




"""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 import save_json







def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	List[Any]:
 A__ =       filter(lambda lowercase_		: p.requires_grad    ,				model.parameters()		)
 A__ =       sum([np.prod(p.size()		) for p in model_parameters]		)
 return params


SCREAMING_SNAKE_CASE       = logging.getLogger(__name__)




class 			UpperCAmelCase_						(		pl.Callback							):




    def 							__magic_name__							(					self		: List[str]			,				snake_case_		: Optional[Any]			,				snake_case_		: Tuple   )   ->							Optional[int]:




     '''simple docstring'''

     A__ =       {F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
     pl_module.logger.log_metrics(snake_case_   )




    @rank_zero_only
    def 							__magic_name__							(					self		: Dict			,				snake_case_		: pl.Trainer			,				snake_case_		: pl.LightningModule			,				snake_case_		: str			,				snake_case_		: List[str]=True   )   ->							None:




     '''simple docstring'''

     logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""   )
     A__ =       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__ =       Path(pl_module.hparams.output_dir   )
     if type_path == "test":
      A__ =       od / "test_results.txt"
      A__ =       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__ =       od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
      A__ =       od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
      results_file.parent.mkdir(exist_ok=snake_case_   )
      generations_file.parent.mkdir(exist_ok=snake_case_   )
     with open(snake_case_			,				"a+"   ) as writer:
      for key in sorted(snake_case_   ):
       if key in ["log", "progress_bar", "preds"]:
        continue
       A__ =       metrics[key]
       if isinstance(snake_case_			,				torch.Tensor   ):
        A__ =       val.item()
       A__ =       F"""{key}: {val:.6f}\n"""
       writer.write(snake_case_   )

     if not save_generations:
      return

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




    @rank_zero_only
    def 							__magic_name__							(					self		: str			,				snake_case_		: Tuple			,				snake_case_		: Dict   )   ->							Any:




     '''simple docstring'''

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

     A__ =       count_trainable_parameters(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 							__magic_name__							(					self		: List[Any]			,				snake_case_		: pl.Trainer			,				snake_case_		: pl.LightningModule   )   ->							Union[str, Any]:




     '''simple docstring'''

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




    @rank_zero_only
    def 							__magic_name__							(					self		: Optional[int]			,				snake_case_		: pl.Trainer			,				snake_case_		: int   )   ->							Optional[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 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_=1    ,				lowercase_=False		)		->	Dict:
 if metric == "rouge2":
  A__ =       "{val_avg_rouge2:.4f}-{step_count}"
 elif metric == "bleu":
  A__ =       "{val_avg_bleu:.4f}-{step_count}"
 elif metric == "loss":
  A__ =       "{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."		)

 A__ =       ModelCheckpoint(
     dirpath=lowercase_    ,				filename=lowercase_    ,				monitor=f"""val_{metric}"""    ,				mode="min" if "loss" in metric else "max"    ,				save_top_k=lowercase_    ,				)
 return checkpoint_callback







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	Union[str, Any]:
 return EarlyStopping(
     monitor=f"""val_{metric}"""    ,				mode="min" if "loss" in metric else "max"    ,				patience=lowercase_    ,				verbose=lowercase_    ,				)











"""simple docstring"""



from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy




class 			UpperCAmelCase_						(		A_							):
    lowercase__		      =   '''MarkupLMFeatureExtractor'''
    lowercase__		      =   ('''MarkupLMTokenizer''', '''MarkupLMTokenizerFast''')
    lowercase__		      =   True




    def __call__(					self		: Dict			,				snake_case_		: Optional[int]=None			,				snake_case_		: Union[str, Any]=None			,				snake_case_		: Tuple=None			,				snake_case_		: str=None			,				snake_case_		: Optional[Any]=None			,				snake_case_		: bool = True			,				snake_case_		: Union[bool, str, PaddingStrategy] = False			,				snake_case_		: Union[bool, str, TruncationStrategy] = None			,				snake_case_		: Optional[int] = None			,				snake_case_		: int = 0			,				snake_case_		: Optional[int] = None			,				snake_case_		: Optional[bool] = None			,				snake_case_		: Optional[bool] = None			,				snake_case_		: bool = False			,				snake_case_		: bool = False			,				snake_case_		: bool = False			,				snake_case_		: bool = False			,				snake_case_		: bool = True			,				snake_case_		: Optional[Union[str, TensorType]] = None			,				**snake_case_		: Dict			,				)   ->							BatchEncoding:




     '''simple docstring'''

     if self.parse_html:
      if html_strings is None:
       raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`"   )

      if nodes is not None or xpaths is not None or node_labels is not None:
       raise ValueError(
           "Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"   )

      A__ =       self.feature_extractor(snake_case_   )
      A__ =       features["nodes"]
      A__ =       features["xpaths"]
     else:
      if html_strings is not None:
       raise ValueError("You have passed HTML strings but `parse_html` is set to `False`."   )
      if nodes is None or xpaths is None:
       raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"   )

        # # second, apply the tokenizer
     if questions is not None and self.parse_html:
      if isinstance(snake_case_			,				snake_case_   ):
       A__ =       [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

     A__ =       self.tokenizer(
         text=questions if questions is not None else nodes			,				text_pair=nodes if questions is not None else None			,				xpaths=snake_case_			,				node_labels=snake_case_			,				add_special_tokens=snake_case_			,				padding=snake_case_			,				truncation=snake_case_			,				max_length=snake_case_			,				stride=snake_case_			,				pad_to_multiple_of=snake_case_			,				return_token_type_ids=snake_case_			,				return_attention_mask=snake_case_			,				return_overflowing_tokens=snake_case_			,				return_special_tokens_mask=snake_case_			,				return_offsets_mapping=snake_case_			,				return_length=snake_case_			,				verbose=snake_case_			,				return_tensors=snake_case_			,				**snake_case_			,				)

     return encoded_inputs




    def 							__magic_name__							(					self		: Optional[Any]			,				*snake_case_		: Any			,				**snake_case_		: Any   )   ->							int:




     '''simple docstring'''

     return self.tokenizer.batch_decode(*snake_case_			,				**snake_case_   )




    def 							__magic_name__							(					self		: Tuple			,				*snake_case_		: Optional[Any]			,				**snake_case_		: List[Any]   )   ->							Any:




     '''simple docstring'''

     return self.tokenizer.decode(*snake_case_			,				**snake_case_   )




    @property
    def 							__magic_name__							(					self		: List[Any]   )   ->							Optional[int]:




     '''simple docstring'''

     A__ =       self.tokenizer.model_input_names
     return tokenizer_input_names





