




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


UpperCamelCase__              =						logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

UpperCamelCase__              =						{
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
UpperCamelCase__              =						{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
UpperCamelCase__              =						sorted(arg_to_scheduler.keys())
UpperCamelCase__              =						'{' + ', '.join(arg_to_scheduler_choices) + '}'


class 				__SCREAMING_SNAKE_CASE							(		pl.LightningModule  ):

					def __init__(				self ,       __lowercase ,       __lowercase=None ,       __lowercase="base" ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       **__lowercase ,       ):







						'''simple docstring'''




						super().__init__()
						# TODO: move to self.save_hyperparameters()
						# self.save_hyperparameters()
						# can also expand arguments into trainer signature for easier reading

						self.save_hyperparameters(__lowercase					)
						lowerCamelCase_							:		List[str]							   =							0
						lowerCamelCase_							:		Optional[Any]							   =							Path(self.hparams.output_dir					)
						lowerCamelCase_							:		Dict							   =							self.hparams.cache_dir if self.hparams.cache_dir else None
						if config is None:
							lowerCamelCase_							:		Tuple							   =							AutoConfig.from_pretrained(
							    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path ,       **({'num_labels': num_labels} if num_labels is not None else {}) ,       cache_dir=__lowercase ,       **__lowercase ,       )
						else:
							lowerCamelCase_							:		PretrainedConfig							   =							config

						lowerCamelCase_							:		Dict							   =							('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
						for p in extra_model_params:
							if getattr(self.hparams ,       __lowercase ,       __lowercase					):
								assert hasattr(self.config ,       __lowercase					), f"model config doesn't have a `{p}` attribute"
								setattr(self.config ,       __lowercase ,       getattr(self.hparams ,       __lowercase					)					)

						if tokenizer is None:
							lowerCamelCase_							:		Union[str, Any]							   =							AutoTokenizer.from_pretrained(
							    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path ,       cache_dir=__lowercase ,       )
						else:
							lowerCamelCase_							:		PreTrainedTokenizer							   =							tokenizer
						lowerCamelCase_							:		str							   =							MODEL_MODES[mode]
						if model is None:
							lowerCamelCase_							:		List[Any]							   =							self.model_type.from_pretrained(
							    self.hparams.model_name_or_path ,       from_tf=bool('.ckpt' in self.hparams.model_name_or_path					) ,       config=self.config ,       cache_dir=__lowercase ,       )
						else:
							lowerCamelCase_							:		List[str]							   =							model

					def 	lowercase_							(				self ,       *__lowercase ,       **__lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		str							   =							self.model_type.from_pretrained(*__lowercase ,       **__lowercase					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							   =							arg_to_scheduler[self.hparams.lr_scheduler]
						lowerCamelCase_							:		List[Any]							   =							get_schedule_func(
						    self.opt ,       num_warmup_steps=self.hparams.warmup_steps ,       num_training_steps=self.total_steps()					)
						lowerCamelCase_							:		int							   =							{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
						return scheduler

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							self.model
						lowerCamelCase_							:		int							   =							['bias', 'LayerNorm.weight']
						lowerCamelCase_							:		int							   =							[
						    {
						        'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay					)],
						        'weight_decay': self.hparams.weight_decay,
						    },
						    {
						        'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay					)],
						        'weight_decay': 0.0,
						    },
						]
						if self.hparams.adafactor:
							lowerCamelCase_							:		int							   =							Adafactor(
							    __lowercase ,       lr=self.hparams.learning_rate ,       scale_parameter=__lowercase ,       relative_step=__lowercase					)

						else:
							lowerCamelCase_							:		int							   =							AdamW(
							    __lowercase ,       lr=self.hparams.learning_rate ,       eps=self.hparams.adam_epsilon					)
						lowerCamelCase_							:		str							   =							optimizer

						lowerCamelCase_							:		int							   =							self.get_lr_scheduler()

						return [optimizer], [scheduler]

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						return self.validation_step(__lowercase ,       __lowercase					)

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						return self.validation_end(__lowercase					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		int							   =							max(1 ,       self.hparams.gpus					)  # TODO: consider num_tpu_cores
						lowerCamelCase_							:		str							   =							self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
						return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						if stage == "test":
							lowerCamelCase_							:		Dict							   =							len(self.test_dataloader().dataset					)
						else:
							lowerCamelCase_							:		List[str]							   =							self.get_dataloader('train' ,       self.hparams.train_batch_size ,       shuffle=__lowercase					)
							lowerCamelCase_							:		int							   =							len(self.train_dataloader().dataset					)

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase = False					):







						'''simple docstring'''




						raise NotImplementedError('You must implement this for your task'					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						return self.train_loader

					def 	lowercase_							(				self					):







						'''simple docstring'''




						return self.get_dataloader('dev' ,       self.hparams.eval_batch_size ,       shuffle=__lowercase					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						return self.get_dataloader('test' ,       self.hparams.eval_batch_size ,       shuffle=__lowercase					)

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						return os.path.join(
						    self.hparams.data_dir ,       'cached_{}_{}_{}'.format(
						        __lowercase ,       list(filter(__lowercase ,       self.hparams.model_name_or_path.split('/'					)					)					).pop() ,       str(self.hparams.max_seq_length					) ,       ) ,       )

					@pl.utilities.rank_zero_only
					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							   =							self.output_dir.joinpath('best_tfmr'					)
						lowerCamelCase_							:		Tuple							   =							self.step_count
						self.model.save_pretrained(__lowercase					)
						self.tokenizer.save_pretrained(__lowercase					)





					@staticmethod
					def 	lowercase_							(				__lowercase ,       __lowercase					):







						'''simple docstring'''




						parser.add_argument(
						    '--model_name_or_path' ,       default=__lowercase ,       type=__lowercase ,       required=__lowercase ,       help='Path to pretrained model or model identifier from huggingface.co/models' ,       )
						parser.add_argument(
						    '--config_name' ,       default='' ,       type=__lowercase ,       help='Pretrained config name or path if not the same as model_name'					)
						parser.add_argument(
						    '--tokenizer_name' ,       default=__lowercase ,       type=__lowercase ,       help='Pretrained tokenizer name or path if not the same as model_name' ,       )
						parser.add_argument(
						    '--cache_dir' ,       default='' ,       type=__lowercase ,       help='Where do you want to store the pre-trained models downloaded from huggingface.co' ,       )
						parser.add_argument(
						    '--encoder_layerdrop' ,       type=__lowercase ,       help='Encoder layer dropout probability (Optional). Goes into model.config' ,       )
						parser.add_argument(
						    '--decoder_layerdrop' ,       type=__lowercase ,       help='Decoder layer dropout probability (Optional). Goes into model.config' ,       )
						parser.add_argument(
						    '--dropout' ,       type=__lowercase ,       help='Dropout probability (Optional). Goes into model.config' ,       )
						parser.add_argument(
						    '--attention_dropout' ,       type=__lowercase ,       help='Attention dropout probability (Optional). Goes into model.config' ,       )
						parser.add_argument('--learning_rate' ,       default=5E-5 ,       type=__lowercase ,       help='The initial learning rate for Adam.'					)
						parser.add_argument(
						    '--lr_scheduler' ,       default='linear' ,       choices=__lowercase ,       metavar=__lowercase ,       type=__lowercase ,       help='Learning rate scheduler' ,       )
						parser.add_argument('--weight_decay' ,       default=0.0 ,       type=__lowercase ,       help='Weight decay if we apply some.'					)
						parser.add_argument('--adam_epsilon' ,       default=1E-8 ,       type=__lowercase ,       help='Epsilon for Adam optimizer.'					)
						parser.add_argument('--warmup_steps' ,       default=0 ,       type=__lowercase ,       help='Linear warmup over warmup_steps.'					)
						parser.add_argument('--num_workers' ,       default=4 ,       type=__lowercase ,       help='kwarg passed to DataLoader'					)
						parser.add_argument('--num_train_epochs' ,       dest='max_epochs' ,       default=3 ,       type=__lowercase					)
						parser.add_argument('--train_batch_size' ,       default=32 ,       type=__lowercase					)
						parser.add_argument('--eval_batch_size' ,       default=32 ,       type=__lowercase					)
						parser.add_argument('--adafactor' ,       action='store_true'					)


class 				__SCREAMING_SNAKE_CASE							(		pl.Callback  ):

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						if (
						    trainer.is_global_zero and trainer.global_rank == 0
						):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
							pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.


class 				__SCREAMING_SNAKE_CASE							(		pl.Callback  ):

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							   =							trainer.lr_schedulers[0]['scheduler']
						lowerCamelCase_							:		Optional[Any]							   =							{f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
						pl_module.logger.log_metrics(__lowercase					)

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						rank_zero_info('***** Validation results *****'					)
						lowerCamelCase_							:		Optional[Any]							   =							trainer.callback_metrics
						# Log results
						for key in sorted(__lowercase					):
							if key not in ["log", "progress_bar"]:
								rank_zero_info('{} = {}\n'.format(__lowercase ,       str(metrics[key]					)					)					)





					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						rank_zero_info('***** Test results *****'					)
						lowerCamelCase_							:		Union[str, Any]							   =							trainer.callback_metrics
						# Log and save results to file
						lowerCamelCase_							:		Tuple							   =							os.path.join(pl_module.hparams.output_dir ,       'test_results.txt'					)
						with open(__lowercase ,       'w'					) as writer:
							for key in sorted(__lowercase					):
								if key not in ["log", "progress_bar"]:
									rank_zero_info('{} = {}\n'.format(__lowercase ,       str(metrics[key]					)					)					)
									writer.write('{} = {}\n'.format(__lowercase ,       str(metrics[key]					)					)					)






def 					snake_case					(							A						,   A			)							->							None:




	'''simple docstring'''


	parser.add_argument(
	    '--output_dir'						,   default=A						,   type=A						,   required=A						,   help='The output directory where the model predictions and checkpoints will be written.'						,   )
	parser.add_argument(
	    '--fp16'						,   action='store_true'						,   help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'						,   )

	parser.add_argument(
	    '--fp16_opt_level'						,   type=A						,   default='O2'						,   help=(
	        'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
	        'See details at https://nvidia.github.io/apex/amp.html'
	    )						,   )
	parser.add_argument('--n_tpu_cores'						,   dest='tpu_cores'						,   type=A			)
	parser.add_argument('--max_grad_norm'						,   dest='gradient_clip_val'						,   default=1.0						,   type=A						,   help='Max gradient norm'			)
	parser.add_argument('--do_train'						,   action='store_true'						,   help='Whether to run training.'			)
	parser.add_argument('--do_predict'						,   action='store_true'						,   help='Whether to run predictions on the test set.'			)
	parser.add_argument(
	    '--gradient_accumulation_steps'						,   dest='accumulate_grad_batches'						,   type=A						,   default=1						,   help='Number of updates steps to accumulate before performing a backward/update pass.'						,   )
	parser.add_argument('--seed'						,   type=A						,   default=42						,   help='random seed for initialization'			)
	parser.add_argument(
	    '--data_dir'						,   default=A						,   type=A						,   required=A						,   help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'						,   )






def 					snake_case					(							A						,   A						,   A=None						,   A=True						,   A=None						,   A=[]						,   A=None						,   A=None						,   **A						,   )							->							Union[str, Any]:




	'''simple docstring'''


	pl.seed_everything(args.seed			)

	# init model
	lowerCamelCase_							:		Tuple							   =							Path(model.hparams.output_dir			)
	odir.mkdir(exist_ok=A			)

	# add custom checkpoints
	if checkpoint_callback is None:
		lowerCamelCase_							:		Dict							   =							pl.callbacks.ModelCheckpoint(
		    filepath=args.output_dir						,   prefix='checkpoint'						,   monitor='val_loss'						,   mode='min'						,   save_top_k=1			)
	if early_stopping_callback:
		extra_callbacks.append(A			)
	if logging_callback is None:
		lowerCamelCase_							:		Optional[Any]							   =							LoggingCallback()

	lowerCamelCase_							:		Tuple							   =							{}

	# TODO: remove with PyTorch 1.6 since pl uses native amp
	if args.fpaa:
		lowerCamelCase_							:		str							   =							16
		# train_params["amp_level"] = args.fp16_opt_level

	if args.gpus > 1:
		lowerCamelCase_							:		Optional[int]							   =							'auto'  # "ddp"
		lowerCamelCase_							:		Any							   =							'ddp'

	lowerCamelCase_							:		Optional[int]							   =							args.accumulate_grad_batches
	lowerCamelCase_							:		str							   =							None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
	lowerCamelCase_							:		Union[str, Any]							   =							'auto'

	lowerCamelCase_							:		Optional[Any]							   =							pl.Trainer.from_argparse_args(
	    A						,   weights_summary=A						,   callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]						,   logger=A						,   **A						,   )

	if args.do_train:
		trainer.fit(A			)

	return trainer






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 				__SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

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







						'''simple docstring'''




						lowerCamelCase_							:		str							   =							parent
						lowerCamelCase_							:		Any							   =							batch_size
						lowerCamelCase_							:		Union[str, Any]							   =							seq_length
						lowerCamelCase_							:		Dict							   =							is_training
						lowerCamelCase_							:		Any							   =							use_attention_mask
						lowerCamelCase_							:		Any							   =							use_token_type_ids
						lowerCamelCase_							:		Any							   =							use_labels
						lowerCamelCase_							:		Any							   =							vocab_size
						lowerCamelCase_							:		Any							   =							hidden_size
						lowerCamelCase_							:		Optional[Any]							   =							embedding_size
						lowerCamelCase_							:		Dict							   =							num_hidden_layers
						lowerCamelCase_							:		Optional[Any]							   =							num_attention_heads
						lowerCamelCase_							:		Optional[Any]							   =							intermediate_size
						lowerCamelCase_							:		List[str]							   =							hidden_act
						lowerCamelCase_							:		Optional[Any]							   =							hidden_dropout_prob
						lowerCamelCase_							:		int							   =							attention_probs_dropout_prob
						lowerCamelCase_							:		Optional[int]							   =							max_position_embeddings
						lowerCamelCase_							:		List[str]							   =							type_vocab_size
						lowerCamelCase_							:		int							   =							type_sequence_label_size
						lowerCamelCase_							:		Tuple							   =							initializer_range
						lowerCamelCase_							:		int							   =							num_choices

					def 	lowercase_							(				self					):







						'''simple docstring'''




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

						lowerCamelCase_							:		List[Any]							   =							None
						if self.use_attention_mask:
							lowerCamelCase_							:		Tuple							   =							random_attention_mask([self.batch_size, self.seq_length]					)

						lowerCamelCase_							:		int							   =							None
						if self.use_token_type_ids:
							lowerCamelCase_							:		List[Any]							   =							ids_tensor([self.batch_size, self.seq_length] ,       self.type_vocab_size					)

						lowerCamelCase_							:		List[str]							   =							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 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							   =							self.prepare_config_and_inputs()
						lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Tuple							   =							config_and_inputs
						lowerCamelCase_							:		Union[str, Any]							   =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
						return config, inputs_dict


@require_flax
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     unittest.TestCase  ):
					_lowerCamelCase		  = True

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

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							   =							FlaxElectraModelTester(self					)





					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						for model_class_name in self.all_model_classes:
							if model_class_name == FlaxElectraForMaskedLM:
								lowerCamelCase_							:		Union[str, Any]							   =							model_class_name.from_pretrained('google/electra-small-generator'					)
							else:
								lowerCamelCase_							:		List[Any]							   =							model_class_name.from_pretrained('google/electra-small-discriminator'					)
							lowerCamelCase_							:		Dict							   =							model(np.ones((1, 1)					)					)
							self.assertIsNotNone(__lowercase					)
