code: 
  
  
  
  
  
  
  """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 	__lowerCamelCase			(						__UpperCamelCase )		->					str:
  
    """simple docstring"""
  
  
  
  
  
  
  
    lowerCAmelCase_     :							List[Any]             =    filter(lambda __UpperCamelCase : p.requires_grad   , model.parameters() )
    lowerCAmelCase_     :							Union[str, Any]             =    sum([np.prod(p.size() ) for p in model_parameters] )
    return params
  
  
  lowercase__      =				logging.getLogger(__name__)
  
  
  class 	__lowerCamelCase       (				pl.Callback    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def      lowerCamelCase (     self							:			str		,    a_							:			Tuple		,    a_							:			Tuple			):
      lowerCAmelCase_     :							List[str]             =    {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      lowerCamelCase (     self							:			Union[str, Any]		,    a_							:			pl.Trainer		,    a_							:			pl.LightningModule		,    a_							:			str		,    a_							:			List[Any]=True			):
      logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''			)
      lowerCAmelCase_     :							int             =    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_     :							Tuple             =    od / "test_results.txt"
        lowerCAmelCase_     :							List[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.
        lowerCAmelCase_     :							int             =    od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
        lowerCAmelCase_     :							int             =    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
          lowerCAmelCase_     :							Any             =    metrics[key]
          if isinstance(a_		,    torch.Tensor			):
            lowerCAmelCase_     :							Optional[int]             =    val.item()
          lowerCAmelCase_     :							Dict             =    f'''{key}: {val:.6f}\n'''
          writer.write(a_			)
  
      if not save_generations:
        return
  
      if "preds" in metrics:
        lowerCAmelCase_     :							Optional[int]             =    "\n".join(metrics["preds"]			)
        generations_file.open("w+"			).write(a_			)
  
  
  
  
  
    @rank_zero_only
    def      lowerCamelCase (     self							:			int		,    a_							:			List[str]		,    a_							:			int			):
      try:
        lowerCAmelCase_     :							List[Any]             =    pl_module.model.model.num_parameters()
      except AttributeError:
        lowerCAmelCase_     :							List[str]             =    pl_module.model.num_parameters()
  
      lowerCAmelCase_     :							Dict             =    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      lowerCamelCase (     self							:			List[str]		,    a_							:			pl.Trainer		,    a_							:			pl.LightningModule			):
      save_json(pl_module.metrics		,    pl_module.metrics_save_path			)
      return self._write_logs(a_		,    a_		,    "test"			)
  
  
  
  
  
    @rank_zero_only
    def      lowerCamelCase (     self							:			str		,    a_							:			pl.Trainer		,    a_							:			List[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 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase=1   , __UpperCamelCase=False )		->					Optional[int]:
  
    """simple docstring"""
  
  
  
  
  
  
  
    if metric == "rouge2":
      lowerCAmelCase_     :							Optional[int]             =    "{val_avg_rouge2:.4f}-{step_count}"
    elif metric == "bleu":
      lowerCAmelCase_     :							List[Any]             =    "{val_avg_bleu:.4f}-{step_count}"
    elif metric == "loss":
      lowerCAmelCase_     :							Tuple             =    "{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=__UpperCamelCase   , filename=__UpperCamelCase   , monitor=f'''val_{metric}'''   , mode="min" if "loss" in metric else "max"   , save_top_k=__UpperCamelCase   , )
    return checkpoint_callback
  
  
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase )		->					Union[str, Any]:
  
    """simple docstring"""
  
  
  
  
  
  
  
    return EarlyStopping(
        monitor=f'''val_{metric}'''   , mode="min" if "loss" in metric else "max"   , patience=__UpperCamelCase   , verbose=__UpperCamelCase   , )
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowercase__      =				{
      """configuration_mobilevitv2""": [
          """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
          """MobileViTV2Config""",
          """MobileViTV2OnnxConfig""",
      ],
  }
  
  
  try:
  					if not is_torch_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					lowercase__      =				[
  					    """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
  					    """MobileViTV2ForImageClassification""",
  					    """MobileViTV2ForSemanticSegmentation""",
  					    """MobileViTV2Model""",
  					    """MobileViTV2PreTrainedModel""",
  					]
  
  if TYPE_CHECKING:
  					from .configuration_mobilevitva import (
  					    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
  					    MobileViTVaConfig,
  					    MobileViTVaOnnxConfig,
  					)
  
  					try:
  										if not is_torch_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_mobilevitva import (
  										    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    MobileViTVaForImageClassification,
  										    MobileViTVaForSemanticSegmentation,
  										    MobileViTVaModel,
  										    MobileViTVaPreTrainedModel,
  										)
  
  else:
  					import sys
  
  					lowercase__      =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  