import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
    from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image


class 	lowerCamelCase_     :






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase=3 , __lowercase=32 , __lowercase=3 , __lowercase=10 , __lowercase=[8, 16, 32, 64] , __lowercase=[1, 1, 2, 1] , __lowercase=True , __lowercase=True , __lowercase="relu" , __lowercase=3 , __lowercase=None , __lowercase=["stage2", "stage3", "stage4"] , __lowercase=[2, 3, 4] , __lowercase=1 , )       ->	Dict:
        __UpperCamelCase     :List[Any]			  =		parent
        __UpperCamelCase     :Optional[Any]			  =		batch_size
        __UpperCamelCase     :int			  =		image_size
        __UpperCamelCase     :Tuple			  =		num_channels
        __UpperCamelCase     :List[Any]			  =		embeddings_size
        __UpperCamelCase     :Dict			  =		hidden_sizes
        __UpperCamelCase     :List[Any]			  =		depths
        __UpperCamelCase     :str			  =		is_training
        __UpperCamelCase     :Optional[Any]			  =		use_labels
        __UpperCamelCase     :int			  =		hidden_act
        __UpperCamelCase     :str			  =		num_labels
        __UpperCamelCase     :Tuple			  =		scope
        __UpperCamelCase     :Dict			  =		len(__lowercase)
        __UpperCamelCase     :Any			  =		out_features
        __UpperCamelCase     :Any			  =		out_indices
        __UpperCamelCase     :Optional[int]			  =		num_groups







    def 			UpperCamelCase__   (	self)       ->	Dict:
        __UpperCamelCase     :List[Any]			  =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        __UpperCamelCase     :List[Any]			  =		None
        if self.use_labels:
            __UpperCamelCase     :int			  =		ids_tensor([self.batch_size] , self.num_labels)

        __UpperCamelCase     :Any			  =		self.get_config()

        return config, pixel_values, labels







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        return BitConfig(
            num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	str:
        __UpperCamelCase     :Tuple			  =		BitModel(config=__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase     :int			  =		model(__lowercase)
        self.parent.assertEqual(
            result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	int:
        __UpperCamelCase     :Dict			  =		self.num_labels
        __UpperCamelCase     :int			  =		BitForImageClassification(__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase     :Tuple			  =		model(__lowercase , labels=__lowercase)
        self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	Any:
        __UpperCamelCase     :Dict			  =		BitBackbone(config=__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase     :List[str]			  =		model(__lowercase)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps) , len(config.out_features))
        self.parent.assertListEqual(list(result.feature_maps[0].shape) , [self.batch_size, self.hidden_sizes[1], 4, 4])

        # verify channels
        self.parent.assertEqual(len(model.channels) , len(config.out_features))
        self.parent.assertListEqual(model.channels , config.hidden_sizes[1:])

        # verify backbone works with out_features=None
        __UpperCamelCase     :Dict			  =		None
        __UpperCamelCase     :str			  =		BitBackbone(config=__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase     :Any			  =		model(__lowercase)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps) , 1)
        self.parent.assertListEqual(list(result.feature_maps[0].shape) , [self.batch_size, self.hidden_sizes[-1], 1, 1])

        # verify channels
        self.parent.assertEqual(len(model.channels) , 1)
        self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]])







    def 			UpperCamelCase__   (	self)       ->	Dict:
        __UpperCamelCase     :Union[str, Any]			  =		self.prepare_config_and_inputs()
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		config_and_inputs
        __UpperCamelCase     :Optional[Any]			  =		{'''pixel_values''': pixel_values}
        return config, inputs_dict


@require_torch
class 	lowerCamelCase_     (   UpperCAmelCase_						,	UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						Optional[int]						=      (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
    a__	:						Union[str, Any]						=      (
        {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
        if is_torch_available()
        else {}
    )

    a__	:						List[str]						=      False
    a__	:						Optional[Any]						=      False
    a__	:						Any						=      False
    a__	:						Union[str, Any]						=      False
    a__	:						List[Any]						=      False







    def 			UpperCamelCase__   (	self)       ->	Dict:
        __UpperCamelCase     :Dict			  =		BitModelTester(self)
        __UpperCamelCase     :List[str]			  =		ConfigTester(self , config_class=__lowercase , has_text_modality=__lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()







    def 			UpperCamelCase__   (	self)       ->	Dict:
        return







    @unittest.skip(reason='''Bit does not output attentions''')
    def 			UpperCamelCase__   (	self)       ->	int:
        pass







    @unittest.skip(reason='''Bit does not use inputs_embeds''')
    def 			UpperCamelCase__   (	self)       ->	Any:
        pass







    @unittest.skip(reason='''Bit does not support input and output embeddings''')
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        pass







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase  ,       __UpperCamelCase     :List[str]			  =		self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCamelCase     :Optional[int]			  =		model_class(__lowercase)
            __UpperCamelCase     :int			  =		inspect.signature(model.forward)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            __UpperCamelCase     :List[str]			  =		[*signature.parameters.keys()]

            __UpperCamelCase     :List[str]			  =		['''pixel_values''']
            self.assertListEqual(arg_names[:1] , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Union[str, Any]			  =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase)







    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        __UpperCamelCase     :Optional[Any]			  =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*__lowercase)







    def 			UpperCamelCase__   (	self)       ->	Dict:
        __UpperCamelCase  ,       __UpperCamelCase     :Optional[int]			  =		self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCamelCase     :Optional[Any]			  =		model_class(config=__lowercase)
            for name, module in model.named_modules():
                if isinstance(__lowercase , (nn.BatchNormad, nn.GroupNorm)):
                    self.assertTrue(
                        torch.all(module.weight == 1) , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
                    self.assertTrue(
                        torch.all(module.bias == 0) , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )







    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        def check_hidden_states_output(__lowercase , __lowercase , __lowercase):
            __UpperCamelCase     :Dict			  =		model_class(__lowercase)
            model.to(__lowercase)
            model.eval()

            with torch.no_grad():
                __UpperCamelCase     :Optional[int]			  =		model(**self._prepare_for_class(__lowercase , __lowercase))

            __UpperCamelCase     :Any			  =		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            __UpperCamelCase     :Any			  =		self.model_tester.num_stages
            self.assertEqual(len(__lowercase) , expected_num_stages + 1)

            # Bit's feature maps are of shape (batch_size, num_channels, height, width)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )

        __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCamelCase     :Tuple			  =		['''preactivation''', '''bottleneck''']
        for model_class in self.all_model_classes:
            for layer_type in layers_type:
                __UpperCamelCase     :str			  =		layer_type
                __UpperCamelCase     :Optional[Any]			  =		True
                check_hidden_states_output(__lowercase , __lowercase , __lowercase)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                __UpperCamelCase     :Dict			  =		True

                check_hidden_states_output(__lowercase , __lowercase , __lowercase)







    @unittest.skip(reason='''Bit does not use feedforward chunking''')
    def 			UpperCamelCase__   (	self)       ->	str:
        pass







    def 			UpperCamelCase__   (	self)       ->	int:
        __UpperCamelCase     :Dict			  =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowercase)







    @slow
    def 			UpperCamelCase__   (	self)       ->	List[str]:
        for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __UpperCamelCase     :Dict			  =		BitModel.from_pretrained(__lowercase)
            self.assertIsNotNone(__lowercase)



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







    __UpperCamelCase     :Union[str, Any]			  =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
    return image


@require_torch
@require_vision
class 	lowerCamelCase_     (   unittest.TestCase ):






    '''simple docstring'''






    @cached_property
    def 			UpperCamelCase__   (	self)       ->	int:
        return (
            BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None
        )







    @slow
    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :List[str]			  =		BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(__lowercase)

        __UpperCamelCase     :List[Any]			  =		self.default_image_processor
        __UpperCamelCase     :List[str]			  =		prepare_img()
        __UpperCamelCase     :Any			  =		image_processor(images=__lowercase , return_tensors='''pt''').to(__lowercase)

        # forward pass
        with torch.no_grad():
            __UpperCamelCase     :Any			  =		model(**__lowercase)

        # verify the logits
        __UpperCamelCase     :Tuple			  =		torch.Size((1, 1_000))
        self.assertEqual(outputs.logits.shape , __lowercase)

        __UpperCamelCase     :Union[str, Any]			  =		torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]).to(__lowercase)

        self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowercase , atol=1E-4))


@require_torch
class 	lowerCamelCase_     (   UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						List[str]						=      (BitBackbone,) if is_torch_available() else ()
    a__	:						Dict						=      BitConfig

    a__	:						Any						=      False







    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        __UpperCamelCase     :Optional[Any]			  =		BitModelTester(self)






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()





