


'''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


__SCREAMING_SNAKE_CASE					:				Optional[int]									=		logging.get_logger(__name__)  # pylint: disable=invalid-name

__SCREAMING_SNAKE_CASE					:				int									=		'''
    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 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






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






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						def __init__(  self							:      int ,  lowerCAmelCase							:      PriorTransformer ,  lowerCAmelCase							:      CLIPVisionModel ,  lowerCAmelCase							:      CLIPImageProcessor ,  lowerCAmelCase							:      HeunDiscreteScheduler ,  lowerCAmelCase							:      ShapERenderer ,  ):
													super().__init__()

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






						def 				_UpperCAmelCase       (  self							:      Dict ,  lowerCAmelCase							:      Tuple ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      Union[str, Any] ,  lowerCAmelCase							:      List[str] ,  lowerCAmelCase							:      Union[str, Any] ,  lowerCAmelCase							:      List[Any]	):
													if latents is None:
																				A_			    =		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}"	)
																				A_			    =		latents.to(lowerCAmelCase	)

													A_			    =		latents * scheduler.init_noise_sigma
													return latents






						def 				_UpperCAmelCase       (  self							:      Optional[int] ,  lowerCAmelCase							:      int=0	):
													if is_accelerate_available():
																				from accelerate import cpu_offload
													else:
																				raise ImportError("Please install accelerate via `pip install accelerate`"	)

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

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






						@property
						def 				_UpperCAmelCase       (  self							:      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(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							:      Tuple ,  lowerCAmelCase							:      Optional[int] ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      Optional[Any] ,  ):
													if isinstance(lowerCAmelCase ,  lowerCAmelCase	) and isinstance(image[0] ,  torch.Tensor	):
																				A_			    =		torch.cat(lowerCAmelCase ,  axis=0	) if image[0].ndim == 4 else torch.stack(lowerCAmelCase ,  axis=0	)

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

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

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

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

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

													return image_embeds






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

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

													A_			    =		self._execution_device

													A_			    =		batch_size * num_images_per_prompt

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

													# prior

													self.scheduler.set_timesteps(lowerCAmelCase ,  device=lowerCAmelCase	)
													A_			    =		self.scheduler.timesteps

													A_			    =		self.prior.config.num_embeddings
													A_			    =		self.prior.config.embedding_dim

													A_			    =		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
													A_			    =		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
																				A_			    =		torch.cat([latents] * 2	) if do_classifier_free_guidance else latents
																				A_			    =		self.scheduler.scale_model_input(lowerCAmelCase ,  lowerCAmelCase	)

																				A_			    =		self.prior(
																				    lowerCAmelCase ,  timestep=lowerCAmelCase ,  proj_embedding=lowerCAmelCase ,  ).predicted_image_embedding

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

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

																				A_			    =		self.scheduler.step(
																				    lowerCAmelCase ,  timestep=lowerCAmelCase ,  sample=lowerCAmelCase ,  ).prev_sample

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

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

																				images.append(lowerCAmelCase	)

													A_			    =		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}"	)

													A_			    =		images.cpu().numpy()

													if output_type == "pil":
																				A_			    =		[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 typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
							import PIL

# soft dependency
if is_pytesseract_available():
							import pytesseract

__SCREAMING_SNAKE_CASE					:				List[str]									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							return [
							    int(1_0_0_0 * (box[0] / width) ),
							    int(1_0_0_0 * (box[1] / height) ),
							    int(1_0_0_0 * (box[2] / width) ),
							    int(1_0_0_0 * (box[3] / height) ),
							]







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		to_pil_image(UpperCamelCase_ )
							A_    ,					A_			    =		pil_image.size
							A_			    =		pytesseract.image_to_data(UpperCamelCase_						,  lang=UpperCamelCase_						,  output_type="dict"						,  config=UpperCamelCase_ )
							A_    ,					A_    ,					A_    ,					A_    ,					A_			    =		data["text"], data["left"], data["top"], data["width"], data["height"]

							# filter empty words and corresponding coordinates
							A_			    =		[idx for idx, word in enumerate(UpperCamelCase_ ) if not word.strip()]
							A_			    =		[word for idx, word in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]

							# turn coordinates into (left, top, left+width, top+height) format
							A_			    =		[]
							for x, y, w, h in zip(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
														A_			    =		[x, y, x + w, y + h]
														actual_boxes.append(UpperCamelCase_ )

							# finally, normalize the bounding boxes
							A_			    =		[]
							for box in actual_boxes:
														normalized_boxes.append(normalize_box(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ) )

							assert len(UpperCamelCase_ ) == len(UpperCamelCase_ ), "Not as many words as there are bounding boxes"

							return words, normalized_boxes







class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Any   						=["pixel_values"]






						def __init__(  self							:      List[Any] ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BILINEAR ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      float = 1 / 2_55 ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[str] = "" ,  **lowerCAmelCase							:      int ,  ):
													super().__init__(**lowerCAmelCase	)
													A_			    =		size if size is not None else {"height": 2_24, "width": 2_24}
													A_			    =		get_size_dict(lowerCAmelCase	)

													A_			    =		do_resize
													A_			    =		size
													A_			    =		resample
													A_			    =		do_rescale
													A_			    =		rescale_value
													A_			    =		do_normalize
													A_			    =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													A_			    =		image_std if image_std is not None else IMAGENET_STANDARD_STD
													A_			    =		apply_ocr
													A_			    =		ocr_lang
													A_			    =		tesseract_config






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Dict[str, int] ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BILINEAR ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      List[str] ,  ):
													A_			    =		get_size_dict(lowerCAmelCase	)
													if "height" not in size or "width" not in size:
																				raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"	)
													A_			    =		(size["height"], size["width"])
													return resize(lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[int, float] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Tuple ,  ):
													return rescale(lowerCAmelCase ,  scale=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      int ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[float, Iterable[float]] ,  lowerCAmelCase							:      Union[float, Iterable[float]] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      int ,  ):
													return normalize(lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      ImageInput ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      Any=None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      float = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[Union[str, TensorType]] = None ,  lowerCAmelCase							:      ChannelDimension = ChannelDimension.FIRST ,  **lowerCAmelCase							:      int ,  ):
													A_			    =		do_resize if do_resize is not None else self.do_resize
													A_			    =		size if size is not None else self.size
													A_			    =		get_size_dict(lowerCAmelCase	)
													A_			    =		resample if resample is not None else self.resample
													A_			    =		do_rescale if do_rescale is not None else self.do_rescale
													A_			    =		rescale_factor if rescale_factor is not None else self.rescale_factor
													A_			    =		do_normalize if do_normalize is not None else self.do_normalize
													A_			    =		image_mean if image_mean is not None else self.image_mean
													A_			    =		image_std if image_std is not None else self.image_std
													A_			    =		apply_ocr if apply_ocr is not None else self.apply_ocr
													A_			    =		ocr_lang if ocr_lang is not None else self.ocr_lang
													A_			    =		tesseract_config if tesseract_config is not None else self.tesseract_config

													A_			    =		make_list_of_images(lowerCAmelCase	)

													if not valid_images(lowerCAmelCase	):
																				raise ValueError(
																				    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																				    "torch.Tensor, tf.Tensor or jax.ndarray."	)

													if do_resize and size is None:
																				raise ValueError("Size must be specified if do_resize is True."	)

													if do_rescale and rescale_factor is None:
																				raise ValueError("Rescale factor must be specified if do_rescale is True."	)

													if do_normalize and (image_mean is None or image_std is None):
																				raise ValueError("If do_normalize is True, image_mean and image_std must be specified."	)

													# All transformations expect numpy arrays.
													A_			    =		[to_numpy_array(lowerCAmelCase	) for image in images]

													# Tesseract OCR to get words + normalized bounding boxes
													if apply_ocr:
																				requires_backends(self ,  "pytesseract"	)
																				A_			    =		[]
																				A_			    =		[]
																				for image in images:
																											A_    ,					A_			    =		apply_tesseract(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase	)
																											words_batch.append(lowerCAmelCase	)
																											boxes_batch.append(lowerCAmelCase	)

													if do_resize:
																				A_			    =		[self.resize(image=lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase	) for image in images]

													if do_rescale:
																				A_			    =		[self.rescale(image=lowerCAmelCase ,  scale=lowerCAmelCase	) for image in images]

													if do_normalize:
																				A_			    =		[self.normalize(image=lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase	) for image in images]

													A_			    =		[to_channel_dimension_format(lowerCAmelCase ,  lowerCAmelCase	) for image in images]

													A_			    =		BatchFeature(data={"pixel_values": images} ,  tensor_type=lowerCAmelCase	)

													if apply_ocr:
																				A_			    =		words_batch
																				A_			    =		boxes_batch
													return data





