



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_									=   False
UpperCamelCase_									=   Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
UpperCamelCase_									=   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       _snake_case				:






   '''simple docstring'''






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

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

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


   def       A__    (    self:     str  ,lowerCamelCase_:     Dict							)	->      Union[str, Any]:
       UpperCAmelCase_     :       List[str]  		=					int(file.split("""."""							)[0].split("""_"""							)[-1]							)
       UpperCAmelCase_     :       int  		=					os.path.join(self.inputdir  ,lowerCamelCase_							)
       return (img_id, filepath)


   @property
   def       A__    (    self:     Tuple							)	->      List[str]:
       UpperCAmelCase_     :       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(lowerCamelCase_							)							)
           if len(lowerCamelCase_							) == self.batch:
               UpperCAmelCase_     :       Tuple  		=					batch
               UpperCAmelCase_     :       Union[str, Any]  		=					[]
               yield list(map(lowerCamelCase_  ,zip(*lowerCamelCase_							)							)							)

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






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







def       lowerCamelCase_  (    _a       :						int    ):



    '''simple docstring'''




    try:
        UpperCAmelCase_     :       Optional[int]  		=					json.load(_a    )
        try:
            UpperCAmelCase_     :       List[Any]  		=					list(data.keys()    )
        except Exception:
            UpperCAmelCase_     :       List[Any]  		=					[d["""img_id"""] for d in data]
    except Exception:
        try:
            UpperCAmelCase_     :       str  		=					eval(stream.read()    )
        except Exception:
            UpperCAmelCase_     :       List[Any]  		=					stream.read().split("""\n"""    )
    return data


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


UpperCamelCase__			=    logging.get_logger(__name__)

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

UpperCamelCase__			=    {
    "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"
        ),
    },
}

UpperCamelCase__			=    {
    "microsoft/deberta-base": 5_12,
    "microsoft/deberta-large": 5_12,
    "microsoft/deberta-xlarge": 5_12,
    "microsoft/deberta-base-mnli": 5_12,
    "microsoft/deberta-large-mnli": 5_12,
    "microsoft/deberta-xlarge-mnli": 5_12,
}

UpperCamelCase__			=    {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}
class       _lowerCAmelCase (			_UpperCAmelCase      ):






			"""simple docstring"""





			lowercase__   :	Dict  			=      VOCAB_FILES_NAMES
			lowercase__   :	Dict  			=      PRETRAINED_VOCAB_FILES_MAP
			lowercase__   :	str  			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase__   :	Tuple  			=      ["""input_ids""", """attention_mask""", """token_type_ids"""]
			lowercase__   :	List[Any]  			=      DebertaTokenizer





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







									super().__init__(
									    lowercase      ,  lowercase      ,  tokenizer_file=lowercase      ,  errors=lowercase      ,  bos_token=lowercase      ,  eos_token=lowercase      ,  unk_token=lowercase      ,  sep_token=lowercase      ,  cls_token=lowercase      ,  pad_token=lowercase      ,  mask_token=lowercase      ,  add_prefix_space=lowercase      ,  **lowercase      ,  )
									__lowercase											=					kwargs.pop("""add_bos_token"""      ,  lowercase )

									__lowercase											=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
									if pre_tok_state.get("""add_prefix_space"""      ,  lowercase ) != add_prefix_space:
															__lowercase											=					getattr(lowercase      ,  pre_tok_state.pop("""type""" ) )
															__lowercase											=					add_prefix_space
															__lowercase											=					pre_tok_class(**lowercase )

									__lowercase											=					add_prefix_space





			@property
			def 		snake_case__      (					self						:							Tuple )    ->       str:
									"""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 		snake_case__      (					self						:							List[Any]      ,  lowercase						:							Tuple )    ->       Optional[int]:
									"""simple docstring"""







									__lowercase											=					AddedToken(lowercase      ,  lstrip=lowercase      ,  rstrip=lowercase ) if isinstance(lowercase      ,  lowercase ) else value
									__lowercase											=					value





			def 		snake_case__      (					self						:							List[str]      ,  lowercase						:							List[int]      ,  lowercase						:							Optional[List[int]] = None )    ->       List[int]:
									"""simple docstring"""







									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									__lowercase											=					[self.cls_token_id]
									__lowercase											=					[self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep





			def 		snake_case__      (					self						:							Dict      ,  lowercase						:							List[int]      ,  lowercase						:							Optional[List[int]] = None )    ->       List[int]:
									"""simple docstring"""







									__lowercase											=					[self.sep_token_id]
									__lowercase											=					[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 		snake_case__      (					self						:							Optional[int]      ,  *lowercase						:							List[str]      ,  **lowercase						:							Tuple )    ->       BatchEncoding:
									"""simple docstring"""







									__lowercase											=					kwargs.get("""is_split_into_words"""      ,  lowercase )
									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(*lowercase      ,  **lowercase )





			def 		snake_case__      (					self						:							List[Any]      ,  *lowercase						:							str      ,  **lowercase						:							List[Any] )    ->       BatchEncoding:
									"""simple docstring"""







									__lowercase											=					kwargs.get("""is_split_into_words"""      ,  lowercase )

									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(*lowercase      ,  **lowercase )





			def 		snake_case__      (					self						:							Union[str, Any]      ,  lowercase						:							str      ,  lowercase						:							Optional[str] = None )    ->       Tuple[str]:
									"""simple docstring"""







									__lowercase											=					self._tokenizer.model.save(lowercase      ,  name=lowercase )
									return tuple(lowercase )







			def 		snake_case__      (					self						:							List[str]      ,  lowercase						:							"Conversation" )    ->       List[int]:
									"""simple docstring"""







									__lowercase											=					[]
									for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(lowercase      ,  add_special_tokens=lowercase ) + [self.eos_token_id] )

									if len(lowercase ) > self.model_max_length:
															__lowercase											=					input_ids[-self.model_max_length :]
									return input_ids





