



"""simple docstring"""






import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
		import torch


_lowercase					:					List[Any] 		=		logging.get_logger(__name__)

class 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





					def __init__(			self				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    Optional[Any]=21,    lowerCamelCase				:    Dict=[],    lowerCamelCase				:    Union[str, Any]=None	)->   Optional[Any]:
								lowerCamelCase__						:						Dict						      =model
								lowerCamelCase__						:						Optional[Any]						      =trace_batch_nums
								lowerCamelCase__						:						Tuple						      =abort_after_batch_num

								# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
								lowerCamelCase__						:						Any						      =collections.deque([],    UpperCAmelCase__	)
								lowerCamelCase__						:						str						      =[]
								lowerCamelCase__						:						Any						      =0
								lowerCamelCase__						:						Tuple						      =0
								lowerCamelCase__						:						Any						      =False
								lowerCamelCase__						:						Dict						      ="""                 """

								self.analyse_model()

								self.register_forward_hook()



					def 						snake_case			(			self				:    int,    lowerCamelCase				:    Tuple=None	)->   Optional[int]:







								if frame is not None:
											self.expand_frame(UpperCAmelCase__	)
								self.frames.append('''\n'''.join(self.frame	)	)
								lowerCamelCase__						:						Dict						      =[]  # start a new frame



					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    List[str]	)->   Optional[Any]:
								self.frame.append(UpperCAmelCase__	)



					def 						snake_case			(			self				:    Dict	)->   List[Any]:
								print('''\n'''.join(self.frames	)	)
								lowerCamelCase__						:						Optional[int]						      =[]



					def 						snake_case			(			self				:    Tuple	)->   str:
								lowerCamelCase__						:						Optional[int]						      =[]



					def 						snake_case			(			self				:    Any	)->   List[Any]:
								print(F'''\nDetected inf/nan during batch_number={self.batch_number}'''	)
								print(F'''Last {len(self.frames	)} forward frames:'''	)
								print(F'''{"abs min":8} {"abs max":8} metadata'''	)
								print('''\n'''.join(self.frames	)	)
								print('''\n\n'''	)
								lowerCamelCase__						:						str						      =[]



					def 						snake_case			(			self				:    int	)->   Tuple:
								lowerCamelCase__						:						Any						      ={m: name for name, m in self.model.named_modules()}
								# self.longest_module_name = max(len(v) for v in self.module_names.values())



					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    Dict,    lowerCamelCase				:    str	)->   Any:







								if torch.is_tensor(UpperCAmelCase__	):
											self.expand_frame(get_abs_min_max(UpperCAmelCase__,    UpperCAmelCase__	)	)
											if detect_overflow(UpperCAmelCase__,    UpperCAmelCase__	):
														lowerCamelCase__						:						List[str]						      =True
								elif var is None:
											self.expand_frame(F'''{"None":>17} {ctx}'''	)
								else:
											self.expand_frame(F'''{"not a tensor":>17} {ctx}'''	)



					def 						snake_case			(			self				:    List[Any]	)->   Union[str, Any]:
								self.expand_frame(F'''\n\n{self.prefix} *** Starting batch number={self.batch_number} ***'''	)
								self.expand_frame(F'''{"abs min":8} {"abs max":8} metadata'''	)



					def 						snake_case			(			self				:    int	)->   Optional[int]:
								self.expand_frame(F'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''	)



					def 						snake_case			(			self				:    Dict,    lowerCamelCase				:    Tuple,    lowerCamelCase				:    Union[str, Any],    lowerCamelCase				:    Optional[Any]	)->   str:
								self.expand_frame(F'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''	)

								# params
								for name, p in module.named_parameters(recurse=UpperCAmelCase__	):
											self.analyse_variable(UpperCAmelCase__,    UpperCAmelCase__	)

								# inputs
								if isinstance(UpperCAmelCase__,    UpperCAmelCase__	):
											for i, x in enumerate(UpperCAmelCase__	):
														self.analyse_variable(UpperCAmelCase__,    F'''input[{i}]'''	)
								else:
											self.analyse_variable(UpperCAmelCase__,    '''input'''	)

								# outputs
								if isinstance(UpperCAmelCase__,    UpperCAmelCase__	):
											for i, x in enumerate(UpperCAmelCase__	):
														# possibly a tuple of tuples
														if isinstance(UpperCAmelCase__,    UpperCAmelCase__	):
																	for j, y in enumerate(UpperCAmelCase__	):
																				self.analyse_variable(UpperCAmelCase__,    F'''output[{i}][{j}]'''	)
														else:
																	self.analyse_variable(UpperCAmelCase__,    F'''output[{i}]'''	)
								else:
											self.analyse_variable(UpperCAmelCase__,    '''output'''	)

								self.save_frame()



					def 						snake_case			(			self				:    List[str]	)->   List[str]:
								self.model.apply(self._register_forward_hook	)



					def 						snake_case			(			self				:    Optional[Any],    lowerCamelCase				:    int	)->   Any:
								module.register_forward_hook(self.forward_hook	)




					def 						snake_case			(			self				:    List[Any],    lowerCamelCase				:    Tuple,    lowerCamelCase				:    List[Any],    lowerCamelCase				:    List[str]	)->   Any:
								lowerCamelCase__						:						Optional[Any]						      =False

								lowerCamelCase__						:						Optional[int]						      =True if self.batch_number in self.trace_batch_nums else False
								if trace_mode:
											self.reset_saved_frames()

								if self.total_calls == 0:
											self.batch_start_frame()
								self.total_calls += 1

								# count batch numbers - the very first forward hook of the batch will be called when the
								# batch completes - i.e. it gets called very last - we know this batch has finished
								if module == self.model:
											self.batch_number += 1
											lowerCamelCase__						:						List[Any]						      =True

								self.create_frame(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__	)

								# if last_frame_of_batch:
								#     self.batch_end_frame()

								if trace_mode:
											self.trace_frames()

								if last_frame_of_batch:
											self.batch_start_frame()

								if self.detected_overflow and not trace_mode:
											self.dump_saved_frames()

											# now we can abort, as it's pointless to continue running
											raise ValueError(
											    '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
											    '''Please scroll up above this traceback to see the activation values prior to this event.'''	)

								# abort after certain batch if requested to do so
								if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
											raise ValueError(
											    F'''DebugUnderflowOverflow: aborting after {self.batch_number} batches due to'''
											    F''' `abort_after_batch_num={self.abort_after_batch_num}` arg'''	)






def 	snake_case__  (						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       str      ):
			"""simple docstring"""
			lowerCamelCase__						:						int						      =var.abs()
			return f'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''






def 	snake_case__  (						__lowerCamelCase							:       List[str]				,						__lowerCamelCase							:       Optional[int]      ):
			"""simple docstring"""
			lowerCamelCase__						:						List[str]						      =False
			if torch.isnan(_A      ).any().item():
						lowerCamelCase__						:						List[Any]						      =True
						print(f'''{ctx} has nans'''      )
			if torch.isinf(_A      ).any().item():
						lowerCamelCase__						:						int						      =True
						print(f'''{ctx} has infs'''      )

			# if needed to monitor large elements can enable the following
			if 0:  # and detected:
						lowerCamelCase__						:						List[Any]						      =var[torch.ge(var.abs()				,						100      )]
						if naaa.numel() > 0:
									print(f'''{ctx}:  n100={naaa.numel()}'''      )
						lowerCamelCase__						:						Union[str, Any]						      =var[torch.ge(var.abs()				,						1000      )]
						if naaaa.numel() > 0:
									print(f'''{ctx}: n1000={naaaa.numel()}'''      )
						lowerCamelCase__						:						Optional[Any]						      =var[torch.ge(var.abs()				,						10000      )]
						if naaaaa.numel() > 0:
									print(f'''{ctx}: n10000={naaaaa.numel()}'''      )

			if 0:
						print(f'''min={var.min():9.2e} max={var.max():9.2e}'''      )

			if 0:
						print(f'''min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})'''      )

			return detected

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				"""underflow_overflow"""
					_a       =				"""tpu_metrics_debug"""







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        __lowercase   (  _A      )  ->    Dict:
      SCREAMING_SNAKE_CASE			:   Dict	  = filter(lambda _A      : p.requires_grad    ,     model.parameters()      )
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = sum([np.prod(p.size()      ) for p in model_parameters]      )
      return params


UpperCAmelCase__			:  List[Any]					     =     logging.getLogger(__name__)







class 							a__							( pl.Callback							):







  """simple docstring"""




  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Any		)       ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[str]	  = {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 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			pl.Trainer					,					UpperCAmelCase__     :			pl.LightningModule					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Tuple=True		)       ->None:
        """simple docstring"""







        logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"		)
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = 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
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = Path(pl_module.hparams.output_dir		)
        if type_path == "test":
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = od / """test_results.txt"""
              SCREAMING_SNAKE_CASE			:   int	  = 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.
              SCREAMING_SNAKE_CASE			:   Any	  = od / f"{type_path}_results/{trainer.global_step:05d}.txt"
              SCREAMING_SNAKE_CASE			:   int	  = 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
                    SCREAMING_SNAKE_CASE			:   Optional[int]	  = metrics[key]
                    if isinstance(UpperCAmelCase__					,					torch.Tensor		):
                          SCREAMING_SNAKE_CASE			:   Optional[int]	  = val.item()
                    SCREAMING_SNAKE_CASE			:   List[str]	  = f"{key}: {val:.6f}\n"
                    writer.write(UpperCAmelCase__		)

        if not save_generations:
              return

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


  @rank_zero_only
  def 			_lowercase   (      self     :			Optional[int]					,					UpperCAmelCase__     :			Optional[int]					,					UpperCAmelCase__     :			Union[str, Any]		)       ->int:
        """simple docstring"""







        try:
              SCREAMING_SNAKE_CASE			:   int	  = pl_module.model.model.num_parameters()
        except AttributeError:
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = pl_module.model.num_parameters()

        SCREAMING_SNAKE_CASE			:   Dict	  = 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 			_lowercase   (      self     :			str					,					UpperCAmelCase__     :			pl.Trainer					,					UpperCAmelCase__     :			pl.LightningModule		)       ->Optional[int]:
        """simple docstring"""







        save_json(pl_module.metrics					,					pl_module.metrics_save_path		)
        return self._write_logs(UpperCAmelCase__					,					UpperCAmelCase__					,					"""test"""		)




  @rank_zero_only
  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			pl.Trainer					,					UpperCAmelCase__     :			List[Any]		)       ->Dict:
        """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        __lowercase   (  _A    ,     _A    ,     _A=1    ,     _A=False      )  ->    Tuple:
      if metric == "rouge2":
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = """{val_avg_rouge2:.4f}-{step_count}"""
      elif metric == "bleu":
            SCREAMING_SNAKE_CASE			:   Tuple	  = """{val_avg_bleu:.4f}-{step_count}"""
      elif metric == "loss":
            SCREAMING_SNAKE_CASE			:   Any	  = """{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."""      )

      SCREAMING_SNAKE_CASE			:   Tuple	  = ModelCheckpoint(
          dirpath=_A    ,     filename=_A    ,     monitor=F"val_{metric}"    ,     mode="""min""" if """loss""" in metric else """max"""    ,     save_top_k=_A    ,     )
      return checkpoint_callback







def        __lowercase   (  _A    ,     _A      )  ->    Optional[int]:
      return EarlyStopping(
          monitor=F"val_{metric}"    ,     mode="""min""" if """loss""" in metric else """max"""    ,     patience=_A    ,     verbose=_A    ,     )




