






'''simple docstring'''







from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
							from ..models.auto import AutoTokenizer




class 		UpperCamelCase__     :




		'''simple docstring'''





		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								raise NotImplementedError()






		def       UpperCamelCase_       (   self							):
								raise NotImplementedError()




class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





		def __init__(   self ,_lowerCAmelCase ,_lowerCAmelCase = False ,**_lowerCAmelCase							):
								lowerCamelCase__								=	tokenizer
								lowerCamelCase__								=	skip_prompt
								lowerCamelCase__								=	decode_kwargs

								# variables used in the streaming process
								lowerCamelCase__								=	[]
								lowerCamelCase__								=	0
								lowerCamelCase__								=	True






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								if len(value.shape							) > 1 and value.shape[0] > 1:
														raise ValueError("""TextStreamer only supports batch size 1"""							)
								elif len(value.shape							) > 1:
														lowerCamelCase__								=	value[0]

								if self.skip_prompt and self.next_tokens_are_prompt:
														lowerCamelCase__								=	False
														return

								# Add the new token to the cache and decodes the entire thing.
								self.token_cache.extend(value.tolist()							)
								lowerCamelCase__								=	self.tokenizer.decode(self.token_cache ,**self.decode_kwargs							)

								# After the symbol for a new line, we flush the cache.
								if text.endswith("""\n"""							):
														lowerCamelCase__								=	text[self.print_len :]
														lowerCamelCase__								=	[]
														lowerCamelCase__								=	0
								# If the last token is a CJK character, we print the characters.
								elif len(_lowerCAmelCase							) > 0 and self._is_chinese_char(ord(text[-1]							)							):
														lowerCamelCase__								=	text[self.print_len :]
														self.print_len += len(_lowerCAmelCase							)
								# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
								# which may change with the subsequent token -- there are probably smarter ways to do this!)
								else:
														lowerCamelCase__								=	text[self.print_len : text.rfind(""" """							) + 1]
														self.print_len += len(_lowerCAmelCase							)

								self.on_finalized_text(_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								# Flush the cache, if it exists
								if len(self.token_cache							) > 0:
														lowerCamelCase__								=	self.tokenizer.decode(self.token_cache ,**self.decode_kwargs							)
														lowerCamelCase__								=	text[self.print_len :]
														lowerCamelCase__								=	[]
														lowerCamelCase__								=	0
								else:
														lowerCamelCase__								=	""""""

								lowerCamelCase__								=	True
								self.on_finalized_text(_lowerCAmelCase ,stream_end=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase = False							):
								print(_lowerCAmelCase ,flush=_lowerCAmelCase ,end="""""" if not stream_end else None							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								# This defines a "chinese character" as anything in the CJK Unicode block:
								#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
								#
								# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
								# despite its name. The modern Korean Hangul alphabet is a different block,
								# as is Japanese Hiragana and Katakana. Those alphabets are used to write
								# space-separated words, so they are not treated specially and handled
								# like the all of the other languages.
								if (
								    (cp >= 0x4e_00 and cp <= 0x9f_ff)
								    or (cp >= 0x34_00 and cp <= 0x4d_bf)  #
								    or (cp >= 0x2_00_00 and cp <= 0x2_a6_df)  #
								    or (cp >= 0x2_a7_00 and cp <= 0x2_b7_3f)  #
								    or (cp >= 0x2_b7_40 and cp <= 0x2_b8_1f)  #
								    or (cp >= 0x2_b8_20 and cp <= 0x2_ce_af)  #
								    or (cp >= 0xf9_00 and cp <= 0xfa_ff)
								    or (cp >= 0x2_f8_00 and cp <= 0x2_fa_1f)  #
								):  #
														return True

								return False




class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





		def __init__(   self ,_lowerCAmelCase ,_lowerCAmelCase = False ,_lowerCAmelCase = None ,**_lowerCAmelCase							):
								super().__init__(_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase							)
								lowerCamelCase__								=	Queue()
								lowerCamelCase__								=	None
								lowerCamelCase__								=	timeout






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase = False							):
								self.text_queue.put(_lowerCAmelCase ,timeout=self.timeout							)
								if stream_end:
														self.text_queue.put(self.stop_signal ,timeout=self.timeout							)






		def __iter__(   self							):
								return self






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.text_queue.get(timeout=self.timeout							)
								if value == self.stop_signal:
														raise StopIteration()
								else:
														return value


'''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


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

__lowercase        =    '''
    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 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Union[PIL.Image.Image, np.ndarray]







class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			def __init__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__()

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



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					if latents is None:
							lowerCAmelCase     =    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}")
							lowerCAmelCase     =    latents.to(__lowerCAmelCase)

					lowerCAmelCase     =    latents * scheduler.init_noise_sigma
					return latents



			def 				a_			(  self			,       __lowerCAmelCase=0):




					"""simple docstring"""


					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(__lowerCAmelCase			,       __lowerCAmelCase)



			@property
			def 				a_			(  self):




					"""simple docstring"""


					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(__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 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       ):




					"""simple docstring"""


					if isinstance(__lowerCAmelCase			,       __lowerCAmelCase) and isinstance(image[0]			,       torch.Tensor):
							lowerCAmelCase     =    torch.cat(__lowerCAmelCase			,       axis=0) if image[0].ndim == 4 else torch.stack(__lowerCAmelCase			,       axis=0)

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

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

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

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

					if do_classifier_free_guidance:
							lowerCAmelCase     =    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
							lowerCAmelCase     =    torch.cat([negative_image_embeds, image_embeds])

					return image_embeds







			@torch.no_grad()
			@replace_example_docstring(__lowerCAmelCase)
			def __call__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = 1			,       __lowerCAmelCase = 25			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = 4.0			,       __lowerCAmelCase = 64			,       __lowerCAmelCase = "pil"			,       __lowerCAmelCase = True			,       ):




					"""simple docstring"""


					if isinstance(__lowerCAmelCase			,       PIL.Image.Image):
							lowerCAmelCase     =    1
					elif isinstance(__lowerCAmelCase			,       torch.Tensor):
							lowerCAmelCase     =    image.shape[0]
					elif isinstance(__lowerCAmelCase			,       __lowerCAmelCase) and isinstance(image[0]			,       (torch.Tensor, PIL.Image.Image)):
							lowerCAmelCase     =    len(__lowerCAmelCase)
					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(__lowerCAmelCase)}")

					lowerCAmelCase     =    self._execution_device

					lowerCAmelCase     =    batch_size * num_images_per_prompt

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

					# prior

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

							lowerCAmelCase     =    self.prior(
							    __lowerCAmelCase			,       timestep=__lowerCAmelCase			,       proj_embedding=__lowerCAmelCase			,       ).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(
							    __lowerCAmelCase			,       timestep=__lowerCAmelCase			,       sample=__lowerCAmelCase			,       ).prev_sample

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

					lowerCAmelCase     =    []
					for i, latent in enumerate(__lowerCAmelCase):
							print()
							lowerCAmelCase     =    self.renderer.decode(
							    latent[None, :]			,       __lowerCAmelCase			,       size=__lowerCAmelCase			,       ray_batch_size=4096			,       n_coarse_samples=64			,       n_fine_samples=128			,       )

							images.append(__lowerCAmelCase)

					lowerCAmelCase     =    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}")

					lowerCAmelCase     =    images.cpu().numpy()

					if output_type == "pil":
							lowerCAmelCase     =    [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)




