'''simple docstring'''





import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   List[str]							=						LongformerTokenizer
    snake_case__							:   str							=						True
    snake_case__							:   Dict							=						LongformerTokenizerFast
    snake_case__							:   List[str]							=						True






    def       _UpperCamelCase    (       self )							-> Dict:
         super().setUp()

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

         SCREAMING_SNAKE_CASE					:			Any    	=       os.path.join(self.tmpdirname    ,							VOCAB_FILES_NAMES['vocab_file'] )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       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       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Dict:
         kwargs.update(self.special_tokens_map )
         return self.tokenizer_class.from_pretrained(self.tmpdirname    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Optional[Any]:
         kwargs.update(self.special_tokens_map )
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Any    	=       'lower newer'
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       'lower newer'
         return input_text, output_text






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

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       tokens + [tokenizer.unk_token]
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__ )    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_tokenizer()

         self.assertListEqual(tokenizer.encode('Hello world!'    ,							add_special_tokens=lowercase__ )    ,							[0, 31_414, 232, 328, 2] )
         self.assertListEqual(
             tokenizer.encode('Hello world! cécé herlolip 418'    ,							add_special_tokens=lowercase__ )    ,							[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]    ,							)






    @slow
    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.tokenizer_class.from_pretrained('allenai/longformer-base-4096' )

         SCREAMING_SNAKE_CASE					:			Any    	=       tokenizer.encode('sequence builders'    ,							add_special_tokens=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Any    	=       tokenizer.encode('multi-sequence build'    ,							add_special_tokens=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       tokenizer.encode(
             'sequence builders'    ,							add_special_tokens=lowercase__    ,							add_prefix_space=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       tokenizer.encode(
             'sequence builders'    ,							'multi-sequence build'    ,							add_special_tokens=lowercase__    ,							add_prefix_space=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       tokenizer.build_inputs_with_special_tokens(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       tokenizer.build_inputs_with_special_tokens(lowercase__    ,							lowercase__ )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.get_tokenizer()

         SCREAMING_SNAKE_CASE					:			Dict    	=       'Encode this sequence.'
         SCREAMING_SNAKE_CASE					:			List[str]    	=       tokenizer.byte_encoder[' '.encode('utf-8' )[0]]

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

         SCREAMING_SNAKE_CASE					:			Dict    	=       tokenizer.encode(lowercase__    ,							add_special_tokens=lowercase__    ,							add_prefix_space=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       tokenizer.convert_ids_to_tokens(encoded[0] )[0]
         self.assertEqual(lowercase__    ,							lowercase__ )

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

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

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       'Encode <mask> sequence'
         SCREAMING_SNAKE_CASE					:			str    	=       'Encode <mask>sequence'

         SCREAMING_SNAKE_CASE					:			str    	=       tokenizer.encode(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       encoded.index(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
         self.assertEqual(lowercase__    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       tokenizer.encode(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       encoded.index(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
         self.assertNotEqual(lowercase__    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         pass






    def       _UpperCamelCase    (       self )							-> List[str]:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       self.rust_tokenizer_class.from_pretrained(lowercase__    ,							**lowercase__ )
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.tokenizer_class.from_pretrained(lowercase__    ,							**lowercase__ )
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       'A, <mask> AllenNLP sentence.'
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       tokenizer_r.encode_plus(lowercase__    ,							add_special_tokens=lowercase__    ,							return_token_type_ids=lowercase__ )
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       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'] )    ,							)

                   SCREAMING_SNAKE_CASE					:			str    	=       tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
                   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       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, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
                   self.assertSequenceEqual(tokens_r['input_ids']    ,							[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 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       _UpperCamelCase    (       self )							-> Optional[Any]:
         for trim_offsets, add_prefix_space in itertools.product([True, False]    ,							repeat=2 ):
              SCREAMING_SNAKE_CASE					:			List[str]    	=       self.rust_tokenizer_class.from_pretrained(
                  self.tmpdirname    ,							use_fast=lowercase__    ,							add_prefix_space=lowercase__    ,							trim_offsets=lowercase__ )

              SCREAMING_SNAKE_CASE					:			List[Any]    	=       json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
              SCREAMING_SNAKE_CASE					:			int    	=       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       _UpperCamelCase    (       self )							-> str:
         # 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})""" ):
                   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       F"""{text_of_1_token} {text_of_1_token}"""

                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.rust_tokenizer_class.from_pretrained(
                       lowercase__    ,							use_fast=lowercase__    ,							add_prefix_space=lowercase__    ,							trim_offsets=lowercase__ )
                   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       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__ ))    ,							)

                   SCREAMING_SNAKE_CASE					:			Any    	=       self.rust_tokenizer_class.from_pretrained(
                       lowercase__    ,							use_fast=lowercase__    ,							add_prefix_space=lowercase__    ,							trim_offsets=lowercase__ )
                   SCREAMING_SNAKE_CASE					:			str    	=       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__ ))    ,							)

                   SCREAMING_SNAKE_CASE					:			str    	=       self.rust_tokenizer_class.from_pretrained(
                       lowercase__    ,							use_fast=lowercase__    ,							add_prefix_space=lowercase__    ,							trim_offsets=lowercase__ )
                   SCREAMING_SNAKE_CASE					:			int    	=       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__ ))    ,							)

                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.rust_tokenizer_class.from_pretrained(
                       lowercase__    ,							use_fast=lowercase__    ,							add_prefix_space=lowercase__    ,							trim_offsets=lowercase__ )
                   SCREAMING_SNAKE_CASE					:			Dict    	=       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__ ))    ,							)

                   SCREAMING_SNAKE_CASE					:			Dict    	=       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)),
                   # )

                   SCREAMING_SNAKE_CASE					:			int    	=       self.rust_tokenizer_class.from_pretrained(
                       lowercase__    ,							use_fast=lowercase__    ,							add_prefix_space=lowercase__    ,							trim_offsets=lowercase__ )
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       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__ ))    ,							)

                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.rust_tokenizer_class.from_pretrained(
                       lowercase__    ,							use_fast=lowercase__    ,							add_prefix_space=lowercase__    ,							trim_offsets=lowercase__ )
                   SCREAMING_SNAKE_CASE					:			Any    	=       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__ ))    ,							)

                   SCREAMING_SNAKE_CASE					:			Tuple    	=       self.rust_tokenizer_class.from_pretrained(
                       lowercase__    ,							use_fast=lowercase__    ,							add_prefix_space=lowercase__    ,							trim_offsets=lowercase__ )
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       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__ ))    ,							)




'''simple docstring'''





import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 how to perform Cross Validation,
# 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
#
########################################################################


_lowerCAmelCase  :Tuple  				=    16
_lowerCAmelCase  :List[Any]  				=    32


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_       ,      a_       ,      a_ = 16		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       AutoTokenizer.from_pretrained('bert-base-cased'		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       DatasetDict(
         {
             'train': dataset['train'].select(a_		),
             'validation': dataset['train'].select(a_		),
             'test': dataset['validation'],
         }		)

     def tokenize_function(a_		):
          # max_length=None => use the model max length (it's actually the default)
          SCREAMING_SNAKE_CASE					:			Any    	=       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():
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       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
     SCREAMING_SNAKE_CASE					:			Dict    	=       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.
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       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":
               SCREAMING_SNAKE_CASE					:			Any    	=       16
          elif accelerator.mixed_precision != "no":
               SCREAMING_SNAKE_CASE					:			Any    	=       8
          else:
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       None

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

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

     SCREAMING_SNAKE_CASE					:			Any    	=       DataLoader(
         tokenized_datasets['test']       ,      shuffle=a_       ,      collate_fn=a_       ,      batch_size=a_		)

     return train_dataloader, eval_dataloader, test_dataloader


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Optional[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[str]    	=       []
     # Download the dataset
     SCREAMING_SNAKE_CASE					:			Any    	=       load_dataset('glue'       ,      'mrpc'		)
     # Create our splits
     SCREAMING_SNAKE_CASE					:			Dict    	=       StratifiedKFold(n_splits=int(args.num_folds		)		)
     # Initialize accelerator
     SCREAMING_SNAKE_CASE					:			Tuple    	=       Accelerator(cpu=args.cpu       ,      mixed_precision=args.mixed_precision		)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     SCREAMING_SNAKE_CASE					:			Dict    	=       config['lr']
     SCREAMING_SNAKE_CASE					:			Tuple    	=       int(config['num_epochs']		)
     SCREAMING_SNAKE_CASE					:			Any    	=       int(config['seed']		)
     SCREAMING_SNAKE_CASE					:			Any    	=       int(config['batch_size']		)

     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       evaluate.load('glue'       ,      'mrpc'		)

     # If the batch size is too big we use gradient accumulation
     SCREAMING_SNAKE_CASE					:			Dict    	=       1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          SCREAMING_SNAKE_CASE					:			int    	=       batch_size // MAX_GPU_BATCH_SIZE
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       MAX_GPU_BATCH_SIZE

     set_seed(a_		)

     # New Code #
     # Create our folds:
     SCREAMING_SNAKE_CASE					:			int    	=       kfold.split(np.zeros(datasets['train'].num_rows		)       ,      datasets['train']['label']		)
     SCREAMING_SNAKE_CASE					:			List[str]    	=       []
     # Iterate over them
     for i, (train_idxs, valid_idxs) in enumerate(a_		):
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       get_fold_dataloaders(
              a_       ,      a_       ,      a_       ,      a_       ,      )
          # Instantiate the model (we build the model here so that the seed also control new weights initialization)
          SCREAMING_SNAKE_CASE					:			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).
          SCREAMING_SNAKE_CASE					:			Dict    	=       model.to(accelerator.device		)

          # Instantiate optimizer
          SCREAMING_SNAKE_CASE					:			int    	=       AdamW(params=model.parameters()       ,      lr=a_		)

          # Instantiate scheduler
          SCREAMING_SNAKE_CASE					:			int    	=       get_linear_schedule_with_warmup(
              optimizer=a_       ,      num_warmup_steps=100       ,      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.
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[Any]    	=       accelerator.prepare(
              a_       ,      a_       ,      a_       ,      a_       ,      a_		)

          # Now we train the model
          for epoch in range(a_		):
               model.train()
               for step, batch in enumerate(a_		):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device		)
                    SCREAMING_SNAKE_CASE					:			Dict    	=       model(**a_		)
                    SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       outputs.loss
                    SCREAMING_SNAKE_CASE					:			List[Any]    	=       loss / gradient_accumulation_steps
                    accelerator.backward(a_		)
                    if step % gradient_accumulation_steps == 0:
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()

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

               SCREAMING_SNAKE_CASE					:			int    	=       metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(f"""epoch {epoch}:"""       ,      a_		)

          # New Code #
          # We also run predictions on the test set at the very end
          SCREAMING_SNAKE_CASE					:			Any    	=       []
          for step, batch in enumerate(a_		):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device		)
               with torch.no_grad():
                    SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model(**a_		)
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       outputs.logits
               SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Tuple    	=       accelerator.gather_for_metrics((predictions, batch['labels'])		)
               fold_predictions.append(predictions.cpu()		)
               if i == 0:
                    # We need all of the test predictions
                    test_references.append(references.cpu()		)
        # Use accelerator.print to print only on the main process.
          test_predictions.append(torch.cat(a_       ,      dim=0		)		)
          # We now need to release all our memory and get rid of the current model, optimizer, etc
          accelerator.free_memory()
     # New Code #
     # Finally we check the accuracy of our folded results:
     SCREAMING_SNAKE_CASE					:			int    	=       torch.cat(a_       ,      dim=0		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.stack(a_       ,      dim=0		).sum(dim=0		).div(int(args.num_folds		)		).argmax(dim=-1		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       metric.compute(predictions=a_       ,      references=a_		)
     accelerator.print('Average test metrics from all folds:'       ,      a_		)


def     __lowerCAmelCase						(       )      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			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.'		)
     # New Code #
     parser.add_argument('--num_folds'       ,      type=a_       ,      default=3       ,      help='The number of splits to perform across the dataset'		)
     SCREAMING_SNAKE_CASE					:			Any    	=       parser.parse_args()
     SCREAMING_SNAKE_CASE					:			Dict    	=       {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(a_       ,      a_		)


if __name__ == "__main__":
 main()



