




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


_UpperCAmelCase						:    int      =  logging.get_logger(__name__)

_UpperCAmelCase						:    Union[str, Any]      =  {
    "ZinengTang/tvlt-base": "https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json",
}




class  __lowerCAmelCase							(				lowerCAmelCase):
       _a			       =    '''tvlt'''






       def __init__( self:       Any							,       _lowerCAmelCase:       Dict=2_24							,       _lowerCAmelCase:       int=20_48							,       _lowerCAmelCase:       str=1_28							,       _lowerCAmelCase:       str=[16, 16]							,       _lowerCAmelCase:       Tuple=[16, 16]							,       _lowerCAmelCase:       Any=3							,       _lowerCAmelCase:       Any=1							,       _lowerCAmelCase:       Union[str, Any]=8							,       _lowerCAmelCase:       Optional[Any]=7_68							,       _lowerCAmelCase:       List[Any]=12							,       _lowerCAmelCase:       Dict=12							,       _lowerCAmelCase:       Optional[int]=30_72							,       _lowerCAmelCase:       Union[str, Any]="gelu"							,       _lowerCAmelCase:       List[Any]=0.0							,       _lowerCAmelCase:       Tuple=0.0							,       _lowerCAmelCase:       Union[str, Any]=0.02							,       _lowerCAmelCase:       str=1e-6							,       _lowerCAmelCase:       Optional[Any]=True							,       _lowerCAmelCase:       Any=False							,       _lowerCAmelCase:       Optional[Any]=16							,       _lowerCAmelCase:       Dict=5_12							,       _lowerCAmelCase:       int=8							,       _lowerCAmelCase:       Union[str, Any]=20_48							,       _lowerCAmelCase:       Tuple=0.75							,       _lowerCAmelCase:       Dict=0.15							,       _lowerCAmelCase:       Tuple="frame-level"							,       _lowerCAmelCase:       Union[str, Any]=True							,       _lowerCAmelCase:       Optional[int]=True							,       _lowerCAmelCase:       Any="classification"							,       **_lowerCAmelCase:       List[Any]							,       ):
         super().__init__(**_lowerCAmelCase					)

         if audio_mask_type not in ("frame-level", "patch_level"):
           raise ValueError(
               "audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
               F"got {audio_mask_type}"					)

         lowercase     :Dict   						=    image_size
         lowercase     :str   						=    spectrogram_length
         lowercase     :str   						=    frequency_length
         lowercase     :Tuple   						=    image_patch_size
         lowercase     :Dict   						=    audio_patch_size
         lowercase     :Optional[Any]   						=    num_image_channels
         lowercase     :Optional[int]   						=    num_audio_channels
         lowercase     :Tuple   						=    num_frames

         lowercase     :str   						=    hidden_size
         lowercase     :Dict   						=    num_hidden_layers
         lowercase     :Optional[int]   						=    num_attention_heads
         lowercase     :List[Any]   						=    intermediate_size
         lowercase     :Tuple   						=    hidden_act
         lowercase     :Union[str, Any]   						=    hidden_dropout_prob
         lowercase     :Tuple   						=    attention_probs_dropout_prob
         lowercase     :Tuple   						=    initializer_range
         lowercase     :Union[str, Any]   						=    layer_norm_eps
         lowercase     :Dict   						=    qkv_bias
         lowercase     :List[str]   						=    use_mean_pooling

         lowercase     :int   						=    decoder_num_attention_heads
         lowercase     :Optional[int]   						=    decoder_hidden_size
         lowercase     :Tuple   						=    decoder_num_hidden_layers
         lowercase     :Any   						=    decoder_intermediate_size
         lowercase     :Any   						=    pixel_mask_ratio
         lowercase     :Any   						=    audio_mask_ratio
         lowercase     :Any   						=    audio_mask_type

         lowercase     :str   						=    task_matching
         lowercase     :Optional[int]   						=    task_mae
         lowercase     :Dict   						=    loss_type









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 ):
  lowercase     :Optional[Any]   						=    filter(lambda lowerCamelCase : p.requires_grad,      model.parameters() )
  lowercase     :Optional[int]   						=    sum([np.prod(p.size() ) for p in model_parameters] )
  return params


_UpperCAmelCase						:    Optional[Any]      =  logging.getLogger(__name__)




class  __lowerCAmelCase							(				pl.Callback):






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       List[Any]							,       _lowerCAmelCase:       Union[str, Any]					):
         lowercase     :Dict   						=    {F"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
         pl_module.logger.log_metrics(_lowerCAmelCase					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Optional[Any]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       pl.LightningModule							,       _lowerCAmelCase:       str							,       _lowerCAmelCase:       List[str]=True					):
         logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"					)
         lowercase     :Tuple   						=    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
         lowercase     :Optional[int]   						=    Path(pl_module.hparams.output_dir					)
         if type_path == "test":
           lowercase     :List[Any]   						=    od / "test_results.txt"
           lowercase     :Optional[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.
           lowercase     :int   						=    od / F"{type_path}_results/{trainer.global_step:05d}.txt"
           lowercase     :Tuple   						=    od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
           results_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
           generations_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
         with open(_lowerCAmelCase							,       "a+"					) as writer:
           for key in sorted(_lowerCAmelCase					):
             if key in ["log", "progress_bar", "preds"]:
               continue
             lowercase     :int   						=    metrics[key]
             if isinstance(_lowerCAmelCase							,       torch.Tensor					):
               lowercase     :int   						=    val.item()
             lowercase     :Optional[Any]   						=    F"{key}: {val:.6f}\n"
             writer.write(_lowerCAmelCase					)

         if not save_generations:
           return

         if "preds" in metrics:
           lowercase     :str   						=    "\n".join(metrics["preds"]					)
           generations_file.open("w+"					).write(_lowerCAmelCase					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]							,       _lowerCAmelCase:       Tuple							,       _lowerCAmelCase:       Tuple					):
         try:
           lowercase     :Optional[int]   						=    pl_module.model.model.num_parameters()
         except AttributeError:
           lowercase     :Dict   						=    pl_module.model.num_parameters()

         lowercase     :Any   						=    count_trainable_parameters(_lowerCAmelCase					)
         # 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        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       pl.LightningModule					):
         save_json(pl_module.metrics							,       pl_module.metrics_save_path					)
         return self._write_logs(_lowerCAmelCase							,       _lowerCAmelCase							,       "test"					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       List[str]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       Dict					):
         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 ):
  if metric == "rouge2":
    lowercase     :str   						=    "{val_avg_rouge2:.4f}-{step_count}"
  elif metric == "bleu":
    lowercase     :int   						=    "{val_avg_bleu:.4f}-{step_count}"
  elif metric == "loss":
    lowercase     :List[str]   						=    "{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." )

  lowercase     :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    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase ):
  return EarlyStopping(
      monitor=F"val_{metric}",      mode="min" if "loss" in metric else "max",      patience=lowerCamelCase,      verbose=lowerCamelCase,      )



