


import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand


def      a	(							SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       return EnvironmentCommand()



class    UpperCAmelCase_	(							_snake_case):
       '''simple docstring'''






       @staticmethod
       def  _lowercase   (     __SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		parser.add_parser('''env'''						)
              download_parser.set_defaults(func=snake_case_						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		huggingface_hub.__version__

              UpperCamelCase    :	int   	=		"not installed"
              UpperCamelCase    :	Tuple   	=		"NA"
              if is_torch_available():
                     import torch

                     UpperCamelCase    :	int   	=		torch.__version__
                     UpperCamelCase    :	int   	=		torch.cuda.is_available()

              UpperCamelCase    :	List[str]   	=		"not installed"
              if is_transformers_available():
                     import transformers

                     UpperCamelCase    :	Optional[Any]   	=		transformers.__version__

              UpperCamelCase    :	Tuple   	=		"not installed"
              if is_accelerate_available():
                     import accelerate

                     UpperCamelCase    :	Optional[int]   	=		accelerate.__version__

              UpperCamelCase    :	List[str]   	=		"not installed"
              if is_xformers_available():
                     import xformers

                     UpperCamelCase    :	Any   	=		xformers.__version__

              UpperCamelCase    :	int   	=		{
                  "`diffusers` version": version,
                  "Platform": platform.platform(),
                  "Python version": platform.python_version(),
                  "PyTorch version (GPU?)": f"""{pt_version} ({pt_cuda_available})""",
                  "Huggingface_hub version": hub_version,
                  "Transformers version": transformers_version,
                  "Accelerate version": accelerate_version,
                  "xFormers version": xformers_version,
                  "Using GPU in script?": "<fill in>",
                  "Using distributed or parallel set-up in script?": "<fill in>",
              }

              print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''						)
              print(self.format_dict(snake_case_						)						)

              return info






       @staticmethod
       def  _lowercase   (     __SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]						) + "\n"





from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt


def      a	(							SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	str   	=		cva.getAffineTransform(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       return cva.warpAffine(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			(rows, cols)       )


if __name__ == "__main__":
  # read original image
  __UpperCAmelCase : Tuple 			=				cva.imread(
      str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
  )
  # turn image in gray scale value
  __UpperCAmelCase : int 			=				cva.cvtColor(image, cva.COLOR_BGR2GRAY)
  # get image shape
  __UpperCAmelCase			,    __UpperCAmelCase : Tuple 			=				gray_img.shape

  # set different points to rotate image
  __UpperCAmelCase : Optional[int] 			=				np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
  __UpperCAmelCase : Optional[int] 			=				np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
  __UpperCAmelCase : Any 			=				np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
  __UpperCAmelCase : int 			=				np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

  # add all rotated images in a list
  __UpperCAmelCase : Union[str, Any] 			=				[
      gray_img,
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
  ]

  # plot different image rotations
  __UpperCAmelCase : List[str] 			=				plt.figure(1)
  __UpperCAmelCase : Dict 			=				["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
  for i, image in enumerate(images):
    plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
    plt.title(titles[i])
    plt.axis("off")
    plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
  plt.show()

