


import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json






def  _UpperCAmelCase						( __lowerCamelCase							)		->List[Any]:


 '''simple docstring'''
 snake_case :			Union[str, Any]							=   filter(lambda __lowerCamelCase							: p.requires_grad			,  model.parameters()							)
 snake_case :			Tuple							=   sum([np.prod(p.size()							) for p in model_parameters]							)
 return params


a					:				List[Any]       =      logging.getLogger(__name__)

class   lowerCamelCase						(	pl.Callback  ):

       def 					UpperCAmelCase					(      self						,    a__						,    a__       ):






        '''simple docstring'''






        snake_case :			Tuple							=   {f'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups       )}
        pl_module.logger.log_metrics(a__       )

       @rank_zero_only
       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__						,    a__=True       ):






        '''simple docstring'''






        logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''       )
        snake_case :			Dict							=   trainer.callback_metrics
        trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']}       )
        # Log results
        snake_case :			List[str]							=   Path(pl_module.hparams.output_dir       )
        if type_path == "test":
         snake_case :			Optional[int]							=   od / '''test_results.txt'''
         snake_case :			int							=   od / '''test_generations.txt'''
        else:
         # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
         # If people want this it will be easy enough to add back.
         snake_case :			Union[str, Any]							=   od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
         snake_case :			List[str]							=   od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
         results_file.parent.mkdir(exist_ok=a__       )
         generations_file.parent.mkdir(exist_ok=a__       )
        with open(a__						,    '''a+'''       ) as writer:
         for key in sorted(a__       ):
          if key in ["log", "progress_bar", "preds"]:
           continue
          snake_case :			Optional[int]							=   metrics[key]
          if isinstance(a__						,    torch.Tensor       ):
           snake_case :			Any							=   val.item()
          snake_case :			int							=   f'''{key}: {val:.6f}\n'''
          writer.write(a__       )

        if not save_generations:
         return

        if "preds" in metrics:
         snake_case :			Optional[int]							=   '''\n'''.join(metrics['''preds''']       )
         generations_file.open('''w+'''       ).write(a__       )

       @rank_zero_only
       def 					UpperCAmelCase					(      self						,    a__						,    a__       ):






        '''simple docstring'''






        try:
         snake_case :			Any							=   pl_module.model.model.num_parameters()
        except AttributeError:
         snake_case :			Union[str, Any]							=   pl_module.model.num_parameters()

        snake_case :			Any							=   count_trainable_parameters(a__       )
        # mp stands for million parameters
        trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1e6, '''grad_mp''': n_trainable_pars / 1e6}       )

       @rank_zero_only
       def 					UpperCAmelCase					(      self						,    a__						,    a__       ):






        '''simple docstring'''






        save_json(pl_module.metrics						,    pl_module.metrics_save_path       )
        return self._write_logs(a__						,    a__						,    '''test'''       )



       @rank_zero_only
       def 					UpperCAmelCase					(      self						,    a__						,    a__       ):






        '''simple docstring'''






        save_json(pl_module.metrics						,    pl_module.metrics_save_path       )
        # Uncommenting this will save val generations
        # return self._write_logs(trainer, pl_module, "valid")







def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase=1			,  __lowerCamelCase=False							)		->Optional[Any]:


 '''simple docstring'''
 if metric == "rouge2":
  snake_case :			Any							=   '''{val_avg_rouge2:.4f}-{step_count}'''
 elif metric == "bleu":
  snake_case :			Optional[Any]							=   '''{val_avg_bleu:.4f}-{step_count}'''
 elif metric == "loss":
  snake_case :			Optional[Any]							=   '''{val_avg_loss:.4f}-{step_count}'''
 else:
  raise NotImplementedError(
      f'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
      ''' this function.'''							)

 snake_case :			Optional[Any]							=   ModelCheckpoint(
     dirpath=__lowerCamelCase			,  filename=__lowerCamelCase			,  monitor=f'''val_{metric}'''			,  mode='''min''' if '''loss''' in metric else '''max'''			,  save_top_k=__lowerCamelCase			,  )
 return checkpoint_callback






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase							)		->int:


 '''simple docstring'''
 return EarlyStopping(
     monitor=f'''val_{metric}'''			,  mode='''min''' if '''loss''' in metric else '''max'''			,  patience=__lowerCamelCase			,  verbose=__lowerCamelCase			,  )










import functools
import operator

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


a					:				Dict       =      logging.get_logger(__name__)

a					:				str       =      {
    'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   """hubert"""

       def __init__(      self						,    a__=32						,    a__=7_68						,    a__=12						,    a__=12						,    a__=30_72						,    a__="gelu"						,    a__=0.1						,    a__=0.1						,    a__=0.1						,    a__=True						,    a__=0.0						,    a__=0.1						,    a__=0.1						,    a__=0.02						,    a__=1e-5						,    a__="group"						,    a__="gelu"						,    a__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)						,    a__=(5, 2, 2, 2, 2, 2, 2)						,    a__=(10, 3, 3, 3, 3, 2, 2)						,    a__=False						,    a__=1_28						,    a__=16						,    a__=False						,    a__=True						,    a__=0.05						,    a__=10						,    a__=2						,    a__=0.0						,    a__=10						,    a__=0						,    a__="sum"						,    a__=False						,    a__=False						,    a__=2_56						,    a__=0						,    a__=1						,    a__=2						,    **a__						,    ):






        '''simple docstring'''






        super().__init__(**a__						,    pad_token_id=a__						,    bos_token_id=a__						,    eos_token_id=a__       )
        snake_case :			List[str]							=   hidden_size
        snake_case :			List[str]							=   feat_extract_norm
        snake_case :			List[str]							=   feat_extract_activation
        snake_case :			Dict							=   list(a__       )
        snake_case :			Tuple							=   list(a__       )
        snake_case :			Dict							=   list(a__       )
        snake_case :			str							=   conv_bias
        snake_case :			Dict							=   num_conv_pos_embeddings
        snake_case :			Union[str, Any]							=   num_conv_pos_embedding_groups
        snake_case :			Optional[int]							=   len(self.conv_dim       )
        snake_case :			Tuple							=   num_hidden_layers
        snake_case :			Any							=   intermediate_size
        snake_case :			Optional[Any]							=   hidden_act
        snake_case :			str							=   num_attention_heads
        snake_case :			List[Any]							=   hidden_dropout
        snake_case :			Union[str, Any]							=   attention_dropout
        snake_case :			Dict							=   activation_dropout
        snake_case :			List[str]							=   feat_proj_layer_norm
        snake_case :			List[str]							=   feat_proj_dropout
        snake_case :			Union[str, Any]							=   final_dropout
        snake_case :			List[str]							=   layerdrop
        snake_case :			str							=   layer_norm_eps
        snake_case :			Tuple							=   initializer_range
        snake_case :			str							=   vocab_size
        snake_case :			Tuple							=   do_stable_layer_norm
        snake_case :			Dict							=   use_weighted_layer_sum
        snake_case :			Optional[Any]							=   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
        snake_case :			Optional[int]							=   apply_spec_augment
        snake_case :			int							=   mask_time_prob
        snake_case :			Dict							=   mask_time_length
        snake_case :			Any							=   mask_time_min_masks
        snake_case :			Any							=   mask_feature_prob
        snake_case :			Union[str, Any]							=   mask_feature_length
        snake_case :			List[Any]							=   mask_feature_min_masks

        # ctc loss
        snake_case :			List[str]							=   ctc_loss_reduction
        snake_case :			Dict							=   ctc_zero_infinity



       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






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






