"""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     						=			False
UpperCAmelCase     						=			Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
UpperCAmelCase     						=			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    :						Optional[int]    ,     lowerCamelCase    :						Tuple=sys.argv[1:]					)	->							Union[str, Any]:
        _UpperCamelCase       :    List[str]					  =					None
        _UpperCamelCase       :    Union[str, Any]					  =					None
        _UpperCamelCase       :    Optional[Any]					  =					None
        _UpperCamelCase       :    Any					  =					1
        _UpperCamelCase       :    str					  =					getopt.getopt(lowerCamelCase    ,     'i:o:b:s'    ,     ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']					)
        for opt, arg in opts:
               if opt in ("-i", "--inputdir"):
                      _UpperCamelCase       :    Union[str, Any]					  =					arg
               elif opt in ("-o", "--outfile"):
                      _UpperCamelCase       :    str					  =					arg
               elif opt in ("-b", "--batch_size"):
                      _UpperCamelCase       :    Optional[int]					  =					int(lowerCamelCase					)
               elif opt in ("-s", "--subset_list"):
                      _UpperCamelCase       :    List[str]					  =					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       :    List[Any]					  =					{self._vqa_file_split()[0] for x in tryload(lowerCamelCase					)}
        else:
               _UpperCamelCase       :    Tuple					  =					None

        _UpperCamelCase       :    Union[str, Any]					  =					CONFIG
        if torch.cuda.is_available():
               _UpperCamelCase       :    Optional[int]					  =					"cuda"
        _UpperCamelCase       :    Optional[Any]					  =					os.path.realpath(lowerCamelCase					)
        _UpperCamelCase       :    List[Any]					  =					os.path.realpath(lowerCamelCase					)
        _UpperCamelCase       :    int					  =					Preprocess(self.config					)
        _UpperCamelCase       :    Union[str, Any]					  =					GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'    ,     config=self.config					)
        _UpperCamelCase       :    Any					  =					batch_size if batch_size != 0 else 1
        _UpperCamelCase       :    List[str]					  =					DEFAULT_SCHEMA

 def    __snake_case   (     self    :						Tuple    ,     lowerCamelCase    :						Optional[int]					)	->							List[Any]:
        _UpperCamelCase       :    List[str]					  =					int(file.split('.'					)[0].split('_'					)[-1]					)
        _UpperCamelCase       :    Dict					  =					os.path.join(self.inputdir    ,     lowerCamelCase					)
        return (img_id, filepath)

 @property
 def    __snake_case   (     self    :						str					)	->							Tuple:
        _UpperCamelCase       :    Union[str, 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       :    Dict					  =					batch
                      _UpperCamelCase       :    List[Any]					  =					[]
                      yield list(map(lowerCamelCase    ,     zip(*lowerCamelCase					)					)					)

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

 def __call__(     self    :						Dict					)	->							List[str]:
        # make writer
        if not TEST:
               _UpperCamelCase       :    Tuple					  =					datasets.ArrowWriter(features=self.schema    ,     path=self.outputfile					)
        # do file generator
        for i, (img_ids, filepaths) in enumerate(self.file_generator					):
               _UpperCamelCase       :    Optional[Any]					  =					self.preprocess(lowerCamelCase					)
               _UpperCamelCase       :    int					  =					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       :    Tuple					  =					output_dict.pop('normalized_boxes'					)
               if not TEST:
                      _UpperCamelCase       :    Dict					  =					np.array(lowerCamelCase					)
                      _UpperCamelCase       :    Optional[Any]					  =					self.schema.encode_batch(lowerCamelCase					)
                      writer.write_batch(lowerCamelCase					)
               if TEST:
                      break
               # finalizer the writer
        if not TEST:
               _UpperCamelCase       :    Dict					  =					writer.finalize()
               print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"					)







def SCREAMING_SNAKE_CASE			(       lowercase_:		Any			):







       try:
              _UpperCamelCase       :    Any					  =					json.load(_UpperCAmelCase			)
              try:
                     _UpperCamelCase       :    int					  =					list(data.keys()			)
              except Exception:
                     _UpperCamelCase       :    Optional[Any]					  =					[d["img_id"] for d in data]
       except Exception:
              try:
                     _UpperCamelCase       :    List[Any]					  =					eval(stream.read()			)
              except Exception:
                     _UpperCamelCase       :    int					  =					stream.read().split('\n'			)
       return data


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




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 				__lowerCAmelCase					(    utils.Parser  ):



 '''simple docstring'''
 _lowerCamelCase  :		str			      =	"halfcheetah-medium-expert-v2"
 _lowerCamelCase  :		str			      =	"config.offline"







def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[Any]						,  lowercase_:		Tuple						,  lowercase_:		Dict						,  lowercase_:		Union[str, Any]						,  lowercase_:		List[Any]			):
       _UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					utils.load_model(lowercase_						,  lowercase_						,  lowercase_						,  epoch=lowercase_						,  device=lowercase_			)
       _UpperCamelCase       :    Tuple					  =					TrajectoryTransformerModel(gpt.config			)

       trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
       _UpperCamelCase       :    Any					  =					gpt.pos_emb
       trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()			)
       trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()			)
       trajectory_transformer.head.load_state_dict(gpt.head.state_dict()			)

       for i, block in enumerate(gpt.blocks			):
              trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
              trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
              trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()			)

              trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()			)
              trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()			)
              trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()			)
              trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()			)

       torch.save(trajectory_transformer.state_dict()						,  'pytorch_model.bin'			)


if __name__ == "__main__":
 UpperCAmelCase     						=			Parser().parse_args('''plan''')
 convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
     args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
 )



