


import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

SCREAMING_SNAKE_CASE				:						int			   = 16
SCREAMING_SNAKE_CASE				:						Union[str, Any]			   = 32




def 			UpperCamelCase			(					_a				,						_a = 1_6						)    ->							Optional[Any]:
     '''simple docstring'''





     lowercase_				:List[Any]       			=				AutoTokenizer.from_pretrained('''bert-base-cased'''						)
     lowercase_				:List[str]       			=				load_dataset('''glue'''				,						'''mrpc'''						)

     def tokenize_function(_a						):
          # max_length=None => use the model max length (it's actually the default)
          lowercase_				:List[str]       			=				tokenizer(examples['''sentence1''']				,						examples['''sentence2''']				,						truncation=a_				,						max_length=a_						)
          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():
          lowercase_				:Optional[int]       			=				datasets.map(
              a_				,						batched=a_				,						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
     lowercase_				:int       			=				tokenized_datasets.rename_column('''label'''				,						'''labels'''						)

     def collate_fn(_a						):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          lowercase_				:int       			=				1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               lowercase_				:Any       			=				1_6
          elif accelerator.mixed_precision != "no":
               lowercase_				:Union[str, Any]       			=				8
          else:
               lowercase_				:Tuple       			=				None

          return tokenizer.pad(
              a_				,						padding='''longest'''				,						max_length=a_				,						pad_to_multiple_of=a_				,						return_tensors='''pt'''				,						)

     # Instantiate dataloaders.
     lowercase_				:List[Any]       			=				DataLoader(
         tokenized_datasets['''train''']				,						shuffle=a_				,						collate_fn=a_				,						batch_size=a_						)
     lowercase_				:Tuple       			=				DataLoader(
         tokenized_datasets['''validation''']				,						shuffle=a_				,						collate_fn=a_				,						batch_size=a_						)

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
   from accelerate.test_utils.training import mocked_dataloaders

   SCREAMING_SNAKE_CASE				:						Union[str, Any]			   = mocked_dataloaders  # noqa: F811




def 			UpperCamelCase			(					_a				,						_a						)    ->							List[str]:
     '''simple docstring'''





     if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,						a_						) == "1":
          lowercase_				:Optional[Any]       			=				2
     # Initialize accelerator
     lowercase_				:Tuple       			=				Accelerator(cpu=args.cpu				,						mixed_precision=args.mixed_precision						)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowercase_				:int       			=				config['''lr''']
     lowercase_				:List[Any]       			=				int(config['''num_epochs''']						)
     lowercase_				:Dict       			=				int(config['''seed''']						)
     lowercase_				:Tuple       			=				int(config['''batch_size''']						)

     # New Code #
     # Parse out whether we are saving every epoch or after a certain number of batches
     if hasattr(args.checkpointing_steps				,						'''isdigit'''						):
          if args.checkpointing_steps == "epoch":
               lowercase_				:Union[str, Any]       			=				args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               lowercase_				:Tuple       			=				int(args.checkpointing_steps						)
          else:
               raise ValueError(
                   f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."						)
     else:
          lowercase_				:Any       			=				None

     set_seed(a_						)

     lowercase_			,					lowercase_				:Dict       			=				get_dataloaders(a_				,						a_						)
     lowercase_				:Optional[Any]       			=				evaluate.load('''glue'''				,						'''mrpc'''						)

     # If the batch size is too big we use gradient accumulation
     lowercase_				:List[str]       			=				1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          lowercase_				:Tuple       			=				batch_size // MAX_GPU_BATCH_SIZE
          lowercase_				:List[Any]       			=				MAX_GPU_BATCH_SIZE

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowercase_				:List[str]       			=				AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''				,						return_dict=a_						)

     # 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).
     lowercase_				:Union[str, Any]       			=				model.to(accelerator.device						)

     # Instantiate optimizer
     lowercase_				:Any       			=				AdamW(params=model.parameters()				,						lr=a_						)

     # Instantiate scheduler
     lowercase_				:int       			=				get_linear_schedule_with_warmup(
         optimizer=a_				,						num_warmup_steps=1_0_0				,						num_training_steps=(len(a_						) * 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.
     lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_				:List[str]       			=				accelerator.prepare(
         a_				,						a_				,						a_				,						a_				,						a_						)

     # New Code #
     # We need to keep track of how many total steps we have iterated over
     lowercase_				:List[Any]       			=				0
     # We also need to keep track of the stating epoch so files are named properly
     lowercase_				:str       			=				0

     # We need to load the checkpoint back in before training here with `load_state`
     # The total number of epochs is adjusted based on where the state is being loaded from,
     # as we assume continuation of the same training script
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}"						)
               accelerator.load_state(args.resume_from_checkpoint						)
               lowercase_				:int       			=				os.path.basename(args.resume_from_checkpoint						)
          else:
               # Get the most recent checkpoint
               lowercase_				:Dict       			=				[f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
               dirs.sort(key=os.path.getctime						)
               lowercase_				:Tuple       			=				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          lowercase_				:Union[str, Any]       			=				os.path.splitext(a_						)[0]

          if "epoch" in training_difference:
               lowercase_				:Any       			=				int(training_difference.replace('''epoch_'''				,						''''''						)						) + 1
               lowercase_				:Dict       			=				None
          else:
               lowercase_				:Optional[Any]       			=				int(training_difference.replace('''step_'''				,						''''''						)						)
               lowercase_				:Any       			=				resume_step // len(a_						)
               resume_step -= starting_epoch * len(a_						)

    # Now we train the model
     for epoch in range(a_				,						a_						):
          model.train()
          # New Code #
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               lowercase_				:Any       			=				accelerator.skip_first_batches(a_				,						a_						)
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               lowercase_				:List[str]       			=				train_dataloader
          for step, batch in enumerate(a_						):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device						)
               lowercase_				:List[Any]       			=				model(**a_						)
               lowercase_				:List[str]       			=				outputs.loss
               lowercase_				:List[str]       			=				loss / gradient_accumulation_steps
               accelerator.backward(a_						)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
               # New Code #
               overall_step += 1

               # New Code #
               # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
               # These are saved to folders named `step_{overall_step}`
               # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
               # If mixed precision was used, will also save a "scalar.bin" file
               if isinstance(a_				,						a_						):
                    lowercase_				:List[Any]       			=				f"step_{overall_step}"
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              lowercase_				:List[Any]       			=				os.path.join(args.output_dir				,						a_						)
                         accelerator.save_state(a_						)

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

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

          # New Code #
          # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
          # These are saved to folders named `epoch_{epoch}`
          # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
          # If mixed precision was used, will also save a "scalar.bin" file
          if checkpointing_steps == "epoch":
               lowercase_				:Union[str, Any]       			=				f"epoch_{epoch}"
               if args.output_dir is not None:
                    lowercase_				:Tuple       			=				os.path.join(args.output_dir				,						a_						)
               accelerator.save_state(a_						)




def 			UpperCamelCase			(					)    ->							Union[str, Any]:
     '''simple docstring'''





     lowercase_				:int       			=				argparse.ArgumentParser(description='''Simple example of training script.'''						)
     parser.add_argument(
         '''--mixed_precision'''				,						type=a_				,						default=a_				,						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.'''						)
     parser.add_argument(
         '''--checkpointing_steps'''				,						type=a_				,						default=a_				,						help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''				,						)
     parser.add_argument(
         '''--output_dir'''				,						type=a_				,						default='''.'''				,						help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''				,						)
     parser.add_argument(
         '''--resume_from_checkpoint'''				,						type=a_				,						default=a_				,						help='''If the training should continue from a checkpoint folder.'''				,						)
     lowercase_				:List[Any]       			=				parser.parse_args()
     lowercase_				:Any       			=				{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
     training_function(a_				,						a_						)


if __name__ == "__main__":
   main()









import os
import re
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 logging


lowerCamelCase_						     =  logging.get_logger(__name__)
lowerCamelCase_						     =  {"""vocab_file""": """spiece.model"""}

lowerCamelCase_						     =  {
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
}

lowerCamelCase_						     =  {
    """google/fnet-base""": 5_1_2,
    """google/fnet-large""": 5_1_2,
}

lowerCamelCase_						     =  """▁"""




class 							a_ (						a_		):


							'''simple docstring'''






							__a:	Optional[int]     =    VOCAB_FILES_NAMES
							__a:	Union[str, Any]     =    PRETRAINED_VOCAB_FILES_MAP
							__a:	List[Any]     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__a:	Any     =    ['''input_ids''', '''token_type_ids''']

							def __init__(		self			,			lowercase_			,			lowercase_=False			,			lowercase_=True			,			lowercase_=True			,			lowercase_="<unk>"			,			lowercase_="[SEP]"			,			lowercase_="<pad>"			,			lowercase_="[CLS]"			,			lowercase_="[MASK]"			,			lowercase_ = None			,			**lowercase_			,			)					->					None:
														'''simple docstring'''

														lowerCAmelCase_										=     (
														    AddedToken(lowercase_			,			lstrip=lowercase_			,			rstrip=lowercase_			,			normalized=lowercase_   )
														    if isinstance(lowercase_			,			lowercase_   )
														    else mask_token
														)

														lowerCAmelCase_										=     {} if sp_model_kwargs is None else sp_model_kwargs

														super().__init__(
														    do_lower_case=lowercase_			,			remove_space=lowercase_			,			keep_accents=lowercase_			,			unk_token=lowercase_			,			sep_token=lowercase_			,			pad_token=lowercase_			,			cls_token=lowercase_			,			mask_token=lowercase_			,			sp_model_kwargs=self.sp_model_kwargs			,			**lowercase_			,			)

														lowerCAmelCase_										=     do_lower_case
														lowerCAmelCase_										=     remove_space
														lowerCAmelCase_										=     keep_accents
														lowerCAmelCase_										=     vocab_file

														lowerCAmelCase_										=     spm.SentencePieceProcessor(**self.sp_model_kwargs   )
														self.sp_model.Load(lowercase_   )

							@property
							def      _lowercase       (		self   )					->					Optional[Any]:
														'''simple docstring'''

														return len(self.sp_model   )

							def      _lowercase       (		self   )					->					Dict:
														'''simple docstring'''

														lowerCAmelCase_										=     {self.convert_ids_to_tokens(lowercase_   ): i for i in range(self.vocab_size   )}
														vocab.update(self.added_tokens_encoder   )
														return vocab

							def __getstate__(		self   )					->					Dict:
														'''simple docstring'''

														lowerCAmelCase_										=     self.__dict__.copy()
														lowerCAmelCase_										=     None
														return state

							def __setstate__(		self			,			lowercase_   )					->					Optional[int]:
														'''simple docstring'''

														lowerCAmelCase_										=     d

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

														lowerCAmelCase_										=     spm.SentencePieceProcessor(**self.sp_model_kwargs   )
														self.sp_model.Load(self.vocab_file   )

							def      _lowercase       (		self			,			lowercase_   )					->					str:
														'''simple docstring'''

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

														if not self.keep_accents:
																					lowerCAmelCase_										=     unicodedata.normalize('NFKD'			,			lowercase_   )
																					lowerCAmelCase_										=     ''.join([c for c in outputs if not unicodedata.combining(lowercase_   )]   )
														if self.do_lower_case:
																					lowerCAmelCase_										=     outputs.lower()

														return outputs

							def      _lowercase       (		self			,			lowercase_   )					->					List[str]:
														'''simple docstring'''

														lowerCAmelCase_										=     self.preprocess_text(lowercase_   )
														lowerCAmelCase_										=     self.sp_model.encode(lowercase_			,			out_type=lowercase_   )
														lowerCAmelCase_										=     []
														for piece in pieces:
																					if len(lowercase_   ) > 1 and piece[-1] == str(','   ) and piece[-2].isdigit():
																												lowerCAmelCase_										=     self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase_			,			''   )   )
																												if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																																			if len(cur_pieces[0]   ) == 1:
																																										lowerCAmelCase_										=     cur_pieces[1:]
																																			else:
																																										lowerCAmelCase_										=     cur_pieces[0][1:]
																												cur_pieces.append(piece[-1]   )
																												new_pieces.extend(lowercase_   )
																					else:
																												new_pieces.append(lowercase_   )

														return new_pieces

							def      _lowercase       (		self			,			lowercase_   )					->					Tuple:
														'''simple docstring'''

														return self.sp_model.PieceToId(lowercase_   )

							def      _lowercase       (		self			,			lowercase_   )					->					Union[str, Any]:
														'''simple docstring'''

														return self.sp_model.IdToPiece(lowercase_   )

							def      _lowercase       (		self			,			lowercase_   )					->					Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     []
														lowerCAmelCase_										=     ''
														lowerCAmelCase_										=     False
														for token in tokens:
																					# make sure that special tokens are not decoded using sentencepiece model
																					if token in self.all_special_tokens:
																												if not prev_is_special:
																																			out_string += " "
																												out_string += self.sp_model.decode(lowercase_   ) + token
																												lowerCAmelCase_										=     True
																												lowerCAmelCase_										=     []
																					else:
																												current_sub_tokens.append(lowercase_   )
																												lowerCAmelCase_										=     False
														out_string += self.sp_model.decode(lowercase_   )
														return out_string.strip()

							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = False			,			lowercase_ = None			,			lowercase_ = True			,			**lowercase_			,			)					->					str:
														'''simple docstring'''

														lowerCAmelCase_										=     kwargs.pop('use_source_tokenizer'			,			lowercase_   )

														lowerCAmelCase_										=     self.convert_ids_to_tokens(lowercase_			,			skip_special_tokens=lowercase_   )

														# 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_										=     []
														lowerCAmelCase_										=     []
														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(lowercase_   )   )
																																			lowerCAmelCase_										=     []
																												sub_texts.append(lowercase_   )
																					else:
																												current_sub_text.append(lowercase_   )
														if current_sub_text:
																					sub_texts.append(self.convert_tokens_to_string(lowercase_   )   )

														# Mimic the behavior of the Rust tokenizer:
														# No space after <unk>
														if spaces_between_special_tokens:
																					lowerCAmelCase_										=     re.sub(R'(<unk>) '			,			R'\1'			,			' '.join(lowercase_   )   )
														else:
																					lowerCAmelCase_										=     ''.join(lowercase_   )

														lowerCAmelCase_										=     (
														    clean_up_tokenization_spaces
														    if clean_up_tokenization_spaces is not None
														    else self.clean_up_tokenization_spaces
														)
														if clean_up_tokenization_spaces:
																					lowerCAmelCase_										=     self.clean_up_tokenization(lowercase_   )
																					return clean_text
														else:
																					return text

							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None   )					->					List[int]:
														'''simple docstring'''

														lowerCAmelCase_										=     [self.sep_token_id]
														lowerCAmelCase_										=     [self.cls_token_id]
														if token_ids_a is None:
																					return cls + token_ids_a + sep
														return cls + token_ids_a + sep + token_ids_a + sep

							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None			,			lowercase_ = False   )					->					List[int]:
														'''simple docstring'''

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

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

							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None   )					->					List[int]:
														'''simple docstring'''

														lowerCAmelCase_										=     [self.sep_token_id]
														lowerCAmelCase_										=     [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   ) * [0] + len(token_ids_a + sep   ) * [1]






							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None   )					->					Tuple[str]:
														'''simple docstring'''

														if not os.path.isdir(lowercase_   ):
																					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
																					return
														lowerCAmelCase_										=     os.path.join(
														    lowercase_			,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

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

														return (out_vocab_file,)



