



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


a_								=   logging.getLogger(__name__)



class 	__lowerCAmelCase       (		pl.Callback				):




	def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase			):



				'''simple docstring'''
				__lowerCamelCase  							=      {F"""lr_group_{i}""": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
				pl_module.logger.log_metrics(snake_case_			)




	@rank_zero_only
	def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase=True			):



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

				if not save_generations:
							return

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




	@rank_zero_only
	def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase			):



				'''simple docstring'''
				try:
							__lowerCamelCase  							=      pl_module.model.model.num_parameters()
				except AttributeError:
							__lowerCamelCase  							=      pl_module.model.num_parameters()

				__lowerCamelCase  							=      count_trainable_parameters(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		,			__UpperCAmelCase		,			__UpperCAmelCase			):



				'''simple docstring'''
				save_json(pl_module.metrics		,			pl_module.metrics_save_path			)
				return self._write_logs(snake_case_		,			snake_case_		,			'''test'''			)




	@rank_zero_only
	def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase			):



				'''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 						a__     (						_UpperCamelCase     :	Optional[int]	,_UpperCamelCase     :	str	,_UpperCamelCase     :	Union[str, Any]=1	,_UpperCamelCase     :	Tuple=False							):
			if metric == "rouge2":
						__lowerCamelCase  							=      """{val_avg_rouge2:.4f}-{step_count}"""
			elif metric == "bleu":
						__lowerCamelCase  							=      """{val_avg_bleu:.4f}-{step_count}"""
			elif metric == "loss":
						__lowerCamelCase  							=      """{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.'''							)

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





def 						a__     (						_UpperCamelCase     :	List[str]	,_UpperCamelCase     :	str							):
			return EarlyStopping(
			    monitor=F"""val_{metric}"""	,mode='''min''' if '''loss''' in metric else '''max'''	,patience=_lowerCamelCase	,verbose=_lowerCamelCase	,)











import unittest

import numpy as np

from transformers import ElectraConfig, 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():
  from transformers.models.electra.modeling_flax_electra import (
      FlaxElectraForCausalLM,
      FlaxElectraForMaskedLM,
      FlaxElectraForMultipleChoice,
      FlaxElectraForPreTraining,
      FlaxElectraForQuestionAnswering,
      FlaxElectraForSequenceClassification,
      FlaxElectraForTokenClassification,
      FlaxElectraModel,
  )



class 	__lowerCAmelCase       (		unittest.TestCase				):




 def __init__(  self		,			__UpperCAmelCase		,			__UpperCAmelCase=13		,			__UpperCAmelCase=7		,			__UpperCAmelCase=True		,			__UpperCAmelCase=True		,			__UpperCAmelCase=True		,			__UpperCAmelCase=True		,			__UpperCAmelCase=99		,			__UpperCAmelCase=24		,			__UpperCAmelCase=32		,			__UpperCAmelCase=5		,			__UpperCAmelCase=4		,			__UpperCAmelCase=37		,			__UpperCAmelCase="gelu"		,			__UpperCAmelCase=0.1		,			__UpperCAmelCase=0.1		,			__UpperCAmelCase=512		,			__UpperCAmelCase=16		,			__UpperCAmelCase=2		,			__UpperCAmelCase=0.02		,			__UpperCAmelCase=4		,			):



    '''simple docstring'''
    __lowerCamelCase  							=      parent
    __lowerCamelCase  							=      batch_size
    __lowerCamelCase  							=      seq_length
    __lowerCamelCase  							=      is_training
    __lowerCamelCase  							=      use_attention_mask
    __lowerCamelCase  							=      use_token_type_ids
    __lowerCamelCase  							=      use_labels
    __lowerCamelCase  							=      vocab_size
    __lowerCamelCase  							=      hidden_size
    __lowerCamelCase  							=      embedding_size
    __lowerCamelCase  							=      num_hidden_layers
    __lowerCamelCase  							=      num_attention_heads
    __lowerCamelCase  							=      intermediate_size
    __lowerCamelCase  							=      hidden_act
    __lowerCamelCase  							=      hidden_dropout_prob
    __lowerCamelCase  							=      attention_probs_dropout_prob
    __lowerCamelCase  							=      max_position_embeddings
    __lowerCamelCase  							=      type_vocab_size
    __lowerCamelCase  							=      type_sequence_label_size
    __lowerCamelCase  							=      initializer_range
    __lowerCamelCase  							=      num_choices




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size			)

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

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

    __lowerCamelCase  							=      ElectraConfig(
        vocab_size=self.vocab_size		,			hidden_size=self.hidden_size		,			embedding_size=self.embedding_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		,			initializer_range=self.initializer_range		,			)

    return config, input_ids, token_type_ids, attention_mask




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.prepare_config_and_inputs()
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase  							=      config_and_inputs
    __lowerCamelCase  							=      {'''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				):
 lowerCAmelCase__       =					True

 lowerCAmelCase__       =					(
     (
         FlaxElectraModel,
         FlaxElectraForCausalLM,
         FlaxElectraForMaskedLM,
         FlaxElectraForPreTraining,
         FlaxElectraForTokenClassification,
         FlaxElectraForQuestionAnswering,
         FlaxElectraForMultipleChoice,
         FlaxElectraForSequenceClassification,
     )
     if is_flax_available()
     else ()
 )




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      FlaxElectraModelTester(self			)




 @slow
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    for model_class_name in self.all_model_classes:
       if model_class_name == FlaxElectraForMaskedLM:
          __lowerCamelCase  							=      model_class_name.from_pretrained('''google/electra-small-generator'''			)
       else:
          __lowerCamelCase  							=      model_class_name.from_pretrained('''google/electra-small-discriminator'''			)
       __lowerCamelCase  							=      model(np.ones((1, 1)			)			)
       self.assertIsNotNone(__UpperCAmelCase			)






