




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						(						_lowerCAmelCase							:      int		):




 """simple docstring"""







 UpperCAmelCase__							       =  filter(lambda _lowerCAmelCase		: 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						(						_lowerCAmelCase							:      Union[str, Any]  ,							_lowerCAmelCase							:      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=_lowerCAmelCase  ,							filename=_lowerCAmelCase  ,							monitor=F'''val_{metric}'''  ,							mode="max"  ,							save_top_k=1  ,							every_n_epochs=1  ,							)

 return checkpoint_callback



def   lowerCAmelCase						(						_lowerCAmelCase							:      Tuple  ,							_lowerCAmelCase							:      Dict		):




 """simple docstring"""







 return EarlyStopping(
     monitor=F'''val_{metric}'''  ,							mode="min" if "loss" in metric else "max"  ,							patience=_lowerCAmelCase  ,							verbose=_lowerCAmelCase  ,							)


class 	_UpperCamelCase					( pl.Callback						):
      def 		UpperCAmelCase_  (	self      :List[Any]       ,					lowerCamelCase      :List[str]       ,					lowerCamelCase      :Union[str, Any]					)   ->	List[str]:
       UpperCAmelCase__							       =  {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 		UpperCAmelCase_  (	self      :Tuple       ,					lowerCamelCase      :pl.Trainer       ,					lowerCamelCase      :pl.LightningModule       ,					lowerCamelCase      :str       ,					lowerCamelCase      :Optional[int]=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=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
         UpperCAmelCase__							       =  metrics[key]
         if isinstance(UpperCamelCase_       ,					torch.Tensor					):
          UpperCAmelCase__							       =  val.item()
         UpperCAmelCase__							       =  f'''{key}: {val:.6f}\n'''
         writer.write(UpperCamelCase_					)

       if not save_generations:
        return

       if "preds" in metrics:
        UpperCAmelCase__							       =  "\n".join(metrics["preds"]					)
        generations_file.open("w+"					).write(UpperCamelCase_					)
      @rank_zero_only
      def 		UpperCAmelCase_  (	self      :Any       ,					lowerCamelCase      :Tuple       ,					lowerCamelCase      :Tuple					)   ->	Optional[int]:







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

       UpperCAmelCase__							       =  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 		UpperCAmelCase_  (	self      :str       ,					lowerCamelCase      :pl.Trainer       ,					lowerCamelCase      :pl.LightningModule					)   ->	Tuple:
       save_json(pl_module.metrics       ,					pl_module.metrics_save_path					)
       return self._write_logs(UpperCamelCase_       ,					UpperCamelCase_       ,					"test"					)







      @rank_zero_only
      def 		UpperCAmelCase_  (	self      :Union[str, Any]       ,					lowerCamelCase      :pl.Trainer       ,					lowerCamelCase      :List[str]					)   ->	Union[str, Any]:
       save_json(pl_module.metrics       ,					pl_module.metrics_save_path					)
       # Uncommenting this will save val generations
       # return self._write_logs(trainer, pl_module, "valid")









from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		try:
				with open(SCREAMING_SNAKE_CASE						,				'''rb'''     ) as flax_state_f:
						lowercase__            =      from_bytes(SCREAMING_SNAKE_CASE						,				flax_state_f.read()     )
		except UnpicklingError as e:
				try:
						with open(SCREAMING_SNAKE_CASE     ) as f:
								if f.read().startswith('''version'''     ):
										raise OSError(
										    '''You seem to have cloned a repository without having git-lfs installed. Please'''
										    ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
										    ''' folder you cloned.'''     )
								else:
										raise ValueError from e
				except (UnicodeDecodeError, ValueError):
						raise EnvironmentError(f'Unable to convert {model_file} to Flax deserializable object. '     )

		return load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		try:
				import torch  # noqa: F401
		except ImportError:
				logger.error(
				    '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
				    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
				    ''' instructions.'''     )
				raise

		# check if we have bf16 weights
		lowercase__            =      flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE     : x.dtype == jnp.bfloataa						,				SCREAMING_SNAKE_CASE     )     ).values()
		if any(SCREAMING_SNAKE_CASE     ):
				# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

				# and bf16 is not fully supported in PT yet.
				logger.warning(
				    '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
				    '''before loading those in PyTorch model.'''     )
				lowercase__            =      jax.tree_util.tree_map(
				    lambda SCREAMING_SNAKE_CASE     : params.astype(np.floataa     ) if params.dtype == jnp.bfloataa else params						,				SCREAMING_SNAKE_CASE     )

		lowercase__            =      ''''''

		lowercase__            =      flatten_dict(SCREAMING_SNAKE_CASE						,				sep='''.'''     )
		lowercase__            =      pt_model.state_dict()

		# keep track of unexpected & missing keys
		lowercase__            =      []
		lowercase__            =      set(pt_model_dict.keys()     )

		for flax_key_tuple, flax_tensor in flax_state_dict.items():
				lowercase__            =      flax_key_tuple.split('''.'''     )

				if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
						lowercase__            =      flax_key_tuple_array[:-1] + ['''weight''']
						lowercase__            =      jnp.transpose(SCREAMING_SNAKE_CASE						,				(3, 2, 0, 1)     )
				elif flax_key_tuple_array[-1] == "kernel":
						lowercase__            =      flax_key_tuple_array[:-1] + ['''weight''']
						lowercase__            =      flax_tensor.T
				elif flax_key_tuple_array[-1] == "scale":
						lowercase__            =      flax_key_tuple_array[:-1] + ['''weight''']

				if "time_embedding" not in flax_key_tuple_array:
						for i, flax_key_tuple_string in enumerate(SCREAMING_SNAKE_CASE     ):
								lowercase__            =      (
								    flax_key_tuple_string.replace('''_0'''						,				'''.0'''     )
								    .replace('''_1'''						,				'''.1'''     )
								    .replace('''_2'''						,				'''.2'''     )
								    .replace('''_3'''						,				'''.3'''     )
								    .replace('''_4'''						,				'''.4'''     )
								    .replace('''_5'''						,				'''.5'''     )
								    .replace('''_6'''						,				'''.6'''     )
								    .replace('''_7'''						,				'''.7'''     )
								    .replace('''_8'''						,				'''.8'''     )
								    .replace('''_9'''						,				'''.9'''     )
								)

				lowercase__            =      '''.'''.join(SCREAMING_SNAKE_CASE     )

				if flax_key in pt_model_dict:
						if flax_tensor.shape != pt_model_dict[flax_key].shape:
								raise ValueError(
								    f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
								    f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'     )
						else:
								# add weight to pytorch dict
								lowercase__            =      np.asarray(SCREAMING_SNAKE_CASE     ) if not isinstance(SCREAMING_SNAKE_CASE						,				np.ndarray     ) else flax_tensor
								lowercase__            =      torch.from_numpy(SCREAMING_SNAKE_CASE     )
								# remove from missing keys
								missing_keys.remove(SCREAMING_SNAKE_CASE     )
				else:
						# weight is not expected by PyTorch model
						unexpected_keys.append(SCREAMING_SNAKE_CASE     )

		pt_model.load_state_dict(SCREAMING_SNAKE_CASE     )

		# re-transform missing_keys to list
		lowercase__            =      list(SCREAMING_SNAKE_CASE     )

		if len(SCREAMING_SNAKE_CASE     ) > 0:
				logger.warning(
				    '''Some weights of the Flax model were not used when initializing the PyTorch model'''
				    f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
				    f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
				    ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
				    f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
				    ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
				    ''' FlaxBertForSequenceClassification model).'''     )
		if len(SCREAMING_SNAKE_CASE     ) > 0:
				logger.warning(
				    f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
				    f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
				    ''' use it for predictions and inference.'''     )

		return pt_model


