




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

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




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




     @property
     def        __snake_case      (			self     :      Any	) ->				List[Any]:
           lowerCamelCase__					     = []
           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:
                       lowerCamelCase__					     = batch
                       lowerCamelCase__					     = []
                       yield list(map(A__		,     zip(*A__	)	)	)

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




     def __call__(			self     :      Any	) ->				Optional[int]:
           # make writer
           if not TEST:
                 lowerCamelCase__					     = datasets.ArrowWriter(features=self.schema		,     path=self.outputfile	)
           # do file generator
           for i, (img_ids, filepaths) in enumerate(self.file_generator	):
                 lowerCamelCase__     ,						lowerCamelCase__     ,						lowerCamelCase__					     = self.preprocess(A__	)
                 lowerCamelCase__					     = self.model(
                     A__		,     A__		,     scales_yx=A__		,     padding='max_detections'		,     max_detections=self.config.MAX_DETECTIONS		,     pad_value=0		,     return_tensors='np'		,     location='cpu'		,     )
                 lowerCamelCase__					     = output_dict.pop('normalized_boxes'	)
                 if not TEST:
                       lowerCamelCase__					     = np.array(A__	)
                       lowerCamelCase__					     = self.schema.encode_batch(A__	)
                       writer.write_batch(A__	)
                 if TEST:
                       break
                 # finalizer the writer
           if not TEST:
                 lowerCamelCase__     ,						lowerCamelCase__					     = writer.finalize()
                 print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"""	)

def 	lowerCAmelCase_	(     lowerCamelCase:				Any  )    ->	Optional[Any]:


      '''simple docstring'''




      try:
            lowerCamelCase__					     = json.load(lowerCamelCase  )
            try:
                  lowerCamelCase__					     = list(data.keys()  )
            except Exception:
                  lowerCamelCase__					     = [d['img_id'] for d in data]
      except Exception:
            try:
                  lowerCamelCase__					     = eval(stream.read()  )
            except Exception:
                  lowerCamelCase__					     = stream.read().split('\n'  )
      return data


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





import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
      import sqlitea

      import sqlalchemy


__SCREAMING_SNAKE_CASE			:      Union[str, Any]      			=datasets.utils.logging.get_logger(__name__)






@dataclass
class 	A	(						datasets.BuilderConfig						):
     """simple docstring"""

     _snake_case:		Union[str, "sqlalchemy.sql.Selectable"]   =None
     _snake_case:		Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]   =None
     _snake_case:		Optional[Union[str, List[str]]]   =None
     _snake_case:		bool   =True
     _snake_case:		Optional[Union[List, Tuple, Dict]]   =None
     _snake_case:		Optional[Union[List, Dict]]   =None
     _snake_case:		Optional[List[str]]   =None
     _snake_case:		Optional[int]   =10_000
     _snake_case:		Optional[datasets.Features]   =None




     def        __snake_case      (			self     :      Optional[Any]	) ->				Union[str, Any]:
           if self.sql is None:
                 raise ValueError('sql must be specified'	)
           if self.con is None:
                 raise ValueError('con must be specified'	)




     def        __snake_case      (			self     :      List[Any]		,     A__     :      dict		,     A__     :      Optional[datasets.Features] = None		,     ) ->				str:
           lowerCamelCase__					     = config_kwargs.copy()
           # We need to stringify the Selectable object to make its hash deterministic

           # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
           lowerCamelCase__					     = config_kwargs['sql']
           if not isinstance(A__		,     A__	):
                 if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
                       import sqlalchemy

                       if isinstance(A__		,     sqlalchemy.sql.Selectable	):
                             lowerCamelCase__					     = sqlalchemy.create_engine(config_kwargs['con'].split('://'	)[0] + '://'	)
                             lowerCamelCase__					     = str(sql.compile(dialect=engine.dialect	)	)
                             lowerCamelCase__					     = sql_str
                       else:
                             raise TypeError(
                                 F"""Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(A__	)}: {sql}"""	)
                 else:
                       raise TypeError(
                           F"""Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(A__	)}: {sql}"""	)
           lowerCamelCase__					     = config_kwargs['con']
           if not isinstance(A__		,     A__	):
                 lowerCamelCase__					     = id(A__	)
                 logger.info(
                     F"""SQL connection 'con' of type {type(A__	)} couldn't be hashed properly. To enable hashing, specify 'con' as URI string instead."""	)

           return super().create_config_id(A__		,     custom_features=A__	)




     @property
     def        __snake_case      (			self     :      Any	) ->				Union[str, Any]:
           lowerCamelCase__					     = {
               'index_col': self.index_col,
               'columns': self.columns,
               'params': self.params,
               'coerce_float': self.coerce_float,
               'parse_dates': self.parse_dates,
           }
           return pd_read_sql_kwargs






class 	A	(						datasets.ArrowBasedBuilder						):
     """simple docstring"""

     _snake_case:		List[str]   =SqlConfig




     def        __snake_case      (			self     :      Tuple	) ->				Dict:
           return datasets.DatasetInfo(features=self.config.features	)




     def        __snake_case      (			self     :      List[Any]		,     A__     :      Any	) ->				List[Any]:
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,     gen_kwargs={}	)]




     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      pa.Table	) ->				pa.Table:
           if self.config.features is not None:
                 lowerCamelCase__					     = self.config.features.arrow_schema
                 if all(not require_storage_cast(A__	) for feature in self.config.features.values()	):
                       # cheaper cast
                       lowerCamelCase__					     = pa.Table.from_arrays([pa_table[field.name] for field in schema]		,     schema=A__	)
                 else:
                       # more expensive cast; allows str <-> int/float or str to Audio for example
                       lowerCamelCase__					     = table_cast(A__		,     A__	)
           return pa_table




     def        __snake_case      (			self     :      int	) ->				List[Any]:
           lowerCamelCase__					     = self.config.chunksize
           lowerCamelCase__					     = pd.read_sql(
               self.config.sql		,     self.config.con		,     chunksize=A__		,     **self.config.pd_read_sql_kwargs	)
           lowerCamelCase__					     = [sql_reader] if chunksize is None else sql_reader
           for chunk_idx, df in enumerate(A__	):
                 lowerCamelCase__					     = pa.Table.from_pandas(A__	)
                 yield chunk_idx, self._cast_table(A__	)