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  lowercase(  lowerCamelCase					)		->  str:







 '''simple docstring'''
 UpperCamelCase      =			filter(lambda lowerCamelCase					: p.requires_grad       ,				model.parameters()					)
 UpperCamelCase      =			sum([np.prod(p.size()					) for p in model_parameters]					)
 return params


_SCREAMING_SNAKE_CASE   		=			logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE_		(			pl.Callback		):







   def 	lowerCamelCase_  (    self	:	int      ,      lowerCamelCase_	:	int      ,      lowerCamelCase_	:	int							):





    """simple docstring"""


    UpperCamelCase      =			{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 	lowerCamelCase_  (    self	:	Optional[Any]      ,      lowerCamelCase_	:	pl.Trainer      ,      lowerCamelCase_	:	pl.LightningModule      ,      lowerCamelCase_	:	str      ,      lowerCamelCase_	:	Dict=True							):





    """simple docstring"""


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

    if not save_generations:
     return

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







   @rank_zero_only
   def 	lowerCamelCase_  (    self	:	str      ,      lowerCamelCase_	:	List[Any]      ,      lowerCamelCase_	:	Optional[int]							):





    """simple docstring"""


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

    UpperCamelCase      =			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 	lowerCamelCase_  (    self	:	List[Any]      ,      lowerCamelCase_	:	pl.Trainer      ,      lowerCamelCase_	:	pl.LightningModule							):





    """simple docstring"""


    save_json(pl_module.metrics      ,      pl_module.metrics_save_path							)
    return self._write_logs(lowerCamelCase_      ,      lowerCamelCase_      ,      """test"""							)







   @rank_zero_only
   def 	lowerCamelCase_  (    self	:	Optional[int]      ,      lowerCamelCase_	:	pl.Trainer      ,      lowerCamelCase_	:	List[Any]							):





    """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  lowercase(  lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase=1       ,				lowerCamelCase=False					)		->  Optional[Any]:







 '''simple docstring'''
 if metric == "rouge2":
  UpperCamelCase      =			"""{val_avg_rouge2:.4f}-{step_count}"""
 elif metric == "bleu":
  UpperCamelCase      =			"""{val_avg_bleu:.4f}-{step_count}"""
 elif metric == "loss":
  UpperCamelCase      =			"""{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."""					)

 UpperCamelCase      =			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  lowercase(  lowerCamelCase       ,				lowerCamelCase					)		->  Any:







 '''simple docstring'''
 return EarlyStopping(
     monitor=f"""val_{metric}"""       ,				mode="""min""" if """loss""" in metric else """max"""       ,				patience=UpperCamelCase_       ,				verbose=UpperCamelCase_       ,				)

from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE   		=			{
    """configuration_align""": [
        """ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AlignConfig""",
        """AlignTextConfig""",
        """AlignVisionConfig""",
    ],
    """processing_align""": ["""AlignProcessor"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE   		=			[
         """ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """AlignModel""",
         """AlignPreTrainedModel""",
         """AlignTextModel""",
         """AlignVisionModel""",
     ]

if TYPE_CHECKING:
     from .configuration_align import (
         ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
         AlignConfig,
         AlignTextConfig,
         AlignVisionConfig,
     )
     from .processing_align import AlignProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_align import (
              ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
              AlignModel,
              AlignPreTrainedModel,
              AlignTextModel,
              AlignVisionModel,
          )

else:
     import sys

     _SCREAMING_SNAKE_CASE   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
