code: 
  """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
  _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  :
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase=sys.argv[1:]  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						None
          UpperCAmelCase__ :       Tuple						=						None
          UpperCAmelCase__ :       Optional[Any]						=						None
          UpperCAmelCase__ :       List[str]						=						1
          UpperCAmelCase__ :       Dict						=						getopt.getopt(_lowerCamelCase       ,					"""i:o:b:s"""       ,					["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]  )
          for opt, arg in opts:
                if opt in ("-i", "--inputdir"):
                      UpperCAmelCase__ :       Optional[int]						=						arg
                elif opt in ("-o", "--outfile"):
                      UpperCAmelCase__ :       Any						=						arg
                elif opt in ("-b", "--batch_size"):
                      UpperCAmelCase__ :       Optional[Any]						=						int(_lowerCamelCase  )
                elif opt in ("-s", "--subset_list"):
                      UpperCAmelCase__ :       Union[str, Any]						=						arg
  
          assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
          assert outputfile is not None and not os.path.isfile(_lowerCamelCase  ), F"""{outputfile}"""
          if subset_list is not None:
                with open(os.path.realpath(_lowerCamelCase  )  ) as f:
                      UpperCAmelCase__ :       int						=						{self._vqa_file_split()[0] for x in tryload(_lowerCamelCase  )}
          else:
                UpperCAmelCase__ :       List[str]						=						None
  
          UpperCAmelCase__ :       Tuple						=						CONFIG
          if torch.cuda.is_available():
                UpperCAmelCase__ :       Union[str, Any]						=						"""cuda"""
          UpperCAmelCase__ :       Optional[Any]						=						os.path.realpath(_lowerCamelCase  )
          UpperCAmelCase__ :       Tuple						=						os.path.realpath(_lowerCamelCase  )
          UpperCAmelCase__ :       Dict						=						Preprocess(self.config  )
          UpperCAmelCase__ :       Optional[int]						=						GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""       ,					config=self.config  )
          UpperCAmelCase__ :       Dict						=						batch_size if batch_size != 0 else 1
          UpperCAmelCase__ :       Tuple						=						DEFAULT_SCHEMA
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						int(file.split("""."""  )[0].split("""_"""  )[-1]  )
          UpperCAmelCase__ :       Optional[int]						=						os.path.join(self.inputdir       ,					_lowerCamelCase  )
          return (img_id, filepath)
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						[]
          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(_lowerCamelCase  )  )
                if len(_lowerCamelCase  ) == self.batch:
                      UpperCAmelCase__ :       Any						=						batch
                      UpperCAmelCase__ :       Tuple						=						[]
                      yield list(map(_lowerCamelCase       ,					zip(*_lowerCamelCase  )  )  )
  
          for i in range(1  ):
                yield list(map(_lowerCamelCase       ,					zip(*_lowerCamelCase  )  )  )
  
  
  
  
    def __call__(self  ):
  
  
          """simple docstring"""
  
          if not TEST:
                UpperCAmelCase__ :       int						=						datasets.ArrowWriter(features=self.schema       ,					path=self.outputfile  )
          # do file generator
          for i, (img_ids, filepaths) in enumerate(self.file_generator  ):
                UpperCAmelCase__ :       Optional[int]						=						self.preprocess(_lowerCamelCase  )
                UpperCAmelCase__ :       Any						=						self.model(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					scales_yx=_lowerCamelCase       ,					padding="""max_detections"""       ,					max_detections=self.config.MAX_DETECTIONS       ,					pad_value=0       ,					return_tensors="""np"""       ,					location="""cpu"""       ,					)
                UpperCAmelCase__ :       List[Any]						=						output_dict.pop("""normalized_boxes"""  )
                if not TEST:
                      UpperCAmelCase__ :       Tuple						=						np.array(_lowerCamelCase  )
                      UpperCAmelCase__ :       Optional[int]						=						self.schema.encode_batch(_lowerCamelCase  )
                      writer.write_batch(_lowerCamelCase  )
                if TEST:
                      break
                # finalizer the writer
          if not TEST:
                UpperCAmelCase__ :       Tuple						=						writer.finalize()
                print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""  )
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   Optional[Any]:
        try:
              UpperCAmelCase__ :       Dict						=						json.load(lowerCAmelCase						)
              try:
                    UpperCAmelCase__ :       Dict						=						list(data.keys()						)
              except Exception:
                    UpperCAmelCase__ :       Optional[Any]						=						[d["""img_id"""] for d in data]
        except Exception:
              try:
                    UpperCAmelCase__ :       Union[str, Any]						=						eval(stream.read()						)
              except Exception:
                    UpperCAmelCase__ :       Any						=						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)
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Tuple:
        UpperCAmelCase__ :       int						=						TaConfig.from_pretrained(lowerCAmelCase						)
        UpperCAmelCase__ :       Optional[int]						=						FlaxTaForConditionalGeneration(config=lowerCAmelCase						)
        UpperCAmelCase__ :       int						=						checkpoints.load_tax_checkpoint(lowerCAmelCase						)
  
        UpperCAmelCase__ :       Optional[Any]						=						"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]
  
        # Encoder
        for layer_index in range(config.num_layers						):
              UpperCAmelCase__ :       Union[str, Any]						=						F"""layers_{str(lowerCAmelCase						)}"""
  
              # Self-Attention
              UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
              UpperCAmelCase__ :       Optional[Any]						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
              UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       int						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
                    UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
              else:
                    UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
              UpperCAmelCase__ :       Tuple						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
              # Assigning
              UpperCAmelCase__ :       str						=						tax_attention_key
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_out
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_query
              UpperCAmelCase__ :       List[Any]						=						tax_attention_value
  
              UpperCAmelCase__ :       Any						=						tax_attention_layer_norm
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       Any						=						tax_mlp_wi_a
                    UpperCAmelCase__ :       List[Any]						=						tax_mlp_wi_a
              else:
                    UpperCAmelCase__ :       Union[str, Any]						=						tax_mlp_wi
  
              UpperCAmelCase__ :       int						=						tax_mlp_wo
              UpperCAmelCase__ :       List[Any]						=						tax_mlp_layer_norm
  
        # Only for layer 0:
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
        UpperCAmelCase__ :       str						=						tax_encoder_rel_embedding
  
        # Assigning
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
        UpperCAmelCase__ :       Optional[Any]						=						tax_encoder_norm
  
        # Decoder
        for layer_index in range(config.num_decoder_layers						):
              UpperCAmelCase__ :       str						=						F"""layers_{str(lowerCAmelCase						)}"""
  
              # Self-Attention
              UpperCAmelCase__ :       List[Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
              UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
              UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
                  """scale"""
              ]
  
              # Encoder-Decoder-Attention
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
                  """kernel"""
              ]
              UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
                  """kernel"""
              ]
              UpperCAmelCase__ :       Optional[Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
                  """kernel"""
              ]
              UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
                  """kernel"""
              ]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]
  
              # MLP
              if split_mlp_wi:
                    UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
                    UpperCAmelCase__ :       int						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
              else:
                    UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
              UpperCAmelCase__ :       Optional[int]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
              # Assigning
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_key
              UpperCAmelCase__ :       str						=						tax_attention_out
              UpperCAmelCase__ :       Tuple						=						tax_attention_query
              UpperCAmelCase__ :       int						=						tax_attention_value
  
              UpperCAmelCase__ :       List[Any]						=						tax_pre_attention_layer_norm
  
              UpperCAmelCase__ :       Union[str, Any]						=						tax_enc_dec_attention_key
              UpperCAmelCase__ :       Optional[int]						=						tax_enc_dec_attention_out
              UpperCAmelCase__ :       List[Any]						=						tax_enc_dec_attention_query
              UpperCAmelCase__ :       Any						=						tax_enc_dec_attention_value
  
              UpperCAmelCase__ :       List[Any]						=						tax_cross_layer_norm
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       Tuple						=						tax_mlp_wi_a
                    UpperCAmelCase__ :       Dict						=						tax_mlp_wi_a
              else:
                    UpperCAmelCase__ :       str						=						tax_mlp_wi
  
              UpperCAmelCase__ :       Tuple						=						tax_mlp_wo
  
              UpperCAmelCase__ :       Any						=						txa_mlp_layer_norm
  
        # Decoder Normalization
        UpperCAmelCase__ :       int						=						tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
        UpperCAmelCase__ :       Union[str, Any]						=						txa_decoder_norm
  
        # Only for layer 0:
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
        UpperCAmelCase__ :       Optional[int]						=						tax_decoder_rel_embedding
  
        # Token Embeddings
        UpperCAmelCase__ :       Tuple						=						tax_model["""target"""]["""token_embedder"""]["""embedding"""]
        UpperCAmelCase__ :       int						=						txa_token_embeddings
  
        # LM Head (only in v1.1 checkpoints)
        if "logits_dense" in tax_model["target"]["decoder"]:
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]
  
        flax_model.save_pretrained(lowerCAmelCase						)
        print("""T5X Model was sucessfully converted!"""						)
  
  
  if __name__ == "__main__":
      _A						=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
      )
      parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
      parser.add_argument(
          """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
      )
      _A						=     parser.parse_args()
      convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
  