
import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
						pass

if is_speech_available():
						from transformers import EncodecFeatureExtractor, MusicgenProcessor


SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       random.Random()



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase=1.0 ,						__UpperCamelCase=None ,						__UpperCamelCase=None     ):
				if rng is None:
								A_       	=  global_rng

				A_       	=  []
				for batch_idx in range(shape[0]     ):
								values.append([]     )
								for _ in range(shape[1]     ):
												values[-1].append(rng.random() * scale     )

				return values






@require_torch
@require_sentencepiece
class 					__lowercase		(					unittest.TestCase       ):


					def        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_       	=  '''facebook/musicgen-small'''
									A_       	=  tempfile.mkdtemp()


					def        lowerCAmelCase_    (	self       ,       **a__						)		->							Optional[int]:





									'''simple docstring'''
									return TaTokenizer.from_pretrained(self.checkpoint       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       **a__						)		->							List[Any]:





									'''simple docstring'''
									return EncodecFeatureExtractor.from_pretrained(self.checkpoint       ,       **a__						)


					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									shutil.rmtree(self.tmpdirname						)


					def        lowerCAmelCase_    (	self						)		->							Any:





									'''simple docstring'''
									A_       	=  self.get_tokenizer()
									A_       	=  self.get_feature_extractor()

									A_       	=  MusicgenProcessor(tokenizer=a__       ,       feature_extractor=a__						)

									processor.save_pretrained(self.tmpdirname						)
									A_       	=  MusicgenProcessor.from_pretrained(self.tmpdirname						)

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

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


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





									'''simple docstring'''
									A_       	=  MusicgenProcessor(tokenizer=self.get_tokenizer()       ,       feature_extractor=self.get_feature_extractor()						)
									processor.save_pretrained(self.tmpdirname						)

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

									A_       	=  MusicgenProcessor.from_pretrained(
									    self.tmpdirname       ,       bos_token='''(BOS)'''       ,       eos_token='''(EOS)'''       ,       do_normalize=a__       ,       padding_value=1.0						)

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

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


					def        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_       	=  self.get_feature_extractor()
									A_       	=  self.get_tokenizer()

									A_       	=  MusicgenProcessor(tokenizer=a__       ,       feature_extractor=a__						)

									A_       	=  floats_list((3, 1_0_0_0)						)

									A_       	=  feature_extractor(a__       ,       return_tensors='''np'''						)
									A_       	=  processor(a__       ,       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        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_       	=  self.get_feature_extractor()
									A_       	=  self.get_tokenizer()

									A_       	=  MusicgenProcessor(tokenizer=a__       ,       feature_extractor=a__						)

									A_       	=  '''This is a test string'''

									A_       	=  processor(text=a__						)

									A_       	=  tokenizer(a__						)

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


					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									A_       	=  self.get_feature_extractor()
									A_       	=  self.get_tokenizer()

									A_       	=  MusicgenProcessor(tokenizer=a__       ,       feature_extractor=a__						)

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

									A_       	=  processor.batch_decode(sequences=a__						)
									A_       	=  tokenizer.batch_decode(a__						)

									self.assertListEqual(a__       ,       a__						)


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





									'''simple docstring'''
									A_       	=  self.get_feature_extractor()
									A_       	=  self.get_tokenizer()

									A_       	=  MusicgenProcessor(tokenizer=a__       ,       feature_extractor=a__						)

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


					def        lowerCAmelCase_    (	self						)		->							List[str]:





									'''simple docstring'''
									A_       	=  self.get_feature_extractor(padding_side='''left'''						)
									A_       	=  self.get_tokenizer()

									A_       	=  MusicgenProcessor(tokenizer=a__       ,       feature_extractor=a__						)

									A_       	=  [floats_list((1, x)						)[0] for x in range(5       ,       2_0       ,       5						)]
									A_       	=  processor(a__						).padding_mask

									A_       	=  np.asarray(floats_list((3, 2_0)						)						)[:, None, :]
									A_       	=  processor.batch_decode(a__       ,       padding_mask=a__						)

									self.assertIsInstance(a__       ,       a__						)

									for audio in decoded_audios:
													self.assertIsInstance(a__       ,       np.ndarray						)

									self.assertTrue(decoded_audios[0].shape == (1, 1_0)						)
									self.assertTrue(decoded_audios[1].shape == (1, 1_5)						)
									self.assertTrue(decoded_audios[2].shape == (1, 2_0)						)

from cva import destroyAllWindows, imread, imshow, waitKey



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_	,					A_       	=  img.shape[0], img.shape[1]

				def to_grayscale(__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
								return 0.2126 * red + 0.587 * green + 0.114 * blue

				def normalize(__UpperCamelCase     ):
								return min(__UpperCamelCase ,						2_55     )

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

				return img


if __name__ == "__main__":
						# read original image
						SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       {
						    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()