

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 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase=sys.argv[1:] )  ->			Optional[Any]:
       '''simple docstring'''






       _lowercase     	=None
       _lowercase     	=None
       _lowercase     	=None
       _lowercase     	=1
       _lowercase	,	_lowercase     	=getopt.getopt(lowerCAmelCase							,       '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(lowerCAmelCase )
            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(lowerCAmelCase ), F'''{outputfile}'''
       if subset_list is not None:
            with open(os.path.realpath(lowerCAmelCase ) ) as f:
                 _lowercase     	={self._vqa_file_split()[0] for x in tryload(lowerCAmelCase )}
       else:
            _lowercase     	=None

       _lowercase     	=CONFIG
       if torch.cuda.is_available():
            _lowercase     	='cuda'
       _lowercase     	=os.path.realpath(lowerCAmelCase )
       _lowercase     	=os.path.realpath(lowerCAmelCase )
       _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   A__					(    self							,       lowerCAmelCase )  ->			List[str]:
       '''simple docstring'''






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




  @property
  def   A__					(    self )  ->			Any:
       '''simple docstring'''






       _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(lowerCAmelCase ) )
            if len(lowerCAmelCase ) == self.batch:
                 _lowercase     	=batch
                 _lowercase     	=[]
                 yield list(map(lowerCAmelCase							,       zip(*lowerCAmelCase ) ) )

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




  def __call__(    self )  ->			Any:
       '''simple docstring'''






       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(lowerCAmelCase )
            _lowercase     	=self.model(
                lowerCAmelCase							,       lowerCAmelCase							,       scales_yx=lowerCAmelCase							,       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(lowerCAmelCase )
                 _lowercase     	=self.schema.encode_batch(lowerCAmelCase )
                 writer.write_batch(lowerCAmelCase )
            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 >> 20} mb''' )
def 					a				(      A__     :       List[str]					)   ->				int:





     """simple docstring"""



     try:
          _lowercase     	=json.load(A__					)
          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__":
  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)





import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list





class 			__lowerCAmelCase					( unittest.TestCase							):




  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	='<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' ' )
        _lowercase     	=dict(zip(lowerCAmelCase							,       range(len(lowerCAmelCase ) ) ) )

        _lowercase     	={
            'pad_token': '<pad>',
            'unk_token': '<unk>',
            'bos_token': '<s>',
            'eos_token': '</s>',
        }
        _lowercase     	={
            'feature_size': 1,
            'padding_value': 0.0,
            'sampling_rate': 16_000,
            'return_attention_mask': False,
            'do_normalize': True,
        }

        _lowercase     	=tempfile.mkdtemp()
        _lowercase     	=os.path.join(self.tmpdirname							,       VOCAB_FILES_NAMES['vocab_file'] )
        _lowercase     	=os.path.join(self.tmpdirname							,       lowerCAmelCase )
        with open(self.vocab_file							,       'w'							,       encoding='utf-8' ) as fp:
              fp.write(json.dumps(lowerCAmelCase ) + '\n' )

        with open(self.feature_extraction_file							,       'w'							,       encoding='utf-8' ) as fp:
              fp.write(json.dumps(lowerCAmelCase ) + '\n' )




  def   A__					(    self							,       **lowerCAmelCase )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=self.add_kwargs_tokens_map.copy()
        kwargs.update(lowerCAmelCase )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname							,       **lowerCAmelCase )




  def   A__					(    self							,       **lowerCAmelCase )  ->			List[str]:
        '''simple docstring'''






        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname							,       **lowerCAmelCase )




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        shutil.rmtree(self.tmpdirname )




  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=self.get_tokenizer()
        _lowercase     	=self.get_feature_extractor()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCAmelCase							,       feature_extractor=lowerCAmelCase )

        processor.save_pretrained(self.tmpdirname )
        _lowercase     	=WavaVecaProcessor.from_pretrained(self.tmpdirname )

        self.assertEqual(processor.tokenizer.get_vocab()							,       tokenizer.get_vocab() )
        self.assertIsInstance(processor.tokenizer							,       lowerCAmelCase )

        self.assertEqual(processor.feature_extractor.to_json_string()							,       feature_extractor.to_json_string() )
        self.assertIsInstance(processor.feature_extractor							,       lowerCAmelCase )




  def   A__					(    self )  ->			str:
        '''simple docstring'''






        _lowercase     	=WavaVecaProcessor(tokenizer=self.get_tokenizer()							,       feature_extractor=self.get_feature_extractor() )
        processor.save_pretrained(self.tmpdirname )

        _lowercase     	=self.get_tokenizer(bos_token='(BOS)'							,       eos_token='(EOS)' )
        _lowercase     	=self.get_feature_extractor(do_normalize=lowerCAmelCase							,       padding_value=1.0 )

        _lowercase     	=WavaVecaProcessor.from_pretrained(
            self.tmpdirname							,       bos_token='(BOS)'							,       eos_token='(EOS)'							,       do_normalize=lowerCAmelCase							,       padding_value=1.0 )

        self.assertEqual(processor.tokenizer.get_vocab()							,       tokenizer_add_kwargs.get_vocab() )
        self.assertIsInstance(processor.tokenizer							,       lowerCAmelCase )

        self.assertEqual(processor.feature_extractor.to_json_string()							,       feature_extractor_add_kwargs.to_json_string() )
        self.assertIsInstance(processor.feature_extractor							,       lowerCAmelCase )




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=self.get_feature_extractor()
        _lowercase     	=self.get_tokenizer()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCAmelCase							,       feature_extractor=lowerCAmelCase )

        _lowercase     	=floats_list((3, 1_000) )

        _lowercase     	=feature_extractor(lowerCAmelCase							,       return_tensors='np' )
        _lowercase     	=processor(lowerCAmelCase							,       return_tensors='np' )

        for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()							,       input_processor[key].sum()							,       delta=1e-2 )




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        _lowercase     	=self.get_feature_extractor()
        _lowercase     	=self.get_tokenizer()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCAmelCase							,       feature_extractor=lowerCAmelCase )

        _lowercase     	='This is a test string'

        _lowercase     	=processor(text=lowerCAmelCase )

        _lowercase     	=tokenizer(lowerCAmelCase )

        for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]							,       encoded_processor[key] )




  def   A__					(    self )  ->			str:
        '''simple docstring'''






        _lowercase     	=self.get_feature_extractor()
        _lowercase     	=self.get_tokenizer()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCAmelCase							,       feature_extractor=lowerCAmelCase )

        _lowercase     	=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _lowercase     	=processor.batch_decode(lowerCAmelCase )
        _lowercase     	=tokenizer.batch_decode(lowerCAmelCase )

        self.assertListEqual(lowerCAmelCase							,       lowerCAmelCase )




  def   A__					(    self )  ->			str:
        '''simple docstring'''






        _lowercase     	=self.get_feature_extractor()
        _lowercase     	=self.get_tokenizer()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCAmelCase							,       feature_extractor=lowerCAmelCase )

        self.assertListEqual(
            processor.model_input_names							,       feature_extractor.model_input_names							,       msg='`processor` and `feature_extractor` model input names do not match'							,       )


