


import functools
import operator

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


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'microsoft/unispeech-large-1500h-cv': (
        'https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str      =  "unispeech"






    def __init__(			self					,   A_=32					,   A_=768					,   A_=12					,   A_=12					,   A_=3072					,   A_="gelu"					,   A_=0.1					,   A_=0.1					,   A_=0.1					,   A_=0.0					,   A_=0.0					,   A_=0.1					,   A_=0.1					,   A_=0.02					,   A_=1E-5					,   A_="group"					,   A_="gelu"					,   A_=(512, 512, 512, 512, 512, 512, 512)					,   A_=(5, 2, 2, 2, 2, 2, 2)					,   A_=(10, 3, 3, 3, 3, 2, 2)					,   A_=False					,   A_=128					,   A_=16					,   A_=False					,   A_=True					,   A_=0.05					,   A_=10					,   A_=2					,   A_=0.0					,   A_=10					,   A_=0					,   A_=320					,   A_=2					,   A_=0.1					,   A_=100					,   A_=256					,   A_=256					,   A_=0.1					,   A_="mean"					,   A_=False					,   A_=False					,   A_=256					,   A_=80					,   A_=0					,   A_=1					,   A_=2					,   A_=0.5					,   **A_					,   )  ->       int:
         super().__init__(**A_					,   pad_token_id=A_					,   bos_token_id=A_					,   eos_token_id=A_    )
         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=feat_extract_norm
         __UpperCamelCase    							=feat_extract_activation
         __UpperCamelCase    							=list(A_    )
         __UpperCamelCase    							=list(A_    )
         __UpperCamelCase    							=list(A_    )
         __UpperCamelCase    							=conv_bias
         __UpperCamelCase    							=num_conv_pos_embeddings
         __UpperCamelCase    							=num_conv_pos_embedding_groups
         __UpperCamelCase    							=len(self.conv_dim    )
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=hidden_dropout
         __UpperCamelCase    							=attention_dropout
         __UpperCamelCase    							=activation_dropout
         __UpperCamelCase    							=feat_proj_dropout
         __UpperCamelCase    							=final_dropout
         __UpperCamelCase    							=layerdrop
         __UpperCamelCase    							=layer_norm_eps
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=num_ctc_classes
         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=do_stable_layer_norm
         __UpperCamelCase    							=use_weighted_layer_sum
         __UpperCamelCase    							=classifier_proj_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)`, but is `len(config.conv_dim) ='
                  f' {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,'
                  f' `len(config.conv_kernel) = {len(self.conv_kernel    )}`.'    )

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         __UpperCamelCase    							=apply_spec_augment
         __UpperCamelCase    							=mask_time_prob
         __UpperCamelCase    							=mask_time_length
         __UpperCamelCase    							=mask_time_min_masks
         __UpperCamelCase    							=mask_feature_prob
         __UpperCamelCase    							=mask_feature_length
         __UpperCamelCase    							=mask_feature_min_masks

         # parameters for pretraining with codevector quantized representations
         __UpperCamelCase    							=num_codevectors_per_group
         __UpperCamelCase    							=num_codevector_groups
         __UpperCamelCase    							=contrastive_logits_temperature
         __UpperCamelCase    							=feat_quantizer_dropout
         __UpperCamelCase    							=num_negatives
         __UpperCamelCase    							=codevector_dim
         __UpperCamelCase    							=proj_codevector_dim
         __UpperCamelCase    							=diversity_loss_weight

         # ctc loss
         __UpperCamelCase    							=ctc_loss_reduction
         __UpperCamelCase    							=ctc_zero_infinity

         # pretraining loss
         __UpperCamelCase    							=replace_prob






    @property
    def _a					(			self    )  ->       Any:
         return functools.reduce(operator.mul					,   self.conv_stride					,   1    )









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
)
_A		       =						logging.getLogger(__name__)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					List[Any]				):
     __UpperCamelCase    							=np.argmax(SCREAMING_SNAKE_CASE__			,     axis=1				)
     return np.sum(outputs == labels				)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[Any]				):
     with open(SCREAMING_SNAKE_CASE__			,     encoding='utf_8'				) as f:
          __UpperCamelCase    							=csv.reader(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=[]
          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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					Any			,     SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					Dict				):
     __UpperCamelCase    							=[]
     for dataset in encoded_datasets:
          __UpperCamelCase    							=len(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=np.zeros((n_batch, 2, input_len)			,     dtype=np.intaa				)
          __UpperCamelCase    							=np.zeros((n_batch, 2)			,     dtype=np.intaa				)
          __UpperCamelCase    							=np.full((n_batch, 2, input_len)			,     fill_value=-1_00			,     dtype=np.intaa				)
          __UpperCamelCase    							=np.zeros((n_batch,)			,     dtype=np.intaa				)
          for (
              i,
              (story, conta, conta, mc_label),
          ) in enumerate(SCREAMING_SNAKE_CASE__				):
               __UpperCamelCase    							=[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               __UpperCamelCase    							=[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               __UpperCamelCase    							=with_conta
               __UpperCamelCase    							=with_conta
               __UpperCamelCase    							=len(SCREAMING_SNAKE_CASE__				) - 1
               __UpperCamelCase    							=len(SCREAMING_SNAKE_CASE__				) - 1
               __UpperCamelCase    							=with_conta
               __UpperCamelCase    							=with_conta
               __UpperCamelCase    							=mc_label
          __UpperCamelCase    							=(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 						_UpperCAmelCase				(			):
     __UpperCamelCase    							=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=6.25E-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=3_74				)

     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.'				)
     __UpperCamelCase    							=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				)

     __UpperCamelCase    							=torch.device('cuda' if torch.cuda.is_available() else 'cpu'				)
     __UpperCamelCase    							=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
     __UpperCamelCase    							=['_start_', '_delimiter_', '_classify_']
     __UpperCamelCase    							=OpenAIGPTTokenizer.from_pretrained(args.model_name				)
     tokenizer.add_tokens(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=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__			:					str				):
          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...'				)
     __UpperCamelCase    							=load_rocstories_dataset(args.train_dataset				)
     __UpperCamelCase    							=load_rocstories_dataset(args.eval_dataset				)
     __UpperCamelCase    							=(train_dataset, eval_dataset)
     __UpperCamelCase    							=tokenize_and_encode(SCREAMING_SNAKE_CASE__				)

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

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

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

     __UpperCamelCase    							=TensorDataset(*SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=SequentialSampler(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=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:
               __UpperCamelCase    							=args.max_steps
               __UpperCamelCase    							=args.max_steps // (len(SCREAMING_SNAKE_CASE__				) // args.gradient_accumulation_steps) + 1
          else:
               __UpperCamelCase    							=len(SCREAMING_SNAKE_CASE__				) // args.gradient_accumulation_steps * args.num_train_epochs

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

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

     if args.do_eval:
          model.eval()
          __UpperCamelCase							,      __UpperCamelCase    							=0, 0
          __UpperCamelCase							,      __UpperCamelCase    							=0, 0
          for batch in tqdm(SCREAMING_SNAKE_CASE__			,     desc='Evaluating'				):
               __UpperCamelCase    							=tuple(t.to(SCREAMING_SNAKE_CASE__				) for t in batch				)
               __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=batch
               with torch.no_grad():
                    __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=model(
                        SCREAMING_SNAKE_CASE__			,     mc_token_ids=SCREAMING_SNAKE_CASE__			,     lm_labels=SCREAMING_SNAKE_CASE__			,     mc_labels=SCREAMING_SNAKE_CASE__				)

               __UpperCamelCase    							=mc_logits.detach().cpu().numpy()
               __UpperCamelCase    							=mc_labels.to('cpu'				).numpy()
               __UpperCamelCase    							=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

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

          __UpperCamelCase    							=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()





