

'''simple docstring'''

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 	lowerCAmelCase  (   pl.LightningModule ):







       def __init__(				self      :			Dict    ,   __lowercase      :			argparse.Namespace    ,   __lowercase      :			Optional[Any]=None    ,   __lowercase      :			Dict="base"    ,   __lowercase      :			List[Any]=None    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			Dict=None    ,   **__lowercase      :			List[Any]    ,   ):







            """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   )
            __lowercase      		=0
            __lowercase      		=Path(self.hparams.output_dir   )
            __lowercase      		=self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                 __lowercase      		=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:
                 __lowercase      		=config

            __lowercase      		=('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:
                 __lowercase      		=AutoTokenizer.from_pretrained(
                     self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,   cache_dir=__lowercase    ,   )
            else:
                 __lowercase      		=tokenizer
            __lowercase      		=MODEL_MODES[mode]
            if model is None:
                 __lowercase      		=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:
                 __lowercase      		=model







       def       snake_case (				self      :			List[str]    ,   *__lowercase      :			List[str]    ,   **__lowercase      :			int   ):







            """simple docstring"""







            __lowercase      		=self.model_type.from_pretrained(*__lowercase    ,   **__lowercase   )







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







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







       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=self.model
            __lowercase      		=['bias', 'LayerNorm.weight']
            __lowercase      		=[
                {
                    'params': [
                        p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay   )
                    ],  # check this named paramters
                    '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:
                 __lowercase      		=Adafactor(
                     __lowercase    ,   lr=self.hparams.learning_rate    ,   scale_parameter=__lowercase    ,   relative_step=__lowercase   )

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

            __lowercase      		=self.get_lr_scheduler()

            return [optimizer], [scheduler]







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			Dict    ,   __lowercase      :			Optional[int]   ):







            """simple docstring"""







            return self.validation_step(__lowercase    ,   __lowercase   )







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			List[str]   ):







            """simple docstring"""







            return self.validation_end(__lowercase   )







       def       snake_case (				self      :			Union[str, Any]   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Tuple    ,   __lowercase      :			Optional[int]   ):







            """simple docstring"""







            if stage == "test":
                 __lowercase      		=len(self.test_dataloader().dataset   )
            else:
                 __lowercase      		=self.get_dataloader('train'    ,   self.hparams.train_batch_size    ,   shuffle=__lowercase   )
                 __lowercase      		=len(self.train_dataloader().dataset   )







       def       snake_case (				self      :			Dict    ,   __lowercase      :			str    ,   __lowercase      :			int    ,   __lowercase      :			bool = False   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Tuple   ):







            """simple docstring"""







            return self.train_loader







       def       snake_case (				self      :			Tuple   ):







            """simple docstring"""







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







       def       snake_case (				self      :			str   ):







            """simple docstring"""







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







       def       snake_case (				self      :			str    ,   __lowercase      :			Dict   ):







            """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       snake_case (				self      :			List[Any]    ,   __lowercase      :			Dict[str, Any]   ):







            """simple docstring"""







            __lowercase      		=self.output_dir.joinpath('best_tfmr'   )
            __lowercase      		=self.step_count
            self.model.save_pretrained(__lowercase   )
            self.tokenizer.save_pretrained(__lowercase   )







       @staticmethod
       def       snake_case (				__lowercase      :			Optional[Any]    ,   __lowercase      :			Any   ):







            """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=str(Path(__lowercase   ).parent / 'test_run' / 'cache'   )    ,   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 	lowerCAmelCase  (   pl.Callback ):







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			Any    ,   __lowercase      :			List[Any]   ):







            """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 	lowerCAmelCase  (   pl.Callback ):







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			Tuple    ,   __lowercase      :			List[Any]   ):







            """simple docstring"""







            for name, param in pl_module.model.rag.named_parameters():
                 if param.grad is None:
                      print(__lowercase   )






class 	lowerCAmelCase  (   pl.Callback ):







       def       snake_case (				self      :			Tuple    ,   __lowercase      :			str    ,   __lowercase      :			str   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Any    ,   __lowercase      :			pl.Trainer    ,   __lowercase      :			pl.LightningModule   ):







            """simple docstring"""







            rank_zero_info('***** Validation results *****'   )
            __lowercase      		=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       snake_case (				self      :			str    ,   __lowercase      :			pl.Trainer    ,   __lowercase      :			pl.LightningModule   ):







            """simple docstring"""







            rank_zero_info('***** Test results *****'   )
            __lowercase      		=trainer.callback_metrics
            # Log and save results to file
            __lowercase      		=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 					__UpperCamelCase				(						lowercase__    :	List[Any],       lowercase__    :	Tuple			):






     '''simple docstring'''





     parser.add_argument(
         '--output_dir',       default=str(Path(lowercase__			).parent / 'test_run' / 'model_checkpoints'			),       type=lowercase__,       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=lowercase__,       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=lowercase__			)
     parser.add_argument('--max_grad_norm',       dest='gradient_clip_val',       default=1.0,       type=lowercase__,       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=lowercase__,       default=1,       help='Number of updates steps to accumulate before performing a backward/update pass.',       )
     parser.add_argument('--seed',       type=lowercase__,       default=42,       help='random seed for initialization'			)
     parser.add_argument(
         '--data_dir',       default=str(Path(lowercase__			).parent / 'test_run' / 'dummy-train-data'			),       type=lowercase__,       help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.',       )







def 					__UpperCamelCase				(						lowercase__    :	BaseTransformer,       lowercase__    :	argparse.Namespace,       lowercase__    :	Any=None,       lowercase__    :	Optional[int]=True,       lowercase__    :	List[str]=[],       lowercase__    :	List[Any]=None,       lowercase__    :	int=None,       **lowercase__    :	List[str],       ):






     '''simple docstring'''





     pl.seed_everything(args.seed			)

     # init model
     __lowercase      		=Path(model.hparams.output_dir			)
     odir.mkdir(exist_ok=lowercase__			)

     # add custom checkpoints
     if checkpoint_callback is None:
          __lowercase      		=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(lowercase__			)
     if logging_callback is None:
          __lowercase      		=LoggingCallback()

     __lowercase      		={}

     if args.fpaa:
          __lowercase      		=16

     if args.gpus > 1:
          __lowercase      		='auto'
          __lowercase      		='ddp'

     __lowercase      		=args.accumulate_grad_batches
     __lowercase      		=None
     __lowercase      		='auto'

     __lowercase      		=pl.Trainer.from_argparse_args(
         lowercase__,       weights_summary=lowercase__,       callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback],       logger=lowercase__,       val_check_interval=1,       num_sanity_val_steps=2,       **lowercase__,       )

     if args.do_train:
          trainer.fit(lowercase__			)

     else:
          print('RAG modeling tests with new set functions successfuly executed!'			)
     return trainer





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase					 =	{
    '''configuration_owlvit''': [
        '''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''OwlViTConfig''',
        '''OwlViTOnnxConfig''',
        '''OwlViTTextConfig''',
        '''OwlViTVisionConfig''',
    ],
    '''processing_owlvit''': ['''OwlViTProcessor'''],
}


try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase					 =	['''OwlViTFeatureExtractor''']
       UpperCAmelCase					 =	['''OwlViTImageProcessor''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase					 =	[
           '''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''OwlViTModel''',
           '''OwlViTPreTrainedModel''',
           '''OwlViTTextModel''',
           '''OwlViTVisionModel''',
           '''OwlViTForObjectDetection''',
       ]

if TYPE_CHECKING:
       from .configuration_owlvit import (
           OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           OwlViTConfig,
           OwlViTOnnxConfig,
           OwlViTTextConfig,
           OwlViTVisionConfig,
       )
       from .processing_owlvit import OwlViTProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_owlvit import OwlViTFeatureExtractor
              from .image_processing_owlvit import OwlViTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_owlvit import (
                  OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OwlViTForObjectDetection,
                  OwlViTModel,
                  OwlViTPreTrainedModel,
                  OwlViTTextModel,
                  OwlViTVisionModel,
              )

else:
       import sys

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


