

'''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


_lowerCAmelCase         =						logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

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






class   _SCREAMING_SNAKE_CASE   (   pl.LightningModule     ):



 def __init__(   self		:   List[Any]			,				a__		:   argparse.Namespace			,				a__		:   Any=None			,				a__		:   Tuple="base"			,				a__		:   int=None			,				a__		:   List[str]=None			,				a__		:   Any=None			,				**a__		:   List[str]			,				):
      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__      )
      __magic_name__						       =    0
      __magic_name__						       =    Path(self.hparams.output_dir      )
      __magic_name__						       =    self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
           __magic_name__						       =    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:
           __magic_name__						       =    config

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



 def 			snake_case__  (   self		:   Any			,				*a__		:   Optional[Any]			,				**a__		:   Any      ):
      __magic_name__						       =    self.model_type.from_pretrained(*a__			,				**a__      )



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



 def 			snake_case__  (   self		:   str      ):
      __magic_name__						       =    self.model
      __magic_name__						       =    ['''bias''', '''LayerNorm.weight''']
      __magic_name__						       =    [
          {
              '''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:
           __magic_name__						       =    Adafactor(
               a__			,				lr=self.hparams.learning_rate			,				scale_parameter=a__			,				relative_step=a__      )

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

      __magic_name__						       =    self.get_lr_scheduler()

      return [optimizer], [scheduler]



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   List[str]			,				a__		:   List[str]      ):
      return self.validation_step(a__			,				a__      )



 def 			snake_case__  (   self		:   str			,				a__		:   Any      ):
      return self.validation_end(a__      )



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    max(1			,				self.hparams.gpus      )  # TODO: consider num_tpu_cores
      __magic_name__						       =    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		:   Union[str, Any]			,				a__		:   List[Any]      ):
      if stage == "test":
           __magic_name__						       =    len(self.test_dataloader().dataset      )
      else:
           __magic_name__						       =    self.get_dataloader('''train'''			,				self.hparams.train_batch_size			,				shuffle=a__      )
           __magic_name__						       =    len(self.train_dataloader().dataset      )



 def 			snake_case__  (   self		:   List[str]			,				a__		:   str			,				a__		:   int			,				a__		:   bool = False      ):
      raise NotImplementedError('''You must implement this for your task'''      )



 def 			snake_case__  (   self		:   Any      ):
      return self.train_loader



 def 			snake_case__  (   self		:   str      ):
      return self.get_dataloader('''dev'''			,				self.hparams.eval_batch_size			,				shuffle=a__      )



 def 			snake_case__  (   self		:   Dict      ):
      return self.get_dataloader('''test'''			,				self.hparams.eval_batch_size			,				shuffle=a__      )



 def 			snake_case__  (   self		:   str			,				a__		:   Any      ):
      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 			snake_case__  (   self		:   Optional[int]			,				a__		:   Dict[str, Any]      ):
      __magic_name__						       =    self.output_dir.joinpath('''best_tfmr'''      )
      __magic_name__						       =    self.step_count
      self.model.save_pretrained(a__      )
      self.tokenizer.save_pretrained(a__      )



 @staticmethod
 def 			snake_case__  (   a__		:   List[Any]			,				a__		:   Any      ):
      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=str(Path(a__      ).parent / '''test_run''' / '''cache'''      )			,				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   _SCREAMING_SNAKE_CASE   (   pl.Callback     ):



 def 			snake_case__  (   self		:   str			,				a__		:   Dict			,				a__		:   List[Any]      ):
      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 			snake_case__  (   self		:   Optional[int]			,				a__		:   Optional[int]			,				a__		:   Tuple      ):
      # print(pl_module.model.rag)
      for name, param in pl_module.model.rag.named_parameters():
           if param.grad is None:
                print(a__      )






class   _SCREAMING_SNAKE_CASE   (   pl.Callback     ):



 def 			snake_case__  (   self		:   Tuple			,				a__		:   Dict			,				a__		:   Optional[Any]      ):
      __magic_name__						       =    trainer.lr_schedulers[0]['''scheduler''']
      __magic_name__						       =    {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()      )}
      pl_module.logger.log_metrics(a__      )



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   pl.Trainer			,				a__		:   pl.LightningModule      ):
      rank_zero_info('''***** Validation results *****'''      )
      __magic_name__						       =    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 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   pl.Trainer			,				a__		:   pl.LightningModule      ):
      rank_zero_info('''***** Test results *****'''      )
      __magic_name__						       =    trainer.callback_metrics
      # Log and save results to file
      __magic_name__						       =    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 						UpperCamelCase	(			a		,						a			)				->     None:






     '''simple docstring'''
     #  To allow all pl args uncomment the following line
     #  parser = pl.Trainer.add_argparse_args(parser)
     parser.add_argument(
         '''--output_dir'''		,						default=str(Path(a			).parent / '''test_run''' / '''model_checkpoints'''			)		,						type=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=str(Path(a			).parent / '''test_run''' / '''dummy-train-data'''			)		,						type=a		,						help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''		,						)




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






     '''simple docstring'''
     pl.seed_everything(args.seed			)

     # init model
     __magic_name__						       =    Path(model.hparams.output_dir			)
     odir.mkdir(exist_ok=a			)

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

     __magic_name__						       =    {}

     if args.fpaa:
          __magic_name__						       =    16

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

     __magic_name__						       =    args.accumulate_grad_batches
     __magic_name__						       =    None
     __magic_name__						       =    '''auto'''

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

     if args.do_train:
          trainer.fit(a			)

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









'''simple docstring'''




import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


_lowerCAmelCase         =						re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_lowerCAmelCase         =						10
_lowerCAmelCase         =						256




def 						UpperCamelCase	(			a			)				->     Optional[MinHash]:






     '''simple docstring'''
     if len(a			) < MIN_NUM_TOKENS:
          return None
     __magic_name__						       =    MinHash(num_perm=a			)
     for token in set(a			):
          min_hash.update(token.encode()			)
     return min_hash




def 						UpperCamelCase	(			a			)				->     Set[str]:






     '''simple docstring'''
     return {t for t in NON_ALPHA.split(a			) if len(t.strip()			) > 0}






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Any			,				*,
     a__		:   float = 0.85			,				):
      __magic_name__						       =    duplication_jaccard_threshold
      __magic_name__						       =    NUM_PERM
      __magic_name__						       =    MinHashLSH(threshold=self._duplication_jaccard_threshold			,				num_perm=self._num_perm      )

      __magic_name__						       =    defaultdict(a__      )



 def 			snake_case__  (   self		:   int			,				a__		:   Tuple			,				a__		:   MinHash      ):
      __magic_name__						       =    self._index.query(a__      )
      if code_key in self._index.keys:
           print(F'''Duplicate key {code_key}'''      )
           return

      self._index.insert(a__			,				a__      )
      if len(a__      ) > 0:
           for base_duplicate in close_duplicates:
                if base_duplicate in self._duplicate_clusters:
                     self._duplicate_clusters[base_duplicate].add(a__      )
                     break
           else:
                self._duplicate_clusters[close_duplicates[0]].add(a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    []
      for base, duplicates in self._duplicate_clusters.items():
           __magic_name__						       =    [base] + list(a__      )
           # reformat the cluster to be a list of dict
           __magic_name__						       =    [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
           duplicate_clusters.append(a__      )
      return duplicate_clusters



 def 			snake_case__  (   self		:   int			,				a__		:   Tuple      ):
      __magic_name__						       =    self.get_duplicate_clusters()
      with open(a__			,				'''w'''      ) as f:
           json.dump(a__			,				a__      )




def 						UpperCamelCase	(			a			)				->     List[Any]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    element
     __magic_name__						       =    get_min_hash([t for t in NON_ALPHA.split(data['''content''']			) if len(t.strip()			) > 0]			)
     if min_hash is not None:
          return (index, data["repo_name"], data["path"]), min_hash




def 						UpperCamelCase	(			a			)				->     List[Any]:






     '''simple docstring'''
     with mp.Pool() as pool:
          for data in pool.imap_unordered(
              _compute_min_hash		,						ThreadedIterator(a		,						max_queue_size=1_0000			)		,						chunksize=100		,						):
               if data is not None:
                    yield data




def 						UpperCamelCase	(			a		,						a			)				->     Tuple:






     '''simple docstring'''
     __magic_name__						       =    DuplicationIndex(duplication_jaccard_threshold=a			)

     for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(a			)			)		,						max_queue_size=100			)			):
          di.add(a		,						a			)

     # Returns a List[Cluster] where Cluster is List[str] with the filenames.
     return di.get_duplicate_clusters()




def 						UpperCamelCase	(			a		,						a			)				->     float:






     '''simple docstring'''
     __magic_name__						       =    get_tokens(a			)
     __magic_name__						       =    get_tokens(a			)
     return len(tokensa & tokensa			) / len(tokensa | tokensa			)


_lowerCAmelCase         =						None




def 						UpperCamelCase	(			a		,						a			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =    []
     for elementa in cluster:
          __magic_name__						       =    _shared_dataset[elementa['''base_index''']]['''content''']
          for elementa in extremes:
               __magic_name__						       =    _shared_dataset[elementa['''base_index''']]['''content''']
               if jaccard_similarity(a		,						a			) >= jaccard_threshold:
                    elementa["copies"] += 1
                    break
          else:
               __magic_name__						       =    1
               extremes.append(a			)
     return extremes




def 						UpperCamelCase	(			a		,						a		,						a			)				->     Optional[Any]:






     '''simple docstring'''
     global _shared_dataset
     __magic_name__						       =    dataset
     __magic_name__						       =    []
     __magic_name__						       =    partial(_find_cluster_extremes_shared		,						jaccard_threshold=a			)
     with mp.Pool() as pool:
          for extremes in tqdm(
              pool.imap_unordered(
                  a		,						a		,						)		,						total=len(a			)		,						):
               extremes_list.append(a			)
     return extremes_list




def 						UpperCamelCase	(			a		,						a = 0.85			)				->     Tuple[Type[Dataset], List[List[Dict]]]:






     '''simple docstring'''
     __magic_name__						       =    make_duplicate_clusters(a		,						a			)
     __magic_name__						       =    {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
     __magic_name__						       =    {}
     __magic_name__						       =    find_extremes(a		,						a		,						a			)
     for extremes in extremes_clusters:
          for element in extremes:
               __magic_name__						       =    element
     __magic_name__						       =    duplicate_indices - set(extreme_dict.keys()			)
     __magic_name__						       =    dataset.filter(lambda a		,						a			: idx not in remove_indices		,						with_indices=a			)

     # update duplicate_clusters
     for cluster in duplicate_clusters:
          for element in cluster:
               __magic_name__						       =    element['''base_index'''] in extreme_dict
               if element["is_extreme"]:
                    __magic_name__						       =    extreme_dict[element['''base_index''']]['''copies''']

     print(F'''Original dataset size: {len(a			)}'''			)
     print(F'''Number of duplicate clusters: {len(a			)}'''			)
     print(F'''Files in duplicate cluster: {len(a			)}'''			)
     print(F'''Unique files in duplicate cluster: {len(a			)}'''			)
     print(F'''Filtered dataset size: {len(a			)}'''			)

     return ds_filter, duplicate_clusters






