






'''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


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



def 							snake_case					(     a_							: Union[List, PIL.Image.Image, torch.Tensor]	)       ->				Optional[int]:





      """simple docstring"""

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

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

            UpperCamelCase_				:   Tuple					    =							[np.array(i.resize((w, h)    ,   resample=PIL_INTERPOLATION["""lanczos"""]	)	)[None, :] for i in image]
            UpperCamelCase_				:   Union[str, Any]					    =							np.concatenate(a_    ,   axis=0	)
            UpperCamelCase_				:   str					    =							np.array(a_	).astype(np.floataa	) / 255.0
            UpperCamelCase_				:   Dict					    =							image.transpose(0    ,   3    ,   1    ,   2	)
            UpperCamelCase_				:   str					    =							2.0 * image - 1.0
            UpperCamelCase_				:   Union[str, Any]					    =							torch.from_numpy(a_	)
      elif isinstance(image[0]    ,   torch.Tensor	):
            UpperCamelCase_				:   Optional[Any]					    =							torch.cat(a_    ,   dim=0	)
      return image



def 							snake_case					(     a_							: Union[List, PIL.Image.Image, torch.Tensor]	)       ->				str:





      """simple docstring"""

      if isinstance(a_    ,   torch.Tensor	):
            return mask
      elif isinstance(a_    ,   PIL.Image.Image	):
            UpperCamelCase_				:   Dict					    =							[mask]

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




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						UNetaDModel
 __a			:						RePaintScheduler
 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       super().__init__()
       self.register_modules(unet=__lowerCAmelCase			,     scheduler=__lowerCAmelCase )





 @torch.no_grad()
 def __call__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = 2_50			,     __lowerCAmelCase = 0.0			,     __lowerCAmelCase = 10			,     __lowerCAmelCase = 10			,     __lowerCAmelCase = None			,     __lowerCAmelCase = "pil"			,     __lowerCAmelCase = True			,     ):
       UpperCamelCase_				:   Union[str, Any]					    =							image

       UpperCamelCase_				:   Dict					    =							_preprocess_image(__lowerCAmelCase )
       UpperCamelCase_				:   Tuple					    =							original_image.to(device=self.device			,     dtype=self.unet.dtype )
       UpperCamelCase_				:   List[str]					    =							_preprocess_mask(__lowerCAmelCase )
       UpperCamelCase_				:   List[str]					    =							mask_image.to(device=self.device			,     dtype=self.unet.dtype )

       UpperCamelCase_				:   Union[str, Any]					    =							original_image.shape[0]

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

       UpperCamelCase_				:   Any					    =							original_image.shape
       UpperCamelCase_				:   int					    =							randn_tensor(__lowerCAmelCase			,     generator=__lowerCAmelCase			,     device=self.device			,     dtype=self.unet.dtype )

       # set step values
       self.scheduler.set_timesteps(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     self.device )
       UpperCamelCase_				:   Optional[int]					    =							eta

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

             else:
                   # compute the reverse: x_t-1 -> x_t
                   UpperCamelCase_				:   Dict					    =							self.scheduler.undo_step(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )
             UpperCamelCase_				:   str					    =							t

       UpperCamelCase_				:   Optional[Any]					    =							(image / 2 + 0.5).clamp(0			,     1 )
       UpperCamelCase_				:   Optional[Any]					    =							image.cpu().permute(0			,     2			,     3			,     1 ).numpy()
       if output_type == "pil":
             UpperCamelCase_				:   str					    =							self.numpy_to_pil(__lowerCAmelCase )

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=__lowerCAmelCase )














'''simple docstring'''

import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler




@dataclass
class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						int      =						0
 __a			:						bool      =						False
 __a			:						float      =						3.0






class     A    (      unittest.TestCase						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       # If no defaults are changed, `to_kwargs` returns an empty dict.
       self.assertDictEqual(MockClass().to_kwargs()			,     {} )
       self.assertDictEqual(MockClass(a=2 ).to_kwargs()			,     {"""a""": 2} )
       self.assertDictEqual(MockClass(a=2			,     b=__lowerCAmelCase ).to_kwargs()			,     {"""a""": 2, """b""": True} )
       self.assertDictEqual(MockClass(a=2			,     c=2.25 ).to_kwargs()			,     {"""a""": 2, """c""": 2.25} )
 @require_cuda
 def   _UpperCAmelCase       (							self ):
       # If no defaults are changed, `to_kwargs` returns an empty dict.
       UpperCamelCase_				:   int					    =							GradScalerKwargs(init_scale=10_24			,     growth_factor=2 )
       AcceleratorState._reset_state()
       UpperCamelCase_				:   Dict					    =							Accelerator(mixed_precision="""fp16"""			,     kwargs_handlers=[scaler_handler] )
       print(accelerator.use_fpaa )
       UpperCamelCase_				:   Any					    =							accelerator.scaler

       # Check the kwargs have been applied
       self.assertEqual(scaler._init_scale			,     10_24.0 )
       self.assertEqual(scaler._growth_factor			,     2.0 )

       # Check the other values are at the default
       self.assertEqual(scaler._backoff_factor			,     0.5 )
       self.assertEqual(scaler._growth_interval			,     20_00 )
       self.assertEqual(scaler._enabled			,     __lowerCAmelCase )





 @require_multi_gpu
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							["""torchrun""", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__ )]
       execute_subprocess_async(__lowerCAmelCase			,     env=os.environ.copy() )


if __name__ == "__main__":
      UpperCamelCase							=DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
      UpperCamelCase							=Accelerator(kwargs_handlers=[ddp_scaler])
      UpperCamelCase							=torch.nn.Linear(100, 200)
      UpperCamelCase							=accelerator.prepare(model)

      # Check the values changed in kwargs
      UpperCamelCase							=""
      UpperCamelCase							=model.bucket_bytes_cap // (1024 * 1024)
      if observed_bucket_cap_map != 15:
            error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
      if model.find_unused_parameters is not True:
            error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

      # Check the values of the defaults
      if model.dim != 0:
            error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
      if model.broadcast_buffers is not True:
            error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
      if model.gradient_as_bucket_view is not False:
            error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

      # Raise error at the end to make sure we don't stop at the first failure.
      if len(error_msg) > 0:
            raise ValueError(error_msg)






