




"""simple docstring"""

from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
       from .features import FeatureType





@dataclass
class 							lowercase_			:



 '''simple docstring'''
 UpperCAmelCase       :	List[str]
 UpperCAmelCase       :	Optional[str]     =      None
 # Automatically constructed
 UpperCAmelCase       :	ClassVar[str]     =      "dict"
 UpperCAmelCase       :	ClassVar[Any]     =      None
 UpperCAmelCase       :	str     =      field(default='''Translation'''     ,      init=__lowerCAmelCase     ,      repr=__lowerCAmelCase				)

 def __call__(					self			:			List[Any]						):
       return pa.struct({lang: pa.string() for lang in sorted(self.languages						)}						)



 def      lowerCAmelCase_				(					self			:			int						):
       from .features import Value

       return {k: Value('string'						) for k in sorted(self.languages						)}





@dataclass
class 							lowercase_			:



 '''simple docstring'''
 UpperCAmelCase       :	Optional[List]     =      None
 UpperCAmelCase       :	Optional[int]     =      None
 UpperCAmelCase       :	Optional[str]     =      None
 # Automatically constructed
 UpperCAmelCase       :	ClassVar[str]     =      "dict"
 UpperCAmelCase       :	ClassVar[Any]     =      None
 UpperCAmelCase       :	str     =      field(default='''TranslationVariableLanguages'''     ,      init=__lowerCAmelCase     ,      repr=__lowerCAmelCase				)

 def      lowerCAmelCase_				(					self			:			Tuple						):
       _A     =  sorted(set(self.languages						)						) if self.languages else None
       _A     =  len(self.languages						) if self.languages else None

 def __call__(					self			:			Tuple						):
       return pa.struct({'language': pa.list_(pa.string()						), 'translation': pa.list_(pa.string()						)}						)

 def      lowerCAmelCase_				(					self			:			str       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  set(self.languages						)
       if self.languages and set(_UpperCAmelCase						) - lang_set:
             raise ValueError(
                 F'''Some languages in example ({", ".join(sorted(set(_UpperCAmelCase						) - lang_set						)						)}) are not in valid set ({", ".join(_UpperCAmelCase						)}).'''						)

       # Convert dictionary into tuples, splitting out cases where there are
       # multiple translations for a single language.
       _A     =  []
       for lang, text in translation_dict.items():
             if isinstance(_UpperCAmelCase       ,				_UpperCAmelCase						):
                   translation_tuples.append((lang, text)						)
             else:
                   translation_tuples.extend([(lang, el) for el in text]						)

        # Ensure translations are in ascending order by language code.
       _A  ,       _A     =  zip(*sorted(_UpperCAmelCase						)						)

       return {"language": languages, "translation": translations}



 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       from .features import Sequence, Value

       return {
           "language": Sequence(Value('string'						)						),
           "translation": Sequence(Value('string'						)						),
       }


'''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_rag import save_json





def    lowerCAmelCase       (		UpperCamelCase__					: Tuple							):


	"""simple docstring"""
	__UpperCAmelCase      					=      filter(lambda UpperCamelCase__							: 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       (		UpperCamelCase__					: Any     ,       UpperCamelCase__					: str							):


	"""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=UpperCamelCase__     ,       filename=UpperCamelCase__     ,       monitor=f"""val_{metric}"""     ,       mode='''max'''     ,       save_top_k=1     ,       every_n_epochs=1     ,       )

	return checkpoint_callback





def    lowerCAmelCase       (		UpperCamelCase__					: Tuple     ,       UpperCamelCase__					: Optional[Any]							):


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






class     A ( pl.Callback ):





							def    snake_case__    (   self					:				Union[str, Any]  ,		__a					:				int  ,		__a					:				Any						)				->						Optional[Any]:
								__UpperCAmelCase      					=      {f"""lr_group_{i}""": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups						)}
								pl_module.logger.log_metrics(__a						)





							@rank_zero_only
							def    snake_case__    (   self					:				Union[str, Any]  ,		__a					:				pl.Trainer  ,		__a					:				pl.LightningModule  ,		__a					:				str  ,		__a					:				Any=True						)				->						None:
								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=__a						)
									generations_file.parent.mkdir(exist_ok=__a						)
								with open(__a  ,		'''a+'''						) as writer:
									for key in sorted(__a						):
										if key in ["log", "progress_bar", "preds"]:
											continue
										__UpperCAmelCase      					=      metrics[key]
										if isinstance(__a  ,		torch.Tensor						):
											__UpperCAmelCase      					=      val.item()
										__UpperCAmelCase      					=      f"""{key}: {val:.6f}\n"""
										writer.write(__a						)

								if not save_generations:
									return

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





							@rank_zero_only
							def    snake_case__    (   self					:				List[Any]  ,		__a					:				Optional[int]  ,		__a					:				Union[str, Any]						)				->						List[Any]:
								try:
									__UpperCAmelCase      					=      pl_module.model.model.num_parameters()
								except AttributeError:
									__UpperCAmelCase      					=      pl_module.model.num_parameters()

								__UpperCAmelCase      					=      count_trainable_parameters(__a						)
								# 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    snake_case__    (   self					:				List[str]  ,		__a					:				pl.Trainer  ,		__a					:				pl.LightningModule						)				->						Optional[int]:
								save_json(pl_module.metrics  ,		pl_module.metrics_save_path						)
								return self._write_logs(__a  ,		__a  ,		'''test'''						)






							@rank_zero_only
							def    snake_case__    (   self					:				Any  ,		__a					:				pl.Trainer  ,		__a					:				Union[str, Any]						)				->						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")
