



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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__A       =						logging.get_logger(__name__)

__A       =						'''▁'''

__A       =						{'''vocab_file''': '''sentencepiece.bpe.model'''}

__A       =						{
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

__A       =						{
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
__A       =						['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']






class 		lowercase						(			snake_case__):






 """simple docstring"""


 a__				: Dict		       =							VOCAB_FILES_NAMES
 a__				: Tuple		       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 a__				: Union[str, Any]		       =							PRETRAINED_VOCAB_FILES_MAP
 a__				: Tuple		       =							["input_ids", "attention_mask"]

 a__				: List[int]		       =							[]
 a__				: List[int]		       =							[]




 def __init__(     self     :						List[str]						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						List[str]="<s>"						,  __UpperCAmelCase     :						Optional[Any]="</s>"						,  __UpperCAmelCase     :						Any="</s>"						,  __UpperCAmelCase     :						Any="<s>"						,  __UpperCAmelCase     :						Tuple="<unk>"						,  __UpperCAmelCase     :						Tuple="<pad>"						,  __UpperCAmelCase     :						Optional[int]="<mask>"						,  __UpperCAmelCase     :						List[str]=None						,  __UpperCAmelCase     :						Tuple=None						,  __UpperCAmelCase     :						str=None						,  __UpperCAmelCase     :						Optional[Dict[str, Any]] = None						,  __UpperCAmelCase     :						List[Any]=None						,  **__UpperCAmelCase     :						Optional[Any]						,  )      ->			Union[str, Any]:
       # Mask token behave like a normal word, i.e. include the space before it
       UpperCAmelCase_=       AddedToken(__UpperCAmelCase						,  lstrip=__UpperCAmelCase						,  rstrip=__UpperCAmelCase					) if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					) else mask_token

       UpperCAmelCase_=       {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=__UpperCAmelCase						,  eos_token=__UpperCAmelCase						,  unk_token=__UpperCAmelCase						,  sep_token=__UpperCAmelCase						,  cls_token=__UpperCAmelCase						,  pad_token=__UpperCAmelCase						,  mask_token=__UpperCAmelCase						,  tokenizer_file=__UpperCAmelCase						,  src_lang=__UpperCAmelCase						,  tgt_lang=__UpperCAmelCase						,  additional_special_tokens=__UpperCAmelCase						,  sp_model_kwargs=self.sp_model_kwargs						,  **__UpperCAmelCase						,  )

       UpperCAmelCase_=       spm.SentencePieceProcessor(**self.sp_model_kwargs					)
       self.sp_model.Load(str(__UpperCAmelCase					)					)
       UpperCAmelCase_=       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'

       # Mimic fairseq token-to-id alignment for the first 4 token
       UpperCAmelCase_=       {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       UpperCAmelCase_=       1

       UpperCAmelCase_=       len(self.sp_model					)
       UpperCAmelCase_=       {
           code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__UpperCAmelCase					)
       }
       UpperCAmelCase_=       {v: k for k, v in self.lang_code_to_id.items()}
       UpperCAmelCase_=       len(self.sp_model					) + len(self.lang_code_to_id					) + self.fairseq_offset

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id					)
       UpperCAmelCase_=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
       UpperCAmelCase_=       list(self.lang_code_to_id.keys()					)

       if additional_special_tokens is not None:
             # Only add those special tokens if they are not already there.
             self._additional_special_tokens.extend(
                 [t for t in additional_special_tokens if t not in self._additional_special_tokens]					)

       UpperCAmelCase_=       src_lang if src_lang is not None else """en_XX"""
       UpperCAmelCase_=       self.lang_code_to_id[self._src_lang]
       UpperCAmelCase_=       tgt_lang
       self.set_src_lang_special_tokens(self._src_lang					)




 def __getstate__(     self     :						Dict					)      ->			Optional[int]:
       UpperCAmelCase_=       self.__dict__.copy()
       UpperCAmelCase_=       None
       UpperCAmelCase_=       self.sp_model.serialized_model_proto()
       return state




 def __setstate__(     self     :						List[Any]						,  __UpperCAmelCase     :						Optional[int]					)      ->			Any:
       UpperCAmelCase_=       d

       # for backward compatibility
       if not hasattr(self						,  """sp_model_kwargs"""					):
             UpperCAmelCase_=       {}

       UpperCAmelCase_=       spm.SentencePieceProcessor(**self.sp_model_kwargs					)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)




 @property
 def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Optional[Any]:
       return len(self.sp_model					) + len(self.lang_code_to_id					) + self.fairseq_offset + 1  # Plus 1 for the mask token




 @property
 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			str:
       return self._src_lang




 @src_lang.setter
 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						str					)      ->			None:
       UpperCAmelCase_=       new_src_lang
       self.set_src_lang_special_tokens(self._src_lang					)




 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None						,  __UpperCAmelCase     :						bool = False					)      ->			List[int]:

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

       UpperCAmelCase_=       [1] * len(self.prefix_tokens					)
       UpperCAmelCase_=       [1] * len(self.suffix_tokens					)
       if token_ids_a is None:
             return prefix_ones + ([0] * len(__UpperCAmelCase					)) + suffix_ones
       return prefix_ones + ([0] * len(__UpperCAmelCase					)) + ([0] * len(__UpperCAmelCase					)) + suffix_ones




 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None					)      ->			List[int]:
       if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




 def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None					)      ->			List[int]:
       UpperCAmelCase_=       [self.sep_token_id]
       UpperCAmelCase_=       [self.cls_token_id]

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




 def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						Dict						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						Optional[str]						,  __UpperCAmelCase     :						Optional[str]						,  **__UpperCAmelCase     :						Any					)      ->			Tuple:
       if src_lang is None or tgt_lang is None:
             raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""					)
       UpperCAmelCase_=       src_lang
       UpperCAmelCase_=       self(__UpperCAmelCase						,  add_special_tokens=__UpperCAmelCase						,  return_tensors=__UpperCAmelCase						,  **__UpperCAmelCase					)
       UpperCAmelCase_=       self.convert_tokens_to_ids(__UpperCAmelCase					)
       UpperCAmelCase_=       tgt_lang_id
       return inputs




 def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			int:
       UpperCAmelCase_=       {self.convert_ids_to_tokens(__UpperCAmelCase					): i for i in range(self.vocab_size					)}
       vocab.update(self.added_tokens_encoder					)
       return vocab




 def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						str					)      ->			List[str]:
       return self.sp_model.encode(__UpperCAmelCase						,  out_type=__UpperCAmelCase					)




 def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						Any					)      ->			Optional[Any]:
       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       UpperCAmelCase_=       self.sp_model.PieceToId(__UpperCAmelCase					)

       # 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   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						List[Any]					)      ->			List[Any]:
       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   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						int					)      ->			Union[str, Any]:
       UpperCAmelCase_=       """""".join(__UpperCAmelCase					).replace(__UpperCAmelCase						,  """ """					).strip()
       return out_string




 def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						Optional[str] = None					)      ->			Tuple[str]:
       if not os.path.isdir(__UpperCAmelCase					):
             logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
             return
       UpperCAmelCase_=       os.path.join(
           __UpperCAmelCase						,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

       if os.path.abspath(self.vocab_file					) != os.path.abspath(__UpperCAmelCase					) and os.path.isfile(self.vocab_file					):
             copyfile(self.vocab_file						,  __UpperCAmelCase					)
       elif not os.path.isfile(self.vocab_file					):
             with open(__UpperCAmelCase						,  """wb"""					) as fi:
                   UpperCAmelCase_=       self.sp_model.serialized_model_proto()
                   fi.write(__UpperCAmelCase					)

       return (out_vocab_file,)




 def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						List[str]						,  __UpperCAmelCase     :						str = "en_XX"						,  __UpperCAmelCase     :						Optional[List[str]] = None						,  __UpperCAmelCase     :						str = "ro_RO"						,  **__UpperCAmelCase     :						Dict						,  )      ->			BatchEncoding:
       UpperCAmelCase_=       src_lang
       UpperCAmelCase_=       tgt_lang
       return super().prepare_seqaseq_batch(__UpperCAmelCase						,  __UpperCAmelCase						,  **__UpperCAmelCase					)




 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Optional[Any]:
       return self.set_src_lang_special_tokens(self.src_lang					)




 def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Any:
       return self.set_tgt_lang_special_tokens(self.tgt_lang					)




 def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						str					)      ->			None:
       UpperCAmelCase_=       self.lang_code_to_id[src_lang]
       UpperCAmelCase_=       []
       UpperCAmelCase_=       [self.eos_token_id, self.cur_lang_code]




 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						str					)      ->			None:
       UpperCAmelCase_=       self.lang_code_to_id[lang]
       UpperCAmelCase_=       []
       UpperCAmelCase_=       [self.eos_token_id, self.cur_lang_code]









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


__A       =						16
__A       =						32



def 	__a					(   lowerCAmelCase_       : Accelerator      ,lowerCAmelCase_       : int = 16      ,lowerCAmelCase_       : str = "bert-base-cased"    )				->			Tuple:






						'''simple docstring'''



						UpperCAmelCase_=       AutoTokenizer.from_pretrained(lowerCAmelCase_    )
						UpperCAmelCase_=       load_dataset("""glue"""      ,"""mrpc"""    )

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

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

						# 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(lowerCAmelCase_       : List[Any]    ):
												# 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(lowerCAmelCase_      ,padding="""max_length"""      ,max_length=1_28      ,return_tensors="""pt"""    )
												return tokenizer.pad(lowerCAmelCase_      ,padding="""longest"""      ,return_tensors="""pt"""    )

						# Instantiate dataloaders.
						UpperCAmelCase_=       DataLoader(
						    tokenized_datasets["""train"""]      ,shuffle=lowerCAmelCase_      ,collate_fn=lowerCAmelCase_      ,batch_size=lowerCAmelCase_    )
						UpperCAmelCase_=       DataLoader(
						    tokenized_datasets["""validation"""]      ,shuffle=lowerCAmelCase_      ,collate_fn=lowerCAmelCase_      ,batch_size=lowerCAmelCase_    )

						return train_dataloader, eval_dataloader



def 	__a					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : List[str]    )				->			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(lowerCAmelCase_    )
						UpperCAmelCase_,       UpperCAmelCase_=       get_dataloaders(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

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

						# 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=lowerCAmelCase_    )

						if accelerator.state.deepspeed_plugin is not None:
												UpperCAmelCase_=       accelerator.state.deepspeed_plugin.deepspeed_config[
												    """gradient_accumulation_steps"""
												]
						else:
												UpperCAmelCase_=       1
						UpperCAmelCase_=       (len(lowerCAmelCase_    ) * 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=lowerCAmelCase_      ,num_warmup_steps=0      ,num_training_steps=lowerCAmelCase_      ,)
						else:
												UpperCAmelCase_=       DummyScheduler(lowerCAmelCase_      ,total_num_steps=lowerCAmelCase_      ,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(
						    lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

						# 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(lowerCAmelCase_      ,lowerCAmelCase_    ):
												model.train()
												for step, batch in enumerate(lowerCAmelCase_    ):
																		UpperCAmelCase_=       model(**lowerCAmelCase_    )
																		UpperCAmelCase_=       outputs.loss
																		UpperCAmelCase_=       loss / gradient_accumulation_steps
																		accelerator.backward(lowerCAmelCase_    )
																		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(lowerCAmelCase_    ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device    )
																		with torch.no_grad():
																								UpperCAmelCase_=       model(**lowerCAmelCase_    )
																		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(lowerCAmelCase_    ) - 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=lowerCAmelCase_      ,references=lowerCAmelCase_      ,)

												UpperCAmelCase_=       metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}:"""      ,lowerCAmelCase_    )
												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(lowerCAmelCase_      ,lowerCAmelCase_    )



def 	__a					(   )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
						parser.add_argument(
						    """--model_name_or_path"""      ,type=lowerCAmelCase_      ,default="""bert-base-cased"""      ,help="""Path to pretrained model or model identifier from huggingface.co/models."""      ,required=lowerCAmelCase_      ,)
						parser.add_argument(
						    """--output_dir"""      ,type=lowerCAmelCase_      ,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=lowerCAmelCase_      ,default=lowerCAmelCase_      ,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=lowerCAmelCase_      ,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(lowerCAmelCase_      ,lowerCAmelCase_    )


if __name__ == "__main__":
	main()




