



'''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 		_lowerCAmelCase				(_lowercase			):

       """simple docstring"""

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


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






def 		_lowerCAmelCase				(_lowercase   ,					_lowercase			):

       """simple docstring"""

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

       a__								= ModelCheckpoint(
           dirpath=_lowerCamelCase   ,					filename=_lowerCamelCase   ,					monitor=F'val_{metric}'   ,					mode="max"   ,					save_top_k=1   ,					every_n_epochs=1   ,					)

       return checkpoint_callback






def 		_lowerCAmelCase				(_lowercase   ,					_lowercase			):

       """simple docstring"""

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



class    lowerCamelCase__				(			pl.Callback						):






      """simple docstring"""




      def lowerCAmelCase_					(       self							:     Any			,a__							:     Tuple			,a__							:     str   ):
             a__								= {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							:     List[Any]			,a__							:     List[str]			,a__							:     int			,a__							:     Tuple			,a__							:     str=True   ):
             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=__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
                           a__								= metrics[key]
                           if isinstance(__SCREAMING_SNAKE_CASE			,torch.Tensor   ):
                                  a__								= val.item()
                           a__								= f'{key}: {val:.6f}\n'
                           writer.write(__SCREAMING_SNAKE_CASE   )

             if not save_generations:
                    return

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




      @rank_zero_only
      def lowerCAmelCase_					(       self							:     List[str]			,a__							:     List[Any]			,a__							:     Any   ):




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

             a__								= 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							:     int			,a__							:     int			,a__							:     str   ):
             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							:     Any			,a__							:     Union[str, Any]			,a__							:     Dict   ):
             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	)	)






