
'''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


_snake_case				:   int     =				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 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Union[PIL.Image.Image, np.ndarray]


class 			lowerCAmelCase (				__UpperCAmelCase			):




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

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




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								if latents is None:
														_SCREAMING_SNAKE_CASE				= randn_tensor(UpperCamelCase					,						generator=UpperCamelCase					,						device=UpperCamelCase					,						dtype=UpperCamelCase      )
								else:
														if latents.shape != shape:
																				raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'      )
														_SCREAMING_SNAKE_CASE				= latents.to(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= latents * scheduler.init_noise_sigma
								return latents




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

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

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




		@property
		def     lowercase       (				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(UpperCamelCase					,						"_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     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						):
								if isinstance(UpperCamelCase					,						UpperCamelCase      ) and isinstance(image[0]					,						torch.Tensor      ):
														_SCREAMING_SNAKE_CASE				= torch.cat(UpperCamelCase					,						axis=0      ) if image[0].ndim == 4 else torch.stack(UpperCamelCase					,						axis=0      )

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

								_SCREAMING_SNAKE_CASE				= image.to(dtype=self.image_encoder.dtype					,						device=UpperCamelCase      )

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

								_SCREAMING_SNAKE_CASE				= image_embeds.repeat_interleave(UpperCamelCase					,						dim=0      )

								if do_classifier_free_guidance:
														_SCREAMING_SNAKE_CASE				= torch.zeros_like(UpperCamelCase      )

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

								return image_embeds




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

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

								_SCREAMING_SNAKE_CASE				= self._execution_device

								_SCREAMING_SNAKE_CASE				= batch_size * num_images_per_prompt

								_SCREAMING_SNAKE_CASE				= guidance_scale > 1.0
								_SCREAMING_SNAKE_CASE				= self._encode_image(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      )

								# prior

								self.scheduler.set_timesteps(UpperCamelCase					,						device=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= self.scheduler.timesteps

								_SCREAMING_SNAKE_CASE				= self.prior.config.num_embeddings
								_SCREAMING_SNAKE_CASE				= self.prior.config.embedding_dim

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

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

														_SCREAMING_SNAKE_CASE				= self.prior(
														    UpperCamelCase					,						timestep=UpperCamelCase					,						proj_embedding=UpperCamelCase					,						).predicted_image_embedding

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

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

														_SCREAMING_SNAKE_CASE				= self.scheduler.step(
														    UpperCamelCase					,						timestep=UpperCamelCase					,						sample=UpperCamelCase					,						).prev_sample

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

								_SCREAMING_SNAKE_CASE				= []
								for i, latent in enumerate(UpperCamelCase      ):
														print()
														_SCREAMING_SNAKE_CASE				= self.renderer.decode(
														    latent[None, :]					,						UpperCamelCase					,						size=UpperCamelCase					,						ray_batch_size=4_096					,						n_coarse_samples=64					,						n_fine_samples=128					,						)

														images.append(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= torch.stack(UpperCamelCase      )

								if output_type not in ["np", "pil"]:
														raise ValueError(F'Only the output types `pil` and `np` are supported not output_type={output_type}'      )

								_SCREAMING_SNAKE_CASE				= images.cpu().numpy()

								if output_type == "pil":
														_SCREAMING_SNAKE_CASE				= [self.numpy_to_pil(UpperCamelCase      ) 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=UpperCamelCase      )

'''simple docstring'''







from typing import TYPE_CHECKING

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


_snake_case				:   int     =				{
    """configuration_longformer""": [
        """LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LongformerConfig""",
        """LongformerOnnxConfig""",
    ],
    """tokenization_longformer""": ["""LongformerTokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Optional[int]     =				["""LongformerTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   int     =				[
						    """LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """LongformerForMaskedLM""",
						    """LongformerForMultipleChoice""",
						    """LongformerForQuestionAnswering""",
						    """LongformerForSequenceClassification""",
						    """LongformerForTokenClassification""",
						    """LongformerModel""",
						    """LongformerPreTrainedModel""",
						    """LongformerSelfAttention""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Tuple     =				[
						    """TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFLongformerForMaskedLM""",
						    """TFLongformerForMultipleChoice""",
						    """TFLongformerForQuestionAnswering""",
						    """TFLongformerForSequenceClassification""",
						    """TFLongformerForTokenClassification""",
						    """TFLongformerModel""",
						    """TFLongformerPreTrainedModel""",
						    """TFLongformerSelfAttention""",
						]


if TYPE_CHECKING:
						from .configuration_longformer import (
						    LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    LongformerConfig,
						    LongformerOnnxConfig,
						)
						from .tokenization_longformer import LongformerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_longformer_fast import LongformerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_longformer import (
												    LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LongformerForMaskedLM,
												    LongformerForMultipleChoice,
												    LongformerForQuestionAnswering,
												    LongformerForSequenceClassification,
												    LongformerForTokenClassification,
												    LongformerModel,
												    LongformerPreTrainedModel,
												    LongformerSelfAttention,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_longformer import (
												    TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLongformerForMaskedLM,
												    TFLongformerForMultipleChoice,
												    TFLongformerForQuestionAnswering,
												    TFLongformerForSequenceClassification,
												    TFLongformerForTokenClassification,
												    TFLongformerModel,
												    TFLongformerPreTrainedModel,
												    TFLongformerSelfAttention,
												)

else:
						import sys

						_snake_case				:   Dict     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)