


import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
lowercase							=logging.getLogger(__name__)





def lowerCamelCase__      (						__lowerCamelCase	:							Optional[Any]	,   __lowerCamelCase	:							Tuple							):







   '''simple docstring'''




   _UpperCAmelCase       :       Any							   =np.argmax(__lowerCamelCase	,   axis=1							)
   return np.sum(outputs == labels							)





def lowerCamelCase__      (						__lowerCamelCase	:							Tuple							):







   '''simple docstring'''




   with open(__lowerCamelCase	,   encoding='utf_8'							) as f:
      _UpperCAmelCase       :       Optional[int]							   =csv.reader(__lowerCamelCase							)
      _UpperCAmelCase       :       str							   =[]
      next(__lowerCamelCase							)  # skip the first line
      for line in tqdm(__lowerCamelCase							):
         output.append((' '.join(line[1:5]							), line[5], line[6], int(line[-1]							) - 1)							)
   return output





def lowerCamelCase__      (						__lowerCamelCase	:							Dict	,   __lowerCamelCase	:							Dict	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							str	,   __lowerCamelCase	:							List[Any]	,   __lowerCamelCase	:							Dict							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[str]							   =[]
   for dataset in encoded_datasets:
      _UpperCAmelCase       :       str							   =len(__lowerCamelCase							)
      _UpperCAmelCase       :       Union[str, Any]							   =np.zeros((n_batch, 2, input_len)	,   dtype=np.intaa							)
      _UpperCAmelCase       :       str							   =np.zeros((n_batch, 2)	,   dtype=np.intaa							)
      _UpperCAmelCase       :       List[str]							   =np.full((n_batch, 2, input_len)	,   fill_value=-1_0_0	,   dtype=np.intaa							)
      _UpperCAmelCase       :       Optional[Any]							   =np.zeros((n_batch,)	,   dtype=np.intaa							)
      for (
          i,
          (story, conta, conta, mc_label),
      ) in enumerate(__lowerCamelCase							):
         _UpperCAmelCase       :       Dict							   =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
         _UpperCAmelCase       :       Optional[int]							   =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
         _UpperCAmelCase       :       Union[str, Any]							   =with_conta
         _UpperCAmelCase       :       List[str]							   =with_conta
         _UpperCAmelCase       :       Any							   =len(__lowerCamelCase							) - 1
         _UpperCAmelCase       :       str							   =len(__lowerCamelCase							) - 1
         _UpperCAmelCase       :       List[Any]							   =with_conta
         _UpperCAmelCase       :       Optional[Any]							   =with_conta
         _UpperCAmelCase       :       Optional[Any]							   =mc_label
      _UpperCAmelCase       :       Dict							   =(input_ids, mc_token_ids, lm_labels, mc_labels)
      tensor_datasets.append(tuple(torch.tensor(__lowerCamelCase							) for t in all_inputs							)							)
   return tensor_datasets





def lowerCamelCase__      (						):







   '''simple docstring'''




   _UpperCAmelCase       :       str							   =argparse.ArgumentParser()
   parser.add_argument('--model_name'	,   type=__lowerCamelCase	,   default='openai-gpt'	,   help='pretrained model name'							)
   parser.add_argument('--do_train'	,   action='store_true'	,   help='Whether to run training.'							)
   parser.add_argument('--do_eval'	,   action='store_true'	,   help='Whether to run eval on the dev set.'							)
   parser.add_argument(
       '--output_dir'	,   default=__lowerCamelCase	,   type=__lowerCamelCase	,   required=__lowerCamelCase	,   help='The output directory where the model predictions and checkpoints will be written.'	,   )
   parser.add_argument('--train_dataset'	,   type=__lowerCamelCase	,   default=''							)
   parser.add_argument('--eval_dataset'	,   type=__lowerCamelCase	,   default=''							)
   parser.add_argument('--seed'	,   type=__lowerCamelCase	,   default=4_2							)
   parser.add_argument('--num_train_epochs'	,   type=__lowerCamelCase	,   default=3							)
   parser.add_argument('--train_batch_size'	,   type=__lowerCamelCase	,   default=8							)
   parser.add_argument('--eval_batch_size'	,   type=__lowerCamelCase	,   default=1_6							)
   parser.add_argument('--adam_epsilon'	,   default=1e-8	,   type=__lowerCamelCase	,   help='Epsilon for Adam optimizer.'							)
   parser.add_argument('--max_grad_norm'	,   type=__lowerCamelCase	,   default=1							)
   parser.add_argument(
       '--max_steps'	,   default=-1	,   type=__lowerCamelCase	,   help=(
           'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
       )	,   )
   parser.add_argument(
       '--gradient_accumulation_steps'	,   type=__lowerCamelCase	,   default=1	,   help='Number of updates steps to accumulate before                        performing a backward/update pass.'	,   )
   parser.add_argument('--learning_rate'	,   type=__lowerCamelCase	,   default=6.25e-5							)
   parser.add_argument('--warmup_steps'	,   default=0	,   type=__lowerCamelCase	,   help='Linear warmup over warmup_steps.'							)
   parser.add_argument('--lr_schedule'	,   type=__lowerCamelCase	,   default='warmup_linear'							)
   parser.add_argument('--weight_decay'	,   type=__lowerCamelCase	,   default=0.01							)
   parser.add_argument('--lm_coef'	,   type=__lowerCamelCase	,   default=0.9							)
   parser.add_argument('--n_valid'	,   type=__lowerCamelCase	,   default=3_7_4							)

   parser.add_argument('--server_ip'	,   type=__lowerCamelCase	,   default=''	,   help='Can be used for distant debugging.'							)
   parser.add_argument('--server_port'	,   type=__lowerCamelCase	,   default=''	,   help='Can be used for distant debugging.'							)
   _UpperCAmelCase       :       List[Any]							   =parser.parse_args()
   print(__lowerCamelCase							)

   if args.server_ip and args.server_port:
      # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
      import ptvsd

      print('Waiting for debugger attach'							)
      ptvsd.enable_attach(address=(args.server_ip, args.server_port)	,   redirect_output=__lowerCamelCase							)
      ptvsd.wait_for_attach()

   random.seed(args.seed							)
   np.random.seed(args.seed							)
   torch.manual_seed(args.seed							)
   torch.cuda.manual_seed_all(args.seed							)

   _UpperCAmelCase       :       Any							   =torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)
   _UpperCAmelCase       :       List[str]							   =torch.cuda.device_count()
   logger.info('device: {}, n_gpu {}'.format(__lowerCamelCase	,   __lowerCamelCase							)							)

   if not args.do_train and not args.do_eval:
      raise ValueError('At least one of `do_train` or `do_eval` must be True.'							)

   if not os.path.exists(args.output_dir							):
      os.makedirs(args.output_dir							)

   # Load tokenizer and model
   # This loading functions also add new tokens and embeddings called `special tokens`
   # These new embeddings will be fine-tuned on the RocStories dataset
   _UpperCAmelCase       :       List[str]							   =["""_start_""", """_delimiter_""", """_classify_"""]
   _UpperCAmelCase       :       Any							   =OpenAIGPTTokenizer.from_pretrained(args.model_name							)
   tokenizer.add_tokens(__lowerCamelCase							)
   _UpperCAmelCase       :       int							   =tokenizer.convert_tokens_to_ids(__lowerCamelCase							)
   _UpperCAmelCase       :       str							   =OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name							)
   model.resize_token_embeddings(len(__lowerCamelCase							)							)
   model.to(__lowerCamelCase							)

   # Load and encode the datasets
   def tokenize_and_encode(__lowerCamelCase	:							str							):
      if isinstance(__lowerCamelCase	,   __lowerCamelCase							):
         return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__lowerCamelCase							)							)
      elif isinstance(__lowerCamelCase	,   __lowerCamelCase							):
         return obj
      return [tokenize_and_encode(__lowerCamelCase							) for o in obj]

   logger.info('Encoding dataset...'							)
   _UpperCAmelCase       :       Tuple							   =load_rocstories_dataset(args.train_dataset							)
   _UpperCAmelCase       :       int							   =load_rocstories_dataset(args.eval_dataset							)
   _UpperCAmelCase       :       str							   =(train_dataset, eval_dataset)
   _UpperCAmelCase       :       Optional[int]							   =tokenize_and_encode(__lowerCamelCase							)

   # Compute the max input length for the Transformer
   _UpperCAmelCase       :       Optional[Any]							   =model.config.n_positions // 2 - 2
   _UpperCAmelCase       :       Optional[int]							   =max(
       len(story[:max_length]							) + max(len(conta[:max_length]							)	,   len(conta[:max_length]							)							) + 3
       for dataset in encoded_datasets
       for story, conta, conta, _ in dataset							)
   _UpperCAmelCase       :       Union[str, Any]							   =min(__lowerCamelCase	,   model.config.n_positions							)  # Max size of input for the pre-trained model

   # Prepare inputs tensors and dataloaders
   _UpperCAmelCase       :       List[str]							   =pre_process_datasets(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   *__lowerCamelCase							)
   _UpperCAmelCase       :       List[str]							   =tensor_datasets[0], tensor_datasets[1]

   _UpperCAmelCase       :       Dict							   =TensorDataset(*__lowerCamelCase							)
   _UpperCAmelCase       :       str							   =RandomSampler(__lowerCamelCase							)
   _UpperCAmelCase       :       Dict							   =DataLoader(__lowerCamelCase	,   sampler=__lowerCamelCase	,   batch_size=args.train_batch_size							)

   _UpperCAmelCase       :       int							   =TensorDataset(*__lowerCamelCase							)
   _UpperCAmelCase       :       List[Any]							   =SequentialSampler(__lowerCamelCase							)
   _UpperCAmelCase       :       Any							   =DataLoader(__lowerCamelCase	,   sampler=__lowerCamelCase	,   batch_size=args.eval_batch_size							)

   # Prepare optimizer
   if args.do_train:
      if args.max_steps > 0:
         _UpperCAmelCase       :       Dict							   =args.max_steps
         _UpperCAmelCase       :       Dict							   =args.max_steps // (len(__lowerCamelCase							) // args.gradient_accumulation_steps) + 1
      else:
         _UpperCAmelCase       :       Optional[Any]							   =len(__lowerCamelCase							) // args.gradient_accumulation_steps * args.num_train_epochs

      _UpperCAmelCase       :       Tuple							   =list(model.named_parameters()							)
      _UpperCAmelCase       :       Dict							   =["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
      _UpperCAmelCase       :       Any							   =[
          {
              """params""": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay							)],
              """weight_decay""": args.weight_decay,
          },
          {"""params""": [p for n, p in param_optimizer if any(nd in n for nd in no_decay							)], """weight_decay""": 0.0},
      ]
      _UpperCAmelCase       :       Dict							   =AdamW(__lowerCamelCase	,   lr=args.learning_rate	,   eps=args.adam_epsilon							)
      _UpperCAmelCase       :       Any							   =get_linear_schedule_with_warmup(
          __lowerCamelCase	,   num_warmup_steps=args.warmup_steps	,   num_training_steps=__lowerCamelCase							)

   if args.do_train:
      _UpperCAmelCase       :       Tuple							   =0, 0, None
      model.train()
      for _ in trange(int(args.num_train_epochs							)	,   desc='Epoch'							):
         _UpperCAmelCase       :       List[Any]							   =0
         _UpperCAmelCase       :       Any							   =0
         _UpperCAmelCase       :       Any							   =tqdm(__lowerCamelCase	,   desc='Training'							)
         for step, batch in enumerate(__lowerCamelCase							):
            _UpperCAmelCase       :       int							   =tuple(t.to(__lowerCamelCase							) for t in batch							)
            _UpperCAmelCase       :       Union[str, Any]							   =batch
            _UpperCAmelCase       :       Any							   =model(__lowerCamelCase	,   mc_token_ids=__lowerCamelCase	,   lm_labels=__lowerCamelCase	,   mc_labels=__lowerCamelCase							)
            _UpperCAmelCase       :       List[str]							   =args.lm_coef * losses[0] + losses[1]
            loss.backward()
            optimizer.step()
            scheduler.step()
            optimizer.zero_grad()
            tr_loss += loss.item()
            _UpperCAmelCase       :       Optional[int]							   =(
                loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
            )
            nb_tr_steps += 1
            _UpperCAmelCase       :       Dict							   ="""Training loss: {:.2e} lr: {:.2e}""".format(__lowerCamelCase	,   scheduler.get_lr()[0]							)

    # Save a trained model
   if args.do_train:
      # Save a trained model, configuration and tokenizer
      _UpperCAmelCase       :       Optional[Any]							   =model.module if hasattr(__lowerCamelCase	,   'module'							) else model  # Only save the model itself

      # If we save using the predefined names, we can load using `from_pretrained`
      _UpperCAmelCase       :       Any							   =os.path.join(args.output_dir	,   __lowerCamelCase							)
      _UpperCAmelCase       :       List[Any]							   =os.path.join(args.output_dir	,   __lowerCamelCase							)

      torch.save(model_to_save.state_dict()	,   __lowerCamelCase							)
      model_to_save.config.to_json_file(__lowerCamelCase							)
      tokenizer.save_vocabulary(args.output_dir							)

      # Load a trained model and vocabulary that you have fine-tuned
      _UpperCAmelCase       :       Optional[int]							   =OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir							)
      _UpperCAmelCase       :       Any							   =OpenAIGPTTokenizer.from_pretrained(args.output_dir							)
      model.to(__lowerCamelCase							)

   if args.do_eval:
      model.eval()
      _UpperCAmelCase       :       Optional[Any]							   =0, 0
      _UpperCAmelCase       :       str							   =0, 0
      for batch in tqdm(__lowerCamelCase	,   desc='Evaluating'							):
         _UpperCAmelCase       :       Optional[int]							   =tuple(t.to(__lowerCamelCase							) for t in batch							)
         _UpperCAmelCase       :       Dict							   =batch
         with torch.no_grad():
            _UpperCAmelCase       :       Any							   =model(
                __lowerCamelCase	,   mc_token_ids=__lowerCamelCase	,   lm_labels=__lowerCamelCase	,   mc_labels=__lowerCamelCase							)

         _UpperCAmelCase       :       Union[str, Any]							   =mc_logits.detach().cpu().numpy()
         _UpperCAmelCase       :       int							   =mc_labels.to('cpu'							).numpy()
         _UpperCAmelCase       :       str							   =accuracy(__lowerCamelCase	,   __lowerCamelCase							)

         eval_loss += mc_loss.mean().item()
         eval_accuracy += tmp_eval_accuracy

         nb_eval_examples += input_ids.size(0							)
         nb_eval_steps += 1

      _UpperCAmelCase       :       Optional[int]							   =eval_loss / nb_eval_steps
      _UpperCAmelCase       :       Union[str, Any]							   =eval_accuracy / nb_eval_examples
      _UpperCAmelCase       :       Any							   =tr_loss / nb_tr_steps if args.do_train else None
      _UpperCAmelCase       :       Optional[Any]							   ={"""eval_loss""": eval_loss, """eval_accuracy""": eval_accuracy, """train_loss""": train_loss}

      _UpperCAmelCase       :       int							   =os.path.join(args.output_dir	,   'eval_results.txt'							)
      with open(__lowerCamelCase	,   'w'							) as writer:
         logger.info('***** Eval results *****'							)
         for key in sorted(result.keys()							):
            logger.info('  %s = %s'	,   __lowerCamelCase	,   str(result[key]							)							)
            writer.write('%s = %s\n' % (key, str(result[key]							))							)


if __name__ == "__main__":
 main()





'''simple docstring'''






from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

lowercase							=logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase    )
class  __magic_name__							(		lowerCAmelCase    ):






       def __init__(      self  ,							**snake_case)						->  Optional[int]:




          '''simple docstring'''
          super().__init__(**snake_case)

          requires_backends(self  ,							'vision')
          self.check_model_type(
              TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
              if self.framework == 'tf'
              else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING)






       def __call__(      self  ,							snake_case  ,							**snake_case)						->  str:




          '''simple docstring'''
          return super().__call__(snake_case  ,							**snake_case)






       def       lowerCAmelCase		(      self  ,							**snake_case)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   ={}
          if "candidate_labels" in kwargs:
             _UpperCAmelCase       :       Union[str, Any]							   =kwargs['candidate_labels']
          if "hypothesis_template" in kwargs:
             _UpperCAmelCase       :       List[Any]							   =kwargs['hypothesis_template']

          return preprocess_params, {}, {}






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case=None  ,							snake_case="This is a photo of {}.")						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =load_image(snake_case)
          _UpperCAmelCase       :       Union[str, Any]							   =self.image_processor(images=[image]  ,							return_tensors=self.framework)
          _UpperCAmelCase       :       Union[str, Any]							   =candidate_labels
          _UpperCAmelCase       :       List[Any]							   =[hypothesis_template.format(snake_case) for x in candidate_labels]
          _UpperCAmelCase       :       str							   =self.tokenizer(snake_case  ,							return_tensors=self.framework  ,							padding=snake_case)
          _UpperCAmelCase       :       Any							   =[text_inputs]
          return inputs






       def       lowerCAmelCase		(      self  ,							snake_case)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =model_inputs.pop('candidate_labels')
          _UpperCAmelCase       :       Tuple							   =model_inputs.pop('text_inputs')
          if isinstance(text_inputs[0]  ,							snake_case):
             _UpperCAmelCase       :       Any							   =text_inputs[0]
          else:
             # Batching case.
             _UpperCAmelCase       :       str							   =text_inputs[0][0]

          _UpperCAmelCase       :       Any							   =self.model(**snake_case  ,							**snake_case)

          _UpperCAmelCase       :       List[str]							   ={
              'candidate_labels': candidate_labels,
              'logits': outputs.logits_per_image,
          }
          return model_outputs






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =model_outputs.pop('candidate_labels')
          _UpperCAmelCase       :       Union[str, Any]							   =model_outputs['logits'][0]
          if self.framework == "pt":
             _UpperCAmelCase       :       Dict							   =logits.softmax(dim=-1).squeeze(-1)
             _UpperCAmelCase       :       Union[str, Any]							   =probs.tolist()
             if not isinstance(snake_case  ,							snake_case):
                _UpperCAmelCase       :       Union[str, Any]							   =[scores]
          elif self.framework == "tf":
             _UpperCAmelCase       :       Dict							   =stable_softmax(snake_case  ,							axis=-1)
             _UpperCAmelCase       :       str							   =probs.numpy().tolist()
          else:
             raise ValueError(f"Unsupported framework: {self.framework}")

          _UpperCAmelCase       :       List[str]							   =[
              {'score': score, 'label': candidate_label}
              for score, candidate_label in sorted(zip(snake_case  ,							snake_case)  ,							key=lambda snake_case: -x[0])
          ]
          return result

