





'''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

SCREAMING_SNAKE_CASE__     =       imread(R'digital_image_processing/image_data/lena_small.jpg')
SCREAMING_SNAKE_CASE__     =       cvtColor(img, COLOR_BGR2GRAY)







def    lowercase__			(				)->			Optional[Any]:
      UpperCamelCase      = cn.convert_to_negative(__UpperCamelCase   )
      # assert negative_img array for at least one True
      assert negative_img.any()







def    lowercase__			(				)->			Dict:
      with Image.open("""digital_image_processing/image_data/lena_small.jpg"""   ) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(__UpperCamelCase		,						110   )   ).startswith(
                """<PIL.Image.Image image mode=RGB size=100x100 at"""   )







def    lowercase__			(				)->			Optional[Any]:
      UpperCamelCase      = canny.gen_gaussian_kernel(9		,						sigma=1.4   )
      # Assert ambiguous array
      assert resp.all()







def    lowercase__			(				)->			Union[str, Any]:
      UpperCamelCase      = imread("""digital_image_processing/image_data/lena_small.jpg"""		,						0   )
      # assert ambiguous array for all == True
      assert canny_img.all()
      UpperCamelCase      = canny.canny(__UpperCamelCase   )
      # assert canny array for at least one True
      assert canny_array.any()







def    lowercase__			(				)->			Tuple:
      assert gg.gaussian_filter(__UpperCamelCase		,						5		,						sigma=0.9   ).all()







def    lowercase__			(				)->			Dict:
      # laplace diagonals
      UpperCamelCase      = array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]   )
      UpperCamelCase      = conv.img_convolve(__UpperCamelCase		,						__UpperCamelCase   ).astype(__UpperCamelCase   )
      assert res.any()







def    lowercase__			(				)->			Optional[int]:
      assert med.median_filter(__UpperCamelCase		,						3   ).any()







def    lowercase__			(				)->			int:
      UpperCamelCase  ,UpperCamelCase      = sob.sobel_filter(__UpperCamelCase   )
      assert grad.any() and theta.any()







def    lowercase__			(				)->			Tuple:
      UpperCamelCase      = sp.make_sepia(__UpperCamelCase		,						20   )
      assert sepia.all()







def    lowercase__			(				__UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"   )->			Any:
      UpperCamelCase      = bs.Burkes(imread(__UpperCamelCase		,						1   )		,						120   )
      burkes.process()
      assert burkes.output_img.any()







def    lowercase__			(				__UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"		,						)->			int:
      UpperCamelCase      = rs.NearestNeighbour(imread(__UpperCamelCase		,						1   )		,						400		,						200   )
      nn.process()
      assert nn.output.any()







def    lowercase__			(				)->			Optional[Any]:
      UpperCamelCase      = """digital_image_processing/image_data/lena.jpg"""

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

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

      UpperCamelCase      = 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      = 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      = lbp.local_binary_value(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )

      assert lbp_image.any()








'''simple docstring'''

import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)







def    lowercase__			(				)->			Tuple:
      # Get the sagemaker specific mp parameters from smp_options variable.
      UpperCamelCase      = os.getenv("""SM_HP_MP_PARAMETERS"""		,						"""{}"""   )
      try:
            # Parse it and check the field "partitions" is included, it is required for model parallel.
            UpperCamelCase      = json.loads(__UpperCamelCase   )
            if "partitions" not in smp_options:
                  return False
      except json.JSONDecodeError:
            return False

      # Get the sagemaker specific framework parameters from mpi_options variable.
      UpperCamelCase      = os.getenv("""SM_FRAMEWORK_PARAMS"""		,						"""{}"""   )
      try:
            # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
            UpperCamelCase      = json.loads(__UpperCamelCase   )
            if not mpi_options.get("""sagemaker_mpi_enabled"""		,						__UpperCamelCase   ):
                  return False
      except json.JSONDecodeError:
            return False
      # Lastly, check if the `smdistributed` module is present.
      return importlib.util.find_spec("""smdistributed"""   ) is not None


if is_sagemaker_model_parallel_available():
     import smdistributed.modelparallel.torch as smp

     smp.init()






@dataclass
class     a_						(      lowerCamelCase    ):
  lowercase							=				field(
      default=""""""     ,	metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""}     ,	)

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        super().__post_init__()
        warnings.warn(
            """`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
            """`TrainingArguments` instead."""      ,		_SCREAMING_SNAKE_CASE      ,		)

  @cached_property
  def   A__						(				self				)  ->				"torch.device":



        """simple docstring"""






        logger.info("""PyTorch: setting up devices"""				)
        if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
              logger.warning(
                  """torch.distributed process group is initialized, but local_rank == -1. """
                  """In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"""				)
        if self.no_cuda:
              UpperCamelCase      = torch.device("""cpu"""				)
              UpperCamelCase      = 0
        elif is_sagemaker_model_parallel_available():
              UpperCamelCase      = smp.local_rank()
              UpperCamelCase      = torch.device("""cuda"""      ,		_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = 1
        elif is_sagemaker_dp_enabled():
              import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

              torch.distributed.init_process_group(backend="""smddp"""      ,		timeout=self.ddp_timeout_delta				)
              UpperCamelCase      = int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK"""				)				)
              UpperCamelCase      = torch.device("""cuda"""      ,		self.local_rank				)
              UpperCamelCase      = 1
        elif self.local_rank == -1:
              # if n_gpu is > 1 we'll use nn.DataParallel.
              # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
              # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
              # trigger an error that a device index is missing. Index 0 takes into account the
              # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
              # will use the first GPU in that env, i.e. GPU#1
              UpperCamelCase      = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""				)
              # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
              # the default value.
              UpperCamelCase      = torch.cuda.device_count()
        else:
              # Here, we'll use torch.distributed.
              # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
              if not torch.distributed.is_initialized():
                    torch.distributed.init_process_group(backend="""nccl"""      ,		timeout=self.ddp_timeout_delta				)
              UpperCamelCase      = torch.device("""cuda"""      ,		self.local_rank				)
              UpperCamelCase      = 1

        if device.type == "cuda":
              torch.cuda.set_device(_SCREAMING_SNAKE_CASE				)

        return device

  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        if is_sagemaker_model_parallel_available():
              return smp.dp_size()

        return super().world_size

  @property
  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        return not is_sagemaker_model_parallel_available()


  @property
  def   A__						(				self				)  ->				str:



        """simple docstring"""






        return False
