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,
)


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

UpperCamelCase__    							= '\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        lowerCAmelCase_			(					__A,  __A,  __A=8							)					->    List[str]:







      '''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							(    UpperCAmelCase_					):


   def __init__(self					:						Any	,  __UpperCAmelCase					:						UNetaDConditionModel	,  __UpperCAmelCase					:						DDPMScheduler	,  __UpperCAmelCase					:						VQModel	,  )						->    Tuple:
         """simple docstring"""



         super().__init__()

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


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						Dict		)						->    Dict:
         """simple docstring"""



         if latents is None:
               UpperCAmelCase__								=  randn_tensor(__UpperCAmelCase	,  generator=__UpperCAmelCase	,  device=__UpperCAmelCase	,  dtype=__UpperCAmelCase		)
         else:
               if latents.shape != shape:
                     raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""		)
               UpperCAmelCase__								=  latents.to(__UpperCAmelCase		)

         UpperCAmelCase__								=  latents * scheduler.init_noise_sigma
         return latents


   def       lowercase_				(self					:						Tuple	,  __UpperCAmelCase					:						Dict=0		)						->    Optional[Any]:
         """simple docstring"""



         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(__UpperCAmelCase	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						List[str]=0		)						->    int:
         """simple docstring"""



         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=__UpperCAmelCase		)
               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__		,				UpperCAmelCase__								=  cpu_offload_with_hook(__UpperCAmelCase	,  __UpperCAmelCase	,  prev_module_hook=__UpperCAmelCase		)

         # 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					:						Optional[Any]		)						->    List[str]:
         """simple docstring"""



         if not hasattr(self.unet	,  "_hf_hook"		):
               return self.device
         for module in self.unet.modules():
               if (
                   hasattr(__UpperCAmelCase	,  "_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(__UpperCAmelCase		)
   def __call__(self					:						Union[str, Any]	,  __UpperCAmelCase					:						Union[torch.FloatTensor, List[torch.FloatTensor]]	,  __UpperCAmelCase					:						Union[torch.FloatTensor, List[torch.FloatTensor]]	,  __UpperCAmelCase					:						torch.FloatTensor	,  __UpperCAmelCase					:						int = 5_1_2	,  __UpperCAmelCase					:						int = 5_1_2	,  __UpperCAmelCase					:						int = 1_0_0	,  __UpperCAmelCase					:						float = 4.0	,  __UpperCAmelCase					:						int = 1	,  __UpperCAmelCase					:						Optional[Union[torch.Generator, List[torch.Generator]]] = None	,  __UpperCAmelCase					:						Optional[torch.FloatTensor] = None	,  __UpperCAmelCase					:						Optional[str] = "pil"	,  __UpperCAmelCase					:						bool = True	,  )						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  self._execution_device

         UpperCAmelCase__								=  guidance_scale > 1.0

         if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		):
               UpperCAmelCase__								=  torch.cat(__UpperCAmelCase	,  dim=0		)
         if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		):
               UpperCAmelCase__								=  torch.cat(__UpperCAmelCase	,  dim=0		)
         if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		):
               UpperCAmelCase__								=  torch.cat(__UpperCAmelCase	,  dim=0		)

         UpperCAmelCase__								=  image_embeds.shape[0] * num_images_per_prompt

         if do_classifier_free_guidance:
               UpperCAmelCase__								=  image_embeds.repeat_interleave(__UpperCAmelCase	,  dim=0		)
               UpperCAmelCase__								=  negative_image_embeds.repeat_interleave(__UpperCAmelCase	,  dim=0		)
               UpperCAmelCase__								=  hint.repeat_interleave(__UpperCAmelCase	,  dim=0		)

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

         UpperCAmelCase__								=  self.movq.config.latent_channels

         UpperCAmelCase__		,				UpperCAmelCase__								=  downscale_height_and_width(__UpperCAmelCase	,  __UpperCAmelCase	,  self.movq_scale_factor		)

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

         for i, t in enumerate(self.progress_bar(__UpperCAmelCase		)		):
               # 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=__UpperCAmelCase	,  timestep=__UpperCAmelCase	,  encoder_hidden_states=__UpperCAmelCase	,  added_cond_kwargs=__UpperCAmelCase	,  return_dict=__UpperCAmelCase	,  )[0]

               if do_classifier_free_guidance:
                     UpperCAmelCase__		,				UpperCAmelCase__								=  noise_pred.split(latents.shape[1]	,  dim=1		)
                     UpperCAmelCase__		,				UpperCAmelCase__								=  noise_pred.chunk(2		)
                     UpperCAmelCase__		,				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__		,				UpperCAmelCase__								=  noise_pred.split(latents.shape[1]	,  dim=1		)

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

         if not return_dict:
               return (image,)

         return ImagePipelineOutput(images=__UpperCAmelCase		)




# Lint as: python3
import itertools
import os
import re


UpperCamelCase__    							= re.compile(R'([A-Z]+)([A-Z][a-z])')
UpperCamelCase__    							= re.compile(R'([a-z\d])([A-Z])')

UpperCamelCase__    							= re.compile(R'(?<!_)_(?!_)')
UpperCamelCase__    							= re.compile(R'(_{2,})')

UpperCamelCase__    							= R'^\w+(\.\w+)*$'

UpperCamelCase__    							= R'<>:/\|?*'




def        lowerCAmelCase_			(					__A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  _uppercase_uppercase_re.sub(r"\1_\2",  __A							)
      UpperCAmelCase__								=  _lowercase_uppercase_re.sub(r"\1_\2",  __A							)
      return name.lower()




def        lowerCAmelCase_			(					__A							)					->    Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  _single_underscore_re.split(__A							)
      UpperCAmelCase__								=  [_multiple_underscores_re.split(__A							) for n in name]
      return "".join(n.capitalize() for n in itertools.chain.from_iterable(__A							) if n != ""							)




def        lowerCAmelCase_			(					__A							)					->    List[str]:







      '''simple docstring'''


      if os.path.basename(__A							) != name:
            raise ValueError(f"""Should be a dataset name, not a path: {name}"""							)
      return camelcase_to_snakecase(__A							)




def        lowerCAmelCase_			(					__A,  __A							)					->    Optional[int]:







      '''simple docstring'''


      if os.path.basename(__A							) != name:
            raise ValueError(f"""Should be a dataset name, not a path: {name}"""							)
      if not re.match(_split_re,  __A							):
            raise ValueError(f"""Split name should match '{_split_re}'' but got '{split}'."""							)
      return f"""{filename_prefix_for_name(__A							)}-{split}"""




def        lowerCAmelCase_			(					__A,  __A,  __A,  __A=None							)					->    str:







      '''simple docstring'''


      UpperCAmelCase__								=  filename_prefix_for_split(__A,  __A							)
      if filetype_suffix:
            prefix += f""".{filetype_suffix}"""
      UpperCAmelCase__								=  os.path.join(__A,  __A							)
      return f"""{filepath}*"""







def        lowerCAmelCase_			(					__A,  __A,  __A,  __A=None,  __A=None							)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  filename_prefix_for_split(__A,  __A							)
      UpperCAmelCase__								=  os.path.join(__A,  __A							)

      if shard_lengths:
            UpperCAmelCase__								=  len(__A							)
            UpperCAmelCase__								=  [f"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(__A							)]
            if filetype_suffix:
                  UpperCAmelCase__								=  [filename + f""".{filetype_suffix}""" for filename in filenames]
            return filenames
      else:
            UpperCAmelCase__								=  prefix
            if filetype_suffix:
                  filename += f""".{filetype_suffix}"""
            return [filename]



