
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  : str						   =							logging.getLogger(__name__)







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ):
       lowercase							:					Optional[Any]	     =				np.argmax(SCREAMING_SNAKE_CASE__ ,     axis=1    )
       return np.sum(outputs == labels    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__    ):
       with open(SCREAMING_SNAKE_CASE__ ,     encoding="""utf_8"""    ) as f:
              lowercase							:					str	     =				csv.reader(SCREAMING_SNAKE_CASE__    )
              lowercase							:					Dict	     =				[]
              next(SCREAMING_SNAKE_CASE__    )  # skip the first line
              for line in tqdm(SCREAMING_SNAKE_CASE__    ):
                     output.append((""" """.join(line[1:5]    ), line[5], line[6], int(line[-1]    ) - 1)    )
       return output







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ):
       lowercase							:					Optional[int]	     =				[]
       for dataset in encoded_datasets:
              lowercase							:					Any	     =				len(SCREAMING_SNAKE_CASE__    )
              lowercase							:					List[Any]	     =				np.zeros((n_batch, 2, input_len) ,     dtype=np.intaa    )
              lowercase							:					Union[str, Any]	     =				np.zeros((n_batch, 2) ,     dtype=np.intaa    )
              lowercase							:					Optional[Any]	     =				np.full((n_batch, 2, input_len) ,     fill_value=-100 ,     dtype=np.intaa    )
              lowercase							:					Optional[int]	     =				np.zeros((n_batch,) ,     dtype=np.intaa    )
              for (
                  i,
                  (story, conta, conta, mc_label),
              ) in enumerate(SCREAMING_SNAKE_CASE__    ):
                     lowercase							:					List[Any]	     =				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     lowercase							:					Any	     =				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     lowercase							:					int	     =				with_conta
                     lowercase							:					Dict	     =				with_conta
                     lowercase							:					Dict	     =				len(SCREAMING_SNAKE_CASE__    ) - 1
                     lowercase							:					Any	     =				len(SCREAMING_SNAKE_CASE__    ) - 1
                     lowercase							:					Dict	     =				with_conta
                     lowercase							:					List[Any]	     =				with_conta
                     lowercase							:					Tuple	     =				mc_label
              lowercase							:					Any	     =				(input_ids, mc_token_ids, lm_labels, mc_labels)
              tensor_datasets.append(tuple(torch.tensor(SCREAMING_SNAKE_CASE__    ) for t in all_inputs    )    )
       return tensor_datasets







def 						_snake_case(  ):
       lowercase							:					List[Any]	     =				argparse.ArgumentParser()
       parser.add_argument("""--model_name""" ,     type=SCREAMING_SNAKE_CASE__ ,     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=SCREAMING_SNAKE_CASE__ ,     type=SCREAMING_SNAKE_CASE__ ,     required=SCREAMING_SNAKE_CASE__ ,     help="""The output directory where the model predictions and checkpoints will be written.""" ,     )
       parser.add_argument("""--train_dataset""" ,     type=SCREAMING_SNAKE_CASE__ ,     default=""""""    )
       parser.add_argument("""--eval_dataset""" ,     type=SCREAMING_SNAKE_CASE__ ,     default=""""""    )
       parser.add_argument("""--seed""" ,     type=SCREAMING_SNAKE_CASE__ ,     default=42    )
       parser.add_argument("""--num_train_epochs""" ,     type=SCREAMING_SNAKE_CASE__ ,     default=3    )
       parser.add_argument("""--train_batch_size""" ,     type=SCREAMING_SNAKE_CASE__ ,     default=8    )
       parser.add_argument("""--eval_batch_size""" ,     type=SCREAMING_SNAKE_CASE__ ,     default=16    )
       parser.add_argument("""--adam_epsilon""" ,     default=1e-8 ,     type=SCREAMING_SNAKE_CASE__ ,     help="""Epsilon for Adam optimizer."""    )
       parser.add_argument("""--max_grad_norm""" ,     type=SCREAMING_SNAKE_CASE__ ,     default=1    )
       parser.add_argument(
           """--max_steps""" ,     default=-1 ,     type=SCREAMING_SNAKE_CASE__ ,     help=(
               """If > 0: set total number of training                         steps to perform. Override num_train_epochs."""
           ) ,     )
       parser.add_argument(
           """--gradient_accumulation_steps""" ,     type=SCREAMING_SNAKE_CASE__ ,     default=1 ,     help="""Number of updates steps to accumulate before                        performing a backward/update pass.""" ,     )
       parser.add_argument("""--learning_rate""" ,     type=SCREAMING_SNAKE_CASE__ ,     default=6.2_5e-5    )
       parser.add_argument("""--warmup_steps""" ,     default=0 ,     type=SCREAMING_SNAKE_CASE__ ,     help="""Linear warmup over warmup_steps."""    )
       parser.add_argument("""--lr_schedule""" ,     type=SCREAMING_SNAKE_CASE__ ,     default="""warmup_linear"""    )
       parser.add_argument("""--weight_decay""" ,     type=SCREAMING_SNAKE_CASE__ ,     default=0.01    )
       parser.add_argument("""--lm_coef""" ,     type=SCREAMING_SNAKE_CASE__ ,     default=0.9    )
       parser.add_argument("""--n_valid""" ,     type=SCREAMING_SNAKE_CASE__ ,     default=374    )

       parser.add_argument("""--server_ip""" ,     type=SCREAMING_SNAKE_CASE__ ,     default="""""" ,     help="""Can be used for distant debugging."""    )
       parser.add_argument("""--server_port""" ,     type=SCREAMING_SNAKE_CASE__ ,     default="""""" ,     help="""Can be used for distant debugging."""    )
       lowercase							:					Any	     =				parser.parse_args()
       print(SCREAMING_SNAKE_CASE__    )

       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=SCREAMING_SNAKE_CASE__    )
              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    )

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

       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
       lowercase							:					str	     =				["""_start_""", """_delimiter_""", """_classify_"""]
       lowercase							:					Optional[int]	     =				OpenAIGPTTokenizer.from_pretrained(args.model_name    )
       tokenizer.add_tokens(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Optional[int]	     =				tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Tuple	     =				OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name    )
       model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE__    )    )
       model.to(SCREAMING_SNAKE_CASE__    )

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

       logger.info("""Encoding dataset..."""    )
       lowercase							:					Tuple	     =				load_rocstories_dataset(args.train_dataset    )
       lowercase							:					Dict	     =				load_rocstories_dataset(args.eval_dataset    )
       lowercase							:					List[str]	     =				(train_dataset, eval_dataset)
       lowercase							:					Dict	     =				tokenize_and_encode(SCREAMING_SNAKE_CASE__    )

       # Compute the max input length for the Transformer
       lowercase							:					str	     =				model.config.n_positions // 2 - 2
       lowercase							:					List[Any]	     =				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    )
       lowercase							:					List[Any]	     =				min(SCREAMING_SNAKE_CASE__ ,     model.config.n_positions    )  # Max size of input for the pre-trained model

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

       lowercase							:					List[str]	     =				TensorDataset(*SCREAMING_SNAKE_CASE__    )
       lowercase							:					List[Any]	     =				RandomSampler(SCREAMING_SNAKE_CASE__    )
       lowercase							:					List[Any]	     =				DataLoader(SCREAMING_SNAKE_CASE__ ,     sampler=SCREAMING_SNAKE_CASE__ ,     batch_size=args.train_batch_size    )

       lowercase							:					List[Any]	     =				TensorDataset(*SCREAMING_SNAKE_CASE__    )
       lowercase							:					List[str]	     =				SequentialSampler(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Tuple	     =				DataLoader(SCREAMING_SNAKE_CASE__ ,     sampler=SCREAMING_SNAKE_CASE__ ,     batch_size=args.eval_batch_size    )

       # Prepare optimizer
       if args.do_train:
              if args.max_steps > 0:
                     lowercase							:					Any	     =				args.max_steps
                     lowercase							:					Tuple	     =				args.max_steps // (len(SCREAMING_SNAKE_CASE__    ) // args.gradient_accumulation_steps) + 1
              else:
                     lowercase							:					str	     =				len(SCREAMING_SNAKE_CASE__    ) // args.gradient_accumulation_steps * args.num_train_epochs

              lowercase							:					str	     =				list(model.named_parameters()    )
              lowercase							:					str	     =				["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
              lowercase							:					str	     =				[
                  {
                      """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},
              ]
              lowercase							:					List[Any]	     =				AdamW(SCREAMING_SNAKE_CASE__ ,     lr=args.learning_rate ,     eps=args.adam_epsilon    )
              lowercase							:					Optional[Any]	     =				get_linear_schedule_with_warmup(
                  SCREAMING_SNAKE_CASE__ ,     num_warmup_steps=args.warmup_steps ,     num_training_steps=SCREAMING_SNAKE_CASE__    )

       if args.do_train:
              lowercase							:					List[Any]	     =				0, 0, None
              model.train()
              for _ in trange(int(args.num_train_epochs    ) ,     desc="""Epoch"""    ):
                     lowercase							:					int	     =				0
                     lowercase							:					Tuple	     =				0
                     lowercase							:					str	     =				tqdm(SCREAMING_SNAKE_CASE__ ,     desc="""Training"""    )
                     for step, batch in enumerate(SCREAMING_SNAKE_CASE__    ):
                            lowercase							:					List[Any]	     =				tuple(t.to(SCREAMING_SNAKE_CASE__    ) for t in batch    )
                            lowercase							:					str	     =				batch
                            lowercase							:					Any	     =				model(SCREAMING_SNAKE_CASE__ ,     mc_token_ids=SCREAMING_SNAKE_CASE__ ,     lm_labels=SCREAMING_SNAKE_CASE__ ,     mc_labels=SCREAMING_SNAKE_CASE__    )
                            lowercase							:					List[Any]	     =				args.lm_coef * losses[0] + losses[1]
                            loss.backward()
                            optimizer.step()
                            scheduler.step()
                            optimizer.zero_grad()
                            tr_loss += loss.item()
                            lowercase							:					Optional[Any]	     =				(
                                loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                            )
                            nb_tr_steps += 1
                            lowercase							:					int	     =				"""Training loss: {:.2e} lr: {:.2e}""".format(SCREAMING_SNAKE_CASE__ ,     scheduler.get_lr()[0]    )

    # Save a trained model
       if args.do_train:
              # Save a trained model, configuration and tokenizer
              lowercase							:					Tuple	     =				model.module if hasattr(SCREAMING_SNAKE_CASE__ ,     """module"""    ) else model  # Only save the model itself

              # If we save using the predefined names, we can load using `from_pretrained`
              lowercase							:					Tuple	     =				os.path.join(args.output_dir ,     SCREAMING_SNAKE_CASE__    )
              lowercase							:					Union[str, Any]	     =				os.path.join(args.output_dir ,     SCREAMING_SNAKE_CASE__    )

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

              # Load a trained model and vocabulary that you have fine-tuned
              lowercase							:					int	     =				OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir    )
              lowercase							:					int	     =				OpenAIGPTTokenizer.from_pretrained(args.output_dir    )
              model.to(SCREAMING_SNAKE_CASE__    )

       if args.do_eval:
              model.eval()
              lowercase							:					int	     =				0, 0
              lowercase							:					List[str]	     =				0, 0
              for batch in tqdm(SCREAMING_SNAKE_CASE__ ,     desc="""Evaluating"""    ):
                     lowercase							:					Any	     =				tuple(t.to(SCREAMING_SNAKE_CASE__    ) for t in batch    )
                     lowercase							:					int	     =				batch
                     with torch.no_grad():
                            lowercase							:					Optional[Any]	     =				model(
                                SCREAMING_SNAKE_CASE__ ,     mc_token_ids=SCREAMING_SNAKE_CASE__ ,     lm_labels=SCREAMING_SNAKE_CASE__ ,     mc_labels=SCREAMING_SNAKE_CASE__    )

                     lowercase							:					Tuple	     =				mc_logits.detach().cpu().numpy()
                     lowercase							:					List[Any]	     =				mc_labels.to("""cpu"""    ).numpy()
                     lowercase							:					str	     =				accuracy(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

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

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

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

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


if __name__ == "__main__":
  main()


from ...processing_utils import ProcessorMixin

class __snake_case		(				lowerCAmelCase					):
    _a		:						Union[str, Any]=    "WhisperFeatureExtractor"
    _a		:						int=    "WhisperTokenizer"
    def __init__(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           super().__init__(snake_case		,snake_case				)
           lowercase							:					Optional[int]	     =				self.feature_extractor
           lowercase							:					Tuple	     =				False
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case=None		,snake_case=None		,snake_case=True				):



           '''simple docstring'''







           return self.tokenizer.get_decoder_prompt_ids(task=snake_case		,language=snake_case		,no_timestamps=snake_case				)
    def __call__(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           if self._in_target_context_manager:
                  return self.current_processor(*snake_case		,**snake_case				)

           lowercase							:					Optional[Any]	     =				kwargs.pop("""audio"""		,snake_case				)
           lowercase							:					str	     =				kwargs.pop("""sampling_rate"""		,snake_case				)
           lowercase							:					Dict	     =				kwargs.pop("""text"""		,snake_case				)
           if len(snake_case				) > 0:
                  lowercase							:					List[Any]	     =				args[0]
                  lowercase							:					Tuple	     =				args[1:]

           if audio is None and text is None:
                  raise ValueError("""You need to specify either an `audio` or `text` input to process."""				)

           if audio is not None:
                  lowercase							:					Any	     =				self.feature_extractor(snake_case		,*snake_case		,sampling_rate=snake_case		,**snake_case				)
           if text is not None:
                  lowercase							:					str	     =				self.tokenizer(snake_case		,**snake_case				)

           if text is None:
                  return inputs

           elif audio is None:
                  return encodings
           else:
                  lowercase							:					List[Any]	     =				encodings["""input_ids"""]
                  return inputs
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.tokenizer.batch_decode(*snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.tokenizer.decode(*snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case="np"				):



           '''simple docstring'''







           return self.tokenizer.get_prompt_ids(snake_case		,return_tensors=snake_case				)
