

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__     :					Optional[int]								=			False
UpperCamelCase__     :					Any								=			Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
UpperCamelCase__     :					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 					__snake_case     :

  def __init__(    self  ,						_A=sys.argv[1:]):
      SCREAMING_SNAKE_CASE_													=  None
      SCREAMING_SNAKE_CASE_													=  None
      SCREAMING_SNAKE_CASE_													=  None
      SCREAMING_SNAKE_CASE_													=  1
      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  getopt.getopt(_A  ,						'i:o:b:s'  ,						['inputdir=', 'outfile=', 'batch_size=', 'subset_list='])
      for opt, arg in opts:
          if opt in ("-i", "--inputdir"):
              SCREAMING_SNAKE_CASE_													=  arg
          elif opt in ("-o", "--outfile"):
              SCREAMING_SNAKE_CASE_													=  arg
          elif opt in ("-b", "--batch_size"):
              SCREAMING_SNAKE_CASE_													=  int(_A)
          elif opt in ("-s", "--subset_list"):
              SCREAMING_SNAKE_CASE_													=  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:
              SCREAMING_SNAKE_CASE_													=  {self._vqa_file_split()[0] for x in tryload(_A)}
      else:
          SCREAMING_SNAKE_CASE_													=  None

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

  def   lowerCAmelCase__   (    self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  int(file.split('.')[0].split('_')[-1])
      SCREAMING_SNAKE_CASE_													=  os.path.join(self.inputdir  ,						_A)
      return (img_id, filepath)

  @property
  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  []
      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:
              SCREAMING_SNAKE_CASE_													=  batch
              SCREAMING_SNAKE_CASE_													=  []
              yield list(map(_A  ,						zip(*_A)))

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


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






def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						Union[str, Any]				):





    """simple docstring"""


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


if __name__ == "__main__":
       UpperCamelCase__     :					Optional[Any]								=			Extract(sys.argv[1:])
       extract()
       if not TEST:
              UpperCamelCase__     :					Any								=			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


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

UpperCamelCase__     :					List[Any]								=			{
    "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 					__snake_case     (			lowerCAmelCase__				):
  __lowerCAmelCase   :		Any         =     'musicgen_decoder'
  __lowerCAmelCase   :		Optional[Any]         =     ['past_key_values']

  def __init__(    self  ,						_A=2048  ,						_A=2048  ,						_A=24  ,						_A=4096  ,						_A=16  ,						_A=0.0  ,						_A=True  ,						_A="gelu"  ,						_A=1024  ,						_A=0.1  ,						_A=0.0  ,						_A=0.0  ,						_A=0.0_2  ,						_A=False  ,						_A=4  ,						_A=2048  ,						_A=2048  ,						_A=None  ,						_A=False  ,						**_A  ,						):
      SCREAMING_SNAKE_CASE_													=  vocab_size
      SCREAMING_SNAKE_CASE_													=  max_position_embeddings
      SCREAMING_SNAKE_CASE_													=  hidden_size
      SCREAMING_SNAKE_CASE_													=  ffn_dim
      SCREAMING_SNAKE_CASE_													=  num_hidden_layers
      SCREAMING_SNAKE_CASE_													=  num_attention_heads
      SCREAMING_SNAKE_CASE_													=  dropout
      SCREAMING_SNAKE_CASE_													=  attention_dropout
      SCREAMING_SNAKE_CASE_													=  activation_dropout
      SCREAMING_SNAKE_CASE_													=  activation_function
      SCREAMING_SNAKE_CASE_													=  initializer_factor
      SCREAMING_SNAKE_CASE_													=  layerdrop
      SCREAMING_SNAKE_CASE_													=  use_cache
      SCREAMING_SNAKE_CASE_													=  scale_embedding  # scale factor will be sqrt(d_model) if True
      SCREAMING_SNAKE_CASE_													=  num_codebooks
      super().__init__(
          pad_token_id=_A  ,						bos_token_id=_A  ,						eos_token_id=_A  ,						tie_word_embeddings=_A  ,						**_A  ,						)






class 					__snake_case     (			lowerCAmelCase__				):
  __lowerCAmelCase   :		int         =     'musicgen'
  __lowerCAmelCase   :		Optional[Any]         =     True

  def __init__(    self  ,						**_A):
      super().__init__(**_A)
      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')

      SCREAMING_SNAKE_CASE_													=  kwargs.pop('text_encoder')
      SCREAMING_SNAKE_CASE_													=  text_encoder_config.pop('model_type')

      SCREAMING_SNAKE_CASE_													=  kwargs.pop('audio_encoder')
      SCREAMING_SNAKE_CASE_													=  audio_encoder_config.pop('model_type')

      SCREAMING_SNAKE_CASE_													=  kwargs.pop('decoder')

      SCREAMING_SNAKE_CASE_													=  AutoConfig.for_model(_A  ,						**_A)
      SCREAMING_SNAKE_CASE_													=  AutoConfig.for_model(_A  ,						**_A)
      SCREAMING_SNAKE_CASE_													=  MusicgenDecoderConfig(**_A)
      SCREAMING_SNAKE_CASE_													=  True

  @classmethod
  def   lowerCAmelCase__   (    cls  ,						_A  ,						_A  ,						_A  ,						**_A  ,						):
      return cls(
          text_encoder=text_encoder_config.to_dict()  ,						audio_encoder=audio_encoder_config.to_dict()  ,						decoder=decoder_config.to_dict()  ,						**_A  ,						)


  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  copy.deepcopy(self.__dict__)
      SCREAMING_SNAKE_CASE_													=  self.text_encoder.to_dict()
      SCREAMING_SNAKE_CASE_													=  self.audio_encoder.to_dict()
      SCREAMING_SNAKE_CASE_													=  self.decoder.to_dict()
      SCREAMING_SNAKE_CASE_													=  self.__class__.model_type
      return output


