


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

import sentencepiece as spm

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


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {"""vocab_file""": """spiece.model"""}

_snake_case             =     {
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    }
}

_snake_case             =     {
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

# Segments (not really needed)
_snake_case             =     0
_snake_case             =     1
_snake_case             =     2
_snake_case             =     3
_snake_case             =     4
class 	UpperCAmelCase_					(					UpperCAmelCase_    ):

   '''simple docstring'''




   __A :      List[Any]     		=		VOCAB_FILES_NAMES
   __A :      List[Any]     		=		PRETRAINED_VOCAB_FILES_MAP
   __A :      List[str]     		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A :      List[Any]     		=		'''left'''






   def __init__(		self						,				__A						,				__A=False						,				__A=True						,				__A=False						,				__A="<s>"						,				__A="</s>"						,				__A="<unk>"						,				__A="<sep>"						,				__A="<pad>"						,				__A="<cls>"						,				__A="<mask>"						,				__A=["<eop>", "<eod>"]						,				__A = None						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				AddedToken(lowerCAmelCase__						,				lstrip=lowerCAmelCase__						,				rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__						,				lowerCAmelCase__ ) else mask_token

     lowerCamelCase :					Optional[Any]    =				{} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         do_lower_case=lowerCAmelCase__						,				remove_space=lowerCAmelCase__						,				keep_accents=lowerCAmelCase__						,				bos_token=lowerCAmelCase__						,				eos_token=lowerCAmelCase__						,				unk_token=lowerCAmelCase__						,				sep_token=lowerCAmelCase__						,				pad_token=lowerCAmelCase__						,				cls_token=lowerCAmelCase__						,				mask_token=lowerCAmelCase__						,				additional_special_tokens=lowerCAmelCase__						,				sp_model_kwargs=self.sp_model_kwargs						,				**lowerCAmelCase__						,				)

     lowerCamelCase :					Dict    =				3

     lowerCamelCase :					List[str]    =				do_lower_case
     lowerCamelCase :					List[Any]    =				remove_space
     lowerCamelCase :					int    =				keep_accents
     lowerCamelCase :					Tuple    =				vocab_file

     lowerCamelCase :					Any    =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
     self.sp_model.Load(lowerCAmelCase__ )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return len(self.sp_model )






   def _snake_case    (		self ):






     """simple docstring"""





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






   def __getstate__(		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				self.__dict__.copy()
     lowerCamelCase :					Optional[int]    =				None
     return state






   def __setstate__(		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Any    =				d

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

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






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     if self.remove_space:
       lowerCamelCase :					int    =				" ".join(inputs.strip().split() )
     else:
       lowerCamelCase :					int    =				inputs
     lowerCamelCase :					Tuple    =				outputs.replace("``"						,				"\"" ).replace("''"						,				"\"" )

     if not self.keep_accents:
       lowerCamelCase :					List[str]    =				unicodedata.normalize("NFKD"						,				lowerCAmelCase__ )
       lowerCamelCase :					int    =				"".join([c for c in outputs if not unicodedata.combining(lowerCAmelCase__ )] )
     if self.do_lower_case:
       lowerCamelCase :					Union[str, Any]    =				outputs.lower()

     return outputs






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					str    =				self.preprocess_text(lowerCAmelCase__ )
     lowerCamelCase :					Optional[Any]    =				self.sp_model.encode(lowerCAmelCase__						,				out_type=lowerCAmelCase__ )
     lowerCamelCase :					Tuple    =				[]
     for piece in pieces:
       if len(lowerCAmelCase__ ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
         lowerCamelCase :					List[str]    =				self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCAmelCase__						,				"" ) )
         if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
           if len(cur_pieces[0] ) == 1:
             lowerCamelCase :					List[str]    =				cur_pieces[1:]
           else:
             lowerCamelCase :					Union[str, Any]    =				cur_pieces[0][1:]
         cur_pieces.append(piece[-1] )
         new_pieces.extend(lowerCAmelCase__ )
       else:
         new_pieces.append(lowerCAmelCase__ )

     return new_pieces






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     return self.sp_model.PieceToId(lowerCAmelCase__ )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     return self.sp_model.IdToPiece(lowerCAmelCase__ )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				"".join(lowerCAmelCase__ ).replace(lowerCAmelCase__						,				" " ).strip()
     return out_string






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






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				kwargs.pop("use_source_tokenizer"						,				lowerCAmelCase__ )

     lowerCamelCase :					int    =				self.convert_ids_to_tokens(lowerCAmelCase__						,				skip_special_tokens=lowerCAmelCase__ )

     # To avoid mixing byte-level and unicode for byte-level BPT
     # we need to build string separately for added tokens and byte-level tokens
     # cf. https://github.com/huggingface/transformers/issues/1133
     lowerCamelCase :					Optional[int]    =				[]
     lowerCamelCase :					Optional[Any]    =				[]
     for token in filtered_tokens:
       if skip_special_tokens and token in self.all_special_ids:
         continue
       if token in self.added_tokens_encoder:
         if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__ ) )
           lowerCamelCase :					Dict    =				[]
         sub_texts.append(lowerCAmelCase__ )
       else:
         current_sub_text.append(lowerCAmelCase__ )
     if current_sub_text:
       sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__ ) )

     # Mimic the behavior of the Rust tokenizer:
     # By default, there are no spaces between special tokens
     lowerCamelCase :					Union[str, Any]    =				"".join(lowerCAmelCase__ )

     lowerCamelCase :					Union[str, Any]    =				(
         clean_up_tokenization_spaces
         if clean_up_tokenization_spaces is not None
         else self.clean_up_tokenization_spaces
     )
     if clean_up_tokenization_spaces:
       lowerCamelCase :					Dict    =				self.clean_up_tokenization(lowerCAmelCase__ )
       return clean_text
     else:
       return text






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






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				[self.sep_token_id]
     lowerCamelCase :					Union[str, Any]    =				[self.cls_token_id]
     if token_ids_a is None:
       return token_ids_a + sep + cls
     return token_ids_a + sep + token_ids_a + sep + cls






   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=lowerCAmelCase__						,				token_ids_a=lowerCAmelCase__						,				already_has_special_tokens=lowerCAmelCase__ )

     if token_ids_a is not None:
       return ([0] * len(lowerCAmelCase__ )) + [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1]
     return ([0] * len(lowerCAmelCase__ )) + [1, 1]






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






     """simple docstring"""





     lowerCamelCase :					Tuple    =				[self.sep_token_id]
     lowerCamelCase :					List[str]    =				[2]

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






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






     """simple docstring"""





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

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

     return (out_vocab_file,)






import argparse

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


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase							:  Optional[Any]   	=						16
lowerCAmelCase							:  List[str]   	=						32







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase = 16    ):
						SCREAMING_SNAKE_CASE_: Tuple  		=       AutoTokenizer.from_pretrained("bert-base-cased"    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       load_dataset("glue"				,	"mrpc"    )

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

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												SCREAMING_SNAKE_CASE_: Tuple  		=       datasets.map(
												    _UpperCAmelCase				,	batched=_UpperCAmelCase				,	remove_columns=["idx", "sentence1", "sentence2"]				,	)

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       tokenized_datasets.rename_column("label"				,	"labels"    )

						def collate_fn(_UpperCAmelCase    ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												SCREAMING_SNAKE_CASE_: List[str]  		=       1_28 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       16
												elif accelerator.mixed_precision != "no":
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       8
												else:
																		SCREAMING_SNAKE_CASE_: List[str]  		=       None

												return tokenizer.pad(
												    _UpperCAmelCase				,	padding="longest"				,	max_length=_UpperCAmelCase				,	pad_to_multiple_of=_UpperCAmelCase				,	return_tensors="pt"				,	)

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE_: Dict  		=       DataLoader(
						    tokenized_datasets["train"]				,	shuffle=_UpperCAmelCase				,	collate_fn=_UpperCAmelCase				,	batch_size=_UpperCAmelCase				,	drop_last=_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       DataLoader(
						    tokenized_datasets["validation"]				,	shuffle=_UpperCAmelCase				,	collate_fn=_UpperCAmelCase				,	batch_size=_UpperCAmelCase				,	drop_last=(accelerator.mixed_precision == "fp8")				,	)

						return train_dataloader, eval_dataloader







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						# Initialize accelerator
						SCREAMING_SNAKE_CASE_: str  		=       Accelerator(cpu=args.cpu				,	mixed_precision=args.mixed_precision    )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						SCREAMING_SNAKE_CASE_: int  		=       config["lr"]
						SCREAMING_SNAKE_CASE_: Any  		=       int(config["num_epochs"]    )
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       int(config["seed"]    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       int(config["batch_size"]    )

						SCREAMING_SNAKE_CASE_: List[str]  		=       evaluate.load("glue"				,	"mrpc"    )

						# If the batch size is too big we use gradient accumulation
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												SCREAMING_SNAKE_CASE_: Tuple  		=       batch_size // MAX_GPU_BATCH_SIZE
												SCREAMING_SNAKE_CASE_: Dict  		=       MAX_GPU_BATCH_SIZE

						set_seed(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       get_dataloaders(_UpperCAmelCase				,	_UpperCAmelCase    )
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						SCREAMING_SNAKE_CASE_: List[Any]  		=       AutoModelForSequenceClassification.from_pretrained("bert-base-cased"				,	return_dict=_UpperCAmelCase    )

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						SCREAMING_SNAKE_CASE_: Tuple  		=       model.to(accelerator.device    )
						# Instantiate optimizer
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       AdamW(params=model.parameters()				,	lr=_UpperCAmelCase    )

						# Instantiate scheduler
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       get_linear_schedule_with_warmup(
						    optimizer=_UpperCAmelCase				,	num_warmup_steps=1_00				,	num_training_steps=(len(_UpperCAmelCase    ) * num_epochs) // gradient_accumulation_steps				,	)

						# 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.

						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       accelerator.prepare(
						    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )

						# Now we train the model
						for epoch in range(_UpperCAmelCase    ):
												model.train()
												for step, batch in enumerate(_UpperCAmelCase    ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device    )
																		SCREAMING_SNAKE_CASE_: Tuple  		=       model(**_UpperCAmelCase    )
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       outputs.loss
																		SCREAMING_SNAKE_CASE_: Tuple  		=       loss / gradient_accumulation_steps
																		accelerator.backward(_UpperCAmelCase    )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												model.eval()
												for step, batch in enumerate(_UpperCAmelCase    ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device    )
																		with torch.no_grad():
																								SCREAMING_SNAKE_CASE_: Optional[int]  		=       model(**_UpperCAmelCase    )
																		SCREAMING_SNAKE_CASE_: int  		=       outputs.logits.argmax(dim=-1    )
																		SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       accelerator.gather_for_metrics((predictions, batch["labels"])    )
																		metric.add_batch(
																		    predictions=_UpperCAmelCase				,	references=_UpperCAmelCase				,	)

												SCREAMING_SNAKE_CASE_: List[Any]  		=       metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}:"				,	_UpperCAmelCase    )







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: Any  		=       argparse.ArgumentParser(description="Simple example of training script."    )
						parser.add_argument(
						    "--mixed_precision"				,	type=_UpperCAmelCase				,	default=_UpperCAmelCase				,	choices=["no", "fp16", "bf16", "fp8"]				,	help="Whether to use mixed precision. Choose"
						    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
						    "and an Nvidia Ampere GPU."				,	)
						parser.add_argument("--cpu"				,	action="store_true"				,	help="If passed, will train on the CPU."    )
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       parser.parse_args()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(_UpperCAmelCase				,	_UpperCAmelCase    )


if __name__ == "__main__":
						main()






