






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       :			List[str]    ):
		snake_case_				:							int						 =       filter(lambda _a    : p.requires_grad  ,      model.parameters()    )
		snake_case_				:							str						 =       sum([np.prod(p.size()    ) for p in model_parameters]    )
		return params


lowercase  :							List[str]			  =    logging.getLogger(__name__)

def      lowerCAmelCase__	(    _a       :			Dict  ,      _a       :			List[str]    ):
		if metric == "rouge2":
				snake_case_				:							List[str]						 =       "{val_avg_rouge2:.4f}-{step_count}"
		elif metric == "bleu":
				snake_case_				:							Any						 =       "{val_avg_bleu:.4f}-{step_count}"
		elif metric == "em":
				snake_case_				:							List[str]						 =       "{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."    )

		snake_case_				:							Optional[int]						 =       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       :			Optional[int]  ,      _a       :			str    ):
		return EarlyStopping(
		    monitor=F'''val_{metric}'''  ,      mode="min" if "loss" in metric else "max"  ,      patience=_a  ,      verbose=_a  ,      )




class 		UpperCAmelCase_						(	pl.Callback					):


						'''simple docstring'''




						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Tuple:
								snake_case_				:							int						 =       {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       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=True				) ->				None:
								logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''				)
								snake_case_				:							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
								snake_case_				:							str						 =       Path(pl_module.hparams.output_dir				)
								if type_path == "test":
										snake_case_				:							Union[str, Any]						 =       od / "test_results.txt"
										snake_case_				:							Tuple						 =       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.
										snake_case_				:							Optional[int]						 =       od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
										snake_case_				:							Optional[int]						 =       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
												snake_case_				:							Union[str, Any]						 =       metrics[key]
												if isinstance(_SCREAMING_SNAKE_CASE		,				torch.Tensor				):
														snake_case_				:							Dict						 =       val.item()
												snake_case_				:							Union[str, Any]						 =       f'''{key}: {val:.6f}\n'''
												writer.write(_SCREAMING_SNAKE_CASE				)

								if not save_generations:
										return

								if "preds" in metrics:
										snake_case_				:							Optional[Any]						 =       "\n".join(metrics["preds"]				)
										generations_file.open("w+"				).write(_SCREAMING_SNAKE_CASE				)






						@rank_zero_only
						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				str:
								try:
										snake_case_				:							Optional[Any]						 =       pl_module.model.model.num_parameters()
								except AttributeError:
										snake_case_				:							Optional[Any]						 =       pl_module.model.num_parameters()

								snake_case_				:							List[str]						 =       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       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				List[Any]:
								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       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Tuple:
								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 typing import TYPE_CHECKING

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


lowercase  :							str			  =    {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							List[str]			  =    [
							    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BioGptForCausalLM''',
							    '''BioGptForTokenClassification''',
							    '''BioGptForSequenceClassification''',
							    '''BioGptModel''',
							    '''BioGptPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase  :							Tuple			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
