'''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     :   Optional[Any]								=   logging.get_logger(__name__)  # pylint: disable=invalid-name

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

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

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

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     if isinstance(__lowerCAmelCase  ,       torch.Tensor							):
          return mask
     elif isinstance(__lowerCAmelCase  ,       PIL.Image.Image							):
          lowercase_					:       Optional[int]     				=					[mask]

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






class       UpperCAmelCase__  (       UpperCAmelCase__			):






   """simple docstring"""
   A							:       UNetaDModel
   A							:       RePaintScheduler
   def __init__(self       ,				_a       ,				_a				)    ->     Optional[int]:
        super().__init__()
        self.register_modules(unet=lowerCamelCase__       ,				scheduler=lowerCamelCase__				)





   @torch.no_grad()
   def __call__(self       ,				_a       ,				_a       ,				_a = 250       ,				_a = 0.0       ,				_a = 10       ,				_a = 10       ,				_a = None       ,				_a = "pil"       ,				_a = True       ,				)    ->     Union[ImagePipelineOutput, Tuple]:
        lowercase_					:       Optional[Any]     				=					image

        lowercase_					:       Tuple     				=					_preprocess_image(lowerCamelCase__				)
        lowercase_					:       List[Any]     				=					original_image.to(device=self.device       ,				dtype=self.unet.dtype				)
        lowercase_					:       int     				=					_preprocess_mask(lowerCamelCase__				)
        lowercase_					:       List[Any]     				=					mask_image.to(device=self.device       ,				dtype=self.unet.dtype				)

        lowercase_					:       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.'''				)

        lowercase_					:       int     				=					original_image.shape
        lowercase_					:       List[str]     				=					randn_tensor(lowerCamelCase__       ,				generator=lowerCamelCase__       ,				device=self.device       ,				dtype=self.unet.dtype				)

        # set step values
        self.scheduler.set_timesteps(lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__       ,				self.device				)
        lowercase_					:       str     				=					eta

        lowercase_					:       Optional[int]     				=					self.scheduler.timesteps[0] + 1
        lowercase_					:       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
                  lowercase_					:       Any     				=					self.unet(lowerCamelCase__       ,				lowerCamelCase__				).sample
                  # compute previous image: x_t -> x_t-1
                  lowercase_					:       Optional[int]     				=					self.scheduler.step(lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__				).prev_sample

             else:
                  # compute the reverse: x_t-1 -> x_t
                  lowercase_					:       Union[str, Any]     				=					self.scheduler.undo_step(lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__				)
             lowercase_					:       Tuple     				=					t

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

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=lowerCamelCase__				)





'''simple docstring'''




import os
import string
import sys


_A								=   1 << 8

_A								=   {
    'tab': ord('\t'),
    'newline': ord('\r'),
    'esc': 2_7,
    'up': 6_5 + ARROW_KEY_FLAG,
    'down': 6_6 + ARROW_KEY_FLAG,
    'right': 6_7 + ARROW_KEY_FLAG,
    'left': 6_8 + ARROW_KEY_FLAG,
    'mod_int': 9_1,
    'undefined': sys.maxsize,
    'interrupt': 3,
    'insert': 5_0,
    'delete': 5_1,
    'pg_up': 5_3,
    'pg_down': 5_4,
}

_A								=   KEYMAP['up']
_A								=   KEYMAP['left']

if sys.platform == "win32":
 _A								=   []
 _A								=   {
     b'\xe0H': KEYMAP['up'] - ARROW_KEY_FLAG,
     b'\x00H': KEYMAP['up'] - ARROW_KEY_FLAG,
     b'\xe0P': KEYMAP['down'] - ARROW_KEY_FLAG,
     b'\x00P': KEYMAP['down'] - ARROW_KEY_FLAG,
     b'\xe0M': KEYMAP['right'] - ARROW_KEY_FLAG,
     b'\x00M': KEYMAP['right'] - ARROW_KEY_FLAG,
     b'\xe0K': KEYMAP['left'] - ARROW_KEY_FLAG,
     b'\x00K': KEYMAP['left'] - ARROW_KEY_FLAG,
 }

for i in range(1_0):
 _A								=   ord(str(i))

def _UpperCamelCase   (       ):
     if os.name == "nt":
          import msvcrt

          lowercase_					:       Optional[Any]     				=					'mbcs'
          # Flush the keyboard buffer
          while msvcrt.kbhit():
               msvcrt.getch()
          if len(SCREAMING_SNAKE_CASE_							) == 0:
               # Read the keystroke
               lowercase_					:       int     				=					msvcrt.getch()

               # If it is a prefix char, get second part
               if ch in (b"\x00", b"\xe0"):
                    lowercase_					:       int     				=					ch + msvcrt.getch()
                    # Translate actual Win chars to bullet char types
                    try:
                         lowercase_					:       List[Any]     				=					chr(WIN_KEYMAP[cha]							)
                         WIN_CH_BUFFER.append(chr(KEYMAP['mod_int']							)							)
                         WIN_CH_BUFFER.append(SCREAMING_SNAKE_CASE_							)
                         if ord(SCREAMING_SNAKE_CASE_							) in (
                             KEYMAP["insert"] - 1 << 9,
                             KEYMAP["delete"] - 1 << 9,
                             KEYMAP["pg_up"] - 1 << 9,
                             KEYMAP["pg_down"] - 1 << 9,
                         ):
                              WIN_CH_BUFFER.append(chr(126							)							)
                         lowercase_					:       Optional[int]     				=					chr(KEYMAP['esc']							)
                    except KeyError:
                         lowercase_					:       Tuple     				=					cha[1]
               else:
                    lowercase_					:       Tuple     				=					ch.decode(SCREAMING_SNAKE_CASE_							)
          else:
               lowercase_					:       Dict     				=					WIN_CH_BUFFER.pop(0							)
     elif os.name == "posix":
          import termios
          import tty

          lowercase_					:       int     				=					sys.stdin.fileno()
          lowercase_					:       Union[str, Any]     				=					termios.tcgetattr(SCREAMING_SNAKE_CASE_							)
          try:
               tty.setraw(SCREAMING_SNAKE_CASE_							)
               lowercase_					:       Any     				=					sys.stdin.read(1							)
          finally:
               termios.tcsetattr(SCREAMING_SNAKE_CASE_  ,       termios.TCSADRAIN  ,       SCREAMING_SNAKE_CASE_							)
     return ch






def _UpperCamelCase   (       ):
     lowercase_					:       Union[str, Any]     				=					get_raw_chars()
     if ord(SCREAMING_SNAKE_CASE_							) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
          return char

     elif ord(SCREAMING_SNAKE_CASE_							) == KEYMAP["esc"]:
          lowercase_					:       Any     				=					get_raw_chars()
          if ord(SCREAMING_SNAKE_CASE_							) == KEYMAP["mod_int"]:
               lowercase_					:       Dict     				=					get_raw_chars()
               if ord(SCREAMING_SNAKE_CASE_							) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(SCREAMING_SNAKE_CASE_							) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
                    return chr(ord(SCREAMING_SNAKE_CASE_							) + ARROW_KEY_FLAG							)
               else:
                    return KEYMAP["undefined"]
          else:
               return get_raw_chars()

     else:
          if char in string.printable:
               return char
          else:
               return KEYMAP["undefined"]




