


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       lowercase_		(		_A			:    List[Any]						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  filter(lambda _A						: p.requires_grad    ,					model.parameters()						)
      lowerCamelCase__		:				Union[str, Any]    =  sum([np.prod(p.size()						) for p in model_parameters]						)
      return params


A      :    List[str]		=					logging.getLogger(__name__)

def       lowercase_		(		_A			:    List[Any]    ,					_A			:    List[Any]						):

      """simple docstring"""




      if metric == "rouge2":
            lowerCamelCase__		:				str    =  "{val_avg_rouge2:.4f}-{step_count}"
      elif metric == "bleu":
            lowerCamelCase__		:				Any    =  "{val_avg_bleu:.4f}-{step_count}"
      elif metric == "em":
            lowerCamelCase__		:				Any    =  "{val_avg_em:.4f}-{step_count}"
      elif metric == "loss":
            lowerCamelCase__		:				Optional[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."						)

      lowerCamelCase__		:				Tuple    =  ModelCheckpoint(
          dirpath=_A    ,					filename=_A    ,					monitor=F"val_{metric}"    ,					mode="max"    ,					save_top_k=1    ,					every_n_epochs=1    ,					)

      return checkpoint_callback

def       lowercase_		(		_A			:    int    ,					_A			:    Optional[Any]						):

      """simple docstring"""




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







class 			_lowercase (							pl.Callback):


       """simple docstring"""




       def 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  {f"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups       )}
             pl_module.logger.log_metrics(__lowerCamelCase       )





       @rank_zero_only
       def 						lowerCAmelCase     (   self							:			int					,      __lowerCamelCase							:			pl.Trainer					,      __lowerCamelCase							:			pl.LightningModule					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			Union[str, Any]=True       ):


             '''simple docstring'''



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

             if not save_generations:
                   return

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





       @rank_zero_only
       def 						lowerCAmelCase     (   self							:			List[str]					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			Union[str, Any]       ):


             '''simple docstring'''



             try:
                   lowerCamelCase__		:				Dict    =  pl_module.model.model.num_parameters()
             except AttributeError:
                   lowerCamelCase__		:				Dict    =  pl_module.model.num_parameters()

             lowerCamelCase__		:				Union[str, Any]    =  count_trainable_parameters(__lowerCamelCase       )
             # 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 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			pl.Trainer					,      __lowerCamelCase							:			pl.LightningModule       ):


             '''simple docstring'''



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





       @rank_zero_only
       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			pl.Trainer					,      __lowerCamelCase							:			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")






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


A      :    str		=					logging.get_logger(__name__)

A      :    int		=					{
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}







class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			"mgp-str"





       def __init__(   self							:			List[str]					,      __lowerCamelCase							:			List[Any]=[32, 128]					,      __lowerCamelCase							:			Optional[int]=4					,      __lowerCamelCase							:			str=3					,      __lowerCamelCase							:			Optional[Any]=27					,      __lowerCamelCase							:			List[str]=38					,      __lowerCamelCase							:			Dict=50257					,      __lowerCamelCase							:			List[Any]=30522					,      __lowerCamelCase							:			Optional[Any]=768					,      __lowerCamelCase							:			str=12					,      __lowerCamelCase							:			Optional[int]=12					,      __lowerCamelCase							:			List[str]=4.0					,      __lowerCamelCase							:			Union[str, Any]=True					,      __lowerCamelCase							:			Any=False					,      __lowerCamelCase							:			Dict=1E-5					,      __lowerCamelCase							:			int=0.0					,      __lowerCamelCase							:			Optional[int]=0.0					,      __lowerCamelCase							:			List[Any]=0.0					,      __lowerCamelCase							:			Union[str, Any]=False					,      __lowerCamelCase							:			str=0.0_2					,      **__lowerCamelCase							:			Dict					,      ):


             '''simple docstring'''



             super().__init__(**__lowerCamelCase       )

             lowerCamelCase__		:				int    =  image_size
             lowerCamelCase__		:				Union[str, Any]    =  patch_size
             lowerCamelCase__		:				Dict    =  num_channels
             lowerCamelCase__		:				Union[str, Any]    =  max_token_length
             lowerCamelCase__		:				Optional[int]    =  num_character_labels
             lowerCamelCase__		:				Union[str, Any]    =  num_bpe_labels
             lowerCamelCase__		:				Optional[int]    =  num_wordpiece_labels
             lowerCamelCase__		:				List[str]    =  hidden_size
             lowerCamelCase__		:				Dict    =  num_hidden_layers
             lowerCamelCase__		:				Any    =  num_attention_heads
             lowerCamelCase__		:				Any    =  mlp_ratio
             lowerCamelCase__		:				List[Any]    =  distilled
             lowerCamelCase__		:				Optional[Any]    =  layer_norm_eps
             lowerCamelCase__		:				Union[str, Any]    =  drop_rate
             lowerCamelCase__		:				List[Any]    =  qkv_bias
             lowerCamelCase__		:				int    =  attn_drop_rate
             lowerCamelCase__		:				List[Any]    =  drop_path_rate
             lowerCamelCase__		:				List[str]    =  output_aa_attentions
             lowerCamelCase__		:				Dict    =  initializer_range


