





'''simple docstring'''

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_rag import save_json




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	List[Any]					     =     filter(lambda lowerCAmelCase_	: p.requires_grad			,      model.parameters()	)
      _snake_case		:	str					     =     sum([np.prod(p.size()	) for p in model_parameters]	)
      return params


UpperCAmelCase					:		Dict		=							logging.getLogger(__name__)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



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

      _snake_case		:	List[str]					     =     ModelCheckpoint(
          dirpath=lowerCAmelCase_			,      filename=lowerCAmelCase_			,      monitor=f'''val_{metric}'''			,      mode='''max'''			,      save_top_k=1			,      every_n_epochs=1			,      )

      return checkpoint_callback




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



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





class lowerCamelCase						(pl.Callback					):







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						List[str]:






        """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(lowercase__		)







  @rank_zero_only
  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__=True		)		->						None:






        """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		:	int					     =     Path(pl_module.hparams.output_dir		)
        if type_path == "test":
              _snake_case		:	str					     =     od / '''test_results.txt'''
              _snake_case		:	Union[str, Any]					     =     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		:	Optional[int]					     =     od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
              _snake_case		:	Optional[int]					     =     od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
              results_file.parent.mkdir(exist_ok=lowercase__		)
              generations_file.parent.mkdir(exist_ok=lowercase__		)
        with open(lowercase__     ,     '''a+'''		) as writer:
              for key in sorted(lowercase__		):
                    if key in ["log", "progress_bar", "preds"]:
                          continue
                    _snake_case		:	Optional[Any]					     =     metrics[key]
                    if isinstance(lowercase__     ,     torch.Tensor		):
                          _snake_case		:	Optional[int]					     =     val.item()
                    _snake_case		:	int					     =     F'''{key}: {val:.6f}\n'''
                    writer.write(lowercase__		)

        if not save_generations:
              return

        if "preds" in metrics:
              _snake_case		:	Dict					     =     '''\n'''.join(metrics['''preds''']		)
              generations_file.open('''w+'''		).write(lowercase__		)







  @rank_zero_only
  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						List[Any]:






        """simple docstring"""





        try:
              _snake_case		:	Dict					     =     pl_module.model.model.num_parameters()
        except AttributeError:
              _snake_case		:	str					     =     pl_module.model.num_parameters()

        _snake_case		:	Any					     =     count_trainable_parameters(lowercase__		)
        # 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     ,     lowercase__     ,     lowercase__		)		->						Optional[int]:






        """simple docstring"""





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







  @rank_zero_only
  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						List[str]:






        """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")









'''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		)


