




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


lowercase__      :			int           =					logging.get_logger(__name__)

lowercase__      :			Optional[Any]           =					{
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}




class 			a__       (					UpperCamelCase__      ):
     a		:			Dict							=	"""pegasus_x"""
     a		:			Union[str, Any]							=	["""past_key_values"""]
     a		:			Tuple							=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







     def __init__(	self  ,		A=96103  ,		A=16384  ,		A=16  ,		A=4096  ,		A=16  ,		A=16  ,		A=4096  ,		A=16  ,		A=0.0  ,		A=0.0  ,		A=True  ,		A=True  ,		A="gelu"  ,		A=1024  ,		A=0.1  ,		A=0.0  ,		A=0.0  ,		A=0.0_2  ,		A=0  ,		A=True  ,		A=0  ,		A=1  ,		A=1  ,		A=32  ,		A=512  ,		A=True  ,		**A  ,		)  ->    str:



          '''simple docstring'''



          a      =      vocab_size
          a      =      max_position_embeddings
          a      =      d_model
          a      =      encoder_ffn_dim
          a      =      encoder_layers
          a      =      encoder_attention_heads
          a      =      decoder_ffn_dim
          a      =      decoder_layers
          a      =      decoder_attention_heads
          a      =      dropout
          a      =      attention_dropout
          a      =      activation_dropout
          a      =      activation_function
          a      =      init_std
          a      =      encoder_layerdrop
          a      =      decoder_layerdrop
          a      =      use_cache
          a      =      encoder_layers
          a      =      scale_embedding  # scale factor will be sqrt(d_model) if True

          a      =      num_global_tokens
          a      =      block_size
          a      =      stagger_local_blocks

          super().__init__(
              pad_token_id=A  ,		eos_token_id=A  ,		is_encoder_decoder=A  ,		decoder_start_token_id=A  ,		forced_eos_token_id=A  ,		**A  ,		)







     @property
     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



          return self.encoder_attention_heads







     @property
     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



          return self.d_model







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							(   __UpperCamelCase)			->     int:
     a      =      filter(lambda __UpperCamelCase: p.requires_grad		,					model.parameters())
     a      =      sum([np.prod(p.size()) for p in model_parameters])
     return params


lowercase__      :			Optional[int]           =					logging.getLogger(__name__)




class 			a__       (					pl.Callback      ):







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    str:



          '''simple docstring'''



          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 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A  ,		A=True )  ->    None:



          '''simple docstring'''



          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 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Any:



          '''simple docstring'''



          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 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Optional[int]:



          '''simple docstring'''



          save_json(pl_module.metrics  ,		pl_module.metrics_save_path )
          return self._write_logs(A  ,		A  ,		"test" )







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Dict:



          '''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							(   __UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase=1		,					__UpperCamelCase=False)			->     Optional[int]:
     if metric == "rouge2":
          a      =      "{val_avg_rouge2:.4f}-{step_count}"
     elif metric == "bleu":
          a      =      "{val_avg_bleu:.4f}-{step_count}"
     elif metric == "loss":
          a      =      "{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.")

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





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

