





'''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				( lowercase						:      Dict						)	->	Any:
				_a								= filter(lambda lowercase						: p.requires_grad    ,							model.parameters()						)
				_a								= sum([np.prod(p.size()						) for p in model_parameters]						)
				return params


lowerCAmelCase_					:    int      	=			logging.getLogger(__name__)






def  _lowerCamelCase				( lowercase						:      List[Any]    ,							lowercase						:      Any						)	->	Any:
				if metric == "rouge2":
								_a								= "{val_avg_rouge2:.4f}-{step_count}"
				elif metric == "bleu":
								_a								= "{val_avg_bleu:.4f}-{step_count}"
				elif metric == "em":
								_a								= "{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."						)

				_a								= ModelCheckpoint(
				    dirpath=lowercase    ,							filename=lowercase    ,							monitor=F'val_{metric}'    ,							mode="max"    ,							save_top_k=3    ,							every_n_epochs=1    ,							)
				return checkpoint_callback






def  _lowerCamelCase				( lowercase						:      Optional[int]    ,							lowercase						:      Optional[int]						)	->	Union[str, Any]:
				return EarlyStopping(
				    monitor=F'val_{metric}'    ,							mode="min" if "loss" in metric else "max"    ,							patience=lowercase    ,							verbose=lowercase    ,							)



class 				__SCREAMING_SNAKE_CASE  (pl.Callback      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					str					,				__a     :					List[Any]					):
								_a								= {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   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					pl.Trainer					,				__a     :					pl.LightningModule					,				__a     :					str					,				__a     :					Tuple=True					):
								logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'					)
								_a								= 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
								_a								= Path(pl_module.hparams.output_dir					)
								if type_path == "test":
												_a								= od / "test_results.txt"
												_a								= 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.
												_a								= od / f'{type_path}_results/{trainer.global_step:05d}.txt'
												_a								= 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
																_a								= metrics[key]
																if isinstance(__a					,				torch.Tensor					):
																				_a								= val.item()
																_a								= f'{key}: {val:.6f}\n'
																writer.write(__a					)

								if not save_generations:
												return

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




				@rank_zero_only
				def   UpperCamelCase__   (  self     :					int					,				__a     :					List[Any]					,				__a     :					Union[str, Any]					):
								try:
												_a								= pl_module.model.model.num_parameters()
								except AttributeError:
												_a								= pl_module.model.num_parameters()

								_a								= 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   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					pl.Trainer					,				__a     :					pl.LightningModule					):
								save_json(pl_module.metrics					,				pl_module.metrics_save_path					)
								return self._write_logs(__a					,				__a					,				"test"					)




				@rank_zero_only
				def   UpperCamelCase__   (  self     :					Any					,				__a     :					pl.Trainer					,				__a     :					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")

"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Union[str, Any]  							=						'transfo-xl'
						lowerCAmelCase__     :  Any  							=						['mems']
						lowerCAmelCase__     :  Tuple  							=						{
						    'n_token': 'vocab_size',
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'n_head',
						    'num_hidden_layers': 'n_layer',
						}





						def __init__(				self						,__UpperCAmelCase=26_77_35						,__UpperCAmelCase=[2_00_00, 4_00_00, 20_00_00]						,__UpperCAmelCase=10_24						,__UpperCAmelCase=10_24						,__UpperCAmelCase=16						,__UpperCAmelCase=64						,__UpperCAmelCase=40_96						,__UpperCAmelCase=4						,__UpperCAmelCase=False						,__UpperCAmelCase=18						,__UpperCAmelCase=16_00						,__UpperCAmelCase=10_00						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=0						,__UpperCAmelCase=-1						,__UpperCAmelCase=True						,__UpperCAmelCase=0.1						,__UpperCAmelCase=0.0						,__UpperCAmelCase=True						,__UpperCAmelCase="normal"						,__UpperCAmelCase=0.0_1						,__UpperCAmelCase=0.0_1						,__UpperCAmelCase=0.0_2						,__UpperCAmelCase=1e-5						,__UpperCAmelCase=0						,**__UpperCAmelCase						,)    -> Tuple:
													A__		   =							vocab_size
													A__		   =							[]
													self.cutoffs.extend(__UpperCAmelCase   )
													if proj_share_all_but_first:
																				A__		   =							[False] + [True] * len(self.cutoffs   )
													else:
																				A__		   =							[False] + [False] * len(self.cutoffs   )
													A__		   =							d_model
													A__		   =							d_embed
													A__		   =							d_head
													A__		   =							d_inner
													A__		   =							div_val
													A__		   =							pre_lnorm
													A__		   =							n_layer
													A__		   =							n_head
													A__		   =							mem_len
													A__		   =							same_length
													A__		   =							attn_type
													A__		   =							clamp_len
													A__		   =							sample_softmax
													A__		   =							adaptive
													A__		   =							dropout
													A__		   =							dropatt
													A__		   =							untie_r
													A__		   =							init
													A__		   =							init_range
													A__		   =							proj_init_std
													A__		   =							init_std
													A__		   =							layer_norm_epsilon
													super().__init__(eos_token_id=__UpperCAmelCase						,**__UpperCAmelCase   )





						@property
						def  snake_case__		(				self   )    -> Optional[Any]:
													# Message copied from Transformer-XL documentation
													logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )
													return -1





						@max_position_embeddings.setter
						def  snake_case__		(				self						,__UpperCAmelCase   )    -> int:
													# Message copied from Transformer-XL documentation
													raise NotImplementedError(
													    f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )





