



'''simple docstring'''





import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil




@pytest.fixture
def lowerCAmelCase  (__A):



     """simple docstring"""



     _a     =  tmp_path / '''text.txt'''
     _a     =  textwrap.dedent(
         '''\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        ''')
     with open(__A  ,     '''w'''  ,     encoding='''utf-8''') as f:
          f.write(__A)
     return str(__A)




@pytest.fixture
def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     _a     =  tmp_path / '''text_with_image.txt'''
     with open(__A  ,     '''w'''  ,     encoding='''utf-8''') as f:
          f.write(__A)
     return str(__A)




@pytest.mark.parametrize('''keep_linebreaks'''  ,     [True, False])
def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     with open(__A  ,     encoding='''utf-8''') as f:
          _a     =  f.read().splitlines(keepends=__A)
     _a     =  Text(keep_linebreaks=__A  ,     encoding='''utf-8''')
     _a     =  text._generate_tables([[text_file]])
     _a     =  pa.concat_tables([table for _, table in generator]).to_pydict()['''text''']
     assert generated_content == expected_content




@require_pil
def lowerCAmelCase  (__A):



     """simple docstring"""



     with open(__A  ,     encoding='''utf-8''') as f:
          _a     =  f.read().splitlines()[0]
     _a     =  Text(encoding='''utf-8'''  ,     features=Features({'''image''': Image()}))
     _a     =  text._generate_tables([[text_file_with_image]])
     _a     =  pa.concat_tables([table for _, table in generator])
     assert pa_table.schema.field('''image''').type == Image()()
     _a     =  pa_table.to_pydict()['''image''']
     assert generated_content == [{"path": image_file, "bytes": None}]




@pytest.mark.parametrize('''sample_by'''  ,     ['''line''', '''paragraph''', '''document'''])
def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     with open(__A  ,     encoding='''utf-8''') as f:
          _a     =  f.read()
     if sample_by == "line":
          _a     =  expected_content.splitlines()
     elif sample_by == "paragraph":
          _a     =  expected_content.split('''\n\n''')
     elif sample_by == "document":
          _a     =  [expected_content]
     _a     =  Text(sample_by=__A  ,     encoding='''utf-8'''  ,     chunksize=100)
     _a     =  text._generate_tables([[text_file]])
     _a     =  pa.concat_tables([table for _, table in generator]).to_pydict()['''text''']
     assert generated_content == expected_content








'''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


lowercase_			=  logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

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






class __A      (  pl.LightningModule						):



 '''simple docstring'''







 def __init__(self			,       A			,       A=None			,       A="base"			,       A=None			,       A=None			,       A=None			,       **A			,       )   ->					int:


      """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(A	)
      _a     =  0
      _a     =  Path(self.hparams.output_dir	)
      _a     =  self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
           _a     =  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=A			,       **A			,       )
      else:
           _a     =  config

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

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



 def  a__  (self			,       *A			,       **A	)   ->					List[str]:


      """simple docstring"""



      _a     =  self.model_type.from_pretrained(*A			,       **A	)



 def  a__  (self	)   ->					Dict:


      """simple docstring"""



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



 def  a__  (self	)   ->					List[str]:


      """simple docstring"""



      _a     =  self.model
      _a     =  ['''bias''', '''LayerNorm.weight''']
      _a     =  [
          {
              '''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:
           _a     =  Adafactor(
               A			,       lr=self.hparams.learning_rate			,       scale_parameter=A			,       relative_step=A	)

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

      _a     =  self.get_lr_scheduler()

      return [optimizer], [scheduler]



 def  a__  (self			,       A			,       A	)   ->					Tuple:


      """simple docstring"""



      return self.validation_step(A			,       A	)



 def  a__  (self			,       A	)   ->					Any:


      """simple docstring"""



      return self.validation_end(A	)



 def  a__  (self	)   ->					int:


      """simple docstring"""



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



 def  a__  (self			,       A	)   ->					int:


      """simple docstring"""



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



 def  a__  (self			,       A			,       A			,       A = False	)   ->					Optional[int]:


      """simple docstring"""



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



 def  a__  (self	)   ->					Optional[Any]:


      """simple docstring"""



      return self.train_loader



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


      """simple docstring"""



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



 def  a__  (self	)   ->					Any:


      """simple docstring"""



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



 def  a__  (self			,       A	)   ->					Union[str, Any]:


      """simple docstring"""



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



 @pl.utilities.rank_zero_only
 def  a__  (self			,       A	)   ->					None:


      """simple docstring"""



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



 @staticmethod
 def  a__  (A			,       A	)   ->					str:


      """simple docstring"""



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






class __A      (  pl.Callback						):



 '''simple docstring'''







 def  a__  (self			,       A			,       A	)   ->					str:


      """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 __A      (  pl.Callback						):



 '''simple docstring'''







 def  a__  (self			,       A			,       A	)   ->					List[Any]:


      """simple docstring"""



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



 def  a__  (self			,       A			,       A	)   ->					Tuple:


      """simple docstring"""



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



 def  a__  (self			,       A			,       A	)   ->					Dict:


      """simple docstring"""



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





def lowerCAmelCase  (__A  ,     __A):



     """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 lowerCAmelCase  (__A  ,     __A  ,     __A=None  ,     __A=True  ,     __A=None  ,     __A=[]  ,     __A=None  ,     __A=None  ,     **__A  ,     ):



     """simple docstring"""



     pl.seed_everything(args.seed)

     # init model
     _a     =  Path(model.hparams.output_dir)
     odir.mkdir(exist_ok=__A)

     # add custom checkpoints
     if checkpoint_callback is None:
          _a     =  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:
          _a     =  LoggingCallback()

     _a     =  {}

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

     if args.gpus > 1:
          _a     =  '''auto'''  # "ddp"
          _a     =  '''ddp'''

     _a     =  args.accumulate_grad_batches
     _a     =  None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
     _a     =  '''auto'''

     _a     =  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



