




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
__SCREAMING_SNAKE_CASE    :   Optional[Any]       =False
__SCREAMING_SNAKE_CASE    :   int       =Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
__SCREAMING_SNAKE_CASE    :   Optional[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, 2_048), 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  :   List[str]	,  A__  :   int=sys.argv[1:]			)				->       Optional[Any]:
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				1
           lowerCamelCase__  ,		lowerCamelCase__        =				getopt.getopt(A__	,  'i:o:b:s'	,  ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']			)
           for opt, arg in opts:
                 if opt in ("-i", "--inputdir"):
                       lowerCamelCase__        =				arg
                 elif opt in ("-o", "--outfile"):
                       lowerCamelCase__        =				arg
                 elif opt in ("-b", "--batch_size"):
                       lowerCamelCase__        =				int(A__			)
                 elif opt in ("-s", "--subset_list"):
                       lowerCamelCase__        =				arg

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

           lowerCamelCase__        =				CONFIG
           if torch.cuda.is_available():
                 lowerCamelCase__        =				'cuda'
           lowerCamelCase__        =				os.path.realpath(A__			)
           lowerCamelCase__        =				os.path.realpath(A__			)
           lowerCamelCase__        =				Preprocess(self.config			)
           lowerCamelCase__        =				GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'	,  config=self.config			)
           lowerCamelCase__        =				batch_size if batch_size != 0 else 1
           lowerCamelCase__        =				DEFAULT_SCHEMA




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




     @property
     def  __snake_case   (    self  :   int			)				->       Optional[Any]:
           lowerCamelCase__        =				[]
           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(A__			)			)
                 if len(A__			) == self.batch:
                       lowerCamelCase__        =				batch
                       lowerCamelCase__        =				[]
                       yield list(map(A__	,  zip(*A__			)			)			)

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




     def __call__(    self  :   List[Any]			)				->       Optional[Any]:
           # make writer
           if not TEST:
                 lowerCamelCase__        =				datasets.ArrowWriter(features=self.schema	,  path=self.outputfile			)
           # do file generator
           for i, (img_ids, filepaths) in enumerate(self.file_generator			):
                 lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__        =				self.preprocess(A__			)
                 lowerCamelCase__        =				self.model(
                     A__	,  A__	,  scales_yx=A__	,  padding='max_detections'	,  max_detections=self.config.MAX_DETECTIONS	,  pad_value=0	,  return_tensors='np'	,  location='cpu'	,  )
                 lowerCamelCase__        =				output_dict.pop('normalized_boxes'			)
                 if not TEST:
                       lowerCamelCase__        =				np.array(A__			)
                       lowerCamelCase__        =				self.schema.encode_batch(A__			)
                       writer.write_batch(A__			)
                 if TEST:
                       break
                 # finalizer the writer
           if not TEST:
                 lowerCamelCase__  ,		lowerCamelCase__        =				writer.finalize()
                 print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"""			)

def 			lowerCAmelCase_			(  lowerCamelCase:       Tuple						)					->			Optional[Any]:


      '''simple docstring'''




      try:
            lowerCamelCase__        =				json.load(lowerCamelCase						)
            try:
                  lowerCamelCase__        =				list(data.keys()						)
            except Exception:
                  lowerCamelCase__        =				[d['img_id'] for d in data]
      except Exception:
            try:
                  lowerCamelCase__        =				eval(stream.read()						)
            except Exception:
                  lowerCamelCase__        =				stream.read().split('\n'						)
      return data


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





import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


__SCREAMING_SNAKE_CASE    :   int       =importlib.metadata.version("""numpy""")
__SCREAMING_SNAKE_CASE    :   Optional[int]       =""".""".join([str(x) for x in sys.version_info[:3]])






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     def  __snake_case   (    self  :   List[Any]			)				->       Optional[int]:
           # lt + different version strings
           require_version_core('numpy<1000.4.5'			)
           require_version_core('numpy<1000.4'			)
           require_version_core('numpy<1000'			)

           # le
           require_version_core('numpy<=1000.4.5'			)
           require_version_core(F"""numpy<={numpy_ver}"""			)

           # eq
           require_version_core(F"""numpy=={numpy_ver}"""			)

           # ne
           require_version_core('numpy!=1000.4.5'			)

           # ge
           require_version_core('numpy>=1.0'			)
           require_version_core('numpy>=1.0.0'			)
           require_version_core(F"""numpy>={numpy_ver}"""			)

           # gt
           require_version_core('numpy>1.0.0'			)

           # mix
           require_version_core('numpy>1.0.0,<1000'			)

           # requirement w/o version
           require_version_core('numpy'			)

           # unmet requirements due to version conflict
           for req in ["numpy==1.0.0", "numpy>=1000.0.0", F"""numpy<{numpy_ver}"""]:
                 try:
                       require_version_core(A__			)
                 except ImportError as e:
                       self.assertIn(F"""{req} is required"""	,  str(A__			)			)
                       self.assertIn('but found'	,  str(A__			)			)

        # unmet requirements due to missing module
           for req in ["numpipypie>1", "numpipypie2"]:
                 try:
                       require_version_core(A__			)
                 except importlib.metadata.PackageNotFoundError as e:
                       self.assertIn(F"""The '{req}' distribution was not found and is required by this application"""	,  str(A__			)			)
                       self.assertIn('Try: pip install transformers -U'	,  str(A__			)			)

        # bogus requirements formats:
        # 1. whole thing
           for req in ["numpy??1.0.0", "numpy1.0.0"]:
                 try:
                       require_version_core(A__			)
                 except ValueError as e:
                       self.assertIn('requirement needs to be in the pip package format'	,  str(A__			)			)
        # 2. only operators
           for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
                 try:
                       require_version_core(A__			)
                 except ValueError as e:
                       self.assertIn('need one of '	,  str(A__			)			)




     def  __snake_case   (    self  :   str			)				->       Optional[int]:
           # matching requirement
           require_version('python>=3.6.0'			)

           # not matching requirements
           for req in ["python>9.9.9", "python<3.0.0"]:
                 try:
                       require_version_core(A__			)
                 except ImportError as e:
                       self.assertIn(F"""{req} is required"""	,  str(A__			)			)
                       self.assertIn(F"""but found python=={python_ver}"""	,  str(A__			)			)