
"""simple docstring"""




import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
    a		:       str     						=       'pt'
elif is_tf_available():
    a		:       Dict     						=       'tf'
else:
    a		:       Optional[Any]     						=       'jax'







class     lowercase(_lowercase    ,						unittest.TestCase       ):
     __snake_case:	List[Any]						=     PerceiverTokenizer
     __snake_case:	Optional[Any]						=     False



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         super().setUp()
         a__		=			PerceiverTokenizer()
         tokenizer.save_pretrained(self.tmpdirname   )



     @cached_property
     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'   )



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     PerceiverTokenizer:







         """simple docstring"""



         return self.tokenizer_class.from_pretrained(self.tmpdirname     ,				**__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=False     ,				__SCREAMING_SNAKE_CASE=2_0     ,				__SCREAMING_SNAKE_CASE=5   )		->     Tuple[str, list]:







         """simple docstring"""



         a__		=			[]
         for i in range(len(__SCREAMING_SNAKE_CASE   )   ):
             try:
                 a__		=			tokenizer.decode([i]     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
             except UnicodeDecodeError:
                 pass
             toks.append((i, tok)   )

         a__		=			list(filter(lambda __SCREAMING_SNAKE_CASE   : re.match(R'^[ a-zA-Z]+$'     ,				t[1]   )     ,				__SCREAMING_SNAKE_CASE   )   )
         a__		=			list(filter(lambda __SCREAMING_SNAKE_CASE   : [t[0]] == tokenizer.encode(t[1]     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )     ,				__SCREAMING_SNAKE_CASE   )   )
         if max_length is not None and len(__SCREAMING_SNAKE_CASE   ) > max_length:
             a__		=			toks[:max_length]
         if min_length is not None and len(__SCREAMING_SNAKE_CASE   ) < min_length and len(__SCREAMING_SNAKE_CASE   ) > 0:
             while len(__SCREAMING_SNAKE_CASE   ) < min_length:
                 a__		=			toks + toks
        # toks_str = [t[1] for t in toks]
         a__		=			[t[0] for t in toks]

         # Ensure consistency
         a__		=			tokenizer.decode(__SCREAMING_SNAKE_CASE     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
         if " " not in output_txt and len(__SCREAMING_SNAKE_CASE   ) > 1:
             a__		=			(
                 tokenizer.decode([toks_ids[0]]     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
                 + ' '
                 + tokenizer.decode(toks_ids[1:]     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
             )
         if with_prefix_space:
             a__		=			' ' + output_txt
         a__		=			tokenizer.encode(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
         return output_txt, output_ids



     def 			lowercase__  (							self   )		->     Optional[Any]:







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         a__		=			'Unicode €.'
         a__		=			tokenizer(__SCREAMING_SNAKE_CASE   )
         a__		=			[4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
         self.assertEqual(encoded['input_ids']     ,				__SCREAMING_SNAKE_CASE   )

         # decoding
         a__		=			tokenizer.decode(__SCREAMING_SNAKE_CASE   )
         self.assertEqual(__SCREAMING_SNAKE_CASE     ,				'[CLS]Unicode €.[SEP]'   )

         a__		=			tokenizer('e è é ê ë'   )
         a__		=			[4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
         self.assertEqual(encoded['input_ids']     ,				__SCREAMING_SNAKE_CASE   )
         # decoding
         a__		=			tokenizer.decode(__SCREAMING_SNAKE_CASE   )
         self.assertEqual(__SCREAMING_SNAKE_CASE     ,				'[CLS]e è é ê ë[SEP]'   )

         # encode/decode, but with `encode` instead of `__call__`
         self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'   )   )     ,				'[CLS]e è é ê ë[SEP]'   )



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         a__		=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
         # fmt: off
         a__		=			[4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
         # fmt: on
         a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				padding=__SCREAMING_SNAKE_CASE     ,				return_tensors=__SCREAMING_SNAKE_CASE   )
         self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         if FRAMEWORK != "jax":
             a__		=			list(batch.input_ids.numpy()[0]   )
         else:
             a__		=			list(batch.input_ids.tolist()[0]   )

         self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         self.assertEqual((2, 3_8)     ,				batch.input_ids.shape   )
         self.assertEqual((2, 3_8)     ,				batch.attention_mask.shape   )



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         a__		=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
         a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				padding=__SCREAMING_SNAKE_CASE     ,				return_tensors=__SCREAMING_SNAKE_CASE   )
         # check if input_ids are returned and no decoder_input_ids
         self.assertIn('input_ids'     ,				__SCREAMING_SNAKE_CASE   )
         self.assertIn('attention_mask'     ,				__SCREAMING_SNAKE_CASE   )
         self.assertNotIn('decoder_input_ids'     ,				__SCREAMING_SNAKE_CASE   )
         self.assertNotIn('decoder_attention_mask'     ,				__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         a__		=			[
             'Summary of the text.',
             'Another summary.',
         ]
         a__		=			tokenizer(
             text_target=__SCREAMING_SNAKE_CASE     ,				max_length=3_2     ,				padding='max_length'     ,				truncation=__SCREAMING_SNAKE_CASE     ,				return_tensors=__SCREAMING_SNAKE_CASE   )
         self.assertEqual(3_2     ,				targets['input_ids'].shape[1]   )



     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         a__		=			self.get_tokenizers()
         for tokenizer in tokenizers:
             with self.subTest(f'{tokenizer.__class__.__name__}'   ):
                 self.assertNotEqual(tokenizer.model_max_length     ,				4_2   )

        # Now let's start the test
         a__		=			self.get_tokenizers()
         for tokenizer in tokenizers:
             with self.subTest(f'{tokenizer.__class__.__name__}'   ):
                 # Isolate this from the other tests because we save additional tokens/etc
                 a__		=			tempfile.mkdtemp()

                 a__		=			' He is very happy, UNwant\u00E9d,running'
                 a__		=			tokenizer.encode(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
                 tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE   )

                 a__		=			tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE   )
                 a__		=			after_tokenizer.encode(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
                 self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

                 shutil.rmtree(__SCREAMING_SNAKE_CASE   )

         a__		=			self.get_tokenizers(model_max_length=4_2   )
         for tokenizer in tokenizers:
             with self.subTest(f'{tokenizer.__class__.__name__}'   ):
                 # Isolate this from the other tests because we save additional tokens/etc
                 a__		=			tempfile.mkdtemp()

                 a__		=			' He is very happy, UNwant\u00E9d,running'
                 tokenizer.add_tokens(['bim', 'bambam']   )
                 a__		=			tokenizer.additional_special_tokens
                 additional_special_tokens.append('new_additional_special_token'   )
                 tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}   )
                 a__		=			tokenizer.encode(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
                 tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE   )

                 a__		=			tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE   )
                 a__		=			after_tokenizer.encode(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
                 self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
                 self.assertIn('new_additional_special_token'     ,				after_tokenizer.additional_special_tokens   )
                 self.assertEqual(after_tokenizer.model_max_length     ,				4_2   )

                 a__		=			tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE     ,				model_max_length=4_3   )
                 self.assertEqual(tokenizer.model_max_length     ,				4_3   )

                 shutil.rmtree(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     List[Any]:







         """simple docstring"""



         a__		=			[]
         if self.test_slow_tokenizer:
             tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())   )

         if self.test_rust_tokenizer:
             tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())   )

         for tokenizer_class, tokenizer_utils in tokenizer_list:
             with tempfile.TemporaryDirectory() as tmp_dir:
                 tokenizer_utils.save_pretrained(__SCREAMING_SNAKE_CASE   )

                 with open(os.path.join(__SCREAMING_SNAKE_CASE     ,				'special_tokens_map.json'   )     ,				encoding='utf-8'   ) as json_file:
                     a__		=			json.load(__SCREAMING_SNAKE_CASE   )

                 with open(os.path.join(__SCREAMING_SNAKE_CASE     ,				'tokenizer_config.json'   )     ,				encoding='utf-8'   ) as json_file:
                     a__		=			json.load(__SCREAMING_SNAKE_CASE   )

                 a__		=			[f'<extra_id_{i}>' for i in range(1_2_5   )]

                 a__		=			added_tokens_extra_ids + [
                     'an_additional_special_token'
                 ]
                 a__		=			added_tokens_extra_ids + [
                     'an_additional_special_token'
                 ]

                 with open(os.path.join(__SCREAMING_SNAKE_CASE     ,				'special_tokens_map.json'   )     ,				'w'     ,				encoding='utf-8'   ) as outfile:
                     json.dump(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
                 with open(os.path.join(__SCREAMING_SNAKE_CASE     ,				'tokenizer_config.json'   )     ,				'w'     ,				encoding='utf-8'   ) as outfile:
                     json.dump(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

                 # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                 # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                 # "special_tokens_map.json" files
                 a__		=			tokenizer_class.from_pretrained(
                     __SCREAMING_SNAKE_CASE     ,				)
                 self.assertIn(
                     'an_additional_special_token'     ,				tokenizer_without_change_in_init.additional_special_tokens   )
                 self.assertEqual(
                     ['an_additional_special_token']     ,				tokenizer_without_change_in_init.convert_ids_to_tokens(
                         tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']   )   )     ,				)

                 # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                 a__		=			added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'     ,				lstrip=__SCREAMING_SNAKE_CASE   )]
                 a__		=			tokenizer_class.from_pretrained(
                     __SCREAMING_SNAKE_CASE     ,				additional_special_tokens=__SCREAMING_SNAKE_CASE     ,				)

                 self.assertIn('a_new_additional_special_token'     ,				tokenizer.additional_special_tokens   )
                 self.assertEqual(
                     ['a_new_additional_special_token']     ,				tokenizer.convert_ids_to_tokens(
                         tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']   )   )     ,				)



     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         self.assertEqual(tokenizer.decode([1_7_8]   )     ,				'�'   )



     def 			lowercase__  (							self   )		->     str:







         """simple docstring"""



         pass



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         pass



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         pass



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         pass




     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			self.get_tokenizers(fast=__SCREAMING_SNAKE_CASE     ,				do_lower_case=__SCREAMING_SNAKE_CASE   )
         for tokenizer in tokenizers:
             with self.subTest(f'{tokenizer.__class__.__name__}'   ):
                 a__		=			['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
                 a__		=			tokenizer.convert_tokens_to_string(__SCREAMING_SNAKE_CASE   )

                 self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )




"""simple docstring"""




import argparse
import os

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

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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)
#
# 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		:       List[str]     						=       16
a		:       str     						=       32



def     __magic_name__		(					UpperCamelCase     :		Accelerator							,		UpperCamelCase     :		int = 16     )     ->						Dict:
    a__		=			AutoTokenizer.from_pretrained('bert-base-cased'     )
    a__		=			load_dataset('glue'							,		'mrpc'     )

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

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        a__		=			datasets.map(
            UpperCamelCase							,		batched=UpperCamelCase							,		remove_columns=['idx', 'sentence1', 'sentence2']							,		)

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

    def collate_fn(UpperCamelCase     :		List[str]     ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        a__		=			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":
            a__		=			16
        elif accelerator.mixed_precision != "no":
            a__		=			8
        else:
            a__		=			None

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

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

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    a		:       Union[str, Any]     						=       mocked_dataloaders  # noqa: F811



def     __magic_name__		(					UpperCamelCase     :		List[Any]							,		UpperCamelCase     :		Optional[int]     )     ->						Optional[Any]:
    # For testing only
    if os.environ.get('TESTING_MOCKED_DATALOADERS'							,		UpperCamelCase     ) == "1":
        a__		=			2
    # New Code #
    a__		=			int(args.gradient_accumulation_steps     )
    a__		=			int(args.local_sgd_steps     )
    # Initialize accelerator
    a__		=			Accelerator(
        cpu=args.cpu							,		mixed_precision=args.mixed_precision							,		gradient_accumulation_steps=UpperCamelCase     )
    if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
        raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'     )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    a__		=			config['lr']
    a__		=			int(config['num_epochs']     )
    a__		=			int(config['seed']     )
    a__		=			int(config['batch_size']     )

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

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

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    a__		=			model.to(accelerator.device     )

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

    # Instantiate scheduler
    a__		=			get_linear_schedule_with_warmup(
        optimizer=UpperCamelCase							,		num_warmup_steps=100							,		num_training_steps=(len(UpperCamelCase     ) * 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.
    a__	,     a__	,     a__	,     a__	,     a__		=			accelerator.prepare(
        UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )

    # Now we train the model
    for epoch in range(UpperCamelCase     ):
        model.train()
        with LocalSGD(
            accelerator=UpperCamelCase							,		model=UpperCamelCase							,		local_sgd_steps=UpperCamelCase							,		enabled=local_sgd_steps is not None     ) as local_sgd:
            for step, batch in enumerate(UpperCamelCase     ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device     )
                # New code #
                # We use the new `accumulate` context manager to perform gradient accumulation
                # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                with accelerator.accumulate(UpperCamelCase     ):
                    a__		=			model(**UpperCamelCase     )
                    a__		=			output.loss
                    accelerator.backward(UpperCamelCase     )
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
                    # LocalSGD-specific line
                    local_sgd.step()

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

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



def     __magic_name__		(					)     ->						Any:
    a__		=			argparse.ArgumentParser(description='Simple example of training script.'     )
    parser.add_argument(
        '--mixed_precision'							,		type=UpperCamelCase							,		default=UpperCamelCase							,		choices=['no', 'fp16', 'bf16', 'fp8']							,		help='Whether to use mixed precision. Choose'
        'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
        'and an Nvidia Ampere GPU.'							,		)
    # New Code #
    parser.add_argument(
        '--gradient_accumulation_steps'							,		type=UpperCamelCase							,		default=1							,		help='The number of minibatches to be ran before gradients are accumulated.'							,		)
    parser.add_argument(
        '--local_sgd_steps'							,		type=UpperCamelCase							,		default=8							,		help='Number of local SGD steps or None to disable local SGD'     )
    parser.add_argument('--cpu'							,		action='store_true'							,		help='If passed, will train on the CPU.'     )
    a__		=			parser.parse_args()
    a__		=			{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
    training_function(UpperCamelCase							,		UpperCamelCase     )


if __name__ == "__main__":
    main()


