
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
lowercase_											=	False
lowercase_											=	Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
lowercase_											=	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   __UpperCamelCase				:






     """simple docstring"""







     def __init__(							self    :    List[Any]      ,		_A    :    Optional[Any]=sys.argv[1:]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						None
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						None
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						None
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						1
       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						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     :					Optional[int]			=						arg
         elif opt in ("-o", "--outfile"):
           __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						arg
         elif opt in ("-b", "--batch_size"):
           __SCREAMING_SNAKE_CASE     :					str			=						int(_A				)
         elif opt in ("-s", "--subset_list"):
           __SCREAMING_SNAKE_CASE     :					int			=						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     :					Dict			=						{self._vqa_file_split()[0] for x in tryload(_A				)}
       else:
         __SCREAMING_SNAKE_CASE     :					Tuple			=						None

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



     def 				UpperCAmelCase__    (							self    :    int      ,		_A    :    Optional[int]				):







       """simple docstring"""




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



     @property
     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						[]
       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     :					Union[str, Any]			=						batch
           __SCREAMING_SNAKE_CASE     :					Dict			=						[]
           yield list(map(_A      ,		zip(*_A				)				)				)

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




     def __call__(							self    :    Optional[Any]				):







       """simple docstring"""




       if not TEST:
         __SCREAMING_SNAKE_CASE     :					int			=						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     :					List[Any]			=						self.preprocess(_A				)
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						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     :					List[Any]			=						output_dict.pop('''normalized_boxes'''				)
         if not TEST:
           __SCREAMING_SNAKE_CASE     :					Dict			=						np.array(_A				)
           __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.schema.encode_batch(_A				)
           writer.write_batch(_A				)
         if TEST:
           break
         # finalizer the writer
       if not TEST:
         __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						writer.finalize()
         print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''				)






def     a__			(    snake_case    ):





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


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


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


lowercase_											=	logging.get_logger(__name__)

lowercase_											=	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase_											=	{
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

lowercase_											=	{
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

lowercase_											=	{
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}





class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	VOCAB_FILES_NAMES
     lowerCAmelCase_     =	PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase_     =	['''input_ids''', '''attention_mask''', '''token_type_ids''']
     lowerCAmelCase_     =	DebertaTokenizer



     def __init__(							self    :    List[str]      ,		_A    :    Tuple=None      ,		_A    :    List[Any]=None      ,		_A    :    Optional[int]=None      ,		_A    :    List[str]="replace"      ,		_A    :    List[Any]="[CLS]"      ,		_A    :    Optional[Any]="[SEP]"      ,		_A    :    Union[str, Any]="[SEP]"      ,		_A    :    List[str]="[CLS]"      ,		_A    :    Optional[Any]="[UNK]"      ,		_A    :    str="[PAD]"      ,		_A    :    List[str]="[MASK]"      ,		_A    :    Dict=False      ,		**_A    :    Optional[int]      ,		):







       """simple docstring"""




       super().__init__(
           _A      ,		_A      ,		tokenizer_file=_A      ,		errors=_A      ,		bos_token=_A      ,		eos_token=_A      ,		unk_token=_A      ,		sep_token=_A      ,		cls_token=_A      ,		pad_token=_A      ,		mask_token=_A      ,		add_prefix_space=_A      ,		**_A      ,		)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						kwargs.pop('''add_bos_token'''      ,		_A				)

       __SCREAMING_SNAKE_CASE     :					int			=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
       if pre_tok_state.get('''add_prefix_space'''      ,		_A				) != add_prefix_space:
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						getattr(_A      ,		pre_tok_state.pop('''type'''				)				)
         __SCREAMING_SNAKE_CASE     :					Dict			=						add_prefix_space
         __SCREAMING_SNAKE_CASE     :					Any			=						pre_tok_class(**_A				)

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						add_prefix_space



     @property
     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       if self._mask_token is None:
         if self.verbose:
           logger.error('''Using mask_token, but it is not set yet.'''				)
         return None
       return str(self._mask_token				)



     @mask_token.setter
     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						AddedToken(_A      ,		lstrip=_A      ,		rstrip=_A				) if isinstance(_A      ,		_A				) else value
       __SCREAMING_SNAKE_CASE     :					Tuple			=						value



     def 				UpperCAmelCase__    (							self    :    List[Any]      ,		_A    :    List[int]      ,		_A    :    Optional[List[int]] = None				):







       """simple docstring"""




       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       __SCREAMING_SNAKE_CASE     :					List[str]			=						[self.cls_token_id]
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep



     def 				UpperCAmelCase__    (							self    :    int      ,		_A    :    List[int]      ,		_A    :    Optional[List[int]] = None				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Tuple			=						[self.sep_token_id]
       __SCREAMING_SNAKE_CASE     :					List[str]			=						[self.cls_token_id]

       if token_ids_a is None:
         return len(cls + token_ids_a + sep				) * [0]
       return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		*_A    :    Optional[Any]      ,		**_A    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						kwargs.get('''is_split_into_words'''      ,		_A				)
       assert self.add_prefix_space or not is_split_into_words, (
           F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
           "to use it with pretokenized inputs."
       )

       return super()._batch_encode_plus(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		*_A    :    Tuple      ,		**_A    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						kwargs.get('''is_split_into_words'''      ,		_A				)

       assert self.add_prefix_space or not is_split_into_words, (
           F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
           "to use it with pretokenized inputs."
       )

       return super()._encode_plus(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    str      ,		_A    :    Optional[str] = None				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self._tokenizer.model.save(_A      ,		name=_A				)
       return tuple(_A				)




     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    "Conversation"				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						[]
       for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(_A      ,		add_special_tokens=_A				) + [self.eos_token_id]				)

       if len(_A				) > self.model_max_length:
         __SCREAMING_SNAKE_CASE     :					Any			=						input_ids[-self.model_max_length :]
       return input_ids
