




'''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       _UpperCamelCase			(_lowerCamelCase				:  Any						)-> Any:




     '''simple docstring'''





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


UpperCAmelCase_							:	Optional[Any]	     =		logging.getLogger(__name__)







def       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any] ,						_lowerCamelCase				:  int						)-> List[str]:




     '''simple docstring'''





     if metric == "rouge2":
          __snake_case            =	'''{val_avg_rouge2:.4f}-{step_count}'''
     elif metric == "bleu":
          __snake_case            =	'''{val_avg_bleu:.4f}-{step_count}'''
     elif metric == "em":
          __snake_case            =	'''{val_avg_em:.4f}-{step_count}'''
     elif metric == "loss":
          __snake_case            =	'''{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.'''						)

     __snake_case            =	ModelCheckpoint(
         dirpath=_lowerCamelCase ,						filename=_lowerCamelCase ,						monitor=f'''val_{metric}''' ,						mode='''max''' ,						save_top_k=1 ,						every_n_epochs=1 ,						)

     return checkpoint_callback







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any] ,						_lowerCamelCase				:  str						)-> str:




     '''simple docstring'''





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







class      lowerCAmelCase (			pl.Callback):


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	{F'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups	)}
            pl_module.logger.log_metrics(__SCREAMING_SNAKE_CASE	)


       @rank_zero_only
       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=True	)   ->	None:



            '''simple docstring'''




            logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''	)
            __snake_case            =	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
            __snake_case            =	Path(pl_module.hparams.output_dir	)
            if type_path == "test":
                 __snake_case            =	od / '''test_results.txt'''
                 __snake_case            =	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.
                 __snake_case            =	od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
                 __snake_case            =	od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
                 results_file.parent.mkdir(exist_ok=__SCREAMING_SNAKE_CASE	)
                 generations_file.parent.mkdir(exist_ok=__SCREAMING_SNAKE_CASE	)
            with open(__SCREAMING_SNAKE_CASE     ,	'''a+'''	) as writer:
                 for key in sorted(__SCREAMING_SNAKE_CASE	):
                      if key in ["log", "progress_bar", "preds"]:
                           continue
                      __snake_case            =	metrics[key]
                      if isinstance(__SCREAMING_SNAKE_CASE     ,	torch.Tensor	):
                           __snake_case            =	val.item()
                      __snake_case            =	F'''{key}: {val:.6f}\n'''
                      writer.write(__SCREAMING_SNAKE_CASE	)

            if not save_generations:
                 return

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


       @rank_zero_only
       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




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

            __snake_case            =	count_trainable_parameters(__SCREAMING_SNAKE_CASE	)
            # 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     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




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




       @rank_zero_only
       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Optional[Any]:



            '''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")












'''simple docstring'''



import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.roformer.modeling_flax_roformer import (
         FlaxRoFormerForMaskedLM,
         FlaxRoFormerForMultipleChoice,
         FlaxRoFormerForQuestionAnswering,
         FlaxRoFormerForSequenceClassification,
         FlaxRoFormerForTokenClassification,
         FlaxRoFormerModel,
     )





class      lowerCAmelCase (			unittest.TestCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=13     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=99     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=5     ,	__SCREAMING_SNAKE_CASE=4     ,	__SCREAMING_SNAKE_CASE=37     ,	__SCREAMING_SNAKE_CASE="gelu"     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=512     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=2     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=4     ,	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	seq_length
            __snake_case            =	is_training
            __snake_case            =	use_attention_mask
            __snake_case            =	use_token_type_ids
            __snake_case            =	use_labels
            __snake_case            =	vocab_size
            __snake_case            =	hidden_size
            __snake_case            =	num_hidden_layers
            __snake_case            =	num_attention_heads
            __snake_case            =	intermediate_size
            __snake_case            =	hidden_act
            __snake_case            =	hidden_dropout_prob
            __snake_case            =	attention_probs_dropout_prob
            __snake_case            =	max_position_embeddings
            __snake_case            =	type_vocab_size
            __snake_case            =	type_sequence_label_size
            __snake_case            =	initializer_range
            __snake_case            =	num_choices


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size	)

            __snake_case            =	None
            if self.use_attention_mask:
                 __snake_case            =	random_attention_mask([self.batch_size, self.seq_length]	)

            __snake_case            =	None
            if self.use_token_type_ids:
                 __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.type_vocab_size	)

            __snake_case            =	RoFormerConfig(
                vocab_size=self.vocab_size     ,	hidden_size=self.hidden_size     ,	num_hidden_layers=self.num_hidden_layers     ,	num_attention_heads=self.num_attention_heads     ,	intermediate_size=self.intermediate_size     ,	hidden_act=self.hidden_act     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	max_position_embeddings=self.max_position_embeddings     ,	type_vocab_size=self.type_vocab_size     ,	is_decoder=__SCREAMING_SNAKE_CASE     ,	initializer_range=self.initializer_range     ,	)

            return config, input_ids, token_type_ids, attention_mask




       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.prepare_config_and_inputs()
            __snake_case       ,     __snake_case       ,     __snake_case       ,     __snake_case            =	config_and_inputs
            __snake_case            =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
            return config, inputs_dict





@require_flax
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   Tuple										=      True

       __lowercase	:   Optional[int]										=      (
           (
               FlaxRoFormerModel,
               FlaxRoFormerForMaskedLM,
               FlaxRoFormerForSequenceClassification,
               FlaxRoFormerForTokenClassification,
               FlaxRoFormerForMultipleChoice,
               FlaxRoFormerForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerModelTester(self	)




       @slow
       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 __snake_case            =	model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''     ,	from_pt=__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	model(np.ones((1, 1)	)	)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)





@require_flax
class      lowerCAmelCase (			unittest.TestCase):


       @slow
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''	)
            __snake_case            =	jnp.array([[0, 1, 2, 3, 4, 5]]	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)[0]

            __snake_case            =	5_0000

            __snake_case            =	(1, 6, vocab_size)
            self.assertEqual(output.shape     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	jnp.array(
                [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]	)

            self.assertTrue(jnp.allclose(output[:, :3, :3]     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-4	)	)






