

import operator as op


UpperCAmelCase_							:					Union[str, Any]		 =   '''scaler.pt'''
UpperCAmelCase_							:					int		 =   '''pytorch_model'''
UpperCAmelCase_							:					Optional[Any]		 =   '''random_states'''
UpperCAmelCase_							:					Dict		 =   '''optimizer'''
UpperCAmelCase_							:					Dict		 =   '''scheduler'''
UpperCAmelCase_							:					int		 =   '''pytorch_model.bin'''
UpperCAmelCase_							:					List[Any]		 =   '''pytorch_model.bin.index.json'''
UpperCAmelCase_							:					Optional[Any]		 =   '''model.safetensors'''
UpperCAmelCase_							:					str		 =   '''model.safetensors.index.json'''
UpperCAmelCase_							:					Tuple		 =   '''1.10.2'''
UpperCAmelCase_							:					str		 =   '''py38'''
UpperCAmelCase_							:					List[str]		 =   '''4.17.0'''
UpperCAmelCase_							:					Any		 =   ['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
UpperCAmelCase_							:					List[Any]		 =   ['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
UpperCAmelCase_							:					Optional[int]		 =   ['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
UpperCAmelCase_							:					Tuple		 =   ['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
UpperCAmelCase_							:					Union[str, Any]		 =   ['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
UpperCAmelCase_							:					int		 =   '''2.0.1'''
UpperCAmelCase_							:					List[Any]		 =   ['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
UpperCAmelCase_							:					List[Any]		 =   ['''default''', '''reduce-overhead''', '''max-autotune''']

UpperCAmelCase_							:					Dict		 =   {'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
UpperCAmelCase_							:					Tuple		 =   [
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

UpperCAmelCase_							:					Tuple		 =   ['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
UpperCAmelCase_							:					Any		 =   ['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']










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 A    (							__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     filter(lambda __UpperCamelCase       : p.requires_grad			,						model.parameters()       )
			A__	  =     sum([np.prod(p.size()       ) for p in model_parameters]       )
			return params


SCREAMING_SNAKE_CASE__   			=   logging.getLogger(__name__)


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Dict:
			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 A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Any:
			return EarlyStopping(
			    monitor=f'''val_{metric}'''			,						mode='min' if 'loss' in metric else 'max'			,						patience=__UpperCamelCase			,						verbose=__UpperCamelCase			,						)






class   __lowerCAmelCase  (		pl.Callback   ):
					"""simple docstring"""






					def      _a				(  self       :							Dict					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							str       ):






								"""simple docstring"""


								A__	  =     {F'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups       )}
								pl_module.logger.log_metrics(_snake_case       )



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






								"""simple docstring"""


								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=_snake_case       )
											generations_file.parent.mkdir(exist_ok=_snake_case       )
								with open(_snake_case					,       'a+'       ) as writer:
											for key in sorted(_snake_case       ):
														if key in ["log", "progress_bar", "preds"]:
																	continue
														A__	  =     metrics[key]
														if isinstance(_snake_case					,       torch.Tensor       ):
																	A__	  =     val.item()
														A__	  =     F'''{key}: {val:.6f}\n'''
														writer.write(_snake_case       )

								if not save_generations:
											return

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



					@rank_zero_only
					def      _a				(  self       :							Dict					,       _snake_case       :							List[str]					,       _snake_case       :							List[Any]       ):






								"""simple docstring"""


								try:
											A__	  =     pl_module.model.model.num_parameters()
								except AttributeError:
											A__	  =     pl_module.model.num_parameters()

								A__	  =     count_trainable_parameters(_snake_case       )
								# 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       :							int					,       _snake_case       :							pl.Trainer					,       _snake_case       :							pl.LightningModule       ):






								"""simple docstring"""


								save_json(pl_module.metrics					,       pl_module.metrics_save_path       )
								return self._write_logs(_snake_case					,       _snake_case					,       'test'       )



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




