






"""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 :				List[str]      					=		False
a :				Tuple      					=		Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
a :				int      					=		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, 2048), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)


class     __UpperCAmelCase:




						"""simple docstring"""




						def __init__(   self       ,  snake_case__=sys.argv[1:]			):
												'''simple docstring'''
												lowercase__				:      int=       None
												lowercase__				:      Dict=       None
												lowercase__				:      Tuple=       None
												lowercase__				:      Any=       1
												lowercase__,	lowercase__				:      List[Any]=       getopt.getopt(snake_case__       ,  "i:o:b:s"       ,  ["inputdir=", "outfile=", "batch_size=", "subset_list="]			)
												for opt, arg in opts:
																		if opt in ("-i", "--inputdir"):
																								lowercase__				:      Tuple=       arg
																		elif opt in ("-o", "--outfile"):
																								lowercase__				:      Union[str, Any]=       arg
																		elif opt in ("-b", "--batch_size"):
																								lowercase__				:      List[Any]=       int(snake_case__			)
																		elif opt in ("-s", "--subset_list"):
																								lowercase__				:      Dict=       arg

												assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
												assert outputfile is not None and not os.path.isfile(snake_case__			), F'''{outputfile}'''
												if subset_list is not None:
																		with open(os.path.realpath(snake_case__			)			) as f:
																								lowercase__				:      Tuple=       {self._vqa_file_split()[0] for x in tryload(snake_case__			)}
												else:
																		lowercase__				:      Optional[Any]=       None

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




						def 		UpperCAmelCase_       (   self       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Tuple=       int(file.split("."			)[0].split("_"			)[-1]			)
												lowercase__				:      Union[str, Any]=       os.path.join(self.inputdir       ,  snake_case__			)
												return (img_id, filepath)




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[str]=       []
												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(snake_case__			)			)
																		if len(snake_case__			) == self.batch:
																								lowercase__				:      List[str]=       batch
																								lowercase__				:      List[str]=       []
																								yield list(map(snake_case__       ,  zip(*snake_case__			)			)			)

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




						def __call__(   self			):
												'''simple docstring'''
												# make writer
												if not TEST:
																		lowercase__				:      Optional[Any]=       datasets.ArrowWriter(features=self.schema       ,  path=self.outputfile			)
												# do file generator
												for i, (img_ids, filepaths) in enumerate(self.file_generator			):
																		lowercase__,	lowercase__,	lowercase__				:      Tuple=       self.preprocess(snake_case__			)
																		lowercase__				:      Optional[Any]=       self.model(
																		    snake_case__       ,  snake_case__       ,  scales_yx=snake_case__       ,  padding="max_detections"       ,  max_detections=self.config.MAX_DETECTIONS       ,  pad_value=0       ,  return_tensors="np"       ,  location="cpu"       ,  )
																		lowercase__				:      Optional[Any]=       output_dict.pop("normalized_boxes"			)
																		if not TEST:
																								lowercase__				:      List[Any]=       np.array(snake_case__			)
																								lowercase__				:      str=       self.schema.encode_batch(snake_case__			)
																								writer.write_batch(snake_case__			)
																		if TEST:
																								break
																		# finalizer the writer
												if not TEST:
																		lowercase__,	lowercase__				:      Optional[Any]=       writer.finalize()
																		print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''			)





def 						lowercase__(A						)				->int:




						"""simple docstring"""



						try:
												lowercase__				:      List[Any]=       json.load(A						)
												try:
																		lowercase__				:      List[Any]=       list(data.keys()						)
												except Exception:
																		lowercase__				:      Tuple=       [d["img_id"] for d in data]
						except Exception:
												try:
																		lowercase__				:      Tuple=       eval(stream.read()						)
												except Exception:
																		lowercase__				:      Dict=       stream.read().split("\n"						)
						return data


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














"""simple docstring"""







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 						lowercase__(A					,		A					,		A						)				->int:




						"""simple docstring"""



						lowercase__				:      List[str]=       RoFormerConfig.from_json_file(A						)
						print(f'''Building PyTorch model from configuration: {config}'''						)
						lowercase__				:      Tuple=       RoFormerForMaskedLM(A						)

						# Load weights from tf checkpoint
						load_tf_weights_in_roformer(A					,		A					,		A						)

						# Save pytorch-model
						print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
						torch.save(model.state_dict()					,		A					,		_use_new_zipfile_serialization=A						)


if __name__ == "__main__":
			a :				List[str]      					=		argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
			)
			parser.add_argument(
			    """--bert_config_file""",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        """The config json file corresponding to the pre-trained BERT model. \n"""
			        """This specifies the model architecture."""
			    ),
			)
			parser.add_argument(
			    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)
			a :				str      					=		parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






