





"""simple docstring"""






import math
import sys

import cva
import numpy as np

def 			__A					(	a_  :np.ndarray      ,				a_  :float)						->						np.ndarray:
				# For applying gaussian function for each element in matrix.
				__a       :  Tuple					=     math.sqrt(_lowerCamelCase)
				__a       :  Dict					=     1 / (sigma * math.sqrt(2 * math.pi))
				return cons * np.exp(-((img / sigma) ** 2) * 0.5)

def 			__A					(	a_  :np.ndarray      ,				a_  :int      ,				a_  :int      ,				a_  :int)						->						np.ndarray:
				__a       :  List[Any]					=     kernel_size // 2
				return img[x - half : x + half + 1, y - half : y + half + 1]

def 			__A					(	a_  :int      ,				a_  :float)						->						np.ndarray:
				# Creates a gaussian kernel of given dimension.
				__a       :  List[str]					=     np.zeros((kernel_size, kernel_size))
				for i in range(0      ,				_lowerCamelCase):
								for j in range(0      ,				_lowerCamelCase):
												__a       :  Union[str, Any]					=     math.sqrt(
												    abs(i - kernel_size // 2) ** 2 + abs(j - kernel_size // 2) ** 2)
				return vec_gaussian(_lowerCamelCase      ,				_lowerCamelCase)

def 			__A					(	a_  :np.ndarray      ,				a_  :float      ,				a_  :float      ,				a_  :int      ,				)						->						np.ndarray:
				__a       :  Union[str, Any]					=     np.zeros(img.shape)
				__a       :  Dict					=     get_gauss_kernel(_lowerCamelCase      ,				_lowerCamelCase)
				__a       :  Tuple					=     img.shape
				for i in range(kernel_size // 2      ,				size_x - kernel_size // 2):
								for j in range(kernel_size // 2      ,				size_y - kernel_size // 2):
												__a       :  Union[str, Any]					=     get_slice(_lowerCamelCase      ,				_lowerCamelCase      ,				_lowerCamelCase      ,				_lowerCamelCase)
												__a       :  List[Any]					=     img_s - img_s[kernel_size // 2, kernel_size // 2]
												__a       :  List[Any]					=     vec_gaussian(_lowerCamelCase      ,				_lowerCamelCase)
												__a       :  Union[str, Any]					=     np.multiply(_lowerCamelCase      ,				_lowerCamelCase)
												__a       :  int					=     np.multiply(_lowerCamelCase      ,				_lowerCamelCase)
												__a       :  str					=     np.sum(_lowerCamelCase) / np.sum(_lowerCamelCase)
												__a       :  Optional[int]					=     val
				return imga

def 			__A					(	a_  :list)						->						tuple:
				__a       :  List[Any]					=     args[1] if args[1:] else """../image_data/lena.jpg"""
				__a       :  Optional[Any]					=     float(args[2]) if args[2:] else 1.0
				__a       :  Dict					=     float(args[3]) if args[3:] else 1.0
				if args[4:]:
								__a       :  List[str]					=     int(args[4])
								__a       :  List[str]					=     kernel_size + abs(kernel_size % 2 - 1)
				else:
								__a       :  Tuple					=     5
				return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
		A       	=     parse_args(sys.argv)
		A       	=     cva.imread(filename, 0)
		cva.imshow('''input image''', img)

		A       	=     img / 255
		A       	=     out.astype('''float32''')
		A       	=     bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
		A       	=     out * 255
		A       	=     np.uinta(out)
		cva.imshow('''output image''', out)
		cva.waitKey(0)
		cva.destroyAllWindows()
"""simple docstring"""




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 						__A							( unittest.TestCase		):






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






     def     __A  (		self				,     **a__  ):
       return AutoTokenizer.from_pretrained(self.checkpoint				,     **a__  )






     def     __A  (		self  ):
       shutil.rmtree(self.tmpdirname  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.get_tokenizer()

       _lowerCAmelCase						:       int  			=     BarkProcessor(tokenizer=a__  )

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

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






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     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						:       Tuple  			=     self.get_tokenizer(bos_token="""(BOS)"""				,     eos_token="""(EOS)"""  )

       _lowerCAmelCase						:       List[Any]  			=     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     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     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						:       Union[str, Any]  			=     2
       _lowerCAmelCase						:       Optional[int]  			=     8

       _lowerCAmelCase						:       Dict  			=     {
           """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						:       Tuple  			=     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						:       List[Any]  			=     os.path.join(self.tmpdirname				,     """file.npz"""  )
       np.savez(a__				,     **a__  )
       _lowerCAmelCase						:       List[Any]  			=     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						:       str  			=     processor(text=self.input_string				,     voice_preset=self.voice_preset  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     self.get_tokenizer()

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

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

       _lowerCAmelCase						:       Tuple  			=     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()  )




