




'''simple docstring'''




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

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


a_   :	int							   = logging.get_logger(__name__)

a_   :	List[Any]							   = {
    """src_vocab_file""": """vocab-src.json""",
    """tgt_vocab_file""": """vocab-tgt.json""",
    """merges_file""": """merges.txt""",
}

a_   :	Any							   = {
    """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"""},
}

a_   :	List[Any]							   = {"""stas/tiny-wmt19-en-de""": 10_24}
a_   :	int							   = {
    """stas/tiny-wmt19-en-de""": {
        """langs""": ["""en""", """de"""],
        """model_max_length""": 10_24,
        """special_tokens_map_file""": None,
        """full_tokenizer_file""": None,
    }
}





def   a_							(   __snake_case							:    str     )							->		Dict:



       """simple docstring"""
       lowerCamelCase_							=set()
       lowerCamelCase_							=word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)     )
              lowerCamelCase_							=char
       return pairs





def   a_							(   __snake_case							:    List[str]     )							->		List[Any]:



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





def   a_							(   __snake_case							:    Optional[Any]     )							->		str:



       """simple docstring"""
       lowerCamelCase_							=[]
       for char in text:
              lowerCamelCase_							=unicodedata.category(__snake_case     )
              if cat.startswith('''C'''     ):
                     continue
              output.append(__snake_case     )
       return "".join(__snake_case     )


class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					str   		=VOCAB_FILES_NAMES
       lowercase			:					List[Any]   		=PRETRAINED_VOCAB_FILES_MAP
       lowercase			:					List[Any]   		=PRETRAINED_INIT_CONFIGURATION
       lowercase			:					str   		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase			:					Optional[int]   		=['input_ids', 'attention_mask']






       def __init__(							self,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=False,	lowerCAmelCase="<unk>",	lowerCAmelCase="<s>",	lowerCAmelCase="</s>",	lowerCAmelCase="<pad>",	**lowerCAmelCase,	):



              """simple docstring"""







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

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

              lowerCamelCase_							=sacremoses

              lowerCamelCase_							=src_vocab_file
              lowerCamelCase_							=tgt_vocab_file
              lowerCamelCase_							=merges_file
              lowerCamelCase_							=do_lower_case

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

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






       def   lowercase__  (							self						):



              """simple docstring"""







              return self.get_src_vocab()






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return self.src_vocab_size






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







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






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







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






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







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






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=replace_unicode_punct(lowerCAmelCase						)
              lowerCamelCase_							=self.moses_punct_norm(lowerCAmelCase,	lowerCAmelCase						)
              lowerCamelCase_							=remove_non_printing_char(lowerCAmelCase						)
              return text






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return len(self.encoder						)






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return len(self.decoder						)






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=tuple(token[:-1]						) + (token[-1] + '''</w>''',)
              if token in self.cache:
                     return self.cache[token]
              lowerCamelCase_							=get_pairs(lowerCAmelCase						)

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

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

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






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase="en",	lowerCAmelCase=False						):



              """simple docstring"""







              lowerCamelCase_							=self.src_lang

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

              if bypass_tokenizer:
                     lowerCamelCase_							=text.split()
              else:
                     lowerCamelCase_							=self.moses_pipeline(lowerCAmelCase,	lang=lowerCAmelCase						)
                     lowerCamelCase_							=self.moses_tokenize(lowerCAmelCase,	lang=lowerCAmelCase						)

              lowerCamelCase_							=[]
              for token in text:
                     if token:
                            split_tokens.extend(list(self.bpe(lowerCAmelCase						).split(''' '''						)						)						)

              return split_tokens






       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







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






       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







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






       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=[t.replace(''' ''',	''''''						).replace('''</w>''',	''' '''						) for t in tokens]
              lowerCamelCase_							=''''''.join(lowerCAmelCase						).split()
              # detokenize
              lowerCamelCase_							=self.moses_detokenize(lowerCAmelCase,	self.tgt_lang						)
              return text






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              lowerCamelCase_							=[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,	lowerCAmelCase,	lowerCAmelCase = None,	lowerCAmelCase = 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						)
              # no bos used in fairseq
              if token_ids_a is not None:
                     return ([0] * len(lowerCAmelCase						)) + [1] + ([0] * len(lowerCAmelCase						)) + [1]
              return ([0] * len(lowerCAmelCase						)) + [1]






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              lowerCamelCase_							=[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,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







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

              lowerCamelCase_							=os.path.join(
                  lowerCAmelCase,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''src_vocab_file''']						)
              lowerCamelCase_							=os.path.join(
                  lowerCAmelCase,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''tgt_vocab_file''']						)
              lowerCamelCase_							=os.path.join(
                  lowerCAmelCase,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']						)

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

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

              lowerCamelCase_							=0
              with open(lowerCAmelCase,	'''w''',	encoding='''utf-8'''						) as writer:
                     for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),	key=lambda lowerCAmelCase						: 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!'''						)
                                   lowerCamelCase_							=token_index
                            writer.write(''' '''.join(lowerCAmelCase						) + '''\n'''						)
                            index += 1

              return src_vocab_file, tgt_vocab_file, merges_file






       def __getstate__(							self						):



              """simple docstring"""







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






       def __setstate__(							self,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=d

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

              lowerCamelCase_							=sacremoses










'''simple docstring'''




import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# 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
#
########################################################################

a_   :	Any							   = 32





def   a_							(   __snake_case							:    Accelerator     , __snake_case							:    int = 16     )							->		List[str]:



       """simple docstring"""
       lowerCamelCase_							=AutoTokenizer.from_pretrained('''bert-base-cased'''     )
       lowerCamelCase_							=load_dataset('''glue'''     , '''mrpc'''     )

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

       def collate_fn(__snake_case							:    Union[str, Any]     ):
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     lowerCamelCase_							=16
              elif accelerator.mixed_precision != "no":
                     lowerCamelCase_							=8
              else:
                     lowerCamelCase_							=None

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

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

       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

     a_   :	List[Any]							   = mocked_dataloaders  # noqa: F811





def   a_							(   __snake_case							:    Optional[int]     , __snake_case							:    List[str]     )							->		Tuple:



       """simple docstring"""
       # For testing only
       if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     , __snake_case     ) == "1":
              lowerCamelCase_							=2
       # Initialize accelerator
       lowerCamelCase_							=Accelerator(cpu=args.cpu     , mixed_precision=args.mixed_precision     )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowerCamelCase_							=config['''lr''']
       lowerCamelCase_							=int(config['''num_epochs''']     )
       lowerCamelCase_							=int(config['''seed''']     )
       lowerCamelCase_							=int(config['''batch_size''']     )

       lowerCamelCase_							=evaluate.load('''glue'''     , '''mrpc'''     )

       # New Code #
       # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
       # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
       # half each time. From this, we can calculate the number of gradient accumulation steps needed
       # and modify the Accelerator object as a result
       @find_executable_batch_size(starting_batch_size=int(__snake_case     )     )
       def inner_training_loop(__snake_case							:    Tuple     ):
              # Since we need to modify the outside accelerator object, we need to bring it
              # to the local scope
              nonlocal accelerator

              # We can calculate the number of gradient accumulation steps based on the current
              # batch size vs the starting batch size
              lowerCamelCase_							=observed_batch_size // batch_size

              # And then set it in the Accelerator directly:
              lowerCamelCase_							=num_gradient_accumulation_steps

              # Next we need to free all of the stored model references in the Accelerator each time
              accelerator.free_memory()

              # And set the seed so our results are reproducable each reset
              set_seed(__snake_case     )

              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              lowerCamelCase_							=AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''     , return_dict=__snake_case     )

              # 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).
              lowerCamelCase_							=model.to(accelerator.device     )

              # Instantiate optimizer
              lowerCamelCase_							=AdamW(params=model.parameters()     , lr=__snake_case     )
              lowerCamelCase_,   lowerCamelCase_							=get_dataloaders(__snake_case     , __snake_case     )

              # Instantiate scheduler
              lowerCamelCase_							=get_linear_schedule_with_warmup(
                  optimizer=__snake_case     , num_warmup_steps=100     , num_training_steps=(len(__snake_case     ) * num_epochs)     , )

              # 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.
              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=accelerator.prepare(
                  __snake_case     , __snake_case     , __snake_case     , __snake_case     , __snake_case     )

              # Now we train the model
              for epoch in range(__snake_case     ):
                     model.train()
                     for step, batch in enumerate(__snake_case     ):
                            # And perform gradient accumulation
                            with accelerator.accumulate(__snake_case     ):
                                   # We could avoid this line since we set the accelerator with `device_placement=True`.
                                   batch.to(accelerator.device     )
                                   lowerCamelCase_							=model(**__snake_case     )
                                   lowerCamelCase_							=outputs.loss
                                   accelerator.backward(__snake_case     )
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

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

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
       inner_training_loop()





def   a_							(   )							->		Any:



       """simple docstring"""
       lowerCamelCase_							=argparse.ArgumentParser(description='''Simple example of training script.'''     )
       parser.add_argument(
           '''--mixed_precision'''     , type=__snake_case     , default=__snake_case     , 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.'''     )
       lowerCamelCase_							=parser.parse_args()
       # New Code #
       # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
       lowerCamelCase_							={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
       training_function(__snake_case     , __snake_case     )


if __name__ == "__main__":
     main()




