






import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


lowerCamelCase			:					Union[str, Any]         =	get_logger(__name__)


lowerCamelCase			:					int         =	R'''
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

'''



class 							_UpperCamelCase			:


      @add_start_docstrings(__UpperCamelCase	)
      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase	)->	jnp.ndarray:
           raise NotImplementedError(
               F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""	)



class 							_UpperCamelCase			:


      @add_start_docstrings(__UpperCamelCase	)
      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase	)->	jnp.ndarray:
           raise NotImplementedError(
               F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""	)



class 							_UpperCamelCase			(a_			):


      @add_start_docstrings(__UpperCamelCase	)
      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						**__UpperCamelCase	)->	jnp.ndarray:
           for processor in self:
                __lowerCAmelCase								=					inspect.signature(processor.__call__	).parameters
                if len(__UpperCamelCase	) > 3:
                     if not all(arg in kwargs for arg in list(function_args.keys()	)[2:]	):
                          raise ValueError(
                              F"""Make sure that all the required parameters: {list(function_args.keys()	)} for """
                              F"""{processor.__class__} are passed to the logits processor."""	)
                     __lowerCAmelCase								=					processor(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						**__UpperCamelCase	)
                else:
                     __lowerCAmelCase								=					processor(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)
           return scores



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase	)->	Optional[Any]:
           if not isinstance(__UpperCamelCase					,						__UpperCamelCase	) or not (temperature > 0):
                raise ValueError(F"""`temperature` has to be a strictly positive float, but is {temperature}"""	)

           __lowerCAmelCase								=					temperature


      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	jnp.ndarray:
           __lowerCAmelCase								=					scores / self.temperature
           return scores



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase = -float("Inf"	)					,						__UpperCamelCase = 1	)->	List[Any]:
           if not isinstance(__UpperCamelCase					,						__UpperCamelCase	) or (top_p < 0 or top_p > 1.0):
                raise ValueError(F"""`top_p` has to be a float > 0 and < 1, but is {top_p}"""	)
           if not isinstance(__UpperCamelCase					,						__UpperCamelCase	) or (min_tokens_to_keep < 1):
                raise ValueError(F"""`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}"""	)

           __lowerCAmelCase								=					top_p
           __lowerCAmelCase								=					filter_value
           __lowerCAmelCase								=					min_tokens_to_keep


      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	jnp.ndarray:
           __lowerCAmelCase			, __lowerCAmelCase								=					lax.top_k(__UpperCamelCase					,						scores.shape[-1]	)

           __lowerCAmelCase								=					jnp.full_like(__UpperCamelCase					,						self.filter_value	)
           __lowerCAmelCase								=					jax.nn.softmax(__UpperCamelCase					,						axis=-1	).cumsum(axis=-1	)
           __lowerCAmelCase								=					cumulative_probs < self.top_p

           # include the token that is higher than top_p as well
           __lowerCAmelCase								=					jnp.roll(__UpperCamelCase					,						1	)
           score_mask |= score_mask.at[:, 0].set(__UpperCamelCase	)

           # min tokens to keep
           __lowerCAmelCase								=					score_mask.at[:, : self.min_tokens_to_keep].set(__UpperCamelCase	)

           __lowerCAmelCase								=					jnp.where(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)
           __lowerCAmelCase								=					jax.lax.sort_key_val(__UpperCamelCase					,						__UpperCamelCase	)[-1]

           return next_scores



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase = -float("Inf"	)					,						__UpperCamelCase = 1	)->	Optional[int]:
           if not isinstance(__UpperCamelCase					,						__UpperCamelCase	) or top_k <= 0:
                raise ValueError(F"""`top_k` has to be a strictly positive integer, but is {top_k}"""	)

           __lowerCAmelCase								=					max(__UpperCamelCase					,						__UpperCamelCase	)
           __lowerCAmelCase								=					filter_value


      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	jnp.ndarray:
           __lowerCAmelCase			, __lowerCAmelCase								=					scores.shape
           __lowerCAmelCase								=					jnp.full(batch_size * vocab_size					,						self.filter_value	)

           __lowerCAmelCase								=					min(self.top_k					,						scores.shape[-1]	)  # Safety check
           __lowerCAmelCase			, __lowerCAmelCase								=					lax.top_k(__UpperCamelCase					,						__UpperCamelCase	)
           __lowerCAmelCase								=					jnp.broadcast_to((jnp.arange(__UpperCamelCase	) * vocab_size)[:, None]					,						(batch_size, topk)	).flatten()
           __lowerCAmelCase								=					topk_scores.flatten()
           __lowerCAmelCase								=					topk_indices.flatten() + shift

           __lowerCAmelCase								=					next_scores_flat.at[topk_indices_flat].set(__UpperCamelCase	)
           __lowerCAmelCase								=					next_scores_flat.reshape(__UpperCamelCase					,						__UpperCamelCase	)
           return next_scores



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase	)->	Optional[Any]:
           __lowerCAmelCase								=					bos_token_id


      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	jnp.ndarray:
           __lowerCAmelCase								=					jnp.full(scores.shape					,						-float("inf"	)	)

           __lowerCAmelCase								=					1 - jnp.bool_(cur_len - 1	)

           __lowerCAmelCase								=					jnp.where(__UpperCamelCase					,						new_scores.at[:, self.bos_token_id].set(0	)					,						__UpperCamelCase	)

           return scores



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase	)->	Optional[int]:
           __lowerCAmelCase								=					max_length
           __lowerCAmelCase								=					eos_token_id


      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	jnp.ndarray:
           __lowerCAmelCase								=					jnp.full(scores.shape					,						-float("inf"	)	)

           __lowerCAmelCase								=					1 - jnp.bool_(cur_len - self.max_length + 1	)

           __lowerCAmelCase								=					jnp.where(__UpperCamelCase					,						new_scores.at[:, self.eos_token_id].set(0	)					,						__UpperCamelCase	)

           return scores



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase	)->	Union[str, Any]:
           if not isinstance(__UpperCamelCase					,						__UpperCamelCase	) or min_length < 0:
                raise ValueError(F"""`min_length` has to be a positive integer, but is {min_length}"""	)

           if not isinstance(__UpperCamelCase					,						__UpperCamelCase	) or eos_token_id < 0:
                raise ValueError(F"""`eos_token_id` has to be a positive integer, but is {eos_token_id}"""	)

           __lowerCAmelCase								=					min_length
           __lowerCAmelCase								=					eos_token_id


      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	jnp.ndarray:
           # create boolean flag to decide if min length penalty should be applied
           __lowerCAmelCase								=					1 - jnp.clip(cur_len - self.min_length					,						0					,						1	)

           __lowerCAmelCase								=					jnp.where(__UpperCamelCase					,						scores.at[:, self.eos_token_id].set(-float("inf"	)	)					,						__UpperCamelCase	)

           return scores



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase	)->	Dict:
           __lowerCAmelCase								=					list(__UpperCamelCase	)
           __lowerCAmelCase								=					begin_index


      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Union[str, Any]:
           __lowerCAmelCase								=					1 - jnp.bool_(cur_len - self.begin_index	)

           __lowerCAmelCase								=					jnp.where(__UpperCamelCase					,						scores.at[:, self.begin_suppress_tokens].set(-float("inf"	)	)					,						__UpperCamelCase	)

           return scores



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase	)->	int:
           __lowerCAmelCase								=					list(__UpperCamelCase	)


      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	jnp.ndarray:
           __lowerCAmelCase								=					scores.at[..., self.suppress_tokens].set(-float("inf"	)	)

           return scores



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase	)->	List[str]:
           __lowerCAmelCase								=					dict(__UpperCamelCase	)
           # Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
           # index of the array corresponds to the index of the token to be forced, for XLA compatibility.
           # Indexes without forced tokens will have a negative value.
           __lowerCAmelCase								=					jnp.ones((max(force_token_map.keys()	) + 1)					,						dtype=jnp.intaa	) * -1
           for index, token in force_token_map.items():
                if token is not None:
                     __lowerCAmelCase								=					force_token_array.at[index].set(__UpperCamelCase	)
           __lowerCAmelCase								=					jnp.intaa(__UpperCamelCase	)


      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	jnp.ndarray:
           def _force_token(__UpperCamelCase	):
                __lowerCAmelCase								=					scores.shape[0]
                __lowerCAmelCase								=					self.force_token_array[generation_idx]

                __lowerCAmelCase								=					jnp.ones_like(__UpperCamelCase					,						dtype=scores.dtype	) * -float("inf"	)
                __lowerCAmelCase								=					jnp.zeros((batch_size, 1)					,						dtype=scores.dtype	)
                __lowerCAmelCase								=					lax.dynamic_update_slice(__UpperCamelCase					,						__UpperCamelCase					,						(0, current_token)	)
                return new_scores

           __lowerCAmelCase								=					lax.cond(
               cur_len >= self.force_token_array.shape[0]					,						lambda: scores					,						lambda: lax.cond(
                   self.force_token_array[cur_len] >= 0					,						lambda: _force_token(__UpperCamelCase	)					,						lambda: scores					,						)					,						)
           return scores



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	int:
           __lowerCAmelCase								=					generate_config.eos_token_id
           __lowerCAmelCase								=					generate_config.no_timestamps_token_id
           __lowerCAmelCase								=					generate_config.no_timestamps_token_id + 1

           __lowerCAmelCase								=					decoder_input_length + 1

           if generate_config.is_multilingual:
                # room for language token and task token
                self.begin_index += 2
           if hasattr(__UpperCamelCase					,						"max_initial_timestamp_index"	):
                __lowerCAmelCase								=					generate_config.max_initial_timestamp_index
           else:
                __lowerCAmelCase								=					model_config.vocab_size
           if self.max_initial_timestamp_index is None:
                __lowerCAmelCase								=					model_config.vocab_size


      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Optional[int]:
           # suppress <|notimestamps|> which is handled by without_timestamps
           __lowerCAmelCase								=					scores.at[:, self.no_timestamps_token_id].set(-float("inf"	)	)

           def handle_pairs(__UpperCamelCase					,						__UpperCamelCase	):
                __lowerCAmelCase								=					jnp.where((cur_len - self.begin_index) >= 1					,						__UpperCamelCase					,						__UpperCamelCase	)
                __lowerCAmelCase								=					jnp.where(
                    input_ids_k[cur_len - 1] >= self.timestamp_begin					,						True and last_was_timestamp					,						__UpperCamelCase					,						)

                __lowerCAmelCase								=					jnp.where((cur_len - self.begin_index) < 2					,						__UpperCamelCase					,						__UpperCamelCase	)
                __lowerCAmelCase								=					jnp.where(
                    input_ids_k[cur_len - 2] >= self.timestamp_begin					,						__UpperCamelCase					,						__UpperCamelCase					,						)

                return jnp.where(
                    __UpperCamelCase					,						jnp.where(
                        penultimate_was_timestamp > 0					,						scores_k.at[self.timestamp_begin :].set(-float("inf"	)	)					,						scores_k.at[: self.eos_token_id].set(-float("inf"	)	)					,						)					,						__UpperCamelCase					,						)

           __lowerCAmelCase								=					jax.vmap(__UpperCamelCase	)(__UpperCamelCase					,						__UpperCamelCase	)

           __lowerCAmelCase								=					jnp.where(cur_len == self.begin_index					,						__UpperCamelCase					,						__UpperCamelCase	)
           __lowerCAmelCase								=					jnp.where(
               self.max_initial_timestamp_index is not None					,						True and apply_max_initial_timestamp					,						__UpperCamelCase					,						)

           __lowerCAmelCase								=					self.timestamp_begin + self.max_initial_timestamp_index

           __lowerCAmelCase								=					jnp.where(
               __UpperCamelCase					,						scores.at[:, last_allowed + 1 :].set(-float("inf"	)	)					,						__UpperCamelCase					,						)

           # if sum of probability over timestamps is above any other token, sample timestamp
           __lowerCAmelCase								=					jax.nn.log_softmax(__UpperCamelCase					,						axis=-1	)

           def handle_cumulative_probs(__UpperCamelCase					,						__UpperCamelCase	):
                __lowerCAmelCase								=					jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]					,						axis=-1	)
                __lowerCAmelCase								=					jnp.max(logprobs_k[: self.timestamp_begin]	)
                return jnp.where(
                    timestamp_logprob > max_text_token_logprob					,						scores_k.at[: self.timestamp_begin].set(-float("inf"	)	)					,						__UpperCamelCase					,						)

           __lowerCAmelCase								=					jax.vmap(__UpperCamelCase	)(__UpperCamelCase					,						__UpperCamelCase	)

           return scores














from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

lowerCamelCase			:					str         =	'''
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        >>> repo = "openai/shap-e-img2img"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"
        >>> image = load_image(image_url).convert("RGB")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")
        ```
'''



@dataclass
class 							_UpperCamelCase			(a_			):
      snake_case_  =							42



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						)->	Dict:
           super().__init__()

           self.register_modules(
               prior=__UpperCamelCase					,						image_encoder=__UpperCamelCase					,						image_processor=__UpperCamelCase					,						scheduler=__UpperCamelCase					,						renderer=__UpperCamelCase					,						)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Union[str, Any]:
           if latents is None:
                __lowerCAmelCase								=					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}"""	)
                __lowerCAmelCase								=					latents.to(__UpperCamelCase	)

           __lowerCAmelCase								=					latents * scheduler.init_noise_sigma
           return latents


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase=0	)->	str:
           if is_accelerate_available():
                from accelerate import cpu_offload
           else:
                raise ImportError("Please install accelerate via `pip install accelerate`"	)

           __lowerCAmelCase								=					torch.device(F"""cuda:{gpu_id}"""	)

           __lowerCAmelCase								=					[self.image_encoder, self.prior]
           for cpu_offloaded_model in models:
                if cpu_offloaded_model is not None:
                     cpu_offload(__UpperCamelCase					,						__UpperCamelCase	)


      @property
      def  __UpperCAmelCase		(	self	)->	List[str]:
           if self.device != torch.device("meta"	) or not hasattr(self.image_encoder					,						"_hf_hook"	):
                return self.device
           for module in self.image_encoder.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


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						)->	Union[str, Any]:
           if isinstance(__UpperCamelCase					,						__UpperCamelCase	) and isinstance(image[0]					,						torch.Tensor	):
                __lowerCAmelCase								=					torch.cat(__UpperCamelCase					,						axis=0	) if image[0].ndim == 4 else torch.stack(__UpperCamelCase					,						axis=0	)

           if not isinstance(__UpperCamelCase					,						torch.Tensor	):
                __lowerCAmelCase								=					self.image_processor(__UpperCamelCase					,						return_tensors="pt"	).pixel_values[0].unsqueeze(0	)

           __lowerCAmelCase								=					image.to(dtype=self.image_encoder.dtype					,						device=__UpperCamelCase	)

           __lowerCAmelCase								=					self.image_encoder(__UpperCamelCase	)["last_hidden_state"]
           __lowerCAmelCase								=					image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

           __lowerCAmelCase								=					image_embeds.repeat_interleave(__UpperCamelCase					,						dim=0	)

           if do_classifier_free_guidance:
                __lowerCAmelCase								=					torch.zeros_like(__UpperCamelCase	)

                # For classifier free guidance, we need to do two forward passes.
                # Here we concatenate the unconditional and text embeddings into a single batch
                # to avoid doing two forward passes
                __lowerCAmelCase								=					torch.cat([negative_image_embeds, image_embeds]	)

           return image_embeds


      @torch.no_grad()
      @replace_example_docstring(__UpperCamelCase	)
      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase = 1					,						__UpperCamelCase = 2_5					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = 4.0					,						__UpperCamelCase = 6_4					,						__UpperCamelCase = "pil"					,						__UpperCamelCase = True					,						)->	Dict:

           if isinstance(__UpperCamelCase					,						PIL.Image.Image	):
                __lowerCAmelCase								=					1
           elif isinstance(__UpperCamelCase					,						torch.Tensor	):
                __lowerCAmelCase								=					image.shape[0]
           elif isinstance(__UpperCamelCase					,						__UpperCamelCase	) and isinstance(image[0]					,						(torch.Tensor, PIL.Image.Image)	):
                __lowerCAmelCase								=					len(__UpperCamelCase	)
           else:
                raise ValueError(
                    F"""`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(__UpperCamelCase	)}"""	)

           __lowerCAmelCase								=					self._execution_device

           __lowerCAmelCase								=					batch_size * num_images_per_prompt

           __lowerCAmelCase								=					guidance_scale > 1.0
           __lowerCAmelCase								=					self._encode_image(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)

           # prior

           self.scheduler.set_timesteps(__UpperCamelCase					,						device=__UpperCamelCase	)
           __lowerCAmelCase								=					self.scheduler.timesteps

           __lowerCAmelCase								=					self.prior.config.num_embeddings
           __lowerCAmelCase								=					self.prior.config.embedding_dim

           __lowerCAmelCase								=					self.prepare_latents(
               (batch_size, num_embeddings * embedding_dim)					,						image_embeds.dtype					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						self.scheduler					,						)

           # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
           __lowerCAmelCase								=					latents.reshape(latents.shape[0]					,						__UpperCamelCase					,						__UpperCamelCase	)

           for i, t in enumerate(self.progress_bar(__UpperCamelCase	)	):
                # expand the latents if we are doing classifier free guidance
                __lowerCAmelCase								=					torch.cat([latents] * 2	) if do_classifier_free_guidance else latents
                __lowerCAmelCase								=					self.scheduler.scale_model_input(__UpperCamelCase					,						__UpperCamelCase	)

                __lowerCAmelCase								=					self.prior(
                    __UpperCamelCase					,						timestep=__UpperCamelCase					,						proj_embedding=__UpperCamelCase					,						).predicted_image_embedding

                # remove the variance
                __lowerCAmelCase			, __lowerCAmelCase								=					noise_pred.split(
                    scaled_model_input.shape[2]					,						dim=2	)  # batch_size, num_embeddings, embedding_dim

                if do_classifier_free_guidance is not None:
                     __lowerCAmelCase			, __lowerCAmelCase								=					noise_pred.chunk(2	)
                     __lowerCAmelCase								=					noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

                __lowerCAmelCase								=					self.scheduler.step(
                    __UpperCamelCase					,						timestep=__UpperCamelCase					,						sample=__UpperCamelCase					,						).prev_sample

           if output_type == "latent":
                return ShapEPipelineOutput(images=__UpperCamelCase	)

           __lowerCAmelCase								=					[]
           for i, latent in enumerate(__UpperCamelCase	):
                print()
                __lowerCAmelCase								=					self.renderer.decode(
                    latent[None, :]					,						__UpperCamelCase					,						size=__UpperCamelCase					,						ray_batch_size=4_0_9_6					,						n_coarse_samples=6_4					,						n_fine_samples=1_2_8					,						)

                images.append(__UpperCamelCase	)

           __lowerCAmelCase								=					torch.stack(__UpperCamelCase	)

           if output_type not in ["np", "pil"]:
                raise ValueError(F"""Only the output types `pil` and `np` are supported not output_type={output_type}"""	)

           __lowerCAmelCase								=					images.cpu().numpy()

           if output_type == "pil":
                __lowerCAmelCase								=					[self.numpy_to_pil(__UpperCamelCase	) for image in images]

           # Offload last model to CPU
           if hasattr(self					,						"final_offload_hook"	) and self.final_offload_hook is not None:
                self.final_offload_hook.offload()

           if not return_dict:
                return (images,)

           return ShapEPipelineOutput(images=__UpperCamelCase	)






