





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        __snake_case       (						lowerCAmelCase_  )							->	Optional[int]:
    SCREAMING_SNAKE_CASE__       							= filter(lambda lowerCAmelCase_  : p.requires_grad   ,       model.parameters()  )
    SCREAMING_SNAKE_CASE__       							= sum([np.prod(p.size()  ) for p in model_parameters]  )
    return params


_A				:							List[str]							= logging.getLogger(__name__)



class 				__snake_case  (     pl.Callback						):


    '''simple docstring'''







    def    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= {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    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							A_=True    ):





        '''simple docstring'''

        logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''    )
        SCREAMING_SNAKE_CASE__       							= 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
        SCREAMING_SNAKE_CASE__       							= Path(pl_module.hparams.output_dir    )
        if type_path == "test":
            SCREAMING_SNAKE_CASE__       							= od / '''test_results.txt'''
            SCREAMING_SNAKE_CASE__       							= 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.
            SCREAMING_SNAKE_CASE__       							= od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
            SCREAMING_SNAKE_CASE__       							= 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
                SCREAMING_SNAKE_CASE__       							= metrics[key]
                if isinstance(A_      ,							torch.Tensor    ):
                    SCREAMING_SNAKE_CASE__       							= val.item()
                SCREAMING_SNAKE_CASE__       							= f'''{key}: {val:.6f}\n'''
                writer.write(A_    )

        if not save_generations:
            return

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



    @rank_zero_only
    def    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

        try:
            SCREAMING_SNAKE_CASE__       							= pl_module.model.model.num_parameters()
        except AttributeError:
            SCREAMING_SNAKE_CASE__       							= pl_module.model.num_parameters()

        SCREAMING_SNAKE_CASE__       							= 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    lowercase_				(					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    lowercase_				(					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        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_=1   ,       lowerCAmelCase_=False  )							->	List[str]:
    if metric == "rouge2":
        SCREAMING_SNAKE_CASE__       							= '''{val_avg_rouge2:.4f}-{step_count}'''
    elif metric == "bleu":
        SCREAMING_SNAKE_CASE__       							= '''{val_avg_bleu:.4f}-{step_count}'''
    elif metric == "loss":
        SCREAMING_SNAKE_CASE__       							= '''{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.'''  )

    SCREAMING_SNAKE_CASE__       							= 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        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Dict:
    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				:							Any							= logging.get_logger(__name__)

_A				:							Any							= {
    """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 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    lowerCamelCase__						:		int							=  """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_    )
        SCREAMING_SNAKE_CASE__       							= hidden_size
        SCREAMING_SNAKE_CASE__       							= feat_extract_norm
        SCREAMING_SNAKE_CASE__       							= feat_extract_activation
        SCREAMING_SNAKE_CASE__       							= list(A_    )
        SCREAMING_SNAKE_CASE__       							= list(A_    )
        SCREAMING_SNAKE_CASE__       							= list(A_    )
        SCREAMING_SNAKE_CASE__       							= conv_bias
        SCREAMING_SNAKE_CASE__       							= num_conv_pos_embeddings
        SCREAMING_SNAKE_CASE__       							= num_conv_pos_embedding_groups
        SCREAMING_SNAKE_CASE__       							= len(self.conv_dim    )
        SCREAMING_SNAKE_CASE__       							= num_hidden_layers
        SCREAMING_SNAKE_CASE__       							= intermediate_size
        SCREAMING_SNAKE_CASE__       							= hidden_act
        SCREAMING_SNAKE_CASE__       							= num_attention_heads
        SCREAMING_SNAKE_CASE__       							= hidden_dropout
        SCREAMING_SNAKE_CASE__       							= attention_dropout
        SCREAMING_SNAKE_CASE__       							= activation_dropout
        SCREAMING_SNAKE_CASE__       							= feat_proj_layer_norm
        SCREAMING_SNAKE_CASE__       							= feat_proj_dropout
        SCREAMING_SNAKE_CASE__       							= final_dropout
        SCREAMING_SNAKE_CASE__       							= layerdrop
        SCREAMING_SNAKE_CASE__       							= layer_norm_eps
        SCREAMING_SNAKE_CASE__       							= initializer_range
        SCREAMING_SNAKE_CASE__       							= vocab_size
        SCREAMING_SNAKE_CASE__       							= do_stable_layer_norm
        SCREAMING_SNAKE_CASE__       							= use_weighted_layer_sum
        SCREAMING_SNAKE_CASE__       							= 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
        SCREAMING_SNAKE_CASE__       							= apply_spec_augment
        SCREAMING_SNAKE_CASE__       							= mask_time_prob
        SCREAMING_SNAKE_CASE__       							= mask_time_length
        SCREAMING_SNAKE_CASE__       							= mask_time_min_masks
        SCREAMING_SNAKE_CASE__       							= mask_feature_prob
        SCREAMING_SNAKE_CASE__       							= mask_feature_length
        SCREAMING_SNAKE_CASE__       							= mask_feature_min_masks

        # ctc loss
        SCREAMING_SNAKE_CASE__       							= ctc_loss_reduction
        SCREAMING_SNAKE_CASE__       							= ctc_zero_infinity




    @property
    def    lowercase_				(					self    ):





        '''simple docstring'''

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