"""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


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

UpperCAmelCase       =  """\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e-img2img\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n        >>> image = load_image(image_url).convert(\"RGB\")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n        ```\n"""







@dataclass
class      UpperCAmelCase_       (						__SCREAMING_SNAKE_CASE):
							snake_case__        =      42







class      UpperCAmelCase_       (						__SCREAMING_SNAKE_CASE):






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

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






							def       _UpperCamelCase			(		self :      List[str]     ,						__UpperCamelCase :      Any     ,						__UpperCamelCase :      Optional[Any]     ,						__UpperCamelCase :      str     ,						__UpperCamelCase :      List[str]     ,						__UpperCamelCase :      List[str]     ,						__UpperCamelCase :      int  )       ->       List[str]:
												if latents is None:
																	_UpperCamelCase      						=	randn_tensor(_a     ,						generator=_a     ,						device=_a     ,						dtype=_a  )
												else:
																	if latents.shape != shape:
																						raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''  )
																	_UpperCamelCase      						=	latents.to(_a  )

												_UpperCamelCase      						=	latents * scheduler.init_noise_sigma
												return latents






							def       _UpperCamelCase			(		self :      int     ,						__UpperCamelCase :      List[Any]=0  )       ->       Tuple:
												if is_accelerate_available():
																	from accelerate import cpu_offload
												else:
																	raise ImportError('''Please install accelerate via `pip install accelerate`'''  )

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

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






							@property
							def       _UpperCamelCase			(		self :      Any  )       ->       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(_a     ,						'''_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]     ,						__UpperCamelCase :      Optional[Any]     ,						__UpperCamelCase :      str     ,						__UpperCamelCase :      str     ,						__UpperCamelCase :      Tuple     ,						)       ->       Dict:
												if isinstance(_a     ,						_a  ) and isinstance(image[0]     ,						torch.Tensor  ):
																	_UpperCamelCase      						=	torch.cat(_a     ,						axis=0  ) if image[0].ndim == 4 else torch.stack(_a     ,						axis=0  )

												if not isinstance(_a     ,						torch.Tensor  ):
																	_UpperCamelCase      						=	self.image_processor(_a     ,						return_tensors='''pt'''  ).pixel_values[0].unsqueeze(0  )

												_UpperCamelCase      						=	image.to(dtype=self.image_encoder.dtype     ,						device=_a  )

												_UpperCamelCase      						=	self.image_encoder(_a  )['''last_hidden_state''']
												_UpperCamelCase      						=	image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

												_UpperCamelCase      						=	image_embeds.repeat_interleave(_a     ,						dim=0  )

												if do_classifier_free_guidance:
																	_UpperCamelCase      						=	torch.zeros_like(_a  )

																	# 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      						=	torch.cat([negative_image_embeds, image_embeds]  )

												return image_embeds






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

												if isinstance(_a     ,						PIL.Image.Image  ):
																	_UpperCamelCase      						=	1
												elif isinstance(_a     ,						torch.Tensor  ):
																	_UpperCamelCase      						=	image.shape[0]
												elif isinstance(_a     ,						_a  ) and isinstance(image[0]     ,						(torch.Tensor, PIL.Image.Image)  ):
																	_UpperCamelCase      						=	len(_a  )
												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(_a  )}'''  )

												_UpperCamelCase      						=	self._execution_device

												_UpperCamelCase      						=	batch_size * num_images_per_prompt

												_UpperCamelCase      						=	guidance_scale > 1.0
												_UpperCamelCase      						=	self._encode_image(_a     ,						_a     ,						_a     ,						_a  )

												# prior

												self.scheduler.set_timesteps(_a     ,						device=_a  )
												_UpperCamelCase      						=	self.scheduler.timesteps

												_UpperCamelCase      						=	self.prior.config.num_embeddings
												_UpperCamelCase      						=	self.prior.config.embedding_dim

												_UpperCamelCase      						=	self.prepare_latents(
												    (batch_size, num_embeddings * embedding_dim)     ,						image_embeds.dtype     ,						_a     ,						_a     ,						_a     ,						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      						=	latents.reshape(latents.shape[0]     ,						_a     ,						_a  )

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

																	_UpperCamelCase      						=	self.prior(
																	    _a     ,						timestep=_a     ,						proj_embedding=_a     ,						).predicted_image_embedding

																	# remove the variance
																	_UpperCamelCase						,							_UpperCamelCase      						=	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      						=	noise_pred.chunk(2  )
																						_UpperCamelCase      						=	noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

																	_UpperCamelCase      						=	self.scheduler.step(
																	    _a     ,						timestep=_a     ,						sample=_a     ,						).prev_sample

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

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

																	images.append(_a  )

												_UpperCamelCase      						=	torch.stack(_a  )

												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      						=	images.cpu().numpy()

												if output_type == "pil":
																	_UpperCamelCase      						=	[self.numpy_to_pil(_a  ) 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=_a  )




"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_			     =  logging.get_logger(__name__)

lowercase_			     =  {
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]										=				'beit'

		def __init__(  self							, _a=8_192							, _a=768							, _a=12							, _a=12							, _a=3_072							, _a="gelu"							, _a=0.0							, _a=0.0							, _a=0.02							, _a=1E-12							, _a=224							, _a=16							, _a=3							, _a=False							, _a=False							, _a=False							, _a=False							, _a=0.1							, _a=0.1							, _a=True							, _a=[3, 5, 7, 11]							, _a=[1, 2, 3, 6]							, _a=True							, _a=0.4							, _a=256							, _a=1							, _a=False							, _a=255							, **_a							, ):
				super().__init__(**_a      )

				__a           =			vocab_size
				__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           =			use_mask_token
				__a           =			use_absolute_position_embeddings
				__a           =			use_relative_position_bias
				__a           =			use_shared_relative_position_bias
				__a           =			layer_scale_init_value
				__a           =			drop_path_rate
				__a           =			use_mean_pooling
				# decode head attributes (semantic segmentation)
				__a           =			out_indices
				__a           =			pool_scales
				# auxiliary head attributes (semantic segmentation)
				__a           =			use_auxiliary_head
				__a           =			auxiliary_loss_weight
				__a           =			auxiliary_channels
				__a           =			auxiliary_num_convs
				__a           =			auxiliary_concat_input
				__a           =			semantic_loss_ignore_index







class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[Any]										=				version.parse('1.11'       )

		@property
		def 			__UpperCAmelCase       (  self      ):
				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )





		@property
		def 			__UpperCAmelCase       (  self      ):
				return 1E-4






