



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    _snake_case (       __snake_case    )     ->     Union[str, Any]:






      '''simple docstring'''







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


__lowerCamelCase  =			logging.getLogger(__name__)







def    _snake_case (       __snake_case					,  __snake_case    )     ->     Any:






      '''simple docstring'''







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

      UpperCAmelCase_  :	str				      =		ModelCheckpoint(
          dirpath=__snake_case					,  filename=__snake_case					,  monitor=F"""val_{metric}"""					,  mode="max"					,  save_top_k=1					,  every_n_epochs=1					,  )

      return checkpoint_callback







def    _snake_case (       __snake_case					,  __snake_case    )     ->     Dict:






      '''simple docstring'''







      return EarlyStopping(
          monitor=F"""val_{metric}"""					,  mode="min" if "loss" in metric else "max"					,  patience=__snake_case					,  verbose=__snake_case					,  )





class   snake_case_ (pl.Callback  ):




       """simple docstring"""






       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	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     ,lowercase     ,lowercase     ,lowercase     ,lowercase=True):

             """simple docstring"""





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

             if not save_generations:
                   return

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



       @rank_zero_only
       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             try:
                   UpperCAmelCase_  :	List[Any]				      =		pl_module.model.model.num_parameters()
             except AttributeError:
                   UpperCAmelCase_  :	Dict				      =		pl_module.model.num_parameters()

             UpperCAmelCase_  :	Union[str, Any]				      =		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     ,lowercase     ,lowercase):

             """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     ,lowercase     ,lowercase):

             """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")





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""trocr"""
       _lowerCamelCase			    =				["""past_key_values"""]
       _lowerCamelCase			    =				{
           """num_attention_heads""": """decoder_attention_heads""",
           """hidden_size""": """d_model""",
           """num_hidden_layers""": """decoder_layers""",
       }



       def __init__(     self     ,lowercase=50265     ,lowercase=1024     ,lowercase=12     ,lowercase=16     ,lowercase=4096     ,lowercase="gelu"     ,lowercase=512     ,lowercase=0.1     ,lowercase=0.0     ,lowercase=0.0     ,lowercase=2     ,lowercase=0.02     ,lowercase=0.0     ,lowercase=True     ,lowercase=False     ,lowercase=True     ,lowercase=True     ,lowercase=1     ,lowercase=0     ,lowercase=2     ,**lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		vocab_size
             UpperCAmelCase_  :	Tuple				      =		d_model
             UpperCAmelCase_  :	List[str]				      =		decoder_layers
             UpperCAmelCase_  :	Union[str, Any]				      =		decoder_attention_heads
             UpperCAmelCase_  :	Optional[Any]				      =		decoder_ffn_dim
             UpperCAmelCase_  :	Tuple				      =		activation_function
             UpperCAmelCase_  :	List[Any]				      =		max_position_embeddings
             UpperCAmelCase_  :	List[Any]				      =		dropout
             UpperCAmelCase_  :	List[str]				      =		attention_dropout
             UpperCAmelCase_  :	Union[str, Any]				      =		activation_dropout
             UpperCAmelCase_  :	Optional[int]				      =		init_std
             UpperCAmelCase_  :	Dict				      =		decoder_layerdrop
             UpperCAmelCase_  :	List[str]				      =		use_cache
             UpperCAmelCase_  :	Dict				      =		scale_embedding
             UpperCAmelCase_  :	Dict				      =		use_learned_position_embeddings
             UpperCAmelCase_  :	Optional[Any]				      =		layernorm_embedding

             super().__init__(
                 pad_token_id=lowercase     ,bos_token_id=lowercase     ,eos_token_id=lowercase     ,decoder_start_token_id=lowercase     ,**lowercase     ,)
