
import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
							import torch

class 				_UpperCamelCase (    unittest.TestCase   ,				_A     ):






			'''simple docstring'''

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]					):
							UpperCamelCase_:							Union[str, Any]		=  load_tool("""speech-to-text"""					)
							self.tool.setup()

			def 					lowerCAmelCase__		(				self       :				List[str]					):
							UpperCamelCase_:							List[str]		=  self.tool(torch.ones(3000					)					)
							self.assertEqual(snake_case_  ,		""" you"""					)




			def 					lowerCAmelCase__		(				self       :				Dict					):
							UpperCamelCase_:							List[Any]		=  self.tool(audio=torch.ones(3000					)					)
							self.assertEqual(snake_case_  ,		""" you"""					)








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
lowerCamelCase_  :       Tuple					      =     False
lowerCamelCase_  :       int					      =     Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
lowerCamelCase_  :       Any					      =     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 				_UpperCamelCase :






			'''simple docstring'''

			def __init__(				self       :				Optional[Any]  ,		snake_case_       :				Optional[Any]=sys.argv[1:]					):
							UpperCamelCase_:							str		=  None
							UpperCamelCase_:							List[str]		=  None
							UpperCamelCase_:							Tuple		=  None
							UpperCamelCase_:							Tuple		=  1
							UpperCamelCase_,      UpperCamelCase_:							Optional[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"):
															UpperCamelCase_:							Dict		=  arg
											elif opt in ("-o", "--outfile"):
															UpperCamelCase_:							Optional[Any]		=  arg
											elif opt in ("-b", "--batch_size"):
															UpperCamelCase_:							List[str]		=  int(snake_case_					)
											elif opt in ("-s", "--subset_list"):
															UpperCamelCase_:							Dict		=  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_:							List[Any]		=  {self._vqa_file_split()[0] for x in tryload(snake_case_					)}
							else:
											UpperCamelCase_:							List[str]		=  None

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

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]  ,		snake_case_       :				int					):
							UpperCamelCase_:							int		=  int(file.split("""."""					)[0].split("""_"""					)[-1]					)
							UpperCamelCase_:							int		=  os.path.join(self.inputdir  ,		snake_case_					)
							return (img_id, filepath)

			@property
			def 					lowerCAmelCase__		(				self       :				int					):
							UpperCamelCase_:							Dict		=  []
							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_:							Dict		=  batch
															UpperCamelCase_:							List[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       :				Optional[int]					):
							# make writer
							if not TEST:
											UpperCamelCase_:							Optional[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_:							Dict		=  self.preprocess(snake_case_					)
											UpperCamelCase_:							Optional[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"""  ,		)
											UpperCamelCase_:							Tuple		=  output_dict.pop("""normalized_boxes"""					)
											if not TEST:
															UpperCamelCase_:							Union[str, Any]		=  np.array(snake_case_					)
															UpperCamelCase_:							int		=  self.schema.encode_batch(snake_case_					)
															writer.write_batch(snake_case_					)
											if TEST:
															break
											# finalizer the writer
							if not TEST:
											UpperCamelCase_,      UpperCamelCase_:							int		=  writer.finalize()
											print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''					)







def 				A__    (  lowerCamelCase  )     ->		Tuple:
				try:
								UpperCamelCase_:							str		=  json.load(lowerCamelCase  )
								try:
												UpperCamelCase_:							List[Any]		=  list(data.keys()  )
								except Exception:
												UpperCamelCase_:							Tuple		=  [d["""img_id"""] for d in data]
				except Exception:
								try:
												UpperCamelCase_:							int		=  eval(stream.read()  )
								except Exception:
												UpperCamelCase_:							Union[str, Any]		=  stream.read().split("""\n"""  )
				return data


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






