
import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
						from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


__a        = get_tests_dir('fixtures/test_sentencepiece.model')
@require_torch
@require_torchaudio
@require_sentencepiece
class 			lowercase__(    unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (	self  :      str	)	->					Any:
					lowercase_					  = tempfile.mkdtemp()

					lowercase_					  = ['''<s>''', '''<pad>''', '''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
					lowercase_					  = dict(zip(_UpperCamelCase	,							range(len(_UpperCamelCase	)	)	)	)
					lowercase_					  = Path(self.tmpdirname	)
					save_json(_UpperCamelCase	,							save_dir / VOCAB_FILES_NAMES['''vocab_file''']	)
					if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
									copyfile(_UpperCamelCase	,							save_dir / VOCAB_FILES_NAMES['''spm_file''']	)

					lowercase_					  = SpeechaTextTokenizer.from_pretrained(self.tmpdirname	)
					tokenizer.save_pretrained(self.tmpdirname	)

					lowercase_					  = {
					    '''feature_size''': 2_4,
					    '''num_mel_bins''': 2_4,
					    '''padding_value''': 0.0,
					    '''sampling_rate''': 1_6_0_0_0,
					    '''return_attention_mask''': False,
					    '''do_normalize''': True,
					}
					save_json(_UpperCamelCase	,							save_dir / FEATURE_EXTRACTOR_NAME	)
	def 						_lowercase  (	self  :      List[Any]	,							**SCREAMING_SNAKE_CASE_  :      int	)	->					Union[str, Any]:
					return SpeechaTextTokenizer.from_pretrained(self.tmpdirname	,							**_UpperCamelCase	)
	def 						_lowercase  (	self  :      Union[str, Any]	,							**SCREAMING_SNAKE_CASE_  :      List[str]	)	->					Dict:
					return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname	,							**_UpperCamelCase	)
	def 						_lowercase  (	self  :      Any	)	->					List[str]:
					shutil.rmtree(self.tmpdirname	)
	def 						_lowercase  (	self  :      Optional[int]	)	->					Optional[Any]:
					lowercase_					  = self.get_tokenizer()
					lowercase_					  = self.get_feature_extractor()

					lowercase_					  = SpeechaTextProcessor(tokenizer=_UpperCamelCase	,							feature_extractor=_UpperCamelCase	)

					processor.save_pretrained(self.tmpdirname	)
					lowercase_					  = SpeechaTextProcessor.from_pretrained(self.tmpdirname	)

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

					self.assertEqual(processor.feature_extractor.to_json_string()	,							feature_extractor.to_json_string()	)
					self.assertIsInstance(processor.feature_extractor	,							_UpperCamelCase	)
	def 						_lowercase  (	self  :      Dict	)	->					Tuple:
					lowercase_					  = SpeechaTextProcessor(
					    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=_UpperCamelCase	,							padding_value=1.0	)

					lowercase_					  = SpeechaTextProcessor.from_pretrained(
					    self.tmpdirname	,							bos_token='''(BOS)'''	,							eos_token='''(EOS)'''	,							do_normalize=_UpperCamelCase	,							padding_value=1.0	)

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

					self.assertEqual(processor.feature_extractor.to_json_string()	,							feature_extractor_add_kwargs.to_json_string()	)
					self.assertIsInstance(processor.feature_extractor	,							_UpperCamelCase	)
	def 						_lowercase  (	self  :      List[Any]	)	->					Tuple:
					lowercase_					  = self.get_feature_extractor()
					lowercase_					  = self.get_tokenizer()

					lowercase_					  = SpeechaTextProcessor(tokenizer=_UpperCamelCase	,							feature_extractor=_UpperCamelCase	)

					lowercase_					  = floats_list((3, 1_0_0_0)	)

					lowercase_					  = feature_extractor(_UpperCamelCase	,							return_tensors='''np'''	)
					lowercase_					  = processor(_UpperCamelCase	,							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 						_lowercase  (	self  :      str	)	->					str:
					lowercase_					  = self.get_feature_extractor()
					lowercase_					  = self.get_tokenizer()

					lowercase_					  = SpeechaTextProcessor(tokenizer=_UpperCamelCase	,							feature_extractor=_UpperCamelCase	)

					lowercase_					  = '''This is a test string'''

					lowercase_					  = processor(text=_UpperCamelCase	)

					lowercase_					  = tokenizer(_UpperCamelCase	)

					for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]	,							encoded_processor[key]	)
	def 						_lowercase  (	self  :      Optional[int]	)	->					Tuple:
					lowercase_					  = self.get_feature_extractor()
					lowercase_					  = self.get_tokenizer()

					lowercase_					  = SpeechaTextProcessor(tokenizer=_UpperCamelCase	,							feature_extractor=_UpperCamelCase	)

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

					lowercase_					  = processor.batch_decode(_UpperCamelCase	)
					lowercase_					  = tokenizer.batch_decode(_UpperCamelCase	)

					self.assertListEqual(_UpperCamelCase	,							_UpperCamelCase	)




	def 						_lowercase  (	self  :      List[Any]	)	->					Optional[int]:
					lowercase_					  = self.get_feature_extractor()
					lowercase_					  = self.get_tokenizer()

					lowercase_					  = SpeechaTextProcessor(tokenizer=_UpperCamelCase	,							feature_extractor=_UpperCamelCase	)

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






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        = False
__a        = Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__a        = 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 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      Dict	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]=sys.argv[1:]	)	->					int:
					lowercase_					  = None
					lowercase_					  = None
					lowercase_					  = None
					lowercase_					  = 1
					lowercase_  ,     lowercase_					  = 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"):
													lowercase_					  = arg
									elif opt in ("-o", "--outfile"):
													lowercase_					  = arg
									elif opt in ("-b", "--batch_size"):
													lowercase_					  = int(SCREAMING_SNAKE_CASE_	)
									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(SCREAMING_SNAKE_CASE_	), f'''{outputfile}'''
					if subset_list is not None:
									with open(os.path.realpath(SCREAMING_SNAKE_CASE_	)	) as f:
													lowercase_					  = {self._vqa_file_split()[0] for x in tryload(SCREAMING_SNAKE_CASE_	)}
					else:
									lowercase_					  = None

					lowercase_					  = CONFIG
					if torch.cuda.is_available():
									lowercase_					  = '''cuda'''
					lowercase_					  = os.path.realpath(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = os.path.realpath(SCREAMING_SNAKE_CASE_	)
					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 						_lowercase  (	self  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      int	)	->					Any:
					lowercase_					  = int(file.split('''.'''	)[0].split('''_'''	)[-1]	)
					lowercase_					  = os.path.join(self.inputdir	,							SCREAMING_SNAKE_CASE_	)
					return (img_id, filepath)
	@property
	def 						_lowercase  (	self  :      List[str]	)	->					List[Any]:
					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(SCREAMING_SNAKE_CASE_	)	)
									if len(SCREAMING_SNAKE_CASE_	) == self.batch:
													lowercase_					  = batch
													lowercase_					  = []
													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  :      Any	)	->					List[str]:
					# make writer
					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(SCREAMING_SNAKE_CASE_	)
									lowercase_					  = 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'''	,							)
									lowercase_					  = output_dict.pop('''normalized_boxes'''	)
									if not TEST:
													lowercase_					  = np.array(SCREAMING_SNAKE_CASE_	)
													lowercase_					  = self.schema.encode_batch(SCREAMING_SNAKE_CASE_	)
													writer.write_batch(SCREAMING_SNAKE_CASE_	)
									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 >> 2_0} mb'''	)




def     a						(							snake_case__: Optional[Any] ):


				'''simple docstring'''
				try:
								lowercase_					  = json.load(snake_case__ )
								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__":
						__a        = Extract(sys.argv[1:])
						extract()
						if not TEST:
												__a        = datasets.Dataset.from_file(extract.outputfile)
												# wala!
												# print(np.array(dataset[0:2]["roi_features"]).shape)




