
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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 KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\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\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        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\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  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase=8						):
   SCREAMING_SNAKE_CASE_	=      height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   SCREAMING_SNAKE_CASE_	=      width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor


def  A__ (       __lowerCamelCase,  __lowerCamelCase=5_12,  __lowerCamelCase=5_12						):
   SCREAMING_SNAKE_CASE_	=      pil_image.resize((w, h),  resample=Image.BICUBIC,  reducing_gap=1						)
   SCREAMING_SNAKE_CASE_	=      np.array(pil_image.convert('''RGB'''						)						)
   SCREAMING_SNAKE_CASE_	=      arr.astype(np.floataa						) / 1_27.5 - 1
   SCREAMING_SNAKE_CASE_	=      np.transpose(__lowerCamelCase,  [2, 0, 1]						)
   SCREAMING_SNAKE_CASE_	=      torch.from_numpy(__lowerCamelCase						).unsqueeze(0						)
   return image





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       def __init__(							self ,     _A ,     _A ,     _A ,     )				->				Tuple:
          super().__init__()

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



       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A	)				->				Optional[int]:
          # get the original timestep using init_timestep
          SCREAMING_SNAKE_CASE_	=      min(int(num_inference_steps * strength	) ,     _A	)

          SCREAMING_SNAKE_CASE_	=      max(num_inference_steps - init_timestep ,     0	)
          SCREAMING_SNAKE_CASE_	=      self.scheduler.timesteps[t_start:]

          return timesteps, num_inference_steps - t_start



       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A=None	)				->				List[Any]:
          if not isinstance(_A ,     (torch.Tensor, PIL.Image.Image, list)	):
             raise ValueError(
                 F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A	)}'''	)

          SCREAMING_SNAKE_CASE_	=      image.to(device=_A ,     dtype=_A	)

          SCREAMING_SNAKE_CASE_	=      batch_size * num_images_per_prompt

          if image.shape[1] == 4:
             SCREAMING_SNAKE_CASE_	=      image

          else:
             if isinstance(_A ,     _A	) and len(_A	) != batch_size:
                raise ValueError(
                    F'''You have passed a list of generators of length {len(_A	)}, but requested an effective batch'''
                    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''	)

             elif isinstance(_A ,     _A	):
                SCREAMING_SNAKE_CASE_	=      [
                    self.movq.encode(image[i : i + 1]	).latent_dist.sample(generator[i]	) for i in range(_A	)
                ]
                SCREAMING_SNAKE_CASE_	=      torch.cat(_A ,     dim=0	)
             else:
                SCREAMING_SNAKE_CASE_	=      self.movq.encode(_A	).latent_dist.sample(_A	)

             SCREAMING_SNAKE_CASE_	=      self.movq.config.scaling_factor * init_latents

          SCREAMING_SNAKE_CASE_	=      torch.cat([init_latents] ,     dim=0	)

          SCREAMING_SNAKE_CASE_	=      init_latents.shape
          SCREAMING_SNAKE_CASE_	=      randn_tensor(_A ,     generator=_A ,     device=_A ,     dtype=_A	)

          # get latents
          SCREAMING_SNAKE_CASE_	=      self.scheduler.add_noise(_A ,     _A ,     _A	)

          SCREAMING_SNAKE_CASE_	=      init_latents

          return latents



       def     _UpperCamelCase	(							self ,     _A=0	)				->				Any:
          if is_accelerate_available():
             from accelerate import cpu_offload
          else:
             raise ImportError('''Please install accelerate via `pip install accelerate`'''	)

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

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



       def     _UpperCamelCase	(							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.'''	)

          SCREAMING_SNAKE_CASE_	=      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)

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

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



       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def     _UpperCamelCase	(							self	)				->				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 ,     _A ,     _A ,     _A ,     _A ,     _A = 512 ,     _A = 512 ,     _A = 100 ,     _A = 4.0 ,     _A = 0.3 ,     _A = 1 ,     _A = None ,     _A = "pil" ,     _A = True ,     )				->				List[str]:
          SCREAMING_SNAKE_CASE_	=      self._execution_device

          SCREAMING_SNAKE_CASE_	=      guidance_scale > 1.0

          if isinstance(_A ,     _A	):
             SCREAMING_SNAKE_CASE_	=      torch.cat(_A ,     dim=0	)
          if isinstance(_A ,     _A	):
             SCREAMING_SNAKE_CASE_	=      torch.cat(_A ,     dim=0	)
          if isinstance(_A ,     _A	):
             SCREAMING_SNAKE_CASE_	=      torch.cat(_A ,     dim=0	)

          SCREAMING_SNAKE_CASE_	=      image_embeds.shape[0]

          if do_classifier_free_guidance:
             SCREAMING_SNAKE_CASE_	=      image_embeds.repeat_interleave(_A ,     dim=0	)
             SCREAMING_SNAKE_CASE_	=      negative_image_embeds.repeat_interleave(_A ,     dim=0	)
             SCREAMING_SNAKE_CASE_	=      hint.repeat_interleave(_A ,     dim=0	)

          SCREAMING_SNAKE_CASE_	=      torch.cat([negative_image_embeds, image_embeds] ,     dim=0	).to(dtype=self.unet.dtype ,     device=_A	)
          SCREAMING_SNAKE_CASE_	=      torch.cat([hint, hint] ,     dim=0	).to(dtype=self.unet.dtype ,     device=_A	)

          if not isinstance(_A ,     _A	):
             SCREAMING_SNAKE_CASE_	=      [image]
          if not all(isinstance(_A ,     (PIL.Image.Image, torch.Tensor)	) for i in image	):
             raise ValueError(
                 F'''Input is in incorrect format: {[type(_A	) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''	)

          SCREAMING_SNAKE_CASE_	=      torch.cat([prepare_image(_A ,     _A ,     _A	) for i in image] ,     dim=0	)
          SCREAMING_SNAKE_CASE_	=      image.to(dtype=image_embeds.dtype ,     device=_A	)

          SCREAMING_SNAKE_CASE_	=      self.movq.encode(_A	)['''latents''']
          SCREAMING_SNAKE_CASE_	=      latents.repeat_interleave(_A ,     dim=0	)
          self.scheduler.set_timesteps(_A ,     device=_A	)
          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      self.get_timesteps(_A ,     _A ,     _A	)
          SCREAMING_SNAKE_CASE_	=      timesteps[:1].repeat(batch_size * num_images_per_prompt	)
          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      downscale_height_and_width(_A ,     _A ,     self.movq_scale_factor	)
          SCREAMING_SNAKE_CASE_	=      self.prepare_latents(
              _A ,     _A ,     _A ,     _A ,     image_embeds.dtype ,     _A ,     _A	)
          for i, t in enumerate(self.progress_bar(_A	)	):
             # expand the latents if we are doing classifier free guidance
             SCREAMING_SNAKE_CASE_	=      torch.cat([latents] * 2	) if do_classifier_free_guidance else latents

             SCREAMING_SNAKE_CASE_	=      {'''image_embeds''': image_embeds, '''hint''': hint}
             SCREAMING_SNAKE_CASE_	=      self.unet(
                 sample=_A ,     timestep=_A ,     encoder_hidden_states=_A ,     added_cond_kwargs=_A ,     return_dict=_A ,     )[0]

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

             # compute the previous noisy sample x_t -> x_t-1

             SCREAMING_SNAKE_CASE_	=      self.scheduler.step(
                 _A ,     _A ,     _A ,     generator=_A ,     )[0]

          # post-processing
          SCREAMING_SNAKE_CASE_	=      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"]:
             SCREAMING_SNAKE_CASE_	=      image * 0.5 + 0.5
             SCREAMING_SNAKE_CASE_	=      image.clamp(0 ,     1	)
             SCREAMING_SNAKE_CASE_	=      image.cpu().permute(0 ,     2 ,     3 ,     1	).float().numpy()

          if output_type == "pil":
             SCREAMING_SNAKE_CASE_	=      self.numpy_to_pil(_A	)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=_A	)




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


__UpperCAmelCase   						=     logging.get_logger(__name__)





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       ="speech-encoder-decoder"
       UpperCAmelCase_				       =True



       def __init__(							self ,     **_A	)				->				Dict:
          super().__init__(**_A	)
          if "encoder" not in kwargs or "decoder" not in kwargs:
             raise ValueError(
                 F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
                 F''' `decoder` sub-configurations are passed, but only {kwargs}'''	)

          SCREAMING_SNAKE_CASE_	=      kwargs.pop('''encoder'''	)
          SCREAMING_SNAKE_CASE_	=      encoder_config.pop('''model_type'''	)
          SCREAMING_SNAKE_CASE_	=      kwargs.pop('''decoder'''	)
          SCREAMING_SNAKE_CASE_	=      decoder_config.pop('''model_type'''	)

          SCREAMING_SNAKE_CASE_	=      AutoConfig.for_model(_A ,     **_A	)
          SCREAMING_SNAKE_CASE_	=      AutoConfig.for_model(_A ,     **_A	)
          SCREAMING_SNAKE_CASE_	=      True



       @classmethod
       def     _UpperCamelCase	(							cls ,     _A ,     _A ,     **_A	)				->				PretrainedConfig:
          logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''	)
          SCREAMING_SNAKE_CASE_	=      True
          SCREAMING_SNAKE_CASE_	=      True

          return cls(encoder=encoder_config.to_dict() ,     decoder=decoder_config.to_dict() ,     **_A	)




       def     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          SCREAMING_SNAKE_CASE_	=      copy.deepcopy(self.__dict__	)
          SCREAMING_SNAKE_CASE_	=      self.encoder.to_dict()
          SCREAMING_SNAKE_CASE_	=      self.decoder.to_dict()
          SCREAMING_SNAKE_CASE_	=      self.__class__.model_type
          return output


