






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		:	str      					=			False
a		:	Optional[Any]      					=			Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
a		:	List[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 :      str		,				__lowercase :      int=sys.argv[1:] )							->				Optional[int]:
       __UpperCAmelCase							:     List[Any]									=  None
       __UpperCAmelCase							:     Any									=  None
       __UpperCAmelCase							:     Optional[int]									=  None
       __UpperCAmelCase							:     Any									=  1
       __UpperCAmelCase							:     Any									=  getopt.getopt(__lowercase		,				"""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							:     Union[str, Any]									=  arg
        elif opt in ("-b", "--batch_size"):
         __UpperCAmelCase							:     int									=  int(__lowercase )
        elif opt in ("-s", "--subset_list"):
         __UpperCAmelCase							:     Optional[int]									=  arg

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

       __UpperCAmelCase							:     Optional[Any]									=  CONFIG
       if torch.cuda.is_available():
        __UpperCAmelCase							:     Any									=  """cuda"""
       __UpperCAmelCase							:     Optional[Any]									=  os.path.realpath(__lowercase )
       __UpperCAmelCase							:     str									=  os.path.realpath(__lowercase )
       __UpperCAmelCase							:     List[Any]									=  Preprocess(self.config )
       __UpperCAmelCase							:     Any									=  GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""		,				config=self.config )
       __UpperCAmelCase							:     Dict									=  batch_size if batch_size != 0 else 1
       __UpperCAmelCase							:     List[Any]									=  DEFAULT_SCHEMA
      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      List[str] )							->				int:
       __UpperCAmelCase							:     str									=  int(file.split(""".""" )[0].split("""_""" )[-1] )
       __UpperCAmelCase							:     int									=  os.path.join(self.inputdir		,				__lowercase )
       return (img_id, filepath)
      @property
      def      UpperCAmelCase				(				self :      Union[str, Any] )							->				Any:
       __UpperCAmelCase							:     Tuple									=  []
       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(__lowercase ) )
        if len(__lowercase ) == self.batch:
         __UpperCAmelCase							:     Any									=  batch
         __UpperCAmelCase							:     Dict									=  []
         yield list(map(__lowercase		,				zip(*__lowercase ) ) )

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




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






def     lowerCamelCase__				(   __lowerCamelCase      :					Optional[Any]		):
 try:
  __UpperCAmelCase							:     Any									=  json.load(__lowerCamelCase		)
  try:
   __UpperCAmelCase							:     List[Any]									=  list(data.keys()		)
  except Exception:
   __UpperCAmelCase							:     Optional[int]									=  [d["""img_id"""] for d in data]
 except Exception:
  try:
   __UpperCAmelCase							:     Tuple									=  eval(stream.read()		)
  except Exception:
   __UpperCAmelCase							:     int									=  stream.read().split("""\n"""		)
 return data


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










import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


a		:	Optional[Any]      					=			logging.get_logger(__name__)

a		:	int      					=			{
    "facebook/musicgen-small": "https://huggingface.co/facebook/musicgen-small/resolve/main/config.json",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}
class    a   (			lowercase__       ):



      """simple docstring"""


      a  :			Optional[int]								=     'musicgen_decoder'
      a  :			Optional[int]								=     ['past_key_values']
      def __init__(				self :      Optional[Any]		,				__lowercase :      str=2048		,				__lowercase :      Optional[int]=2048		,				__lowercase :      List[Any]=24		,				__lowercase :      Optional[Any]=4096		,				__lowercase :      Dict=16		,				__lowercase :      List[Any]=0.0		,				__lowercase :      Tuple=True		,				__lowercase :      List[Any]="gelu"		,				__lowercase :      Any=1024		,				__lowercase :      Optional[Any]=0.1		,				__lowercase :      List[Any]=0.0		,				__lowercase :      int=0.0		,				__lowercase :      List[str]=0.02		,				__lowercase :      Dict=False		,				__lowercase :      List[str]=4		,				__lowercase :      List[str]=2048		,				__lowercase :      List[Any]=2048		,				__lowercase :      Union[str, Any]=None		,				__lowercase :      List[str]=False		,				**__lowercase :      int		,				)							->				str:
        __UpperCAmelCase							:     Union[str, Any]									=  vocab_size
        __UpperCAmelCase							:     Dict									=  max_position_embeddings
        __UpperCAmelCase							:     str									=  hidden_size
        __UpperCAmelCase							:     Optional[Any]									=  ffn_dim
        __UpperCAmelCase							:     List[Any]									=  num_hidden_layers
        __UpperCAmelCase							:     Any									=  num_attention_heads
        __UpperCAmelCase							:     List[Any]									=  dropout
        __UpperCAmelCase							:     List[str]									=  attention_dropout
        __UpperCAmelCase							:     List[Any]									=  activation_dropout
        __UpperCAmelCase							:     int									=  activation_function
        __UpperCAmelCase							:     Optional[int]									=  initializer_factor
        __UpperCAmelCase							:     List[Any]									=  layerdrop
        __UpperCAmelCase							:     Tuple									=  use_cache
        __UpperCAmelCase							:     Tuple									=  scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCAmelCase							:     int									=  num_codebooks
        super().__init__(
            pad_token_id=__lowercase		,				bos_token_id=__lowercase		,				eos_token_id=__lowercase		,				tie_word_embeddings=__lowercase		,				**__lowercase		,				)


class    a   (			lowercase__       ):



      """simple docstring"""


      a  :			Tuple								=     'musicgen'
      a  :			int								=     True
      def __init__(				self :      Any		,				**__lowercase :      Tuple )							->				Union[str, Any]:
        super().__init__(**__lowercase )
        if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
          raise ValueError("""Config has to be initialized with text_encoder, audio_encoder and decoder config""" )

        __UpperCAmelCase							:     int									=  kwargs.pop("""text_encoder""" )
        __UpperCAmelCase							:     str									=  text_encoder_config.pop("""model_type""" )

        __UpperCAmelCase							:     Tuple									=  kwargs.pop("""audio_encoder""" )
        __UpperCAmelCase							:     Optional[Any]									=  audio_encoder_config.pop("""model_type""" )

        __UpperCAmelCase							:     Optional[Any]									=  kwargs.pop("""decoder""" )

        __UpperCAmelCase							:     Any									=  AutoConfig.for_model(__lowercase		,				**__lowercase )
        __UpperCAmelCase							:     Dict									=  AutoConfig.for_model(__lowercase		,				**__lowercase )
        __UpperCAmelCase							:     Any									=  MusicgenDecoderConfig(**__lowercase )
        __UpperCAmelCase							:     int									=  True
      @classmethod
      def      UpperCAmelCase				(				cls :      Optional[Any]		,				__lowercase :      PretrainedConfig		,				__lowercase :      PretrainedConfig		,				__lowercase :      MusicgenDecoderConfig		,				**__lowercase :      List[Any]		,				)							->				List[str]:
        return cls(
            text_encoder=text_encoder_config.to_dict()		,				audio_encoder=audio_encoder_config.to_dict()		,				decoder=decoder_config.to_dict()		,				**__lowercase		,				)




      def      UpperCAmelCase				(				self :      Any )							->				int:
        __UpperCAmelCase							:     Optional[Any]									=  copy.deepcopy(self.__dict__ )
        __UpperCAmelCase							:     List[Any]									=  self.text_encoder.to_dict()
        __UpperCAmelCase							:     str									=  self.audio_encoder.to_dict()
        __UpperCAmelCase							:     List[str]									=  self.decoder.to_dict()
        __UpperCAmelCase							:     List[str]									=  self.__class__.model_type
        return output


