



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase__     =	{"""configuration_yoso""": ["""YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YosoConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCamelCase__     =	[
          """YOSO_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """YosoForMaskedLM""",
          """YosoForMultipleChoice""",
          """YosoForQuestionAnswering""",
          """YosoForSequenceClassification""",
          """YosoForTokenClassification""",
          """YosoLayer""",
          """YosoModel""",
          """YosoPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_yoso import (
                YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
                YosoForMaskedLM,
                YosoForMultipleChoice,
                YosoForQuestionAnswering,
                YosoForSequenceClassification,
                YosoForTokenClassification,
                YosoLayer,
                YosoModel,
                YosoPreTrainedModel,
            )


else:
      import sys

      lowerCamelCase__     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)





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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Dict      ):
				"""simple docstring"""







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


lowerCamelCase__     =	logging.getLogger(__name__)






class 				SCREAMING_SNAKE_CASE     (							pl.Callback   ):





							def   UpperCamelCase_   (				self						:						Dict ,			__lowercase						:						Dict ,			__lowercase						:						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(__lowercase		)





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Optional[Any] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						pl.LightningModule ,			__lowercase						:						str ,			__lowercase						:						Tuple=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=__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
																			__a					       =						metrics[key]
																			if isinstance(__lowercase ,			torch.Tensor		):
																							__a					       =						val.item()
																			__a					       =						F"{key}: {val:.6f}\n"
																			writer.write(__lowercase		)

											if not save_generations:
															return

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





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						List[Any]		):

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

											__a					       =						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   UpperCamelCase_   (				self						:						Union[str, Any] ,			__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   UpperCamelCase_   (				self						:						Optional[int] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[int]			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]			,       _SCREAMING_SNAKE_CASE    :     Tuple=1			,       _SCREAMING_SNAKE_CASE    :     List[str]=False      ):
				"""simple docstring"""







				if metric == "rouge2":
								__a					       =						"""{val_avg_rouge2:.4f}-{step_count}"""
				elif metric == "bleu":
								__a					       =						"""{val_avg_bleu:.4f}-{step_count}"""
				elif metric == "loss":
								__a					       =						"""{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."""      )

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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     int			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







				return EarlyStopping(
				    monitor=f"val_{metric}"			,       mode="""min""" if """loss""" in metric else """max"""			,       patience=_SCREAMING_SNAKE_CASE			,       verbose=_SCREAMING_SNAKE_CASE			,       )
