


"""simple docstring"""






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


lowercase  		=			get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_torch
@require_torchaudio
@require_sentencepiece
class 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						str		=      tempfile.mkdtemp()

								snake_case__  :						Optional[Any]		=      ["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
								snake_case__  :						Union[str, Any]		=      dict(zip(__lowerCamelCase	,					range(len(__lowerCamelCase))))
								snake_case__  :						Optional[int]		=      Path(self.tmpdirname)
								save_json(__lowerCamelCase	,					save_dir / VOCAB_FILES_NAMES["vocab_file"])
								if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
									copyfile(__lowerCamelCase	,					save_dir / VOCAB_FILES_NAMES["spm_file"])

								snake_case__  :						Optional[int]		=      SpeechaTextTokenizer.from_pretrained(self.tmpdirname)
								tokenizer.save_pretrained(self.tmpdirname)

								snake_case__  :						int		=      {
								    "feature_size": 24,
								    "num_mel_bins": 24,
								    "padding_value": 0.0,
								    "sampling_rate": 16_000,
								    "return_attention_mask": False,
								    "do_normalize": True,
								}
								save_json(__lowerCamelCase	,					save_dir / FEATURE_EXTRACTOR_NAME)




							def        UpperCAmelCase (						self	,					**lowerCamelCase__)   ->	Tuple:







								'''simple docstring'''




								return SpeechaTextTokenizer.from_pretrained(self.tmpdirname	,					**__lowerCamelCase)




							def        UpperCAmelCase (						self	,					**lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




								return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname	,					**__lowerCamelCase)




							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								shutil.rmtree(self.tmpdirname)




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







								'''simple docstring'''




								snake_case__  :						str		=      self.get_tokenizer()
								snake_case__  :						List[str]		=      self.get_feature_extractor()

								snake_case__  :						Union[str, Any]		=      SpeechaTextProcessor(tokenizer=__lowerCamelCase	,					feature_extractor=__lowerCamelCase)

								processor.save_pretrained(self.tmpdirname)
								snake_case__  :						Tuple		=      SpeechaTextProcessor.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        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      SpeechaTextProcessor(
								    tokenizer=self.get_tokenizer()	,					feature_extractor=self.get_feature_extractor())
								processor.save_pretrained(self.tmpdirname)

								snake_case__  :						int		=      self.get_tokenizer(bos_token="(BOS)"	,					eos_token="(EOS)")
								snake_case__  :						Tuple		=      self.get_feature_extractor(do_normalize=__lowerCamelCase	,					padding_value=1.0)

								snake_case__  :						Dict		=      SpeechaTextProcessor.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        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      self.get_feature_extractor()
								snake_case__  :						int		=      self.get_tokenizer()

								snake_case__  :						List[Any]		=      SpeechaTextProcessor(tokenizer=__lowerCamelCase	,					feature_extractor=__lowerCamelCase)

								snake_case__  :						int		=      floats_list((3, 1_000))

								snake_case__  :						Dict		=      feature_extractor(__lowerCamelCase	,					return_tensors="np")
								snake_case__  :						List[Any]		=      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        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.get_feature_extractor()
								snake_case__  :						int		=      self.get_tokenizer()

								snake_case__  :						List[Any]		=      SpeechaTextProcessor(tokenizer=__lowerCamelCase	,					feature_extractor=__lowerCamelCase)

								snake_case__  :						int		=      "This is a test string"

								snake_case__  :						List[str]		=      processor(text=__lowerCamelCase)

								snake_case__  :						Optional[Any]		=      tokenizer(__lowerCamelCase)

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




							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						str		=      self.get_feature_extractor()
								snake_case__  :						Tuple		=      self.get_tokenizer()

								snake_case__  :						Dict		=      SpeechaTextProcessor(tokenizer=__lowerCamelCase	,					feature_extractor=__lowerCamelCase)

								snake_case__  :						Dict		=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								snake_case__  :						List[Any]		=      processor.batch_decode(__lowerCamelCase)
								snake_case__  :						str		=      tokenizer.batch_decode(__lowerCamelCase)

								self.assertListEqual(__lowerCamelCase	,					__lowerCamelCase)







							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.get_feature_extractor()
								snake_case__  :						Tuple		=      self.get_tokenizer()

								snake_case__  :						Tuple		=      SpeechaTextProcessor(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"	,					)













"""simple docstring"""

from cva import destroyAllWindows, imread, imshow, waitKey






def _lowerCAmelCase						(	__lowerCamelCase:Tuple			,    __lowerCamelCase:int ):

						'''simple docstring'''






						__magic_name__   ,     __magic_name__      =  img.shape[0], img.shape[1]

						def to_grayscale(__lowerCamelCase:List[str]			,    __lowerCamelCase:Optional[Any]			,    __lowerCamelCase:int ):
												return 0.2_1_2_6 * red + 0.5_8_7 * green + 0.1_1_4 * blue

						def normalize(__lowerCamelCase:Union[str, Any] ):
												return min(__lowerCamelCase			,    2_5_5 )

						for i in range(__lowerCamelCase ):
												for j in range(__lowerCamelCase ):
																		__magic_name__      =  int(to_grayscale(*img[i][j] ) )
																		__magic_name__      =  [
																		    normalize(__lowerCamelCase ),
																		    normalize(greyscale + factor ),
																		    normalize(greyscale + 2 * factor ),
																		]

						return img


if __name__ == "__main__":
			# read original image
			lowercase           =							{
			    percentage: imread('''image_data/lena.jpg''', 1) for percentage in (10, 20, 30, 40)
			}

			for percentage, img in images.items():
						make_sepia(img, percentage)

			for percentage, img in images.items():
						imshow(f'''Original image with sepia (factor: {percentage})''', img)

			waitKey(0)
			destroyAllWindows()



