






"""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_rag import save_json

def 				snake_case      (   A__						):
			UpperCAmelCase_     : Dict			     =					filter(lambda A__						: p.requires_grad	,model.parameters()						)
			UpperCAmelCase_     : Dict			     =					sum([np.prod(p.size()						) for p in model_parameters]						)
			return params


lowerCamelCase_					=      logging.getLogger(__name__)

def 				snake_case      (   A__	,A__						):


			if metric == "rouge2":
						UpperCAmelCase_     : Dict			     =					"{val_avg_rouge2:.4f}-{step_count}"
			elif metric == "bleu":
						UpperCAmelCase_     : Tuple			     =					"{val_avg_bleu:.4f}-{step_count}"
			elif metric == "em":
						UpperCAmelCase_     : Optional[Any]			     =					"{val_avg_em:.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_     : List[Any]			     =					ModelCheckpoint(
			    dirpath=__A	,filename=__A	,monitor=F"""val_{metric}"""	,mode="max"	,save_top_k=3	,every_n_epochs=1	,)
			return checkpoint_callback

def 				snake_case      (   A__	,A__						):
			return EarlyStopping(
			    monitor=F"""val_{metric}"""	,mode="min" if "loss" in metric else "max"	,patience=__A	,verbose=__A	,)







class 			UpperCamelCase_       (pl.Callback     ):





	def      _SCREAMING_SNAKE_CASE      (      self					: int	,	lowerCAmelCase_					: Any	,	lowerCAmelCase_					: List[Any]						)						->			List[str]:
				UpperCAmelCase_     : Optional[Any]			     =					{f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups						)}
				pl_module.logger.log_metrics(__UpperCAmelCase						)





	@rank_zero_only
	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: pl.Trainer	,	lowerCAmelCase_					: pl.LightningModule	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: List[Any]=True						)						->			None:
				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_     : List[str]			     =					Path(pl_module.hparams.output_dir						)
				if type_path == "test":
							UpperCAmelCase_     : Tuple			     =					od / "test_results.txt"
							UpperCAmelCase_     : Optional[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_     : Dict			     =					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=__UpperCAmelCase						)
							generations_file.parent.mkdir(exist_ok=__UpperCAmelCase						)
				with open(__UpperCAmelCase	,	"a+"						) as writer:
							for key in sorted(__UpperCAmelCase						):
										if key in ["log", "progress_bar", "preds"]:
													continue
										UpperCAmelCase_     : Dict			     =					metrics[key]
										if isinstance(__UpperCAmelCase	,	torch.Tensor						):
													UpperCAmelCase_     : Tuple			     =					val.item()
										UpperCAmelCase_     : int			     =					f"""{key}: {val:.6f}\n"""
										writer.write(__UpperCAmelCase						)

				if not save_generations:
							return

				if "preds" in metrics:
							UpperCAmelCase_     : List[str]			     =					"\n".join(metrics["preds"]						)
							generations_file.open("w+"						).write(__UpperCAmelCase						)





	@rank_zero_only
	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: Any	,	lowerCAmelCase_					: Union[str, Any]						)						->			Optional[int]:



				try:
							UpperCAmelCase_     : int			     =					pl_module.model.model.num_parameters()
				except AttributeError:
							UpperCAmelCase_     : str			     =					pl_module.model.num_parameters()

				UpperCAmelCase_     : Union[str, Any]			     =					count_trainable_parameters(__UpperCAmelCase						)
				# 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      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: pl.Trainer	,	lowerCAmelCase_					: pl.LightningModule						)						->			Union[str, Any]:
				save_json(pl_module.metrics	,	pl_module.metrics_save_path						)
				return self._write_logs(__UpperCAmelCase	,	__UpperCAmelCase	,	"test"						)





	@rank_zero_only
	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: pl.Trainer	,	lowerCAmelCase_					: Union[str, Any]						)						->			int:
				save_json(pl_module.metrics	,	pl_module.metrics_save_path						)
				# Uncommenting this will save val generations
				# return self._write_logs(trainer, pl_module, "valid")




import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment





class      A							(				unittest.TestCase     ):


   def 			lowercase_      (self     :						Union[str, Any]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  inspect.getfile(accelerate.test_utils		)
         UpperCAmelCase__								=  os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_script.py"]		)
         UpperCAmelCase__								=  os.path.sep.join(
             mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_distributed_data_loop.py"]		)
         UpperCAmelCase__								=  os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_ops.py"]		)


   @require_multi_gpu
   def 			lowercase_      (self     :						List[str]		)						->				Any:
         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices."""		)
         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
         with patch_environment(omp_num_threads=1		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)


   @require_multi_gpu
   def 			lowercase_      (self     :						str		)						->				str:
         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices."""		)
         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
         print(f"""Command: {cmd}"""		)
         with patch_environment(omp_num_threads=1		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)


   @require_multi_gpu
   def 			lowercase_      (self     :						Tuple		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__		)]
         with patch_environment(omp_num_threads=1		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)





   @require_multi_gpu
   def 			lowercase_      (self     :						Dict		)						->				str:
         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""		)
         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
         with patch_environment(omp_num_threads=1       ,  cuda_visible_devices="0,1"		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)


if __name__ == "__main__":
      UpperCamelCase__											=					Accelerator()
      UpperCamelCase__											=					(accelerator.state.process_index + 2, 1_0)
      UpperCamelCase__											=					torch.randint(0, 1_0, shape).to(accelerator.device)

      UpperCamelCase__											=					''

      UpperCamelCase__											=					accelerator.pad_across_processes(tensor)
      if tensora.shape[0] != accelerator.state.num_processes + 1:
            error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
      if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
            error_msg += "Tensors have different values."
      if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
            error_msg += "Padding was not done with the right value (0)."

      UpperCamelCase__											=					accelerator.pad_across_processes(tensor, pad_first=True)
      if tensora.shape[0] != accelerator.state.num_processes + 1:
            error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
      UpperCamelCase__											=					accelerator.state.num_processes - accelerator.state.process_index - 1
      if not torch.equal(tensora[index:], tensor):
            error_msg += "Tensors have different values."
      if not torch.all(tensora[:index] == 0):
            error_msg += "Padding was not done with the right value (0)."

      # Raise error at the end to make sure we don't stop at the first failure.
      if len(error_msg) > 0:
            raise ValueError(error_msg)



