"""simple docstring"""
import copy

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


lowercase__   			=      logging.get_logger(__name__)

lowercase__   			=      {}




class   __snake_case			(	__lowerCAmelCase							):
      a__								=						"""fsmt_decoder"""



      def __init__(		self       ,     lowercase=0       ,     lowercase=0)      ->     Optional[int]:





        '''simple docstring'''





        super().__init__()
        a__:	Dict     				=      vocab_size
        a__:	str     				=      bos_token_id







class   __snake_case			(	__lowerCAmelCase							):
      a__								=						"""fsmt"""
      a__								=						{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



      def __init__(		self       ,     lowercase=["en", "de"]       ,     lowercase=4_20_24       ,     lowercase=4_20_24       ,     lowercase="relu"       ,     lowercase=10_24       ,     lowercase=2_00       ,     lowercase=10_24       ,     lowercase=40_96       ,     lowercase=12       ,     lowercase=16       ,     lowercase=0.0       ,     lowercase=40_96       ,     lowercase=12       ,     lowercase=16       ,     lowercase=0.0       ,     lowercase=0.0       ,     lowercase=0.1       ,     lowercase=0.0       ,     lowercase=0.02       ,     lowercase=2       ,     lowercase=True       ,     lowercase=True       ,     lowercase=False       ,     lowercase=5       ,     lowercase=1.0       ,     lowercase=False       ,     lowercase=True       ,     lowercase=1       ,     lowercase=0       ,     lowercase=2       ,     lowercase=2       ,     **lowercase       ,     )      ->     List[Any]:





        '''simple docstring'''





        a__:	Optional[int]     				=      langs
        a__:	Union[str, Any]     				=      src_vocab_size
        a__:	Tuple     				=      tgt_vocab_size
        a__:	Optional[Any]     				=      d_model  # encoder_embed_dim and decoder_embed_dim

        a__:	str     				=      encoder_ffn_dim
        a__:	Optional[int]     				=      encoder_layers
        a__:	Dict     				=      encoder_attention_heads
        a__:	Optional[int]     				=      encoder_layerdrop
        a__:	Dict     				=      decoder_layerdrop
        a__:	Union[str, Any]     				=      decoder_ffn_dim
        a__:	List[Any]     				=      decoder_layers
        a__:	Dict     				=      decoder_attention_heads
        a__:	List[str]     				=      max_position_embeddings
        a__:	Optional[Any]     				=      init_std  # Normal(0, this parameter)
        a__:	Optional[Any]     				=      activation_function

        a__:	List[Any]     				=      DecoderConfig(vocab_size=lowercase       ,     bos_token_id=lowercase)
        if "decoder" in common_kwargs:
          del common_kwargs["decoder"]

        a__:	List[str]     				=      scale_embedding  # scale factor will be sqrt(d_model) if True

        # 3 Types of Dropout
        a__:	Dict     				=      attention_dropout
        a__:	Union[str, Any]     				=      activation_dropout
        a__:	List[Any]     				=      dropout

        a__:	Optional[int]     				=      use_cache
        super().__init__(
            pad_token_id=lowercase       ,     bos_token_id=lowercase       ,     eos_token_id=lowercase       ,     decoder_start_token_id=lowercase       ,     is_encoder_decoder=lowercase       ,     tie_word_embeddings=lowercase       ,     forced_eos_token_id=lowercase       ,     max_length=lowercase       ,     num_beams=lowercase       ,     length_penalty=lowercase       ,     early_stopping=lowercase       ,     **lowercase       ,     )



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      copy.deepcopy(self.__dict__)
        a__:	str     				=      self.decoder.to_dict()
        a__:	int     				=      self.__class__.model_type
        return output




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 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]     )	->		str:
				__lowercase								=   filter(lambda SCREAMING_SNAKE_CASE     : p.requires_grad       ,				model.parameters()     )
				__lowercase								=   sum([np.prod(p.size()     ) for p in model_parameters]     )
				return params


SCREAMING_SNAKE_CASE__				=							logging.getLogger(__name__)
class       A__  (		pl.Callback							):





							def      a__					(					self    :  List[str]	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  List[str]							)		->						Tuple:



											"""simple docstring"""


											__lowercase								=   {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      a__					(					self    :  Dict	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule	,					_UpperCAmelCase    :  str	,					_UpperCAmelCase    :  int=True							)		->						None:



											"""simple docstring"""


											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=_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
																			__lowercase								=   metrics[key]
																			if isinstance(_UpperCAmelCase	,					torch.Tensor							):
																							__lowercase								=   val.item()
																			__lowercase								=   f"""{key}: {val:.6f}\n"""
																			writer.write(_UpperCAmelCase							)

											if not save_generations:
															return

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





							@rank_zero_only
							def      a__					(					self    :  str	,					_UpperCAmelCase    :  Optional[int]	,					_UpperCAmelCase    :  str							)		->						Optional[int]:



											"""simple docstring"""


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

											__lowercase								=   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      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule							)		->						Optional[Any]:



											"""simple docstring"""


											save_json(pl_module.metrics	,					pl_module.metrics_save_path							)
											return self._write_logs(_UpperCAmelCase	,					_UpperCAmelCase	,					'test'							)





							@rank_zero_only
							def      a__					(					self    :  int	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  Tuple							)		->						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")






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: List[str]       ,				SCREAMING_SNAKE_CASE			: Union[str, Any]=1       ,				SCREAMING_SNAKE_CASE			: str=False     )	->		List[str]:
				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=SCREAMING_SNAKE_CASE       ,				filename=SCREAMING_SNAKE_CASE       ,				monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				save_top_k=SCREAMING_SNAKE_CASE       ,				)
				return checkpoint_callback




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





