



import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow



@require_torch
class 						UpperCAmelCase_					( unittest.TestCase):







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					"ylacombe/bark-small"
														_lowerCAmelCase  :							int				     =					tempfile.mkdtemp()
														_lowerCAmelCase  :							Dict				     =					"en_speaker_1"
														_lowerCAmelCase  :							str				     =					"This is a test string"
														_lowerCAmelCase  :							List[Any]				     =					"speaker_embeddings_path.json"
														_lowerCAmelCase  :							Any				     =					"speaker_embeddings"







							def      snake_case__   (     self,    **__a):

														'''simple docstring'''

														return AutoTokenizer.from_pretrained(self.checkpoint,    **__a)







							def      snake_case__   (     self):

														'''simple docstring'''

														shutil.rmtree(self.tmpdirname)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					self.get_tokenizer()

														_lowerCAmelCase  :							int				     =					BarkProcessor(tokenizer=__a)

														processor.save_pretrained(self.tmpdirname)
														_lowerCAmelCase  :							Tuple				     =					BarkProcessor.from_pretrained(self.tmpdirname)

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







							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					BarkProcessor.from_pretrained(
														    pretrained_processor_name_or_path=self.checkpoint,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path,    )
														processor.save_pretrained(
														    self.tmpdirname,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path,    speaker_embeddings_directory=self.speaker_embeddings_directory,    )

														_lowerCAmelCase  :							str				     =					self.get_tokenizer(bos_token="(BOS)",    eos_token="(EOS)")

														_lowerCAmelCase  :							Optional[int]				     =					BarkProcessor.from_pretrained(
														    self.tmpdirname,    self.speaker_embeddings_dict_path,    bos_token="(BOS)",    eos_token="(EOS)",    )

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					BarkProcessor.from_pretrained(
														    pretrained_processor_name_or_path=self.checkpoint,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path,    )

														_lowerCAmelCase  :							Union[str, Any]				     =					35
														_lowerCAmelCase  :							Dict				     =					2
														_lowerCAmelCase  :							int				     =					8

														_lowerCAmelCase  :							str				     =					{
														    "semantic_prompt": np.ones(__a),
														    "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)),
														    "fine_prompt": np.ones((nb_codebooks_total, seq_len)),
														}

														# test providing already loaded voice_preset
														_lowerCAmelCase  :							Dict				     =					processor(text=self.input_string,    voice_preset=__a)

														_lowerCAmelCase  :							List[str]				     =					inputs["history_prompt"]
														for key in voice_preset:
																					self.assertListEqual(voice_preset[key].tolist(),    processed_voice_preset.get(__a,    np.array([])).tolist())

														# test loading voice preset from npz file
														_lowerCAmelCase  :							Dict				     =					os.path.join(self.tmpdirname,    "file.npz")
														np.savez(__a,    **__a)
														_lowerCAmelCase  :							List[str]				     =					processor(text=self.input_string,    voice_preset=__a)
														_lowerCAmelCase  :							Optional[int]				     =					inputs["history_prompt"]

														for key in voice_preset:
																					self.assertListEqual(voice_preset[key].tolist(),    processed_voice_preset.get(__a,    np.array([])).tolist())

														# test loading voice preset from the hub
														_lowerCAmelCase  :							int				     =					processor(text=self.input_string,    voice_preset=self.voice_preset)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					self.get_tokenizer()

														_lowerCAmelCase  :							List[Any]				     =					BarkProcessor(tokenizer=__a)

														_lowerCAmelCase  :							Dict				     =					processor(text=self.input_string)

														_lowerCAmelCase  :							Union[str, Any]				     =					tokenizer(
														    self.input_string,    padding="max_length",    max_length=256,    add_special_tokens=__a,    return_attention_mask=__a,    return_token_type_ids=__a,    )

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





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							if (ksize % 2) == 0:
														_lowerCAmelCase  :							str				     =					ksize + 1
							_lowerCAmelCase  :							List[str]				     =					np.zeros((ksize, ksize)       ,				dtype=np.floataa						)

							# each value
							for y in range(_lowerCamelCase						):
														for x in range(_lowerCamelCase						):
																					# distance from center
																					_lowerCAmelCase  :							int				     =					x - ksize // 2
																					_lowerCAmelCase  :							Dict				     =					y - ksize // 2

																					# degree to radiant
																					_lowerCAmelCase  :							List[Any]				     =					theta / 180 * np.pi
																					_lowerCAmelCase  :							int				     =					np.cos(_theta						)
																					_lowerCAmelCase  :							Optional[int]				     =					np.sin(_theta						)

																					# get kernel x
																					_lowerCAmelCase  :							int				     =					cos_theta * px + sin_theta * py

																					# get kernel y
																					_lowerCAmelCase  :							str				     =					-sin_theta * px + cos_theta * py

																					# fill kernel
																					_lowerCAmelCase  :							Union[str, Any]				     =					np.exp(
																					    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)						) * np.cos(2 * np.pi * _x / lambd + psi						)

							return gabor


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						# read original image
						_snake_case	=						imread("../image_data/lena.jpg")
						# turn image in gray scale value
						_snake_case	=						cvtColor(img, COLOR_BGR2GRAY)

						# Apply multiple Kernel to detect edges
						_snake_case	=						np.zeros(gray.shape[:2])
						for theta in [0, 30, 60, 90, 120, 150]:
												_snake_case	=						gabor_filter_kernel(10, 8, theta, 10, 0, 0)
												out += filteraD(gray, CV_8UC3, kernel_aa)
						_snake_case	=						out / out.max() * 255
						_snake_case	=						out.astype(np.uinta)

						imshow("Original", gray)
						imshow("Gabor filter with 20x20 mask and 6 directions", out)

						waitKey(0)
