


import argparse
from collections import defaultdict

import yaml


UpperCAmelCase_				:			Dict   =	'docs/source/en/_toctree.yml'







def 	SCREAMING_SNAKE_CASE_						(      __A			:      List[Any]    )     ->		Optional[Any]:

       """simple docstring"""

       a_ :		int							=  defaultdict(__A    )
       for doc in model_doc:
              counts[doc["local"]] += 1
       a_ :		Optional[int]							=  [key for key, value in counts.items() if value > 1]

       a_ :		List[str]							=  []
       for duplicate_key in duplicates:
              a_ :		str							=  list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}    )
              if len(__A    ) > 1:
                     raise ValueError(
                         F"""{duplicate_key} is present several times in the documentation table of content at """
                         '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                         'others.'    )
              # Only add this once
              new_doc.append({'local': duplicate_key, 'title': titles[0]}    )

       # Add none duplicate-keys
       new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]    )

       # Sort
       return sorted(__A  ,   key=lambda __A    : s["title"].lower()    )







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]=False    )     ->		List[Any]:

       """simple docstring"""

       with open(__A  ,   encoding='utf-8'    ) as f:
              a_ :		int							=  yaml.safe_load(f.read()    )

       # Get to the API doc
       a_ :		List[Any]							=  0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       a_ :		Optional[Any]							=  content[api_idx]['sections']

       # Then to the model doc
       a_ :		Union[str, Any]							=  0
       while api_doc[model_idx]["title"] != "Models":
              model_idx += 1

       a_ :		Any							=  api_doc[model_idx]['sections']

       a_ :		str							=  [(idx, section) for idx, section in enumerate(__A    ) if 'sections' in section]
       a_ :		Dict							=  False
       for idx, modality_doc in modalities_docs:
              a_ :		Tuple							=  modality_doc['sections']
              a_ :		Dict							=  clean_model_doc_toc(__A    )

              if old_modality_doc != new_modality_doc:
                     a_ :		str							=  True
                     if overwrite:
                            a_ :		Tuple							=  new_modality_doc

       if diff:
              if overwrite:
                     a_ :		str							=  model_doc
                     a_ :		Union[str, Any]							=  api_doc
                     with open(__A  ,   'w'  ,   encoding='utf-8'    ) as f:
                            f.write(yaml.dump(__A  ,   allow_unicode=__A    )    )
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'    )


if __name__ == "__main__":
   UpperCAmelCase_				:			str   =	argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   UpperCAmelCase_				:			List[str]   =	parser.parse_args()

   check_model_doc(args.fix_and_overwrite)






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 	SCREAMING_SNAKE_CASE_						(      __A			:      Any    )     ->		Optional[int]:

       """simple docstring"""

       a_ :		str							=  filter(lambda __A    : p.requires_grad  ,   model.parameters()    )
       a_ :		List[Any]							=  sum([np.prod(p.size()    ) for p in model_parameters]    )
       return params


UpperCAmelCase_				:			Dict   =	logging.getLogger(__name__)







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int  ,   __A			:      int    )     ->		List[str]:

       """simple docstring"""

       if metric == "rouge2":
              a_ :		Dict							=  '{val_avg_rouge2:.4f}-{step_count}'
       elif metric == "bleu":
              a_ :		Dict							=  '{val_avg_bleu:.4f}-{step_count}'
       elif metric == "em":
              a_ :		Union[str, Any]							=  '{val_avg_em:.4f}-{step_count}'
       elif metric == "loss":
              a_ :		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.'    )

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

       return checkpoint_callback







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int  ,   __A			:      List[Any]    )     ->		int:

       """simple docstring"""

       return EarlyStopping(
           monitor=F"""val_{metric}"""  ,   mode='min' if 'loss' in metric else 'max'  ,   patience=__A  ,   verbose=__A  ,   )
class 			SCREAMING_SNAKE_CASE__  (    pl.Callback      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	List[str]					)		->      Any:
             a_ :		int							=  {F"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
             pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE__					)
      @rank_zero_only
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	pl.Trainer					,							SCREAMING_SNAKE_CASE__    :	pl.LightningModule					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	List[str]=True					)		->      None:
             logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""					)
             a_ :		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
             a_ :		Optional[int]							=  Path(pl_module.hparams.output_dir					)
             if type_path == "test":
                    a_ :		Tuple							=  od / 'test_results.txt'
                    a_ :		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.
                    a_ :		str							=  od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
                    a_ :		Optional[Any]							=  od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
                    results_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__					)
                    generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__					)
             with open(SCREAMING_SNAKE_CASE__					,							'a+'					) as writer:
                    for key in sorted(SCREAMING_SNAKE_CASE__					):
                           if key in ["log", "progress_bar", "preds"]:
                                  continue
                           a_ :		int							=  metrics[key]
                           if isinstance(SCREAMING_SNAKE_CASE__					,							torch.Tensor					):
                                  a_ :		List[str]							=  val.item()
                           a_ :		int							=  F"""{key}: {val:.6f}\n"""
                           writer.write(SCREAMING_SNAKE_CASE__					)

             if not save_generations:
                    return

             if "preds" in metrics:
                    a_ :		Optional[Any]							=  '\n'.join(metrics['preds']					)
                    generations_file.open('w+'					).write(SCREAMING_SNAKE_CASE__					)
      @rank_zero_only
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      Dict:
             try:
                    a_ :		Any							=  pl_module.model.model.num_parameters()
             except AttributeError:
                    a_ :		List[str]							=  pl_module.model.num_parameters()

             a_ :		Any							=  count_trainable_parameters(SCREAMING_SNAKE_CASE__					)
             # 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]					,							SCREAMING_SNAKE_CASE__    :	pl.Trainer					,							SCREAMING_SNAKE_CASE__    :	pl.LightningModule					)		->      Optional[int]:
             save_json(pl_module.metrics					,							pl_module.metrics_save_path					)
             return self._write_logs(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							'test'					)

      @rank_zero_only
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	pl.Trainer					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					)		->      Optional[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")


