



"""simple docstring"""


from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

__A :				Optional[int]					    = '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'







def 			lowercase						(   _SCREAMING_SNAKE_CASE :    Tuple       ,  _SCREAMING_SNAKE_CASE :    Tuple       ,  _SCREAMING_SNAKE_CASE :    int=8					):






				'''simple docstring'''



				_UpperCAmelCase      		=     height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				_UpperCAmelCase      		=     width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor
class       _a			(       lowerCAmelCase):
			"""simple docstring"""



			def __init__(		self  :     Union[str, Any]					, __UpperCamelCase  :     Optional[int]					, __UpperCamelCase  :     int					, __UpperCamelCase  :     Any					, )->int:
							super().__init__()

							self.register_modules(
							    unet=A_					, scheduler=A_					, movq=A_					, )
							_UpperCAmelCase      		=     2 ** (len(self.movq.config.block_out_channels ) - 1)



			def 			lowercase__    (		self  :     int					, __UpperCamelCase  :     Optional[int]					, __UpperCamelCase  :     str					, __UpperCamelCase  :     List[str]					, __UpperCamelCase  :     Union[str, Any]					, __UpperCamelCase  :     str					, __UpperCamelCase  :     int )->List[Any]:
							if latents is None:
											_UpperCAmelCase      		=     randn_tensor(A_					, generator=A_					, device=A_					, dtype=A_ )
							else:
											if latents.shape != shape:
															raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}' )
											_UpperCAmelCase      		=     latents.to(A_ )

							_UpperCAmelCase      		=     latents * scheduler.init_noise_sigma
							return latents



			def 			lowercase__    (		self  :     Dict					, __UpperCamelCase  :     Optional[Any]=0 )->str:
							if is_accelerate_available():
											from accelerate import cpu_offload
							else:
											raise ImportError('''Please install accelerate via `pip install accelerate`''' )

							_UpperCAmelCase      		=     torch.device(F'cuda:{gpu_id}' )

							_UpperCAmelCase      		=     [
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
											if cpu_offloaded_model is not None:
															cpu_offload(A_					, A_ )



			def 			lowercase__    (		self  :     str					, __UpperCamelCase  :     Optional[int]=0 )->Tuple:
							if is_accelerate_available() and is_accelerate_version('''>='''					, '''0.17.0.dev0''' ):
											from accelerate import cpu_offload_with_hook
							else:
											raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''' )

							_UpperCAmelCase      		=     torch.device(F'cuda:{gpu_id}' )

							if self.device.type != "cpu":
											self.to('''cpu'''					, silence_dtype_warnings=A_ )
											torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							_UpperCAmelCase      		=     None
							for cpu_offloaded_model in [self.unet, self.movq]:
											_UpperCAmelCase      		=     cpu_offload_with_hook(A_					, A_					, prev_module_hook=A_ )

							# We'll offload the last model manually.
							_UpperCAmelCase      		=     hook



			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 			lowercase__    (		self  :     int )->List[Any]:
							if not hasattr(self.unet					, '''_hf_hook''' ):
											return self.device
							for module in self.unet.modules():
											if (
											    hasattr(A_					, '''_hf_hook''' )
											    and hasattr(module._hf_hook					, '''execution_device''' )
											    and module._hf_hook.execution_device is not None
											):
															return torch.device(module._hf_hook.execution_device )
							return self.device



			@torch.no_grad()
			@replace_example_docstring(A_ )
			def __call__(		self  :     List[Any]					, __UpperCamelCase  :     Tuple					, __UpperCamelCase  :     Dict					, __UpperCamelCase  :     Any					, __UpperCamelCase  :     Union[str, Any] = 5_1_2					, __UpperCamelCase  :     Optional[Any] = 5_1_2					, __UpperCamelCase  :     List[Any] = 1_0_0					, __UpperCamelCase  :     Optional[int] = 4.0					, __UpperCamelCase  :     int = 1					, __UpperCamelCase  :     Dict = None					, __UpperCamelCase  :     Optional[Any] = None					, __UpperCamelCase  :     List[Any] = "pil"					, __UpperCamelCase  :     Optional[Any] = True					, )->Any:
							_UpperCAmelCase      		=     self._execution_device

							_UpperCAmelCase      		=     guidance_scale > 1.0

							if isinstance(A_					, A_ ):
											_UpperCAmelCase      		=     torch.cat(A_					, dim=0 )
							if isinstance(A_					, A_ ):
											_UpperCAmelCase      		=     torch.cat(A_					, dim=0 )
							if isinstance(A_					, A_ ):
											_UpperCAmelCase      		=     torch.cat(A_					, dim=0 )

							_UpperCAmelCase      		=     image_embeds.shape[0] * num_images_per_prompt

							if do_classifier_free_guidance:
											_UpperCAmelCase      		=     image_embeds.repeat_interleave(A_					, dim=0 )
											_UpperCAmelCase      		=     negative_image_embeds.repeat_interleave(A_					, dim=0 )
											_UpperCAmelCase      		=     hint.repeat_interleave(A_					, dim=0 )

							_UpperCAmelCase      		=     torch.cat([negative_image_embeds, image_embeds]					, dim=0 ).to(dtype=self.unet.dtype					, device=A_ )
							_UpperCAmelCase      		=     torch.cat([hint, hint]					, dim=0 ).to(dtype=self.unet.dtype					, device=A_ )
							self.scheduler.set_timesteps(A_					, device=A_ )
							_UpperCAmelCase      		=     self.scheduler.timesteps

							_UpperCAmelCase      		=     self.movq.config.latent_channels

							_UpperCAmelCase      		=     downscale_height_and_width(A_					, A_					, self.movq_scale_factor )

							# create initial latent
							_UpperCAmelCase      		=     self.prepare_latents(
							    (batch_size, num_channels_latents, height, width)					, image_embeds.dtype					, A_					, A_					, A_					, self.scheduler					, )

							for i, t in enumerate(self.progress_bar(A_ ) ):
											# expand the latents if we are doing classifier free guidance
											_UpperCAmelCase      		=     torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

											_UpperCAmelCase      		=     {"""image_embeds""": image_embeds, """hint""": hint}
											_UpperCAmelCase      		=     self.unet(
											    sample=A_					, timestep=A_					, encoder_hidden_states=A_					, added_cond_kwargs=A_					, return_dict=A_					, )[0]

											if do_classifier_free_guidance:
															_UpperCAmelCase      		=     noise_pred.split(latents.shape[1]					, dim=1 )
															_UpperCAmelCase      		=     noise_pred.chunk(2 )
															_UpperCAmelCase      		=     variance_pred.chunk(2 )
															_UpperCAmelCase      		=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
															_UpperCAmelCase      		=     torch.cat([noise_pred, variance_pred_text]					, dim=1 )

											if not (
											    hasattr(self.scheduler.config					, '''variance_type''' )
											    and self.scheduler.config.variance_type in ["learned", "learned_range"]
											):
															_UpperCAmelCase      		=     noise_pred.split(latents.shape[1]					, dim=1 )

											# compute the previous noisy sample x_t -> x_t-1
											_UpperCAmelCase      		=     self.scheduler.step(
											    A_					, A_					, A_					, generator=A_					, )[0]
							# post-processing
							_UpperCAmelCase      		=     self.movq.decode(A_					, force_not_quantize=A_ )["""sample"""]

							if output_type not in ["pt", "np", "pil"]:
											raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}' )

							if output_type in ["np", "pil"]:
											_UpperCAmelCase      		=     image * 0.5 + 0.5
											_UpperCAmelCase      		=     image.clamp(0					, 1 )
											_UpperCAmelCase      		=     image.cpu().permute(0					, 2					, 3					, 1 ).float().numpy()

							if output_type == "pil":
											_UpperCAmelCase      		=     self.numpy_to_pil(A_ )

							if not return_dict:
											return (image,)

							return ImagePipelineOutput(images=A_ )







import os
import string
import sys


SCREAMING_SNAKE_CASE__					:     List[str]       =     1 << 8

SCREAMING_SNAKE_CASE__					:     str       =     {
    'tab': ord('\t'),
    'newline': ord('\r'),
    'esc': 27,
    'up': 65 + ARROW_KEY_FLAG,
    'down': 66 + ARROW_KEY_FLAG,
    'right': 67 + ARROW_KEY_FLAG,
    'left': 68 + ARROW_KEY_FLAG,
    'mod_int': 91,
    'undefined': sys.maxsize,
    'interrupt': 3,
    'insert': 50,
    'delete': 51,
    'pg_up': 53,
    'pg_down': 54,
}

SCREAMING_SNAKE_CASE__					:     List[str]       =     KEYMAP['up']
SCREAMING_SNAKE_CASE__					:     Optional[int]       =     KEYMAP['left']

if sys.platform == "win32":
 SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     []
 SCREAMING_SNAKE_CASE__					:     Dict       =     {
     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(10):
 SCREAMING_SNAKE_CASE__					:     List[str]       =     ord(str(i))




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

    _UpperCAmelCase		:							int							=     """mbcs"""
    # Flush the keyboard buffer
    while msvcrt.kbhit():
      msvcrt.getch()
    if len(snake_case__     ) == 0:
      # Read the keystroke
      _UpperCAmelCase		:							Any							=     msvcrt.getch()

      # If it is a prefix char, get second part
      if ch in (b"\x00", b"\xe0"):
        _UpperCAmelCase		:							str							=     ch + msvcrt.getch()
        # Translate actual Win chars to bullet char types
        try:
          _UpperCAmelCase		:							Optional[int]							=     chr(WIN_KEYMAP[cha]     )
          WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""]     )     )
          WIN_CH_BUFFER.append(snake_case__     )
          if ord(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     )     )
          _UpperCAmelCase		:							Any							=     chr(KEYMAP["""esc"""]     )
        except KeyError:
          _UpperCAmelCase		:							Optional[int]							=     cha[1]
      else:
        _UpperCAmelCase		:							Union[str, Any]							=     ch.decode(snake_case__     )
    else:
      _UpperCAmelCase		:							Optional[int]							=     WIN_CH_BUFFER.pop(0     )
  elif os.name == "posix":
    import termios
    import tty

    _UpperCAmelCase		:							List[str]							=     sys.stdin.fileno()
    _UpperCAmelCase		:							Optional[Any]							=     termios.tcgetattr(snake_case__     )
    try:
      tty.setraw(snake_case__     )
      _UpperCAmelCase		:							Tuple							=     sys.stdin.read(1     )
    finally:
      termios.tcsetattr(snake_case__						,   termios.TCSADRAIN						,   snake_case__     )
  return ch





def    a__    (		):
  _UpperCAmelCase		:							int							=     get_raw_chars()
  if ord(snake_case__     ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
    return char

  elif ord(snake_case__     ) == KEYMAP["esc"]:
    _UpperCAmelCase		:							int							=     get_raw_chars()
    if ord(snake_case__     ) == KEYMAP["mod_int"]:
      _UpperCAmelCase		:							Optional[Any]							=     get_raw_chars()
      if ord(snake_case__     ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(snake_case__     ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
        return chr(ord(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"]





