code: 
  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				(     UpperCAmelCase      )->		str:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					SCREAMING_SNAKE_CASE_	      =       filter(lambda UpperCAmelCase      : p.requires_grad				,model.parameters()      )
  					SCREAMING_SNAKE_CASE_	      =       sum([np.prod(p.size()      ) for p in model_parameters]      )
  					return params
  
  
  A_														=      logging.getLogger(__name__)
  
  class 						snake_case			(       pl.Callback	):
  			'''simple docstring'''
  
  			def _lowercase (	self     :     Tuple							,							lowerCAmelCase_     :     Union[str, Any]							,							lowerCAmelCase_     :     Any					)					->					List[Any]:
  
  
  
  
  								"""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(UpperCamelCase__					)
  
  			@rank_zero_only
  			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     pl.Trainer							,							lowerCAmelCase_     :     pl.LightningModule							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     int=True					)					->					None:
  
  
  
  
  								"""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=UpperCamelCase__					)
  													generations_file.parent.mkdir(exist_ok=UpperCamelCase__					)
  								with open(UpperCamelCase__							,							'''a+'''					) as writer:
  													for key in sorted(UpperCamelCase__					):
  																		if key in ["log", "progress_bar", "preds"]:
  																							continue
  																		SCREAMING_SNAKE_CASE_	      =       metrics[key]
  																		if isinstance(UpperCamelCase__							,							torch.Tensor					):
  																							SCREAMING_SNAKE_CASE_	      =       val.item()
  																		SCREAMING_SNAKE_CASE_	      =       F'''{key}: {val:.6f}\n'''
  																		writer.write(UpperCamelCase__					)
  
  								if not save_generations:
  													return
  
  								if "preds" in metrics:
  													SCREAMING_SNAKE_CASE_	      =       '''\n'''.join(metrics['''preds''']					)
  													generations_file.open('''w+'''					).write(UpperCamelCase__					)
  
  			@rank_zero_only
  			def _lowercase (	self     :     List[Any]							,							lowerCAmelCase_     :     Union[str, Any]							,							lowerCAmelCase_     :     Union[str, Any]					)					->					int:
  
  
  
  
  								"""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(UpperCamelCase__					)
  								# 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     :     List[Any]							,							lowerCAmelCase_     :     pl.Trainer							,							lowerCAmelCase_     :     pl.LightningModule					)					->					Dict:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								save_json(pl_module.metrics							,							pl_module.metrics_save_path					)
  								return self._write_logs(UpperCamelCase__							,							UpperCamelCase__							,							'''test'''					)
  
  
  
  
  			@rank_zero_only
  			def _lowercase (	self     :     int							,							lowerCAmelCase_     :     pl.Trainer							,							lowerCAmelCase_     :     Any					)					->					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")
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase=1				,UpperCAmelCase=False      )->		int:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					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=SCREAMING_SNAKE_CASE__				,filename=SCREAMING_SNAKE_CASE__				,monitor=f'''val_{metric}'''				,mode='''min''' if '''loss''' in metric else '''max'''				,save_top_k=SCREAMING_SNAKE_CASE__				,)
  					return checkpoint_callback
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					return EarlyStopping(
  					    monitor=f'''val_{metric}'''				,mode='''min''' if '''loss''' in metric else '''max'''				,patience=SCREAMING_SNAKE_CASE__				,verbose=SCREAMING_SNAKE_CASE__				,)
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A_														=      {
      "configuration_switch_transformers": [
          "SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "SwitchTransformersConfig",
          "SwitchTransformersOnnxConfig",
      ]
  }
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      [
  		    "SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST",
  		    "SwitchTransformersEncoderModel",
  		    "SwitchTransformersForConditionalGeneration",
  		    "SwitchTransformersModel",
  		    "SwitchTransformersPreTrainedModel",
  		    "SwitchTransformersTop1Router",
  		    "SwitchTransformersSparseMLP",
  		]
  
  
  if TYPE_CHECKING:
  		from .configuration_switch_transformers import (
  		    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
  		    SwitchTransformersConfig,
  		    SwitchTransformersOnnxConfig,
  		)
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_switch_transformers import (
  				    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    SwitchTransformersEncoderModel,
  				    SwitchTransformersForConditionalGeneration,
  				    SwitchTransformersModel,
  				    SwitchTransformersPreTrainedModel,
  				    SwitchTransformersSparseMLP,
  				    SwitchTransformersTopaRouter,
  				)
  
  
  else:
  		import sys
  
  		A_														=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  