





'''simple docstring'''
import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
				from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


a__							:							Dict			=get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_torch
@require_torchaudio
@require_sentencepiece
class 					snake_case					(   unittest.TestCase	):




	"""simple docstring"""



	def 	_lowerCamelCase (	self	:							List[Any]     ):
								__UpperCamelCase	=  tempfile.mkdtemp()

								__UpperCamelCase	=  ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
								__UpperCamelCase	=  dict(zip(__A							,     range(len(__A     )     )     )     )
								__UpperCamelCase	=  Path(self.tmpdirname     )
								save_json(__A							,     save_dir / VOCAB_FILES_NAMES['vocab_file']     )
								if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
															copyfile(__A							,     save_dir / VOCAB_FILES_NAMES['spm_file']     )

								__UpperCamelCase	=  SpeechaTextTokenizer.from_pretrained(self.tmpdirname     )
								tokenizer.save_pretrained(self.tmpdirname     )

								__UpperCamelCase	=  {
								    'feature_size': 2_4,
								    'num_mel_bins': 2_4,
								    'padding_value': 0.0,
								    'sampling_rate': 1_6_0_0_0,
								    'return_attention_mask': False,
								    'do_normalize': True,
								}
								save_json(__A							,     save_dir / FEATURE_EXTRACTOR_NAME     )






	def 	_lowerCamelCase (	self	:							Optional[int]							,     **__A	:							List[str]     ):
								return SpeechaTextTokenizer.from_pretrained(self.tmpdirname							,     **__A     )






	def 	_lowerCamelCase (	self	:							List[Any]							,     **__A	:							str     ):
								return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname							,     **__A     )






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






	def 	_lowerCamelCase (	self	:							Dict     ):
								__UpperCamelCase	=  self.get_tokenizer()
								__UpperCamelCase	=  self.get_feature_extractor()

								__UpperCamelCase	=  SpeechaTextProcessor(tokenizer=__A							,     feature_extractor=__A     )

								processor.save_pretrained(self.tmpdirname     )
								__UpperCamelCase	=  SpeechaTextProcessor.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	:							Optional[Any]     ):
								__UpperCamelCase	=  SpeechaTextProcessor(
								    tokenizer=self.get_tokenizer()							,     feature_extractor=self.get_feature_extractor()     )
								processor.save_pretrained(self.tmpdirname     )

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

								__UpperCamelCase	=  SpeechaTextProcessor.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	:							Any     ):
								__UpperCamelCase	=  self.get_feature_extractor()
								__UpperCamelCase	=  self.get_tokenizer()

								__UpperCamelCase	=  SpeechaTextProcessor(tokenizer=__A							,     feature_extractor=__A     )

								__UpperCamelCase	=  floats_list((3, 1_0_0_0)     )

								__UpperCamelCase	=  feature_extractor(__A							,     return_tensors='np'     )
								__UpperCamelCase	=  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	:							str     ):
								__UpperCamelCase	=  self.get_feature_extractor()
								__UpperCamelCase	=  self.get_tokenizer()

								__UpperCamelCase	=  SpeechaTextProcessor(tokenizer=__A							,     feature_extractor=__A     )

								__UpperCamelCase	=  'This is a test string'

								__UpperCamelCase	=  processor(text=__A     )

								__UpperCamelCase	=  tokenizer(__A     )

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






	def 	_lowerCamelCase (	self	:							str     ):
								__UpperCamelCase	=  self.get_feature_extractor()
								__UpperCamelCase	=  self.get_tokenizer()

								__UpperCamelCase	=  SpeechaTextProcessor(tokenizer=__A							,     feature_extractor=__A     )

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

								__UpperCamelCase	=  processor.batch_decode(__A     )
								__UpperCamelCase	=  tokenizer.batch_decode(__A     )

								self.assertListEqual(__A							,     __A     )






	def 	_lowerCamelCase (	self	:							Tuple     ):
								__UpperCamelCase	=  self.get_feature_extractor()
								__UpperCamelCase	=  self.get_tokenizer()

								__UpperCamelCase	=  SpeechaTextProcessor(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'							,     )







'''simple docstring'''
import numpy as np
from PIL import Image
def     lowercase__       (       __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       __UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	=  rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b
def     lowercase__       (       __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       return (gray > 127) & (gray <= 255)
def     lowercase__       (       __lowercase						:  np.array    ,  __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       __UpperCamelCase	=  np.zeros_like(__lowercase					)
       __UpperCamelCase	=  np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)					)

       # Copy image to padded image
       __UpperCamelCase	=  image

       # Iterate over image & apply kernel
       for x in range(image.shape[1]					):
              for y in range(image.shape[0]					):
                     __UpperCamelCase	=  (
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     __UpperCamelCase	=  int(summation == 5					)
       return output


# kernel to be applied
a__							:							Dict			=np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
    # read original image
    a__							:							Any			=np.array(Image.open(r'''..\image_data\lena.jpg'''))
    # Apply erosion operation to a binary image
    a__							:							Union[str, Any]			=erosion(grayabinary(rgbagray(image)), structuring_element)
    # Save the output image
    a__							:							Tuple			=Image.fromarray(output).convert('''RGB''')
    pil_img.save('''result_erosion.png''')
