
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, 20_48), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)







class 	lowerCamelCase						:





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

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





     def 						UpperCAmelCase(self :       Tuple							,       _A :       List[Any]  )		->		Optional[int]:
         snake_case						=   int(file.split("."  )[0].split("_"  )[-1]  )
         snake_case						=   os.path.join(self.inputdir							,       _A  )
         return (img_id, filepath)





     @property
     def 						UpperCAmelCase(self :       Tuple  )		->		List[str]:
         snake_case						=   []
         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:
                 snake_case						=   batch
                 snake_case						=   []
                 yield list(map(_A							,       zip(*_A  )  )  )

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





     def __call__(self :       List[str]  )		->		Optional[int]:
         # make writer
         if not TEST:
             snake_case						=   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						=   self.preprocess(_A  )
             snake_case						=   self.model(
                 _A							,       _A							,       scales_yx=_A							,       padding="max_detections"							,       max_detections=self.config.MAX_DETECTIONS							,       pad_value=0							,       return_tensors="np"							,       location="cpu"							,       )
             snake_case						=   output_dict.pop("normalized_boxes"  )
             if not TEST:
                 snake_case						=   np.array(_A  )
                 snake_case						=   self.schema.encode_batch(_A  )
                 writer.write_batch(_A  )
             if TEST:
                 break
             # finalizer the writer
         if not TEST:
             snake_case	,				snake_case						=   writer.finalize()
             print(f'Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'  )



def 		lowercase_     (  A__						)				->				Optional[Any]:
    """simple docstring"""

    try:
        snake_case						=   json.load(A__						)
        try:
            snake_case						=   list(data.keys()						)
        except Exception:
            snake_case						=   [d["img_id"] for d in data]
    except Exception:
        try:
            snake_case						=   eval(stream.read()						)
        except Exception:
            snake_case						=   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 transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types







class 	lowerCamelCase						(    unittest.TestCase				,					A_    ):





     def 						UpperCAmelCase(self :       Any  )		->		int:
         snake_case						=   load_tool("translation"  )
         self.tool.setup()
         snake_case						=   load_tool("translation"							,       remote=_A  )





     def 						UpperCAmelCase(self :       Optional[Any]  )		->		Optional[int]:
         snake_case						=   self.tool("Hey, what's up?"							,       src_lang="English"							,       tgt_lang="French"  )
         self.assertEqual(_A							,       "- Hé, comment ça va?"  )





     def 						UpperCAmelCase(self :       str  )		->		int:
         snake_case						=   self.remote_tool("Hey, what's up?"							,       src_lang="English"							,       tgt_lang="French"  )
         self.assertEqual(_A							,       "- Hé, comment ça va?"  )





     def 						UpperCAmelCase(self :       str  )		->		List[str]:
         snake_case						=   self.tool(text="Hey, what's up?"							,       src_lang="English"							,       tgt_lang="French"  )
         self.assertEqual(_A							,       "- Hé, comment ça va?"  )





     def 						UpperCAmelCase(self :       Optional[Any]  )		->		str:
         snake_case						=   self.remote_tool(text="Hey, what's up?"							,       src_lang="English"							,       tgt_lang="French"  )
         self.assertEqual(_A							,       "- Hé, comment ça va?"  )





     def 						UpperCAmelCase(self :       Any  )		->		Tuple:
         snake_case						=   ["Hey, what's up?", "English", "Spanish"]
         snake_case						=   self.tool(*_A  )

         # There is a single output
         if len(self.tool.outputs  ) == 1:
             snake_case						=   [outputs]

         self.assertListEqual(output_types(_A  )							,       self.tool.outputs  )





     def 						UpperCAmelCase(self :       List[str]  )		->		List[Any]:
         snake_case						=   ["Hey, what's up?", "English", "Spanish"]
         snake_case						=   self.tool(*_A  )

         if not isinstance(_A							,       _A  ):
             snake_case						=   [outputs]

         self.assertEqual(len(_A  )							,       len(self.tool.outputs  )  )

         for output, output_type in zip(_A							,       self.tool.outputs  ):
             snake_case						=   AGENT_TYPE_MAPPING[output_type]
             self.assertTrue(isinstance(_A							,       _A  )  )





     def 						UpperCAmelCase(self :       int  )		->		Any:
         snake_case						=   ["Hey, what's up?", "English", "Spanish"]

         snake_case						=   []

         for _input, input_type in zip(_A							,       self.tool.inputs  ):
             if isinstance(_A							,       _A  ):
                 _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input  ) for _input_type in input_type]  )
             else:
                 _inputs.append(AGENT_TYPE_MAPPING[input_type](_input  )  )

        # Should not raise an error
         snake_case						=   self.tool(*_A  )

         if not isinstance(_A							,       _A  ):
             snake_case						=   [outputs]

         self.assertEqual(len(_A  )							,       len(self.tool.outputs  )  )

