


'''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					(      lowerCamelCase  :      Optional[Any]):
      A_						:  Tuple          =				filter(lambda lowerCamelCase: p.requires_grad							,						model.parameters())
      A_						:  List[str]          =				sum([np.prod(p.size()) for p in model_parameters])
      return params


__magic_name__			=      logging.getLogger(__name__)




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      str):
      if metric == "rouge2":
            A_						:  List[str]          =				"""{val_avg_rouge2:.4f}-{step_count}"""
      elif metric == "bleu":
            A_						:  List[str]          =				"""{val_avg_bleu:.4f}-{step_count}"""
      elif metric == "em":
            A_						:  Dict          =				"""{val_avg_em:.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_						:  Any          =				ModelCheckpoint(
          dirpath=lowerCamelCase							,						filename=lowerCamelCase							,						monitor=F'val_{metric}'							,						mode="""max"""							,						save_top_k=3							,						every_n_epochs=1							,						)
      return checkpoint_callback




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






class 	__lowerCAmelCase		(							pl.Callback    ):


  '''simple docstring'''







  def        _a							(							self :       str	,_a :       Optional[Any]	,_a :       Optional[Any] ):

        '''simple docstring'''




        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(_a )






  @rank_zero_only
  def        _a							(							self :       Dict	,_a :       pl.Trainer	,_a :       pl.LightningModule	,_a :       str	,_a :       Union[str, Any]=True ):

        '''simple docstring'''




        logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****' )
        A_						:  Dict          =				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_						:  str          =				Path(pl_module.hparams.output_dir )
        if type_path == "test":
              A_						:  Dict          =				od / """test_results.txt"""
              A_						:  Optional[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.
              A_						:  Union[str, Any]          =				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=_a )
              generations_file.parent.mkdir(exist_ok=_a )
        with open(_a	,"""a+""" ) as writer:
              for key in sorted(_a ):
                    if key in ["log", "progress_bar", "preds"]:
                          continue
                    A_						:  Optional[int]          =				metrics[key]
                    if isinstance(_a	,torch.Tensor ):
                          A_						:  Union[str, Any]          =				val.item()
                    A_						:  Dict          =				f'{key}: {val:.6f}\n'
                    writer.write(_a )

        if not save_generations:
              return

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






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

        '''simple docstring'''




        try:
              A_						:  Union[str, Any]          =				pl_module.model.model.num_parameters()
        except AttributeError:
              A_						:  Tuple          =				pl_module.model.num_parameters()

        A_						:  str          =				count_trainable_parameters(_a )
        # 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        _a							(							self :       Optional[Any]	,_a :       pl.Trainer	,_a :       pl.LightningModule ):

        '''simple docstring'''




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






  @rank_zero_only
  def        _a							(							self :       Union[str, Any]	,_a :       pl.Trainer	,_a :       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")




'''simple docstring'''


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""image_processor""", """tokenizer"""]
  a_  					=							"""ViltImageProcessor"""
  a_  					=							("""BertTokenizer""", """BertTokenizerFast""")






  def __init__(							self :       List[Any]	,_a :       Optional[Any]=None	,_a :       List[str]=None	,**_a :       Any ):

        '''simple docstring'''




        A_						:  Any          =				None
        if "feature_extractor" in kwargs:
              warnings.warn(
                  """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                  """ instead."""	,_a	,)
              A_						:  List[str]          =				kwargs.pop("""feature_extractor""" )

        A_						:  List[Any]          =				image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
              raise ValueError("""You need to specify an `image_processor`.""" )
        if tokenizer is None:
              raise ValueError("""You need to specify a `tokenizer`.""" )

        super().__init__(_a	,_a )
        A_						:  Optional[Any]          =				self.image_processor






  def __call__(							self :       Any	,_a :       Tuple	,_a :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,_a :       bool = True	,_a :       Union[bool, str, PaddingStrategy] = False	,_a :       Union[bool, str, TruncationStrategy] = None	,_a :       Optional[int] = None	,_a :       int = 0	,_a :       Optional[int] = None	,_a :       Optional[bool] = None	,_a :       Optional[bool] = None	,_a :       bool = False	,_a :       bool = False	,_a :       bool = False	,_a :       bool = False	,_a :       bool = True	,_a :       Optional[Union[str, TensorType]] = None	,**_a :       Tuple	,):

        '''simple docstring'''




        A_						:  int          =				self.tokenizer(
            text=_a	,add_special_tokens=_a	,padding=_a	,truncation=_a	,max_length=_a	,stride=_a	,pad_to_multiple_of=_a	,return_token_type_ids=_a	,return_attention_mask=_a	,return_overflowing_tokens=_a	,return_special_tokens_mask=_a	,return_offsets_mapping=_a	,return_length=_a	,verbose=_a	,return_tensors=_a	,**_a	,)
        # add pixel_values + pixel_mask
        A_						:  Optional[int]          =				self.image_processor(_a	,return_tensors=_a )
        encoding.update(_a )

        return encoding






  def        _a							(							self :       List[Any]	,*_a :       Any	,**_a :       Any ):

        '''simple docstring'''




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






  def        _a							(							self :       int	,*_a :       int	,**_a :       Optional[int] ):

        '''simple docstring'''




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






  @property
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  Optional[int]          =				self.tokenizer.model_input_names
        A_						:  str          =				self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""	,_a	,)
        return self.image_processor_class






  @property
  def        _a							(							self :       int ):

        '''simple docstring'''




        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""	,_a	,)
        return self.image_processor
