
'''simple docstring'''
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
				from transformers import TvltImageProcessor

if is_speech_available():
				from transformers import TvltFeatureExtractor

from transformers import TvltProcessor


@require_torch
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							def 	lowercase							(self  ):
														_snake_case       =  """ZinengTang/tvlt-base"""
														_snake_case       =  tempfile.mkdtemp()

							def 	lowercase							(self  ,  **UpperCAmelCase  ):
														return TvltImageProcessor.from_pretrained(self.checkpoint  ,  **__lowerCAmelCase  )

							def 	lowercase							(self  ,  **UpperCAmelCase  ):
														return TvltFeatureExtractor.from_pretrained(self.checkpoint  ,  **__lowerCAmelCase  )

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

							def 	lowercase							(self  ):
														_snake_case       =  self.get_image_processor()
														_snake_case       =  self.get_feature_extractor()

														_snake_case       =  TvltProcessor(image_processor=__lowerCAmelCase  ,  feature_extractor=__lowerCAmelCase  )
														processor.save_pretrained(self.tmpdirname  )
														_snake_case       =  TvltProcessor.from_pretrained(self.tmpdirname  )

														self.assertIsInstance(processor.feature_extractor  ,  __lowerCAmelCase  )
														self.assertIsInstance(processor.image_processor  ,  __lowerCAmelCase  )

							def 	lowercase							(self  ):
														_snake_case       =  self.get_image_processor()
														_snake_case       =  self.get_feature_extractor()

														_snake_case       =  TvltProcessor(image_processor=__lowerCAmelCase  ,  feature_extractor=__lowerCAmelCase  )

														_snake_case       =  np.ones([12000]  )

														_snake_case       =  feature_extractor(__lowerCAmelCase  ,  return_tensors="""np"""  )
														_snake_case       =  processor(audio=__lowerCAmelCase  ,  return_tensors="""np"""  )

														for key in audio_dict.keys():
																					self.assertAlmostEqual(audio_dict[key].sum()  ,  input_processor[key].sum()  ,  delta=1e-2  )

							def 	lowercase							(self  ):
														_snake_case       =  self.get_image_processor()
														_snake_case       =  self.get_feature_extractor()

														_snake_case       =  TvltProcessor(image_processor=__lowerCAmelCase  ,  feature_extractor=__lowerCAmelCase  )

														_snake_case       =  np.ones([3, 224, 224]  )

														_snake_case       =  image_processor(__lowerCAmelCase  ,  return_tensors="""np"""  )
														_snake_case       =  processor(images=__lowerCAmelCase  ,  return_tensors="""np"""  )

														for key in image_dict.keys():
																					self.assertAlmostEqual(image_dict[key].sum()  ,  input_processor[key].sum()  ,  delta=1e-2  )

							def 	lowercase							(self  ):
														_snake_case       =  self.get_image_processor()
														_snake_case       =  self.get_feature_extractor()

														_snake_case       =  TvltProcessor(image_processor=__lowerCAmelCase  ,  feature_extractor=__lowerCAmelCase  )

														_snake_case       =  np.ones([12000]  )
														_snake_case       =  np.ones([3, 224, 224]  )

														_snake_case       =  processor(audio=__lowerCAmelCase  ,  images=__lowerCAmelCase  )

														self.assertListEqual(list(inputs.keys()  )  ,  ["""audio_values""", """audio_mask""", """pixel_values""", """pixel_mask"""]  )

														# test if it raises when no input is passed
														with pytest.raises(__lowerCAmelCase  ):
																					processor()





							def 	lowercase							(self  ):
														_snake_case       =  self.get_image_processor()
														_snake_case       =  self.get_feature_extractor()

														_snake_case       =  TvltProcessor(image_processor=__lowerCAmelCase  ,  feature_extractor=__lowerCAmelCase  )

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

'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class       _lowerCAmelCase   :







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       List[str]:
														if dst_width < 0 or dst_height < 0:
																					raise ValueError("""Destination width/height should be > 0"""  )

														_snake_case       =  img
														_snake_case       =  img.shape[1]
														_snake_case       =  img.shape[0]
														_snake_case       =  dst_width
														_snake_case       =  dst_height

														_snake_case       =  self.src_w / self.dst_w
														_snake_case       =  self.src_h / self.dst_h

														_snake_case       =  _snake_case       =  (
														    np.ones((self.dst_h, self.dst_w, 3)  ,  np.uinta  ) * 255
														)

							def 	lowercase							(self  )					->       List[Any]:
														for i in range(self.dst_h  ):
																					for j in range(self.dst_w  ):
																												_snake_case       =  self.img[self.get_y(UpperCAmelCase  )][self.get_x(UpperCAmelCase  )]

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														return int(self.ratio_x * x  )





							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														return int(self.ratio_y * y  )


if __name__ == "__main__":
				__lowerCAmelCase							,    __lowerCAmelCase												=	800, 600
				__lowerCAmelCase												=	imread('image_data/lena.jpg', 1)
				__lowerCAmelCase												=	NearestNeighbour(im, dst_w, dst_h)
				n.process()

				imshow(
				    f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
				)
				waitKey(0)
				destroyAllWindows()