
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     a						(							snake_case__: Dict ):


				'''simple docstring'''
				lowercase_					  = filter(lambda snake_case__ : p.requires_grad						,       model.parameters() )
				lowercase_					  = sum([np.prod(p.size() ) for p in model_parameters] )
				return params


__a        = logging.getLogger(__name__)
class 			lowercase__(    pl.Callback							):



	"""simple docstring"""




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

					if not save_generations:
									return

					if "preds" in metrics:
									lowercase_					  = '''\n'''.join(metrics['''preds''']	)
									generations_file.open('''w+'''	).write(SCREAMING_SNAKE_CASE_	)
	@rank_zero_only
	def 						_lowercase  (	self  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      Any	,							SCREAMING_SNAKE_CASE_  :      Any	)	->					Union[str, Any]:
					try:
									lowercase_					  = pl_module.model.model.num_parameters()
					except AttributeError:
									lowercase_					  = pl_module.model.num_parameters()

					lowercase_					  = 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 						_lowercase  (	self  :      Optional[Any]	,							SCREAMING_SNAKE_CASE_  :      pl.Trainer	,							SCREAMING_SNAKE_CASE_  :      pl.LightningModule	)	->					Optional[int]:
					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 						_lowercase  (	self  :      Optional[Any]	,							SCREAMING_SNAKE_CASE_  :      pl.Trainer	,							SCREAMING_SNAKE_CASE_  :      str	)	->					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")




def     a						(							snake_case__: Dict						,       snake_case__: int						,       snake_case__: Optional[int]=1						,       snake_case__: Optional[int]=False ):


				'''simple docstring'''
				if metric == "rouge2":
								lowercase_					  = '''{val_avg_rouge2:.4f}-{step_count}'''
				elif metric == "bleu":
								lowercase_					  = '''{val_avg_bleu:.4f}-{step_count}'''
				elif metric == "loss":
								lowercase_					  = '''{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.''' )

				lowercase_					  = ModelCheckpoint(
				    dirpath=snake_case__						,       filename=snake_case__						,       monitor=F'''val_{metric}'''						,       mode='''min''' if '''loss''' in metric else '''max'''						,       save_top_k=snake_case__						,       )
				return checkpoint_callback




def     a						(							snake_case__: Optional[int]						,       snake_case__: Optional[int] ):


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






from typing import TYPE_CHECKING

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


__a        = {
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'NystromformerForMaskedLM',
						    'NystromformerForMultipleChoice',
						    'NystromformerForQuestionAnswering',
						    'NystromformerForSequenceClassification',
						    'NystromformerForTokenClassification',
						    'NystromformerLayer',
						    'NystromformerModel',
						    'NystromformerPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nystromformer import (
												    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NystromformerForMaskedLM,
												    NystromformerForMultipleChoice,
												    NystromformerForQuestionAnswering,
												    NystromformerForSequenceClassification,
												    NystromformerForTokenClassification,
												    NystromformerLayer,
												    NystromformerModel,
												    NystromformerPreTrainedModel,
												)


else:
						import sys

						__a        = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




