


"""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 import save_json





def     lowercase__	(  lowerCamelCase     :  Union[str, Any]				)					->     Union[str, Any]:
  lowerCAmelCase__    :    Optional[Any]								=					filter(lambda lowerCamelCase				: p.requires_grad				,	model.parameters()				)
  lowerCAmelCase__    :    Union[str, Any]								=					sum([np.prod(p.size()				) for p in model_parameters]				)
  return params


__UpperCAmelCase    				=       logging.getLogger(__name__)

class  lowercase_  ( pl.Callback						):







      def 				_lowerCAmelCase       (      self :							int		,  _lowercase :							Dict		,  _lowercase :							Optional[int]    ):
        lowerCAmelCase__    :    Optional[Any]								=					{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 				_lowerCAmelCase       (      self :							str		,  _lowercase :							pl.Trainer		,  _lowercase :							pl.LightningModule		,  _lowercase :							str		,  _lowercase :							List[str]=True    ):
        logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"    )
        lowerCAmelCase__    :    List[str]								=					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
        lowerCAmelCase__    :    List[str]								=					Path(pl_module.hparams.output_dir    )
        if type_path == "test":
          lowerCAmelCase__    :    Union[str, Any]								=					od / "test_results.txt"
          lowerCAmelCase__    :    str								=					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.
          lowerCAmelCase__    :    List[Any]								=					od / f"{type_path}_results/{trainer.global_step:05d}.txt"
          lowerCAmelCase__    :    List[str]								=					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
            lowerCAmelCase__    :    List[str]								=					metrics[key]
            if isinstance(_lowercase		,  torch.Tensor    ):
              lowerCAmelCase__    :    List[Any]								=					val.item()
            lowerCAmelCase__    :    List[Any]								=					f"{key}: {val:.6f}\n"
            writer.write(_lowercase    )

        if not save_generations:
          return

        if "preds" in metrics:
          lowerCAmelCase__    :    List[Any]								=					"\n".join(metrics["preds"]    )
          generations_file.open("w+"    ).write(_lowercase    )







      @rank_zero_only
      def 				_lowerCAmelCase       (      self :							Optional[int]		,  _lowercase :							Tuple		,  _lowercase :							Any    ):
        try:
          lowerCAmelCase__    :    Dict								=					pl_module.model.model.num_parameters()
        except AttributeError:
          lowerCAmelCase__    :    List[Any]								=					pl_module.model.num_parameters()

        lowerCAmelCase__    :    int								=					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 				_lowerCAmelCase       (      self :							Optional[int]		,  _lowercase :							pl.Trainer		,  _lowercase :							pl.LightningModule    ):
        save_json(pl_module.metrics		,  pl_module.metrics_save_path    )
        return self._write_logs(_lowercase		,  _lowercase		,  "test"    )







      @rank_zero_only
      def 				_lowerCAmelCase       (      self :							Tuple		,  _lowercase :							pl.Trainer		,  _lowercase :							Optional[Any]    ):
        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     lowercase__	(  lowerCamelCase     :  Union[str, Any]				,	lowerCamelCase     :  Tuple				,	lowerCamelCase     :  Tuple=1				,	lowerCamelCase     :  int=False				)					->     List[str]:
  if metric == "rouge2":
    lowerCAmelCase__    :    Any								=					"{val_avg_rouge2:.4f}-{step_count}"
  elif metric == "bleu":
    lowerCAmelCase__    :    int								=					"{val_avg_bleu:.4f}-{step_count}"
  elif metric == "loss":
    lowerCAmelCase__    :    Union[str, 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."				)

  lowerCAmelCase__    :    int								=					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     lowercase__	(  lowerCamelCase     :  List[str]				,	lowerCamelCase     :  str				)					->     Any:
  return EarlyStopping(
      monitor=F"val_{metric}"				,	mode="min" if "loss" in metric else "max"				,	patience=lowerCamelCase				,	verbose=lowerCamelCase				,	)




"""simple docstring"""





import functools
import operator

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


__UpperCAmelCase    				=      logging.get_logger(__name__)

__UpperCAmelCase    				=      {
    "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  lowercase_  ( a_						):
      __magic_name__     :							List[Any]          =				"""hubert"""







      def __init__(      self :							List[Any]		,  _lowercase :							Optional[int]=3_2		,  _lowercase :							Tuple=7_6_8		,  _lowercase :							List[Any]=1_2		,  _lowercase :							Dict=1_2		,  _lowercase :							Tuple=3_0_7_2		,  _lowercase :							str="gelu"		,  _lowercase :							Optional[int]=0.1		,  _lowercase :							int=0.1		,  _lowercase :							Tuple=0.1		,  _lowercase :							str=True		,  _lowercase :							Optional[Any]=0.0		,  _lowercase :							List[Any]=0.1		,  _lowercase :							Union[str, Any]=0.1		,  _lowercase :							Union[str, Any]=0.02		,  _lowercase :							List[Any]=1e-5		,  _lowercase :							List[Any]="group"		,  _lowercase :							Tuple="gelu"		,  _lowercase :							List[str]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)		,  _lowercase :							List[Any]=(5, 2, 2, 2, 2, 2, 2)		,  _lowercase :							Tuple=(1_0, 3, 3, 3, 3, 2, 2)		,  _lowercase :							Any=False		,  _lowercase :							str=1_2_8		,  _lowercase :							int=1_6		,  _lowercase :							str=False		,  _lowercase :							Union[str, Any]=True		,  _lowercase :							Optional[Any]=0.05		,  _lowercase :							List[Any]=1_0		,  _lowercase :							Dict=2		,  _lowercase :							Optional[int]=0.0		,  _lowercase :							List[str]=1_0		,  _lowercase :							List[str]=0		,  _lowercase :							Optional[Any]="sum"		,  _lowercase :							int=False		,  _lowercase :							Any=False		,  _lowercase :							Optional[Any]=2_5_6		,  _lowercase :							int=0		,  _lowercase :							Optional[int]=1		,  _lowercase :							List[Any]=2		,  **_lowercase :							Tuple		,  ):
        super().__init__(**_lowercase		,  pad_token_id=_lowercase		,  bos_token_id=_lowercase		,  eos_token_id=_lowercase    )
        lowerCAmelCase__    :    Optional[Any]								=					hidden_size
        lowerCAmelCase__    :    int								=					feat_extract_norm
        lowerCAmelCase__    :    Optional[Any]								=					feat_extract_activation
        lowerCAmelCase__    :    Optional[Any]								=					list(_lowercase    )
        lowerCAmelCase__    :    Union[str, Any]								=					list(_lowercase    )
        lowerCAmelCase__    :    Tuple								=					list(_lowercase    )
        lowerCAmelCase__    :    str								=					conv_bias
        lowerCAmelCase__    :    Optional[Any]								=					num_conv_pos_embeddings
        lowerCAmelCase__    :    List[str]								=					num_conv_pos_embedding_groups
        lowerCAmelCase__    :    List[Any]								=					len(self.conv_dim    )
        lowerCAmelCase__    :    Union[str, Any]								=					num_hidden_layers
        lowerCAmelCase__    :    str								=					intermediate_size
        lowerCAmelCase__    :    List[Any]								=					hidden_act
        lowerCAmelCase__    :    Optional[int]								=					num_attention_heads
        lowerCAmelCase__    :    Optional[int]								=					hidden_dropout
        lowerCAmelCase__    :    Dict								=					attention_dropout
        lowerCAmelCase__    :    Dict								=					activation_dropout
        lowerCAmelCase__    :    Optional[Any]								=					feat_proj_layer_norm
        lowerCAmelCase__    :    Union[str, Any]								=					feat_proj_dropout
        lowerCAmelCase__    :    Optional[Any]								=					final_dropout
        lowerCAmelCase__    :    Dict								=					layerdrop
        lowerCAmelCase__    :    Optional[int]								=					layer_norm_eps
        lowerCAmelCase__    :    str								=					initializer_range
        lowerCAmelCase__    :    int								=					vocab_size
        lowerCAmelCase__    :    int								=					do_stable_layer_norm
        lowerCAmelCase__    :    Union[str, Any]								=					use_weighted_layer_sum
        lowerCAmelCase__    :    Tuple								=					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
        lowerCAmelCase__    :    int								=					apply_spec_augment
        lowerCAmelCase__    :    Tuple								=					mask_time_prob
        lowerCAmelCase__    :    int								=					mask_time_length
        lowerCAmelCase__    :    Dict								=					mask_time_min_masks
        lowerCAmelCase__    :    Union[str, Any]								=					mask_feature_prob
        lowerCAmelCase__    :    Dict								=					mask_feature_length
        lowerCAmelCase__    :    Tuple								=					mask_feature_min_masks

        # ctc loss
        lowerCAmelCase__    :    Tuple								=					ctc_loss_reduction
        lowerCAmelCase__    :    List[str]								=					ctc_zero_infinity







      @property
      def 				_lowerCAmelCase       (      self :							Dict    ):
        return functools.reduce(operator.mul		,  self.conv_stride		,  1    )
