"""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 					a__ (						UpperCAmelCase__    :     Optional[Any]     )->		Optional[int]:
						UpperCAmelCase__   		=						filter(lambda UpperCAmelCase__     : p.requires_grad						,     model.parameters()     )
						UpperCAmelCase__   		=						sum([np.prod(p.size()     ) for p in model_parameters]     )
						return params


UpperCamelCase_    :    Any											=						logging.getLogger(__name__)


class      _lowercase						(				pl.Callback					):

				def     A	(       self    :      Any    ,      _lowercase    :      Dict    ,      _lowercase    :      Dict       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						{f'lr_group_{i}': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups       )}
										pl_module.logger.log_metrics(_lowercase       )

				@rank_zero_only
				def     A	(       self    :      int    ,      _lowercase    :      pl.Trainer    ,      _lowercase    :      pl.LightningModule    ,      _lowercase    :      str    ,      _lowercase    :      List[str]=True       ):


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

										if not save_generations:
																return

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

				@rank_zero_only
				def     A	(       self    :      Tuple    ,      _lowercase    :      Dict    ,      _lowercase    :      str       ):


										'''simple docstring'''
										try:
																UpperCAmelCase__   		=						pl_module.model.model.num_parameters()
										except AttributeError:
																UpperCAmelCase__   		=						pl_module.model.num_parameters()

										UpperCAmelCase__   		=						count_trainable_parameters(_lowercase       )
										# 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[int]    ,      _lowercase    :      pl.Trainer    ,      _lowercase    :      pl.LightningModule       ):


										'''simple docstring'''
										save_json(pl_module.metrics    ,      pl_module.metrics_save_path       )
										return self._write_logs(_lowercase    ,      _lowercase    ,      """test"""       )

				@rank_zero_only
				def     A	(       self    :      int    ,      _lowercase    :      pl.Trainer    ,      _lowercase    :      List[str]       ):


										'''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 					a__ (						UpperCAmelCase__    :     List[Any]						,     UpperCAmelCase__    :     Any						,     UpperCAmelCase__    :     Dict=1						,     UpperCAmelCase__    :     str=False     )->		List[Any]:
						if metric == "rouge2":
												UpperCAmelCase__   		=						"""{val_avg_rouge2:.4f}-{step_count}"""
						elif metric == "bleu":
												UpperCAmelCase__   		=						"""{val_avg_bleu:.4f}-{step_count}"""
						elif metric == "loss":
												UpperCAmelCase__   		=						"""{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."""     )

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







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


"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_    :    Optional[Any]											=						logging.get_logger(__name__)

UpperCamelCase_    :    Optional[int]											=						{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}


class      _lowercase						(				__lowerCamelCase					):
				lowerCamelCase__									=    'rembert'

				def __init__(       self    :      List[Any]    ,      _lowercase    :      Tuple=2_5_0_3_0_0    ,      _lowercase    :      Tuple=1_1_5_2    ,      _lowercase    :      List[Any]=3_2    ,      _lowercase    :      Dict=1_8    ,      _lowercase    :      str=2_5_6    ,      _lowercase    :      Optional[Any]=1_6_6_4    ,      _lowercase    :      Union[str, Any]=4_6_0_8    ,      _lowercase    :      Optional[Any]="gelu"    ,      _lowercase    :      Optional[Any]=0.0    ,      _lowercase    :      Tuple=0.0    ,      _lowercase    :      Dict=0.1    ,      _lowercase    :      str=5_1_2    ,      _lowercase    :      Tuple=2    ,      _lowercase    :      int=0.02    ,      _lowercase    :      Tuple=1e-12    ,      _lowercase    :      Union[str, Any]=True    ,      _lowercase    :      Optional[int]=0    ,      _lowercase    :      Any=3_1_2    ,      _lowercase    :      Optional[Any]=3_1_3    ,      **_lowercase    :      str    ,      ):


										'''simple docstring'''
										super().__init__(pad_token_id=_lowercase    ,      bos_token_id=_lowercase    ,      eos_token_id=_lowercase    ,      **_lowercase       )

										UpperCAmelCase__   		=						vocab_size
										UpperCAmelCase__   		=						input_embedding_size
										UpperCAmelCase__   		=						output_embedding_size
										UpperCAmelCase__   		=						max_position_embeddings
										UpperCAmelCase__   		=						hidden_size
										UpperCAmelCase__   		=						num_hidden_layers
										UpperCAmelCase__   		=						num_attention_heads
										UpperCAmelCase__   		=						intermediate_size
										UpperCAmelCase__   		=						hidden_act
										UpperCAmelCase__   		=						hidden_dropout_prob
										UpperCAmelCase__   		=						attention_probs_dropout_prob
										UpperCAmelCase__   		=						classifier_dropout_prob
										UpperCAmelCase__   		=						initializer_range
										UpperCAmelCase__   		=						type_vocab_size
										UpperCAmelCase__   		=						layer_norm_eps
										UpperCAmelCase__   		=						use_cache
										UpperCAmelCase__   		=						False


class      _lowercase						(				__lowerCamelCase					):

				@property
				def     A	(       self    :      str       ):


										'''simple docstring'''
										if self.task == "multiple-choice":
																UpperCAmelCase__   		=						{0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
																UpperCAmelCase__   		=						{0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										        ("""token_type_ids""", dynamic_axis),
										    ]       )

				@property
				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										return 1e-4

