'''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_rag import save_json




def 							lowerCamelCase			(   UpperCAmelCase__ :      Optional[int]				)    ->   Dict:
       lowercase_      :    List[str]       		= filter(lambda UpperCAmelCase__				: p.requires_grad   ,   model.parameters()				)
       lowercase_      :    Dict       		= sum([np.prod(p.size()				) for p in model_parameters]				)
       return params


_lowercase		:				Dict	       =		logging.getLogger(__name__)




def 							lowerCamelCase			(   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      Optional[Any]				)    ->   Union[str, Any]:
       if metric == "rouge2":
              lowercase_      :    Optional[int]       		= """{val_avg_rouge2:.4f}-{step_count}"""
       elif metric == "bleu":
              lowercase_      :    str       		= """{val_avg_bleu:.4f}-{step_count}"""
       elif metric == "em":
              lowercase_      :    List[Any]       		= """{val_avg_em:.4f}-{step_count}"""
       elif metric == "loss":
              lowercase_      :    List[str]       		= """{val_avg_loss:.4f}-{step_count}"""
       else:
              raise NotImplementedError(
                  F'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
                  """ function."""				)

       lowercase_      :    Dict       		= ModelCheckpoint(
           dirpath=UpperCAmelCase__   ,   filename=UpperCAmelCase__   ,   monitor=F'''val_{metric}'''   ,   mode="""max"""   ,   save_top_k=1   ,   every_n_epochs=1   ,   )

       return checkpoint_callback




def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      Optional[int]				)    ->   Tuple:
       return EarlyStopping(
           monitor=F'''val_{metric}'''   ,   mode="""min""" if """loss""" in metric else """max"""   ,   patience=UpperCAmelCase__   ,   verbose=UpperCAmelCase__   ,   )





class 					__magic_name__		(					pl.Callback):







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

          if not save_generations:
                 return

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







   @rank_zero_only
   def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]       ,    lowercase_       :						str       ,    lowercase_       :						List[str]					):
          try:
                 lowercase_      :    List[Any]       		= pl_module.model.model.num_parameters()
          except AttributeError:
                 lowercase_      :    List[Any]       		= pl_module.model.num_parameters()

          lowercase_      :    Optional[Any]       		= 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 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    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 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						pl.Trainer       ,    lowercase_       :						int					):
          save_json(pl_module.metrics       ,    pl_module.metrics_save_path					)
          # Uncommenting this will save val generations
          # return self._write_logs(trainer, pl_module, "valid")






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowercase		:				Optional[Any]	       =		{
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Any	       =		[
						    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SwiftFormerForImageClassification",
						    "SwiftFormerModel",
						    "SwiftFormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

						_lowercase		:				Optional[int]	       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





