

"""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


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

_A						=			'\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 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     4_2






class 	_lowercase       (    __UpperCAmelCase		):




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			)		->							Optional[int]:
			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_ )		->							Optional[int]:
			if latents is None:
					lowerCamelCase					:      Any          =   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}""" )
					lowerCamelCase					:      str          =   latents.to(UpperCAmelCase_ )

			lowerCamelCase					:      str          =   latents * scheduler.init_noise_sigma
			return latents




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

			lowerCamelCase					:      List[Any]          =   torch.device(F"""cuda:{gpu_id}""" )

			lowerCamelCase					:      Any          =   [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 )		->							Dict:
			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_		,			)		->							List[Any]:
			if isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ) and isinstance(image[0]		,			torch.Tensor ):
					lowerCamelCase					:      Union[str, Any]          =   torch.cat(UpperCAmelCase_		,			axis=0 ) if image[0].ndim == 4 else torch.stack(UpperCAmelCase_		,			axis=0 )

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

			lowerCamelCase					:      Optional[int]          =   image.to(dtype=self.image_encoder.dtype		,			device=UpperCAmelCase_ )

			lowerCamelCase					:      Optional[int]          =   self.image_encoder(UpperCAmelCase_ )['last_hidden_state']
			lowerCamelCase					:      Optional[int]          =   image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

			lowerCamelCase					:      List[Any]          =   image_embeds.repeat_interleave(UpperCAmelCase_		,			dim=0 )

			if do_classifier_free_guidance:
					lowerCamelCase					:      Tuple          =   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
					lowerCamelCase					:      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_ = 25		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = 4.0		,			UpperCAmelCase_ = 64		,			UpperCAmelCase_ = "pil"		,			UpperCAmelCase_ = True		,			)		->							Optional[int]:

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

			lowerCamelCase					:      Optional[Any]          =   self._execution_device

			lowerCamelCase					:      str          =   batch_size * num_images_per_prompt

			lowerCamelCase					:      Tuple          =   guidance_scale > 1.0
			lowerCamelCase					:      List[str]          =   self._encode_image(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )

			# prior

			self.scheduler.set_timesteps(UpperCAmelCase_		,			device=UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   self.scheduler.timesteps

			lowerCamelCase					:      Optional[int]          =   self.prior.config.num_embeddings
			lowerCamelCase					:      Dict          =   self.prior.config.embedding_dim

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

					lowerCamelCase					:      Tuple          =   self.prior(
					    UpperCAmelCase_		,			timestep=UpperCAmelCase_		,			proj_embedding=UpperCAmelCase_		,			).predicted_image_embedding

					# remove the variance
					lowerCamelCase					:      Union[str, Any]          =   noise_pred.split(
					    scaled_model_input.shape[2]		,			dim=2 )  # batch_size, num_embeddings, embedding_dim

					if do_classifier_free_guidance is not None:
							lowerCamelCase					:      Optional[Any]          =   noise_pred.chunk(2 )
							lowerCamelCase					:      List[Any]          =   noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

					lowerCamelCase					:      str          =   self.scheduler.step(
					    UpperCAmelCase_		,			timestep=UpperCAmelCase_		,			sample=UpperCAmelCase_		,			).prev_sample

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

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

					images.append(UpperCAmelCase_ )

			lowerCamelCase					:      Tuple          =   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}""" )

			lowerCamelCase					:      List[Any]          =   images.cpu().numpy()

			if output_type == "pil":
					lowerCamelCase					:      Dict          =   [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 ....configuration_utils import PretrainedConfig
from ....utils import logging


_A						=			logging.get_logger(__name__)

_A						=			{
    'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
        'https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'trajectory_transformer'
	lowercase_				=     ['past_key_values']
	lowercase_				=     {
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}




	def __init__(			self		,			UpperCAmelCase_=100		,			UpperCAmelCase_=5		,			UpperCAmelCase_=1		,			UpperCAmelCase_=1		,			UpperCAmelCase_=249		,			UpperCAmelCase_=6		,			UpperCAmelCase_=17		,			UpperCAmelCase_=25		,			UpperCAmelCase_=4		,			UpperCAmelCase_=4		,			UpperCAmelCase_=128		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.0006		,			UpperCAmelCase_=512		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=1E-1_2		,			UpperCAmelCase_=1		,			UpperCAmelCase_=True		,			UpperCAmelCase_=1		,			UpperCAmelCase_=50256		,			UpperCAmelCase_=50256		,			**UpperCAmelCase_		,			)		->							List[Any]:
			lowerCamelCase					:      int          =   vocab_size
			lowerCamelCase					:      List[str]          =   action_weight
			lowerCamelCase					:      List[Any]          =   reward_weight
			lowerCamelCase					:      List[str]          =   value_weight
			lowerCamelCase					:      Tuple          =   max_position_embeddings
			lowerCamelCase					:      List[str]          =   block_size
			lowerCamelCase					:      Any          =   action_dim
			lowerCamelCase					:      List[Any]          =   observation_dim
			lowerCamelCase					:      Any          =   transition_dim
			lowerCamelCase					:      int          =   learning_rate
			lowerCamelCase					:      Union[str, Any]          =   n_layer
			lowerCamelCase					:      Tuple          =   n_head
			lowerCamelCase					:      Any          =   n_embd
			lowerCamelCase					:      Union[str, Any]          =   embd_pdrop
			lowerCamelCase					:      Optional[int]          =   attn_pdrop
			lowerCamelCase					:      int          =   resid_pdrop
			lowerCamelCase					:      Optional[int]          =   initializer_range
			lowerCamelCase					:      List[str]          =   layer_norm_eps
			lowerCamelCase					:      Any          =   kaiming_initializer_range
			lowerCamelCase					:      str          =   use_cache
			super().__init__(pad_token_id=UpperCAmelCase_		,			bos_token_id=UpperCAmelCase_		,			eos_token_id=UpperCAmelCase_		,			**UpperCAmelCase_ )






