


"""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


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

lowercase_			     =  "\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					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



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







class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''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 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a      ):
				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 			__UpperCAmelCase       (  self							, _a=0      ):
				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 			__UpperCAmelCase       (  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 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, ):
				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							, _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           =			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=4_096							, n_coarse_samples=64							, n_fine_samples=128							, )

						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      )










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


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {
    '''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 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      str     		=		"decision_transformer"
   __A :      Union[str, Any]     		=		["past_key_values"]
   __A :      Optional[int]     		=		{
       "max_position_embeddings": "n_positions",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }






   def __init__(		self						,				__A=17						,				__A=4						,				__A=128						,				__A=4096						,				__A=True						,				__A=1						,				__A=1024						,				__A=3						,				__A=1						,				__A=None						,				__A="relu"						,				__A=0.1						,				__A=0.1						,				__A=0.1						,				__A=1e-5						,				__A=0.02						,				__A=True						,				__A=True						,				__A=5_0256						,				__A=5_0256						,				__A=False						,				__A=False						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				state_dim
     lowerCamelCase :					Tuple    =				act_dim
     lowerCamelCase :					List[str]    =				hidden_size
     lowerCamelCase :					Optional[Any]    =				max_ep_len
     lowerCamelCase :					Union[str, Any]    =				action_tanh
     lowerCamelCase :					int    =				vocab_size
     lowerCamelCase :					List[Any]    =				n_positions
     lowerCamelCase :					Dict    =				n_layer
     lowerCamelCase :					int    =				n_head
     lowerCamelCase :					List[Any]    =				n_inner
     lowerCamelCase :					Any    =				activation_function
     lowerCamelCase :					Optional[int]    =				resid_pdrop
     lowerCamelCase :					str    =				embd_pdrop
     lowerCamelCase :					Tuple    =				attn_pdrop
     lowerCamelCase :					List[Any]    =				layer_norm_epsilon
     lowerCamelCase :					Dict    =				initializer_range
     lowerCamelCase :					Optional[int]    =				scale_attn_weights
     lowerCamelCase :					List[Any]    =				use_cache
     lowerCamelCase :					Tuple    =				scale_attn_by_inverse_layer_idx
     lowerCamelCase :					Optional[int]    =				reorder_and_upcast_attn

     lowerCamelCase :					Dict    =				bos_token_id
     lowerCamelCase :					Any    =				eos_token_id

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



