

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

a__												=						"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> 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\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""



def   __UpperCAmelCase					(      __a :     Any						,__a :     Optional[int]						,__a :     Optional[Any]=8      )     ->	Any:

					"""simple docstring"""

					_a      :       Any   =						height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					_a      :       Union[str, Any]   =						width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor







class 							UpperCAmelCase_						(     _UpperCamelCase  ):
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a			,				_a			,				)       ->       List[Any]:
												super().__init__()

												self.register_modules(
												    unet=_a			,				scheduler=_a			,				movq=_a			,				)
												_a      :       int   =						2 ** (len(self.movq.config.block_out_channels			) - 1)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			,				_a			,				_a			,				_a			)       ->       Optional[Any]:





												if latents is None:
																	_a      :       str   =						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}"""			)
																	_a      :       Tuple   =						latents.to(_a			)

												_a      :       int   =						latents * scheduler.init_noise_sigma
												return latents


							def 							__lowercase							(     self			,				_a=0			)       ->       Dict:





												if is_accelerate_available():
																	from accelerate import cpu_offload
												else:
																	raise ImportError('''Please install accelerate via `pip install accelerate`'''			)

												_a      :       Tuple   =						torch.device(F"""cuda:{gpu_id}"""			)

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


							def 							__lowercase							(     self			,				_a=0			)       ->       List[Any]:





												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.'''			)

												_a      :       Tuple   =						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)

												_a      :       str   =						None
												for cpu_offloaded_model in [self.unet, self.movq]:
																	_a      :       int   =						cpu_offload_with_hook(_a			,				_a			,				prev_module_hook=_a			)

												# We'll offload the last model manually.
												_a      :       Tuple   =						hook


							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def 							__lowercase							(     self			)       ->       List[str]:





												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			,				_a			,				_a			,				_a			,				_a = 5_1_2			,				_a = 5_1_2			,				_a = 1_0_0			,				_a = 4.0			,				_a = 1			,				_a = None			,				_a = None			,				_a = "pil"			,				_a = True			,				)       ->       Optional[Any]:
												_a      :       str   =						self._execution_device

												_a      :       List[str]   =						guidance_scale > 1.0

												if isinstance(_a			,				_a			):
																	_a      :       Union[str, Any]   =						torch.cat(_a			,				dim=0			)
												if isinstance(_a			,				_a			):
																	_a      :       Any   =						torch.cat(_a			,				dim=0			)
												if isinstance(_a			,				_a			):
																	_a      :       Optional[Any]   =						torch.cat(_a			,				dim=0			)

												_a      :       List[Any]   =						image_embeds.shape[0] * num_images_per_prompt

												if do_classifier_free_guidance:
																	_a      :       int   =						image_embeds.repeat_interleave(_a			,				dim=0			)
																	_a      :       Any   =						negative_image_embeds.repeat_interleave(_a			,				dim=0			)
																	_a      :       List[Any]   =						hint.repeat_interleave(_a			,				dim=0			)

												_a      :       int   =						torch.cat([negative_image_embeds, image_embeds]			,				dim=0			).to(dtype=self.unet.dtype			,				device=_a			)
												_a      :       Any   =						torch.cat([hint, hint]			,				dim=0			).to(dtype=self.unet.dtype			,				device=_a			)
												self.scheduler.set_timesteps(_a			,				device=_a			)
												_a      :       List[Any]   =						self.scheduler.timesteps

												_a      :       Any   =						self.movq.config.latent_channels

												_a      :       Any   =						downscale_height_and_width(_a			,				_a			,				self.movq_scale_factor			)

												# create initial latent
												_a      :       Union[str, Any]   =						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
																	_a      :       List[Any]   =						torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

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

																	if do_classifier_free_guidance:
																						_a      :       Any   =						noise_pred.split(latents.shape[1]			,				dim=1			)
																						_a      :       List[str]   =						noise_pred.chunk(2			)
																						_a      :       Union[str, Any]   =						variance_pred.chunk(2			)
																						_a      :       List[Any]   =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						_a      :       Any   =						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"]
																	):
																						_a      :       Any   =						noise_pred.split(latents.shape[1]			,				dim=1			)

																	# compute the previous noisy sample x_t -> x_t-1
																	_a      :       List[Any]   =						self.scheduler.step(
																	    _a			,				_a			,				_a			,				generator=_a			,				)[0]
												# post-processing
												_a      :       Any   =						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"]:
																	_a      :       Tuple   =						image * 0.5 + 0.5
																	_a      :       Any   =						image.clamp(0			,				1			)
																	_a      :       Optional[int]   =						image.cpu().permute(0			,				2			,				3			,				1			).float().numpy()

												if output_type == "pil":
																	_a      :       Any   =						self.numpy_to_pil(_a			)

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=_a			)










import os
import string
import sys


SCREAMING_SNAKE_CASE__							:      int         =    1 << 8

SCREAMING_SNAKE_CASE__							:      List[str]         =    {
    """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,
}

SCREAMING_SNAKE_CASE__							:      Any         =    KEYMAP["""up"""]
SCREAMING_SNAKE_CASE__							:      Tuple         =    KEYMAP["""left"""]

if sys.platform == "win32":
  SCREAMING_SNAKE_CASE__							:      Optional[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(1_0):
  SCREAMING_SNAKE_CASE__							:      Dict         =    ord(str(i))






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

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

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

      a__						:    Tuple   		=	sys.stdin.fileno()
      a__						:    int   		=	termios.tcgetattr(lowerCamelCase )
      try:
         tty.setraw(lowerCamelCase )
         a__						:    Tuple   		=	sys.stdin.read(1 )
      finally:
         termios.tcsetattr(lowerCamelCase				,			termios.TCSADRAIN				,			lowerCamelCase )
   return ch






def    _A	(   ):
   a__						:    str   		=	get_raw_chars()
   if ord(lowerCamelCase ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
      return char

   elif ord(lowerCamelCase ) == KEYMAP["esc"]:
      a__						:    Tuple   		=	get_raw_chars()
      if ord(lowerCamelCase ) == KEYMAP["mod_int"]:
         a__						:    List[Any]   		=	get_raw_chars()
         if ord(lowerCamelCase ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(lowerCamelCase ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
            return chr(ord(lowerCamelCase ) + ARROW_KEY_FLAG )
         else:
            return KEYMAP["undefined"]
      else:
         return get_raw_chars()

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

