
"""simple docstring"""


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


A_						       =				logging.getLogger(__name__)




class    __lowerCamelCase     (   pl.Callback):




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase      ):
							lowerCamelCase_ 		=     {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 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase=True      ):
							logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"      )
							lowerCamelCase_ 		=     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
							lowerCamelCase_ 		=     Path(pl_module.hparams.output_dir      )
							if type_path == "test":
										lowerCamelCase_ 		=     od / '''test_results.txt'''
										lowerCamelCase_ 		=     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.
										lowerCamelCase_ 		=     od / f"{type_path}_results/{trainer.global_step:05d}.txt"
										lowerCamelCase_ 		=     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
													lowerCamelCase_ 		=     metrics[key]
													if isinstance(UpperCAmelCase       ,	torch.Tensor      ):
																lowerCamelCase_ 		=     val.item()
													lowerCamelCase_ 		=     f"{key}: {val:.6f}\n"
													writer.write(UpperCAmelCase      )

							if not save_generations:
										return

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




				@rank_zero_only
				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase      ):
							try:
										lowerCamelCase_ 		=     pl_module.model.model.num_parameters()
							except AttributeError:
										lowerCamelCase_ 		=     pl_module.model.num_parameters()

							lowerCamelCase_ 		=     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 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase      ):
							save_json(pl_module.metrics       ,	pl_module.metrics_save_path      )
							return self._write_logs(UpperCAmelCase       ,	UpperCAmelCase       ,	'''test'''      )





				@rank_zero_only
				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase      ):
							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	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__=1  ,lowerCAmelCase__=False	):
			if metric == "rouge2":
						lowerCamelCase_ 		=     '''{val_avg_rouge2:.4f}-{step_count}'''
			elif metric == "bleu":
						lowerCamelCase_ 		=     '''{val_avg_bleu:.4f}-{step_count}'''
			elif metric == "loss":
						lowerCamelCase_ 		=     '''{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.'''	)

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






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			return EarlyStopping(
			    monitor=f"val_{metric}"  ,mode='''min''' if '''loss''' in metric else '''max'''  ,patience=lowerCAmelCase__  ,verbose=lowerCAmelCase__  ,)





"""simple docstring"""


from typing import TYPE_CHECKING

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


A_						       =				{
    """configuration_flava""": [
        """FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """FlavaConfig""",
        """FlavaImageCodebookConfig""",
        """FlavaImageConfig""",
        """FlavaMultimodalConfig""",
        """FlavaTextConfig""",
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_						       =				["""FlavaFeatureExtractor"""]
						A_						       =				["""FlavaImageProcessor"""]
						A_						       =				["""FlavaProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_						       =				[
						    """FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """FlavaForPreTraining""",
						    """FlavaImageCodebook""",
						    """FlavaImageModel""",
						    """FlavaModel""",
						    """FlavaMultimodalModel""",
						    """FlavaPreTrainedModel""",
						    """FlavaTextModel""",
						]

if TYPE_CHECKING:
						from .configuration_flava import (
						    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    FlavaConfig,
						    FlavaImageCodebookConfig,
						    FlavaImageConfig,
						    FlavaMultimodalConfig,
						    FlavaTextConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_flava import FlavaFeatureExtractor
												from .image_processing_flava import FlavaImageProcessor
												from .processing_flava import FlavaProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flava import (
												    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlavaForPreTraining,
												    FlavaImageCodebook,
												    FlavaImageModel,
												    FlavaModel,
												    FlavaMultimodalModel,
												    FlavaPreTrainedModel,
												    FlavaTextModel,
												)

else:
						import sys

						A_						       =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



