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   _lowerCAmelCase		(     unittest.TestCase				):







							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:Tuple						  =     '''ylacombe/bark-small'''
										lowerCamelCase						:int						  =     tempfile.mkdtemp()
										lowerCamelCase						:Dict						  =     '''en_speaker_1'''
										lowerCamelCase						:int						  =     '''This is a test string'''
										lowerCamelCase						:int						  =     '''speaker_embeddings_path.json'''
										lowerCamelCase						:Dict						  =     '''speaker_embeddings'''







							def 				snake_case  (				self     :    Tuple	,					**__snake_case     :    int		):
										return AutoTokenizer.from_pretrained(self.checkpoint	,					**__UpperCamelCase		)







							def 				snake_case  (				self     :    str		):
										shutil.rmtree(self.tmpdirname		)







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:List[Any]						  =     self.get_tokenizer()

										lowerCamelCase						:Optional[int]						  =     BarkProcessor(tokenizer=__UpperCamelCase		)

										processor.save_pretrained(self.tmpdirname		)
										lowerCamelCase						:Optional[int]						  =     BarkProcessor.from_pretrained(self.tmpdirname		)

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







							@slow
							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						: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						:Optional[Any]						  =     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 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:Tuple						  =     BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint	,					speaker_embeddings_dict_path=self.speaker_embeddings_dict_path	,					)

										lowerCamelCase						:Optional[int]						  =     35
										lowerCamelCase						:Optional[int]						  =     2
										lowerCamelCase						:str						  =     8

										lowerCamelCase						:Optional[int]						  =     {
										    '''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
										lowerCamelCase						:Optional[int]						  =     processor(text=self.input_string	,					voice_preset=__UpperCamelCase		)

										lowerCamelCase						:int						  =     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
										lowerCamelCase						:int						  =     os.path.join(self.tmpdirname	,					'''file.npz'''		)
										np.savez(__UpperCamelCase	,					**__UpperCamelCase		)
										lowerCamelCase						:Optional[Any]						  =     processor(text=self.input_string	,					voice_preset=__UpperCamelCase		)
										lowerCamelCase						:str						  =     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
										lowerCamelCase						:str						  =     processor(text=self.input_string	,					voice_preset=self.voice_preset		)







							def 				snake_case  (				self     :    int		):
										lowerCamelCase						:int						  =     self.get_tokenizer()

										lowerCamelCase						:List[str]						  =     BarkProcessor(tokenizer=__UpperCamelCase		)

										lowerCamelCase						:List[Any]						  =     processor(text=self.input_string		)

										lowerCamelCase						:Tuple						  =     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()		)




import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A__           =							imread(R"""digital_image_processing/image_data/lena_small.jpg""")
A__           =							cvtColor(img, COLOR_BGR2GRAY)





def  _lowerCamelCase					(   ):
			lowerCamelCase						:int						  =     cn.convert_to_negative(a_)
			# assert negative_img array for at least one True
			assert negative_img.any()





def  _lowerCamelCase					(   ):
			with Image.open('''digital_image_processing/image_data/lena_small.jpg''') as img:
						# Work around assertion for response
						assert str(cc.change_contrast(a_						,			1_10)).startswith(
						    '''<PIL.Image.Image image mode=RGB size=100x100 at''')





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Optional[Any]						  =     canny.gen_gaussian_kernel(9						,			sigma=1.4)
			# Assert ambiguous array
			assert resp.all()





def  _lowerCamelCase					(   ):
			lowerCamelCase						:str						  =     imread('''digital_image_processing/image_data/lena_small.jpg'''						,			0)
			# assert ambiguous array for all == True
			assert canny_img.all()
			lowerCamelCase						:Optional[Any]						  =     canny.canny(a_)
			# assert canny array for at least one True
			assert canny_array.any()





def  _lowerCamelCase					(   ):
			assert gg.gaussian_filter(a_						,			5						,			sigma=0.9).all()





def  _lowerCamelCase					(   ):
			# laplace diagonals
			lowerCamelCase						:List[Any]						  =     array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]])
			lowerCamelCase						:List[Any]						  =     conv.img_convolve(a_						,			a_).astype(a_)
			assert res.any()





def  _lowerCamelCase					(   ):
			assert med.median_filter(a_						,			3).any()





def  _lowerCamelCase					(   ):
			lowerCamelCase  ,    lowerCamelCase						:Union[str, Any]						  =     sob.sobel_filter(a_)
			assert grad.any() and theta.any()





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Dict						  =     sp.make_sepia(a_						,			20)
			assert sepia.all()





def  _lowerCamelCase					(   a_				: str = "digital_image_processing/image_data/lena_small.jpg"):
			lowerCamelCase						:Any						  =     bs.Burkes(imread(a_						,			1)						,			1_20)
			burkes.process()
			assert burkes.output_img.any()





def  _lowerCamelCase					(   a_				: str = "digital_image_processing/image_data/lena_small.jpg"						,			):
			lowerCamelCase						:Tuple						  =     rs.NearestNeighbour(imread(a_						,			1)						,			4_00						,			2_00)
			nn.process()
			assert nn.output.any()





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Tuple						  =     '''digital_image_processing/image_data/lena.jpg'''

			# Reading the image and converting it to grayscale.
			lowerCamelCase						:Tuple						  =     imread(a_						,			0)

			# Test for get_neighbors_pixel function() return not None
			lowerCamelCase						:Dict						  =     0
			lowerCamelCase						:Optional[Any]						  =     0
			lowerCamelCase						:str						  =     image[x_coordinate][y_coordinate]

			lowerCamelCase						:Any						  =     lbp.get_neighbors_pixel(
			    a_						,			a_						,			a_						,			a_)

			assert neighbors_pixels is not None

			# Test for local_binary_pattern function()
			# Create a numpy array as the same height and width of read image
			lowerCamelCase						:int						  =     np.zeros((image.shape[0], image.shape[1]))

			# Iterating through the image and calculating the local binary pattern value
			# for each pixel.
			for i in range(0						,			image.shape[0]):
						for j in range(0						,			image.shape[1]):
									lowerCamelCase						:Optional[int]						  =     lbp.local_binary_value(a_						,			a_						,			a_)

			assert lbp_image.any()



