

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,
)


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

snake_case_			  = '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

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

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[int]	,     SCREAMING_SNAKE_CASE_		:    Optional[Any]	,     SCREAMING_SNAKE_CASE_		:    Optional[Any]=8			):



     '''simple docstring'''





     lowercase__    :   Optional[int]      =       height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     lowercase__    :   Any      =       width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Union[str, Any]	,     SCREAMING_SNAKE_CASE_		:    List[Any]=512	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]=512			):



     '''simple docstring'''





     lowercase__    :   Dict      =       pil_image.resize((w, h)	,     resample=Image.BICUBIC	,     reducing_gap=1			)
     lowercase__    :   Optional[Any]      =       np.array(pil_image.convert('RGB'			)			)
     lowercase__    :   Union[str, Any]      =       arr.astype(np.floataa			) / 127.5 - 1
     lowercase__    :   str      =       np.transpose(SCREAMING_SNAKE_CASE_	,     [2, 0, 1]			)
     lowercase__    :   Dict      =       torch.from_numpy(SCREAMING_SNAKE_CASE_			).unsqueeze(0			)
     return image


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   a						,   a						,   a						,   ):
         super().__init__()

         self.register_modules(
             unet=a						,   scheduler=a						,   movq=a						,   )
         lowercase__    :   List[Any]      =       2 ** (len(self.movq.config.block_out_channels) - 1)





    def       snake_case_				(      self						,   a						,   a						,   a):
         # get the original timestep using init_timestep
         lowercase__    :   Optional[Any]      =       min(int(num_inference_steps * strength)						,   a)

         lowercase__    :   Tuple      =       max(num_inference_steps - init_timestep						,   0)
         lowercase__    :   List[Any]      =       self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start





    def       snake_case_				(      self						,   a						,   a						,   a						,   a						,   a						,   a						,   a=None):
         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)}""")

         lowercase__    :   Optional[Any]      =       image.to(device=a						,   dtype=a)

         lowercase__    :   Tuple      =       batch_size * num_images_per_prompt

         if image.shape[1] == 4:
              lowercase__    :   Dict      =       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):
                   lowercase__    :   Tuple      =       [
                       self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(a)
                   ]
                   lowercase__    :   Optional[int]      =       torch.cat(a						,   dim=0)
              else:
                   lowercase__    :   Union[str, Any]      =       self.movq.encode(a).latent_dist.sample(a)

              lowercase__    :   Union[str, Any]      =       self.movq.config.scaling_factor * init_latents

         lowercase__    :   Union[str, Any]      =       torch.cat([init_latents]						,   dim=0)

         lowercase__    :   Union[str, Any]      =       init_latents.shape
         lowercase__    :   Tuple      =       randn_tensor(a						,   generator=a						,   device=a						,   dtype=a)

         # get latents
         lowercase__    :   Optional[Any]      =       self.scheduler.add_noise(a						,   a						,   a)

         lowercase__    :   Union[str, Any]      =       init_latents

         return latents





    def       snake_case_				(      self						,   a=0):
         if is_accelerate_available():
              from accelerate import cpu_offload
         else:
              raise ImportError('Please install accelerate via `pip install accelerate`')

         lowercase__    :   Dict      =       torch.device(f"""cuda:{gpu_id}""")

         lowercase__    :   Union[str, Any]      =       [
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
              if cpu_offloaded_model is not None:
                   cpu_offload(a						,   a)





    def       snake_case_				(      self						,   a=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.')

         lowercase__    :   Optional[int]      =       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)

         lowercase__    :   List[str]      =       None
         for cpu_offloaded_model in [self.unet, self.movq]:
              lowercase__      , lowercase__    :   Optional[Any]      =       cpu_offload_with_hook(a						,   a						,   prev_module_hook=a)

         # We'll offload the last model manually.
         lowercase__    :   str      =       hook





    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def       snake_case_				(      self):
         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 = 512						,   a = 512						,   a = 100						,   a = 4.0						,   a = 0.3						,   a = 1						,   a = None						,   a = "pil"						,   a = True						,   ):
         lowercase__    :   Any      =       self._execution_device

         lowercase__    :   str      =       guidance_scale > 1.0

         if isinstance(a						,   a):
              lowercase__    :   List[str]      =       torch.cat(a						,   dim=0)
         lowercase__    :   str      =       image_embeds.shape[0]
         if isinstance(a						,   a):
              lowercase__    :   Optional[int]      =       torch.cat(a						,   dim=0)

         if do_classifier_free_guidance:
              lowercase__    :   Optional[int]      =       image_embeds.repeat_interleave(a						,   dim=0)
              lowercase__    :   Tuple      =       negative_image_embeds.repeat_interleave(a						,   dim=0)

         lowercase__    :   Any      =       torch.cat([negative_image_embeds, image_embeds]						,   dim=0).to(dtype=self.unet.dtype						,   device=a)

         if not isinstance(a						,   a):
              lowercase__    :   str      =       [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""")

         lowercase__    :   List[Any]      =       torch.cat([prepare_image(a						,   a						,   a) for i in image]						,   dim=0)
         lowercase__    :   Tuple      =       image.to(dtype=image_embeds.dtype						,   device=a)

         lowercase__    :   str      =       self.movq.encode(a)['latents']
         lowercase__    :   Optional[int]      =       latents.repeat_interleave(a						,   dim=0)
         self.scheduler.set_timesteps(a						,   device=a)
         lowercase__      , lowercase__    :   str      =       self.get_timesteps(a						,   a						,   a)
         lowercase__    :   Union[str, Any]      =       timesteps[:1].repeat(batch_size * num_images_per_prompt)
         lowercase__      , lowercase__    :   Any      =       downscale_height_and_width(a						,   a						,   self.movq_scale_factor)
         lowercase__    :   Optional[Any]      =       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
              lowercase__    :   int      =       torch.cat([latents] * 2) if do_classifier_free_guidance else latents

              lowercase__    :   int      =       {'image_embeds': image_embeds}
              lowercase__    :   int      =       self.unet(
                  sample=a						,   timestep=a						,   encoder_hidden_states=a						,   added_cond_kwargs=a						,   return_dict=a						,   )[0]

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

              # compute the previous noisy sample x_t -> x_t-1
              lowercase__    :   int      =       self.scheduler.step(
                  a						,   a						,   a						,   generator=a						,   )[0]

         # post-processing
         lowercase__    :   Union[str, 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"]:
              lowercase__    :   Union[str, Any]      =       image * 0.5 + 0.5
              lowercase__    :   int      =       image.clamp(0						,   1)
              lowercase__    :   str      =       image.cpu().permute(0						,   2						,   3						,   1).float().numpy()

         if output_type == "pil":
              lowercase__    :   str      =       self.numpy_to_pil(a)

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=a)




import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


snake_case_			  = logging.get_logger(__name__)


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   *a						,   **a):
         warnings.warn(
             'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use FlavaImageProcessor instead.'						,   a						,   )
         super().__init__(*a						,   **a)

