





"""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

lowercase__				:List[Any]				   =       imread(R'digital_image_processing/image_data/lena_small.jpg')
lowercase__				:int				   =       cvtColor(img, COLOR_BGR2GRAY)


def       lowerCamelCase_       (   )						->Tuple:





  """simple docstring"""


  __UpperCAmelCase    :						Union[str, Any]       =						cn.convert_to_negative(UpperCAmelCase_  )
  # assert negative_img array for at least one True
  assert negative_img.any()


def       lowerCamelCase_       (   )						->Tuple:





  """simple docstring"""


  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       lowerCamelCase_       (   )						->int:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[int]       =						canny.gen_gaussian_kernel(9  ,			sigma=1.4  )
  # Assert ambiguous array
  assert resp.all()


def       lowerCamelCase_       (   )						->Union[str, Any]:





  """simple docstring"""


  __UpperCAmelCase    :						int       =						imread('''digital_image_processing/image_data/lena_small.jpg'''  ,			0  )
  # assert ambiguous array for all == True
  assert canny_img.all()
  __UpperCAmelCase    :						Any       =						canny.canny(UpperCAmelCase_  )
  # assert canny array for at least one True
  assert canny_array.any()


def       lowerCamelCase_       (   )						->List[str]:





  """simple docstring"""


  assert gg.gaussian_filter(UpperCAmelCase_  ,			5  ,			sigma=0.9  ).all()


def       lowerCamelCase_       (   )						->Union[str, Any]:





  """simple docstring"""


  __UpperCAmelCase    :						List[str]       =						array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]  )
  __UpperCAmelCase    :						Tuple       =						conv.img_convolve(UpperCAmelCase_  ,			UpperCAmelCase_  ).astype(UpperCAmelCase_  )
  assert res.any()


def       lowerCamelCase_       (   )						->Tuple:





  """simple docstring"""


  assert med.median_filter(UpperCAmelCase_  ,			3  ).any()


def       lowerCamelCase_       (   )						->Dict:





  """simple docstring"""


  __UpperCAmelCase       ,     __UpperCAmelCase    :						Tuple       =						sob.sobel_filter(UpperCAmelCase_  )
  assert grad.any() and theta.any()


def       lowerCamelCase_       (   )						->Tuple:





  """simple docstring"""


  __UpperCAmelCase    :						Union[str, Any]       =						sp.make_sepia(UpperCAmelCase_  ,			20  )
  assert sepia.all()


def       lowerCamelCase_       (   UpperCAmelCase_ = "digital_image_processing/image_data/lena_small.jpg"  )						->Union[str, Any]:





  """simple docstring"""


  __UpperCAmelCase    :						int       =						bs.Burkes(imread(UpperCAmelCase_  ,			1  )  ,			1_20  )
  burkes.process()
  assert burkes.output_img.any()


def       lowerCamelCase_       (   UpperCAmelCase_ = "digital_image_processing/image_data/lena_small.jpg"  ,			)						->List[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Tuple       =						rs.NearestNeighbour(imread(UpperCAmelCase_  ,			1  )  ,			4_00  ,			2_00  )
  nn.process()
  assert nn.output.any()







def       lowerCamelCase_       (   )						->int:





  """simple docstring"""


  __UpperCAmelCase    :						Dict       =						'''digital_image_processing/image_data/lena.jpg'''

  # Reading the image and converting it to grayscale.
  __UpperCAmelCase    :						Optional[int]       =						imread(UpperCAmelCase_  ,			0  )

  # Test for get_neighbors_pixel function() return not None
  __UpperCAmelCase    :						Tuple       =						0
  __UpperCAmelCase    :						List[Any]       =						0
  __UpperCAmelCase    :						Optional[int]       =						image[x_coordinate][y_coordinate]

  __UpperCAmelCase    :						List[str]       =						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
  __UpperCAmelCase    :						Tuple       =						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]  ):
      __UpperCAmelCase    :						Optional[int]       =						lbp.local_binary_value(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )

  assert lbp_image.any()






"""simple docstring"""





# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


lowercase__				:int				   =       '3'

print('Python version:', sys.version)
print('transformers version:', transformers.__version__)

try:
  import torch

  print('Torch version:', torch.__version__)
  print('Cuda available:', torch.cuda.is_available())
  print('Cuda version:', torch.version.cuda)
  print('CuDNN version:', torch.backends.cudnn.version())
  print('Number of GPUs available:', torch.cuda.device_count())
  print('NCCL version:', torch.cuda.nccl.version())
except ImportError:
  print('Torch version:', None)

try:
  import deepspeed

  print('DeepSpeed version:', deepspeed.__version__)
except ImportError:
  print('DeepSpeed version:', None)

try:
  import tensorflow as tf

  print('TensorFlow version:', tf.__version__)
  print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))
  print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))
except ImportError:
  print('TensorFlow version:', None)