


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__:		Union[str, Any]	)  ->		Optional[int]:


							'''simple docstring'''





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


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

def 	lowerCAmelCase__			(      a__:		Any      ,  a__:		Dict	)  ->		Dict:


							'''simple docstring'''





							if metric == "rouge2":
														_UpperCAmelCase					=      '{val_avg_rouge2:.4f}-{step_count}'
							elif metric == "bleu":
														_UpperCAmelCase					=      '{val_avg_bleu:.4f}-{step_count}'
							elif metric == "em":
														_UpperCAmelCase					=      '{val_avg_em:.4f}-{step_count}'
							elif metric == "loss":
														_UpperCAmelCase					=      '{val_avg_loss:.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.'	)

							_UpperCAmelCase					=      ModelCheckpoint(
							    dirpath=a__      ,  filename=a__      ,  monitor=F'''val_{metric}'''      ,  mode='max'      ,  save_top_k=1      ,  every_n_epochs=1      ,  )

							return checkpoint_callback

def 	lowerCAmelCase__			(      a__:		Union[str, Any]      ,  a__:		Optional[Any]	)  ->		str:


							'''simple docstring'''





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







class 		__a				(    pl.Callback      ):
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      {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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=True						)			->	None:




									"""simple docstring"""





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

									if not save_generations:
																return

									if "preds" in metrics:
																_UpperCAmelCase					=      '\n'.join(metrics['preds']						)
																generations_file.open('w+'						).write(_SCREAMING_SNAKE_CASE						)
		@rank_zero_only
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									try:
																_UpperCAmelCase					=      pl_module.model.model.num_parameters()
									except AttributeError:
																_UpperCAmelCase					=      pl_module.model.num_parameters()

									_UpperCAmelCase					=      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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[str]:




									"""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")




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__  :List[str]             =							{
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :Any             =							[
		    '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XCLIPModel''',
		    '''XCLIPPreTrainedModel''',
		    '''XCLIPTextModel''',
		    '''XCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_x_clip import (
		    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XCLIPConfig,
		    XCLIPTextConfig,
		    XCLIPVisionConfig,
		)
		from .processing_x_clip import XCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_x_clip import (
				    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XCLIPModel,
				    XCLIPPreTrainedModel,
				    XCLIPTextModel,
				    XCLIPVisionModel,
				)

else:
		import sys

		lowerCAmelCase__  :str             =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
