



'''simple docstring'''




import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
      import jax






@require_flax
class 						_UpperCamelCase  :


   '''simple docstring'''

   def UpperCamelCase__ (							self					:			int						):





     """simple docstring"""






     __SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.prepare_init_args_and_inputs_for_common()

     __SCREAMING_SNAKE_CASE	:		Tuple   	=		self.model_class(**__lowerCamelCase						)
     __SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		model.init(inputs_dict["""prng_key"""]							,					inputs_dict["""sample"""]						)
     jax.lax.stop_gradient(__lowerCamelCase						)

     __SCREAMING_SNAKE_CASE	:		int   	=		model.apply(__lowerCamelCase							,					inputs_dict["""sample"""]						)

     if isinstance(__lowerCamelCase							,					__lowerCamelCase						):
       __SCREAMING_SNAKE_CASE	:		str   	=		output.sample

     self.assertIsNotNone(__lowerCamelCase						)
     __SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		inputs_dict["sample"].shape
     self.assertEqual(output.shape							,					__lowerCamelCase							,					"""Input and output shapes do not match"""						)

   def UpperCamelCase__ (							self					:			Union[str, Any]						):





     """simple docstring"""






     __SCREAMING_SNAKE_CASE	:		List[str]   	=		self.prepare_init_args_and_inputs_for_common()

     __SCREAMING_SNAKE_CASE	:		List[Any]   	=		1_6
     __SCREAMING_SNAKE_CASE	:		int   	=		(1_6, 3_2)

     __SCREAMING_SNAKE_CASE	:		Tuple   	=		self.model_class(**__lowerCamelCase						)
     __SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		model.init(inputs_dict["""prng_key"""]							,					inputs_dict["""sample"""]						)
     jax.lax.stop_gradient(__lowerCamelCase						)

     __SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		model.apply(__lowerCamelCase							,					inputs_dict["""sample"""]						)

     if isinstance(__lowerCamelCase							,					__lowerCamelCase						):
       __SCREAMING_SNAKE_CASE	:		Dict   	=		output.sample

     self.assertIsNotNone(__lowerCamelCase						)
     __SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		inputs_dict["sample"].shape
     self.assertEqual(output.shape							,					__lowerCamelCase							,					"""Input and output shapes do not match"""						)



   def UpperCamelCase__ (							self					:			Tuple						):





     """simple docstring"""






     __SCREAMING_SNAKE_CASE	:		List[str]   	=		"kwargs" in inspect.signature(self.model_class.__init__						).parameters
     __SCREAMING_SNAKE_CASE	:		Any   	=		len(self.model_class._deprecated_kwargs						) > 0

     if has_kwarg_in_model_class and not has_deprecated_kwarg:
       raise ValueError(
           F"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
           """ under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"""
           """ no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="""
           """ [<deprecated_argument>]`"""						)

     if not has_kwarg_in_model_class and has_deprecated_kwarg:
       raise ValueError(
           F"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
           """ under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"""
           F" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
           """ from `_deprecated_kwargs = [<deprecated_argument>]`"""						)




'''simple docstring'''




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

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

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


UpperCamelCase__	:     List[str]    =			logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase__	:     Tuple    =			'''
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif

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

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

        >>> guidance_scale = 15.0
        >>> prompt = "a shark"

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

        >>> gif_path = export_to_gif(images[0], "shark_3d.gif")
        ```
'''






@dataclass
class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			def __init__(							self					:			List[str]							,					lowerCAmelCase__					:			PriorTransformer							,					lowerCAmelCase__					:			CLIPTextModelWithProjection							,					lowerCAmelCase__					:			CLIPTokenizer							,					lowerCAmelCase__					:			HeunDiscreteScheduler							,					lowerCAmelCase__					:			ShapERenderer							,					):





					"""simple docstring"""






					super().__init__()

					self.register_modules(
					    prior=lowerCAmelCase__							,					text_encoder=lowerCAmelCase__							,					tokenizer=lowerCAmelCase__							,					scheduler=lowerCAmelCase__							,					renderer=lowerCAmelCase__							,					)

			def UpperCamelCase__ (							self					:			Tuple							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			List[str]							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			Dict							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			int						):





					"""simple docstring"""






					if latents is None:
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		randn_tensor(lowerCAmelCase__							,					generator=lowerCAmelCase__							,					device=lowerCAmelCase__							,					dtype=lowerCAmelCase__						)
					else:
							if latents.shape != shape:
									raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"						)
							__SCREAMING_SNAKE_CASE	:		Any   	=		latents.to(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Dict   	=		latents * scheduler.init_noise_sigma
					return latents

			def UpperCamelCase__ (							self					:			str							,					lowerCAmelCase__					:			int=0						):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		torch.device(F"cuda:{gpu_id}"						)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		[self.text_encoder, self.prior]
					for cpu_offloaded_model in models:
							if cpu_offloaded_model is not None:
									cpu_offload(lowerCAmelCase__							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int							,					lowerCAmelCase__					:			str=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."""						)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		torch.device(F"cuda:{gpu_id}"						)

					if self.device.type != "cpu":
							self.to("""cpu"""							,					silence_dtype_warnings=lowerCAmelCase__						)
							torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		None
					for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Dict   	=		cpu_offload_with_hook(lowerCAmelCase__							,					lowerCAmelCase__							,					prev_module_hook=lowerCAmelCase__						)

					if self.safety_checker is not None:
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[str]   	=		cpu_offload_with_hook(self.safety_checker							,					lowerCAmelCase__							,					prev_module_hook=lowerCAmelCase__						)

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

			@property
			def UpperCamelCase__ (							self					:			List[str]						):





					"""simple docstring"""






					if self.device != torch.device("""meta"""						) or not hasattr(self.text_encoder							,					"""_hf_hook"""						):
							return self.device
					for module in self.text_encoder.modules():
							if (
							    hasattr(lowerCAmelCase__							,					"""_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]							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			Tuple							,					):





					"""simple docstring"""






					len(lowerCAmelCase__						) if isinstance(lowerCAmelCase__							,					lowerCAmelCase__						) else 1

					# YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		0
					# get prompt text embeddings
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.tokenizer(
					    lowerCAmelCase__							,					padding="""max_length"""							,					max_length=self.tokenizer.model_max_length							,					truncation=lowerCAmelCase__							,					return_tensors="""pt"""							,					)
					__SCREAMING_SNAKE_CASE	:		str   	=		text_inputs.input_ids
					__SCREAMING_SNAKE_CASE	:		Dict   	=		self.tokenizer(lowerCAmelCase__							,					padding="""longest"""							,					return_tensors="""pt"""						).input_ids

					if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowerCAmelCase__							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		Dict   	=		self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]						)
							logger.warning(
							    """The following part of your input was truncated because CLIP can only handle sequences up to"""
							    F" {self.tokenizer.model_max_length} tokens: {removed_text}"						)

					__SCREAMING_SNAKE_CASE	:		Any   	=		self.text_encoder(text_input_ids.to(lowerCAmelCase__						)						)
					__SCREAMING_SNAKE_CASE	:		Any   	=		text_encoder_output.text_embeds

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		prompt_embeds.repeat_interleave(lowerCAmelCase__							,					dim=0						)
					# in Shap-E it normalize the prompt_embeds and then later rescale it
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		prompt_embeds / torch.linalg.norm(lowerCAmelCase__							,					dim=-1							,					keepdim=lowerCAmelCase__						)

					if do_classifier_free_guidance:
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.zeros_like(lowerCAmelCase__						)

							# 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
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		torch.cat([negative_prompt_embeds, prompt_embeds]						)

					# Rescale the features to have unit variance
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		math.sqrt(prompt_embeds.shape[1]						) * prompt_embeds

					return prompt_embeds



			@torch.no_grad()
			@replace_example_docstring(lowerCAmelCase__						)
			def __call__(							self					:			Union[str, Any]							,					lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			int = 1							,					lowerCAmelCase__					:			int = 2_5							,					lowerCAmelCase__					:			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					lowerCAmelCase__					:			Optional[torch.FloatTensor] = None							,					lowerCAmelCase__					:			float = 4.0							,					lowerCAmelCase__					:			int = 6_4							,					lowerCAmelCase__					:			Optional[str] = "pil"							,					lowerCAmelCase__					:			bool = True							,					):





					"""simple docstring"""






					if isinstance(lowerCAmelCase__							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		str   	=		1
					elif isinstance(lowerCAmelCase__							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		len(lowerCAmelCase__						)
					else:
							raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__						)}"						)

					__SCREAMING_SNAKE_CASE	:		Dict   	=		self._execution_device

					__SCREAMING_SNAKE_CASE	:		str   	=		batch_size * num_images_per_prompt

					__SCREAMING_SNAKE_CASE	:		str   	=		guidance_scale > 1.0
					__SCREAMING_SNAKE_CASE	:		int   	=		self._encode_prompt(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)

					# prior

					self.scheduler.set_timesteps(lowerCAmelCase__							,					device=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.scheduler.timesteps

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.prior.config.num_embeddings
					__SCREAMING_SNAKE_CASE	:		int   	=		self.prior.config.embedding_dim

					__SCREAMING_SNAKE_CASE	:		str   	=		self.prepare_latents(
					    (batch_size, num_embeddings * embedding_dim)							,					prompt_embeds.dtype							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					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
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		latents.reshape(latents.shape[0]							,					lowerCAmelCase__							,					lowerCAmelCase__						)

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

							__SCREAMING_SNAKE_CASE	:		str   	=		self.prior(
							    lowerCAmelCase__							,					timestep=lowerCAmelCase__							,					proj_embedding=lowerCAmelCase__							,					).predicted_image_embedding

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

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

							__SCREAMING_SNAKE_CASE	:		str   	=		self.scheduler.step(
							    lowerCAmelCase__							,					timestep=lowerCAmelCase__							,					sample=lowerCAmelCase__							,					).prev_sample

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

					__SCREAMING_SNAKE_CASE	:		Dict   	=		[]
					for i, latent in enumerate(lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.renderer.decode(
							    latent[None, :]							,					lowerCAmelCase__							,					size=lowerCAmelCase__							,					ray_batch_size=4_0_9_6							,					n_coarse_samples=6_4							,					n_fine_samples=1_2_8							,					)
							images.append(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.stack(lowerCAmelCase__						)

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

					__SCREAMING_SNAKE_CASE	:		str   	=		images.cpu().numpy()

					if output_type == "pil":
							__SCREAMING_SNAKE_CASE	:		int   	=		[self.numpy_to_pil(lowerCAmelCase__						) 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=lowerCAmelCase__						)