


"""simple docstring"""


import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
   import os

   import jax.numpy as jnp
   from jax import jit

   from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
   from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

   _snake_case          =							"0.12"  # assumed parallelism: 8


if is_torch_available():
   import torch



def   snake_case						(				_a:     Optional[int]		,   _a:     List[Any]		,   _a:     List[Any]=None							)->		str:
      '''simple docstring'''


      if rng is None:
            lowerCamelCase__           =		random.Random()

      lowerCamelCase__           =		1
      for dim in shape:
            total_dims *= dim

      lowerCamelCase__           =		[]
      for _ in range(_a							):
            values.append(rng.randint(0		,   vocab_size - 1							)							)

      lowerCamelCase__           =		np.array(_a		,   dtype=jnp.intaa							).reshape(_a							)

      return output



def   snake_case						(				_a:     Optional[Any]		,   _a:     Tuple=None							)->		Optional[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		ids_tensor(_a		,   vocab_size=2		,   rng=_a							)
      # make sure that at least one token is attended to for each batch
      lowerCamelCase__           =		1
      return attn_mask



@require_flax
class   _a      :
    a_    :				Optional[Any]             =    None
    a_    :				List[Any]             =    ()
    def      _UpperCamelCase       ( self							:					List[Any]				):
          lowerCamelCase__    ,  lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs_for_common()

          # cut to half length & take max batch_size 3
          lowerCamelCase__           =		2
          lowerCamelCase__           =		inputs['input_ids'].shape[-1] // 2
          lowerCamelCase__           =		inputs['input_ids'][:max_batch_size, :sequence_length]

          lowerCamelCase__           =		jnp.ones_like(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		attention_mask[:max_batch_size, :sequence_length]

          # generate max 5 tokens
          lowerCamelCase__           =		input_ids.shape[-1] + 5
          if config.eos_token_id is not None and config.pad_token_id is None:
                # hack to allow generate for models such as GPT2 as is done in `generate()`
                lowerCamelCase__           =		config.eos_token_id
          return config, input_ids, attention_mask, max_length
    @is_pt_flax_cross_test
    def      _UpperCamelCase       ( self							:					Any				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()
          lowerCamelCase__           =		False
          lowerCamelCase__           =		max_length
          lowerCamelCase__           =		0

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
                lowerCamelCase__           =		getattr(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		pt_model_class(SCREAMING_SNAKE_CASE__				).eval()
                lowerCamelCase__           =		load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE__							,					flax_model.params				)

                lowerCamelCase__           =		flax_model.generate(SCREAMING_SNAKE_CASE__				).sequences
                lowerCamelCase__           =		pt_model.generate(torch.tensor(SCREAMING_SNAKE_CASE__							,					dtype=torch.long				)				)

                if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                      lowerCamelCase__           =		flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

                self.assertListEqual(pt_generation_outputs.numpy().tolist()							,					flax_generation_outputs.tolist()				)
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()
          lowerCamelCase__           =		False
          lowerCamelCase__           =		max_length

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,					jit_generation_outputs.tolist()				)
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()
          lowerCamelCase__           =		True
          lowerCamelCase__           =		max_length

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,					jit_generation_outputs.tolist()				)
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()
          lowerCamelCase__           =		False
          lowerCamelCase__           =		max_length
          lowerCamelCase__           =		2

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,					jit_generation_outputs.tolist()				)
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()
          lowerCamelCase__           =		False
          lowerCamelCase__           =		max_length
          lowerCamelCase__           =		2
          lowerCamelCase__           =		2

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[0]							,					input_ids.shape[0] * config.num_return_sequences				)
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()
          lowerCamelCase__           =		True
          lowerCamelCase__           =		max_length
          lowerCamelCase__           =		0.8
          lowerCamelCase__           =		10
          lowerCamelCase__           =		0.3
          lowerCamelCase__           =		1
          lowerCamelCase__           =		8
          lowerCamelCase__           =		9

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,					jit_generation_outputs.tolist()				)
    def      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()
          lowerCamelCase__           =		max_length
          lowerCamelCase__           =		1
          lowerCamelCase__           =		8
          lowerCamelCase__           =		9

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,					jit_generation_outputs.tolist()				)
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()
          lowerCamelCase__           =		max_length
          lowerCamelCase__           =		2
          lowerCamelCase__           =		1
          lowerCamelCase__           =		8
          lowerCamelCase__           =		9

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,					jit_generation_outputs.tolist()				)
    def      _UpperCamelCase       ( self							:					Any				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()

          # pad attention mask on the left
          lowerCamelCase__           =		attention_mask.at[(0, 0)].set(0				)

          lowerCamelCase__           =		False
          lowerCamelCase__           =		max_length

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__							,					attention_mask=SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__							,					attention_mask=SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,					jit_generation_outputs.tolist()				)
    def      _UpperCamelCase       ( self							:					Dict				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()

          # pad attention mask on the left
          lowerCamelCase__           =		attention_mask.at[(0, 0)].set(0				)

          lowerCamelCase__           =		True
          lowerCamelCase__           =		max_length

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__							,					attention_mask=SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__							,					attention_mask=SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,					jit_generation_outputs.tolist()				)


    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()

          # pad attention mask on the left
          lowerCamelCase__           =		attention_mask.at[(0, 0)].set(0				)

          lowerCamelCase__           =		2
          lowerCamelCase__           =		max_length

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__							,					attention_mask=SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__							,					attention_mask=SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,					jit_generation_outputs.tolist()				)





@require_flax
class   _a      (      unittest.TestCase ):
    def      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__           =		AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'				)
          lowerCamelCase__           =		FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'				)

          lowerCamelCase__           =		'Hello world'
          lowerCamelCase__           =		tokenizer(SCREAMING_SNAKE_CASE__							,					return_tensors='np'				).input_ids

          # typos are quickly detected (the correct argument is `do_sample`)
          with self.assertRaisesRegex(SCREAMING_SNAKE_CASE__							,					'do_samples'				):
                model.generate(SCREAMING_SNAKE_CASE__							,					do_samples=SCREAMING_SNAKE_CASE__				)

          # arbitrary arguments that will not be used anywhere are also not accepted
          with self.assertRaisesRegex(SCREAMING_SNAKE_CASE__							,					'foo'				):
                lowerCamelCase__           =		{'foo': 'bar'}
                model.generate(SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				)







"""simple docstring"""


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


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

_snake_case          =							"\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e-img2img\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n        >>> image = load_image(image_url).convert(\"RGB\")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n        ```\n"



@dataclass
class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Union[PIL.Image.Image, np.ndarray]





class   _a      (      SCREAMING_SNAKE_CASE_ ):
    def __init__( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					PriorTransformer							,					SCREAMING_SNAKE_CASE__							:					CLIPVisionModel							,					SCREAMING_SNAKE_CASE__							:					CLIPImageProcessor							,					SCREAMING_SNAKE_CASE__							:					HeunDiscreteScheduler							,					SCREAMING_SNAKE_CASE__							:					ShapERenderer							,					):
          super().__init__()

          self.register_modules(
              prior=SCREAMING_SNAKE_CASE__							,					image_encoder=SCREAMING_SNAKE_CASE__							,					image_processor=SCREAMING_SNAKE_CASE__							,					scheduler=SCREAMING_SNAKE_CASE__							,					renderer=SCREAMING_SNAKE_CASE__							,					)
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					List[str]				):
          if latents is None:
                lowerCamelCase__           =		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}'				)
                lowerCamelCase__           =		latents.to(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		latents * scheduler.init_noise_sigma
          return latents
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					List[str]=0				):
          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(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          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(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
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					int							,					):
          if isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				) and isinstance(image[0]							,					torch.Tensor				):
                lowerCamelCase__           =		torch.cat(SCREAMING_SNAKE_CASE__							,					axis=0				) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE__							,					axis=0				)

          if not isinstance(SCREAMING_SNAKE_CASE__							,					torch.Tensor				):
                lowerCamelCase__           =		self.image_processor(SCREAMING_SNAKE_CASE__							,					return_tensors='pt'				).pixel_values[0].unsqueeze(0				)

          lowerCamelCase__           =		image.to(dtype=self.image_encoder.dtype							,					device=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.image_encoder(SCREAMING_SNAKE_CASE__				)['last_hidden_state']
          lowerCamelCase__           =		image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

          lowerCamelCase__           =		image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__							,					dim=0				)

          if do_classifier_free_guidance:
                lowerCamelCase__           =		torch.zeros_like(SCREAMING_SNAKE_CASE__				)

                # 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(SCREAMING_SNAKE_CASE__				)
    def __call__( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Union[PIL.Image.Image, List[PIL.Image.Image]]							,					SCREAMING_SNAKE_CASE__							:					int = 1							,					SCREAMING_SNAKE_CASE__							:					int = 25							,					SCREAMING_SNAKE_CASE__							:					Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					SCREAMING_SNAKE_CASE__							:					Optional[torch.FloatTensor] = None							,					SCREAMING_SNAKE_CASE__							:					float = 4.0							,					SCREAMING_SNAKE_CASE__							:					int = 64							,					SCREAMING_SNAKE_CASE__							:					Optional[str] = "pil"							,					SCREAMING_SNAKE_CASE__							:					bool = True							,					):

          if isinstance(SCREAMING_SNAKE_CASE__							,					PIL.Image.Image				):
                lowerCamelCase__           =		1
          elif isinstance(SCREAMING_SNAKE_CASE__							,					torch.Tensor				):
                lowerCamelCase__           =		image.shape[0]
          elif isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				) and isinstance(image[0]							,					(torch.Tensor, PIL.Image.Image)				):
                lowerCamelCase__           =		len(SCREAMING_SNAKE_CASE__				)
          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(SCREAMING_SNAKE_CASE__				)}'				)

          lowerCamelCase__           =		self._execution_device

          lowerCamelCase__           =		batch_size * num_images_per_prompt

          lowerCamelCase__           =		guidance_scale > 1.0
          lowerCamelCase__           =		self._encode_image(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

          # prior

          self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__							,					device=SCREAMING_SNAKE_CASE__				)
          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							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					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]							,					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
                lowerCamelCase__           =		torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
                lowerCamelCase__           =		self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		self.prior(
                    SCREAMING_SNAKE_CASE__							,					timestep=SCREAMING_SNAKE_CASE__							,					proj_embedding=SCREAMING_SNAKE_CASE__							,					).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(
                    SCREAMING_SNAKE_CASE__							,					timestep=SCREAMING_SNAKE_CASE__							,					sample=SCREAMING_SNAKE_CASE__							,					).prev_sample

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

          lowerCamelCase__           =		[]
          for i, latent in enumerate(SCREAMING_SNAKE_CASE__				):
                print()
                lowerCamelCase__           =		self.renderer.decode(
                    latent[None, :]							,					SCREAMING_SNAKE_CASE__							,					size=SCREAMING_SNAKE_CASE__							,					ray_batch_size=40_96							,					n_coarse_samples=64							,					n_fine_samples=1_28							,					)

                images.append(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		torch.stack(SCREAMING_SNAKE_CASE__				)

          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(SCREAMING_SNAKE_CASE__				) 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=SCREAMING_SNAKE_CASE__				)



