



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__    (					UpperCamelCase_				:       Tuple	)->  Tuple:
    A__		 =					filter(lambda UpperCamelCase_	: p.requires_grad ,       model.parameters()	)
    A__		 =					sum([np.prod(p.size()	) for p in model_parameters]	)
    return params


_lowercase						  =		logging.getLogger(__name__)



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

    return checkpoint_callback



def 		lowerCAmelCase__    (					UpperCamelCase_				:       str ,       UpperCamelCase_				:       Optional[int]	)->  str:
    return EarlyStopping(
        monitor=f"val_{metric}" ,       mode='''min''' if '''loss''' in metric else '''max''' ,       patience=UpperCamelCase_ ,       verbose=UpperCamelCase_ ,       )



class     _UpperCAmelCase			(       pl.Callback     ):




     def 		snake_case_		(				self ,						a__ ,						a__):
         A__		 =					{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 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__=True):
         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=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__		 =					metrics[key]
                 if isinstance(a__ ,						torch.Tensor):
                     A__		 =					val.item()
                 A__		 =					F"{key}: {val:.6f}\n"
                 writer.write(a__)

         if not save_generations:
             return

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




     @rank_zero_only
     def 		snake_case_		(				self ,						a__ ,						a__):
         try:
             A__		 =					pl_module.model.model.num_parameters()
         except AttributeError:
             A__		 =					pl_module.model.num_parameters()

         A__		 =					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 		snake_case_		(				self ,						a__ ,						a__):
         save_json(pl_module.metrics ,						pl_module.metrics_save_path)
         return self._write_logs(a__ ,						a__ ,						'''test''')




     @rank_zero_only
     def 		snake_case_		(				self ,						a__ ,						a__):
         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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_lowercase						  =		{
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''albert'''




     def __init__(				self ,						a__=3_0_0_0_0 ,						a__=1_2_8 ,						a__=4_0_9_6 ,						a__=1_2 ,						a__=1 ,						a__=6_4 ,						a__=1_6_3_8_4 ,						a__=1 ,						a__="gelu_new" ,						a__=0 ,						a__=0 ,						a__=5_1_2 ,						a__=2 ,						a__=0.0_2 ,						a__=1e-12 ,						a__=0.1 ,						a__="absolute" ,						a__=0 ,						a__=2 ,						a__=3 ,						**a__ ,						):
         super().__init__(pad_token_id=a__ ,						bos_token_id=a__ ,						eos_token_id=a__ ,						**a__)

         A__		 =					vocab_size
         A__		 =					embedding_size
         A__		 =					hidden_size
         A__		 =					num_hidden_layers
         A__		 =					num_hidden_groups
         A__		 =					num_attention_heads
         A__		 =					inner_group_num
         A__		 =					hidden_act
         A__		 =					intermediate_size
         A__		 =					hidden_dropout_prob
         A__		 =					attention_probs_dropout_prob
         A__		 =					max_position_embeddings
         A__		 =					type_vocab_size
         A__		 =					initializer_range
         A__		 =					layer_norm_eps
         A__		 =					classifier_dropout_prob
         A__		 =					position_embedding_type







class     _UpperCAmelCase			(       A__     ):




     @property
     def 		snake_case_		(				self):
         if self.task == "multiple-choice":
             A__		 =					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             A__		 =					{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
                 ('''token_type_ids''', dynamic_axis),
             ])
