


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


snake_case__     							= logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

snake_case__     							= {
    "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
snake_case__     							= {
    "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
}
snake_case__     							= sorted(arg_to_scheduler.keys())
snake_case__     							= "{" + ", ".join(arg_to_scheduler_choices) + "}"



class     snake_case__  (      pl.LightningModule  ):
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase="base"     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      **_UpperCAmelCase     ,      ):




										"""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(__snake_case      )
										__magic_name__				:							Union[str, Any]				=      0
										__magic_name__				:							int				=      Path(self.hparams.output_dir      )
										__magic_name__				:							Tuple				=      self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
																__magic_name__				:							Dict				=      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=__snake_case     ,      **__snake_case     ,      )
										else:
																__magic_name__				:							List[str]				=      config

										__magic_name__				:							int				=      ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
										for p in extra_model_params:
																if getattr(self.hparams     ,      __snake_case     ,      __snake_case      ):
																						assert hasattr(self.config     ,      __snake_case      ), F"model config doesn\'t have a `{p}` attribute"
																						setattr(self.config     ,      __snake_case     ,      getattr(self.hparams     ,      __snake_case      )      )

										if tokenizer is None:
																__magic_name__				:							str				=      AutoTokenizer.from_pretrained(
																    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,      cache_dir=__snake_case     ,      )
										else:
																__magic_name__				:							Optional[int]				=      tokenizer
										__magic_name__				:							Dict				=      MODEL_MODES[mode]
										if model is None:
																__magic_name__				:							str				=      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=__snake_case     ,      )
										else:
																__magic_name__				:							List[Any]				=      model
				def  UpperCamelCase_						(  self     ,      *_UpperCAmelCase     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      self.model_type.from_pretrained(*__snake_case     ,      **__snake_case      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




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




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      self.model
										__magic_name__				:							str				=      ['bias', 'LayerNorm.weight']
										__magic_name__				:							Optional[Any]				=      [
										    {
										        '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:
																__magic_name__				:							Dict				=      Adafactor(
																    __snake_case     ,      lr=self.hparams.learning_rate     ,      scale_parameter=__snake_case     ,      relative_step=__snake_case      )

										else:
																__magic_name__				:							List[Any]				=      AdamW(
																    __snake_case     ,      lr=self.hparams.learning_rate     ,      eps=self.hparams.adam_epsilon      )
										__magic_name__				:							str				=      optimizer

										__magic_name__				:							int				=      self.get_lr_scheduler()

										return [optimizer], [scheduler]
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										return self.validation_step(__snake_case     ,      __snake_case      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										return self.validation_end(__snake_case      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							str				=      max(1     ,      self.hparams.gpus      )  # TODO: consider num_tpu_cores
										__magic_name__				:							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  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										if stage == "test":
																__magic_name__				:							Dict				=      len(self.test_dataloader().dataset      )
										else:
																__magic_name__				:							Dict				=      self.get_dataloader('train'     ,      self.hparams.train_batch_size     ,      shuffle=__snake_case      )
																__magic_name__				:							str				=      len(self.train_dataloader().dataset      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = False      ):




										"""simple docstring"""




										raise NotImplementedError('You must implement this for your task'      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return self.train_loader
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return self.get_dataloader('dev'     ,      self.hparams.eval_batch_size     ,      shuffle=__snake_case      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return self.get_dataloader('test'     ,      self.hparams.eval_batch_size     ,      shuffle=__snake_case      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										return os.path.join(
										    self.hparams.data_dir     ,      'cached_{}_{}_{}'.format(
										        __snake_case     ,      list(filter(__snake_case     ,      self.hparams.model_name_or_path.split('/'      )      )      ).pop()     ,      str(self.hparams.max_seq_length      )     ,      )     ,      )
				@pl.utilities.rank_zero_only
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      self.output_dir.joinpath('best_tfmr'      )
										__magic_name__				:							Any				=      self.step_count
										self.model.save_pretrained(__snake_case      )
										self.tokenizer.save_pretrained(__snake_case      )





				@staticmethod
				def  UpperCamelCase_						(  _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




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






class     snake_case__  (      pl.Callback  ):
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""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     snake_case__  (      pl.Callback  ):
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      trainer.lr_schedulers[0]['scheduler']
										__magic_name__				:							Dict				=      {F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()      )}
										pl_module.logger.log_metrics(__snake_case      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




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





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




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






def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[Any]      ,						lowercase__  :Tuple				)       ->  int:
						parser.add_argument(
						    '--output_dir'      ,						default=UpperCamelCase_      ,						type=UpperCamelCase_      ,						required=UpperCamelCase_      ,						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=UpperCamelCase_      ,						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=UpperCamelCase_				)
						parser.add_argument('--max_grad_norm'      ,						dest='gradient_clip_val'      ,						default=1.0      ,						type=UpperCamelCase_      ,						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=UpperCamelCase_      ,						default=1      ,						help='Number of updates steps to accumulate before performing a backward/update pass.'      ,						)
						parser.add_argument('--seed'      ,						type=UpperCamelCase_      ,						default=4_2      ,						help='random seed for initialization'				)
						parser.add_argument(
						    '--data_dir'      ,						default=UpperCamelCase_      ,						type=UpperCamelCase_      ,						required=UpperCamelCase_      ,						help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'      ,						)



def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple      ,						lowercase__  :List[str]      ,						lowercase__  :str=None      ,						lowercase__  :Union[str, Any]=True      ,						lowercase__  :str=None      ,						lowercase__  :int=[]      ,						lowercase__  :Optional[int]=None      ,						lowercase__  :Tuple=None      ,						**lowercase__  :List[str]      ,						)       ->  List[Any]:
						pl.seed_everything(args.seed				)

						# init model
						__magic_name__				:							int				=      Path(model.hparams.output_dir				)
						odir.mkdir(exist_ok=UpperCamelCase_				)

						# add custom checkpoints
						if checkpoint_callback is None:
												__magic_name__				:							Union[str, Any]				=      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(UpperCamelCase_				)
						if logging_callback is None:
												__magic_name__				:							Any				=      LoggingCallback()

						__magic_name__				:							Dict				=      {}

						# TODO: remove with PyTorch 1.6 since pl uses native amp
						if args.fpaa:
												__magic_name__				:							Dict				=      1_6
												# train_params["amp_level"] = args.fp16_opt_level

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

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

						__magic_name__				:							Optional[int]				=      pl.Trainer.from_argparse_args(
						    UpperCamelCase_      ,						weights_summary=UpperCamelCase_      ,						callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]      ,						logger=UpperCamelCase_      ,						**UpperCamelCase_      ,						)

						if args.do_train:
												trainer.fit(UpperCamelCase_				)

						return trainer






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case__     							= {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'FlaubertForMultipleChoice',
			    'FlaubertForQuestionAnswering',
			    'FlaubertForQuestionAnsweringSimple',
			    'FlaubertForSequenceClassification',
			    'FlaubertForTokenClassification',
			    'FlaubertModel',
			    'FlaubertWithLMHeadModel',
			    'FlaubertPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFFlaubertForMultipleChoice',
			    'TFFlaubertForQuestionAnsweringSimple',
			    'TFFlaubertForSequenceClassification',
			    'TFFlaubertForTokenClassification',
			    'TFFlaubertModel',
			    'TFFlaubertPreTrainedModel',
			    'TFFlaubertWithLMHeadModel',
			]


if TYPE_CHECKING:
			from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
			from .tokenization_flaubert import FlaubertTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flaubert import (
						    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaubertForMultipleChoice,
						    FlaubertForQuestionAnswering,
						    FlaubertForQuestionAnsweringSimple,
						    FlaubertForSequenceClassification,
						    FlaubertForTokenClassification,
						    FlaubertModel,
						    FlaubertPreTrainedModel,
						    FlaubertWithLMHeadModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_flaubert import (
						    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFlaubertForMultipleChoice,
						    TFFlaubertForQuestionAnsweringSimple,
						    TFFlaubertForSequenceClassification,
						    TFFlaubertForTokenClassification,
						    TFFlaubertModel,
						    TFFlaubertPreTrainedModel,
						    TFFlaubertWithLMHeadModel,
						)

else:
			import sys

			snake_case__     							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


