'''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
__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_0_4_8), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)






class     a__:


			'''simple docstring'''






			def __init__(       self	,   __lowerCAmelCase=sys.argv[1:]):




					"""simple docstring"""


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

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



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  int(file.split(""".""")[0].split("""_""")[-1])
					lowerCAmelCase        =  os.path.join(self.inputdir	,   __lowerCAmelCase)
					return (img_id, filepath)



			@property
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  []
					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:
									lowerCAmelCase        =  batch
									lowerCAmelCase        =  []
									yield list(map(__lowerCAmelCase	,   zip(*__lowerCAmelCase)))

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







			def __call__(       self):




					"""simple docstring"""


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







def     snake_case__							(      _A: str			)   ->     List[str]:

		'''simple docstring'''


		try:
				lowerCAmelCase        =  json.load(_A			)
				try:
						lowerCAmelCase        =  list(data.keys()			)
				except Exception:
						lowerCAmelCase        =  [d["""img_id"""] for d in data]
		except Exception:
				try:
						lowerCAmelCase        =  eval(stream.read()			)
				except Exception:
						lowerCAmelCase        =  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)





'''simple docstring'''




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

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
						if is_torch_available():
												import torch
						if is_tf_available():
												import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


__lowercase				   =  logging.get_logger(__name__)

__lowercase				   =  {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

__lowercase				   =  {
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
}

__lowercase				   =  {
    '''Salesforce/codegen-350M-mono''': 2_0_4_8,
}






@lru_cache()
def     snake_case__							(      )   ->     int:

		'''simple docstring'''


		lowerCAmelCase        =  (
		    list(range(ord("""!"""			)				,       ord("""~"""			) + 1			)			) + list(range(ord("""¡"""			)				,       ord("""¬"""			) + 1			)			) + list(range(ord("""®"""			)				,       ord("""ÿ"""			) + 1			)			)
		)
		lowerCAmelCase        =  bs[:]
		lowerCAmelCase        =  0
		for b in range(2**8			):
				if b not in bs:
						bs.append(_A			)
						cs.append(2**8 + n			)
						n += 1
		lowerCAmelCase        =  [chr(_A			) for n in cs]
		return dict(zip(_A				,       _A			)			)






def     snake_case__							(      _A: int			)   ->     int:

		'''simple docstring'''


		lowerCAmelCase        =  set()
		lowerCAmelCase        =  word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)			)
				lowerCAmelCase        =  char
		return pairs






class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			UpperCAmelCase_      :					Tuple								=	VOCAB_FILES_NAMES
			UpperCAmelCase_      :					List[str]								=	PRETRAINED_VOCAB_FILES_MAP
			UpperCAmelCase_      :					Any								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCAmelCase_      :					str								=	['''input_ids''', '''attention_mask''']



			def __init__(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase="replace"	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase=None	,   __lowerCAmelCase=False	,   __lowerCAmelCase=False	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else bos_token
					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else eos_token
					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else unk_token
					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else pad_token
					super().__init__(
					    errors=__lowerCAmelCase	,   unk_token=__lowerCAmelCase	,   bos_token=__lowerCAmelCase	,   eos_token=__lowerCAmelCase	,   pad_token=__lowerCAmelCase	,   add_prefix_space=__lowerCAmelCase	,   add_bos_token=__lowerCAmelCase	,   **__lowerCAmelCase	,   )
					lowerCAmelCase        =  add_bos_token

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

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



			@property
			def 							a_	(       self):




					"""simple docstring"""


					return len(self.encoder)



			def 							a_	(       self):




					"""simple docstring"""


					return dict(self.encoder	,   **self.added_tokens_encoder)



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					if token in self.cache:
							return self.cache[token]
					lowerCAmelCase        =  tuple(__lowerCAmelCase)
					lowerCAmelCase        =  get_pairs(__lowerCAmelCase)

					if not pairs:
							return token

					while True:
							lowerCAmelCase        =  min(__lowerCAmelCase	,   key=lambda __lowerCAmelCase: self.bpe_ranks.get(__lowerCAmelCase	,   float("""inf""")))
							if bigram not in self.bpe_ranks:
									break
							lowerCAmelCase ,					lowerCAmelCase        =  bigram
							lowerCAmelCase        =  []
							lowerCAmelCase        =  0
							while i < len(__lowerCAmelCase):
									try:
											lowerCAmelCase        =  word.index(__lowerCAmelCase	,   __lowerCAmelCase)
									except ValueError:
											new_word.extend(word[i:])
											break
									else:
											new_word.extend(word[i:j])
											lowerCAmelCase        =  j

									if word[i] == first and i < len(__lowerCAmelCase) - 1 and word[i + 1] == second:
											new_word.append(first + second)
											i += 2
									else:
											new_word.append(word[i])
											i += 1
							lowerCAmelCase        =  tuple(__lowerCAmelCase)
							lowerCAmelCase        =  new_word
							if len(__lowerCAmelCase) == 1:
									break
							else:
									lowerCAmelCase        =  get_pairs(__lowerCAmelCase)
					lowerCAmelCase        =  """ """.join(__lowerCAmelCase)
					lowerCAmelCase        =  word
					return word



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase=None):




					"""simple docstring"""


					if self.add_bos_token:
							lowerCAmelCase        =  [self.bos_token_id]
					else:
							lowerCAmelCase        =  []

					lowerCAmelCase        =  bos_token_ids + token_ids_a

					if token_ids_a is None:
							return output

					return output + bos_token_ids + token_ids_a



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  []
					for token in re.findall(self.pat	,   __lowerCAmelCase):
							lowerCAmelCase        =  """""".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(__lowerCAmelCase).split(""" """))
					return bpe_tokens



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					return self.encoder.get(__lowerCAmelCase	,   self.encoder.get(self.unk_token))



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					return self.decoder.get(__lowerCAmelCase)



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  """""".join(__lowerCAmelCase)
					lowerCAmelCase        =  bytearray([self.byte_decoder[c] for c in text]).decode("""utf-8"""	,   errors=self.errors)
					return text



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = None):




					"""simple docstring"""


					if not os.path.isdir(__lowerCAmelCase):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory")
							return
					lowerCAmelCase        =  os.path.join(
					    __lowerCAmelCase	,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
					lowerCAmelCase        =  os.path.join(
					    __lowerCAmelCase	,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""])

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

					lowerCAmelCase        =  0
					with open(__lowerCAmelCase	,   """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 __lowerCAmelCase: 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!""")
											lowerCAmelCase        =  token_index
									writer.write(""" """.join(__lowerCAmelCase) + """\n""")
									index += 1

					return vocab_file, merge_file



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase=False	,   **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  kwargs.pop("""add_prefix_space"""	,   self.add_prefix_space)
					if is_split_into_words or add_prefix_space:
							lowerCAmelCase        =  """ """ + text
					return (text, kwargs)



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = False	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  super()._decode(
					    token_ids=__lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase	,   clean_up_tokenization_spaces=__lowerCAmelCase	,   **__lowerCAmelCase	,   )

					if truncate_before_pattern is not None and len(__lowerCAmelCase) > 0:
							lowerCAmelCase        =  self.truncate(__lowerCAmelCase	,   __lowerCAmelCase)

					return decoded_text







			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					def find_re(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):
							lowerCAmelCase        =  pattern.search(__lowerCAmelCase	,   __lowerCAmelCase)
							return m.start() if m else -1

					lowerCAmelCase        =  [re.compile(__lowerCAmelCase	,   re.MULTILINE) for pattern in truncate_before_pattern]

					lowerCAmelCase        =  list(re.finditer("""^print"""	,   __lowerCAmelCase	,   re.MULTILINE))

					if len(__lowerCAmelCase) > 1:
							lowerCAmelCase        =  completion[: prints[1].start()]

					lowerCAmelCase        =  list(re.finditer("""^def"""	,   __lowerCAmelCase	,   re.MULTILINE))

					if len(__lowerCAmelCase) > 1:
							lowerCAmelCase        =  completion[: defs[1].start()]

					lowerCAmelCase        =  0

					lowerCAmelCase        =  [
					    pos for pos in [find_re(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase) for terminal in terminals] if pos != -1
					]

					if len(__lowerCAmelCase) > 0:
							return completion[: min(__lowerCAmelCase)]
					else:
							return completion




