'''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, 2_048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)





class 	_lowercase		:

 def __init__(				self			:							int     ,   snake_case			:							List[str]=sys.argv[1:]    )     ->		Any:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      None
  UpperCamelCase_      :		str				=      None
  UpperCamelCase_      :		str				=      None
  UpperCamelCase_      :		str				=      1
  UpperCamelCase_,				UpperCamelCase_      :		Optional[int]				=      getopt.getopt(snake_case     ,   'i:o:b:s'     ,   ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']    )
  for opt, arg in opts:
   if opt in ("-i", "--inputdir"):
    UpperCamelCase_      :		int				=      arg
   elif opt in ("-o", "--outfile"):
    UpperCamelCase_      :		Union[str, Any]				=      arg
   elif opt in ("-b", "--batch_size"):
    UpperCamelCase_      :		Optional[Any]				=      int(snake_case    )
   elif opt in ("-s", "--subset_list"):
    UpperCamelCase_      :		str				=      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:
    UpperCamelCase_      :		Optional[Any]				=      {self._vqa_file_split()[0] for x in tryload(snake_case    )}
  else:
   UpperCamelCase_      :		Dict				=      None

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

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							Optional[Any]    )     ->		str:

  """simple docstring"""





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

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any    )     ->		Optional[Any]:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      []
  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:
    UpperCamelCase_      :		Optional[int]				=      batch
    UpperCamelCase_      :		Any				=      []
    yield list(map(snake_case     ,   zip(*snake_case    )    )    )

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


 def __call__(				self			:							List[Any]    )     ->		Tuple:

  """simple docstring"""





  if not TEST:
   UpperCamelCase_      :		List[Any]				=      datasets.ArrowWriter(features=self.schema     ,   path=self.outputfile    )
  # do file generator
  for i, (img_ids, filepaths) in enumerate(self.file_generator    ):
   UpperCamelCase_,				UpperCamelCase_,				UpperCamelCase_      :		str				=      self.preprocess(snake_case    )
   UpperCamelCase_      :		int				=      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'     ,   )
   UpperCamelCase_      :		str				=      output_dict.pop('normalized_boxes'    )
   if not TEST:
    UpperCamelCase_      :		Optional[Any]				=      np.array(snake_case    )
    UpperCamelCase_      :		List[str]				=      self.schema.encode_batch(snake_case    )
    writer.write_batch(snake_case    )
   if TEST:
    break
   # finalizer the writer
  if not TEST:
   UpperCamelCase_,				UpperCamelCase_      :		List[str]				=      writer.finalize()
   print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"    )





def      __lowercase  (		lowerCamelCase      :  List[Any]			):




 try:
  UpperCamelCase_      :		str				=      json.load(lowerCamelCase__			)
  try:
   UpperCamelCase_      :		Optional[int]				=      list(data.keys()			)
  except Exception:
   UpperCamelCase_      :		List[str]				=      [d['img_id'] for d in data]
 except Exception:
  try:
   UpperCamelCase_      :		List[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)




import unittest

from cer import CER


a_					      = CER()





class 	_lowercase		(      unittest.TestCase ):

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      ['White House']
  UpperCamelCase_      :		Optional[Any]				=      ['white house']
  # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
  UpperCamelCase_      :		int				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.1818181818    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		List[Any]:

  """simple docstring"""





  UpperCamelCase_      :		str				=      ['were wolf']
  UpperCamelCase_      :		str				=      ['werewolf']
  # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
  UpperCamelCase_      :		List[str]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.1111111    ) < 1e-6    )

  UpperCamelCase_      :		Tuple				=      ['werewolf']
  UpperCamelCase_      :		Union[str, Any]				=      ['weae     wolf']
  # S = 1, D = 1, I = 0, N = 8, CER = 0.25
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.25    ) < 1e-6    )

  # consecutive whitespaces case 1
  UpperCamelCase_      :		Optional[int]				=      ['were wolf']
  UpperCamelCase_      :		Dict				=      ['were               wolf']
  # S = 0, D = 0, I = 0, N = 9, CER = 0
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.0    ) < 1e-6    )

  # consecutive whitespaces case 2
  UpperCamelCase_      :		Optional[Any]				=      ['were   wolf']
  UpperCamelCase_      :		int				=      ['were               wolf']
  # S = 0, D = 0, I = 0, N = 9, CER = 0
  UpperCamelCase_      :		str				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.0    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      ['werewolf']
  UpperCamelCase_      :		int				=      ['weaewolf']
  # S = 1, D = 0, I = 0, N = 8, CER = 0.125
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      ['werewolf']
  UpperCamelCase_      :		Dict				=      ['wereawolf']
  # S = 0, D = 1, I = 0, N = 8, CER = 0.125
  UpperCamelCase_      :		str				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      ['werewolf']
  UpperCamelCase_      :		Any				=      ['wereolf']
  # S = 0, D = 0, I = 1, N = 8, CER = 0.125
  UpperCamelCase_      :		Optional[Any]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      ['werewolf']
  UpperCamelCase_      :		str				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertEqual(snake_case     ,   0.0    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      ['werewolf', 'I am your father']
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertEqual(snake_case     ,   0.0    )

  UpperCamelCase_      :		Any				=      ['werewolf', 'I am your father', 'doge']
  UpperCamelCase_      :		List[str]				=      ['werxwolf', 'I       am your father', 'doge']
  # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
  UpperCamelCase_      :		Dict				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.03571428    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      ['My hovercraft', 'is full of eels']
  UpperCamelCase_      :		Union[str, Any]				=      ['My hovercraft is full', ' of eels']
  # S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
  # whitespace at the front of " of eels" will be strip during preporcessing
  # so need to insert 2 whitespaces
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case     ,   concatenate_texts=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.071428    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      ['我能吞下玻璃而不伤身体']
  UpperCamelCase_      :		int				=      [' 能吞虾玻璃而 不霜身体啦']
  # S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
  UpperCamelCase_      :		Dict				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.4545454545    ) < 1e-6    )

  UpperCamelCase_      :		Union[str, Any]				=      ['我能吞下玻璃', '而不伤身体']
  UpperCamelCase_      :		int				=      ['我    能 吞 下 玻 璃', '而不伤身体']
  # S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
  UpperCamelCase_      :		Any				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.454545454545    ) < 1e-6    )

  UpperCamelCase_      :		Any				=      ['我能吞下玻璃而不伤身体']
  UpperCamelCase_      :		int				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertFalse(snake_case     ,   0.0    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		int:

  """simple docstring"""





  UpperCamelCase_      :		str				=      ['']
  UpperCamelCase_      :		Optional[Any]				=      ['Hypothesis']
  with self.assertRaises(snake_case    ):
   cer.compute(predictions=snake_case     ,   references=snake_case    )


if __name__ == "__main__":
   unittest.main()



