
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,
)


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

lowerCamelCase_						     =					'''
    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 						__magic_name__			(   __a      :	Dict	,		__a      :	List[str]	,		__a      :	Union[str, Any]=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	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      ):
       super().__init__()

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







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       if latents is None:
            UpperCamelCase__				=    randn_tensor(SCREAMING_SNAKE_CASE_	,      generator=SCREAMING_SNAKE_CASE_	,      device=SCREAMING_SNAKE_CASE_	,      dtype=SCREAMING_SNAKE_CASE_						)
       else:
            if latents.shape != shape:
                 raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"						)
            UpperCamelCase__				=    latents.to(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    latents * scheduler.init_noise_sigma
       return latents







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_=0						):
       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(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_=0						):
       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=SCREAMING_SNAKE_CASE_						)
            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(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      prev_module_hook=SCREAMING_SNAKE_CASE_						)

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







  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 	UpperCAmelCase_ (self						):
       if not hasattr(self.unet	,      """_hf_hook"""						):
            return self.device
       for module in self.unet.modules():
            if (
                hasattr(SCREAMING_SNAKE_CASE_	,      """_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(SCREAMING_SNAKE_CASE_						)
  def __call__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = 5_12	,      SCREAMING_SNAKE_CASE_ = 5_12	,      SCREAMING_SNAKE_CASE_ = 1_00	,      SCREAMING_SNAKE_CASE_ = 4.0	,      SCREAMING_SNAKE_CASE_ = 1	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = "pil"	,      SCREAMING_SNAKE_CASE_ = True	,      ):
       UpperCamelCase__				=    self._execution_device

       UpperCamelCase__				=    guidance_scale > 1.0

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

       UpperCamelCase__				=    image_embeds.shape[0] * num_images_per_prompt

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

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

       UpperCamelCase__				=    self.movq.config.latent_channels

       UpperCamelCase__		,	UpperCamelCase__				=    downscale_height_and_width(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      self.movq_scale_factor						)

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

       for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_						)						):
            # 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=SCREAMING_SNAKE_CASE_	,      timestep=SCREAMING_SNAKE_CASE_	,      encoder_hidden_states=SCREAMING_SNAKE_CASE_	,      added_cond_kwargs=SCREAMING_SNAKE_CASE_	,      return_dict=SCREAMING_SNAKE_CASE_	,      )[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(
                SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      generator=SCREAMING_SNAKE_CASE_	,      )[0]
       # post-processing
       UpperCamelCase__				=    self.movq.decode(SCREAMING_SNAKE_CASE_	,      force_not_quantize=SCREAMING_SNAKE_CASE_						)["""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(SCREAMING_SNAKE_CASE_						)

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_						)







import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCamelCase_						     =					logging.get_logger(__name__)






def 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    R"""\w+[.]\d+"""
     UpperCamelCase__				=    re.findall(__a	,		__a						)
     for pat in pats:
          UpperCamelCase__				=    key.replace(__a	,		"""_""".join(pat.split("""."""						)						)						)
     return key






def 						__magic_name__			(   __a      :	str	,		__a      :	Dict	,		__a      :	int						):



     '''simple docstring'''




     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""scale""",)
     if (
         any("""norm""" in str_ for str_ in pt_tuple_key						)
         and (pt_tuple_key[-1] == "bias")
         and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
         and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
     ):
          UpperCamelCase__				=    pt_tuple_key[:-1] + ("""scale""",)
          return renamed_pt_tuple_key, pt_tensor
     elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
          UpperCamelCase__				=    pt_tuple_key[:-1] + ("""scale""",)
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
          UpperCamelCase__				=    pt_tuple_key[:-1] + ("""embedding""",)
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""kernel""",)
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
          UpperCamelCase__				=    pt_tensor.transpose(2	,		3	,		1	,		0						)
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""kernel""",)
     if pt_tuple_key[-1] == "weight":
          UpperCamelCase__				=    pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""weight""",)
     if pt_tuple_key[-1] == "gamma":
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm bias
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""bias""",)
     if pt_tuple_key[-1] == "beta":
          return renamed_pt_tuple_key, pt_tensor

     return pt_tuple_key, pt_tensor






def 						__magic_name__			(   __a      :	List[Any]	,		__a      :	List[Any]	,		__a      :	Optional[int]=42						):



     '''simple docstring'''




     UpperCamelCase__				=    {k: v.numpy() for k, v in pt_state_dict.items()}

     # Step 2: Since the model is stateless, get random Flax params
     UpperCamelCase__				=    flax_model.init_weights(PRNGKey(__a						)						)

     UpperCamelCase__				=    flatten_dict(__a						)
     UpperCamelCase__				=    {}

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          UpperCamelCase__				=    rename_key(__a						)
          UpperCamelCase__				=    tuple(renamed_pt_key.split("""."""						)						)

          # Correctly rename weight parameters
          UpperCamelCase__		,	UpperCamelCase__				=    rename_key_and_reshape_tensor(__a	,		__a	,		__a						)

          if flax_key in random_flax_state_dict:
               if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
                        f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."						)

        # also add unexpected weight so that warning is thrown
          UpperCamelCase__				=    jnp.asarray(__a						)

     return unflatten_dict(__a						)





