argument(
     '--info'   , nargs='?'   , type=SCREAMING_SNAKE_CASE   , const=datetime_now()   , help=(
         'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
         ' lang=en-ru. If no value is passed, the current datetime string will be used.'
     )   , )
 # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
 __lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				parser.parse_known_args()
 __lowerCAmelCase:       Union[str, Any]            =				parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE		)
 if parsed_args and verbose:
  print(f'''parsed the following generate kwargs: {parsed_args}'''		)
 with open(args.input_path		) as f:
  __lowerCAmelCase:       Optional[int]            =				[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
 if args.n_obs > 0:
  __lowerCAmelCase:       Tuple            =				examples[: args.n_obs]
 Path(args.save_path		).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE		)
 if args.reference_path is None and Path(args.score_path		).exists():
  warnings.warn(f'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''		)
 __lowerCAmelCase:       Tuple            =				generate_summaries_or_translations(
     SCREAMING_SNAKE_CASE   , args.save_path   , args.model_name   , batch_size=args.bs   , device=args.device   , fpaa=args.fpaa   , task=args.task   , prefix=args.prefix   , **SCREAMING_SNAKE_CASE   , )

 if args.reference_path is None:
  return {}

 # Compute scores
 __lowerCAmelCase:       Dict            =				calculate_bleu if 'translation' in args.task else calculate_rouge
 __lowerCAmelCase:       Optional[Any]            =				[x.rstrip() for x in open(args.save_path		).readlines()]
 __lowerCAmelCase:       Union[str, Any]            =				[x.rstrip() for x in open(args.reference_path		).readlines()][: len(SCREAMING_SNAKE_CASE		)]
 __lowerCAmelCase:       dict            =				score_fn(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
 scores.update(SCREAMING_SNAKE_CASE		)

 if args.dump_args:
  scores.update(SCREAMING_SNAKE_CASE		)
 if args.info:
  __lowerCAmelCase:       Any            =				args.info

 if verbose:
  print(SCREAMING_SNAKE_CASE		)

 if args.score_path is not None:
  json.dump(SCREAMING_SNAKE_CASE   , open(args.score_path   , 'w'		)		)

 return scores


if __name__ == "__main__":
  # Usage for MT:
  # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
  run_generate(verbose=True)








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, 2_0_4_8), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)






class 							A_			:


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

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


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


     @property
     def   UpperCAmelCase  (							self      :		int )							->     Dict:
      __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(UpperCAmelCase ) )
       if len(UpperCAmelCase ) == self.batch:
        __lowerCAmelCase:       Optional[int]            =				batch
        __lowerCAmelCase:       Tuple            =				[]
        yield list(map(UpperCAmelCase      ,  zip(*UpperCAmelCase ) ) )

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


     def __call__(							self      :		Tuple )							->     Dict:
      # make writer
      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:       List[Any]            =				self.preprocess(UpperCAmelCase )
       __lowerCAmelCase:       str            =				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:       str            =				output_dict.pop('normalized_boxes' )
       if not TEST:
        __lowerCAmelCase:       Dict            =				np.array(UpperCAmelCase )
        __lowerCAmelCase:       Optional[int]            =				self.schema.encode_batch(UpperCAmelCase )
        writer.write_batch(UpperCAmelCase )
       if TEST:
        break
       # finalizer the writer
      if not TEST:
       __lowerCAmelCase     ,							__lowerCAmelCase:       Optional[int]            =				writer.finalize()
       print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb''' )







def _a			(	SCREAMING_SNAKE_CASE		:      Optional[int]		)   ->   Any:

 """simple docstring"""






 try:
  __lowerCAmelCase:       Optional[Any]            =				json.load(SCREAMING_SNAKE_CASE		)
  try:
   __lowerCAmelCase:       Tuple            =				list(data.keys()		)
  except Exception:
   __lowerCAmelCase:       int            =				[d['img_id'] for d in data]
 except Exception:
  try:
   __lowerCAmelCase:       Dict            =				eval(stream.read()		)
  except Exception:
   __lowerCAmelCase:       Dict            =				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)





