





"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFDebertaForMaskedLM,
						    TFDebertaForQuestionAnswering,
						    TFDebertaForSequenceClassification,
						    TFDebertaForTokenClassification,
						    TFDebertaModel,
						)




class 					_a		:






				def __init__(					self						:       Tuple,					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Optional[Any]=1_3,					lowerCAmelCase__						:       List[str]=7,					lowerCAmelCase__						:       Optional[Any]=True,					lowerCAmelCase__						:       str=True,					lowerCAmelCase__						:       Optional[Any]=True,					lowerCAmelCase__						:       Optional[Any]=True,					lowerCAmelCase__						:       Any=9_9,					lowerCAmelCase__						:       List[str]=3_2,					lowerCAmelCase__						:       Optional[int]=2,					lowerCAmelCase__						:       str=4,					lowerCAmelCase__						:       Optional[int]=3_7,					lowerCAmelCase__						:       Dict="gelu",					lowerCAmelCase__						:       Optional[int]=0.1,					lowerCAmelCase__						:       Dict=0.1,					lowerCAmelCase__						:       Tuple=5_1_2,					lowerCAmelCase__						:       Optional[int]=1_6,					lowerCAmelCase__						:       Optional[int]=2,					lowerCAmelCase__						:       Optional[Any]=0.02,					lowerCAmelCase__						:       Dict=3,					lowerCAmelCase__						:       Tuple=4,					lowerCAmelCase__						:       Optional[Any]=None,					)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       parent
										_UpperCamelCase     :   Tuple      =       1_3
										_UpperCamelCase     :   Optional[Any]      =       7
										_UpperCamelCase     :   Optional[Any]      =       True
										_UpperCamelCase     :   str      =       True
										_UpperCamelCase     :   List[str]      =       True
										_UpperCamelCase     :   Union[str, Any]      =       True
										_UpperCamelCase     :   List[str]      =       9_9
										_UpperCamelCase     :   Any      =       3_2
										_UpperCamelCase     :   Dict      =       2
										_UpperCamelCase     :   List[str]      =       4
										_UpperCamelCase     :   List[str]      =       3_7
										_UpperCamelCase     :   List[Any]      =       '''gelu'''
										_UpperCamelCase     :   Optional[Any]      =       0.1
										_UpperCamelCase     :   List[Any]      =       0.1
										_UpperCamelCase     :   Any      =       5_1_2
										_UpperCamelCase     :   Union[str, Any]      =       1_6
										_UpperCamelCase     :   List[str]      =       False
										_UpperCamelCase     :   Any      =       -1
										_UpperCamelCase     :   int      =       True
										_UpperCamelCase     :   Optional[Any]      =       2
										_UpperCamelCase     :   Optional[int]      =       0.02
										_UpperCamelCase     :   Optional[Any]      =       3
										_UpperCamelCase     :   str      =       4
										_UpperCamelCase     :   int      =       None






				def 							snake_case      (					self						:       Optional[int]				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       ids_tensor([self.batch_size, self.seq_length],					self.vocab_size				)

										_UpperCamelCase     :   Optional[int]      =       None
										if self.use_input_mask:
																_UpperCamelCase     :   Any      =       random_attention_mask([self.batch_size, self.seq_length]				)

										_UpperCamelCase     :   Union[str, Any]      =       None
										if self.use_token_type_ids:
																_UpperCamelCase     :   int      =       ids_tensor([self.batch_size, self.seq_length],					self.type_vocab_size				)

										_UpperCamelCase     :   Dict      =       None
										_UpperCamelCase     :   Optional[int]      =       None
										_UpperCamelCase     :   str      =       None
										if self.use_labels:
																_UpperCamelCase     :   Dict      =       ids_tensor([self.batch_size],					self.type_sequence_label_size				)
																_UpperCamelCase     :   Any      =       ids_tensor([self.batch_size, self.seq_length],					self.num_labels				)

										_UpperCamelCase     :   str      =       DebertaConfig(
										    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,					relative_attention=self.relative_attention,					max_relative_positions=self.max_relative_positions,					position_biased_input=self.position_biased_input,					initializer_range=self.initializer_range,					return_dict=lowerCAmelCase__,					)

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






				def 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Dict				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   Any      =       TFDebertaModel(config=lowerCAmelCase__				)
										_UpperCamelCase     :   List[str]      =       {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

										_UpperCamelCase     :   int      =       [input_ids, input_mask]
										_UpperCamelCase     :   Union[str, Any]      =       model(lowerCAmelCase__				)

										_UpperCamelCase     :   List[str]      =       model(lowerCAmelCase__				)

										self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)				)






				def 							snake_case      (					self						:       Optional[Any],					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       List[Any]				)      -> Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       TFDebertaForMaskedLM(config=lowerCAmelCase__				)
										_UpperCamelCase     :   Tuple      =       {
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}
										_UpperCamelCase     :   Optional[Any]      =       model(lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.vocab_size)				)






				def 							snake_case      (					self						:       Union[str, Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       str				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       self.num_labels
										_UpperCamelCase     :   int      =       TFDebertaForSequenceClassification(config=lowerCAmelCase__				)
										_UpperCamelCase     :   Union[str, Any]      =       {
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}

										_UpperCamelCase     :   Any      =       model(lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)				)






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       int				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       self.num_labels
										_UpperCamelCase     :   Optional[int]      =       TFDebertaForTokenClassification(config=lowerCAmelCase__				)
										_UpperCamelCase     :   Tuple      =       {
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}
										_UpperCamelCase     :   Optional[Any]      =       model(lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.num_labels)				)






				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       Tuple				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       TFDebertaForQuestionAnswering(config=lowerCAmelCase__				)
										_UpperCamelCase     :   str      =       {
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}

										_UpperCamelCase     :   List[str]      =       model(lowerCAmelCase__				)
										self.parent.assertEqual(result.start_logits.shape,					(self.batch_size, self.seq_length)				)
										self.parent.assertEqual(result.end_logits.shape,					(self.batch_size, self.seq_length)				)






				def 							snake_case      (					self						:       List[Any]				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       self.prepare_config_and_inputs()
										(
										    (
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				
										)     :   List[Any]      =       config_and_inputs
										_UpperCamelCase     :   Dict      =       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
										return config, inputs_dict




@require_tf
class 					_a		( _lowerCAmelCase      ,							_lowerCAmelCase      ,							unittest.TestCase							):
				UpperCamelCase								=       (
				    (
				        TFDebertaModel,
				        TFDebertaForMaskedLM,
				        TFDebertaForQuestionAnswering,
				        TFDebertaForSequenceClassification,
				        TFDebertaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				UpperCamelCase								=       (
				    {
				        '''feature-extraction''': TFDebertaModel,
				        '''fill-mask''': TFDebertaForMaskedLM,
				        '''question-answering''': TFDebertaForQuestionAnswering,
				        '''text-classification''': TFDebertaForSequenceClassification,
				        '''token-classification''': TFDebertaForTokenClassification,
				        '''zero-shot''': TFDebertaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				UpperCamelCase								=       False
				UpperCamelCase								=       False






				def 							snake_case      (					self						:       List[Any]				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       TFDebertaModelTester(self				)
										_UpperCamelCase     :   int      =       ConfigTester(self,					config_class=lowerCAmelCase__,					hidden_size=3_7				)






				def 							snake_case      (					self						:       Optional[int]				)      -> List[Any]:




										'''simple docstring'''



										self.config_tester.run_common_tests()






				def 							snake_case      (					self						:       Dict				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       Tuple				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       List[Any]				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__				)






				@slow
				def 							snake_case      (					self						:       Optional[int]				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''				)
										self.assertIsNotNone(lowerCAmelCase__				)




@require_tf
class 					_a		( unittest.TestCase							):






				@unittest.skip(reason='''Model not available yet'''				)
				def 							snake_case      (					self						:       Tuple				)      -> Optional[Any]:




										'''simple docstring'''



										pass






				@slow
				def 							snake_case      (					self						:       Any				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''				)
										_UpperCamelCase     :   List[Any]      =       tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]				)
										_UpperCamelCase     :   str      =       tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
										_UpperCamelCase     :   Tuple      =       model(lowerCAmelCase__,					attention_mask=lowerCAmelCase__				)[0]

										_UpperCamelCase     :   Union[str, Any]      =       tf.constant(
										    [
										        [
										            [-0.59_855_896, -0.80_552_566, -0.8_462_135],
										            [1.4_484_025, -0.93_483_794, -0.80_593_085],
										            [0.3_122_741, 0.00_316_059, -1.4_131_377],
										        ]
										    ]				)
										tf.debugging.assert_near(output[:, 1:4, 1:4],					lowerCAmelCase__,					atol=1e-4				)







'''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 import save_json






def 			__lowerCAmelCase			(__lowerCAmelCase ):
		_UpperCAmelCase		:							Dict             =   filter(lambda __lowerCAmelCase : p.requires_grad   ,			model.parameters() )
		_UpperCAmelCase		:							List[Any]             =   sum([np.prod(p.size() ) for p in model_parameters] )
		return params


lowerCamelCase__     						=  logging.getLogger(__name__)






class 						lowerCAmelCase__      (      pl.Callback						):




			def 				lowerCAmelCase__     (		self	:  Optional[Any]						,	lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  Union[str, Any]			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   {F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
					pl_module.logger.log_metrics(lowerCamelCase__			)




			@rank_zero_only
			def 				lowerCAmelCase__     (		self	:  Dict						,	lowerCamelCase__	:  pl.Trainer						,	lowerCamelCase__	:  pl.LightningModule						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  Any=True			) ->None:



					'''simple docstring'''







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

					if not save_generations:
							return

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




			@rank_zero_only
			def 				lowerCAmelCase__     (		self	:  List[Any]						,	lowerCamelCase__	:  Dict						,	lowerCamelCase__	:  int			) ->Optional[int]:



					'''simple docstring'''







					try:
							_UpperCAmelCase		:							int             =   pl_module.model.model.num_parameters()
					except AttributeError:
							_UpperCAmelCase		:							Dict             =   pl_module.model.num_parameters()

					_UpperCAmelCase		:							Dict             =   count_trainable_parameters(lowerCamelCase__			)
					# 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	:  List[str]						,	lowerCamelCase__	:  pl.Trainer						,	lowerCamelCase__	:  pl.LightningModule			) ->int:



					'''simple docstring'''







					save_json(pl_module.metrics						,	pl_module.metrics_save_path			)
					return self._write_logs(lowerCamelCase__						,	lowerCamelCase__						,	"test"			)






			@rank_zero_only
			def 				lowerCAmelCase__     (		self	:  List[str]						,	lowerCamelCase__	:  pl.Trainer						,	lowerCamelCase__	:  Tuple			) ->Dict:



					'''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 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase=1   ,			__lowerCAmelCase=False ):
		if metric == "rouge2":
				_UpperCAmelCase		:							Tuple             =   "{val_avg_rouge2:.4f}-{step_count}"
		elif metric == "bleu":
				_UpperCAmelCase		:							List[str]             =   "{val_avg_bleu:.4f}-{step_count}"
		elif metric == "loss":
				_UpperCAmelCase		:							Union[str, Any]             =   "{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." )

		_UpperCAmelCase		:							Optional[int]             =   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 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase ):
		return EarlyStopping(
		    monitor=F"""val_{metric}"""   ,			mode="min" if "loss" in metric else "max"   ,			patience=__lowerCAmelCase   ,			verbose=__lowerCAmelCase   ,			)



