


import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
__lowerCAmelCase			=							logging.get_logger(__name__)

__lowerCAmelCase			=							{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
__lowerCAmelCase			=							[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)  ->    Dict:
       for attribute in key.split('.'					):
              lowercase__:       Tuple        =       getattr(snake_case      ,							snake_case					)

       if weight_type is not None:
              lowercase__:       int        =       getattr(snake_case      ,							snake_case					).shape
       else:
              lowercase__:       int        =       hf_pointer.shape

       if hf_shape != value.shape:
              raise ValueError(
                  f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
                  f' {value.shape} for {full_name}'					)

       if weight_type == "weight":
              lowercase__:       int        =       value
       elif weight_type == "weight_g":
              lowercase__:       List[Any]        =       value
       elif weight_type == "weight_v":
              lowercase__:       List[Any]        =       value
       elif weight_type == "bias":
              lowercase__:       Optional[int]        =       value
       else:
              lowercase__:       Optional[Any]        =       value

       logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'					)




def        snake_case_					(  snake_case      ,							snake_case					)  ->    Tuple:
       lowercase__:       str        =       []
       lowercase__:       Union[str, Any]        =       fairseq_model.state_dict()

       lowercase__:       Optional[Any]        =       hf_model.unispeech_sat.feature_extractor

       for name, value in fairseq_dict.items():
              lowercase__:       Optional[Any]        =       False
              if "conv_layers" in name:
                     load_conv_layer(
                         snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							hf_model.config.feat_extract_norm == 'group'      ,							)
                     lowercase__:       Dict        =       True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowercase__:       List[str]        =       'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                            if key in name or key.split('w2v_model.'					)[-1] == name.split('.'					)[0]:
                                   if "layer_norm_for_extract" in name and (".".join(name.split('.'					)[:-1]					) != key):
                                          # special case since naming is very similar
                                          continue
                                   lowercase__:       int        =       True
                                   if "*" in mapped_key:
                                          lowercase__:       List[Any]        =       name.split(snake_case					)[0].split('.'					)[-2]
                                          lowercase__:       List[str]        =       mapped_key.replace('*'      ,							snake_case					)
                                   if "weight_g" in name:
                                          lowercase__:       Optional[Any]        =       'weight_g'
                                   elif "weight_v" in name:
                                          lowercase__:       Optional[int]        =       'weight_v'
                                   elif "bias" in name:
                                          lowercase__:       Dict        =       'bias'
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          lowercase__:       List[str]        =       'weight'
                                   else:
                                          lowercase__:       List[Any]        =       None
                                   set_recursively(snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)
                            continue
              if not is_used:
                     unused_weights.append(snake_case					)

       logger.warning(f'Unused weights: {unused_weights}'					)




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)  ->    Union[str, Any]:
       lowercase__:       Union[str, Any]        =       full_name.split('conv_layers.'					)[-1]
       lowercase__:       List[str]        =       name.split('.'					)
       lowercase__:       int        =       int(items[0]					)
       lowercase__:       str        =       int(items[1]					)

       if type_id == 0:
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                            raise ValueError(
                                f'{full_name} has size {value.shape}, but'
                                f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'					)
                     lowercase__:       List[str]        =       value
                     logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                            raise ValueError(
                                f'{full_name} has size {value.shape}, but'
                                f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'					)
                     lowercase__:       str        =       value
                     logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                            raise ValueError(
                                f'{full_name} has size {value.shape}, but'
                                f' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'					)
                     lowercase__:       Tuple        =       value
                     logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                            raise ValueError(
                                f'{full_name} has size {value.shape}, but'
                                f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'					)
                     lowercase__:       List[str]        =       value
                     logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
       else:
              unused_weights.append(snake_case					)




@torch.no_grad()
def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case=None      ,							snake_case=None      ,							snake_case=True					)  ->    List[Any]:
       if config_path is not None:
              lowercase__:       List[Any]        =       UniSpeechSatConfig.from_pretrained(snake_case					)
       else:
              lowercase__:       Dict        =       UniSpeechSatConfig()

       lowercase__:       Any        =       ''

       if is_finetuned:
              lowercase__:       Tuple        =       UniSpeechSatForCTC(snake_case					)
       else:
              lowercase__:       Optional[int]        =       UniSpeechSatForPreTraining(snake_case					)

       lowercase__      ,						lowercase__      ,						lowercase__:       str        =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path]      ,							arg_overrides={'data': '/'.join(dict_path.split('/'					)[:-1]					)}					)
       lowercase__:       List[Any]        =       model[0].eval()

       recursively_load_weights(snake_case      ,							snake_case					)

       hf_wavavec.save_pretrained(snake_case					)


if __name__ == "__main__":
 __lowerCAmelCase			=							argparse.ArgumentParser()
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
 parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
 parser.add_argument(
     '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
 )
 __lowerCAmelCase			=							parser.parse_args()
 convert_unispeech_sat_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )






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        snake_case_					(  snake_case      ,							snake_case					)  ->    Optional[int]:
       lowercase__:       Optional[int]        =       np.argmax(snake_case      ,							axis=1					)
       return np.sum(outputs == labels					)




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




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)  ->    Tuple:
       lowercase__:       List[Any]        =       []
       for dataset in encoded_datasets:
              lowercase__:       Dict        =       len(snake_case					)
              lowercase__:       int        =       np.zeros((n_batch, 2, input_len)      ,							dtype=np.intaa					)
              lowercase__:       int        =       np.zeros((n_batch, 2)      ,							dtype=np.intaa					)
              lowercase__:       Optional[int]        =       np.full((n_batch, 2, input_len)      ,							fill_value=-1_00      ,							dtype=np.intaa					)
              lowercase__:       Optional[Any]        =       np.zeros((n_batch,)      ,							dtype=np.intaa					)
              for (
                  i,
                  (story, conta, conta, mc_label),
              ) in enumerate(snake_case					):
                     lowercase__:       List[Any]        =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     lowercase__:       List[Any]        =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     lowercase__:       Union[str, Any]        =       with_conta
                     lowercase__:       List[Any]        =       with_conta
                     lowercase__:       Any        =       len(snake_case					) - 1
                     lowercase__:       Dict        =       len(snake_case					) - 1
                     lowercase__:       Optional[Any]        =       with_conta
                     lowercase__:       Tuple        =       with_conta
                     lowercase__:       int        =       mc_label
              lowercase__:       Any        =       (input_ids, mc_token_ids, lm_labels, mc_labels)
              tensor_datasets.append(tuple(torch.tensor(snake_case					) for t in all_inputs					)					)
       return tensor_datasets




def        snake_case_					(  )  ->    Union[str, Any]:
       lowercase__:       Optional[Any]        =       argparse.ArgumentParser()
       parser.add_argument('--model_name'      ,							type=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=snake_case      ,							type=snake_case      ,							required=snake_case      ,							help='The output directory where the model predictions and checkpoints will be written.'      ,							)
       parser.add_argument('--train_dataset'      ,							type=snake_case      ,							default=''					)
       parser.add_argument('--eval_dataset'      ,							type=snake_case      ,							default=''					)
       parser.add_argument('--seed'      ,							type=snake_case      ,							default=42					)
       parser.add_argument('--num_train_epochs'      ,							type=snake_case      ,							default=3					)
       parser.add_argument('--train_batch_size'      ,							type=snake_case      ,							default=8					)
       parser.add_argument('--eval_batch_size'      ,							type=snake_case      ,							default=16					)
       parser.add_argument('--adam_epsilon'      ,							default=1e-8      ,							type=snake_case      ,							help='Epsilon for Adam optimizer.'					)
       parser.add_argument('--max_grad_norm'      ,							type=snake_case      ,							default=1					)
       parser.add_argument(
           '--max_steps'      ,							default=-1      ,							type=snake_case      ,							help=(
               'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
           )      ,							)
       parser.add_argument(
           '--gradient_accumulation_steps'      ,							type=snake_case      ,							default=1      ,							help='Number of updates steps to accumulate before                        performing a backward/update pass.'      ,							)
       parser.add_argument('--learning_rate'      ,							type=snake_case      ,							default=6.25e-5					)
       parser.add_argument('--warmup_steps'      ,							default=0      ,							type=snake_case      ,							help='Linear warmup over warmup_steps.'					)
       parser.add_argument('--lr_schedule'      ,							type=snake_case      ,							default='warmup_linear'					)
       parser.add_argument('--weight_decay'      ,							type=snake_case      ,							default=0.0_1					)
       parser.add_argument('--lm_coef'      ,							type=snake_case      ,							default=0.9					)
       parser.add_argument('--n_valid'      ,							type=snake_case      ,							default=3_74					)

       parser.add_argument('--server_ip'      ,							type=snake_case      ,							default=''      ,							help='Can be used for distant debugging.'					)
       parser.add_argument('--server_port'      ,							type=snake_case      ,							default=''      ,							help='Can be used for distant debugging.'					)
       lowercase__:       List[str]        =       parser.parse_args()
       print(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=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__:       Tuple        =       torch.cuda.device_count()
       logger.info('device: {}, n_gpu {}'.format(snake_case      ,							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__:       Any        =       ['_start_', '_delimiter_', '_classify_']
       lowercase__:       Any        =       OpenAIGPTTokenizer.from_pretrained(args.model_name					)
       tokenizer.add_tokens(snake_case					)
       lowercase__:       int        =       tokenizer.convert_tokens_to_ids(snake_case					)
       lowercase__:       int        =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name					)
       model.resize_token_embeddings(len(snake_case					)					)
       model.to(snake_case					)

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

       logger.info('Encoding dataset...'					)
       lowercase__:       Dict        =       load_rocstories_dataset(args.train_dataset					)
       lowercase__:       Dict        =       load_rocstories_dataset(args.eval_dataset					)
       lowercase__:       str        =       (train_dataset, eval_dataset)
       lowercase__:       Any        =       tokenize_and_encode(snake_case					)

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

       # Prepare inputs tensors and dataloaders
       lowercase__:       str        =       pre_process_datasets(snake_case      ,							snake_case      ,							snake_case      ,							*snake_case					)
       lowercase__      ,						lowercase__:       Optional[Any]        =       tensor_datasets[0], tensor_datasets[1]

       lowercase__:       List[str]        =       TensorDataset(*snake_case					)
       lowercase__:       Dict        =       RandomSampler(snake_case					)
       lowercase__:       Optional[int]        =       DataLoader(snake_case      ,							sampler=snake_case      ,							batch_size=args.train_batch_size					)

       lowercase__:       str        =       TensorDataset(*snake_case					)
       lowercase__:       str        =       SequentialSampler(snake_case					)
       lowercase__:       Optional[Any]        =       DataLoader(snake_case      ,							sampler=snake_case      ,							batch_size=args.eval_batch_size					)

       # Prepare optimizer
       if args.do_train:
              if args.max_steps > 0:
                     lowercase__:       Union[str, Any]        =       args.max_steps
                     lowercase__:       Tuple        =       args.max_steps // (len(snake_case					) // args.gradient_accumulation_steps) + 1
              else:
                     lowercase__:       Optional[Any]        =       len(snake_case					) // args.gradient_accumulation_steps * args.num_train_epochs

              lowercase__:       str        =       list(model.named_parameters()					)
              lowercase__:       Any        =       ['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__:       Tuple        =       AdamW(snake_case      ,							lr=args.learning_rate      ,							eps=args.adam_epsilon					)
              lowercase__:       Tuple        =       get_linear_schedule_with_warmup(
                  snake_case      ,							num_warmup_steps=args.warmup_steps      ,							num_training_steps=snake_case					)

       if args.do_train:
              lowercase__      ,						lowercase__      ,						lowercase__:       int        =       0, 0, None
              model.train()
              for _ in trange(int(args.num_train_epochs					)      ,							desc='Epoch'					):
                     lowercase__:       str        =       0
                     lowercase__:       Optional[Any]        =       0
                     lowercase__:       List[Any]        =       tqdm(snake_case      ,							desc='Training'					)
                     for step, batch in enumerate(snake_case					):
                            lowercase__:       Union[str, Any]        =       tuple(t.to(snake_case					) for t in batch					)
                            lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__:       List[Any]        =       batch
                            lowercase__:       List[str]        =       model(snake_case      ,							mc_token_ids=snake_case      ,							lm_labels=snake_case      ,							mc_labels=snake_case					)
                            lowercase__:       Optional[Any]        =       args.lm_coef * losses[0] + losses[1]
                            loss.backward()
                            optimizer.step()
                            scheduler.step()
                            optimizer.zero_grad()
                            tr_loss += loss.item()
                            lowercase__:       Union[str, Any]        =       (
                                loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                            )
                            nb_tr_steps += 1
                            lowercase__:       Tuple        =       'Training loss: {:.2e} lr: {:.2e}'.format(snake_case      ,							scheduler.get_lr()[0]					)

    # Save a trained model
       if args.do_train:
              # Save a trained model, configuration and tokenizer
              lowercase__:       Any        =       model.module if hasattr(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      ,							snake_case					)
              lowercase__:       List[str]        =       os.path.join(args.output_dir      ,							snake_case					)

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

              # Load a trained model and vocabulary that you have fine-tuned
              lowercase__:       Optional[Any]        =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir					)
              lowercase__:       Any        =       OpenAIGPTTokenizer.from_pretrained(args.output_dir					)
              model.to(snake_case					)

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

                     lowercase__:       Dict        =       mc_logits.detach().cpu().numpy()
                     lowercase__:       Tuple        =       mc_labels.to('cpu'					).numpy()
                     lowercase__:       Dict        =       accuracy(snake_case      ,							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__:       Optional[int]        =       eval_loss / nb_eval_steps
              lowercase__:       Optional[int]        =       eval_accuracy / nb_eval_examples
              lowercase__:       int        =       tr_loss / nb_tr_steps if args.do_train else None
              lowercase__:       Optional[Any]        =       {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

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


if __name__ == "__main__":
 main()


