



import unittest

from cer import CER


UpperCAmelCase_			      =  CER()





class 		__SCREAMING_SNAKE_CASE      (				unittest.TestCase      ):






				"""simple docstring"""

				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							str					     =   ["""White House"""]
					_snake_case					:							Dict					     =   ["""white house"""]
					# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
					_snake_case					:							Any					     =   cer.compute(predictions=SCREAMING_SNAKE_CASE__						,	references=SCREAMING_SNAKE_CASE__      )
					self.assertTrue(abs(char_error_rate - 0.18_1818_1818      ) < 1e-6      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							str					     =   ["""were wolf"""]
					_snake_case					:							Dict					     =   ["""werewolf"""]
					# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
					_snake_case					:							Optional[Any]					     =   cer.compute(predictions=SCREAMING_SNAKE_CASE__						,	references=SCREAMING_SNAKE_CASE__      )
					self.assertTrue(abs(char_error_rate - 0.111_1111      ) < 1e-6      )

					_snake_case					:							List[Any]					     =   ["""werewolf"""]
					_snake_case					:							List[Any]					     =   ["""weae     wolf"""]
					# S = 1, D = 1, I = 0, N = 8, CER = 0.25
					_snake_case					:							Optional[int]					     =   cer.compute(predictions=SCREAMING_SNAKE_CASE__						,	references=SCREAMING_SNAKE_CASE__      )
					self.assertTrue(abs(char_error_rate - 0.25      ) < 1e-6      )

					# consecutive whitespaces case 1
					_snake_case					:							List[str]					     =   ["""were wolf"""]
					_snake_case					:							Union[str, Any]					     =   ["""were               wolf"""]
					# S = 0, D = 0, I = 0, N = 9, CER = 0
					_snake_case					:							int					     =   cer.compute(predictions=SCREAMING_SNAKE_CASE__						,	references=SCREAMING_SNAKE_CASE__      )
					self.assertTrue(abs(char_error_rate - 0.0      ) < 1e-6      )

					# consecutive whitespaces case 2
					_snake_case					:							Tuple					     =   ["""were   wolf"""]
					_snake_case					:							Union[str, Any]					     =   ["""were               wolf"""]
					# S = 0, D = 0, I = 0, N = 9, CER = 0
					_snake_case					:							List[Any]					     =   cer.compute(predictions=SCREAMING_SNAKE_CASE__						,	references=SCREAMING_SNAKE_CASE__      )
					self.assertTrue(abs(char_error_rate - 0.0      ) < 1e-6      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

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







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

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







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Dict					     =   ["""werewolf"""]
					_snake_case					:							Any					     =   ["""wereolf"""]
					# S = 0, D = 0, I = 1, N = 8, CER = 0.125
					_snake_case					:							Any					     =   cer.compute(predictions=SCREAMING_SNAKE_CASE__						,	references=SCREAMING_SNAKE_CASE__      )
					self.assertTrue(abs(char_error_rate - 0.125      ) < 1e-6      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							List[Any]					     =   ["""werewolf"""]
					_snake_case					:							Dict					     =   cer.compute(predictions=SCREAMING_SNAKE_CASE__						,	references=SCREAMING_SNAKE_CASE__      )
					self.assertEqual(SCREAMING_SNAKE_CASE__						,	0.0      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							int					     =   ["""werewolf""", """I am your father"""]
					_snake_case					:							Any					     =   cer.compute(predictions=SCREAMING_SNAKE_CASE__						,	references=SCREAMING_SNAKE_CASE__      )
					self.assertEqual(SCREAMING_SNAKE_CASE__						,	0.0      )

					_snake_case					:							List[str]					     =   ["""werewolf""", """I am your father""", """doge"""]
					_snake_case					:							Optional[Any]					     =   ["""werxwolf""", """I       am your father""", """doge"""]
					# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
					_snake_case					:							Union[str, Any]					     =   cer.compute(predictions=SCREAMING_SNAKE_CASE__						,	references=SCREAMING_SNAKE_CASE__      )
					self.assertTrue(abs(char_error_rate - 0.0357_1428      ) < 1e-6      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   ["""My hovercraft""", """is full of eels"""]
					_snake_case					:							Tuple					     =   ["""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
					_snake_case					:							Tuple					     =   cer.compute(predictions=SCREAMING_SNAKE_CASE__						,	references=SCREAMING_SNAKE_CASE__						,	concatenate_texts=SCREAMING_SNAKE_CASE__      )
					self.assertTrue(abs(char_error_rate - 0.07_1428      ) < 1e-6      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							str					     =   ["""我能吞下玻璃而不伤身体"""]
					_snake_case					:							Dict					     =   [""" 能吞虾玻璃而 不霜身体啦"""]
					# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
					_snake_case					:							Union[str, Any]					     =   cer.compute(predictions=SCREAMING_SNAKE_CASE__						,	references=SCREAMING_SNAKE_CASE__      )
					self.assertTrue(abs(char_error_rate - 0.45_4545_4545      ) < 1e-6      )

					_snake_case					:							Union[str, Any]					     =   ["""我能吞下玻璃""", """而不伤身体"""]
					_snake_case					:							Dict					     =   ["""我    能 吞 下 玻 璃""", """而不伤身体"""]
					# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
					_snake_case					:							Optional[Any]					     =   cer.compute(predictions=SCREAMING_SNAKE_CASE__						,	references=SCREAMING_SNAKE_CASE__      )
					self.assertTrue(abs(char_error_rate - 0.4545_4545_4545      ) < 1e-6      )

					_snake_case					:							Any					     =   ["""我能吞下玻璃而不伤身体"""]
					_snake_case					:							Union[str, Any]					     =   cer.compute(predictions=SCREAMING_SNAKE_CASE__						,	references=SCREAMING_SNAKE_CASE__      )
					self.assertFalse(SCREAMING_SNAKE_CASE__						,	0.0      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							List[Any]					     =   [""""""]
					_snake_case					:							str					     =   ["""Hypothesis"""]
					with self.assertRaises(SCREAMING_SNAKE_CASE__      ):
						cer.compute(predictions=SCREAMING_SNAKE_CASE__						,	references=SCREAMING_SNAKE_CASE__      )


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










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





class 		__SCREAMING_SNAKE_CASE      :






				"""simple docstring"""

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






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   None
					_snake_case					:							List[Any]					     =   None
					_snake_case					:							Optional[int]					     =   None
					_snake_case					:							Union[str, Any]					     =   1
					_snake_case     , _snake_case					:							Dict					     =   getopt.getopt(SCREAMING_SNAKE_CASE__						,	"""i:o:b:s"""						,	["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]      )
					for opt, arg in opts:
						if opt in ("-i", "--inputdir"):
							_snake_case					:							List[Any]					     =   arg
						elif opt in ("-o", "--outfile"):
							_snake_case					:							Dict					     =   arg
						elif opt in ("-b", "--batch_size"):
							_snake_case					:							Union[str, Any]					     =   int(SCREAMING_SNAKE_CASE__      )
						elif opt in ("-s", "--subset_list"):
							_snake_case					:							List[Any]					     =   arg

					assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
					assert outputfile is not None and not os.path.isfile(SCREAMING_SNAKE_CASE__      ), f'''{outputfile}'''
					if subset_list is not None:
						with open(os.path.realpath(SCREAMING_SNAKE_CASE__      )      ) as f:
							_snake_case					:							int					     =   {self._vqa_file_split()[0] for x in tryload(SCREAMING_SNAKE_CASE__      )}
					else:
						_snake_case					:							List[str]					     =   None

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







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

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







				@property
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							int					     =   []
					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(SCREAMING_SNAKE_CASE__      )      )
						if len(SCREAMING_SNAKE_CASE__      ) == self.batch:
							_snake_case					:							Union[str, Any]					     =   batch
							_snake_case					:							Optional[Any]					     =   []
							yield list(map(SCREAMING_SNAKE_CASE__						,	zip(*SCREAMING_SNAKE_CASE__      )      )      )

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







				def __call__(				self      ):






					"""simple docstring"""

					if not TEST:
						_snake_case					:							Optional[Any]					     =   datasets.ArrowWriter(features=self.schema						,	path=self.outputfile      )
					# do file generator
					for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
						_snake_case     , _snake_case     , _snake_case					:							List[Any]					     =   self.preprocess(SCREAMING_SNAKE_CASE__      )
						_snake_case					:							Tuple					     =   self.model(
						    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	scales_yx=SCREAMING_SNAKE_CASE__						,	padding="""max_detections"""						,	max_detections=self.config.MAX_DETECTIONS						,	pad_value=0						,	return_tensors="""np"""						,	location="""cpu"""						,	)
						_snake_case					:							Dict					     =   output_dict.pop("""normalized_boxes"""      )
						if not TEST:
							_snake_case					:							List[str]					     =   np.array(SCREAMING_SNAKE_CASE__      )
							_snake_case					:							int					     =   self.schema.encode_batch(SCREAMING_SNAKE_CASE__      )
							writer.write_batch(SCREAMING_SNAKE_CASE__      )
						if TEST:
							break
						# finalizer the writer
					if not TEST:
						_snake_case     , _snake_case					:							Optional[int]					     =   writer.finalize()
						print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''      )
def   UpperCAmelCase							(		A__     )							->					List[str]:
	try:
		_snake_case					:							Optional[int]					     =   json.load(A__     )
		try:
			_snake_case					:							Union[str, Any]					     =   list(data.keys()     )
		except Exception:
			_snake_case					:							Optional[int]					     =   [d["""img_id"""] for d in data]
	except Exception:
		try:
			_snake_case					:							List[str]					     =   eval(stream.read()     )
		except Exception:
			_snake_case					:							Any					     =   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)





