


import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_snake_case			 =	logging.get_logger(__name__)

_snake_case			 =	'''▁'''

_snake_case			 =	{'''vocab_file''': '''prophetnet.tokenizer'''}

_snake_case			 =	{
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

_snake_case			 =	{
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

_snake_case			 =	{
    '''microsoft/xprophetnet-large-wiki100-cased''': 5_12,
}






def 						lowercase_(    SCREAMING_SNAKE_CASE_      ):


  '''simple docstring'''

  lowerCamelCase					:   Union[str, Any]       =							collections.OrderedDict()
  with open(_lowercase ,		"r" ,		encoding="utf-8"      ) as reader:
    lowerCamelCase					:   int       =							reader.readlines()
  for index, token in enumerate(_lowercase      ):
    lowerCamelCase					:   str       =							token.rstrip("\n"      )
    lowerCamelCase					:   List[Any]       =							index
  return vocab







class       UpperCAmelCase_   (   _a       ):

   '''simple docstring'''




   __A					: int							=						VOCAB_FILES_NAMES
   __A					: Tuple							=						PRETRAINED_VOCAB_FILES_MAP
   __A					: str							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A					: List[str]							=						["input_ids", "attention_mask"]






   def __init__(						self		,							__A		,							__A="[SEP]"		,							__A="[SEP]"		,							__A="[SEP]"		,							__A="[UNK]"		,							__A="[PAD]"		,							__A="[CLS]"		,							__A="[MASK]"		,							__A = None		,							**__A		,							):






     """simple docstring"""





     lowerCamelCase					:   Any       =							{} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=A_		,							eos_token=A_		,							sep_token=A_		,							unk_token=A_		,							pad_token=A_		,							cls_token=A_		,							mask_token=A_		,							sp_model_kwargs=self.sp_model_kwargs		,							**A_		,							)

     try:
       import sentencepiece as spm
     except ImportError:
       logger.warning(
           "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
           " pip install sentencepiece"					)
       raise

     lowerCamelCase					:   Union[str, Any]       =							spm.SentencePieceProcessor(**self.sp_model_kwargs					)
     self.sp_model.Load(str(A_					)					)
     lowerCamelCase					:   Tuple       =							vocab_file

     # Original fairseq vocab and spm vocab must be "aligned":
     # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
     # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
     # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
     # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

     # put special tokens and [unused] tokens into the vocab
     lowerCamelCase					:   Union[str, Any]       =							{"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

     for i in range(10					):
       lowerCamelCase					:   Tuple       =							F"""[unused{i}]"""
       lowerCamelCase					:   Optional[int]       =							5 + i

     # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
     lowerCamelCase					:   Any       =							12
     lowerCamelCase					:   List[Any]       =							{v: k for k, v in self.fairseq_tokens_to_ids.items()}
     for k in self.fairseq_tokens_to_ids.keys():
       self.unique_no_split_tokens.append(A_					)






   def __getstate__(						self					):






     """simple docstring"""





     lowerCamelCase					:   Dict       =							self.__dict__.copy()
     lowerCamelCase					:   List[Any]       =							None
     return state






   def __setstate__(						self		,							__A					):






     """simple docstring"""





     lowerCamelCase					:   Any       =							d
     try:
       import sentencepiece as spm
     except ImportError:
       logger.warning(
           "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
           " pip install sentencepiece"					)
       raise

     # for backward compatibility
     if not hasattr(self		,							"sp_model_kwargs"					):
       lowerCamelCase					:   List[Any]       =							{}

     lowerCamelCase					:   str       =							spm.SentencePieceProcessor(**self.sp_model_kwargs					)
     self.sp_model.Load(self.vocab_file					)






   def _snake_case       (						self		,							__A		,							__A = None		,							__A = False					):






     """simple docstring"""





     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=A_		,							token_ids_a=A_		,							already_has_special_tokens=A_					)

     if token_ids_a is None:
       return ([0] * len(A_					)) + [1]
     return ([0] * len(A_					)) + [1] + ([0] * len(A_					)) + [1]






   def _snake_case       (						self		,							__A		,							__A = None					):






     """simple docstring"""





     lowerCamelCase					:   Optional[int]       =							[self.sep_token_id]

     if token_ids_a is None:
       return len(token_ids_a + sep					) * [0]
     return len(token_ids_a + sep + sep + token_ids_a + sep					) * [0]






   @property
   def _snake_case       (						self					):






     """simple docstring"""





     return len(self.sp_model					) + self.fairseq_offset






   def _snake_case       (						self					):






     """simple docstring"""





     lowerCamelCase					:   Any       =							{self.convert_ids_to_tokens(A_					): i for i in range(self.vocab_size					)}
     vocab.update(self.added_tokens_encoder					)
     return vocab






   def _snake_case       (						self		,							__A					):






     """simple docstring"""





     return self.sp_model.encode(A_		,							out_type=A_					)






   def _snake_case       (						self		,							__A					):






     """simple docstring"""





     if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
     lowerCamelCase					:   Tuple       =							self.sp_model.PieceToId(A_					)

     # Need to return unknown token if the SP model returned 0
     return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






   def _snake_case       (						self		,							__A					):






     """simple docstring"""





     if index in self.fairseq_ids_to_tokens:
       return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(index - self.fairseq_offset					)






   def _snake_case       (						self		,							__A					):






     """simple docstring"""





     lowerCamelCase					:   Tuple       =							"".join(A_					).replace(A_		,							" "					).strip()
     return out_string






   def _snake_case       (						self		,							__A		,							__A = None					):






     """simple docstring"""





     if not os.path.isdir(A_					):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
       return
     lowerCamelCase					:   List[str]       =							os.path.join(
         A_		,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)

     if os.path.abspath(self.vocab_file					) != os.path.abspath(A_					) and os.path.isfile(self.vocab_file					):
       copyfile(self.vocab_file		,							A_					)
     elif not os.path.isfile(self.vocab_file					):
       with open(A_		,							"wb"					) as fi:
         lowerCamelCase					:   Optional[Any]       =							self.sp_model.serialized_model_proto()
         fi.write(A_					)

     return (out_vocab_file,)






   def _snake_case       (						self		,							__A		,							__A = None					):






     """simple docstring"""





     if token_ids_a is None:
       return token_ids_a + [self.sep_token_id]
     lowerCamelCase					:   int       =							[self.sep_token_id]
     return token_ids_a + sep + token_ids_a + sep







import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__snake_case   							=						1_6
__snake_case   							=						3_2






def 		_A		(		_lowercase					,  _lowercase = 16					,  _lowercase = "bert-base-cased" )					-> Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase =			AutoTokenizer.from_pretrained(_lowercase )
     __UpperCamelCase =			load_dataset('glue'					,  'mrpc' )

     def tokenize_function(_lowercase ):
          # max_length=None => use the model max length (it's actually the default)
          __UpperCamelCase =			tokenizer(examples['sentence1']					,  examples['sentence2']					,  truncation=_lowercase					,  max_length=_lowercase )
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     __UpperCamelCase =			datasets.map(
         _lowercase					,  batched=_lowercase					,  remove_columns=['idx', 'sentence1', 'sentence2']					,  load_from_cache_file=_lowercase )

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     __UpperCamelCase =			tokenized_datasets.rename_column('label'					,  'labels' )

     def collate_fn(_lowercase ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(_lowercase					,  padding='max_length'					,  max_length=1_28					,  return_tensors='pt' )
          return tokenizer.pad(_lowercase					,  padding='longest'					,  return_tensors='pt' )

     # Instantiate dataloaders.
     __UpperCamelCase =			DataLoader(
         tokenized_datasets['train']					,  shuffle=_lowercase					,  collate_fn=_lowercase					,  batch_size=_lowercase )
     __UpperCamelCase =			DataLoader(
         tokenized_datasets['validation']					,  shuffle=_lowercase					,  collate_fn=_lowercase					,  batch_size=_lowercase )

     return train_dataloader, eval_dataloader






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     __UpperCamelCase =			Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __UpperCamelCase =			config['lr']
     __UpperCamelCase =			int(config['num_epochs'] )
     __UpperCamelCase =			int(config['seed'] )
     __UpperCamelCase =			int(config['batch_size'] )
     __UpperCamelCase =			args.model_name_or_path

     set_seed(_lowercase )
     __UpperCamelCase,    __UpperCamelCase =			get_dataloaders(_lowercase					,  _lowercase					,  _lowercase )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __UpperCamelCase =			AutoModelForSequenceClassification.from_pretrained(_lowercase					,  return_dict=_lowercase )

     # Instantiate optimizer
     __UpperCamelCase =			(
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     __UpperCamelCase =			optimizer_cls(params=model.parameters()					,  lr=_lowercase )

     if accelerator.state.deepspeed_plugin is not None:
          __UpperCamelCase =			accelerator.state.deepspeed_plugin.deepspeed_config[
              'gradient_accumulation_steps'
          ]
     else:
          __UpperCamelCase =			1
     __UpperCamelCase =			(len(_lowercase ) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          __UpperCamelCase =			get_linear_schedule_with_warmup(
              optimizer=_lowercase					,  num_warmup_steps=0					,  num_training_steps=_lowercase					,  )
     else:
          __UpperCamelCase =			DummyScheduler(_lowercase					,  total_num_steps=_lowercase					,  warmup_num_steps=0 )

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			accelerator.prepare(
         _lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )

     # We need to keep track of how many total steps we have iterated over
     __UpperCamelCase =			0
     # We also need to keep track of the stating epoch so files are named properly
     __UpperCamelCase =			0

     # Now we train the model
     __UpperCamelCase =			evaluate.load('glue'					,  'mrpc' )
     __UpperCamelCase =			0
     __UpperCamelCase =			{}
     for epoch in range(_lowercase					,  _lowercase ):
          model.train()
          for step, batch in enumerate(_lowercase ):
               __UpperCamelCase =			model(**_lowercase )
               __UpperCamelCase =			outputs.loss
               __UpperCamelCase =			loss / gradient_accumulation_steps
               accelerator.backward(_lowercase )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

          model.eval()
          __UpperCamelCase =			0
          for step, batch in enumerate(_lowercase ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device )
               with torch.no_grad():
                    __UpperCamelCase =			model(**_lowercase )
               __UpperCamelCase =			outputs.logits.argmax(dim=-1 )
               # It is slightly faster to call this once, than multiple times
               __UpperCamelCase,    __UpperCamelCase =			accelerator.gather(
                   (predictions, batch['labels']) )  # If we are in a multiprocess environment, the last batch has duplicates
               if accelerator.use_distributed:
                    if step == len(_lowercase ) - 1:
                         __UpperCamelCase =			predictions[: len(eval_dataloader.dataset ) - samples_seen]
                         __UpperCamelCase =			references[: len(eval_dataloader.dataset ) - samples_seen]
                    else:
                         samples_seen += references.shape[0]
               metric.add_batch(
                   predictions=_lowercase					,  references=_lowercase					,  )

          __UpperCamelCase =			metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}:'''					,  _lowercase )
          __UpperCamelCase =			eval_metric['accuracy']

          if best_performance < eval_metric["accuracy"]:
               __UpperCamelCase =			eval_metric['accuracy']

     if args.performance_lower_bound is not None:
          assert (
              args.performance_lower_bound <= best_performance
          ), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
          with open(os.path.join(args.output_dir					,  'all_results.json' )					,  'w' ) as f:
               json.dump(_lowercase					,  _lowercase )






def 		_A		(		)					-> List[str]:


     """simple docstring"""


     __UpperCamelCase =			argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
     parser.add_argument(
         '--model_name_or_path'					,  type=_lowercase					,  default='bert-base-cased'					,  help='Path to pretrained model or model identifier from huggingface.co/models.'					,  required=_lowercase					,  )
     parser.add_argument(
         '--output_dir'					,  type=_lowercase					,  default='.'					,  help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'					,  )
     parser.add_argument(
         '--performance_lower_bound'					,  type=_lowercase					,  default=_lowercase					,  help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'					,  )
     parser.add_argument(
         '--num_epochs'					,  type=_lowercase					,  default=3					,  help='Number of train epochs.'					,  )
     __UpperCamelCase =			parser.parse_args()
     __UpperCamelCase =			{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
     training_function(_lowercase					,  _lowercase )


if __name__ == "__main__":
    main()


