





import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

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


_UpperCAmelCase										=					logging.get_logger(__name__)

_UpperCAmelCase										=					{
    'src_vocab_file': 'vocab-src.json',
    'tgt_vocab_file': 'vocab-tgt.json',
    'merges_file': 'merges.txt',
}

_UpperCAmelCase										=					{
    'src_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'
    },
    'tgt_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'
    },
    'merges_file': {'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'},
}

_UpperCAmelCase										=					{'stas/tiny-wmt19-en-de': 1_0_2_4}
_UpperCAmelCase										=					{
    'stas/tiny-wmt19-en-de': {
        'langs': ['en', 'de'],
        'model_max_length': 1_0_2_4,
        'special_tokens_map_file': None,
        'full_tokenizer_file': None,
    }
}




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Any:
       UpperCamelCase_ 							=	set()
       UpperCamelCase_ 							=	word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)	)
              UpperCamelCase_ 							=	char
       return pairs




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		int:
       UpperCamelCase_ 							=	text.replace("，"       ,		","	)
       UpperCamelCase_ 							=	re.sub(r"。\s*"       ,		". "       ,		UpperCamelCase_	)
       UpperCamelCase_ 							=	text.replace("、"       ,		","	)
       UpperCamelCase_ 							=	text.replace("”"       ,		"\""	)
       UpperCamelCase_ 							=	text.replace("“"       ,		"\""	)
       UpperCamelCase_ 							=	text.replace("∶"       ,		":"	)
       UpperCamelCase_ 							=	text.replace("："       ,		":"	)
       UpperCamelCase_ 							=	text.replace("？"       ,		"?"	)
       UpperCamelCase_ 							=	text.replace("《"       ,		"\""	)
       UpperCamelCase_ 							=	text.replace("》"       ,		"\""	)
       UpperCamelCase_ 							=	text.replace("）"       ,		")"	)
       UpperCamelCase_ 							=	text.replace("！"       ,		"!"	)
       UpperCamelCase_ 							=	text.replace("（"       ,		"("	)
       UpperCamelCase_ 							=	text.replace("；"       ,		";"	)
       UpperCamelCase_ 							=	text.replace("１"       ,		"1"	)
       UpperCamelCase_ 							=	text.replace("」"       ,		"\""	)
       UpperCamelCase_ 							=	text.replace("「"       ,		"\""	)
       UpperCamelCase_ 							=	text.replace("０"       ,		"0"	)
       UpperCamelCase_ 							=	text.replace("３"       ,		"3"	)
       UpperCamelCase_ 							=	text.replace("２"       ,		"2"	)
       UpperCamelCase_ 							=	text.replace("５"       ,		"5"	)
       UpperCamelCase_ 							=	text.replace("６"       ,		"6"	)
       UpperCamelCase_ 							=	text.replace("９"       ,		"9"	)
       UpperCamelCase_ 							=	text.replace("７"       ,		"7"	)
       UpperCamelCase_ 							=	text.replace("８"       ,		"8"	)
       UpperCamelCase_ 							=	text.replace("４"       ,		"4"	)
       UpperCamelCase_ 							=	re.sub(r"．\s*"       ,		". "       ,		UpperCamelCase_	)
       UpperCamelCase_ 							=	text.replace("～"       ,		"~"	)
       UpperCamelCase_ 							=	text.replace("’"       ,		"'"	)
       UpperCamelCase_ 							=	text.replace("…"       ,		"..."	)
       UpperCamelCase_ 							=	text.replace("━"       ,		"-"	)
       UpperCamelCase_ 							=	text.replace("〈"       ,		"<"	)
       UpperCamelCase_ 							=	text.replace("〉"       ,		">"	)
       UpperCamelCase_ 							=	text.replace("【"       ,		"["	)
       UpperCamelCase_ 							=	text.replace("】"       ,		"]"	)
       UpperCamelCase_ 							=	text.replace("％"       ,		"%"	)
       return text




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		int:
       UpperCamelCase_ 							=	[]
       for char in text:
              UpperCamelCase_ 							=	unicodedata.category(UpperCamelCase_	)
              if cat.startswith("C"	):
                     continue
              output.append(UpperCamelCase_	)
       return "".join(UpperCamelCase_	)







class 				_UpperCamelCase       (						lowerCAmelCase_  ):
       _UpperCamelCase					:       Optional[int]       = VOCAB_FILES_NAMES
       _UpperCamelCase					:       Union[str, Any]       = PRETRAINED_VOCAB_FILES_MAP
       _UpperCamelCase					:       Dict       = PRETRAINED_INIT_CONFIGURATION
       _UpperCamelCase					:       Tuple       = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCamelCase					:       Optional[Any]       = ['''input_ids''', '''attention_mask''']

       def __init__(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			List[Any]=None       , _SCREAMING_SNAKE_CASE:			int=None       , _SCREAMING_SNAKE_CASE:			int=None       , _SCREAMING_SNAKE_CASE:			List[Any]=None       , _SCREAMING_SNAKE_CASE:			Optional[int]=False       , _SCREAMING_SNAKE_CASE:			Optional[int]="<unk>"       , _SCREAMING_SNAKE_CASE:			Any="<s>"       , _SCREAMING_SNAKE_CASE:			List[Any]="</s>"       , _SCREAMING_SNAKE_CASE:			Dict="<pad>"       , **_SCREAMING_SNAKE_CASE:			Any       , )							->			Dict:




              """simple docstring"""


              super().__init__(
                  langs=_SCREAMING_SNAKE_CASE       , src_vocab_file=_SCREAMING_SNAKE_CASE       , tgt_vocab_file=_SCREAMING_SNAKE_CASE       , merges_file=_SCREAMING_SNAKE_CASE       , do_lower_case=_SCREAMING_SNAKE_CASE       , unk_token=_SCREAMING_SNAKE_CASE       , bos_token=_SCREAMING_SNAKE_CASE       , sep_token=_SCREAMING_SNAKE_CASE       , pad_token=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE       , )

              try:
                     import sacremoses
              except ImportError:
                     raise ImportError(
                         "You need to install sacremoses to use XLMTokenizer. "
                         "See https://pypi.org/project/sacremoses/ for installation."		)

              UpperCamelCase_ 							=	sacremoses

              UpperCamelCase_ 							=	src_vocab_file
              UpperCamelCase_ 							=	tgt_vocab_file
              UpperCamelCase_ 							=	merges_file
              UpperCamelCase_ 							=	do_lower_case

              # cache of sm.MosesPunctNormalizer instance
              UpperCamelCase_ 							=	{}
              # cache of sm.MosesTokenizer instance
              UpperCamelCase_ 							=	{}
              UpperCamelCase_ 							=	{}

              if langs and len(_SCREAMING_SNAKE_CASE		) == 2:
                     UpperCamelCase_							,			UpperCamelCase_ 							=	langs
              else:
                     raise ValueError(
                         f'''arg `langs` needs to be a list of 2 langs, e.g. [\'en\', \'ru\'], but got {langs}. '''
                         "Usually that means that tokenizer can't find a mapping for the given model path "
                         "in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer."		)

              with open(_SCREAMING_SNAKE_CASE       , encoding="utf-8"		) as src_vocab_handle:
                     UpperCamelCase_ 							=	json.load(_SCREAMING_SNAKE_CASE		)
              with open(_SCREAMING_SNAKE_CASE       , encoding="utf-8"		) as tgt_vocab_handle:
                     UpperCamelCase_ 							=	json.load(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	{v: k for k, v in tgt_vocab.items()}
              with open(_SCREAMING_SNAKE_CASE       , encoding="utf-8"		) as merges_handle:
                     UpperCamelCase_ 							=	merges_handle.read().split("\n"		)[:-1]
              UpperCamelCase_ 							=	[tuple(merge.split()[:2]		) for merge in merges]
              UpperCamelCase_ 							=	dict(zip(_SCREAMING_SNAKE_CASE       , range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
              UpperCamelCase_ 							=	{}

       def lowercase			(		self:			Dict		)							->			Dict[str, int]:




              """simple docstring"""


              return self.get_src_vocab()

       @property
       def lowercase			(		self:			Dict		)							->			int:




              """simple docstring"""


              return self.src_vocab_size

       def lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Dict		)							->			Union[str, Any]:




              """simple docstring"""


              if lang not in self.cache_moses_punct_normalizer:
                     UpperCamelCase_ 							=	self.sm.MosesPunctNormalizer(lang=_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	punct_normalizer
              return self.cache_moses_punct_normalizer[lang].normalize(_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Dict       , _SCREAMING_SNAKE_CASE:			Optional[Any]		)							->			Optional[Any]:




              """simple docstring"""


              if lang not in self.cache_moses_tokenizer:
                     UpperCamelCase_ 							=	self.sm.MosesTokenizer(lang=_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	moses_tokenizer
              return self.cache_moses_tokenizer[lang].tokenize(
                  _SCREAMING_SNAKE_CASE       , aggressive_dash_splits=_SCREAMING_SNAKE_CASE       , return_str=_SCREAMING_SNAKE_CASE       , escape=_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Optional[int]		)							->			Union[str, Any]:




              """simple docstring"""


              if lang not in self.cache_moses_detokenizer:
                     UpperCamelCase_ 							=	self.sm.MosesDetokenizer(lang=_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	moses_detokenizer
              return self.cache_moses_detokenizer[lang].detokenize(_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			str		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	replace_unicode_punct(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	self.moses_punct_norm(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	remove_non_printing_char(_SCREAMING_SNAKE_CASE		)
              return text

       @property
       def lowercase			(		self:			List[Any]		)							->			int:




              """simple docstring"""


              return len(self.encoder		)

       @property
       def lowercase			(		self:			List[str]		)							->			Optional[int]:




              """simple docstring"""


              return len(self.decoder		)

       def lowercase			(		self:			Tuple		)							->			List[str]:




              """simple docstring"""


              return dict(self.encoder       , **self.added_tokens_encoder		)

       def lowercase			(		self:			int		)							->			Optional[Any]:




              """simple docstring"""


              return dict(self.decoder       , **self.added_tokens_decoder		)

       def lowercase			(		self:			str       , _SCREAMING_SNAKE_CASE:			Optional[int]		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	tuple(token[:-1]		) + (token[-1] + "</w>",)
              if token in self.cache:
                     return self.cache[token]
              UpperCamelCase_ 							=	get_pairs(_SCREAMING_SNAKE_CASE		)

              if not pairs:
                     return token + "</w>"

              while True:
                     UpperCamelCase_ 							=	min(_SCREAMING_SNAKE_CASE       , key=lambda _SCREAMING_SNAKE_CASE		: self.bpe_ranks.get(_SCREAMING_SNAKE_CASE       , float("inf"		)		)		)
                     if bigram not in self.bpe_ranks:
                            break
                     UpperCamelCase_							,			UpperCamelCase_ 							=	bigram
                     UpperCamelCase_ 							=	[]
                     UpperCamelCase_ 							=	0
                     while i < len(_SCREAMING_SNAKE_CASE		):
                            try:
                                   UpperCamelCase_ 							=	word.index(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
                            except ValueError:
                                   new_word.extend(word[i:]		)
                                   break
                            else:
                                   new_word.extend(word[i:j]		)
                                   UpperCamelCase_ 							=	j

                            if word[i] == first and i < len(_SCREAMING_SNAKE_CASE		) - 1 and word[i + 1] == second:
                                   new_word.append(first + second		)
                                   i += 2
                            else:
                                   new_word.append(word[i]		)
                                   i += 1
                     UpperCamelCase_ 							=	tuple(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	new_word
                     if len(_SCREAMING_SNAKE_CASE		) == 1:
                            break
                     else:
                            UpperCamelCase_ 							=	get_pairs(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	" ".join(_SCREAMING_SNAKE_CASE		)
              if word == "\n  </w>":
                     UpperCamelCase_ 							=	"\n</w>"
              UpperCamelCase_ 							=	word
              return word

       def lowercase			(		self:			Any       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			int="en"       , _SCREAMING_SNAKE_CASE:			Tuple=False		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	self.src_lang

              if self.do_lower_case:
                     UpperCamelCase_ 							=	text.lower()

              if bypass_tokenizer:
                     UpperCamelCase_ 							=	text.split()
              else:
                     UpperCamelCase_ 							=	self.moses_pipeline(_SCREAMING_SNAKE_CASE       , lang=_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	self.moses_tokenize(_SCREAMING_SNAKE_CASE       , lang=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	[]
              for token in text:
                     if token:
                            split_tokens.extend(list(self.bpe(_SCREAMING_SNAKE_CASE		).split(" "		)		)		)

              return split_tokens

       def lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			str		)							->			int:




              """simple docstring"""


              return self.encoder.get(_SCREAMING_SNAKE_CASE       , self.encoder.get(self.unk_token		)		)

       def lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Dict		)							->			int:




              """simple docstring"""


              return self.decoder.get(_SCREAMING_SNAKE_CASE       , self.unk_token		)

       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			Optional[int]		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	[t.replace(" "       , ""		).replace("</w>"       , " "		) for t in tokens]
              UpperCamelCase_ 							=	"".join(_SCREAMING_SNAKE_CASE		).split()
              # detokenize
              UpperCamelCase_ 							=	self.moses_detokenize(_SCREAMING_SNAKE_CASE       , self.tgt_lang		)
              return text

       def lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			List[int]       , _SCREAMING_SNAKE_CASE:			Optional[List[int]] = None		)							->			List[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	[self.sep_token_id]

              # no bos used in fairseq
              if token_ids_a is None:
                     return token_ids_a + sep
              return token_ids_a + sep + token_ids_a + sep

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




              """simple docstring"""


              if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=_SCREAMING_SNAKE_CASE       , token_ids_a=_SCREAMING_SNAKE_CASE       , already_has_special_tokens=_SCREAMING_SNAKE_CASE		)
              # no bos used in fairseq
              if token_ids_a is not None:
                     return ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
              return ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]

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




              """simple docstring"""


              UpperCamelCase_ 							=	[self.sep_token_id]

              # no bos used in fairseq
              if token_ids_a is None:
                     return len(token_ids_a + sep		) * [0]
              return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]

       def lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Optional[str] = None		)							->			Tuple[str]:




              """simple docstring"""


              if not os.path.isdir(_SCREAMING_SNAKE_CASE		):
                     logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
                     return

              UpperCamelCase_ 							=	os.path.join(
                  _SCREAMING_SNAKE_CASE       , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["src_vocab_file"]		)
              UpperCamelCase_ 							=	os.path.join(
                  _SCREAMING_SNAKE_CASE       , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["tgt_vocab_file"]		)
              UpperCamelCase_ 							=	os.path.join(
                  _SCREAMING_SNAKE_CASE       , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

              with open(_SCREAMING_SNAKE_CASE       , "w"       , encoding="utf-8"		) as f:
                     f.write(json.dumps(self.encoder       , indent=2       , sort_keys=_SCREAMING_SNAKE_CASE       , ensure_ascii=_SCREAMING_SNAKE_CASE		) + "\n"		)

              with open(_SCREAMING_SNAKE_CASE       , "w"       , encoding="utf-8"		) as f:
                     UpperCamelCase_ 							=	{v: k for k, v in self.decoder.items()}
                     f.write(json.dumps(_SCREAMING_SNAKE_CASE       , indent=2       , sort_keys=_SCREAMING_SNAKE_CASE       , ensure_ascii=_SCREAMING_SNAKE_CASE		) + "\n"		)

              UpperCamelCase_ 							=	0
              with open(_SCREAMING_SNAKE_CASE       , "w"       , encoding="utf-8"		) as writer:
                     for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       , key=lambda _SCREAMING_SNAKE_CASE		: kv[1]		):
                            if index != token_index:
                                   logger.warning(
                                       f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
                                       " Please check that the tokenizer is not corrupted!"		)
                                   UpperCamelCase_ 							=	token_index
                            writer.write(" ".join(_SCREAMING_SNAKE_CASE		) + "\n"		)
                            index += 1

              return src_vocab_file, tgt_vocab_file, merges_file

       def __getstate__(		self:			str		)							->			str:




              """simple docstring"""


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



       def __setstate__(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			List[str]		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	d

              try:
                     import sacremoses
              except ImportError:
                     raise ImportError(
                         "You need to install sacremoses to use XLMTokenizer. "
                         "See https://pypi.org/project/sacremoses/ for installation."		)

              UpperCamelCase_ 							=	sacremoses








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
#
########################################################################

_UpperCAmelCase										=					1_6
_UpperCAmelCase										=					3_2




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_ = 16	)  ->		Dict:
       UpperCamelCase_ 							=	AutoTokenizer.from_pretrained("bert-base-cased"	)
       UpperCamelCase_ 							=	load_dataset("glue"       ,		"mrpc"	)

       def tokenize_function(UpperCamelCase_	):
              # max_length=None => use the model max length (it's actually the default)
              UpperCamelCase_ 							=	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():
              UpperCamelCase_ 							=	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
       UpperCamelCase_ 							=	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.
              UpperCamelCase_ 							=	128 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     UpperCamelCase_ 							=	16
              elif accelerator.mixed_precision != "no":
                     UpperCamelCase_ 							=	8
              else:
                     UpperCamelCase_ 							=	None

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

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

       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

     _UpperCAmelCase										=					mocked_dataloaders  # noqa: F811




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[Any]:
       # For testing only
       if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,		UpperCamelCase_	) == "1":
              UpperCamelCase_ 							=	2
       # Initialize accelerator
       UpperCamelCase_ 							=	Accelerator(cpu=args.cpu       ,		mixed_precision=args.mixed_precision	)
       # 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"]	)

       # 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":
                     UpperCamelCase_ 							=	args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     UpperCamelCase_ 							=	int(args.checkpointing_steps	)
              else:
                     raise ValueError(
                         F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''	)
       else:
              UpperCamelCase_ 							=	None

       set_seed(UpperCamelCase_	)

       UpperCamelCase_							,			UpperCamelCase_ 							=	get_dataloaders(UpperCamelCase_       ,		UpperCamelCase_	)
       UpperCamelCase_ 							=	evaluate.load("glue"       ,		"mrpc"	)

       # If the batch size is too big we use gradient accumulation
       UpperCamelCase_ 							=	1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              UpperCamelCase_ 							=	batch_size // MAX_GPU_BATCH_SIZE
              UpperCamelCase_ 							=	MAX_GPU_BATCH_SIZE

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       UpperCamelCase_ 							=	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).
       UpperCamelCase_ 							=	model.to(accelerator.device	)

       # Instantiate optimizer
       UpperCamelCase_ 							=	AdamW(params=model.parameters()       ,		lr=UpperCamelCase_	)

       # Instantiate scheduler
       UpperCamelCase_ 							=	get_linear_schedule_with_warmup(
           optimizer=UpperCamelCase_       ,		num_warmup_steps=100       ,		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.
       UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	accelerator.prepare(
           UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)

       # New Code #
       # 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

       # 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	)
                     UpperCamelCase_ 							=	os.path.basename(args.resume_from_checkpoint	)
              else:
                     # Get the most recent checkpoint
                     UpperCamelCase_ 							=	[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
                     dirs.sort(key=os.path.getctime	)
                     UpperCamelCase_ 							=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              UpperCamelCase_ 							=	os.path.splitext(UpperCamelCase_	)[0]

              if "epoch" in training_difference:
                     UpperCamelCase_ 							=	int(training_difference.replace("epoch_"       ,		""	)	) + 1
                     UpperCamelCase_ 							=	None
              else:
                     UpperCamelCase_ 							=	int(training_difference.replace("step_"       ,		""	)	)
                     UpperCamelCase_ 							=	resume_step // len(UpperCamelCase_	)
                     resume_step -= starting_epoch * len(UpperCamelCase_	)

    # Now we train the model
       for epoch in range(UpperCamelCase_       ,		UpperCamelCase_	):
              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
                     UpperCamelCase_ 							=	accelerator.skip_first_batches(UpperCamelCase_       ,		UpperCamelCase_	)
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     UpperCamelCase_ 							=	train_dataloader
              for step, batch in enumerate(UpperCamelCase_	):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device	)
                     UpperCamelCase_ 							=	model(**UpperCamelCase_	)
                     UpperCamelCase_ 							=	outputs.loss
                     UpperCamelCase_ 							=	loss / gradient_accumulation_steps
                     accelerator.backward(UpperCamelCase_	)
                     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(UpperCamelCase_       ,		UpperCamelCase_	):
                            UpperCamelCase_ 							=	F'''step_{overall_step}'''
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          UpperCamelCase_ 							=	os.path.join(args.output_dir       ,		UpperCamelCase_	)
                                   accelerator.save_state(UpperCamelCase_	)

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

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

              # 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":
                     UpperCamelCase_ 							=	F'''epoch_{epoch}'''
                     if args.output_dir is not None:
                            UpperCamelCase_ 							=	os.path.join(args.output_dir       ,		UpperCamelCase_	)
                     accelerator.save_state(UpperCamelCase_	)




def 			lowerCAmelCase_			(				)  ->		Dict:
       UpperCamelCase_ 							=	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."	)
       parser.add_argument(
           "--checkpointing_steps"       ,		type=UpperCamelCase_       ,		default=UpperCamelCase_       ,		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=UpperCamelCase_       ,		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=UpperCamelCase_       ,		default=UpperCamelCase_       ,		help="If the training should continue from a checkpoint folder."       ,		)
       UpperCamelCase_ 							=	parser.parse_args()
       UpperCamelCase_ 							=	{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
       training_function(UpperCamelCase_       ,		UpperCamelCase_	)


if __name__ == "__main__":
     main()

