code: 
  
  
  import logging
  
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  
  from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
  from transformers.models.albert.modeling_albert import (
      ALBERT_INPUTS_DOCSTRING,
      ALBERT_START_DOCSTRING,
      AlbertModel,
      AlbertPreTrainedModel,
      AlbertTransformer,
  )
  
  
  SCREAMING_SNAKE_CASE__				:    Dict         =    logging.getLogger(__name__)
  
  
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
  
  
       def 					__snake_case(     self       ,				A_       ,				A_       ,				A_=None       ,				A_=None						):
         if current_layer == 0:
           _UpperCAmelCase		:       Any       =    self.embedding_hidden_mapping_in(A_						)
         else:
           _UpperCAmelCase		:       Optional[int]       =    hidden_states[0]
  
         _UpperCAmelCase		:       Tuple       =    int(self.config.num_hidden_layers / self.config.num_hidden_groups						)
  
         # Index of the hidden group
         _UpperCAmelCase		:       Union[str, Any]       =    int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)						)
  
         _UpperCAmelCase		:       int       =    self.albert_layer_groups[group_idx](
             A_       ,				A_       ,				head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]       ,				)
         _UpperCAmelCase		:       Dict       =    layer_group_output[0]
  
         return (hidden_states,)
  
  
  @add_start_docstrings(
      '''The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''       ,  A       ,  )
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
  
  
       def __init__(     self       ,				A_						):
         super().__init__(A_						)
  
         _UpperCAmelCase		:       List[Any]       =    AlbertTransformerWithPabee(A_						)
  
         self.init_weights()
         _UpperCAmelCase		:       Tuple       =    0
         _UpperCAmelCase		:       Optional[Any]       =    0
         _UpperCAmelCase		:       int       =    0
  
         _UpperCAmelCase		:       Dict       =    0
  
  
       def 					__snake_case(     self       ,				A_						):
         _UpperCAmelCase		:       str       =    threshold
  
  
       def 					__snake_case(     self       ,				A_						):
         _UpperCAmelCase		:       Optional[Any]       =    patience
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Tuple       =    0
         _UpperCAmelCase		:       Tuple       =    0
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Dict       =    self.inference_layers_num / self.inference_instances_num
         _UpperCAmelCase		:       Optional[int]       =    (
             F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
             F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
         )
         print(A_						)
  
  
  
  
       @add_start_docstrings_to_model_forward(A_						)
       def 					__snake_case(     self       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=False       ,				):
  
         if input_ids is not None and inputs_embeds is not None:
           raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""						)
         elif input_ids is not None:
           _UpperCAmelCase		:       List[str]       =    input_ids.size()
         elif inputs_embeds is not None:
           _UpperCAmelCase		:       Optional[Any]       =    inputs_embeds.size()[:-1]
         else:
           raise ValueError("""You have to specify either input_ids or inputs_embeds"""						)
  
         _UpperCAmelCase		:       List[str]       =    input_ids.device if input_ids is not None else inputs_embeds.device
  
         if attention_mask is None:
           _UpperCAmelCase		:       Any       =    torch.ones(A_       ,				device=A_						)
         if token_type_ids is None:
           _UpperCAmelCase		:       List[str]       =    torch.zeros(A_       ,				dtype=torch.long       ,				device=A_						)
  
         _UpperCAmelCase		:       str       =    attention_mask.unsqueeze(1						).unsqueeze(2						)
         _UpperCAmelCase		:       Tuple       =    extended_attention_mask.to(dtype=self.dtype						)  # fp16 compatibility
         _UpperCAmelCase		:       Tuple       =    (1.0 - extended_attention_mask) * -1_00_00.0
         _UpperCAmelCase		:       Union[str, Any]       =    self.get_head_mask(A_       ,				self.config.num_hidden_layers						)
  
         _UpperCAmelCase		:       Dict       =    self.embeddings(
             A_       ,				position_ids=A_       ,				token_type_ids=A_       ,				inputs_embeds=A_						)
         _UpperCAmelCase		:       str       =    embedding_output
  
         if self.training:
           _UpperCAmelCase		:       Any       =    []
           for i in range(self.config.num_hidden_layers						):
             _UpperCAmelCase		:       Tuple       =    self.encoder.adaptive_forward(
                 A_       ,				current_layer=A_       ,				attention_mask=A_       ,				head_mask=A_       ,				)
  
             _UpperCAmelCase		:       List[Any]       =    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
             _UpperCAmelCase		:       List[str]       =    output_layers[i](output_dropout(A_						)						)
             res.append(A_						)
         elif self.patience == 0:  # Use all layers for inference
           _UpperCAmelCase		:       Optional[int]       =    self.encoder(A_       ,				A_       ,				head_mask=A_						)
           _UpperCAmelCase		:       Optional[int]       =    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
           _UpperCAmelCase		:       List[Any]       =    [output_layers[self.config.num_hidden_layers - 1](A_						)]
         else:
           _UpperCAmelCase		:       str       =    0
           _UpperCAmelCase		:       str       =    None
           _UpperCAmelCase		:       str       =    0
           for i in range(self.config.num_hidden_layers						):
             calculated_layer_num += 1
             _UpperCAmelCase		:       int       =    self.encoder.adaptive_forward(
                 A_       ,				current_layer=A_       ,				attention_mask=A_       ,				head_mask=A_       ,				)
  
             _UpperCAmelCase		:       Tuple       =    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
             _UpperCAmelCase		:       List[str]       =    output_layers[i](A_						)
             if regression:
               _UpperCAmelCase		:       Optional[Any]       =    logits.detach()
               if patient_result is not None:
                 _UpperCAmelCase		:       str       =    patient_result.detach()
               if (patient_result is not None) and torch.abs(patient_result - labels						) < self.regression_threshold:
                 patient_counter += 1
               else:
                 _UpperCAmelCase		:       Union[str, Any]       =    0
             else:
               _UpperCAmelCase		:       Dict       =    logits.detach().argmax(dim=1						)
               if patient_result is not None:
                 _UpperCAmelCase		:       Optional[Any]       =    patient_result.detach().argmax(dim=1						)
               if (patient_result is not None) and torch.all(labels.eq(A_						)						):
                 patient_counter += 1
               else:
                 _UpperCAmelCase		:       Dict       =    0
  
             _UpperCAmelCase		:       int       =    logits
             if patient_counter == self.patience:
               break
           _UpperCAmelCase		:       Tuple       =    [patient_result]
           self.inference_layers_num += calculated_layer_num
           self.inference_instances_num += 1
  
         return res
  
  
  @add_start_docstrings(
      '''Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
      the pooled output) e.g. for GLUE tasks. '''       ,  A       ,  )
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
  
  
       def __init__(     self       ,				A_						):
         super().__init__(A_						)
         _UpperCAmelCase		:       List[str]       =    config.num_labels
  
         _UpperCAmelCase		:       List[Any]       =    AlbertModelWithPabee(A_						)
         _UpperCAmelCase		:       Optional[int]       =    nn.Dropout(config.classifier_dropout_prob						)
         _UpperCAmelCase		:       Tuple       =    nn.ModuleList(
             [nn.Linear(config.hidden_size       ,				self.config.num_labels						) for _ in range(config.num_hidden_layers						)]						)
  
         self.init_weights()
  
  
  
  
       @add_start_docstrings_to_model_forward(A_						)
       def 					__snake_case(     self       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				):
         _UpperCAmelCase		:       Optional[Any]       =    self.albert(
             input_ids=A_       ,				attention_mask=A_       ,				token_type_ids=A_       ,				position_ids=A_       ,				head_mask=A_       ,				inputs_embeds=A_       ,				output_dropout=self.dropout       ,				output_layers=self.classifiers       ,				regression=self.num_labels == 1       ,				)
  
         _UpperCAmelCase		:       str       =    (logits[-1],)
  
         if labels is not None:
           _UpperCAmelCase		:       List[Any]       =    None
           _UpperCAmelCase		:       Optional[int]       =    0
           for ix, logits_item in enumerate(A_						):
             if self.num_labels == 1:
               #  We are doing regression
               _UpperCAmelCase		:       int       =    MSELoss()
               _UpperCAmelCase		:       Union[str, Any]       =    loss_fct(logits_item.view(-1						)       ,				labels.view(-1						)						)
             else:
               _UpperCAmelCase		:       List[Any]       =    CrossEntropyLoss()
               _UpperCAmelCase		:       List[Any]       =    loss_fct(logits_item.view(-1       ,				self.num_labels						)       ,				labels.view(-1						)						)
             if total_loss is None:
               _UpperCAmelCase		:       List[str]       =    loss
             else:
               total_loss += loss * (ix + 1)
             total_weights += ix + 1
           _UpperCAmelCase		:       List[str]       =    (total_loss / total_weights,) + outputs
  
         return outputs
  
  
  
  
  
  
style_context: 
  
  
  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
  
  
  SCREAMING_SNAKE_CASE__				:    Optional[int]         =    logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  SCREAMING_SNAKE_CASE__				:    Tuple         =    '\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 		_SCREAMING_SNAKE_CASE    (	A    ):
       __SCREAMING_SNAKE_CASE    				=						42
  
  
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
  
  
       def __init__(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				):
         super().__init__()
  
         self.register_modules(
             prior=A_       ,				text_encoder=A_       ,				tokenizer=A_       ,				scheduler=A_       ,				renderer=A_       ,				)
  
  
       def 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_						):
         if latents is None:
           _UpperCAmelCase		:       List[Any]       =    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		:       Optional[int]       =    latents.to(A_						)
  
         _UpperCAmelCase		:       str       =    latents * scheduler.init_noise_sigma
         return latents
  
  
       def 					__snake_case(     self       ,				A_=0						):
         if is_accelerate_available():
           from accelerate import cpu_offload
         else:
           raise ImportError("""Please install accelerate via `pip install accelerate`"""						)
  
         _UpperCAmelCase		:       Any       =    torch.device(F'''cuda:{gpu_id}'''						)
  
         _UpperCAmelCase		:       Dict       =    [self.text_encoder, self.prior]
         for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
             cpu_offload(A_       ,				A_						)
  
  
       def 					__snake_case(     self       ,				A_=0						):
         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."""						)
  
         _UpperCAmelCase		:       int       =    torch.device(F'''cuda:{gpu_id}'''						)
  
         if self.device.type != "cpu":
           self.to("""cpu"""       ,				silence_dtype_warnings=A_						)
           torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
         _UpperCAmelCase		:       List[str]       =    None
         for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
           _UpperCAmelCase,_UpperCAmelCase		:       List[Any]       =    cpu_offload_with_hook(A_       ,				A_       ,				prev_module_hook=A_						)
  
         if self.safety_checker is not None:
           _UpperCAmelCase,_UpperCAmelCase		:       int       =    cpu_offload_with_hook(self.safety_checker       ,				A_       ,				prev_module_hook=A_						)
  
         # We'll offload the last model manually.
         _UpperCAmelCase		:       Optional[Any]       =    hook
  
  
       @property
       def 					__snake_case(     self						):
         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(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 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				):
         len(A_						) if isinstance(A_       ,				A_						) else 1
  
         # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
         _UpperCAmelCase		:       str       =    0
         # get prompt text embeddings
         _UpperCAmelCase		:       List[str]       =    self.tokenizer(
             A_       ,				padding="""max_length"""       ,				max_length=self.tokenizer.model_max_length       ,				truncation=A_       ,				return_tensors="""pt"""       ,				)
         _UpperCAmelCase		:       Any       =    text_inputs.input_ids
         _UpperCAmelCase		:       Dict       =    self.tokenizer(A_       ,				padding="""longest"""       ,				return_tensors="""pt"""						).input_ids
  
         if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A_       ,				A_						):
           _UpperCAmelCase		:       Union[str, Any]       =    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}'''						)
  
         _UpperCAmelCase		:       List[Any]       =    self.text_encoder(text_input_ids.to(A_						)						)
         _UpperCAmelCase		:       Union[str, Any]       =    text_encoder_output.text_embeds
  
         _UpperCAmelCase		:       Tuple       =    prompt_embeds.repeat_interleave(A_       ,				dim=0						)
         # in Shap-E it normalize the prompt_embeds and then later rescale it
         _UpperCAmelCase		:       Any       =    prompt_embeds / torch.linalg.norm(A_       ,				dim=-1       ,				keepdim=A_						)
  
         if do_classifier_free_guidance:
           _UpperCAmelCase		:       Union[str, Any]       =    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		:       Optional[Any]       =    torch.cat([negative_prompt_embeds, prompt_embeds]						)
  
         # Rescale the features to have unit variance
         _UpperCAmelCase		:       List[Any]       =    math.sqrt(prompt_embeds.shape[1]						) * prompt_embeds
  
         return prompt_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_       ,				A_						):
           _UpperCAmelCase		:       Optional[int]       =    1
         elif isinstance(A_       ,				A_						):
           _UpperCAmelCase		:       Union[str, Any]       =    len(A_						)
         else:
           raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(A_						)}'''						)
  
         _UpperCAmelCase		:       Tuple       =    self._execution_device
  
         _UpperCAmelCase		:       List[str]       =    batch_size * num_images_per_prompt
  
         _UpperCAmelCase		:       str       =    guidance_scale > 1.0
         _UpperCAmelCase		:       Tuple       =    self._encode_prompt(A_       ,				A_       ,				A_       ,				A_						)
  
         # prior
  
         self.scheduler.set_timesteps(A_       ,				device=A_						)
         _UpperCAmelCase		:       Any       =    self.scheduler.timesteps
  
         _UpperCAmelCase		:       List[str]       =    self.prior.config.num_embeddings
         _UpperCAmelCase		:       Union[str, Any]       =    self.prior.config.embedding_dim
  
         _UpperCAmelCase		:       Optional[Any]       =    self.prepare_latents(
             (batch_size, num_embeddings * embedding_dim)       ,				prompt_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		:       Optional[Any]       =    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		:       Optional[int]       =    torch.cat([latents] * 2						) if do_classifier_free_guidance else latents
           _UpperCAmelCase		:       Union[str, Any]       =    self.scheduler.scale_model_input(A_       ,				A_						)
  
           _UpperCAmelCase		:       Any       =    self.prior(
               A_       ,				timestep=A_       ,				proj_embedding=A_       ,				).predicted_image_embedding
  
           # remove the variance
           _UpperCAmelCase,_UpperCAmelCase		:       Dict       =    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		:       Union[str, Any]       =    noise_pred.chunk(2						)
             _UpperCAmelCase		:       List[Any]       =    noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
  
           _UpperCAmelCase		:       Tuple       =    self.scheduler.step(
               A_       ,				timestep=A_       ,				sample=A_       ,				).prev_sample
  
         if output_type == "latent":
           return ShapEPipelineOutput(images=A_						)
  
         _UpperCAmelCase		:       Dict       =    []
         for i, latent in enumerate(A_						):
           _UpperCAmelCase		:       int       =    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		:       List[Any]       =    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		:       str       =    images.cpu().numpy()
  
         if output_type == "pil":
           _UpperCAmelCase		:       List[str]       =    [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_						)
  
  
  
  
  
  