






"""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        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						Tuple							)					->       List[Any]:
   _a  :       Dict             =	"""ylacombe/bark-small"""
   _a  :       Union[str, Any]             =	tempfile.mkdtemp()
   _a  :       Dict             =	"""en_speaker_1"""
   _a  :       Any             =	"""This is a test string"""
   _a  :       str             =	"""speaker_embeddings_path.json"""
   _a  :       Union[str, Any]             =	"""speaker_embeddings"""


 def _lowercase	(     self		:						Any							,						**UpperCAmelCase__		:						Union[str, Any]							)					->       Optional[Any]:
   return AutoTokenizer.from_pretrained(self.checkpoint							,						**UpperCAmelCase__							)


 def _lowercase	(     self		:						Dict							)					->       Any:
   shutil.rmtree(self.tmpdirname							)


 def _lowercase	(     self		:						Optional[Any]							)					->       Dict:
   _a  :       List[Any]             =	self.get_tokenizer()

   _a  :       List[Any]             =	BarkProcessor(tokenizer=UpperCAmelCase__							)

   processor.save_pretrained(self.tmpdirname							)
   _a  :       Any             =	BarkProcessor.from_pretrained(self.tmpdirname							)

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


 @slow
 def _lowercase	(     self		:						str							)					->       Any:
   _a  :       Dict             =	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							,						)

   _a  :       Dict             =	self.get_tokenizer(bos_token="""(BOS)"""							,						eos_token="""(EOS)"""							)

   _a  :       str             =	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 _lowercase	(     self		:						Tuple							)					->       Any:
   _a  :       Union[str, Any]             =	BarkProcessor.from_pretrained(
       pretrained_processor_name_or_path=self.checkpoint							,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,						)

   _a  :       Dict             =	35
   _a  :       Any             =	2
   _a  :       str             =	8

   _a  :       Tuple             =	{
       """semantic_prompt""": np.ones(UpperCAmelCase__							),
       """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)							),
       """fine_prompt""": np.ones((nb_codebooks_total, seq_len)							),
   }

   # test providing already loaded voice_preset
   _a  :       Optional[int]             =	processor(text=self.input_string							,						voice_preset=UpperCAmelCase__							)

   _a  :       Any             =	inputs["""history_prompt"""]
   for key in voice_preset:
     self.assertListEqual(voice_preset[key].tolist()							,						processed_voice_preset.get(UpperCAmelCase__							,						np.array([]							)							).tolist()							)

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

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

   # test loading voice preset from the hub
   _a  :       Optional[Any]             =	processor(text=self.input_string							,						voice_preset=self.voice_preset							)







 def _lowercase	(     self		:						int							)					->       List[str]:
   _a  :       Union[str, Any]             =	self.get_tokenizer()

   _a  :       Union[str, Any]             =	BarkProcessor(tokenizer=UpperCAmelCase__							)

   _a  :       Any             =	processor(text=self.input_string							)

   _a  :       List[str]             =	tokenizer(
       self.input_string							,						padding="""max_length"""							,						max_length=256							,						add_special_tokens=UpperCAmelCase__							,						return_attention_mask=UpperCAmelCase__							,						return_token_type_ids=UpperCAmelCase__							,						)

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









"""simple docstring"""




import cva
import numpy as np






class        UpperCamelCase			:


 def __init__(     self		:						Optional[int]							,						UpperCAmelCase__		:						float							,						UpperCAmelCase__		:						int							)					->       Dict:

   if k in (0.0_4, 0.0_6):
     _a  :       List[str]             =	k
     _a  :       List[Any]             =	window_size
   else:
     raise ValueError("""invalid k value"""							)


 def __str__(     self		:						Dict							)					->       str:
   return str(self.k							)







 def _lowercase	(     self		:						int							,						UpperCAmelCase__		:						str							)					->       tuple[cva.Mat, list[list[int]]]:
   _a  :       Dict             =	cva.imread(UpperCAmelCase__							,						0							)
   _a      ,  _a  :       List[Any]             =	img.shape
   _a  :       list[list[int]]             =	[]
   _a  :       List[Any]             =	img.copy()
   _a  :       int             =	cva.cvtColor(UpperCAmelCase__							,						cva.COLOR_GRAY2RGB							)
   _a      ,  _a  :       Any             =	np.gradient(UpperCAmelCase__							)
   _a  :       Tuple             =	dx**2
   _a  :       Union[str, Any]             =	dy**2
   _a  :       Union[str, Any]             =	dx * dy
   _a  :       int             =	0.0_4
   _a  :       List[str]             =	self.window_size // 2
   for y in range(UpperCAmelCase__							,						h - offset							):
     for x in range(UpperCAmelCase__							,						w - offset							):
       _a  :       str             =	ixx[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()
       _a  :       List[Any]             =	iyy[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()
       _a  :       Tuple             =	ixy[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()

       _a  :       Any             =	(wxx * wyy) - (wxy**2)
       _a  :       Tuple             =	wxx + wyy
       _a  :       Any             =	det - k * (trace**2)
       # Can change the value
       if r > 0.5:
         corner_list.append([x, y, r]							)
         color_img.itemset((y, x, 0)							,						0							)
         color_img.itemset((y, x, 1)							,						0							)
         color_img.itemset((y, x, 2)							,						255							)
   return color_img, corner_list


if __name__ == "__main__":
 _snake_case          =   HarrisCorner(0.04, 3)
 _snake_case      , _snake_case          =   edge_detect.detect('path_to_image')
 cva.imwrite('detect.png', color_img)

