






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,
)


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

lowerCamelCase     :							Optional[Any]    ='''
    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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase=8      )			->     Dict:
     UpperCamelCase__      :       int	    =  height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     UpperCamelCase__      :       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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase=512 ,   __lowerCAmelCase=512      )			->     str:
     UpperCamelCase__      :       Any	    =  pil_image.resize((w, h) ,   resample=Image.BICUBIC ,   reducing_gap=1      )
     UpperCamelCase__      :       Any	    =  np.array(pil_image.convert("RGB"      )      )
     UpperCamelCase__      :       Tuple	    =  arr.astype(np.floataa      ) / 127.5 - 1
     UpperCamelCase__      :       str	    =  np.transpose(__lowerCAmelCase ,   [2, 0, 1]      )
     UpperCamelCase__      :       int	    =  torch.from_numpy(__lowerCAmelCase      ).unsqueeze(0      )
     return image





class       __a  (   A__	):







 def __init__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       UNetaDConditionModel							,       SCREAMING_SNAKE_CASE :       DDPMScheduler							,       SCREAMING_SNAKE_CASE :       VQModel							,       ):


      '''simple docstring'''






      super().__init__()

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







 def  __lowercase (		self :       Optional[int]							,       SCREAMING_SNAKE_CASE :       Tuple							,       SCREAMING_SNAKE_CASE :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       int						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  min(int(num_inference_steps * strength						)							,       SCREAMING_SNAKE_CASE						)

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

      return timesteps, num_inference_steps - t_start







 def  __lowercase (		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       str							,       SCREAMING_SNAKE_CASE :       str							,       SCREAMING_SNAKE_CASE :       List[str]							,       SCREAMING_SNAKE_CASE :       List[str]							,       SCREAMING_SNAKE_CASE :       int=None						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,       (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(SCREAMING_SNAKE_CASE						)}'						)

      UpperCamelCase__      :       Dict	    =  image.to(device=SCREAMING_SNAKE_CASE							,       dtype=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Optional[Any]	    =  batch_size * num_images_per_prompt

      if image.shape[1] == 4:
           UpperCamelCase__      :       str	    =  image

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

           elif isinstance(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						):
                UpperCamelCase__      :       Dict	    =  [
                    self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(SCREAMING_SNAKE_CASE						)
                ]
                UpperCamelCase__      :       str	    =  torch.cat(SCREAMING_SNAKE_CASE							,       dim=0						)
           else:
                UpperCamelCase__      :       Optional[Any]	    =  self.movq.encode(SCREAMING_SNAKE_CASE						).latent_dist.sample(SCREAMING_SNAKE_CASE						)

           UpperCamelCase__      :       int	    =  self.movq.config.scaling_factor * init_latents

      UpperCamelCase__      :       Optional[Any]	    =  torch.cat([init_latents]							,       dim=0						)

      UpperCamelCase__      :       Optional[int]	    =  init_latents.shape
      UpperCamelCase__      :       str	    =  randn_tensor(SCREAMING_SNAKE_CASE							,       generator=SCREAMING_SNAKE_CASE							,       device=SCREAMING_SNAKE_CASE							,       dtype=SCREAMING_SNAKE_CASE						)

      # get latents
      UpperCamelCase__      :       str	    =  self.scheduler.add_noise(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Optional[int]	    =  init_latents

      return latents







 def  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       List[Any]=0						):


      '''simple docstring'''






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

      UpperCamelCase__      :       Optional[Any]	    =  torch.device(F'cuda:{gpu_id}'						)

      UpperCamelCase__      :       Tuple	    =  [
          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  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       Dict=0						):


      '''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__      :       int	    =  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__      :       Union[str, Any]	    =  None
      for cpu_offloaded_model in [self.unet, self.movq]:
           UpperCamelCase__      :       int	    =  cpu_offload_with_hook(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       prev_module_hook=SCREAMING_SNAKE_CASE						)

      # We'll offload the last model manually.
      UpperCamelCase__      :       Optional[Any]	    =  hook







 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      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 :       List[str]							,       SCREAMING_SNAKE_CASE :       Union[torch.FloatTensor, List[torch.FloatTensor]]							,       SCREAMING_SNAKE_CASE :       Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]							,       SCREAMING_SNAKE_CASE :       Union[torch.FloatTensor, List[torch.FloatTensor]]							,       SCREAMING_SNAKE_CASE :       int = 5_12							,       SCREAMING_SNAKE_CASE :       int = 5_12							,       SCREAMING_SNAKE_CASE :       int = 1_00							,       SCREAMING_SNAKE_CASE :       float = 4.0							,       SCREAMING_SNAKE_CASE :       float = 0.3							,       SCREAMING_SNAKE_CASE :       int = 1							,       SCREAMING_SNAKE_CASE :       Optional[Union[torch.Generator, List[torch.Generator]]] = None							,       SCREAMING_SNAKE_CASE :       Optional[str] = "pil"							,       SCREAMING_SNAKE_CASE :       bool = True							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       Any	    =  self._execution_device

      UpperCamelCase__      :       Union[str, Any]	    =  guidance_scale > 1.0

      if isinstance(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						):
           UpperCamelCase__      :       Dict	    =  torch.cat(SCREAMING_SNAKE_CASE							,       dim=0						)
      UpperCamelCase__      :       Optional[int]	    =  image_embeds.shape[0]
      if isinstance(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						):
           UpperCamelCase__      :       Union[str, Any]	    =  torch.cat(SCREAMING_SNAKE_CASE							,       dim=0						)

      if do_classifier_free_guidance:
           UpperCamelCase__      :       List[Any]	    =  image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE							,       dim=0						)
           UpperCamelCase__      :       Dict	    =  negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE							,       dim=0						)

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

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

      UpperCamelCase__      :       Union[str, Any]	    =  torch.cat([prepare_image(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						) for i in image]							,       dim=0						)
      UpperCamelCase__      :       int	    =  image.to(dtype=image_embeds.dtype							,       device=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Optional[Any]	    =  self.movq.encode(SCREAMING_SNAKE_CASE						)["latents"]
      UpperCamelCase__      :       str	    =  latents.repeat_interleave(SCREAMING_SNAKE_CASE							,       dim=0						)
      self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE							,       device=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       int	    =  self.get_timesteps(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Optional[int]	    =  timesteps[:1].repeat(batch_size * num_images_per_prompt						)
      UpperCamelCase__      :       Dict	    =  downscale_height_and_width(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       self.movq_scale_factor						)
      UpperCamelCase__      :       int	    =  self.prepare_latents(
          SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       image_embeds.dtype							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)
      for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE						)						):
           # expand the latents if we are doing classifier free guidance
           UpperCamelCase__      :       Optional[int]	    =  torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

           UpperCamelCase__      :       List[Any]	    =  {"image_embeds": image_embeds}
           UpperCamelCase__      :       Tuple	    =  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__      :       str	    =  noise_pred.split(latents.shape[1]							,       dim=1						)
                UpperCamelCase__      :       Optional[Any]	    =  noise_pred.chunk(2						)
                UpperCamelCase__      :       Any	    =  variance_pred.chunk(2						)
                UpperCamelCase__      :       str	    =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                UpperCamelCase__      :       str	    =  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__      :       Optional[int]	    =  noise_pred.split(latents.shape[1]							,       dim=1						)

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

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

      if output_type == "pil":
           UpperCamelCase__      :       List[str]	    =  self.numpy_to_pil(SCREAMING_SNAKE_CASE						)

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE						)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase     :							Optional[int]    =logging.get_logger(__name__)

lowerCamelCase     :							Optional[Any]    ={
    '''edbeeching/decision-transformer-gym-hopper-medium''': (
        '''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}





class       __a  (   A__	):
 _lowerCAmelCase	:	str				=			'''decision_transformer'''
 _lowerCAmelCase	:	Optional[Any]				=			['''past_key_values''']
 _lowerCAmelCase	:	Optional[int]				=			{
     '''max_position_embeddings''': '''n_positions''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }







 def __init__(		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       Optional[int]=17							,       SCREAMING_SNAKE_CASE :       Optional[int]=4							,       SCREAMING_SNAKE_CASE :       Optional[int]=1_28							,       SCREAMING_SNAKE_CASE :       Dict=40_96							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=True							,       SCREAMING_SNAKE_CASE :       Tuple=1							,       SCREAMING_SNAKE_CASE :       Optional[int]=10_24							,       SCREAMING_SNAKE_CASE :       Any=3							,       SCREAMING_SNAKE_CASE :       str=1							,       SCREAMING_SNAKE_CASE :       Optional[int]=None							,       SCREAMING_SNAKE_CASE :       Dict="relu"							,       SCREAMING_SNAKE_CASE :       Optional[int]=0.1							,       SCREAMING_SNAKE_CASE :       str=0.1							,       SCREAMING_SNAKE_CASE :       str=0.1							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=1e-5							,       SCREAMING_SNAKE_CASE :       Optional[int]=0.0_2							,       SCREAMING_SNAKE_CASE :       Optional[Any]=True							,       SCREAMING_SNAKE_CASE :       Tuple=True							,       SCREAMING_SNAKE_CASE :       int=5_02_56							,       SCREAMING_SNAKE_CASE :       List[Any]=5_02_56							,       SCREAMING_SNAKE_CASE :       Any=False							,       SCREAMING_SNAKE_CASE :       List[Any]=False							,       **SCREAMING_SNAKE_CASE :       Optional[int]							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  state_dim
      UpperCamelCase__      :       Optional[int]	    =  act_dim
      UpperCamelCase__      :       Dict	    =  hidden_size
      UpperCamelCase__      :       Any	    =  max_ep_len
      UpperCamelCase__      :       Optional[Any]	    =  action_tanh
      UpperCamelCase__      :       str	    =  vocab_size
      UpperCamelCase__      :       List[str]	    =  n_positions
      UpperCamelCase__      :       Tuple	    =  n_layer
      UpperCamelCase__      :       Union[str, Any]	    =  n_head
      UpperCamelCase__      :       Dict	    =  n_inner
      UpperCamelCase__      :       int	    =  activation_function
      UpperCamelCase__      :       List[str]	    =  resid_pdrop
      UpperCamelCase__      :       Optional[int]	    =  embd_pdrop
      UpperCamelCase__      :       Optional[Any]	    =  attn_pdrop
      UpperCamelCase__      :       Optional[Any]	    =  layer_norm_epsilon
      UpperCamelCase__      :       int	    =  initializer_range
      UpperCamelCase__      :       Dict	    =  scale_attn_weights
      UpperCamelCase__      :       Tuple	    =  use_cache
      UpperCamelCase__      :       List[str]	    =  scale_attn_by_inverse_layer_idx
      UpperCamelCase__      :       Union[str, Any]	    =  reorder_and_upcast_attn

      UpperCamelCase__      :       Optional[Any]	    =  bos_token_id
      UpperCamelCase__      :       Optional[int]	    =  eos_token_id

      super().__init__(bos_token_id=SCREAMING_SNAKE_CASE							,       eos_token_id=SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)