





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 A_	(		A__				)  ->		str:
       a__						:	Any =				filter(lambda A__				: p.requires_grad		,      model.parameters()				)
       a__						:	Union[str, Any] =				sum([np.prod(p.size()				) for p in model_parameters]				)
       return params


lowercase      :       Union[str, Any]									=  logging.getLogger(__name__)



def A_	(		A__		,      A__				)  ->		int:
       if metric == "rouge2":
              a__						:	Any =				'{val_avg_rouge2:.4f}-{step_count}'
       elif metric == "bleu":
              a__						:	Tuple =				'{val_avg_bleu:.4f}-{step_count}'
       elif metric == "em":
              a__						:	Union[str, Any] =				'{val_avg_em:.4f}-{step_count}'
       elif metric == "loss":
              a__						:	int =				'{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__						:	Tuple =				ModelCheckpoint(
           dirpath=A__		,      filename=A__		,      monitor=F'val_{metric}'		,      mode='max'		,      save_top_k=1		,      every_n_epochs=1		,      )

       return checkpoint_callback



def A_	(		A__		,      A__				)  ->		str:
       return EarlyStopping(
           monitor=F'val_{metric}'		,      mode='min' if 'loss' in metric else 'max'		,      patience=A__		,      verbose=A__		,      )





class  A__    (							pl.Callback	):




   """simple docstring"""



   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Any =				{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 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase=True)			->						None:







          '''simple docstring'''

          logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****')
          a__						:	Optional[Any] =				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__						:	Any =				Path(pl_module.hparams.output_dir)
          if type_path == "test":
                 a__						:	Dict =				od / 'test_results.txt'
                 a__						:	str =				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__						:	Any =				od / F'{type_path}_results/{trainer.global_step:05d}.txt'
                 a__						:	Union[str, Any] =				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
                        a__						:	Dict =				metrics[key]
                        if isinstance(lowercase					,  torch.Tensor):
                               a__						:	Optional[Any] =				val.item()
                        a__						:	Dict =				F'{key}: {val:.6f}\n'
                        writer.write(lowercase)

          if not save_generations:
                 return

          if "preds" in metrics:
                 a__						:	Tuple =				'\n'.join(metrics['preds'])
                 generations_file.open('w+').write(lowercase)

   @rank_zero_only
   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						int:







          '''simple docstring'''

          try:
                 a__						:	int =				pl_module.model.model.num_parameters()
          except AttributeError:
                 a__						:	str =				pl_module.model.num_parameters()

          a__						:	Union[str, 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 							__lowercase		(					self					,  lowercase					,  lowercase)			->						Any:







          '''simple docstring'''

          save_json(pl_module.metrics					,  pl_module.metrics_save_path)
          return self._write_logs(lowercase					,  lowercase					,  'test')

   @rank_zero_only
   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						Dict:







          '''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")









import argparse
from collections import defaultdict

import yaml


lowercase      :       int									=  """docs/source/en/_toctree.yml"""



def A_	(		A__				)  ->		Tuple:
       a__						:	int =				defaultdict(A__				)
       a__						:	List[str] =				[]
       a__						:	Any =				[]
       for doc in doc_list:
              if "local" in doc:
                     counts[doc["local"]] += 1

              if doc["title"].lower() == "overview":
                     overview_doc.append({'local': doc['local'], 'title': doc['title']}				)
              else:
                     new_doc_list.append(A__				)

       a__						:	int =				new_doc_list
       a__						:	List[str] =				[key for key, value in counts.items() if value > 1]

       a__						:	Tuple =				[]
       for duplicate_key in duplicates:
              a__						:	int =				list({doc['title'] for doc in doc_list 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 doc_list if 'local' not in counts or counts[doc['local']] == 1]				)
       a__						:	Any =				sorted(A__		,      key=lambda A__				: s["title"].lower()				)

       # "overview" gets special treatment and is always first
       if len(A__				) > 1:
              raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'				)

       overview_doc.extend(A__				)

       # Sort
       return overview_doc



def A_	(		A__=False				)  ->		str:
       with open(A__		,      encoding='utf-8'				) as f:
              a__						:	int =				yaml.safe_load(f.read()				)

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

       # Then to the model doc
       a__						:	List[str] =				0
       while api_doc[scheduler_idx]["title"] != "Schedulers":
              scheduler_idx += 1

       a__						:	Tuple =				api_doc[scheduler_idx]['sections']
       a__						:	Optional[int] =				clean_doc_toc(A__				)

       a__						:	int =				False
       if new_scheduler_doc != scheduler_doc:
              a__						:	int =				True
              if overwrite:
                     a__						:	int =				new_scheduler_doc

       if diff:
              if overwrite:
                     a__						:	Optional[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.'				)



def A_	(		A__=False				)  ->		Tuple:
       with open(A__		,      encoding='utf-8'				) as f:
              a__						:	Optional[Any] =				yaml.safe_load(f.read()				)

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

       # Then to the model doc
       a__						:	List[Any] =				0
       while api_doc[pipeline_idx]["title"] != "Pipelines":
              pipeline_idx += 1

       a__						:	Optional[int] =				False
       a__						:	Union[str, Any] =				api_doc[pipeline_idx]['sections']
       a__						:	List[str] =				[]

       # sort sub pipeline docs
       for pipeline_doc in pipeline_docs:
              if "section" in pipeline_doc:
                     a__						:	Any =				pipeline_doc['section']
                     a__						:	Dict =				clean_doc_toc(A__				)
                     if overwrite:
                            a__						:	Optional[Any] =				new_sub_pipeline_doc
              new_pipeline_docs.append(A__				)

       # sort overall pipeline doc
       a__						:	Optional[Any] =				clean_doc_toc(A__				)

       if new_pipeline_docs != pipeline_docs:
              a__						:	List[Any] =				True
              if overwrite:
                     a__						:	Union[str, Any] =				new_pipeline_docs

       if diff:
              if overwrite:
                     a__						:	Dict =				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__":
    lowercase      :       List[Any]									=  argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    lowercase      :       str									=  parser.parse_args()

    check_scheduler_doc(args.fix_and_overwrite)
    check_pipeline_doc(args.fix_and_overwrite)


