




"""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      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			Tuple    =    filter(lambda lowercase__		: p.requires_grad							,    model.parameters()		)
				_lowerCamelCase     :			int    =    sum([np.prod(p.size()		) for p in model_parameters]		)
				return params


lowercase__     						=						logging.getLogger(__name__)







def      _snake_case			(				lowercase__							,    lowercase__		):
				if metric == "rouge2":
								_lowerCamelCase     :			str    =    '{val_avg_rouge2:.4f}-{step_count}'
				elif metric == "bleu":
								_lowerCamelCase     :			List[str]    =    '{val_avg_bleu:.4f}-{step_count}'
				elif metric == "em":
								_lowerCamelCase     :			Union[str, Any]    =    '{val_avg_em:.4f}-{step_count}'
				elif metric == "loss":
								_lowerCamelCase     :			Dict    =    '{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.'		)

				_lowerCamelCase     :			List[Any]    =    ModelCheckpoint(
				    dirpath=lowercase__							,    filename=lowercase__							,    monitor=f'''val_{metric}'''							,    mode='max'							,    save_top_k=1							,    every_n_epochs=1							,    )

				return checkpoint_callback







def      _snake_case			(				lowercase__							,    lowercase__		):
				return EarlyStopping(
				    monitor=f'''val_{metric}'''							,    mode='min' if 'loss' in metric else 'max'							,    patience=lowercase__							,    verbose=lowercase__							,    )






class lowerCAmelCase__	(     pl.Callback						):






			'''simple docstring'''



			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			int    =    {F'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups  )}
							pl_module.logger.log_metrics(lowercase  )
			@rank_zero_only
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase=True  ):
							logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''  )
							_lowerCamelCase     :			Dict    =    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
							_lowerCamelCase     :			List[str]    =    Path(pl_module.hparams.output_dir  )
							if type_path == "test":
											_lowerCamelCase     :			Union[str, Any]    =    od / 'test_results.txt'
											_lowerCamelCase     :			Dict    =    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.
											_lowerCamelCase     :			int    =    od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
											_lowerCamelCase     :			str    =    od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
											results_file.parent.mkdir(exist_ok=lowercase  )
											generations_file.parent.mkdir(exist_ok=lowercase  )
							with open(lowercase  ,      'a+'  ) as writer:
											for key in sorted(lowercase  ):
															if key in ["log", "progress_bar", "preds"]:
																			continue
															_lowerCamelCase     :			Tuple    =    metrics[key]
															if isinstance(lowercase  ,      torch.Tensor  ):
																			_lowerCamelCase     :			str    =    val.item()
															_lowerCamelCase     :			Tuple    =    F'''{key}: {val:.6f}\n'''
															writer.write(lowercase  )

							if not save_generations:
											return

							if "preds" in metrics:
											_lowerCamelCase     :			Any    =    '\n'.join(metrics['preds']  )
											generations_file.open('w+'  ).write(lowercase  )
			@rank_zero_only
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							try:
											_lowerCamelCase     :			Union[str, Any]    =    pl_module.model.model.num_parameters()
							except AttributeError:
											_lowerCamelCase     :			str    =    pl_module.model.num_parameters()

							_lowerCamelCase     :			List[Any]    =    count_trainable_parameters(lowercase  )
							# 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  ,      lowercase  ,      lowercase  ):
							save_json(pl_module.metrics  ,      pl_module.metrics_save_path  )
							return self._write_logs(lowercase  ,      lowercase  ,      'test'  )






			@rank_zero_only
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							save_json(pl_module.metrics  ,      pl_module.metrics_save_path  )
							# Uncommenting this will save val generations
							# return self._write_logs(trainer, pl_module, "valid")
import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
__lowerCamelCase			: List[str]							   =		object()

# For specifying empty leaf dict `{}`
__lowerCamelCase			: Optional[int]							   =		object()



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       List[str]	)							->					Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      tuple((re.compile(x + """$"""	) for x in qs)	)
  for i in range(len(__UpperCamelCase	) - len(__UpperCamelCase	) + 1	):
    SCREAMING_SNAKE_CASE__										=      [x.match(__UpperCamelCase	) for x, y in zip(__UpperCamelCase ,     ks[i:]	)]
    if matches and all(__UpperCamelCase	):
      return True
  return False



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int]	)							->					List[str]:







  """simple docstring"""





  def replace(__UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       Any	):
    for rule, replacement in rules:
      if _match(__UpperCamelCase ,     __UpperCamelCase	):
        return replacement
    return val

  return replace



def 	__SCREAMING_SNAKE_CASE  (					)							->					List[Any]:







  """simple docstring"""





  return [
      # embeddings
      (("transformer", "wpe", "embedding"), P("""mp""" ,     __UpperCamelCase	)),
      (("transformer", "wte", "embedding"), P("""mp""" ,     __UpperCamelCase	)),
      # atention
      (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__UpperCamelCase ,     """mp"""	)),
      (("attention", "out_proj", "kernel"), P("""mp""" ,     __UpperCamelCase	)),
      (("attention", "out_proj", "bias"), None),
      # mlp
      (("mlp", "c_fc", "kernel"), P(__UpperCamelCase ,     """mp"""	)),
      (("mlp", "c_fc", "bias"), P("""mp"""	)),
      (("mlp", "c_proj", "kernel"), P("""mp""" ,     __UpperCamelCase	)),
      (("mlp", "c_proj", "bias"), None),
      # layer norms
      ((r"ln_\d+", "bias"), None),
      ((r"\d+", r"ln_\d+", "scale"), None),
      (("ln_f", "bias"), None),
      (("ln_f", "scale"), None),
  ]






def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any]	)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      _get_partition_rules()
  SCREAMING_SNAKE_CASE__										=      _replacement_rules(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      {k: _unmatched for k in flatten_dict(__UpperCamelCase	)}
  SCREAMING_SNAKE_CASE__										=      {k: replace(__UpperCamelCase ,     __UpperCamelCase	) for k, v in initd.items()}
  assert _unmatched not in result.values(), "Incomplete partition spec."
  return freeze(unflatten_dict(__UpperCamelCase	)	)
