
import os
import unittest

from transformers import ElectraTokenizerFast
from transformers.models.electra.tokenization_electra import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    ElectraTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english

@require_tokenizers
class __snake_case	(   A_      ,			unittest.TestCase  ):
    _a	:		Tuple=       ElectraTokenizer
    _a	:		Tuple=       ElectraTokenizerFast
    _a	:		Any=       True
    _a	:		List[str]=       True
    _a	:		Any=       filter_non_english
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           super().setUp()

           lowercase							:  List[Any] 			=   [
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """[PAD]""",
               """[MASK]""",
               """want""",
               """##want""",
               """##ed""",
               """wa""",
               """un""",
               """runn""",
               """##ing""",
               """,""",
               """low""",
               """lowest""",
           ]
           lowercase							:  Optional[int] 			=   os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES["""vocab_file"""]   )
           with open(self.vocab_file	,"""w"""	,encoding="""utf-8"""   ) as vocab_writer:
                  vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]   )   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   """UNwant\u00E9d,running"""
           lowercase							:  Any 			=   """unwanted, running"""
           return input_text, output_text
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[Any] 			=   self.tokenizer_class(self.vocab_file   )

           lowercase							:  Tuple 			=   tokenizer.tokenize("""UNwant\u00E9d,running"""   )
           self.assertListEqual(snake_case	,["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]   )
           self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case   )	,[9, 6, 7, 12, 10, 11]   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           if not self.test_rust_tokenizer:
                  return

           lowercase							:  List[str] 			=   self.get_tokenizer()
           lowercase							:  Any 			=   self.get_rust_tokenizer()

           lowercase							:  Optional[Any] 			=   """UNwant\u00E9d,running"""

           lowercase							:  Any 			=   tokenizer.tokenize(snake_case   )
           lowercase							:  Optional[Any] 			=   rust_tokenizer.tokenize(snake_case   )
           self.assertListEqual(snake_case	,snake_case   )

           lowercase							:  List[str] 			=   tokenizer.encode(snake_case	,add_special_tokens=snake_case   )
           lowercase							:  Any 			=   rust_tokenizer.encode(snake_case	,add_special_tokens=snake_case   )
           self.assertListEqual(snake_case	,snake_case   )

           lowercase							:  int 			=   self.get_rust_tokenizer()
           lowercase							:  List[Any] 			=   tokenizer.encode(snake_case   )
           lowercase							:  List[str] 			=   rust_tokenizer.encode(snake_case   )
           self.assertListEqual(snake_case	,snake_case   )

           # With lower casing
           lowercase							:  List[str] 			=   self.get_tokenizer(do_lower_case=snake_case   )
           lowercase							:  Optional[int] 			=   self.get_rust_tokenizer(do_lower_case=snake_case   )

           lowercase							:  List[str] 			=   """UNwant\u00E9d,running"""

           lowercase							:  Union[str, Any] 			=   tokenizer.tokenize(snake_case   )
           lowercase							:  Union[str, Any] 			=   rust_tokenizer.tokenize(snake_case   )
           self.assertListEqual(snake_case	,snake_case   )

           lowercase							:  Any 			=   tokenizer.encode(snake_case	,add_special_tokens=snake_case   )
           lowercase							:  List[Any] 			=   rust_tokenizer.encode(snake_case	,add_special_tokens=snake_case   )
           self.assertListEqual(snake_case	,snake_case   )

           lowercase							:  int 			=   self.get_rust_tokenizer()
           lowercase							:  List[Any] 			=   tokenizer.encode(snake_case   )
           lowercase							:  List[Any] 			=   rust_tokenizer.encode(snake_case   )
           self.assertListEqual(snake_case	,snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   BasicTokenizer()

           self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""   )	,["""ah""", """\u535A""", """\u63A8""", """zz"""]   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   BasicTokenizer(do_lower_case=snake_case   )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """   )	,["""hello""", """!""", """how""", """are""", """you""", """?"""]   )
           self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""   )	,["""hello"""]   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[Any] 			=   BasicTokenizer(do_lower_case=snake_case	,strip_accents=snake_case   )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """   )	,["""hällo""", """!""", """how""", """are""", """you""", """?"""]   )
           self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""   )	,["""h\u00E9llo"""]   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[Any] 			=   BasicTokenizer(do_lower_case=snake_case	,strip_accents=snake_case   )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """   )	,["""hallo""", """!""", """how""", """are""", """you""", """?"""]   )
           self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""   )	,["""hello"""]   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[int] 			=   BasicTokenizer(do_lower_case=snake_case   )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """   )	,["""hallo""", """!""", """how""", """are""", """you""", """?"""]   )
           self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""   )	,["""hello"""]   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  int 			=   BasicTokenizer(do_lower_case=snake_case   )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """   )	,["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  str 			=   BasicTokenizer(do_lower_case=snake_case	,strip_accents=snake_case   )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """   )	,["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[Any] 			=   BasicTokenizer(do_lower_case=snake_case	,strip_accents=snake_case   )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """   )	,["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[int] 			=   BasicTokenizer(do_lower_case=snake_case	,never_split=["""[UNK]"""]   )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""   )	,["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  int 			=   ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

           lowercase							:  List[str] 			=   {}
           for i, token in enumerate(snake_case   ):
                  lowercase							:  List[str] 			=   i
           lowercase							:  int 			=   WordpieceTokenizer(vocab=snake_case	,unk_token="""[UNK]"""   )

           self.assertListEqual(tokenizer.tokenize(""""""   )	,[]   )

           self.assertListEqual(tokenizer.tokenize("""unwanted running"""   )	,["""un""", """##want""", """##ed""", """runn""", """##ing"""]   )

           self.assertListEqual(tokenizer.tokenize("""unwantedX running"""   )	,["""[UNK]""", """runn""", """##ing"""]   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           self.assertTrue(_is_whitespace(""" """   )   )
           self.assertTrue(_is_whitespace("""\t"""   )   )
           self.assertTrue(_is_whitespace("""\r"""   )   )
           self.assertTrue(_is_whitespace("""\n"""   )   )
           self.assertTrue(_is_whitespace("""\u00A0"""   )   )

           self.assertFalse(_is_whitespace("""A"""   )   )
           self.assertFalse(_is_whitespace("""-"""   )   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           self.assertTrue(_is_control("""\u0005"""   )   )

           self.assertFalse(_is_control("""A"""   )   )
           self.assertFalse(_is_control(""" """   )   )
           self.assertFalse(_is_control("""\t"""   )   )
           self.assertFalse(_is_control("""\r"""   )   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           self.assertTrue(_is_punctuation("""-"""   )   )
           self.assertTrue(_is_punctuation("""$"""   )   )
           self.assertTrue(_is_punctuation("""`"""   )   )
           self.assertTrue(_is_punctuation("""."""   )   )

           self.assertFalse(_is_punctuation("""A"""   )   )
           self.assertFalse(_is_punctuation(""" """   )   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[int] 			=   self.get_tokenizer()
           lowercase							:  Optional[Any] 			=   self.get_rust_tokenizer()

           # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
           self.assertListEqual([tokenizer.tokenize(snake_case   ) for t in ["""Test""", """\xad""", """test"""]]	,[["""[UNK]"""], [], ["""[UNK]"""]]   )

           self.assertListEqual(
               [rust_tokenizer.tokenize(snake_case   ) for t in ["""Test""", """\xad""", """test"""]]	,[["""[UNK]"""], [], ["""[UNK]"""]]   )
    @slow
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  int 			=   self.tokenizer_class.from_pretrained("""google/electra-base-discriminator"""   )

           lowercase							:  Union[str, Any] 			=   tokenizer.encode("""sequence builders"""	,add_special_tokens=snake_case   )
           lowercase							:  List[str] 			=   tokenizer.encode("""multi-sequence build"""	,add_special_tokens=snake_case   )

           lowercase							:  List[Any] 			=   tokenizer.build_inputs_with_special_tokens(snake_case   )
           lowercase							:  Dict 			=   tokenizer.build_inputs_with_special_tokens(snake_case	,snake_case   )

           assert encoded_sentence == [101] + text + [102]
           assert encoded_pair == [101] + text + [102] + text_a + [102]
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"   ):
                         lowercase							:  int 			=   self.rust_tokenizer_class.from_pretrained(snake_case	,**snake_case   )

                         lowercase							:  List[Any] 			=   f"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
                         lowercase							:  List[str] 			=   tokenizer_r.encode_plus(
                             snake_case	,return_attention_mask=snake_case	,return_token_type_ids=snake_case	,return_offsets_mapping=snake_case	,add_special_tokens=snake_case	,)

                         lowercase							:  Tuple 			=   tokenizer_r.do_lower_case if hasattr(snake_case	,"""do_lower_case"""   ) else False
                         lowercase							:  Any 			=   (
                             [
                                 ((0, 0), tokenizer_r.cls_token),
                                 ((0, 1), """A"""),
                                 ((1, 2), ""","""),
                                 ((3, 5), """na"""),
                                 ((5, 6), """##ï"""),
                                 ((6, 8), """##ve"""),
                                 ((9, 15), tokenizer_r.mask_token),
                                 ((16, 21), """Allen"""),
                                 ((21, 23), """##NL"""),
                                 ((23, 24), """##P"""),
                                 ((25, 33), """sentence"""),
                                 ((33, 34), """."""),
                                 ((0, 0), tokenizer_r.sep_token),
                             ]
                             if not do_lower_case
                             else [
                                 ((0, 0), tokenizer_r.cls_token),
                                 ((0, 1), """a"""),
                                 ((1, 2), ""","""),
                                 ((3, 8), """naive"""),
                                 ((9, 15), tokenizer_r.mask_token),
                                 ((16, 21), """allen"""),
                                 ((21, 23), """##nl"""),
                                 ((23, 24), """##p"""),
                                 ((25, 33), """sentence"""),
                                 ((33, 34), """."""),
                                 ((0, 0), tokenizer_r.sep_token),
                             ]
                         )

                         self.assertEqual(
                             [e[1] for e in expected_results]	,tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]   )   )
                         self.assertEqual([e[0] for e in expected_results]	,tokens["""offset_mapping"""]   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[int] 			=   ["""的""", """人""", """有"""]
           lowercase							:  Optional[int] 			=   """""".join(snake_case   )
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"   ):
                         lowercase							:  str 			=   True
                         lowercase							:  Any 			=   self.tokenizer_class.from_pretrained(snake_case	,**snake_case   )
                         lowercase							:  Tuple 			=   self.rust_tokenizer_class.from_pretrained(snake_case	,**snake_case   )

                         lowercase							:  Any 			=   tokenizer_p.encode(snake_case	,add_special_tokens=snake_case   )
                         lowercase							:  List[Any] 			=   tokenizer_r.encode(snake_case	,add_special_tokens=snake_case   )

                         lowercase							:  str 			=   tokenizer_r.convert_ids_to_tokens(snake_case   )
                         lowercase							:  List[Any] 			=   tokenizer_p.convert_ids_to_tokens(snake_case   )

                         # it is expected that each Chinese character is not preceded by "##"
                         self.assertListEqual(snake_case	,snake_case   )
                         self.assertListEqual(snake_case	,snake_case   )

                         lowercase							:  int 			=   False
                         lowercase							:  str 			=   self.rust_tokenizer_class.from_pretrained(snake_case	,**snake_case   )
                         lowercase							:  Optional[int] 			=   self.tokenizer_class.from_pretrained(snake_case	,**snake_case   )

                         lowercase							:  Tuple 			=   tokenizer_r.encode(snake_case	,add_special_tokens=snake_case   )
                         lowercase							:  int 			=   tokenizer_p.encode(snake_case	,add_special_tokens=snake_case   )

                         lowercase							:  str 			=   tokenizer_r.convert_ids_to_tokens(snake_case   )
                         lowercase							:  Tuple 			=   tokenizer_p.convert_ids_to_tokens(snake_case   )

                         # it is expected that only the first Chinese character is not preceded by "##".
                         lowercase							:  int 			=   [
                             f"##{token}" if idx != 0 else token for idx, token in enumerate(snake_case   )
                         ]
                         self.assertListEqual(snake_case	,snake_case   )
                         self.assertListEqual(snake_case	,snake_case   )


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						:      List[Any]							=							False
lowercase						:      Union[str, Any]							=							Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
lowercase						:      List[str]							=							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	,snake_case=sys.argv[1:]   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   None
           lowercase							:  Optional[Any] 			=   None
           lowercase							:  Tuple 			=   None
           lowercase							:  Optional[int] 			=   1
           lowercase			,    lowercase							:  str 			=   getopt.getopt(snake_case	,"""i:o:b:s"""	,["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]   )
           for opt, arg in opts:
                  if opt in ("-i", "--inputdir"):
                         lowercase							:  List[Any] 			=   arg
                  elif opt in ("-o", "--outfile"):
                         lowercase							:  Any 			=   arg
                  elif opt in ("-b", "--batch_size"):
                         lowercase							:  Any 			=   int(snake_case   )
                  elif opt in ("-s", "--subset_list"):
                         lowercase							:  str 			=   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:
                         lowercase							:  Dict 			=   {self._vqa_file_split()[0] for x in tryload(snake_case   )}
           else:
                  lowercase							:  Optional[Any] 			=   None

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



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   int(file.split("""."""   )[0].split("""_"""   )[-1]   )
           lowercase							:  List[str] 			=   os.path.join(self.inputdir	,snake_case   )
           return (img_id, filepath)
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   []
           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:
                         lowercase							:  Dict 			=   batch
                         lowercase							:  int 			=   []
                         yield list(map(snake_case	,zip(*snake_case   )   )   )

           for i in range(1   ):
                  yield list(map(snake_case	,zip(*snake_case   )   )   )
    def __call__(						self   ):



           '''simple docstring'''







           if not TEST:
                  lowercase							:  Any 			=   datasets.ArrowWriter(features=self.schema	,path=self.outputfile   )
           # do file generator
           for i, (img_ids, filepaths) in enumerate(self.file_generator   ):
                  lowercase			,    lowercase			,    lowercase							:  Tuple 			=   self.preprocess(snake_case   )
                  lowercase							:  Dict 			=   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"""	,)
                  lowercase							:  int 			=   output_dict.pop("""normalized_boxes"""   )
                  if not TEST:
                         lowercase							:  Optional[int] 			=   np.array(snake_case   )
                         lowercase							:  Tuple 			=   self.schema.encode_batch(snake_case   )
                         writer.write_batch(snake_case   )
                  if TEST:
                         break
                  # finalizer the writer
           if not TEST:
                  lowercase			,    lowercase							:  Dict 			=   writer.finalize()
                  print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"   )







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					List[Any]:
       try:
              lowercase							:  Dict 			=   json.load(SCREAMING_SNAKE_CASE__       )
              try:
                     lowercase							:  Tuple 			=   list(data.keys()       )
              except Exception:
                     lowercase							:  Tuple 			=   [d["""img_id"""] for d in data]
       except Exception:
              try:
                     lowercase							:  Optional[Any] 			=   eval(stream.read()       )
              except Exception:
                     lowercase							:  Any 			=   stream.read().split("""\n"""       )
       return data


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