
"""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														=		"""
    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    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				42






class    _UpperCAmelCase(   lowerCamelCase     ):



		def __init__( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		)	->	Union[str, Any]:






							'''simple docstring'''
							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)	->	Union[str, 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 						UpperCAmelCase							( self    ,		__a=0)	->	Optional[Any]:






							'''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 						UpperCAmelCase							( self)	->	List[str]:






							'''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 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		)	->	Optional[int]:






							'''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    ,		__a    ,		__a = 1    ,		__a = 25    ,		__a = None    ,		__a = None    ,		__a = 4.0    ,		__a = 64    ,		__a = "pil"    ,		__a = True    ,		)	->	Optional[int]:






							'''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)








"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
				from PIL import Image






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'dandelin/vilt-b32-finetuned-vqa'
		lowercase__      =				(
		    'This is a tool that answers a question about an image. It takes an input named `image` which should be the '
		    'image containing the information, as well as a `question` which should be the question in English. It '
		    'returns a text that is the answer to the question.'
		)
		lowercase__      =				'image_qa'
		lowercase__      =				AutoProcessor
		lowercase__      =				AutoModelForVisualQuestionAnswering

		lowercase__      =				['image', 'text']
		lowercase__      =				['text']



		def __init__( self    ,		*__a    ,		**__a)	->	int:






							'''simple docstring'''
							requires_backends(self    ,		['''vision'''])
							super().__init__(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Dict:






							'''simple docstring'''
							return self.pre_processor(__a    ,		__a    ,		return_tensors='''pt''')



		def 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






							'''simple docstring'''
							with torch.no_grad():
												return self.model(**__a).logits




		def 						UpperCAmelCase							( self    ,		__a)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  outputs.argmax(-1).item()
							return self.model.config.idalabel[idx]






