code: import math
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  from transformers import CLIPTextModelWithProjection, CLIPTokenizer
  
  from ...models import PriorTransformer
  from ...pipelines import DiffusionPipeline
  from ...schedulers import HeunDiscreteScheduler
  from ...utils import (
      BaseOutput,
      is_accelerate_available,
      is_accelerate_version,
      logging,
      randn_tensor,
      replace_example_docstring,
  )
  from .renderer import ShapERenderer
  
  
  A_:      int        =			logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  A_:      Optional[Any]        =			'\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = "a shark"\n\n        >>> images = pipe(\n        ...     prompt,\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], "shark_3d.gif")\n        ```\n'
  
  
  
  
  
  
  @dataclass
  class _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							lowerCAmelCase__       =					42
  
  
  
  
  
  
  class _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														super().__init__()
  
  														self.register_modules(
  														    prior=UpperCAmelCase     ,   text_encoder=UpperCAmelCase     ,   tokenizer=UpperCAmelCase     ,   scheduler=UpperCAmelCase     ,   renderer=UpperCAmelCase     ,   )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if latents is None:
  																					_lowercase				=	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}'''  )
  																					_lowercase				=	latents.to(UpperCAmelCase  )
  
  														_lowercase				=	latents * scheduler.init_noise_sigma
  														return latents
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase=0  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if is_accelerate_available():
  																					from accelerate import cpu_offload
  														else:
  																					raise ImportError("""Please install accelerate via `pip install accelerate`"""  )
  
  														_lowercase				=	torch.device(F'''cuda:{gpu_id}'''  )
  
  														_lowercase				=	[self.text_encoder, self.prior]
  														for cpu_offloaded_model in models:
  																					if cpu_offloaded_model is not None:
  																												cpu_offload(UpperCAmelCase     ,   UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase=0  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if is_accelerate_available() and is_accelerate_version(""">="""     ,   """0.17.0.dev0"""  ):
  																					from accelerate import cpu_offload_with_hook
  														else:
  																					raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""  )
  
  														_lowercase				=	torch.device(F'''cuda:{gpu_id}'''  )
  
  														if self.device.type != "cpu":
  																					self.to("""cpu"""     ,   silence_dtype_warnings=UpperCAmelCase  )
  																					torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
  														_lowercase				=	None
  														for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
  																					_lowercase				,       _lowercase				=	cpu_offload_with_hook(UpperCAmelCase     ,   UpperCAmelCase     ,   prev_module_hook=UpperCAmelCase  )
  
  														if self.safety_checker is not None:
  																					_lowercase				,       _lowercase				=	cpu_offload_with_hook(self.safety_checker     ,   UpperCAmelCase     ,   prev_module_hook=UpperCAmelCase  )
  
  														# We'll offload the last model manually.
  														_lowercase				=	hook
  
  
  
  							@property
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if self.device != torch.device("""meta"""  ) or not hasattr(self.text_encoder     ,   """_hf_hook"""  ):
  																					return self.device
  														for module in self.text_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     ,   ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														len(UpperCAmelCase  ) if isinstance(UpperCAmelCase     ,   UpperCAmelCase  ) else 1
  
  														# YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
  														_lowercase				=	0
  														# get prompt text embeddings
  														_lowercase				=	self.tokenizer(
  														    UpperCAmelCase     ,   padding="""max_length"""     ,   max_length=self.tokenizer.model_max_length     ,   truncation=UpperCAmelCase     ,   return_tensors="""pt"""     ,   )
  														_lowercase				=	text_inputs.input_ids
  														_lowercase				=	self.tokenizer(UpperCAmelCase     ,   padding="""longest"""     ,   return_tensors="""pt"""  ).input_ids
  
  														if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(UpperCAmelCase     ,   UpperCAmelCase  ):
  																					_lowercase				=	self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]  )
  																					logger.warning(
  																					    """The following part of your input was truncated because CLIP can only handle sequences up to"""
  																					    F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''  )
  
  														_lowercase				=	self.text_encoder(text_input_ids.to(UpperCAmelCase  )  )
  														_lowercase				=	text_encoder_output.text_embeds
  
  														_lowercase				=	prompt_embeds.repeat_interleave(UpperCAmelCase     ,   dim=0  )
  														# in Shap-E it normalize the prompt_embeds and then later rescale it
  														_lowercase				=	prompt_embeds / torch.linalg.norm(UpperCAmelCase     ,   dim=-1     ,   keepdim=UpperCAmelCase  )
  
  														if do_classifier_free_guidance:
  																					_lowercase				=	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
  																					_lowercase				=	torch.cat([negative_prompt_embeds, prompt_embeds]  )
  
  														# Rescale the features to have unit variance
  														_lowercase				=	math.sqrt(prompt_embeds.shape[1]  ) * prompt_embeds
  
  														return prompt_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     ,   ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if isinstance(UpperCAmelCase     ,   UpperCAmelCase  ):
  																					_lowercase				=	1
  														elif isinstance(UpperCAmelCase     ,   UpperCAmelCase  ):
  																					_lowercase				=	len(UpperCAmelCase  )
  														else:
  																					raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase  )}'''  )
  
  														_lowercase				=	self._execution_device
  
  														_lowercase				=	batch_size * num_images_per_prompt
  
  														_lowercase				=	guidance_scale > 1.0
  														_lowercase				=	self._encode_prompt(UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  )
  
  														# prior
  
  														self.scheduler.set_timesteps(UpperCAmelCase     ,   device=UpperCAmelCase  )
  														_lowercase				=	self.scheduler.timesteps
  
  														_lowercase				=	self.prior.config.num_embeddings
  														_lowercase				=	self.prior.config.embedding_dim
  
  														_lowercase				=	self.prepare_latents(
  														    (batch_size, num_embeddings * embedding_dim)     ,   prompt_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
  														_lowercase				=	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
  																					_lowercase				=	torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents
  																					_lowercase				=	self.scheduler.scale_model_input(UpperCAmelCase     ,   UpperCAmelCase  )
  
  																					_lowercase				=	self.prior(
  																					    UpperCAmelCase     ,   timestep=UpperCAmelCase     ,   proj_embedding=UpperCAmelCase     ,   ).predicted_image_embedding
  
  																					# remove the variance
  																					_lowercase				,       _lowercase				=	noise_pred.split(
  																					    scaled_model_input.shape[2]     ,   dim=2  )  # batch_size, num_embeddings, embedding_dim
  
  																					if do_classifier_free_guidance is not None:
  																												_lowercase				,       _lowercase				=	noise_pred.chunk(2  )
  																												_lowercase				=	noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
  
  																					_lowercase				=	self.scheduler.step(
  																					    UpperCAmelCase     ,   timestep=UpperCAmelCase     ,   sample=UpperCAmelCase     ,   ).prev_sample
  
  														if output_type == "latent":
  																					return ShapEPipelineOutput(images=UpperCAmelCase  )
  
  														_lowercase				=	[]
  														for i, latent in enumerate(UpperCAmelCase  ):
  																					_lowercase				=	self.renderer.decode(
  																					    latent[None, :]     ,   UpperCAmelCase     ,   size=UpperCAmelCase     ,   ray_batch_size=4096     ,   n_coarse_samples=64     ,   n_fine_samples=128     ,   )
  																					images.append(UpperCAmelCase  )
  
  														_lowercase				=	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}'''  )
  
  														_lowercase				=	images.cpu().numpy()
  
  														if output_type == "pil":
  																					_lowercase				=	[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  )
  
  
  
style_context: import random
  
  
  class _lowercase					:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase = 0.01     ,   UpperCAmelCase = 1000     ,   UpperCAmelCase = -1     ,   ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	sample
  														if len(self.sample  ) == 0:
  																					raise ValueError("""Sample data can not be empty"""  )
  														_lowercase				=	target
  														if len(self.target  ) == 0:
  																					raise ValueError("""Target data can not be empty"""  )
  														if len(self.sample  ) != len(self.target  ):
  																					raise ValueError("""Sample data and Target data do not have matching lengths"""  )
  														_lowercase				=	learning_rate
  														_lowercase				=	epoch_number
  														_lowercase				=	bias
  														_lowercase				=	len(UpperCAmelCase  )
  														_lowercase				=	len(sample[0]  )  # number of columns in dataset
  														_lowercase				=	[]
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														for sample in self.sample:
  																					sample.insert(0     ,   self.bias  )
  
  														for _ in range(self.col_sample  ):
  																					self.weight.append(random.random()  )
  
  														self.weight.insert(0     ,   self.bias  )
  
  														_lowercase				=	0
  
  														while True:
  																					_lowercase				=	False
  																					for i in range(self.number_sample  ):
  																												_lowercase				=	0
  																												for j in range(self.col_sample + 1  ):
  																																			_lowercase				=	u + self.weight[j] * self.sample[i][j]
  																												_lowercase				=	self.sign(UpperCAmelCase  )
  																												if y != self.target[i]:
  																																			for j in range(self.col_sample + 1  ):
  																																										_lowercase				=	(
  																																										    self.weight[j]
  																																										    + self.learning_rate
  																																										    * (self.target[i] - y)
  																																										    * self.sample[i][j]
  																																										)
  																																			_lowercase				=	True
              # print('Epoch: \n',epoch_count)
  																					_lowercase				=	epoch_count + 1
  																					# if you want control the epoch or just by error
  																					if not has_misclassified:
  																												print(("""\nEpoch:\n""", epoch_count)  )
  																												print("""------------------------\n"""  )
  																												# if epoch_count > self.epoch_number or not error:
  																												break
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if len(self.sample  ) == 0:
  																					raise ValueError("""Sample data can not be empty"""  )
  														sample.insert(0     ,   self.bias  )
  														_lowercase				=	0
  														for i in range(self.col_sample + 1  ):
  																					_lowercase				=	u + self.weight[i] * sample[i]
  
  														_lowercase				=	self.sign(UpperCAmelCase  )
  
  														if y == -1:
  																					print(("""Sample: """, sample)  )
  																					print("""classification: P1"""  )
  														else:
  																					print(("""Sample: """, sample)  )
  																					print("""classification: P2"""  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return 1 if u >= 0 else -1
  
  
  
  
  
  
  A_:      Optional[int]        =			[
      [-0.6_5_0_8, 0.1_0_9_7, 4.0_0_0_9],
      [-1.4_4_9_2, 0.8_8_9_6, 4.4_0_0_5],
      [2.0_8_5_0, 0.6_8_7_6, 1_2.0_7_1_0],
      [0.2_6_2_6, 1.1_4_7_6, 7.7_9_8_5],
      [0.6_4_1_8, 1.0_2_3_4, 7.0_4_2_7],
      [0.2_5_6_9, 0.6_7_3_0, 8.3_2_6_5],
      [1.1_1_5_5, 0.6_0_4_3, 7.4_4_4_6],
      [0.0_9_1_4, 0.3_3_9_9, 7.0_6_7_7],
      [0.0_1_2_1, 0.5_2_5_6, 4.6_3_1_6],
      [-0.0_4_2_9, 0.4_6_6_0, 5.4_3_2_3],
      [0.4_3_4_0, 0.6_8_7_0, 8.2_2_8_7],
      [0.2_7_3_5, 1.0_2_8_7, 7.1_9_3_4],
      [0.4_8_3_9, 0.4_8_5_1, 7.4_8_5_0],
      [0.4_0_8_9, -0.1_2_6_7, 5.5_0_1_9],
      [1.4_3_9_1, 0.1_6_1_4, 8.5_8_4_3],
      [-0.9_1_1_5, -0.1_9_7_3, 2.1_9_6_2],
      [0.3_6_5_4, 1.0_4_7_5, 7.4_8_5_8],
      [0.2_1_4_4, 0.7_5_1_5, 7.1_6_9_9],
      [0.2_0_1_3, 1.0_0_1_4, 6.5_4_8_9],
      [0.6_4_8_3, 0.2_1_8_3, 5.8_9_9_1],
      [-0.1_1_4_7, 0.2_2_4_2, 7.2_4_3_5],
      [-0.7_9_7_0, 0.8_7_9_5, 3.8_7_6_2],
      [-1.0_6_2_5, 0.6_3_6_6, 2.4_7_0_7],
      [0.5_3_0_7, 0.1_2_8_5, 5.6_8_8_3],
      [-1.2_2_0_0, 0.7_7_7_7, 1.7_2_5_2],
      [0.3_9_5_7, 0.1_0_7_6, 5.6_6_2_3],
      [-0.1_0_1_3, 0.5_9_8_9, 7.1_8_1_2],
      [2.4_4_8_2, 0.9_4_5_5, 1_1.2_0_9_5],
      [2.0_1_4_9, 0.6_1_9_2, 1_0.9_2_6_3],
      [0.2_0_1_2, 0.2_6_1_1, 5.4_6_3_1],
  ]
  
  A_:      Optional[int]        =			[
      -1,
      -1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      -1,
      -1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      -1,
      1,
      -1,
      1,
  ]
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  				A_:      str        =			Perceptron(
  				    sample=samples, target=target, learning_rate=0.0_1, epoch_number=1000, bias=-1
  				)
  				network.training()
  				print('Finished training perceptron')
  				print('Enter values to predict or q to exit')
  				while True:
  								A_:      list        =			[]
  								for i in range(len(samples[0])):
  												A_:      Dict        =			input('value: ').strip()
  												if user_input == "q":
  																break
  												A_:      Union[str, Any]        =			float(user_input)
  												sample.insert(i, observation)
  								network.sort(sample)
  
  
  