

'''simple docstring'''







import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
      import torch


@require_torch
@require_optimum
@slow
class 					A__      (      unittest.TestCase			):



    def 					snake_case_			(					self       )						->      Optional[int]:





           '''simple docstring'''
           A_						=      """hf-internal-testing/tiny-random-t5"""
           A_						=      AutoTokenizer.from_pretrained(UpperCamelCase__       )
           A_						=      AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__       )

           A_						=      tokenizer("""This is me"""		,				return_tensors="""pt"""       )

           A_						=      model.to_bettertransformer()

           self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()       )       )

           A_						=      model.generate(**UpperCamelCase__       )

           A_						=      model.reverse_bettertransformer()

           self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()       )       )

           with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_pretrained(UpperCamelCase__       )

                  A_						=      AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__       )

                  self.assertFalse(
                      any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()       )       )

                  A_						=      model_reloaded.generate(**UpperCamelCase__       )
                  self.assertTrue(torch.allclose(UpperCamelCase__		,				UpperCamelCase__       )       )







    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           A_						=      """hf-internal-testing/tiny-random-t5"""
           A_						=      AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__       )

           A_						=      model.to_bettertransformer()

           with tempfile.TemporaryDirectory() as tmpdirname:
                  with self.assertRaises(UpperCamelCase__       ):
                         model.save_pretrained(UpperCamelCase__       )

                  A_						=      model.reverse_bettertransformer()
                  model.save_pretrained(UpperCamelCase__       )



'''simple docstring'''







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

__lowerCamelCase		       =    imread(r'''digital_image_processing/image_data/lena_small.jpg''')
__lowerCamelCase		       =    cvtColor(img, COLOR_BGR2GRAY)
def       UpperCAmelCase__    (      )			->			Dict:
       A_						=      cn.convert_to_negative(UpperCAmelCase__				)
       # assert negative_img array for at least one True
       assert negative_img.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
              # Work around assertion for response
              assert str(cc.change_contrast(UpperCAmelCase__,       1_10				)				).startswith(
                  """<PIL.Image.Image image mode=RGB size=100x100 at"""				)
def       UpperCAmelCase__    (      )			->			str:
       A_						=      canny.gen_gaussian_kernel(9,       sigma=1.4				)
       # Assert ambiguous array
       assert resp.all()
def       UpperCAmelCase__    (      )			->			Union[str, Any]:
       A_						=      imread("""digital_image_processing/image_data/lena_small.jpg""",       0				)
       # assert ambiguous array for all == True
       assert canny_img.all()
       A_						=      canny.canny(UpperCAmelCase__				)
       # assert canny array for at least one True
       assert canny_array.any()
def       UpperCAmelCase__    (      )			->			Dict:
       assert gg.gaussian_filter(UpperCAmelCase__,       5,       sigma=0.9				).all()
def       UpperCAmelCase__    (      )			->			int:
       # laplace diagonals
       A_						=      array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
       A_						=      conv.img_convolve(UpperCAmelCase__,       UpperCAmelCase__				).astype(UpperCAmelCase__				)
       assert res.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       assert med.median_filter(UpperCAmelCase__,       3				).any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       A_					,			A_						=      sob.sobel_filter(UpperCAmelCase__				)
       assert grad.any() and theta.any()
def       UpperCAmelCase__    (      )			->			List[str]:
       A_						=      sp.make_sepia(UpperCAmelCase__,       20				)
       assert sepia.all()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg"				)			->			List[Any]:
       A_						=      bs.Burkes(imread(UpperCAmelCase__,       1				),       1_20				)
       burkes.process()
       assert burkes.output_img.any()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg",       )			->			Optional[int]:
       A_						=      rs.NearestNeighbour(imread(UpperCAmelCase__,       1				),       4_00,       2_00				)
       nn.process()
       assert nn.output.any()







def       UpperCAmelCase__    (      )			->			Optional[int]:
       A_						=      """digital_image_processing/image_data/lena.jpg"""

       # Reading the image and converting it to grayscale.
       A_						=      imread(UpperCAmelCase__,       0				)

       # Test for get_neighbors_pixel function() return not None
       A_						=      0
       A_						=      0
       A_						=      image[x_coordinate][y_coordinate]

       A_						=      lbp.get_neighbors_pixel(
           UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       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
       A_						=      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]				):
                     A_						=      lbp.local_binary_value(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       assert lbp_image.any()
