
"""simple docstring"""





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_a								=						logging.get_logger(__name__)  # pylint: disable=invalid-name



def  lowerCamelCase__						(     __snake_case      )				->						str:


					"""simple docstring"""





					warnings.warn(
					    '''The preprocess method is deprecated and will be removed in a future version. Please'''
					    ''' use VaeImageProcessor.preprocess instead''',	__snake_case,	)
					if isinstance(__snake_case,	torch.Tensor      ):
										return image
					elif isinstance(__snake_case,	PIL.Image.Image      ):
										_UpperCamelCase									=    [image]

					if isinstance(image[0],	PIL.Image.Image      ):
										_UpperCamelCase					,    _UpperCamelCase									=    image[0].size
										_UpperCamelCase					,    _UpperCamelCase									=    (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

										_UpperCamelCase									=    [np.array(i.resize((w, h),	resample=PIL_INTERPOLATION['''lanczos''']      )      )[None, :] for i in image]
										_UpperCamelCase									=    np.concatenate(__snake_case,	axis=0      )
										_UpperCamelCase									=    np.array(__snake_case      ).astype(np.floataa      ) / 255.0
										_UpperCamelCase									=    image.transpose(0,	3,	1,	2      )
										_UpperCamelCase									=    2.0 * image - 1.0
										_UpperCamelCase									=    torch.from_numpy(__snake_case      )
					elif isinstance(image[0],	torch.Tensor      ):
										_UpperCamelCase									=    torch.cat(__snake_case,	dim=0      )
					return image



def  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					if isinstance(__snake_case,	torch.Tensor      ):
										return mask
					elif isinstance(__snake_case,	PIL.Image.Image      ):
										_UpperCamelCase									=    [mask]

					if isinstance(mask[0],	PIL.Image.Image      ):
										_UpperCamelCase					,    _UpperCamelCase									=    mask[0].size
										_UpperCamelCase					,    _UpperCamelCase									=    (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
										_UpperCamelCase									=    [np.array(m.convert('''L'''      ).resize((w, h),	resample=PIL_INTERPOLATION['''nearest''']      )      )[None, :] for m in mask]
										_UpperCamelCase									=    np.concatenate(__snake_case,	axis=0      )
										_UpperCamelCase									=    mask.astype(np.floataa      ) / 255.0
										_UpperCamelCase									=    0
										_UpperCamelCase									=    1
										_UpperCamelCase									=    torch.from_numpy(__snake_case      )
					elif isinstance(mask[0],	torch.Tensor      ):
										_UpperCamelCase									=    torch.cat(__snake_case,	dim=0      )
					return mask






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = 42
		lowercase__         = 42



		def __init__(   self							,						__a							,						__a)			->			List[str]:






							'''simple docstring'''
							super().__init__()
							self.register_modules(unet=__a							,						scheduler=__a)




		@torch.no_grad()
		def __call__(   self							,						__a							,						__a							,						__a = 2_50							,						__a = 0.0							,						__a = 10							,						__a = 10							,						__a = None							,						__a = "pil"							,						__a = True							,						)			->			Union[ImagePipelineOutput, Tuple]:






							'''simple docstring'''
							_UpperCamelCase									=    image

							_UpperCamelCase									=    _preprocess_image(__a)
							_UpperCamelCase									=    original_image.to(device=self.device							,						dtype=self.unet.dtype)
							_UpperCamelCase									=    _preprocess_mask(__a)
							_UpperCamelCase									=    mask_image.to(device=self.device							,						dtype=self.unet.dtype)

							_UpperCamelCase									=    original_image.shape[0]

							# sample gaussian noise to begin the loop
							if isinstance(__a							,						__a) and len(__a) != batch_size:
												raise ValueError(
												    F'''You have passed a list of generators of length {len(__a)}, but requested an effective batch'''
												    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.''')

							_UpperCamelCase									=    original_image.shape
							_UpperCamelCase									=    randn_tensor(__a							,						generator=__a							,						device=self.device							,						dtype=self.unet.dtype)

							# set step values
							self.scheduler.set_timesteps(__a							,						__a							,						__a							,						self.device)
							_UpperCamelCase									=    eta

							_UpperCamelCase									=    self.scheduler.timesteps[0] + 1
							_UpperCamelCase									=    generator[0] if isinstance(__a							,						__a) else generator
							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
												if t < t_last:
																	# predict the noise residual
																	_UpperCamelCase									=    self.unet(__a							,						__a).sample
																	# compute previous image: x_t -> x_t-1
																	_UpperCamelCase									=    self.scheduler.step(__a							,						__a							,						__a							,						__a							,						__a							,						__a).prev_sample

												else:
																	# compute the reverse: x_t-1 -> x_t
																	_UpperCamelCase									=    self.scheduler.undo_step(__a							,						__a							,						__a)
												_UpperCamelCase									=    t

							_UpperCamelCase									=    (image / 2 + 0.5).clamp(0							,						1)
							_UpperCamelCase									=    image.cpu().permute(0							,						2							,						3							,						1).numpy()
							if output_type == "pil":
												_UpperCamelCase									=    self.numpy_to_pil(__a)

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=__a)









# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__lowercase				      =						subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
__lowercase				      =						(
    subprocess.check_output(f'git diff --diff-filter=d --name-only {fork_point_sha}'.split()).decode("""utf-8""").split()
)

__lowercase				      =						"""|""".join(sys.argv[1:])
__lowercase				      =						re.compile(rf'^({joined_dirs}).*?\.py$')

__lowercase				      =						[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")





