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
  
  
  UpperCamelCase__				       =			logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  UpperCamelCase__				       =			'\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 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  4_2
  
  
  
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       str	,      UpperCamelCase       :       PriorTransformer	,      UpperCamelCase       :       CLIPTextModelWithProjection	,      UpperCamelCase       :       CLIPTokenizer	,      UpperCamelCase       :       HeunDiscreteScheduler	,      UpperCamelCase       :       ShapERenderer	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
  
              self.register_modules(
                  prior=UpperCamelCase	,      text_encoder=UpperCamelCase	,      tokenizer=UpperCamelCase	,      scheduler=UpperCamelCase	,      renderer=UpperCamelCase	,      )
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Union[str, Any]	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       str	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       Union[str, Any]	,      UpperCamelCase       :       Any	,      UpperCamelCase       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              if latents is None:
                     _lowercase					:				int           =			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					:				Optional[int]           =			latents.to(UpperCamelCase		)
  
              _lowercase					:				str           =			latents * scheduler.init_noise_sigma
              return latents
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]	,      UpperCamelCase       :       List[str]=0		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              if is_accelerate_available():
                     from accelerate import cpu_offload
              else:
                     raise ImportError('''Please install accelerate via `pip install accelerate`'''		)
  
              _lowercase					:				Union[str, Any]           =			torch.device(F'cuda:{gpu_id}'		)
  
              _lowercase					:				Tuple           =			[self.text_encoder, self.prior]
              for cpu_offloaded_model in models:
                     if cpu_offloaded_model is not None:
                            cpu_offload(UpperCamelCase	,      UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]	,      UpperCamelCase       :       Tuple=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					:				List[Any]           =			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					:				Any           =			None
              for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
                     _lowercase					:				Tuple           =			cpu_offload_with_hook(UpperCamelCase	,      UpperCamelCase	,      prev_module_hook=UpperCamelCase		)
  
              if self.safety_checker is not None:
                     _lowercase					:				Dict           =			cpu_offload_with_hook(self.safety_checker	,      UpperCamelCase	,      prev_module_hook=UpperCamelCase		)
  
              # We'll offload the last model manually.
              _lowercase					:				str           =			hook
  
  
  
  
       @property
       def   lowerCAmelCase_   ( self       :       Dict		):
  
  
  
  
  
              """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   lowerCAmelCase_   ( self       :       Union[str, Any]	,      UpperCamelCase       :       Dict	,      UpperCamelCase       :       Union[str, Any]	,      UpperCamelCase       :       Dict	,      UpperCamelCase       :       Tuple	,      ):
  
  
  
  
  
              """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					:				Dict           =			0
              # get prompt text embeddings
              _lowercase					:				Optional[int]           =			self.tokenizer(
                  UpperCamelCase	,      padding='''max_length'''	,      max_length=self.tokenizer.model_max_length	,      truncation=UpperCamelCase	,      return_tensors='''pt'''	,      )
              _lowercase					:				int           =			text_inputs.input_ids
              _lowercase					:				Union[str, Any]           =			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					:				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}'		)
  
              _lowercase					:				str           =			self.text_encoder(text_input_ids.to(UpperCamelCase		)		)
              _lowercase					:				str           =			text_encoder_output.text_embeds
  
              _lowercase					:				Dict           =			prompt_embeds.repeat_interleave(UpperCamelCase	,      dim=0		)
              # in Shap-E it normalize the prompt_embeds and then later rescale it
              _lowercase					:				int           =			prompt_embeds / torch.linalg.norm(UpperCamelCase	,      dim=-1	,      keepdim=UpperCamelCase		)
  
              if do_classifier_free_guidance:
                     _lowercase					:				Union[str, Any]           =			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					:				int           =			torch.cat([negative_prompt_embeds, prompt_embeds]		)
  
              # Rescale the features to have unit variance
              _lowercase					:				List[str]           =			math.sqrt(prompt_embeds.shape[1]		) * prompt_embeds
  
              return prompt_embeds
  
  
  
  
       @torch.no_grad()
       @replace_example_docstring(UpperCamelCase		)
       def __call__( self       :       str	,      UpperCamelCase       :       str	,      UpperCamelCase       :       int = 1	,      UpperCamelCase       :       int = 25	,      UpperCamelCase       :       Optional[Union[torch.Generator, List[torch.Generator]]] = None	,      UpperCamelCase       :       Optional[torch.FloatTensor] = None	,      UpperCamelCase       :       float = 4.0	,      UpperCamelCase       :       int = 64	,      UpperCamelCase       :       Optional[str] = "pil"	,      UpperCamelCase       :       bool = True	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              if isinstance(UpperCamelCase	,      UpperCamelCase		):
                     _lowercase					:				Optional[Any]           =			1
              elif isinstance(UpperCamelCase	,      UpperCamelCase		):
                     _lowercase					:				Any           =			len(UpperCamelCase		)
              else:
                     raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase		)}'		)
  
              _lowercase					:				Optional[Any]           =			self._execution_device
  
              _lowercase					:				List[Any]           =			batch_size * num_images_per_prompt
  
              _lowercase					:				List[str]           =			guidance_scale > 1.0
              _lowercase					:				List[str]           =			self._encode_prompt(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		)
  
              # prior
  
              self.scheduler.set_timesteps(UpperCamelCase	,      device=UpperCamelCase		)
              _lowercase					:				Any           =			self.scheduler.timesteps
  
              _lowercase					:				int           =			self.prior.config.num_embeddings
              _lowercase					:				Any           =			self.prior.config.embedding_dim
  
              _lowercase					:				int           =			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					:				Optional[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
                     _lowercase					:				Optional[int]           =			torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
                     _lowercase					:				Any           =			self.scheduler.scale_model_input(UpperCamelCase	,      UpperCamelCase		)
  
                     _lowercase					:				Any           =			self.prior(
                         UpperCamelCase	,      timestep=UpperCamelCase	,      proj_embedding=UpperCamelCase	,      ).predicted_image_embedding
  
                     # remove the variance
                     _lowercase					:				List[str]           =			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					:				Union[str, Any]           =			noise_pred.chunk(2		)
                            _lowercase					:				List[str]           =			noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
  
                     _lowercase					:				Union[str, Any]           =			self.scheduler.step(
                         UpperCamelCase	,      timestep=UpperCamelCase	,      sample=UpperCamelCase	,      ).prev_sample
  
              if output_type == "latent":
                     return ShapEPipelineOutput(images=UpperCamelCase		)
  
              _lowercase					:				Optional[int]           =			[]
              for i, latent in enumerate(UpperCamelCase		):
                     _lowercase					:				str           =			self.renderer.decode(
                         latent[None, :]	,      UpperCamelCase	,      size=UpperCamelCase	,      ray_batch_size=40_96	,      n_coarse_samples=64	,      n_fine_samples=1_28	,      )
                     images.append(UpperCamelCase		)
  
              _lowercase					:				Any           =			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					:				Tuple           =			images.cpu().numpy()
  
              if output_type == "pil":
                     _lowercase					:				List[str]           =			[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 logging
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import List, Optional
  
  import torch
  from datasets import Dataset
  from torch import nn
  from tqdm.auto import tqdm
  
  from transformers import (
      AutoModelForSequenceClassification,
      AutoTokenizer,
      HfArgumentParser,
      Trainer,
      TrainingArguments,
      set_seed,
      utils,
  )
  from transformers.trainer_utils import get_last_checkpoint, is_main_process
  
  
  UpperCamelCase__				       =			'\nDistills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class\nnames. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but\nwhen only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled\ndataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided\nclass names.\n'
  
  UpperCamelCase__				       =			logging.getLogger(__name__)
  
  @dataclass
  class 						UpperCAmelCase__   :
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  field(
           default='''roberta-large-mnli'''      ,						metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''}		)
       UpperCAmelCase_         =  field(
           default='''This example is {}.'''      ,						metadata={
               '''help''': (
                   '''Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}'''
                   '''where class name is inserted.'''
               )
           }      ,						)
       UpperCAmelCase_         =  field(
           default=3_2      ,						metadata={'''help''': '''Batch size for generating teacher predictions.'''}		)
       UpperCAmelCase_         =  field(
           default=A_      ,						metadata={
               '''help''': (
                   '''Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called'''
                   '''multi-class multi-label classification).'''
               )
           }      ,						)
       UpperCAmelCase_         =  field(
           default=1.0      ,						metadata={'''help''': '''Temperature applied to teacher softmax for distillation.'''}		)
  
  
  
  
  @dataclass
  class 						UpperCAmelCase__   :
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  field(
           default='''distilbert-base-uncased'''      ,						metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''}		)
  
  
  
  
  @dataclass
  class 						UpperCAmelCase__   :
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  field(metadata={'''help''': '''Text file with one unlabeled instance per line.'''}		)
       UpperCAmelCase_         =  field(metadata={'''help''': '''Text file with one class name per line.'''}		)
       UpperCAmelCase_         =  field(
           default=A_      ,						metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not.'''}      ,						)
  
  
  
  
  @dataclass
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  field(
           default=A_      ,						metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}      ,						)
       UpperCAmelCase_         =  field(
           default=3_2      ,						metadata={'''help''': '''Batch size per GPU/TPU core/CPU for training.'''}		)
       UpperCAmelCase_         =  field(
           default=1_2_8      ,						metadata={'''help''': '''Batch size per GPU/TPU core/CPU for evaluation.'''}		)
       UpperCAmelCase_         =  field(default=1.0      ,						metadata={'''help''': '''Total number of training epochs to perform.'''}		)
       UpperCAmelCase_         =  field(default=A_      ,						metadata={'''help''': '''Whether to run training of student model.'''}		)
       UpperCAmelCase_         =  field(
           default=A_      ,						metadata={
               '''help''': (
                   '''Whether to evaluate the agreement of the final student predictions and the teacher predictions'''
                   '''after training.'''
               )
           }      ,						)
       UpperCAmelCase_         =  field(
           default=0      ,						metadata={
               '''help''': (
                   '''Limit the total amount of checkpoints.'''
                   '''Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints).'''
               )
           }      ,						)
  
  
  
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Dict	,      UpperCamelCase       :       Optional[int]	,      UpperCamelCase       :       List[str]	,      UpperCamelCase       :       Dict=False		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[Any]           =			inputs['''labels''']
              _lowercase					:				List[Any]           =			model(inputs['''input_ids''']	,      attention_mask=inputs['''attention_mask''']		)
              _lowercase					:				int           =			outputs[0]
  
              _lowercase					:				List[str]           =			-torch.sum(target_p * logits.log_softmax(dim=-1		)	,      axis=-1		).mean()
  
              if return_outputs:
                     return loss, outputs
  
              return loss
  
  
  
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Tuple:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Tuple           =			[]
         with open(UpperCAmelCase_    ,     '''r'''			) as f:
                for line in f:
                       _lowercase					:				Optional[int]           =			line.strip()
                       if len(UpperCAmelCase_			) > 0:
                              lines.append(UpperCAmelCase_			)
         return lines
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Dict:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Any           =			[]
         _lowercase					:				int           =			[]
         for example in examples:
                for name in class_names:
                       premises.append(UpperCAmelCase_			)
                       hypotheses.append(hypothesis_template.format(UpperCAmelCase_			)			)
         return premises, hypotheses
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							List[Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         for label, ind in config.labelaid.items():
                if label.lower().startswith('''entail'''			):
                       return ind
         logger.warning('''Could not identify entailment dimension from teacher config label2id. Setting to -1.'''			)
         return -1
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     )						->							int:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Optional[Any]           =			AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase_			)
         _lowercase					:				Any           =			model.config
         if not no_cuda and torch.cuda.is_available():
                _lowercase					:				int           =			nn.DataParallel(model.cuda()			)
                batch_size *= len(model.device_ids			)
         _lowercase					:				Optional[int]           =			AutoTokenizer.from_pretrained(UpperCAmelCase_    ,     use_fast=UpperCAmelCase_			)
  
         _lowercase       ,	_lowercase					:				Any           =			get_premise_hypothesis_pairs(UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)
         _lowercase					:				List[str]           =			[]
  
         for i in tqdm(range(0    ,     len(UpperCAmelCase_			)    ,     UpperCAmelCase_			)			):
                _lowercase					:				Tuple           =			premises[i : i + batch_size]
                _lowercase					:				Optional[int]           =			hypotheses[i : i + batch_size]
  
                _lowercase					:				Union[str, Any]           =			tokenizer(
                    UpperCAmelCase_    ,     UpperCAmelCase_    ,     padding=UpperCAmelCase_    ,     truncation='''only_first'''    ,     return_tensors='''pt'''    ,     )
  
                with torch.cuda.amp.autocast(enabled=UpperCAmelCase_			):
                       with torch.no_grad():
                              _lowercase					:				Dict           =			model(**UpperCAmelCase_			)
                logits.append(outputs.logits.detach().cpu().float()			)
  
         _lowercase					:				Any           =			get_entailment_id(UpperCAmelCase_			)
         _lowercase					:				Tuple           =			-1 if entail_id == 0 else 0
         _lowercase					:				Any           =			torch.cat(UpperCAmelCase_    ,     dim=0			)  # N*K x 3
         _lowercase					:				List[Any]           =			logits.reshape(len(UpperCAmelCase_			)    ,     len(UpperCAmelCase_			)    ,     -1			)[..., [contr_id, entail_id]]  # N x K x 2
  
         if multi_label:
                # softmax over (contr, entail) logits for each class independently
                _lowercase					:				Optional[int]           =			(nli_logits / temperature).softmax(-1			)
         else:
                # softmax over entail logits across classes s.t. class probabilities sum to 1.
                _lowercase					:				Union[str, Any]           =			(nli_logits / temperature).softmax(1			)
  
         return nli_prob[..., 1]  # N x K
  
  def   UpperCamelCase__   (    )						->							Union[str, Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Optional[Any]           =			HfArgumentParser(
             (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)    ,     description=UpperCAmelCase_    ,     )
  
         if len(sys.argv			) == 2 and sys.argv[1].endswith('''.json'''			):
                # If we pass only one argument to the script and it's the path to a json file,
                # let's parse it to get our arguments.
                _lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase					:				Union[str, Any]           =			parser.parse_json_file(
                    json_file=os.path.abspath(sys.argv[1]			)			)
         else:
                _lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase					:				Tuple           =			parser.parse_args_into_dataclasses()
  
         # Detecting last checkpoint.
         _lowercase					:				Tuple           =			None
         if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
                _lowercase					:				Optional[Any]           =			get_last_checkpoint(training_args.output_dir			)
                if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
                       raise ValueError(
                           F'Output directory ({training_args.output_dir}) already exists and is not empty. '
                           '''Use --overwrite_output_dir to overcome.'''			)
                elif last_checkpoint is not None:
                       logger.info(
                           F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                           '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''			)
  
      # Setup logging
         logging.basicConfig(
             format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''    ,     datefmt='''%m/%d/%Y %H:%M:%S'''    ,     handlers=[logging.StreamHandler(sys.stdout			)]    ,     )
         logger.setLevel(logging.INFO if is_main_process(training_args.local_rank			) else logging.WARN			)
  
         # Log on each process the small summary:
         logger.warning(
             F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
             + F'distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}'			)
         # Set the verbosity to info of the Transformers logger (on main process only):
         if is_main_process(training_args.local_rank			):
                utils.logging.set_verbosity_info()
                utils.logging.enable_default_handler()
                utils.logging.enable_explicit_format()
  
         if training_args.local_rank != -1:
                raise ValueError('''Distributed training is not currently supported.'''			)
         if training_args.tpu_num_cores is not None:
                raise ValueError('''TPU acceleration is not currently supported.'''			)
  
         logger.info(F'Training/evaluation parameters {training_args}'			)
  
         # Set seed before initializing model.
         set_seed(training_args.seed			)
  
         # 1. read in data
         _lowercase					:				str           =			read_lines(data_args.data_file			)
         _lowercase					:				List[str]           =			read_lines(data_args.class_names_file			)
  
         # 2. get teacher predictions and load into dataset
         logger.info('''Generating predictions from zero-shot teacher model'''			)
         _lowercase					:				Optional[int]           =			get_teacher_predictions(
             teacher_args.teacher_name_or_path    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     teacher_args.hypothesis_template    ,     teacher_args.teacher_batch_size    ,     teacher_args.temperature    ,     teacher_args.multi_label    ,     data_args.use_fast_tokenizer    ,     training_args.no_cuda    ,     training_args.fpaa    ,     )
         _lowercase					:				Optional[Any]           =			Dataset.from_dict(
             {
                 '''text''': examples,
                 '''labels''': teacher_soft_preds,
             }			)
  
         # 3. create student
         logger.info('''Initializing student model'''			)
         _lowercase					:				List[Any]           =			AutoModelForSequenceClassification.from_pretrained(
             student_args.student_name_or_path    ,     num_labels=len(UpperCAmelCase_			)			)
         _lowercase					:				Optional[Any]           =			AutoTokenizer.from_pretrained(student_args.student_name_or_path    ,     use_fast=data_args.use_fast_tokenizer			)
         _lowercase					:				Optional[int]           =			dict(enumerate(UpperCAmelCase_			)			)
         _lowercase					:				Union[str, Any]           =			{label: i for i, label in enumerate(UpperCAmelCase_			)}
  
         # 4. train student on teacher predictions
         _lowercase					:				List[str]           =			dataset.map(UpperCAmelCase_    ,     input_columns='''text'''			)
         dataset.set_format('''torch'''			)
  
         def compute_metrics(UpperCAmelCase_    ,     UpperCAmelCase_=False			):
                _lowercase					:				Optional[Any]           =			p.predictions.argmax(-1			)
                _lowercase					:				List[Any]           =			p.label_ids.argmax(-1			)  # "label_ids" are actually distributions
                return {"agreement": (preds == proxy_labels).mean().item()}
  
         _lowercase					:				Optional[int]           =			DistillationTrainer(
             model=UpperCAmelCase_    ,     tokenizer=UpperCAmelCase_    ,     args=UpperCAmelCase_    ,     train_dataset=UpperCAmelCase_    ,     compute_metrics=UpperCAmelCase_    ,     )
  
         if training_args.do_train:
                logger.info('''Training student model on teacher predictions'''			)
                trainer.train()
  
         if training_args.do_eval:
                _lowercase					:				Tuple           =			trainer.evaluate(eval_dataset=UpperCAmelCase_			)['''eval_agreement''']
                logger.info(F'Agreement of student and teacher predictions: {agreement * 100:0.2f}%'			)
  
         trainer.save_model()
  
  
  if __name__ == "__main__":
     main()