

'''simple docstring'''






import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			lowercase_	(lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			RobertaTokenizer
  SCREAMING_SNAKE_CASE    :  str							=			RobertaTokenizerFast
  SCREAMING_SNAKE_CASE    :  List[str]							=			True
  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			{'cls_token': '<s>'}






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   __lowercase							  = [
       '''l''',
       '''o''',
       '''w''',
       '''e''',
       '''r''',
       '''s''',
       '''t''',
       '''i''',
       '''d''',
       '''n''',
       '''\u0120''',
       '''\u0120l''',
       '''\u0120n''',
       '''\u0120lo''',
       '''\u0120low''',
       '''er''',
       '''\u0120lowest''',
       '''\u0120newer''',
       '''\u0120wider''',
       '''<unk>''',
   ]
   __lowercase							  = dict(zip(lowercase__					,range(len(lowercase__			)			)			)			)
   __lowercase							  = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
   __lowercase							  = {'''unk_token''': '''<unk>'''}

   __lowercase							  = os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['''vocab_file''']			)
   __lowercase							  = os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['''merges_file''']			)
   with open(self.vocab_file					,'''w'''					,encoding='''utf-8'''			) as fp:
    fp.write(json.dumps(lowercase__			) + '''\n'''			)
   with open(self.merges_file					,'''w'''					,encoding='''utf-8'''			) as fp:
    fp.write('''\n'''.join(lowercase__			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]					,**lowercase__   :     Dict			):
   kwargs.update(self.special_tokens_map			)
   return self.tokenizer_class.from_pretrained(self.tmpdirname					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,**lowercase__   :     Optional[Any]			):
   kwargs.update(self.special_tokens_map			)
   return RobertaTokenizerFast.from_pretrained(self.tmpdirname					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     Union[str, Any]			):
   __lowercase							  = '''lower newer'''
   __lowercase							  = '''lower newer'''
   return input_text, output_text






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = self.tokenizer_class(self.vocab_file					,self.merges_file					,**self.special_tokens_map			)
   __lowercase							  = '''lower newer'''
   __lowercase							  = ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
   __lowercase							  = tokenizer.tokenize(lowercase__			)  # , add_prefix_space=True)
   self.assertListEqual(lowercase__					,lowercase__			)

   __lowercase							  = tokens + [tokenizer.unk_token]
   __lowercase							  = [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__			)					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   __lowercase							  = self.get_tokenizer()

   self.assertListEqual(tokenizer.encode('''Hello world!'''					,add_special_tokens=lowercase__			)					,[0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]			)
   self.assertListEqual(
       tokenizer.encode('''Hello world! cécé herlolip 418'''					,add_special_tokens=lowercase__			)					,[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]					,)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.tokenizer_class.from_pretrained('''roberta-base'''			)

   __lowercase							  = tokenizer.encode('''sequence builders'''					,add_special_tokens=lowercase__			)
   __lowercase							  = tokenizer.encode('''multi-sequence build'''					,add_special_tokens=lowercase__			)

   __lowercase							  = tokenizer.encode(
       '''sequence builders'''					,add_special_tokens=lowercase__					,add_prefix_space=lowercase__			)
   __lowercase							  = tokenizer.encode(
       '''sequence builders'''					,'''multi-sequence build'''					,add_special_tokens=lowercase__					,add_prefix_space=lowercase__			)

   __lowercase							  = tokenizer.build_inputs_with_special_tokens(lowercase__			)
   __lowercase							  = tokenizer.build_inputs_with_special_tokens(lowercase__					,lowercase__			)

   assert encoded_sentence == encoded_text_from_decode
   assert encoded_pair == encoded_pair_from_decode






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.get_tokenizer()

   __lowercase							  = '''Encode this sequence.'''
   __lowercase							  = tokenizer.byte_encoder[''' '''.encode('''utf-8'''			)[0]]

   # Testing encoder arguments
   __lowercase							  = tokenizer.encode(lowercase__					,add_special_tokens=lowercase__					,add_prefix_space=lowercase__			)
   __lowercase							  = tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
   self.assertNotEqual(lowercase__					,lowercase__			)

   __lowercase							  = tokenizer.encode(lowercase__					,add_special_tokens=lowercase__					,add_prefix_space=lowercase__			)
   __lowercase							  = tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
   self.assertEqual(lowercase__					,lowercase__			)

   tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}			)
   __lowercase							  = tokenizer.encode(lowercase__					,add_special_tokens=lowercase__			)
   __lowercase							  = tokenizer.convert_ids_to_tokens(encoded[1]			)[0]
   self.assertNotEqual(lowercase__					,lowercase__			)

   # Testing spaces after special tokens
   __lowercase							  = '''<mask>'''
   tokenizer.add_special_tokens(
       {'''mask_token''': AddedToken(lowercase__					,lstrip=lowercase__					,rstrip=lowercase__			)}			)  # mask token has a left space
   __lowercase							  = tokenizer.convert_tokens_to_ids(lowercase__			)

   __lowercase							  = '''Encode <mask> sequence'''
   __lowercase							  = '''Encode <mask>sequence'''

   __lowercase							  = tokenizer.encode(lowercase__			)
   __lowercase							  = encoded.index(lowercase__			)
   __lowercase							  = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
   self.assertEqual(lowercase__					,lowercase__			)

   __lowercase							  = tokenizer.encode(lowercase__			)
   __lowercase							  = encoded.index(lowercase__			)
   __lowercase							  = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
   self.assertNotEqual(lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   pass






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
    with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"			):
     __lowercase							  = self.rust_tokenizer_class.from_pretrained(lowercase__					,**lowercase__			)
     __lowercase							  = self.tokenizer_class.from_pretrained(lowercase__					,**lowercase__			)
     __lowercase							  = '''A, <mask> AllenNLP sentence.'''
     __lowercase							  = tokenizer_r.encode_plus(lowercase__					,add_special_tokens=lowercase__					,return_token_type_ids=lowercase__			)
     __lowercase							  = tokenizer_p.encode_plus(lowercase__					,add_special_tokens=lowercase__					,return_token_type_ids=lowercase__			)

     # token_type_ids should put 0 everywhere
     self.assertEqual(sum(tokens_r['''token_type_ids''']			)					,sum(tokens_p['''token_type_ids''']			)			)

     # attention_mask should put 1 everywhere, so sum over length should be 1
     self.assertEqual(
         sum(tokens_r['''attention_mask''']			) / len(tokens_r['''attention_mask''']			)					,sum(tokens_p['''attention_mask''']			) / len(tokens_p['''attention_mask''']			)					,)

     __lowercase							  = tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']			)
     __lowercase							  = tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']			)

     # Rust correctly handles the space before the mask while python doesnt
     self.assertSequenceEqual(tokens_p['''input_ids''']					,[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]			)
     self.assertSequenceEqual(tokens_r['''input_ids''']					,[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]			)

     self.assertSequenceEqual(
         lowercase__					,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)
     self.assertSequenceEqual(
         lowercase__					,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   for trim_offsets, add_prefix_space in itertools.product([True, False]					,repeat=2			):
    __lowercase							  = self.rust_tokenizer_class.from_pretrained(
        self.tmpdirname					,use_fast=lowercase__					,add_prefix_space=lowercase__					,trim_offsets=lowercase__			)

    __lowercase							  = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()			)
    __lowercase							  = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()			)

    self.assertEqual(pre_tokenizer_state['''add_prefix_space''']					,lowercase__			)

    self.assertEqual(post_processor_state['''add_prefix_space''']					,lowercase__			)
    self.assertEqual(post_processor_state['''trim_offsets''']					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
   # `trim_offsets`
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
    with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"			):
     __lowercase							  = '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
     __lowercase							  = F"{text_of_1_token} {text_of_1_token}"

     __lowercase							  = self.rust_tokenizer_class.from_pretrained(
         lowercase__					,use_fast=lowercase__					,add_prefix_space=lowercase__					,trim_offsets=lowercase__			)
     __lowercase							  = tokenizer_r(lowercase__					,return_offsets_mapping=lowercase__					,add_special_tokens=lowercase__			)
     self.assertEqual(encoding.offset_mapping[0]					,(0, len(lowercase__			))			)
     self.assertEqual(
         encoding.offset_mapping[1]					,(len(lowercase__			) + 1, len(lowercase__			) + 1 + len(lowercase__			))					,)

     __lowercase							  = self.rust_tokenizer_class.from_pretrained(
         lowercase__					,use_fast=lowercase__					,add_prefix_space=lowercase__					,trim_offsets=lowercase__			)
     __lowercase							  = tokenizer_r(lowercase__					,return_offsets_mapping=lowercase__					,add_special_tokens=lowercase__			)
     self.assertEqual(encoding.offset_mapping[0]					,(0, len(lowercase__			))			)
     self.assertEqual(
         encoding.offset_mapping[1]					,(len(lowercase__			) + 1, len(lowercase__			) + 1 + len(lowercase__			))					,)

     __lowercase							  = self.rust_tokenizer_class.from_pretrained(
         lowercase__					,use_fast=lowercase__					,add_prefix_space=lowercase__					,trim_offsets=lowercase__			)
     __lowercase							  = tokenizer_r(lowercase__					,return_offsets_mapping=lowercase__					,add_special_tokens=lowercase__			)
     self.assertEqual(encoding.offset_mapping[0]					,(0, len(lowercase__			))			)
     self.assertEqual(
         encoding.offset_mapping[1]					,(len(lowercase__			), len(lowercase__			) + 1 + len(lowercase__			))					,)

     __lowercase							  = self.rust_tokenizer_class.from_pretrained(
         lowercase__					,use_fast=lowercase__					,add_prefix_space=lowercase__					,trim_offsets=lowercase__			)
     __lowercase							  = tokenizer_r(lowercase__					,return_offsets_mapping=lowercase__					,add_special_tokens=lowercase__			)
     self.assertEqual(encoding.offset_mapping[0]					,(0, len(lowercase__			))			)
     self.assertEqual(
         encoding.offset_mapping[1]					,(len(lowercase__			), len(lowercase__			) + 1 + len(lowercase__			))					,)

     __lowercase							  = F" {text}"

     # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
     #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
     # )
     # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
     # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
     # self.assertEqual(
     #     encoding.offset_mapping[1],
     #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
     # )

     __lowercase							  = self.rust_tokenizer_class.from_pretrained(
         lowercase__					,use_fast=lowercase__					,add_prefix_space=lowercase__					,trim_offsets=lowercase__			)
     __lowercase							  = tokenizer_r(lowercase__					,return_offsets_mapping=lowercase__					,add_special_tokens=lowercase__			)
     self.assertEqual(encoding.offset_mapping[0]					,(1, 1 + len(lowercase__			))			)
     self.assertEqual(
         encoding.offset_mapping[1]					,(1 + len(lowercase__			) + 1, 1 + len(lowercase__			) + 1 + len(lowercase__			))					,)

     __lowercase							  = self.rust_tokenizer_class.from_pretrained(
         lowercase__					,use_fast=lowercase__					,add_prefix_space=lowercase__					,trim_offsets=lowercase__			)
     __lowercase							  = tokenizer_r(lowercase__					,return_offsets_mapping=lowercase__					,add_special_tokens=lowercase__			)
     self.assertEqual(encoding.offset_mapping[0]					,(0, 1 + len(lowercase__			))			)
     self.assertEqual(
         encoding.offset_mapping[1]					,(1 + len(lowercase__			), 1 + len(lowercase__			) + 1 + len(lowercase__			))					,)

     __lowercase							  = self.rust_tokenizer_class.from_pretrained(
         lowercase__					,use_fast=lowercase__					,add_prefix_space=lowercase__					,trim_offsets=lowercase__			)
     __lowercase							  = tokenizer_r(lowercase__					,return_offsets_mapping=lowercase__					,add_special_tokens=lowercase__			)
     self.assertEqual(encoding.offset_mapping[0]					,(0, 1 + len(lowercase__			))			)
     self.assertEqual(
         encoding.offset_mapping[1]					,(1 + len(lowercase__			), 1 + len(lowercase__			) + 1 + len(lowercase__			))					,)


import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__lowercase							 =					16
__lowercase							 =					32



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = 16  ,			SCREAMING_SNAKE_CASE = "bert-base-cased"	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Any			  =		load_dataset('''glue'''  ,			'''mrpc'''	)

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

    # Apply the method we just defined to all the examples in all the splits of the dataset
    __UpperCamelCase     :Tuple			  =		datasets.map(
        SCREAMING_SNAKE_CASE  ,			batched=SCREAMING_SNAKE_CASE  ,			remove_columns=['''idx''', '''sentence1''', '''sentence2''']  ,			load_from_cache_file=SCREAMING_SNAKE_CASE	)

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

    def collate_fn(SCREAMING_SNAKE_CASE	):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(SCREAMING_SNAKE_CASE  ,			padding='''max_length'''  ,			max_length=128  ,			return_tensors='''pt'''	)
        return tokenizer.pad(SCREAMING_SNAKE_CASE  ,			padding='''longest'''  ,			return_tensors='''pt'''	)

    # Instantiate dataloaders.
    __UpperCamelCase     :Union[str, Any]			  =		DataLoader(
        tokenized_datasets['''train''']  ,			shuffle=SCREAMING_SNAKE_CASE  ,			collate_fn=SCREAMING_SNAKE_CASE  ,			batch_size=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Dict			  =		DataLoader(
        tokenized_datasets['''validation''']  ,			shuffle=SCREAMING_SNAKE_CASE  ,			collate_fn=SCREAMING_SNAKE_CASE  ,			batch_size=SCREAMING_SNAKE_CASE	)

    return train_dataloader, eval_dataloader



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		Accelerator()

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __UpperCamelCase     :int			  =		config['''lr''']
    __UpperCamelCase     :str			  =		int(config['''num_epochs''']	)
    __UpperCamelCase     :Any			  =		int(config['''seed''']	)
    __UpperCamelCase     :Dict			  =		int(config['''batch_size''']	)
    __UpperCamelCase     :Optional[Any]			  =		args.model_name_or_path

    set_seed(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		get_dataloaders(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __UpperCamelCase     :Any			  =		AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE  ,			return_dict=SCREAMING_SNAKE_CASE	)

    # Instantiate optimizer
    __UpperCamelCase     :List[str]			  =		(
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    __UpperCamelCase     :Optional[Any]			  =		optimizer_cls(params=model.parameters()  ,			lr=SCREAMING_SNAKE_CASE	)

    if accelerator.state.deepspeed_plugin is not None:
        __UpperCamelCase     :Dict			  =		accelerator.state.deepspeed_plugin.deepspeed_config[
            '''gradient_accumulation_steps'''
        ]
    else:
        __UpperCamelCase     :Dict			  =		1
    __UpperCamelCase     :Tuple			  =		(len(SCREAMING_SNAKE_CASE	) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        __UpperCamelCase     :str			  =		get_linear_schedule_with_warmup(
            optimizer=SCREAMING_SNAKE_CASE  ,			num_warmup_steps=0  ,			num_training_steps=SCREAMING_SNAKE_CASE  ,			)
    else:
        __UpperCamelCase     :Dict			  =		DummyScheduler(SCREAMING_SNAKE_CASE  ,			total_num_steps=SCREAMING_SNAKE_CASE  ,			warmup_num_steps=0	)

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :int			  =		accelerator.prepare(
        SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

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

    # Now we train the model
    __UpperCamelCase     :Any			  =		evaluate.load('''glue'''  ,			'''mrpc'''	)
    __UpperCamelCase     :Union[str, Any]			  =		0
    __UpperCamelCase     :Optional[int]			  =		{}
    for epoch in range(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
        model.train()
        for step, batch in enumerate(SCREAMING_SNAKE_CASE	):
            __UpperCamelCase     :Optional[Any]			  =		model(**SCREAMING_SNAKE_CASE	)
            __UpperCamelCase     :Tuple			  =		outputs.loss
            __UpperCamelCase     :str			  =		loss / gradient_accumulation_steps
            accelerator.backward(SCREAMING_SNAKE_CASE	)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

        model.eval()
        __UpperCamelCase     :Any			  =		0
        for step, batch in enumerate(SCREAMING_SNAKE_CASE	):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device	)
            with torch.no_grad():
                __UpperCamelCase     :Any			  =		model(**SCREAMING_SNAKE_CASE	)
            __UpperCamelCase     :Optional[int]			  =		outputs.logits.argmax(dim=-1	)
            # It is slightly faster to call this once, than multiple times
            __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		accelerator.gather(
                (predictions, batch['''labels'''])	)  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                if step == len(SCREAMING_SNAKE_CASE	) - 1:
                    __UpperCamelCase     :List[str]			  =		predictions[: len(eval_dataloader.dataset	) - samples_seen]
                    __UpperCamelCase     :Optional[int]			  =		references[: len(eval_dataloader.dataset	) - samples_seen]
                else:
                    samples_seen += references.shape[0]
            metric.add_batch(
                predictions=SCREAMING_SNAKE_CASE  ,			references=SCREAMING_SNAKE_CASE  ,			)

        __UpperCamelCase     :Dict			  =		metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"""epoch {epoch}:"""  ,			SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :str			  =		eval_metric['''accuracy''']

        if best_performance < eval_metric["accuracy"]:
            __UpperCamelCase     :int			  =		eval_metric['''accuracy''']

    if args.performance_lower_bound is not None:
        assert (
            args.performance_lower_bound <= best_performance
        ), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        with open(os.path.join(args.output_dir  ,			'''all_results.json'''	)  ,			'''w'''	) as f:
            json.dump(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''	)
    parser.add_argument(
        '''--model_name_or_path'''  ,			type=SCREAMING_SNAKE_CASE  ,			default='''bert-base-cased'''  ,			help='''Path to pretrained model or model identifier from huggingface.co/models.'''  ,			required=SCREAMING_SNAKE_CASE  ,			)
    parser.add_argument(
        '''--output_dir'''  ,			type=SCREAMING_SNAKE_CASE  ,			default='''.'''  ,			help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,			)
    parser.add_argument(
        '''--performance_lower_bound'''  ,			type=SCREAMING_SNAKE_CASE  ,			default=SCREAMING_SNAKE_CASE  ,			help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''  ,			)
    parser.add_argument(
        '''--num_epochs'''  ,			type=SCREAMING_SNAKE_CASE  ,			default=3  ,			help='''Number of train epochs.'''  ,			)
    __UpperCamelCase     :List[str]			  =		parser.parse_args()
    __UpperCamelCase     :Tuple			  =		{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
    training_function(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    main()





