
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase_  :       str					      =     logging.get_logger(__name__)

lowerCamelCase_  :       str					      =     {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

lowerCamelCase_  :       int					      =     {
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
}

lowerCamelCase_  :       Dict					      =     {
    """roberta-base""": 5_12,
    """roberta-large""": 5_12,
    """roberta-large-mnli""": 5_12,
    """distilroberta-base""": 5_12,
    """roberta-base-openai-detector""": 5_12,
    """roberta-large-openai-detector""": 5_12,
}







@lru_cache()
def 				A__    (  )     ->		Any:
				UpperCamelCase_:							Union[str, Any]		=  (
				    list(range(ord("""!"""  )   ,			ord("""~"""  ) + 1  )  ) + list(range(ord("""¡"""  )   ,			ord("""¬"""  ) + 1  )  ) + list(range(ord("""®"""  )   ,			ord("""ÿ"""  ) + 1  )  )
				)
				UpperCamelCase_:							List[str]		=  bs[:]
				UpperCamelCase_:							Tuple		=  0
				for b in range(2**8  ):
								if b not in bs:
												bs.append(lowerCamelCase  )
												cs.append(2**8 + n  )
												n += 1
				UpperCamelCase_:							str		=  [chr(lowerCamelCase  ) for n in cs]
				return dict(zip(lowerCamelCase   ,			lowerCamelCase  )  )







def 				A__    (  lowerCamelCase  )     ->		Dict:
				UpperCamelCase_:							str		=  set()
				UpperCamelCase_:							str		=  word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)  )
								UpperCamelCase_:							Optional[int]		=  char
				return pairs





class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			__UpperCamelCase					:  int    					=       VOCAB_FILES_NAMES
			__UpperCamelCase					:  Tuple    					=       PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase					:  Dict    					=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase					:  Optional[int]    					=       ["""input_ids""", """attention_mask"""]

			def __init__(				self       :				Tuple  ,		snake_case_       :				Optional[int]  ,		snake_case_       :				Dict  ,		snake_case_       :				Dict="replace"  ,		snake_case_       :				Optional[int]="<s>"  ,		snake_case_       :				str="</s>"  ,		snake_case_       :				Optional[Any]="</s>"  ,		snake_case_       :				int="<s>"  ,		snake_case_       :				int="<unk>"  ,		snake_case_       :				Optional[Any]="<pad>"  ,		snake_case_       :				Optional[Any]="<mask>"  ,		snake_case_       :				List[str]=False  ,		**snake_case_       :				List[Any]  ,		):
							UpperCamelCase_:							List[Any]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_					) if isinstance(snake_case_  ,		snake_case_					) else bos_token
							UpperCamelCase_:							List[str]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_					) if isinstance(snake_case_  ,		snake_case_					) else eos_token
							UpperCamelCase_:							List[str]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_					) if isinstance(snake_case_  ,		snake_case_					) else sep_token
							UpperCamelCase_:							str		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_					) if isinstance(snake_case_  ,		snake_case_					) else cls_token
							UpperCamelCase_:							List[Any]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_					) if isinstance(snake_case_  ,		snake_case_					) else unk_token
							UpperCamelCase_:							Any		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_					) if isinstance(snake_case_  ,		snake_case_					) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							UpperCamelCase_:							List[str]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_					) if isinstance(snake_case_  ,		snake_case_					) else mask_token

							super().__init__(
							    errors=snake_case_  ,		bos_token=snake_case_  ,		eos_token=snake_case_  ,		unk_token=snake_case_  ,		sep_token=snake_case_  ,		cls_token=snake_case_  ,		pad_token=snake_case_  ,		mask_token=snake_case_  ,		add_prefix_space=snake_case_  ,		**snake_case_  ,		)

							with open(snake_case_  ,		encoding="""utf-8"""					) as vocab_handle:
											UpperCamelCase_:							Any		=  json.load(snake_case_					)
							UpperCamelCase_:							Tuple		=  {v: k for k, v in self.encoder.items()}
							UpperCamelCase_:							int		=  errors  # how to handle errors in decoding
							UpperCamelCase_:							List[Any]		=  bytes_to_unicode()
							UpperCamelCase_:							Union[str, Any]		=  {v: k for k, v in self.byte_encoder.items()}
							with open(snake_case_  ,		encoding="""utf-8"""					) as merges_handle:
											UpperCamelCase_:							Dict		=  merges_handle.read().split("""\n"""					)[1:-1]
							UpperCamelCase_:							Optional[int]		=  [tuple(merge.split()					) for merge in bpe_merges]
							UpperCamelCase_:							int		=  dict(zip(snake_case_  ,		range(len(snake_case_					)					)					)					)
							UpperCamelCase_:							Any		=  {}
							UpperCamelCase_:							Union[str, Any]		=  add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							UpperCamelCase_:							Tuple		=  re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""					)

			@property
			def 					lowerCAmelCase__		(				self       :				Tuple					):
							return len(self.encoder					)

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]					):
							return dict(self.encoder  ,		**self.added_tokens_encoder					)

			def 					lowerCAmelCase__		(				self       :				str  ,		snake_case_       :				Any					):
							if token in self.cache:
											return self.cache[token]
							UpperCamelCase_:							int		=  tuple(snake_case_					)
							UpperCamelCase_:							int		=  get_pairs(snake_case_					)

							if not pairs:
											return token

							while True:
											UpperCamelCase_:							Union[str, Any]		=  min(snake_case_  ,		key=lambda snake_case_					: self.bpe_ranks.get(snake_case_  ,		float("""inf"""					)					)					)
											if bigram not in self.bpe_ranks:
															break
											UpperCamelCase_,      UpperCamelCase_:							Tuple		=  bigram
											UpperCamelCase_:							Dict		=  []
											UpperCamelCase_:							Union[str, Any]		=  0
											while i < len(snake_case_					):
															try:
																			UpperCamelCase_:							Tuple		=  word.index(snake_case_  ,		snake_case_					)
															except ValueError:
																			new_word.extend(word[i:]					)
																			break
															else:
																			new_word.extend(word[i:j]					)
																			UpperCamelCase_:							int		=  j

															if word[i] == first and i < len(snake_case_					) - 1 and word[i + 1] == second:
																			new_word.append(first + second					)
																			i += 2
															else:
																			new_word.append(word[i]					)
																			i += 1
											UpperCamelCase_:							Tuple		=  tuple(snake_case_					)
											UpperCamelCase_:							Optional[int]		=  new_word
											if len(snake_case_					) == 1:
															break
											else:
															UpperCamelCase_:							Optional[Any]		=  get_pairs(snake_case_					)
							UpperCamelCase_:							Tuple		=  """ """.join(snake_case_					)
							UpperCamelCase_:							Union[str, Any]		=  word
							return word

			def 					lowerCAmelCase__		(				self       :				int  ,		snake_case_       :				str					):
							UpperCamelCase_:							Union[str, Any]		=  []
							for token in re.findall(self.pat  ,		snake_case_					):
											UpperCamelCase_:							Dict		=  """""".join(
											    self.byte_encoder[b] for b in token.encode("""utf-8"""					)					)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
											bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case_					).split(""" """					)					)
							return bpe_tokens

			def 					lowerCAmelCase__		(				self       :				List[str]  ,		snake_case_       :				List[str]					):
							return self.encoder.get(snake_case_  ,		self.encoder.get(self.unk_token					)					)

			def 					lowerCAmelCase__		(				self       :				List[Any]  ,		snake_case_       :				Any					):
							return self.decoder.get(snake_case_					)

			def 					lowerCAmelCase__		(				self       :				Tuple  ,		snake_case_       :				Any					):
							UpperCamelCase_:							List[str]		=  """""".join(snake_case_					)
							UpperCamelCase_:							Dict		=  bytearray([self.byte_decoder[c] for c in text]					).decode("""utf-8"""  ,		errors=self.errors					)
							return text

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]  ,		snake_case_       :				str  ,		snake_case_       :				Optional[str] = None					):
							if not os.path.isdir(snake_case_					):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
											return
							UpperCamelCase_:							List[str]		=  os.path.join(
							    snake_case_  ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
							UpperCamelCase_:							Tuple		=  os.path.join(
							    snake_case_  ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]					)

							with open(snake_case_  ,		"""w"""  ,		encoding="""utf-8"""					) as f:
											f.write(json.dumps(self.encoder  ,		indent=2  ,		sort_keys=snake_case_  ,		ensure_ascii=snake_case_					) + """\n"""					)

							UpperCamelCase_:							List[Any]		=  0
							with open(snake_case_  ,		"""w"""  ,		encoding="""utf-8"""					) as writer:
											writer.write("""#version: 0.2\n"""					)
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,		key=lambda snake_case_					: kv[1]					):
															if index != token_index:
																			logger.warning(
																			    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																			    """ Please check that the tokenizer is not corrupted!"""					)
																			UpperCamelCase_:							Optional[Any]		=  token_index
															writer.write(""" """.join(snake_case_					) + """\n"""					)
															index += 1

							return vocab_file, merge_file

			def 					lowerCAmelCase__		(				self       :				Tuple  ,		snake_case_       :				List[int]  ,		snake_case_       :				Optional[List[int]] = None					):
							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							UpperCamelCase_:							Tuple		=  [self.cls_token_id]
							UpperCamelCase_:							Optional[int]		=  [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep

			def 					lowerCAmelCase__		(				self       :				Optional[int]  ,		snake_case_       :				List[int]  ,		snake_case_       :				Optional[List[int]] = None  ,		snake_case_       :				bool = False					):
							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=snake_case_  ,		token_ids_a=snake_case_  ,		already_has_special_tokens=snake_case_					)

							if token_ids_a is None:
											return [1] + ([0] * len(snake_case_					)) + [1]
							return [1] + ([0] * len(snake_case_					)) + [1, 1] + ([0] * len(snake_case_					)) + [1]

			def 					lowerCAmelCase__		(				self       :				Dict  ,		snake_case_       :				List[int]  ,		snake_case_       :				Optional[List[int]] = None					):
							UpperCamelCase_:							str		=  [self.sep_token_id]
							UpperCamelCase_:							Optional[int]		=  [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 + sep + token_ids_a + sep					) * [0]




			def 					lowerCAmelCase__		(				self       :				Dict  ,		snake_case_       :				List[Any]  ,		snake_case_       :				Union[str, Any]=False  ,		**snake_case_       :				Optional[int]					):
							UpperCamelCase_:							str		=  kwargs.pop("""add_prefix_space"""  ,		self.add_prefix_space					)
							if (is_split_into_words or add_prefix_space) and (len(snake_case_					) > 0 and not text[0].isspace()):
											UpperCamelCase_:							Optional[int]		=  """ """ + text
							return (text, kwargs)








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
lowerCamelCase_  :       Tuple					      =     False
lowerCamelCase_  :       int					      =     Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
lowerCamelCase_  :       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, 20_48), 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       :				Optional[Any]  ,		snake_case_       :				Optional[Any]=sys.argv[1:]					):
							UpperCamelCase_:							str		=  None
							UpperCamelCase_:							List[str]		=  None
							UpperCamelCase_:							Tuple		=  None
							UpperCamelCase_:							Tuple		=  1
							UpperCamelCase_,      UpperCamelCase_:							Optional[Any]		=  getopt.getopt(snake_case_  ,		"""i:o:b:s"""  ,		["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]					)
							for opt, arg in opts:
											if opt in ("-i", "--inputdir"):
															UpperCamelCase_:							Dict		=  arg
											elif opt in ("-o", "--outfile"):
															UpperCamelCase_:							Optional[Any]		=  arg
											elif opt in ("-b", "--batch_size"):
															UpperCamelCase_:							List[str]		=  int(snake_case_					)
											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(snake_case_					), f'''{outputfile}'''
							if subset_list is not None:
											with open(os.path.realpath(snake_case_					)					) as f:
															UpperCamelCase_:							List[Any]		=  {self._vqa_file_split()[0] for x in tryload(snake_case_					)}
							else:
											UpperCamelCase_:							List[str]		=  None

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

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]  ,		snake_case_       :				int					):
							UpperCamelCase_:							int		=  int(file.split("""."""					)[0].split("""_"""					)[-1]					)
							UpperCamelCase_:							int		=  os.path.join(self.inputdir  ,		snake_case_					)
							return (img_id, filepath)

			@property
			def 					lowerCAmelCase__		(				self       :				int					):
							UpperCamelCase_:							Dict		=  []
							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(snake_case_					)					)
											if len(snake_case_					) == self.batch:
															UpperCamelCase_:							Dict		=  batch
															UpperCamelCase_:							List[Any]		=  []
															yield list(map(snake_case_  ,		zip(*snake_case_					)					)					)

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




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







def 				A__    (  lowerCamelCase  )     ->		Tuple:
				try:
								UpperCamelCase_:							str		=  json.load(lowerCamelCase  )
								try:
												UpperCamelCase_:							List[Any]		=  list(data.keys()  )
								except Exception:
												UpperCamelCase_:							Tuple		=  [d["""img_id"""] for d in data]
				except Exception:
								try:
												UpperCamelCase_:							int		=  eval(stream.read()  )
								except Exception:
												UpperCamelCase_:							Union[str, Any]		=  stream.read().split("""\n"""  )
				return data


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






