



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   (      A						):
			UpperCamelCase        =					4_2







class       __lowerCAmelCase   (      A						):



			def __init__(						self       :  List[Any]			,		A       :  PriorTransformer			,		A       :  CLIPVisionModel			,		A       :  CLIPImageProcessor			,		A       :  HeunDiscreteScheduler			,		A       :  ShapERenderer			,		)       ->   Tuple:



						"""simple docstring"""






						super().__init__()

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



			def    _lowerCamelCase	(						self       :  int			,		A       :  Dict			,		A       :  str			,		A       :  Optional[int]			,		A       :  int			,		A       :  str			,		A       :  Tuple)       ->   Optional[Any]:



						"""simple docstring"""






						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    _lowerCamelCase	(						self       :  Tuple			,		A       :  Union[str, Any]=0)       ->   Dict:



						"""simple docstring"""






						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    _lowerCamelCase	(						self       :  str)       ->   List[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    _lowerCamelCase	(						self       :  List[str]			,		A       :  Union[str, Any]			,		A       :  Any			,		A       :  Optional[Any]			,		A       :  Optional[int]			,		)       ->   str:



						"""simple docstring"""






						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       :  Dict			,		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			,		)       ->   Union[str, Any]:



						"""simple docstring"""






						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=40_96			,		n_coarse_samples=64			,		n_fine_samples=1_28			,		)

									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)





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


UpperCAmelCase__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{
    "edbeeching/decision-transformer-gym-hopper-medium": (
        "https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''decision_transformer'''
			UpperCamelCase        =					['''past_key_values''']
			UpperCamelCase        =					{
			    '''max_position_embeddings''': '''n_positions''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}



			def __init__(						self       :  Any			,		A       :  Optional[int]=17			,		A       :  List[str]=4			,		A       :  int=1_28			,		A       :  Union[str, Any]=40_96			,		A       :  Any=True			,		A       :  Any=1			,		A       :  List[Any]=10_24			,		A       :  List[Any]=3			,		A       :  Tuple=1			,		A       :  Any=None			,		A       :  Optional[int]="relu"			,		A       :  Union[str, Any]=0.1			,		A       :  Optional[int]=0.1			,		A       :  Optional[int]=0.1			,		A       :  Optional[int]=1E-5			,		A       :  List[Any]=0.0_2			,		A       :  Tuple=True			,		A       :  Union[str, Any]=True			,		A       :  str=5_02_56			,		A       :  Union[str, Any]=5_02_56			,		A       :  List[Any]=False			,		A       :  Optional[int]=False			,		**A       :  int			,		)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       state_dim
						_UpperCAmelCase			=       act_dim
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       max_ep_len
						_UpperCAmelCase			=       action_tanh
						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       n_positions
						_UpperCAmelCase			=       n_layer
						_UpperCAmelCase			=       n_head
						_UpperCAmelCase			=       n_inner
						_UpperCAmelCase			=       activation_function
						_UpperCAmelCase			=       resid_pdrop
						_UpperCAmelCase			=       embd_pdrop
						_UpperCAmelCase			=       attn_pdrop
						_UpperCAmelCase			=       layer_norm_epsilon
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       scale_attn_weights
						_UpperCAmelCase			=       use_cache
						_UpperCAmelCase			=       scale_attn_by_inverse_layer_idx
						_UpperCAmelCase			=       reorder_and_upcast_attn

						_UpperCAmelCase			=       bos_token_id
						_UpperCAmelCase			=       eos_token_id

						super().__init__(bos_token_id=A			,		eos_token_id=A			,		**A)
