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
)
__lowerCamelCase    :				Any							     =	logging.getLogger(__name__)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[Any]				,  __UpperCamelCase					: int			)							->      Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.argmax(__UpperCamelCase				,  axis=1			)
  return np.sum(outputs == labels			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      Any:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Dict				,  __UpperCamelCase					: Optional[Any]				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Optional[Any]				,  __UpperCamelCase					: Dict				,  __UpperCamelCase					: Optional[Any]			)							->      List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   []
  for dataset in encoded_datasets:
    SCREAMING_SNAKE_CASE__    							=   len(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   np.zeros((n_batch, 2, input_len)				,  dtype=np.intaa			)
    SCREAMING_SNAKE_CASE__    							=   np.zeros((n_batch, 2)				,  dtype=np.intaa			)
    SCREAMING_SNAKE_CASE__    							=   np.full((n_batch, 2, input_len)				,  fill_value=-1_00				,  dtype=np.intaa			)
    SCREAMING_SNAKE_CASE__    							=   np.zeros((n_batch,)				,  dtype=np.intaa			)
    for (
        i,
        (story, conta, conta, mc_label),
    ) in enumerate(__UpperCamelCase			):
      SCREAMING_SNAKE_CASE__    							=   [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
      SCREAMING_SNAKE_CASE__    							=   [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
      SCREAMING_SNAKE_CASE__    							=   with_conta
      SCREAMING_SNAKE_CASE__    							=   with_conta
      SCREAMING_SNAKE_CASE__    							=   len(__UpperCamelCase			) - 1
      SCREAMING_SNAKE_CASE__    							=   len(__UpperCamelCase			) - 1
      SCREAMING_SNAKE_CASE__    							=   with_conta
      SCREAMING_SNAKE_CASE__    							=   with_conta
      SCREAMING_SNAKE_CASE__    							=   mc_label
    SCREAMING_SNAKE_CASE__    							=   (input_ids, mc_token_ids, lm_labels, mc_labels)
    tensor_datasets.append(tuple(torch.tensor(__UpperCamelCase			) for t in all_inputs			)			)
  return tensor_datasets



def 			__SCREAMING_SNAKE_CASE						(      )							->      Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   argparse.ArgumentParser()
  parser.add_argument("""--model_name"""				,  type=__UpperCamelCase				,  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=__UpperCamelCase				,  type=__UpperCamelCase				,  required=__UpperCamelCase				,  help="""The output directory where the model predictions and checkpoints will be written."""				,  )
  parser.add_argument("""--train_dataset"""				,  type=__UpperCamelCase				,  default=""""""			)
  parser.add_argument("""--eval_dataset"""				,  type=__UpperCamelCase				,  default=""""""			)
  parser.add_argument("""--seed"""				,  type=__UpperCamelCase				,  default=42			)
  parser.add_argument("""--num_train_epochs"""				,  type=__UpperCamelCase				,  default=3			)
  parser.add_argument("""--train_batch_size"""				,  type=__UpperCamelCase				,  default=8			)
  parser.add_argument("""--eval_batch_size"""				,  type=__UpperCamelCase				,  default=16			)
  parser.add_argument("""--adam_epsilon"""				,  default=1E-8				,  type=__UpperCamelCase				,  help="""Epsilon for Adam optimizer."""			)
  parser.add_argument("""--max_grad_norm"""				,  type=__UpperCamelCase				,  default=1			)
  parser.add_argument(
      """--max_steps"""				,  default=-1				,  type=__UpperCamelCase				,  help=(
          """If > 0: set total number of training                         steps to perform. Override num_train_epochs."""
      )				,  )
  parser.add_argument(
      """--gradient_accumulation_steps"""				,  type=__UpperCamelCase				,  default=1				,  help="""Number of updates steps to accumulate before                        performing a backward/update pass."""				,  )
  parser.add_argument("""--learning_rate"""				,  type=__UpperCamelCase				,  default=6.25E-5			)
  parser.add_argument("""--warmup_steps"""				,  default=0				,  type=__UpperCamelCase				,  help="""Linear warmup over warmup_steps."""			)
  parser.add_argument("""--lr_schedule"""				,  type=__UpperCamelCase				,  default="""warmup_linear"""			)
  parser.add_argument("""--weight_decay"""				,  type=__UpperCamelCase				,  default=0.01			)
  parser.add_argument("""--lm_coef"""				,  type=__UpperCamelCase				,  default=0.9			)
  parser.add_argument("""--n_valid"""				,  type=__UpperCamelCase				,  default=3_74			)

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

  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=__UpperCamelCase			)
    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			)

  SCREAMING_SNAKE_CASE__    							=   torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""			)
  SCREAMING_SNAKE_CASE__    							=   torch.cuda.device_count()
  logger.info("""device: {}, n_gpu {}""".format(__UpperCamelCase				,  __UpperCamelCase			)			)

  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
  SCREAMING_SNAKE_CASE__    							=   ["""_start_""", """_delimiter_""", """_classify_"""]
  SCREAMING_SNAKE_CASE__    							=   OpenAIGPTTokenizer.from_pretrained(args.model_name			)
  tokenizer.add_tokens(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   tokenizer.convert_tokens_to_ids(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name			)
  model.resize_token_embeddings(len(__UpperCamelCase			)			)
  model.to(__UpperCamelCase			)

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

  logger.info("""Encoding dataset..."""			)
  SCREAMING_SNAKE_CASE__    							=   load_rocstories_dataset(args.train_dataset			)
  SCREAMING_SNAKE_CASE__    							=   load_rocstories_dataset(args.eval_dataset			)
  SCREAMING_SNAKE_CASE__    							=   (train_dataset, eval_dataset)
  SCREAMING_SNAKE_CASE__    							=   tokenize_and_encode(__UpperCamelCase			)

  # Compute the max input length for the Transformer
  SCREAMING_SNAKE_CASE__    							=   model.config.n_positions // 2 - 2
  SCREAMING_SNAKE_CASE__    							=   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			)
  SCREAMING_SNAKE_CASE__    							=   min(__UpperCamelCase				,  model.config.n_positions			)  # Max size of input for the pre-trained model

  # Prepare inputs tensors and dataloaders
  SCREAMING_SNAKE_CASE__    							=   pre_process_datasets(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  *__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   tensor_datasets[0], tensor_datasets[1]

  SCREAMING_SNAKE_CASE__    							=   TensorDataset(*__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   RandomSampler(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   DataLoader(__UpperCamelCase				,  sampler=__UpperCamelCase				,  batch_size=args.train_batch_size			)

  SCREAMING_SNAKE_CASE__    							=   TensorDataset(*__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   SequentialSampler(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   DataLoader(__UpperCamelCase				,  sampler=__UpperCamelCase				,  batch_size=args.eval_batch_size			)

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

    SCREAMING_SNAKE_CASE__    							=   list(model.named_parameters()			)
    SCREAMING_SNAKE_CASE__    							=   ["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
    SCREAMING_SNAKE_CASE__    							=   [
        {
            """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},
    ]
    SCREAMING_SNAKE_CASE__    							=   AdamW(__UpperCamelCase				,  lr=args.learning_rate				,  eps=args.adam_epsilon			)
    SCREAMING_SNAKE_CASE__    							=   get_linear_schedule_with_warmup(
        __UpperCamelCase				,  num_warmup_steps=args.warmup_steps				,  num_training_steps=__UpperCamelCase			)

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

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

    # If we save using the predefined names, we can load using `from_pretrained`
    SCREAMING_SNAKE_CASE__    							=   os.path.join(args.output_dir				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   os.path.join(args.output_dir				,  __UpperCamelCase			)

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

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

  if args.do_eval:
    model.eval()
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   0, 0
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   0, 0
    for batch in tqdm(__UpperCamelCase				,  desc="""Evaluating"""			):
      SCREAMING_SNAKE_CASE__    							=   tuple(t.to(__UpperCamelCase			) for t in batch			)
      SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   batch
      with torch.no_grad():
        SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   model(
            __UpperCamelCase				,  mc_token_ids=__UpperCamelCase				,  lm_labels=__UpperCamelCase				,  mc_labels=__UpperCamelCase			)

      SCREAMING_SNAKE_CASE__    							=   mc_logits.detach().cpu().numpy()
      SCREAMING_SNAKE_CASE__    							=   mc_labels.to("""cpu"""			).numpy()
      SCREAMING_SNAKE_CASE__    							=   accuracy(__UpperCamelCase				,  __UpperCamelCase			)

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

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

    SCREAMING_SNAKE_CASE__    							=   eval_loss / nb_eval_steps
    SCREAMING_SNAKE_CASE__    							=   eval_accuracy / nb_eval_examples
    SCREAMING_SNAKE_CASE__    							=   tr_loss / nb_tr_steps if args.do_train else None
    SCREAMING_SNAKE_CASE__    							=   {"""eval_loss""": eval_loss, """eval_accuracy""": eval_accuracy, """train_loss""": train_loss}

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


if __name__ == "__main__":
   main()

from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"EncodecFeatureExtractor"
   lowerCAmelCase_														=							("T5Tokenizer", "T5TokenizerFast")




   def __init__(					self    :       List[Any] ,			_lowercase    :       int ,			_lowercase    :       Any   ):



     """simple docstring"""






     super().__init__(_lowercase ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.feature_extractor
     SCREAMING_SNAKE_CASE__    							=   False




   def 			__a		(					self    :       Tuple ,			_lowercase    :       str=None ,			_lowercase    :       int=None ,			_lowercase    :       Any=True   ):



     """simple docstring"""






     return self.tokenizer.get_decoder_prompt_ids(task=_lowercase ,			language=_lowercase ,			no_timestamps=_lowercase   )




   def __call__(					self    :       Tuple ,			*_lowercase    :       Tuple ,			**_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""audio""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""sampling_rate""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""text""" ,			_lowercase   )
     if len(_lowercase   ) > 0:
       SCREAMING_SNAKE_CASE__    							=   args[0]
       SCREAMING_SNAKE_CASE__    							=   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 text is not None:
       SCREAMING_SNAKE_CASE__    							=   self.tokenizer(_lowercase ,			**_lowercase   )

     if audio is not None:
       SCREAMING_SNAKE_CASE__    							=   self.feature_extractor(_lowercase ,			*_lowercase ,			sampling_rate=_lowercase ,			**_lowercase   )

     if audio is None:
       return inputs

     elif text is None:
       return audio_inputs

     else:
       SCREAMING_SNAKE_CASE__    							=   audio_inputs["""input_values"""]
       if "padding_mask" in audio_inputs:
         SCREAMING_SNAKE_CASE__    							=   audio_inputs["""padding_mask"""]
       return inputs




   def 			__a		(					self    :       Dict ,			*_lowercase    :       Any ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""audio""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""padding_mask""" ,			_lowercase   )

     if len(_lowercase   ) > 0:
       SCREAMING_SNAKE_CASE__    							=   args[0]
       SCREAMING_SNAKE_CASE__    							=   args[1:]

     if audio_values is not None:
       return self._decode_audio(_lowercase ,			padding_mask=_lowercase   )
     else:
       return self.tokenizer.batch_decode(*_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       List[str] ,			*_lowercase    :       Tuple ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     return self.tokenizer.decode(*_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       Tuple ,			_lowercase    :       Optional = None   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   to_numpy(_lowercase   )
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   audio_values.shape

     if padding_mask is None:
       return list(_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   to_numpy(_lowercase   )

     # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
     # token (so that the generated audio values are **not** treated as padded tokens)
     SCREAMING_SNAKE_CASE__    							=   seq_len - padding_mask.shape[-1]
     SCREAMING_SNAKE_CASE__    							=   1 - self.feature_extractor.padding_value
     SCREAMING_SNAKE_CASE__    							=   np.pad(_lowercase ,			((0, 0), (0, difference)) ,			"""constant""" ,			constant_values=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   audio_values.tolist()
     for i in range(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   np.asarray(audio_values[i]   )[
           padding_mask[i][None, :] != self.feature_extractor.padding_value
       ]
       SCREAMING_SNAKE_CASE__    							=   sliced_audio.reshape(_lowercase ,			-1   )

     return audio_values
