
"""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)




"""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)

