


"""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


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

a_       							=    "\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 snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			42





class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       int    ,   a__      :       PriorTransformer    ,   a__      :       CLIPVisionModel    ,   a__      :       CLIPImageProcessor    ,   a__      :       HeunDiscreteScheduler    ,   a__      :       ShapERenderer    ,   )       ->						Optional[Any]:





						'''simple docstring'''







						super().__init__()

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







		def  a_ (					self      :       Optional[int]    ,   a__      :       List[Any]    ,   a__      :       List[str]    ,   a__      :       Dict    ,   a__      :       List[Any]    ,   a__      :       List[str]    ,   a__      :       Optional[int]		)       ->						Optional[Any]:





						'''simple docstring'''







						if latents is None:
										_A					  =			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}"""		)
										_A					  =			latents.to(a__		)

						_A					  =			latents * scheduler.init_noise_sigma
						return latents







		def  a_ (					self      :       Optional[int]    ,   a__      :       Tuple=0		)       ->						List[str]:





						'''simple docstring'''







						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(a__    ,   a__		)







		@property
		def  a_ (					self      :       Optional[int]		)       ->						Any:





						'''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(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  a_ (					self      :       Tuple    ,   a__      :       str    ,   a__      :       Union[str, Any]    ,   a__      :       Tuple    ,   a__      :       Any    ,   )       ->						List[str]:





						'''simple docstring'''







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

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

						_A					  =			image.to(dtype=self.image_encoder.dtype    ,   device=a__		)

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

						_A					  =			image_embeds.repeat_interleave(a__    ,   dim=0		)

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

						return image_embeds







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





						'''simple docstring'''







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

						_A					  =			self._execution_device

						_A					  =			batch_size * num_images_per_prompt

						_A					  =			guidance_scale > 1.0
						_A					  =			self._encode_image(a__    ,   a__    ,   a__    ,   a__		)

						# prior

						self.scheduler.set_timesteps(a__    ,   device=a__		)
						_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    ,   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
						_A					  =			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
										_A					  =			torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
										_A					  =			self.scheduler.scale_model_input(a__    ,   a__		)

										_A					  =			self.prior(
										    a__    ,   timestep=a__    ,   proj_embedding=a__    ,   ).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(
										    a__    ,   timestep=a__    ,   sample=a__    ,   ).prev_sample

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

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

										images.append(a__		)

						_A					  =			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}"""		)

						_A					  =			images.cpu().numpy()

						if output_type == "pil":
										_A					  =			[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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_       							=    {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    ["RoFormerTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RoFormerForCausalLM",
		    "RoFormerForMaskedLM",
		    "RoFormerForMultipleChoice",
		    "RoFormerForQuestionAnswering",
		    "RoFormerForSequenceClassification",
		    "RoFormerForTokenClassification",
		    "RoFormerLayer",
		    "RoFormerModel",
		    "RoFormerPreTrainedModel",
		    "load_tf_weights_in_roformer",
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFRoFormerForCausalLM",
		    "TFRoFormerForMaskedLM",
		    "TFRoFormerForMultipleChoice",
		    "TFRoFormerForQuestionAnswering",
		    "TFRoFormerForSequenceClassification",
		    "TFRoFormerForTokenClassification",
		    "TFRoFormerLayer",
		    "TFRoFormerModel",
		    "TFRoFormerPreTrainedModel",
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "FlaxRoFormerForMaskedLM",
		    "FlaxRoFormerForMultipleChoice",
		    "FlaxRoFormerForQuestionAnswering",
		    "FlaxRoFormerForSequenceClassification",
		    "FlaxRoFormerForTokenClassification",
		    "FlaxRoFormerModel",
		    "FlaxRoFormerPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		a_       							=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)