
'''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__						:				Dict           =    False
a__						:				Optional[int]           =    Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
a__						:				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, 2_0_4_8), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)


class 	UpperCamelCase__					:






 def __init__(     self					:Any		,			_A					:Optional[Any]=sys.argv[1:]     )			->     str:







       '''simple docstring'''



       __A    		=     None
       __A    		=     None
       __A    		=     None
       __A    		=     1
       __A		, __A    		=     getopt.getopt(__SCREAMING_SNAKE_CASE		,			'i:o:b:s'		,			['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']     )
       for opt, arg in opts:
             if opt in ("-i", "--inputdir"):
                   __A    		=     arg
             elif opt in ("-o", "--outfile"):
                   __A    		=     arg
             elif opt in ("-b", "--batch_size"):
                   __A    		=     int(__SCREAMING_SNAKE_CASE     )
             elif opt in ("-s", "--subset_list"):
                   __A    		=     arg

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

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






 def 		lowercase_  (     self					:List[Any]		,			_A					:int     )			->     Optional[int]:







       '''simple docstring'''



       __A    		=     int(file.split('.'     )[0].split('_'     )[-1]     )
       __A    		=     os.path.join(self.inputdir		,			__SCREAMING_SNAKE_CASE     )
       return (img_id, filepath)






 @property
 def 		lowercase_  (     self					:Any     )			->     int:







       '''simple docstring'''



       __A    		=     []
       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(__SCREAMING_SNAKE_CASE     )     )
             if len(__SCREAMING_SNAKE_CASE     ) == self.batch:
                   __A    		=     batch
                   __A    		=     []
                   yield list(map(__SCREAMING_SNAKE_CASE		,			zip(*__SCREAMING_SNAKE_CASE     )     )     )

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






 def __call__(     self					:Union[str, Any]     )			->     Any:







       '''simple docstring'''



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





def     snake_case							(						UpperCAmelCase		)->     Dict:







      """simple docstring"""





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


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








'''simple docstring'''





import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 	UpperCamelCase__					(      SCREAMING_SNAKE_CASE ,       unittest.TestCase):
 UpperCAmelCase__      :      Tuple  			=   PreTrainedTokenizerFast
 UpperCAmelCase__      :      Tuple  			=   False
 UpperCAmelCase__      :      str  			=   True
 UpperCAmelCase__      :      str  			=   'tokenizer_file'






 def 		lowercase_  (     self					:Union[str, Any]     )			->     List[Any]:







       '''simple docstring'''



       __A    		=     False  # because we don't have pretrained_vocab_files_map
       super().setUp()
       __A    		=     True

       __A    		=     ['robot-test/dummy-tokenizer-fast', 'robot-test/dummy-tokenizer-wordlevel']
       __A    		=     'SaulLu/dummy-tokenizer-bytelevel-bpe'

       # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
       __A    		=     [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

       __A    		=     PreTrainedTokenizerFast.from_pretrained(model_paths[0]     )
       tokenizer.save_pretrained(self.tmpdirname     )






 def 		lowercase_  (     self					:Any     )			->     List[str]:







       '''simple docstring'''



       pass






 def 		lowercase_  (     self					:Optional[Any]     )			->     Optional[Any]:







       '''simple docstring'''



       pass






 def 		lowercase_  (     self					:Tuple     )			->     str:







       '''simple docstring'''



       pass






 def 		lowercase_  (     self					:List[Any]     )			->     Tuple:







       '''simple docstring'''



       pass






 def 		lowercase_  (     self					:List[Any]     )			->     str:







       '''simple docstring'''



       __A    		=     self.tmpdirname
       # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
                   try:
                         __A    		=     tempfile.mkdtemp()
                         __A    		=     self.rust_tokenizer_class.from_pretrained(_A		,			**_A     )

                         tokenizer.save_pretrained(self.tmpdirname     )
                         super().test_training_new_tokenizer()
                   finally:
                         # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                         # is restored
                         shutil.rmtree(self.tmpdirname     )
                         __A    		=     tmpdirname_orig






 def 		lowercase_  (     self					:int     )			->     int:







       '''simple docstring'''



       __A    		=     self.tmpdirname
       # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
                   try:
                         __A    		=     tempfile.mkdtemp()
                         __A    		=     self.rust_tokenizer_class.from_pretrained(_A		,			**_A     )

                         tokenizer.save_pretrained(self.tmpdirname     )
                         super().test_training_new_tokenizer_with_special_tokens_change()
                   finally:
                         # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                         # is restored
                         shutil.rmtree(self.tmpdirname     )
                         __A    		=     tmpdirname_orig






 def 		lowercase_  (     self					:Optional[Any]     )			->     Any:







       '''simple docstring'''



       __A    		=     self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name     )

       __A    		=     ('a' for _ in range(1_000     ))
       __A    		=     tokenizer.train_new_from_iterator(text_iterator=_A		,			length=1_000		,			vocab_size=50     )

       __A    		=     new_tokenizer.encode('a🤗'     )
       self.assertEqual(_A		,			[64, 172, 253, 97, 245]     )


@require_tokenizers
class 	UpperCamelCase__					(      unittest.TestCase):






 def 		lowercase_  (     self					:Tuple     )			->     List[Any]:







       '''simple docstring'''



       __A    		=     AutoTokenizer.from_pretrained('bert-base-cased'     )
       __A    		=     json.loads(tokenizer._tokenizer.to_str()     )
       __A    		=     len(_A     )

       with tempfile.TemporaryDirectory() as tmp_dir:
             # Hack to save this in the tokenizer_config.json
             __A    		=     ['tokenizer.4.0.0.json']
             tokenizer.save_pretrained(_A     )
             json.dump(_A		,			open(os.path.join(_A		,			'tokenizer.4.0.0.json'     )		,			'w'     )     )

             # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
             __A    		=     AutoTokenizer.from_pretrained(_A     )
             self.assertEqual(len(_A     )		,			len(_A     ) + 1     )
             __A    		=     json.loads(new_tokenizer._tokenizer.to_str()     )
             self.assertIn('huggingface'		,			json_tokenizer['model']['vocab']     )

             # Will need to be adjusted if we reach v42 and this test is still here.
             # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
             shutil.move(os.path.join(_A		,			'tokenizer.4.0.0.json'     )		,			os.path.join(_A		,			'tokenizer.42.0.0.json'     )     )
             __A    		=     ['tokenizer.42.0.0.json']
             tokenizer.save_pretrained(_A     )
             __A    		=     AutoTokenizer.from_pretrained(_A     )
             self.assertEqual(len(_A     )		,			len(_A     )     )
             __A    		=     json.loads(new_tokenizer._tokenizer.to_str()     )
             self.assertNotIn('huggingface'		,			json_tokenizer['model']['vocab']     )






 def 		lowercase_  (     self					:List[Any]     )			->     Optional[int]:







       '''simple docstring'''



       __A    		=     'hf-internal-testing/test-two-tokenizers'

       # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
       __A    		=     AutoTokenizer.from_pretrained(_A     )
       self.assertEqual(len(_A     )		,			28_997     )
       __A    		=     json.loads(tokenizer._tokenizer.to_str()     )
       self.assertIn('huggingface'		,			json_tokenizer['model']['vocab']     )

       # Testing an older version by monkey-patching the version in the module it's used.
       import transformers as old_transformers

       __A    		=     '3.0.0'
       __A    		=     old_transformers.models.auto.AutoTokenizer.from_pretrained(_A     )
       self.assertEqual(len(_A     )		,			28_996     )
       __A    		=     json.loads(old_tokenizer._tokenizer.to_str()     )
       self.assertNotIn('huggingface'		,			json_tokenizer['model']['vocab']     )


@require_tokenizers
class 	UpperCamelCase__					(      unittest.TestCase):






 def 		lowercase_  (     self					:List[str]     )			->     List[Any]:







       '''simple docstring'''



       __A    		=     PreTrainedTokenizerFast.from_pretrained('robot-test/dummy-tokenizer-wordlevel'     )
       __A    		=     'The Matrix is a 1999 science fiction action film.'

       with concurrent.futures.ThreadPoolExecutor() as executor:
             __A    		=     [executor.submit(self.fetch		,			_A		,			_A     ) for i in range(10     )]
             __A    		=     [future.result() for future in futures]
             self.assertEqual(_A		,			[[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10     )]     )






 def 		lowercase_  (     self					:Optional[int]		,			_A					:str		,			_A					:Any     )			->     Optional[int]:







       '''simple docstring'''



       return tokenizer.encode(_A		,			truncation='longest_first'		,			padding='longest'     )






