
'''simple docstring'''




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     :							List[Any]       						=     16
lowercase     :							Dict       						=     32







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__ = 16						,							snake_case__ = "bert-base-cased"		):




    '''simple docstring'''



    A			:  List[str]   =		AutoTokenizer.from_pretrained(snake_case__		)
    A			:  Union[str, Any]   =		load_dataset('''glue'''						,							'''mrpc'''		)

    def tokenize_function(snake_case__		):
        # max_length=None => use the model max length (it's actually the default)
        A			:  Dict   =		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
    A			:  Tuple   =		datasets.map(
        snake_case__						,							batched=snake_case__						,							remove_columns=['''idx''', '''sentence1''', '''sentence2''']						,							load_from_cache_file=snake_case__		)

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

    def collate_fn(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(snake_case__						,							padding='''max_length'''						,							max_length=128						,							return_tensors='''pt'''		)
        return tokenizer.pad(snake_case__						,							padding='''longest'''						,							return_tensors='''pt'''		)

    # Instantiate dataloaders.
    A			:  Optional[int]   =		DataLoader(
        tokenized_datasets['''train''']						,							shuffle=snake_case__						,							collate_fn=snake_case__						,							batch_size=snake_case__		)
    A			:  int   =		DataLoader(
        tokenized_datasets['''validation''']						,							shuffle=snake_case__						,							collate_fn=snake_case__						,							batch_size=snake_case__		)

    return train_dataloader, eval_dataloader







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  Optional[Any]   =		Accelerator()

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

    set_seed(snake_case__		)
    A,    A			:  List[str]   =		get_dataloaders(snake_case__						,							snake_case__						,							snake_case__		)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    A			:  List[str]   =		AutoModelForSequenceClassification.from_pretrained(snake_case__						,							return_dict=snake_case__		)

    # Instantiate optimizer
    A			:  str   =		(
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    A			:  Dict   =		optimizer_cls(params=model.parameters()						,							lr=snake_case__		)

    if accelerator.state.deepspeed_plugin is not None:
        A			:  int   =		accelerator.state.deepspeed_plugin.deepspeed_config[
            '''gradient_accumulation_steps'''
        ]
    else:
        A			:  str   =		1
    A			:  str   =		(len(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
    ):
        A			:  Tuple   =		get_linear_schedule_with_warmup(
            optimizer=snake_case__						,							num_warmup_steps=0						,							num_training_steps=snake_case__						,							)
    else:
        A			:  Union[str, Any]   =		DummyScheduler(snake_case__						,							total_num_steps=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.
    A,    A,    A,    A,    A			:  Union[str, Any]   =		accelerator.prepare(
        snake_case__						,							snake_case__						,							snake_case__						,							snake_case__						,							snake_case__		)

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

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

            overall_step += 1

        model.eval()
        A			:  Tuple   =		0
        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():
                A			:  Optional[int]   =		model(**snake_case__		)
            A			:  str   =		outputs.logits.argmax(dim=-1		)
            # It is slightly faster to call this once, than multiple times
            A,    A			:  Optional[int]   =		accelerator.gather(
                (predictions, batch['''labels'''])		)  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                if step == len(snake_case__		) - 1:
                    A			:  int   =		predictions[: len(eval_dataloader.dataset		) - samples_seen]
                    A			:  Tuple   =		references[: len(eval_dataloader.dataset		) - samples_seen]
                else:
                    samples_seen += references.shape[0]
            metric.add_batch(
                predictions=snake_case__						,							references=snake_case__						,							)

        A			:  List[str]   =		metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'						,							snake_case__		)
        A			:  str   =		eval_metric['''accuracy''']

        if best_performance < eval_metric["accuracy"]:
            A			:  List[str]   =		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(snake_case__						,							snake_case__		)







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    A			:  Optional[Any]   =		argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''		)
    parser.add_argument(
        '''--model_name_or_path'''						,							type=snake_case__						,							default='''bert-base-cased'''						,							help='''Path to pretrained model or model identifier from huggingface.co/models.'''						,							required=snake_case__						,							)
    parser.add_argument(
        '''--output_dir'''						,							type=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=snake_case__						,							default=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=snake_case__						,							default=3						,							help='''Number of train epochs.'''						,							)
    A			:  Union[str, Any]   =		parser.parse_args()
    A			:  Dict   =		{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
    training_function(snake_case__						,							snake_case__		)


if __name__ == "__main__":
      main()






'''simple docstring'''




import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 					A							(				__snake_case       ,     unittest.TestCase		):
     __magic_name__          =						DebertaTokenizer
     __magic_name__          =						True
     __magic_name__          =						DebertaTokenizerFast





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         super().setUp()

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

         A			:  Optional[int]   =		os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''vocab_file''']      )
         A			:  str   =		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(SCREAMING_SNAKE_CASE      ) + '''\n'''      )
         with open(self.merges_file		,     '''w'''		,     encoding='''utf-8'''      ) as fp:
             fp.write('''\n'''.join(SCREAMING_SNAKE_CASE      )      )





     def 							__lowerCAmelCase				(				self		,     **SCREAMING_SNAKE_CASE      )					->							Optional[Any]:






         """simple docstring"""






         kwargs.update(self.special_tokens_map      )
         return self.tokenizer_class.from_pretrained(self.tmpdirname		,     **SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  Union[str, Any]   =		'''lower newer'''
         A			:  List[Any]   =		'''lower newer'''
         return input_text, output_text





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  str   =		self.get_tokenizer()
         A			:  List[str]   =		'''lower newer'''
         A			:  int   =		['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
         A			:  Any   =		tokenizer.tokenize(SCREAMING_SNAKE_CASE      )
         self.assertListEqual(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

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





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  List[Any]   =		self.get_tokenizer()
         A			:  Tuple   =		tokenizer('''Hello'''		,     '''World'''      )
         A			:  str   =		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
         self.assertListEqual(tokd['''token_type_ids''']		,     SCREAMING_SNAKE_CASE      )





     @slow
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  Union[str, Any]   =		self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''      )

         A			:  Union[str, Any]   =		tokenizer.encode('''sequence builders'''		,     add_special_tokens=SCREAMING_SNAKE_CASE      )
         A			:  Any   =		tokenizer.encode('''multi-sequence build'''		,     add_special_tokens=SCREAMING_SNAKE_CASE      )

         A			:  Tuple   =		tokenizer.encode(
             '''sequence builders'''		,     add_special_tokens=SCREAMING_SNAKE_CASE		,     add_prefix_space=SCREAMING_SNAKE_CASE      )
         A			:  Union[str, Any]   =		tokenizer.encode(
             '''sequence builders'''		,     '''multi-sequence build'''		,     add_special_tokens=SCREAMING_SNAKE_CASE		,     add_prefix_space=SCREAMING_SNAKE_CASE      )

         A			:  List[str]   =		tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE      )
         A			:  Union[str, Any]   =		tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode





     @slow
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  Any   =		[self.tokenizer_class]
         if self.test_rust_tokenizer:
             tokenizer_classes.append(self.rust_tokenizer_class      )

         for tokenizer_class in tokenizer_classes:
             A			:  List[str]   =		tokenizer_class.from_pretrained('''microsoft/deberta-base'''      )

             A			:  Dict   =		[
                 '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                 '''ALBERT incorporates two parameter reduction techniques''',
                 '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                 ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                 ''' vocabulary embedding.''',
             ]

             A			:  List[Any]   =		tokenizer(SCREAMING_SNAKE_CASE		,     padding=SCREAMING_SNAKE_CASE      )
             A			:  List[str]   =		[tokenizer.decode(SCREAMING_SNAKE_CASE		,     skip_special_tokens=SCREAMING_SNAKE_CASE      ) for seq in encoding['''input_ids''']]

             # fmt: off
             A			:  List[str]   =		{
                 '''input_ids''': [
                     [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
                 ],
                 '''token_type_ids''': [
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                 ],
                 '''attention_mask''': [
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                 ]
             }
             # fmt: on

             A			:  Any   =		[
                 '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                 '''ALBERT incorporates two parameter reduction techniques''',
                 '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                 ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                 ''' vocabulary embedding.''',
             ]

             self.assertDictEqual(encoding.data		,     SCREAMING_SNAKE_CASE      )

             for expected, decoded in zip(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
                 self.assertEqual(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )




