
"""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


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

__magic_name__       =			"\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 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





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



class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  ):
				super().__init__()

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




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
				if latents is None:
						__SCREAMING_SNAKE_CASE            = randn_tensor(lowerCAmelCase__							,  generator=lowerCAmelCase__							,  device=lowerCAmelCase__							,  dtype=lowerCAmelCase__)
				else:
						if latents.shape != shape:
								raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
						__SCREAMING_SNAKE_CASE            = latents.to(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = latents * scheduler.init_noise_sigma
				return latents




		def 	snake_case_     (     self							,  lowerCAmelCase__=0):
				if is_accelerate_available():
						from accelerate import cpu_offload
				else:
						raise ImportError("""Please install accelerate via `pip install accelerate`""")

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

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




		@property
		def 	snake_case_     (     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(lowerCAmelCase__							,  """_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 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  ):
				if isinstance(lowerCAmelCase__							,  lowerCAmelCase__) and isinstance(image[0]							,  torch.Tensor):
						__SCREAMING_SNAKE_CASE            = torch.cat(lowerCAmelCase__							,  axis=0) if image[0].ndim == 4 else torch.stack(lowerCAmelCase__							,  axis=0)

				if not isinstance(lowerCAmelCase__							,  torch.Tensor):
						__SCREAMING_SNAKE_CASE            = self.image_processor(lowerCAmelCase__							,  return_tensors="""pt""").pixel_values[0].unsqueeze(0)

				__SCREAMING_SNAKE_CASE            = image.to(dtype=self.image_encoder.dtype							,  device=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = self.image_encoder(lowerCAmelCase__)["""last_hidden_state"""]
				__SCREAMING_SNAKE_CASE            = image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

				__SCREAMING_SNAKE_CASE            = image_embeds.repeat_interleave(lowerCAmelCase__							,  dim=0)

				if do_classifier_free_guidance:
						__SCREAMING_SNAKE_CASE            = torch.zeros_like(lowerCAmelCase__)

						# 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
						__SCREAMING_SNAKE_CASE            = torch.cat([negative_image_embeds, image_embeds])

				return image_embeds




		@torch.no_grad()
		@replace_example_docstring(lowerCAmelCase__)
		def __call__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = 1							,  lowerCAmelCase__ = 2_5							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = 4.0							,  lowerCAmelCase__ = 6_4							,  lowerCAmelCase__ = "pil"							,  lowerCAmelCase__ = True							,  ):

				if isinstance(lowerCAmelCase__							,  PIL.Image.Image):
						__SCREAMING_SNAKE_CASE            = 1
				elif isinstance(lowerCAmelCase__							,  torch.Tensor):
						__SCREAMING_SNAKE_CASE            = image.shape[0]
				elif isinstance(lowerCAmelCase__							,  lowerCAmelCase__) and isinstance(image[0]							,  (torch.Tensor, PIL.Image.Image)):
						__SCREAMING_SNAKE_CASE            = len(lowerCAmelCase__)
				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(lowerCAmelCase__)}")

				__SCREAMING_SNAKE_CASE            = self._execution_device

				__SCREAMING_SNAKE_CASE            = batch_size * num_images_per_prompt

				__SCREAMING_SNAKE_CASE            = guidance_scale > 1.0
				__SCREAMING_SNAKE_CASE            = self._encode_image(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__)

				# prior

				self.scheduler.set_timesteps(lowerCAmelCase__							,  device=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = self.scheduler.timesteps

				__SCREAMING_SNAKE_CASE            = self.prior.config.num_embeddings
				__SCREAMING_SNAKE_CASE            = self.prior.config.embedding_dim

				__SCREAMING_SNAKE_CASE            = self.prepare_latents(
				    (batch_size, num_embeddings * embedding_dim)							,  image_embeds.dtype							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  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
				__SCREAMING_SNAKE_CASE            = latents.reshape(latents.shape[0]							,  lowerCAmelCase__							,  lowerCAmelCase__)

				for i, t in enumerate(self.progress_bar(lowerCAmelCase__)):
						# expand the latents if we are doing classifier free guidance
						__SCREAMING_SNAKE_CASE            = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
						__SCREAMING_SNAKE_CASE            = self.scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__)

						__SCREAMING_SNAKE_CASE            = self.prior(
						    lowerCAmelCase__							,  timestep=lowerCAmelCase__							,  proj_embedding=lowerCAmelCase__							,  ).predicted_image_embedding

						# remove the variance
						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = noise_pred.split(
						    scaled_model_input.shape[2]							,  dim=2)  # batch_size, num_embeddings, embedding_dim

						if do_classifier_free_guidance is not None:
								__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = noise_pred.chunk(2)
								__SCREAMING_SNAKE_CASE            = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

						__SCREAMING_SNAKE_CASE            = self.scheduler.step(
						    lowerCAmelCase__							,  timestep=lowerCAmelCase__							,  sample=lowerCAmelCase__							,  ).prev_sample

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

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

						images.append(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = torch.stack(lowerCAmelCase__)

				if output_type not in ["np", "pil"]:
						raise ValueError(f"Only the output types `pil` and `np` are supported not output_type={output_type}")

				__SCREAMING_SNAKE_CASE            = images.cpu().numpy()

				if output_type == "pil":
						__SCREAMING_SNAKE_CASE            = [self.numpy_to_pil(lowerCAmelCase__) 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=lowerCAmelCase__)






"""simple docstring"""





import requests
from bsa import BeautifulSoup



def 						_lowerCAmelCase				(				UpperCamelCase_ = "AAPL"				):
		__SCREAMING_SNAKE_CASE            = f"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
		__SCREAMING_SNAKE_CASE            = BeautifulSoup(requests.get(UpperCamelCase_				).text  ,				"""html.parser"""				)
		__SCREAMING_SNAKE_CASE            = """My(6px) Pos(r) smartphone_Mt(6px)"""
		return soup.find("""div"""  ,				class_=class_				).find("""span"""				).text


if __name__ == "__main__":
							for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
														print(F"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")




