




'''simple docstring'''

import shutil
import tempfile
import unittest

import pytest

from transformers import WhisperTokenizer, is_speech_available
from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio

from .test_feature_extraction_whisper import floats_list


if is_speech_available():
		from transformers import WhisperFeatureExtractor, WhisperProcessor


a__         =     50_358
a__         =     50_362





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






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									snake_case__          =						"""openai/whisper-small.en"""
									snake_case__          =						tempfile.mkdtemp()






						def       __lowerCAmelCase(   self						:						List[str]					,   **__UpperCamelCase						:						Dict       ):



									'''simple docstring'''






									return WhisperTokenizer.from_pretrained(self.checkpoint					,   **__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Dict					,   **__UpperCamelCase						:						Union[str, Any]       ):



									'''simple docstring'''






									return WhisperFeatureExtractor.from_pretrained(self.checkpoint					,   **__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									shutil.rmtree(self.tmpdirname       )






						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									snake_case__          =						self.get_tokenizer()
									snake_case__          =						self.get_feature_extractor()

									snake_case__          =						WhisperProcessor(tokenizer=__UpperCamelCase					,   feature_extractor=__UpperCamelCase       )

									processor.save_pretrained(self.tmpdirname       )
									snake_case__          =						WhisperProcessor.from_pretrained(self.tmpdirname       )

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

									self.assertEqual(processor.feature_extractor.to_json_string()					,   feature_extractor.to_json_string()       )
									self.assertIsInstance(processor.feature_extractor					,   __UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									snake_case__          =						WhisperProcessor(tokenizer=self.get_tokenizer()					,   feature_extractor=self.get_feature_extractor()       )
									processor.save_pretrained(self.tmpdirname       )

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

									snake_case__          =						WhisperProcessor.from_pretrained(
									    self.tmpdirname					,   bos_token="""(BOS)"""					,   eos_token="""(EOS)"""					,   do_normalize=__UpperCamelCase					,   padding_value=1.0       )

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

									self.assertEqual(processor.feature_extractor.to_json_string()					,   feature_extractor_add_kwargs.to_json_string()       )
									self.assertIsInstance(processor.feature_extractor					,   __UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






									snake_case__          =						self.get_feature_extractor()
									snake_case__          =						self.get_tokenizer()

									snake_case__          =						WhisperProcessor(tokenizer=__UpperCamelCase					,   feature_extractor=__UpperCamelCase       )

									snake_case__          =						floats_list((3, 1_0_0_0)       )

									snake_case__          =						feature_extractor(__UpperCamelCase					,   return_tensors="""np"""       )
									snake_case__          =						processor(__UpperCamelCase					,   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						:						Union[str, Any]       ):



									'''simple docstring'''






									snake_case__          =						self.get_feature_extractor()
									snake_case__          =						self.get_tokenizer()

									snake_case__          =						WhisperProcessor(tokenizer=__UpperCamelCase					,   feature_extractor=__UpperCamelCase       )

									snake_case__          =						"""This is a test string"""

									snake_case__          =						processor(text=__UpperCamelCase       )

									snake_case__          =						tokenizer(__UpperCamelCase       )

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






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									snake_case__          =						self.get_feature_extractor()
									snake_case__          =						self.get_tokenizer()

									snake_case__          =						WhisperProcessor(tokenizer=__UpperCamelCase					,   feature_extractor=__UpperCamelCase       )

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

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

									self.assertListEqual(__UpperCamelCase					,   __UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						List[str]       ):



									'''simple docstring'''






									snake_case__          =						self.get_feature_extractor()
									snake_case__          =						self.get_tokenizer()

									snake_case__          =						WhisperProcessor(tokenizer=__UpperCamelCase					,   feature_extractor=__UpperCamelCase       )

									self.assertListEqual(
									    processor.model_input_names					,   feature_extractor.model_input_names					,   msg="""`processor` and `feature_extractor` model input names do not match"""					,   )






						def       __lowerCAmelCase(   self						:						Union[str, Any]       ):



									'''simple docstring'''






									snake_case__          =						self.get_feature_extractor()
									snake_case__          =						self.get_tokenizer()

									snake_case__          =						WhisperProcessor(tokenizer=__UpperCamelCase					,   feature_extractor=__UpperCamelCase       )
									snake_case__          =						processor.get_decoder_prompt_ids(task="""transcribe"""					,   no_timestamps=__UpperCamelCase       )

									self.assertIsInstance(__UpperCamelCase					,   __UpperCamelCase       )
									for ids in forced_decoder_ids:
												self.assertIsInstance(__UpperCamelCase					,   (list, tuple)       )

									snake_case__          =						[TRANSCRIBE, NOTIMESTAMPS]
									self.assertListEqual([ids[-1] for ids in forced_decoder_ids]					,   __UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






									snake_case__          =						WhisperProcessor(tokenizer=self.get_tokenizer()					,   feature_extractor=self.get_feature_extractor()       )
									snake_case__          =						processor.get_prompt_ids("""Mr. Quilter"""       )
									snake_case__          =						processor.tokenizer.decode(__UpperCamelCase       )

									self.assertListEqual(prompt_ids.tolist()					,   [5_0_3_6_0, 1_7_7_0, 1_3, 2_2_6_4, 3_4_6, 3_5_3]       )
									self.assertEqual(__UpperCamelCase					,   """<|startofprev|> Mr. Quilter"""       )






						def       __lowerCAmelCase(   self						:						Optional[Any]       ):



									'''simple docstring'''






									snake_case__          =						WhisperProcessor(tokenizer=self.get_tokenizer()					,   feature_extractor=self.get_feature_extractor()       )
									snake_case__          =						processor.get_prompt_ids(""""""       )
									snake_case__          =						processor.tokenizer.decode(__UpperCamelCase       )

									self.assertListEqual(prompt_ids.tolist()					,   [5_0_3_6_0, 2_2_0]       )
									self.assertEqual(__UpperCamelCase					,   """<|startofprev|> """       )







						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									snake_case__          =						WhisperProcessor(tokenizer=self.get_tokenizer()					,   feature_extractor=self.get_feature_extractor()       )

									def _test_prompt_error_raised_helper(__UpperCamelCase						:						Optional[int]					,   __UpperCamelCase						:						List[str]       ):
												with pytest.raises(__UpperCamelCase       ) as excinfo:
															processor.get_prompt_ids(__UpperCamelCase       )
												snake_case__          =						f"""Encountered text in the prompt corresponding to disallowed special token: {special_token}."""
												self.assertEqual(__UpperCamelCase					,   str(excinfo.value       )       )

									_test_prompt_error_raised_helper("""<|startofprev|> test"""					,   """<|startofprev|>"""       )
									_test_prompt_error_raised_helper("""test <|notimestamps|>"""					,   """<|notimestamps|>"""       )
									_test_prompt_error_raised_helper("""test <|zh|> test <|transcribe|>"""					,   """<|zh|>"""       )
'''simple docstring'''
import numpy as np
from PIL import Image







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				,      lowercase				,      lowercase				:							Dict									=   rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
  return 0.2989 * r + 0.5870 * g + 0.1140 * b







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  return (gray > 127) & (gray <= 255)







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array ,   __UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				:							str									=   np.zeros_like(__UpperCAmelCase			)
  lowercase				:							Tuple									=   np.zeros(
      (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)			)

  # Copy image to padded image
  lowercase				:							int									=   image

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


# kernel to be applied
_A		:      Union[str, Any]						   =np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

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