





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

UpperCamelCase_	 ="""
    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 					_a		( _lowerCAmelCase							):
				UpperCamelCase								=       42




class 					_a		( _lowerCAmelCase							):






				def __init__(					self						:       int,					lowerCAmelCase__						:       PriorTransformer,					lowerCAmelCase__						:       CLIPTextModelWithProjection,					lowerCAmelCase__						:       CLIPTokenizer,					lowerCAmelCase__						:       HeunDiscreteScheduler,					lowerCAmelCase__						:       ShapERenderer,					)      -> Optional[int]:




										'''simple docstring'''



										super().__init__()

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






				def 							snake_case      (					self						:       List[Any],					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       List[str]				)      -> Optional[int]:




										'''simple docstring'''



										if latents is None:
																_UpperCamelCase     :   Any      =       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}"""				)
																_UpperCamelCase     :   List[Any]      =       latents.to(lowerCAmelCase__				)

										_UpperCamelCase     :   Union[str, Any]      =       latents * scheduler.init_noise_sigma
										return latents






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       str=0				)      -> Dict:




										'''simple docstring'''



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

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

										_UpperCamelCase     :   List[str]      =       [self.text_encoder, self.prior]
										for cpu_offloaded_model in models:
																if cpu_offloaded_model is not None:
																						cpu_offload(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       Tuple=0				)      -> List[Any]:




										'''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     :   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)

										_UpperCamelCase     :   Tuple      =       None
										for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
																_UpperCamelCase  ,				_UpperCamelCase     :   List[Any]      =       cpu_offload_with_hook(lowerCAmelCase__,					lowerCAmelCase__,					prev_module_hook=lowerCAmelCase__				)

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

										# We'll offload the last model manually.
										_UpperCamelCase     :   Union[str, Any]      =       hook






				@property
				def 							snake_case      (					self						:       Any				)      -> Tuple:




										'''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 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Any,					)      -> List[str]:




										'''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
										_UpperCamelCase     :   Optional[Any]      =       0
										# get prompt text embeddings
										_UpperCamelCase     :   Union[str, Any]      =       self.tokenizer(
										    lowerCAmelCase__,					padding='''max_length''',					max_length=self.tokenizer.model_max_length,					truncation=lowerCAmelCase__,					return_tensors='''pt''',					)
										_UpperCamelCase     :   Union[str, Any]      =       text_inputs.input_ids
										_UpperCamelCase     :   Union[str, Any]      =       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__				):
																_UpperCamelCase     :   Tuple      =       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}"""				)

										_UpperCamelCase     :   Dict      =       self.text_encoder(text_input_ids.to(lowerCAmelCase__				)				)
										_UpperCamelCase     :   Optional[int]      =       text_encoder_output.text_embeds

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

										if do_classifier_free_guidance:
																_UpperCamelCase     :   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
																_UpperCamelCase     :   Optional[Any]      =       torch.cat([negative_prompt_embeds, prompt_embeds]				)

										# Rescale the features to have unit variance
										_UpperCamelCase     :   Union[str, Any]      =       math.sqrt(prompt_embeds.shape[1]				) * prompt_embeds

										return prompt_embeds






				@torch.no_grad()
				@replace_example_docstring(lowerCAmelCase__				)
				def __call__(					self						:       Optional[int],					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,					)      -> Any:




										'''simple docstring'''



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

										_UpperCamelCase     :   Dict      =       self._execution_device

										_UpperCamelCase     :   List[Any]      =       batch_size * num_images_per_prompt

										_UpperCamelCase     :   List[str]      =       guidance_scale > 1.0
										_UpperCamelCase     :   str      =       self._encode_prompt(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)

										# prior

										self.scheduler.set_timesteps(lowerCAmelCase__,					device=lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       self.scheduler.timesteps

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

										_UpperCamelCase     :   Tuple      =       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
										_UpperCamelCase     :   Dict      =       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
																_UpperCamelCase     :   Optional[int]      =       torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
																_UpperCamelCase     :   Dict      =       self.scheduler.scale_model_input(lowerCAmelCase__,					lowerCAmelCase__				)

																_UpperCamelCase     :   Tuple      =       self.prior(
																    lowerCAmelCase__,					timestep=lowerCAmelCase__,					proj_embedding=lowerCAmelCase__,					).predicted_image_embedding

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

																if do_classifier_free_guidance is not None:
																						_UpperCamelCase  ,				_UpperCamelCase     :   List[str]      =       noise_pred.chunk(2				)
																						_UpperCamelCase     :   Optional[Any]      =       noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

																_UpperCamelCase     :   Dict      =       self.scheduler.step(
																    lowerCAmelCase__,					timestep=lowerCAmelCase__,					sample=lowerCAmelCase__,					).prev_sample

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

										_UpperCamelCase     :   List[Any]      =       []
										for i, latent in enumerate(lowerCAmelCase__				):
																_UpperCamelCase     :   Union[str, 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__				)

										_UpperCamelCase     :   str      =       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}"""				)

										_UpperCamelCase     :   Dict      =       images.cpu().numpy()

										if output_type == "pil":
																_UpperCamelCase     :   Any      =       [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__				)




"""simple docstring"""




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


__lowerCamelCase									=	logging.get_logger(__name__)

__lowerCamelCase									=	{
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    str    				=		'vilt'





						def __init__(							self		,__UpperCAmelCase=3_05_22		,__UpperCAmelCase=2		,__UpperCAmelCase=2		,__UpperCAmelCase=40		,__UpperCAmelCase=7_68		,__UpperCAmelCase=12		,__UpperCAmelCase=12		,__UpperCAmelCase=30_72		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=1e-12		,__UpperCAmelCase=3_84		,__UpperCAmelCase=32		,__UpperCAmelCase=3		,__UpperCAmelCase=True		,__UpperCAmelCase=-1		,__UpperCAmelCase=False		,__UpperCAmelCase=-1		,**__UpperCAmelCase		,)       ->					Tuple:
													super().__init__(tie_word_embeddings=__UpperCAmelCase		,**__UpperCAmelCase  )

													A__						  =				vocab_size
													A__						  =				type_vocab_size
													A__						  =				modality_type_vocab_size
													A__						  =				max_position_embeddings

													A__						  =				hidden_size
													A__						  =				num_hidden_layers
													A__						  =				num_attention_heads
													A__						  =				intermediate_size
													A__						  =				hidden_act
													A__						  =				hidden_dropout_prob
													A__						  =				attention_probs_dropout_prob
													A__						  =				initializer_range
													A__						  =				layer_norm_eps

													A__						  =				image_size
													A__						  =				patch_size
													A__						  =				num_channels
													A__						  =				qkv_bias
													A__						  =				max_image_length
													A__						  =				num_images





