





'''simple docstring'''

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
)
UpperCAmelCase					:		List[str]		=							logging.getLogger(__name__)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[int]					     =     np.argmax(lowerCAmelCase_			,      axis=1	)
      return np.sum(outputs == labels	)




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      with open(lowerCAmelCase_			,      encoding='''utf_8'''	) as f:
            _snake_case		:	Dict					     =     csv.reader(lowerCAmelCase_	)
            _snake_case		:	Tuple					     =     []
            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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Union[str, Any]					     =     []
      for dataset in encoded_datasets:
            _snake_case		:	List[Any]					     =     len(lowerCAmelCase_	)
            _snake_case		:	int					     =     np.zeros((n_batch, 2, input_len)			,      dtype=np.intaa	)
            _snake_case		:	Optional[Any]					     =     np.zeros((n_batch, 2)			,      dtype=np.intaa	)
            _snake_case		:	Optional[Any]					     =     np.full((n_batch, 2, input_len)			,      fill_value=-100			,      dtype=np.intaa	)
            _snake_case		:	int					     =     np.zeros((n_batch,)			,      dtype=np.intaa	)
            for (
                i,
                (story, conta, conta, mc_label),
            ) in enumerate(lowerCAmelCase_	):
                  _snake_case		:	Any					     =     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                  _snake_case		:	List[Any]					     =     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                  _snake_case		:	List[Any]					     =     with_conta
                  _snake_case		:	Dict					     =     with_conta
                  _snake_case		:	str					     =     len(lowerCAmelCase_	) - 1
                  _snake_case		:	List[Any]					     =     len(lowerCAmelCase_	) - 1
                  _snake_case		:	Optional[Any]					     =     with_conta
                  _snake_case		:	List[Any]					     =     with_conta
                  _snake_case		:	Optional[Any]					     =     mc_label
            _snake_case		:	Optional[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    _a    (							):





      """simple docstring"""



      _snake_case		:	List[Any]					     =     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=42	)
      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=16	)
      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=374	)

      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.'''	)
      _snake_case		:	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	)

      _snake_case		:	Optional[Any]					     =     torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''	)
      _snake_case		:	Dict					     =     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
      _snake_case		:	Optional[Any]					     =     ['''_start_''', '''_delimiter_''', '''_classify_''']
      _snake_case		:	Optional[int]					     =     OpenAIGPTTokenizer.from_pretrained(args.model_name	)
      tokenizer.add_tokens(lowerCAmelCase_	)
      _snake_case		:	Optional[Any]					     =     tokenizer.convert_tokens_to_ids(lowerCAmelCase_	)
      _snake_case		:	List[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_	):
            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...'''	)
      _snake_case		:	Optional[Any]					     =     load_rocstories_dataset(args.train_dataset	)
      _snake_case		:	List[Any]					     =     load_rocstories_dataset(args.eval_dataset	)
      _snake_case		:	Dict					     =     (train_dataset, eval_dataset)
      _snake_case		:	Optional[Any]					     =     tokenize_and_encode(lowerCAmelCase_	)

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

      # Prepare inputs tensors and dataloaders
      _snake_case		:	int					     =     pre_process_datasets(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      *lowerCAmelCase_	)
      _snake_case		:	Dict					     =     tensor_datasets[0], tensor_datasets[1]

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

      _snake_case		:	str					     =     TensorDataset(*lowerCAmelCase_	)
      _snake_case		:	Any					     =     SequentialSampler(lowerCAmelCase_	)
      _snake_case		:	str					     =     DataLoader(lowerCAmelCase_			,      sampler=lowerCAmelCase_			,      batch_size=args.eval_batch_size	)

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

            _snake_case		:	Union[str, Any]					     =     list(model.named_parameters()	)
            _snake_case		:	str					     =     ['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
            _snake_case		:	Union[str, 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},
            ]
            _snake_case		:	Any					     =     AdamW(lowerCAmelCase_			,      lr=args.learning_rate			,      eps=args.adam_epsilon	)
            _snake_case		:	str					     =     get_linear_schedule_with_warmup(
                lowerCAmelCase_			,      num_warmup_steps=args.warmup_steps			,      num_training_steps=lowerCAmelCase_	)

      if args.do_train:
            _snake_case		:	Optional[int]					     =     0, 0, None
            model.train()
            for _ in trange(int(args.num_train_epochs	)			,      desc='''Epoch'''	):
                  _snake_case		:	str					     =     0
                  _snake_case		:	List[str]					     =     0
                  _snake_case		:	Tuple					     =     tqdm(lowerCAmelCase_			,      desc='''Training'''	)
                  for step, batch in enumerate(lowerCAmelCase_	):
                        _snake_case		:	Any					     =     tuple(t.to(lowerCAmelCase_	) for t in batch	)
                        _snake_case		:	Optional[Any]					     =     batch
                        _snake_case		:	Union[str, Any]					     =     model(lowerCAmelCase_			,      mc_token_ids=lowerCAmelCase_			,      lm_labels=lowerCAmelCase_			,      mc_labels=lowerCAmelCase_	)
                        _snake_case		:	List[Any]					     =     args.lm_coef * losses[0] + losses[1]
                        loss.backward()
                        optimizer.step()
                        scheduler.step()
                        optimizer.zero_grad()
                        tr_loss += loss.item()
                        _snake_case		:	Optional[Any]					     =     (
                            loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                        )
                        nb_tr_steps += 1
                        _snake_case		:	int					     =     '''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
            _snake_case		:	Optional[int]					     =     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`
            _snake_case		:	Dict					     =     os.path.join(args.output_dir			,      lowerCAmelCase_	)
            _snake_case		:	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
            _snake_case		:	Dict					     =     OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir	)
            _snake_case		:	Optional[int]					     =     OpenAIGPTTokenizer.from_pretrained(args.output_dir	)
            model.to(lowerCAmelCase_	)

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

                  _snake_case		:	int					     =     mc_logits.detach().cpu().numpy()
                  _snake_case		:	Any					     =     mc_labels.to('''cpu'''	).numpy()
                  _snake_case		:	Optional[Any]					     =     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

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

            _snake_case		:	str					     =     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'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase					:		List[str]		=							logging.get_logger(__name__)

UpperCAmelCase					:		Dict		=							{
    'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}





class lowerCamelCase						(a__					):
  _lowercase  :	List[str]       					=				"""sew-d"""







  def __init__(		self     ,     lowercase__=32     ,     lowercase__=768     ,     lowercase__=12     ,     lowercase__=12     ,     lowercase__=3_072     ,     lowercase__=2     ,     lowercase__=512     ,     lowercase__=256     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__=("p2c", "c2p")     ,     lowercase__="layer_norm"     ,     lowercase__="gelu_python"     ,     lowercase__=0.1     ,     lowercase__=0.1     ,     lowercase__=0.1     ,     lowercase__=0.0     ,     lowercase__=0.1     ,     lowercase__=0.02     ,     lowercase__=1E-7     ,     lowercase__=1E-5     ,     lowercase__="group"     ,     lowercase__="gelu"     ,     lowercase__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)     ,     lowercase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)     ,     lowercase__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)     ,     lowercase__=False     ,     lowercase__=128     ,     lowercase__=16     ,     lowercase__=True     ,     lowercase__=0.05     ,     lowercase__=10     ,     lowercase__=2     ,     lowercase__=0.0     ,     lowercase__=10     ,     lowercase__=0     ,     lowercase__="mean"     ,     lowercase__=False     ,     lowercase__=False     ,     lowercase__=256     ,     lowercase__=0     ,     lowercase__=1     ,     lowercase__=2     ,     **lowercase__     ,     )		->						Dict:






        """simple docstring"""





        super().__init__(**lowercase__     ,     pad_token_id=lowercase__     ,     bos_token_id=lowercase__     ,     eos_token_id=lowercase__		)
        _snake_case		:	List[str]					     =     hidden_size
        _snake_case		:	Optional[Any]					     =     feat_extract_norm
        _snake_case		:	Tuple					     =     feat_extract_activation
        _snake_case		:	Tuple					     =     list(lowercase__		)
        _snake_case		:	Any					     =     list(lowercase__		)
        _snake_case		:	Any					     =     list(lowercase__		)
        _snake_case		:	Any					     =     conv_bias
        _snake_case		:	List[Any]					     =     num_conv_pos_embeddings
        _snake_case		:	Any					     =     num_conv_pos_embedding_groups
        _snake_case		:	Union[str, Any]					     =     len(self.conv_dim		)
        _snake_case		:	Optional[Any]					     =     num_hidden_layers
        _snake_case		:	Optional[int]					     =     intermediate_size
        _snake_case		:	Any					     =     squeeze_factor
        _snake_case		:	Optional[Any]					     =     max_position_embeddings
        _snake_case		:	Tuple					     =     position_buckets
        _snake_case		:	Tuple					     =     share_att_key
        _snake_case		:	Any					     =     relative_attention
        _snake_case		:	Optional[int]					     =     norm_rel_ebd
        _snake_case		:	Optional[Any]					     =     list(lowercase__		)
        _snake_case		:	List[Any]					     =     hidden_act
        _snake_case		:	List[Any]					     =     num_attention_heads
        _snake_case		:	Dict					     =     hidden_dropout
        _snake_case		:	Tuple					     =     attention_dropout
        _snake_case		:	Union[str, Any]					     =     activation_dropout
        _snake_case		:	List[Any]					     =     feat_proj_dropout
        _snake_case		:	Optional[int]					     =     final_dropout
        _snake_case		:	Optional[Any]					     =     layer_norm_eps
        _snake_case		:	Dict					     =     feature_layer_norm_eps
        _snake_case		:	List[Any]					     =     initializer_range
        _snake_case		:	Dict					     =     vocab_size

        if (
            (len(self.conv_stride		) != self.num_feat_extract_layers)
            or (len(self.conv_kernel		) != self.num_feat_extract_layers)
            or (len(self.conv_dim		) != self.num_feat_extract_layers)
        ):
              raise ValueError(
                  '''Configuration for convolutional layers is incorrect.'''
                  '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
                  F'''but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)'''
                  F'''= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`.'''		)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        _snake_case		:	Union[str, Any]					     =     apply_spec_augment
        _snake_case		:	Any					     =     mask_time_prob
        _snake_case		:	List[str]					     =     mask_time_length
        _snake_case		:	Dict					     =     mask_time_min_masks
        _snake_case		:	Union[str, Any]					     =     mask_feature_prob
        _snake_case		:	Tuple					     =     mask_feature_length
        _snake_case		:	Union[str, Any]					     =     mask_feature_min_masks

        # ctc loss
        _snake_case		:	Optional[Any]					     =     ctc_loss_reduction
        _snake_case		:	Optional[Any]					     =     ctc_zero_infinity

        # sequence classification
        _snake_case		:	List[Any]					     =     use_weighted_layer_sum
        _snake_case		:	Any					     =     classifier_proj_size







  @property
  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        return functools.reduce(operator.mul     ,     self.conv_stride     ,     1		)


