




'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a  :				Optional[Any]									=  logging.get_logger(__name__)

a  :				str									=  {
    'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json',
    'funnel-transformer/small-base': 'https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json',
    'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/config.json',
    'funnel-transformer/medium-base': 'https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json',
    'funnel-transformer/intermediate': (
        'https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'
    ),
    'funnel-transformer/intermediate-base': (
        'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'
    ),
    'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/config.json',
    'funnel-transformer/large-base': 'https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json',
    'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json',
    'funnel-transformer/xlarge-base': 'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json',
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"funnel"
				snake_case_      =		{
				    "hidden_size": "d_model",
				    "num_attention_heads": "n_head",
				}


				def __init__(		self    :					List[Any]       ,				lowercase_    :					Optional[int]=3_0522       ,				lowercase_    :					Optional[int]=[4, 4, 4]       ,				lowercase_    :					str=None       ,				lowercase_    :					Union[str, Any]=2       ,				lowercase_    :					Union[str, Any]=768       ,				lowercase_    :					List[str]=12       ,				lowercase_    :					List[str]=64       ,				lowercase_    :					Optional[int]=3072       ,				lowercase_    :					Optional[int]="gelu_new"       ,				lowercase_    :					List[Any]=0.1       ,				lowercase_    :					Dict=0.1       ,				lowercase_    :					str=0.0       ,				lowercase_    :					Union[str, Any]=0.1       ,				lowercase_    :					List[Any]=None       ,				lowercase_    :					int=1e-9       ,				lowercase_    :					Union[str, Any]="mean"       ,				lowercase_    :					Dict="relative_shift"       ,				lowercase_    :					Optional[int]=True       ,				lowercase_    :					Optional[int]=True       ,				lowercase_    :					Optional[Any]=True       ,				**lowercase_    :					Dict       ,				):
									snake_case_									=				vocab_size
									snake_case_									=				block_sizes
									snake_case_									=				[1] * len(lowercase_					) if block_repeats is None else block_repeats
									assert len(lowercase_					) == len(
									    self.block_repeats					), "`block_sizes` and `block_repeats` should have the same length."
									snake_case_									=				num_decoder_layers
									snake_case_									=				d_model
									snake_case_									=				n_head
									snake_case_									=				d_head
									snake_case_									=				d_inner
									snake_case_									=				hidden_act
									snake_case_									=				hidden_dropout
									snake_case_									=				attention_dropout
									snake_case_									=				activation_dropout
									snake_case_									=				initializer_range
									snake_case_									=				initializer_std
									snake_case_									=				layer_norm_eps
									assert pooling_type in [
									    "mean",
									    "max",
									], F"Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."
									snake_case_									=				pooling_type
									assert attention_type in [
									    "relative_shift",
									    "factorized",
									], F"Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."
									snake_case_									=				attention_type
									snake_case_									=				separate_cls
									snake_case_									=				truncate_seq
									snake_case_									=				pool_q_only

									super().__init__(**lowercase_					)


				@property
				def   A_ (		self    :					Optional[int]					):
									return sum(self.block_sizes					)


				@num_hidden_layers.setter
				def   A_ (		self    :					Union[str, Any]       ,				lowercase_    :					Dict					):
									raise NotImplementedError(
									    '''This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.'''					)


				@property
				def   A_ (		self    :					Optional[Any]					):
									return len(self.block_sizes					)


				@num_blocks.setter
				def   A_ (		self    :					Tuple       ,				lowercase_    :					Union[str, Any]					):
									raise NotImplementedError('''This model does not support the setting of `num_blocks`. Please set `block_sizes`.'''					)









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   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



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





class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							Any:
							super().__init__()

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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							List[Any]:
							if latents is None:
												UpperCAmelCase_    :      str            =						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}"   )
												UpperCAmelCase_    :      Tuple            =						latents.to(_UpperCamelCase   )

							UpperCAmelCase_    :      Tuple            =						latents * scheduler.init_noise_sigma
							return latents



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

							UpperCAmelCase_    :      int            =						torch.device(f"cuda:{gpu_id}"   )

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



		@property
		def 					__UpperCAmelCase			(							self   )	->							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(_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 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							str:
							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ) and isinstance(image[0]				,		torch.Tensor   ):
												UpperCAmelCase_    :      int            =						torch.cat(_UpperCamelCase				,		axis=0   ) if image[0].ndim == 4 else torch.stack(_UpperCamelCase				,		axis=0   )

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

							UpperCAmelCase_    :      Tuple            =						image.to(dtype=self.image_encoder.dtype				,		device=_UpperCamelCase   )

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

							UpperCAmelCase_    :      List[str]            =						image_embeds.repeat_interleave(_UpperCamelCase				,		dim=0   )

							if do_classifier_free_guidance:
												UpperCAmelCase_    :      Dict            =						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
												UpperCAmelCase_    :      Optional[int]            =						torch.cat([negative_image_embeds, image_embeds]   )

							return image_embeds



		@torch.no_grad()
		@replace_example_docstring(_UpperCamelCase   )
		def __call__(							self				,		_UpperCamelCase				,		_UpperCamelCase = 1				,		_UpperCamelCase = 2_5				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = 4.0				,		_UpperCamelCase = 6_4				,		_UpperCamelCase = "pil"				,		_UpperCamelCase = True				,		)	->							Union[str, Any]:

							if isinstance(_UpperCamelCase				,		PIL.Image.Image   ):
												UpperCAmelCase_    :      Tuple            =						1
							elif isinstance(_UpperCamelCase				,		torch.Tensor   ):
												UpperCAmelCase_    :      str            =						image.shape[0]
							elif isinstance(_UpperCamelCase				,		_UpperCamelCase   ) and isinstance(image[0]				,		(torch.Tensor, PIL.Image.Image)   ):
												UpperCAmelCase_    :      Optional[int]            =						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   )}"   )

							UpperCAmelCase_    :      Tuple            =						self._execution_device

							UpperCAmelCase_    :      str            =						batch_size * num_images_per_prompt

							UpperCAmelCase_    :      str            =						guidance_scale > 1.0
							UpperCAmelCase_    :      str            =						self._encode_image(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )

							# prior

							self.scheduler.set_timesteps(_UpperCamelCase				,		device=_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						self.scheduler.timesteps

							UpperCAmelCase_    :      int            =						self.prior.config.num_embeddings
							UpperCAmelCase_    :      Any            =						self.prior.config.embedding_dim

							UpperCAmelCase_    :      List[str]            =						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
							UpperCAmelCase_    :      List[Any]            =						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
												UpperCAmelCase_    :      Tuple            =						torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
												UpperCAmelCase_    :      Optional[Any]            =						self.scheduler.scale_model_input(_UpperCamelCase				,		_UpperCamelCase   )

												UpperCAmelCase_    :      int            =						self.prior(
												    _UpperCamelCase				,		timestep=_UpperCamelCase				,		proj_embedding=_UpperCamelCase				,		).predicted_image_embedding

												# remove the variance
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						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_    :      str            =						noise_pred.chunk(2   )
																	UpperCAmelCase_    :      List[Any]            =						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

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

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

							UpperCAmelCase_    :      List[Any]            =						[]
							for i, latent in enumerate(_UpperCamelCase   ):
												print()
												UpperCAmelCase_    :      List[str]            =						self.renderer.decode(
												    latent[None, :]				,		_UpperCamelCase				,		size=_UpperCamelCase				,		ray_batch_size=4_0_9_6				,		n_coarse_samples=6_4				,		n_fine_samples=1_2_8				,		)

												images.append(_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[int]            =						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}"   )

							UpperCAmelCase_    :      Dict            =						images.cpu().numpy()

							if output_type == "pil":
												UpperCAmelCase_    :      List[str]            =						[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   )






