


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__			(      a__:		Optional[Any]	)  ->		Any:


							'''simple docstring'''





							_UpperCAmelCase					=      filter(lambda a__	: p.requires_grad      ,  model.parameters()	)
							_UpperCAmelCase					=      sum([np.prod(p.size()	) for p in model_parameters]	)
							return params


lowerCAmelCase__  :Any             =							logging.getLogger(__name__)

def 	lowerCAmelCase__			(      a__:		Tuple      ,  a__:		List[str]	)  ->		Union[str, Any]:


							'''simple docstring'''





							if metric == "rouge2":
														_UpperCAmelCase					=      '{val_avg_rouge2:.4f}-{step_count}'
							elif metric == "bleu":
														_UpperCAmelCase					=      '{val_avg_bleu:.4f}-{step_count}'
							elif metric == "em":
														_UpperCAmelCase					=      '{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					=      ModelCheckpoint(
							    dirpath=a__      ,  filename=a__      ,  monitor=F'''val_{metric}'''      ,  mode='max'      ,  save_top_k=3      ,  every_n_epochs=1      ,  )
							return checkpoint_callback

def 	lowerCAmelCase__			(      a__:		Any      ,  a__:		Dict	)  ->		Any:


							'''simple docstring'''





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







class 		__a				(    pl.Callback      ):
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""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(_SCREAMING_SNAKE_CASE						)
		@rank_zero_only
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=True						)			->	None:




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

									if not save_generations:
																return

									if "preds" in metrics:
																_UpperCAmelCase					=      '\n'.join(metrics['preds']						)
																generations_file.open('w+'						).write(_SCREAMING_SNAKE_CASE						)
		@rank_zero_only
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[str]:




									"""simple docstring"""





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

									_UpperCAmelCase					=      count_trainable_parameters(_SCREAMING_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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





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






		@rank_zero_only
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	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")




from importlib import import_module

from .logging import get_logger


lowerCAmelCase__  :Optional[Any]             =							get_logger(__name__)

class 		__a				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      attrs or []
									if module is not None:
																for key in module.__dict__:
																							if key in attrs or not key.startswith('__'						):
																														setattr(self      ,   _SCREAMING_SNAKE_CASE      ,   getattr(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)						)
									_UpperCAmelCase					=      module._original_module if isinstance(_SCREAMING_SNAKE_CASE      ,   _PatchedModuleObj						) else module



class 		__a				:
		_a     :  Any           =     []
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      obj
									_UpperCAmelCase					=      target
									_UpperCAmelCase					=      new
									_UpperCAmelCase					=      target.split('.'						)[0]
									_UpperCAmelCase					=      {}
									_UpperCAmelCase					=      attrs or []
		def __enter__(      self						)			->	int:




									"""simple docstring"""





									*_UpperCAmelCase		,	_UpperCAmelCase					=      self.target.split('.'						)

									# Patch modules:
									# it's used to patch attributes of submodules like "os.path.join";
									# in this case we need to patch "os" and "os.path"

									for i in range(len(_SCREAMING_SNAKE_CASE						)						):
																try:
																							_UpperCAmelCase					=      import_module('.'.join(submodules[: i + 1]						)						)
																except ModuleNotFoundError:
																							continue
																# We iterate over all the globals in self.obj in case we find "os" or "os.path"
																for attr in self.obj.__dir__():
																							_UpperCAmelCase					=      getattr(self.obj      ,   _SCREAMING_SNAKE_CASE						)
																							# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
																							# This allows to patch renamed modules like "from os import path as ospath".
																							if obj_attr is submodule or (
																							    (isinstance(_SCREAMING_SNAKE_CASE      ,   _PatchedModuleObj						) and obj_attr._original_module is submodule)
																							):
																														_UpperCAmelCase					=      obj_attr
																														# patch at top level
																														setattr(self.obj      ,   _SCREAMING_SNAKE_CASE      ,   _PatchedModuleObj(_SCREAMING_SNAKE_CASE      ,   attrs=self.attrs						)						)
																														_UpperCAmelCase					=      getattr(self.obj      ,   _SCREAMING_SNAKE_CASE						)
																														# construct lower levels patches
																														for key in submodules[i + 1 :]:
																																					setattr(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _PatchedModuleObj(getattr(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)      ,   attrs=self.attrs						)						)
																																					_UpperCAmelCase					=      getattr(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																														# finally set the target attribute
																														setattr(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   self.new						)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

									if submodules:  # if it's an attribute of a submodule like "os.path.join"
																try:
																							_UpperCAmelCase					=      getattr(import_module('.'.join(_SCREAMING_SNAKE_CASE						)						)      ,   _SCREAMING_SNAKE_CASE						)
																except (AttributeError, ModuleNotFoundError):
																							return
																# We iterate over all the globals in self.obj in case we find "os.path.join"
																for attr in self.obj.__dir__():
																							# We don't check for the name of the global, but rather if its value *is* "os.path.join".
																							# This allows to patch renamed attributes like "from os.path import join as pjoin".
																							if getattr(self.obj      ,   _SCREAMING_SNAKE_CASE						) is attr_value:
																														_UpperCAmelCase					=      getattr(self.obj      ,   _SCREAMING_SNAKE_CASE						)
																														setattr(self.obj      ,   _SCREAMING_SNAKE_CASE      ,   self.new						)
									elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
																_UpperCAmelCase					=      globals()['__builtins__'][target_attr]
																setattr(self.obj      ,   _SCREAMING_SNAKE_CASE      ,   self.new						)
									else:
																raise RuntimeError(f'''Tried to patch attribute {target_attr} instead of a submodule.'''						)
		def __exit__(      self      ,   *_SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





									for attr in list(self.original						):
																setattr(self.obj      ,   _SCREAMING_SNAKE_CASE      ,   self.original.pop(_SCREAMING_SNAKE_CASE						)						)
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									self.__enter__()
									self._active_patches.append(self						)






		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									try:
																self._active_patches.remove(self						)
									except ValueError:
																# If the patch hasn't been started this will fail
																return None

									return self.__exit__()
