

"""simple docstring"""

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







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

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







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_				)      ->    int:
											__lowercase        =      int(file.split('''.'''				)[0].split('''_'''				)[-1]				)
											__lowercase        =      os.path.join(self.inputdir  ,				lowercase_				)
											return (img_id, filepath)







						@property
						def 							_UpperCamelCase     (self				)      ->    List[str]:
											__lowercase        =      []
											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:
																					__lowercase        =      batch
																					__lowercase        =      []
																					yield list(map(lowercase_  ,				zip(*lowercase_				)				)				)

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







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





def 					__UpperCamelCase (     a__:			Dict      )					->      List[Any]:




					"""simple docstring"""



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


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







"""simple docstring"""


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


snake_case_								=			logging.get_logger(__name__)

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

snake_case_								=			{
    """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"""
        ),
    },
}

snake_case_								=			{
    """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,
}

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







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				VOCAB_FILES_NAMES
						__UpperCamelCase													=				PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase													=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase													=				["""input_ids""", """attention_mask""", """token_type_ids"""]
						__UpperCamelCase													=				DebertaTokenizer






						def __init__(   self					:Optional[Any]  ,    lowercase_					:List[str]=None  ,    lowercase_					:List[str]=None  ,    lowercase_					:List[Any]=None  ,    lowercase_					:Optional[Any]="replace"  ,    lowercase_					:int="[CLS]"  ,    lowercase_					:Union[str, Any]="[SEP]"  ,    lowercase_					:int="[SEP]"  ,    lowercase_					:int="[CLS]"  ,    lowercase_					:Optional[int]="[UNK]"  ,    lowercase_					:int="[PAD]"  ,    lowercase_					:List[Any]="[MASK]"  ,    lowercase_					:Union[str, Any]=False  ,    **lowercase_					:int  ,    )							->     Tuple:
												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_  ,    )
												UpperCAmelCase			 =					kwargs.pop('add_bos_token'  ,    lowercase_   )

												UpperCAmelCase			 =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
												if pre_tok_state.get('add_prefix_space'  ,    lowercase_   ) != add_prefix_space:
																		UpperCAmelCase			 =					getattr(lowercase_  ,    pre_tok_state.pop('type'   )   )
																		UpperCAmelCase			 =					add_prefix_space
																		UpperCAmelCase			 =					pre_tok_class(**lowercase_   )

												UpperCAmelCase			 =					add_prefix_space






						@property
						def       UpperCAmelCase__	(   self					:str   )							->     str:
												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  ,    lowercase_					:Any   )							->     Any:
												UpperCAmelCase			 =					AddedToken(lowercase_  ,    lstrip=lowercase_  ,    rstrip=lowercase_   ) if isinstance(lowercase_  ,    lowercase_   ) else value
												UpperCAmelCase			 =					value






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												UpperCAmelCase			 =					[self.cls_token_id]
												UpperCAmelCase			 =					[self.sep_token_id]
												return cls + token_ids_a + sep + token_ids_a + sep






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.sep_token_id]
												UpperCAmelCase			 =					[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					:int  ,    *lowercase_					:str  ,    **lowercase_					:Optional[Any]   )							->     BatchEncoding:
												UpperCAmelCase			 =					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       UpperCAmelCase__	(   self					:Optional[Any]  ,    *lowercase_					:Dict  ,    **lowercase_					:Any   )							->     BatchEncoding:
												UpperCAmelCase			 =					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       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:str  ,    lowercase_					:Optional[str] = None   )							->     Tuple[str]:
												UpperCAmelCase			 =					self._tokenizer.model.save(lowercase_  ,    name=lowercase_   )
												return tuple(lowercase_   )






						def       UpperCAmelCase__	(   self					:int  ,    lowercase_					:"Conversation"   )							->     List[int]:
												UpperCAmelCase			 =					[]
												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:
																		UpperCAmelCase			 =					input_ids[-self.model_max_length :]
												return input_ids





