





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 				=     logging.getLogger(__name__)



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	np.argmax(_lowerCAmelCase  ,axis=1     )
   return np.sum(outputs == labels     )



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   with open(_lowerCAmelCase  ,encoding="""utf_8"""     ) as f:
      A_     :				Optional[int]			      =	csv.reader(_lowerCAmelCase     )
      A_     :				Optional[Any]			      =	[]
      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  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Dict			      =	[]
   for dataset in encoded_datasets:
      A_     :				Optional[Any]			      =	len(_lowerCAmelCase     )
      A_     :				List[Any]			      =	np.zeros((n_batch, 2, input_len)  ,dtype=np.intaa     )
      A_     :				Union[str, Any]			      =	np.zeros((n_batch, 2)  ,dtype=np.intaa     )
      A_     :				Tuple			      =	np.full((n_batch, 2, input_len)  ,fill_value=-1_0_0  ,dtype=np.intaa     )
      A_     :				List[str]			      =	np.zeros((n_batch,)  ,dtype=np.intaa     )
      for (
          i,
          (story, conta, conta, mc_label),
      ) in enumerate(_lowerCAmelCase     ):
         A_     :				str			      =	[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
         A_     :				Any			      =	[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
         A_     :				Union[str, Any]			      =	with_conta
         A_     :				Optional[int]			      =	with_conta
         A_     :				Tuple			      =	len(_lowerCAmelCase     ) - 1
         A_     :				Dict			      =	len(_lowerCAmelCase     ) - 1
         A_     :				Any			      =	with_conta
         A_     :				List[str]			      =	with_conta
         A_     :				Tuple			      =	mc_label
      A_     :				Union[str, Any]			      =	(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'''


   A_     :				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."""     )
   A_     :				Optional[int]			      =	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     )

   A_     :				List[Any]			      =	torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""     )
   A_     :				List[Any]			      =	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
   A_     :				Dict			      =	["""_start_""", """_delimiter_""", """_classify_"""]
   A_     :				Any			      =	OpenAIGPTTokenizer.from_pretrained(args.model_name     )
   tokenizer.add_tokens(_lowerCAmelCase     )
   A_     :				Dict			      =	tokenizer.convert_tokens_to_ids(_lowerCAmelCase     )
   A_     :				Dict			      =	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     ):
      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..."""     )
   A_     :				int			      =	load_rocstories_dataset(args.train_dataset     )
   A_     :				Optional[int]			      =	load_rocstories_dataset(args.eval_dataset     )
   A_     :				str			      =	(train_dataset, eval_dataset)
   A_     :				Tuple			      =	tokenize_and_encode(_lowerCAmelCase     )

   # Compute the max input length for the Transformer
   A_     :				Any			      =	model.config.n_positions // 2 - 2
   A_     :				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     )
   A_     :				Optional[int]			      =	min(_lowerCAmelCase  ,model.config.n_positions     )  # Max size of input for the pre-trained model

   # Prepare inputs tensors and dataloaders
   A_     :				int			      =	pre_process_datasets(_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,*_lowerCAmelCase     )
   A_      ,				A_     :				Optional[Any]			      =	tensor_datasets[0], tensor_datasets[1]

   A_     :				List[str]			      =	TensorDataset(*_lowerCAmelCase     )
   A_     :				Union[str, Any]			      =	RandomSampler(_lowerCAmelCase     )
   A_     :				Union[str, Any]			      =	DataLoader(_lowerCAmelCase  ,sampler=_lowerCAmelCase  ,batch_size=args.train_batch_size     )

   A_     :				Optional[Any]			      =	TensorDataset(*_lowerCAmelCase     )
   A_     :				Dict			      =	SequentialSampler(_lowerCAmelCase     )
   A_     :				Any			      =	DataLoader(_lowerCAmelCase  ,sampler=_lowerCAmelCase  ,batch_size=args.eval_batch_size     )

   # Prepare optimizer
   if args.do_train:
      if args.max_steps > 0:
         A_     :				Optional[int]			      =	args.max_steps
         A_     :				Optional[Any]			      =	args.max_steps // (len(_lowerCAmelCase     ) // args.gradient_accumulation_steps) + 1
      else:
         A_     :				Tuple			      =	len(_lowerCAmelCase     ) // args.gradient_accumulation_steps * args.num_train_epochs

      A_     :				Optional[int]			      =	list(model.named_parameters()     )
      A_     :				Any			      =	["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
      A_     :				Optional[int]			      =	[
          {
              """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},
      ]
      A_     :				Any			      =	AdamW(_lowerCAmelCase  ,lr=args.learning_rate  ,eps=args.adam_epsilon     )
      A_     :				Tuple			      =	get_linear_schedule_with_warmup(
          _lowerCAmelCase  ,num_warmup_steps=args.warmup_steps  ,num_training_steps=_lowerCAmelCase     )

   if args.do_train:
      A_      ,				A_      ,				A_     :				Optional[Any]			      =	0, 0, None
      model.train()
      for _ in trange(int(args.num_train_epochs     )  ,desc="""Epoch"""     ):
         A_     :				Optional[int]			      =	0
         A_     :				int			      =	0
         A_     :				Any			      =	tqdm(_lowerCAmelCase  ,desc="""Training"""     )
         for step, batch in enumerate(_lowerCAmelCase     ):
            A_     :				Dict			      =	tuple(t.to(_lowerCAmelCase     ) for t in batch     )
            A_      ,				A_      ,				A_      ,				A_     :				Optional[int]			      =	batch
            A_     :				int			      =	model(_lowerCAmelCase  ,mc_token_ids=_lowerCAmelCase  ,lm_labels=_lowerCAmelCase  ,mc_labels=_lowerCAmelCase     )
            A_     :				int			      =	args.lm_coef * losses[0] + losses[1]
            loss.backward()
            optimizer.step()
            scheduler.step()
            optimizer.zero_grad()
            tr_loss += loss.item()
            A_     :				int			      =	(
                loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
            )
            nb_tr_steps += 1
            A_     :				Any			      =	"""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
      A_     :				Union[str, 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`
      A_     :				Dict			      =	os.path.join(args.output_dir  ,_lowerCAmelCase     )
      A_     :				Dict			      =	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
      A_     :				str			      =	OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir     )
      A_     :				Any			      =	OpenAIGPTTokenizer.from_pretrained(args.output_dir     )
      model.to(_lowerCAmelCase     )

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

         A_     :				Optional[Any]			      =	mc_logits.detach().cpu().numpy()
         A_     :				List[Any]			      =	mc_labels.to("""cpu"""     ).numpy()
         A_     :				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

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

      A_     :				Tuple			      =	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()













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


_lowerCAmelCase 				=     {
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     ["""SpeechT5Tokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     [
          """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """SpeechT5ForSpeechToText""",
          """SpeechT5ForSpeechToSpeech""",
          """SpeechT5ForTextToSpeech""",
          """SpeechT5Model""",
          """SpeechT5PreTrainedModel""",
          """SpeechT5HifiGan""",
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      _lowerCAmelCase 				=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






