


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

_snake_case  :	Any			      =       '''
    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	(a__   ):




							"""simple docstring"""


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

class        a	(a__   ):




							"""simple docstring"""


							def __init__(	self :						List[str]					,			lowerCamelCase :						int					,			lowerCamelCase :						List[str]					,			lowerCamelCase :						List[str]					,			lowerCamelCase :						Tuple					,			lowerCamelCase :						Tuple					,			)			->					Optional[int]:
														super().__init__()

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

							def 					__snake_case      (	self :						str					,			lowerCamelCase :						int					,			lowerCamelCase :						Any					,			lowerCamelCase :						Tuple					,			lowerCamelCase :						List[str]					,			lowerCamelCase :						Tuple					,			lowerCamelCase :						List[Any]       )			->					str:
														if latents is None:
																					__snake_case	:    List[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}'       )
																					__snake_case	:    Optional[int]											=     latents.to(lowerCAmelCase__       )

														__snake_case	:    Dict											=     latents * scheduler.init_noise_sigma
														return latents

							def 					__snake_case      (	self :						int					,			lowerCamelCase :						List[str]=0       )			->					List[str]:
														if is_accelerate_available():
																					from accelerate import cpu_offload
														else:
																					raise ImportError("Please install accelerate via `pip install accelerate`"       )

														__snake_case	:    Tuple											=     torch.device(F'cuda:{gpu_id}'       )

														__snake_case	:    Union[str, Any]											=     [self.image_encoder, self.prior]
														for cpu_offloaded_model in models:
																					if cpu_offloaded_model is not None:
																												cpu_offload(lowerCAmelCase__					,			lowerCAmelCase__       )

							@property
							def 					__snake_case      (	self :						Tuple       )			->					int:
														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 					__snake_case      (	self :						Tuple					,			lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						Union[str, Any]					,			)			->					str:
														if isinstance(lowerCAmelCase__					,			lowerCAmelCase__       ) and isinstance(image[0]					,			torch.Tensor       ):
																					__snake_case	:    Tuple											=     torch.cat(lowerCAmelCase__					,			axis=0       ) if image[0].ndim == 4 else torch.stack(lowerCAmelCase__					,			axis=0       )

														if not isinstance(lowerCAmelCase__					,			torch.Tensor       ):
																					__snake_case	:    Union[str, Any]											=     self.image_processor(lowerCAmelCase__					,			return_tensors="pt"       ).pixel_values[0].unsqueeze(0       )

														__snake_case	:    List[Any]											=     image.to(dtype=self.image_encoder.dtype					,			device=lowerCAmelCase__       )

														__snake_case	:    Dict											=     self.image_encoder(lowerCAmelCase__       )["last_hidden_state"]
														__snake_case	:    Union[str, Any]											=     image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

														__snake_case	:    Any											=     image_embeds.repeat_interleave(lowerCAmelCase__					,			dim=0       )

														if do_classifier_free_guidance:
																					__snake_case	:    str											=     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
																					__snake_case	:    Dict											=     torch.cat([negative_image_embeds, image_embeds]       )

														return image_embeds






							@torch.no_grad()
							@replace_example_docstring(lowerCAmelCase__       )
							def __call__(	self :						List[str]					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						Optional[Any] = 1					,			lowerCamelCase :						Optional[Any] = 25					,			lowerCamelCase :						List[Any] = None					,			lowerCamelCase :						Any = None					,			lowerCamelCase :						List[Any] = 4.0					,			lowerCamelCase :						List[Any] = 64					,			lowerCamelCase :						Tuple = "pil"					,			lowerCamelCase :						Optional[int] = True					,			)			->					List[Any]:

														if isinstance(lowerCAmelCase__					,			PIL.Image.Image       ):
																					__snake_case	:    str											=     1
														elif isinstance(lowerCAmelCase__					,			torch.Tensor       ):
																					__snake_case	:    str											=     image.shape[0]
														elif isinstance(lowerCAmelCase__					,			lowerCAmelCase__       ) and isinstance(image[0]					,			(torch.Tensor, PIL.Image.Image)       ):
																					__snake_case	:    Union[str, Any]											=     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__       )}'       )

														__snake_case	:    Tuple											=     self._execution_device

														__snake_case	:    Optional[Any]											=     batch_size * num_images_per_prompt

														__snake_case	:    int											=     guidance_scale > 1.0
														__snake_case	:    int											=     self._encode_image(lowerCAmelCase__					,			lowerCAmelCase__					,			lowerCAmelCase__					,			lowerCAmelCase__       )

														# prior

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

														__snake_case	:    str											=     self.prior.config.num_embeddings
														__snake_case	:    Tuple											=     self.prior.config.embedding_dim

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

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

																					# remove the variance
																					__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:
																												__snake_case	:    Optional[Any]											=     noise_pred.chunk(2       )
																												__snake_case	:    Optional[Any]											=     noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

																					__snake_case	:    List[str]											=     self.scheduler.step(
																					    lowerCAmelCase__					,			timestep=lowerCAmelCase__					,			sample=lowerCAmelCase__					,			).prev_sample

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

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

																					images.append(lowerCAmelCase__       )

														__snake_case	:    int											=     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}'       )

														__snake_case	:    Any											=     images.cpu().numpy()

														if output_type == "pil":
																					__snake_case	:    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__       )





import unittest

from transformers import DonutProcessor


_snake_case  :	Dict			      =       "naver-clova-ix/donut-base"

class        a	(unittest.TestCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Dict       )			->					int:
														__snake_case	:    Any											=     DonutProcessor.from_pretrained(lowerCamelCase       )






							def 					__snake_case      (							self :						str       )			->					List[Any]:
														__snake_case	:    Union[str, Any]											=     {
														    "name": "John Doe",
														    "age": "99",
														    "city": "Atlanta",
														    "state": "GA",
														    "zip": "30301",
														    "phone": "123-4567",
														    "nicknames": [{"nickname": "Johnny"}, {"nickname": "JD"}],
														}

														__snake_case	:    Union[str, Any]											=     (
														    "<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"
														    "<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"
														    "<s_nicknames><s_nickname>Johnny</s_nickname>"
														    "<sep/><s_nickname>JD</s_nickname></s_nicknames>"
														)
														__snake_case	:    Tuple											=     self.processor.tokenajson(lowerCamelCase       )

														self.assertDictEqual(lowerCamelCase					,			lowerCamelCase       )

