
'''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
UpperCamelCase__					:						int											=     False
UpperCamelCase__					:						Union[str, Any]											=     Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
UpperCamelCase__					:						str											=     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				,							A__=sys.argv[1:]					)			->	int:
          _SCREAMING_SNAKE_CASE          = None
          _SCREAMING_SNAKE_CASE          = None
          _SCREAMING_SNAKE_CASE          = None
          _SCREAMING_SNAKE_CASE          = 1
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_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"):
                _SCREAMING_SNAKE_CASE          = arg
             elif opt in ("-o", "--outfile"):
                _SCREAMING_SNAKE_CASE          = arg
             elif opt in ("-b", "--batch_size"):
                _SCREAMING_SNAKE_CASE          = int(A__					)
             elif opt in ("-s", "--subset_list"):
                _SCREAMING_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:
                _SCREAMING_SNAKE_CASE          = {self._vqa_file_split()[0] for x in tryload(A__					)}
          else:
             _SCREAMING_SNAKE_CASE          = None

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






       def 				UpperCamelCase (							self				,							A__					)			->	List[Any]:
          _SCREAMING_SNAKE_CASE          = int(file.split("""."""					)[0].split("""_"""					)[-1]					)
          _SCREAMING_SNAKE_CASE          = os.path.join(self.inputdir				,							A__					)
          return (img_id, filepath)






       @property
       def 				UpperCamelCase (							self					)			->	int:
          _SCREAMING_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:
                _SCREAMING_SNAKE_CASE          = batch
                _SCREAMING_SNAKE_CASE          = []
                yield list(map(A__				,							zip(*A__					)					)					)

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






       def __call__(							self					)			->	Any:
          # make writer
          if not TEST:
             _SCREAMING_SNAKE_CASE          = datasets.ArrowWriter(features=self.schema				,							path=self.outputfile					)
          # do file generator
          for i, (img_ids, filepaths) in enumerate(self.file_generator					):
             _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.preprocess(A__					)
             _SCREAMING_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"""				,							)
             _SCREAMING_SNAKE_CASE          = output_dict.pop("""normalized_boxes"""					)
             if not TEST:
                _SCREAMING_SNAKE_CASE          = np.array(A__					)
                _SCREAMING_SNAKE_CASE          = self.schema.encode_batch(A__					)
                writer.write_batch(A__					)
             if TEST:
                break
             # finalizer the writer
          if not TEST:
             _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = writer.finalize()
             print(F"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"					)






def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   Any:




   """simple docstring"""
   try:
      _SCREAMING_SNAKE_CASE          = json.load(SCREAMING_SNAKE_CASE_  )
      try:
         _SCREAMING_SNAKE_CASE          = list(data.keys()  )
      except Exception:
         _SCREAMING_SNAKE_CASE          = [d["""img_id"""] for d in data]
   except Exception:
      try:
         _SCREAMING_SNAKE_CASE          = eval(stream.read()  )
      except Exception:
         _SCREAMING_SNAKE_CASE          = stream.read().split("""\n"""  )
   return data


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








'''simple docstring'''



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase__					:						Any											=     logging.get_logger(__name__)

class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'mt5'
       SCREAMING_SNAKE_CASE					=     ['past_key_values']






       def __init__(							self				,							A__=25_01_12				,							A__=5_12				,							A__=64				,							A__=10_24				,							A__=8				,							A__=None				,							A__=6				,							A__=32				,							A__=1_28				,							A__=0.1				,							A__=1E-6				,							A__=1.0				,							A__="gated-gelu"				,							A__=True				,							A__=True				,							A__="T5Tokenizer"				,							A__=False				,							A__=0				,							A__=1				,							A__=0				,							**A__				,							)			->	Union[str, Any]:
          super().__init__(
              is_encoder_decoder=A__				,							tokenizer_class=A__				,							tie_word_embeddings=A__				,							pad_token_id=A__				,							eos_token_id=A__				,							decoder_start_token_id=A__				,							**A__				,							)
          _SCREAMING_SNAKE_CASE          = vocab_size
          _SCREAMING_SNAKE_CASE          = d_model
          _SCREAMING_SNAKE_CASE          = d_kv
          _SCREAMING_SNAKE_CASE          = d_ff
          _SCREAMING_SNAKE_CASE          = num_layers
          _SCREAMING_SNAKE_CASE          = (
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          _SCREAMING_SNAKE_CASE          = num_heads
          _SCREAMING_SNAKE_CASE          = relative_attention_num_buckets
          _SCREAMING_SNAKE_CASE          = relative_attention_max_distance
          _SCREAMING_SNAKE_CASE          = dropout_rate
          _SCREAMING_SNAKE_CASE          = layer_norm_epsilon
          _SCREAMING_SNAKE_CASE          = initializer_factor
          _SCREAMING_SNAKE_CASE          = feed_forward_proj
          _SCREAMING_SNAKE_CASE          = use_cache

          _SCREAMING_SNAKE_CASE          = self.feed_forward_proj.split("""-"""					)
          _SCREAMING_SNAKE_CASE          = act_info[-1]
          _SCREAMING_SNAKE_CASE          = act_info[0] == """gated"""

          if len(A__					) > 1 and act_info[0] != "gated" or len(A__					) > 2:
             raise ValueError(
                 F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
                 """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                 """'gated-gelu' or 'relu'"""					)

          # for backwards compatibility
          if feed_forward_proj == "gated-gelu":
             _SCREAMING_SNAKE_CASE          = """gelu_new"""






       @property
       def 				UpperCamelCase (							self					)			->	int:
          return self.d_model






       @property
       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          return self.num_heads






       @property
       def 				UpperCamelCase (							self					)			->	Dict:
          return self.num_layers






class       _a (_lowerCamelCase):





       """simple docstring"""




       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
       def 				UpperCamelCase (							self					)			->	Mapping[str, Mapping[int, str]]:
          _SCREAMING_SNAKE_CASE          = {
              """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
              """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
          }
          if self.use_past:
             _SCREAMING_SNAKE_CASE          = """past_encoder_sequence + sequence"""
             _SCREAMING_SNAKE_CASE          = {0: """batch"""}
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
          else:
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """decoder_sequence"""}
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """decoder_sequence"""}

          if self.use_past:
             self.fill_with_past_key_values_(A__				,							direction="""inputs"""					)

          return common_inputs






       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
       def 				UpperCamelCase (							self					)			->	int:
          return 13






       @property
       def 				UpperCamelCase (							self					)			->	float:
          return 5E-4






