

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

A_			:					Any    			=     '\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        _a		(__magic_name__							):


					'''simple docstring'''






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



class        _a		(__magic_name__							):


					'''simple docstring'''






					def __init__(					self       ,			A__       ,			A__       ,			A__       ,			A__       ,			A__       ,			):
						super().__init__()

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






					def     __A     (					self       ,			A__       ,			A__       ,			A__       ,			A__       ,			A__       ,			A__     ):
						if latents is None:
							A__				:       Optional[Any]	  =						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__				:       List[Any]	  =						latents.to(A__     )

						A__				:       Any	  =						latents * scheduler.init_noise_sigma
						return latents






					def     __A     (					self       ,			A__=0     ):
						if is_accelerate_available():
							from accelerate import cpu_offload
						else:
							raise ImportError("""Please install accelerate via `pip install accelerate`"""     )

						A__				:       Union[str, Any]	  =						torch.device(F"""cuda:{gpu_id}"""     )

						A__				:       Union[str, Any]	  =						[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     ):
						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       ,			A__       ,			A__       ,			A__       ,			A__       ,			):
						if isinstance(A__       ,			A__     ) and isinstance(image[0]       ,			torch.Tensor     ):
							A__				:       Any	  =						torch.cat(A__       ,			axis=0     ) if image[0].ndim == 4 else torch.stack(A__       ,			axis=0     )

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

						A__				:       int	  =						image.to(dtype=self.image_encoder.dtype       ,			device=A__     )

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

						A__				:       int	  =						image_embeds.repeat_interleave(A__       ,			dim=0     )

						if do_classifier_free_guidance:
							A__				:       Dict	  =						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__				:       Any	  =						torch.cat([negative_image_embeds, image_embeds]     )

						return image_embeds






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

						if isinstance(A__       ,			PIL.Image.Image     ):
							A__				:       Union[str, Any]	  =						1
						elif isinstance(A__       ,			torch.Tensor     ):
							A__				:       List[Any]	  =						image.shape[0]
						elif isinstance(A__       ,			A__     ) and isinstance(image[0]       ,			(torch.Tensor, PIL.Image.Image)     ):
							A__				:       List[Any]	  =						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__				:       List[Any]	  =						self._execution_device

						A__				:       Tuple	  =						batch_size * num_images_per_prompt

						A__				:       List[Any]	  =						guidance_scale > 1.0
						A__				:       Any	  =						self._encode_image(A__       ,			A__       ,			A__       ,			A__     )

						# prior

						self.scheduler.set_timesteps(A__       ,			device=A__     )
						A__				:       List[Any]	  =						self.scheduler.timesteps

						A__				:       Optional[Any]	  =						self.prior.config.num_embeddings
						A__				:       Optional[Any]	  =						self.prior.config.embedding_dim

						A__				:       Any	  =						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__				:       List[str]	  =						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__				:       Union[str, Any]	  =						torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents
							A__				:       Dict	  =						self.scheduler.scale_model_input(A__       ,			A__     )

							A__				:       Union[str, Any]	  =						self.prior(
							    A__       ,			timestep=A__       ,			proj_embedding=A__       ,			).predicted_image_embedding

							# remove the variance
							A__  ,  A__				:       List[str]	  =						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__				:       List[str]	  =						noise_pred.chunk(2     )
								A__				:       Tuple	  =						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

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

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

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

							images.append(A__     )

						A__				:       Optional[int]	  =						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__				:       Tuple	  =						images.cpu().numpy()

						if output_type == "pil":
							A__				:       Union[str, Any]	  =						[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__     )






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A_			:					Tuple    			=     {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					Dict    			=     [
	    'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'XLMForMultipleChoice',
	    'XLMForQuestionAnswering',
	    'XLMForQuestionAnsweringSimple',
	    'XLMForSequenceClassification',
	    'XLMForTokenClassification',
	    'XLMModel',
	    'XLMPreTrainedModel',
	    'XLMWithLMHeadModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					str    			=     [
	    'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFXLMForMultipleChoice',
	    'TFXLMForQuestionAnsweringSimple',
	    'TFXLMForSequenceClassification',
	    'TFXLMForTokenClassification',
	    'TFXLMMainLayer',
	    'TFXLMModel',
	    'TFXLMPreTrainedModel',
	    'TFXLMWithLMHeadModel',
	]


if TYPE_CHECKING:
	from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
	from .tokenization_xlm import XLMTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm import (
		    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMPreTrainedModel,
		    XLMWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMMainLayer,
		    TFXLMModel,
		    TFXLMPreTrainedModel,
		    TFXLMWithLMHeadModel,
		)

else:
	import sys

	A_			:					Tuple    			=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



