


'''simple docstring'''
import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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


if is_torch_available():
		import torch

		from transformers import UperNetForSemanticSegmentation
		from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor
class        lowercase     :

		"""simple docstring"""

		def __init__(		self   ,       UpperCamelCase_   ,       UpperCamelCase_=13   ,       UpperCamelCase_=32   ,       UpperCamelCase_=3   ,       UpperCamelCase_=4   ,       UpperCamelCase_=[10, 20, 30, 40]   ,       UpperCamelCase_=[2, 2, 3, 2]   ,       UpperCamelCase_=True   ,       UpperCamelCase_=True   ,       UpperCamelCase_=37   ,       UpperCamelCase_="gelu"   ,       UpperCamelCase_=10   ,       UpperCamelCase_=0.02   ,       UpperCamelCase_=["stage2", "stage3", "stage4"]   ,       UpperCamelCase_=3   ,       UpperCamelCase_=None   ,       ):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			parent
							UpperCamelCase__       :str							 =			batch_size
							UpperCamelCase__       :Union[str, Any]							 =			image_size
							UpperCamelCase__       :List[Any]							 =			num_channels
							UpperCamelCase__       :List[str]							 =			num_stages
							UpperCamelCase__       :int							 =			hidden_sizes
							UpperCamelCase__       :Dict							 =			depths
							UpperCamelCase__       :int							 =			is_training
							UpperCamelCase__       :Union[str, Any]							 =			use_labels
							UpperCamelCase__       :Optional[int]							 =			intermediate_size
							UpperCamelCase__       :Any							 =			hidden_act
							UpperCamelCase__       :List[str]							 =			type_sequence_label_size
							UpperCamelCase__       :Optional[int]							 =			initializer_range
							UpperCamelCase__       :Union[str, Any]							 =			out_features
							UpperCamelCase__       :Union[str, Any]							 =			num_labels
							UpperCamelCase__       :str							 =			scope
							UpperCamelCase__       :List[str]							 =			num_stages




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

							UpperCamelCase__       :Dict							 =			None
							if self.use_labels:
												UpperCamelCase__       :str							 =			ids_tensor([self.batch_size]   ,       self.type_sequence_label_size							)

							UpperCamelCase__       :Dict							 =			self.get_config()

							return config, pixel_values, labels




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return ConvNextConfig(
							    num_channels=self.num_channels   ,       num_stages=self.num_stages   ,       hidden_sizes=self.hidden_sizes   ,       depths=self.depths   ,       is_training=self.is_training   ,       intermediate_size=self.intermediate_size   ,       hidden_act=self.hidden_act   ,       out_features=self.out_features   ,       )




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return UperNetConfig(
							    backbone_config=self.get_backbone_config()   ,       hidden_size=512   ,       pool_scales=[1, 2, 3, 6]   ,       use_auxiliary_head=UpperCamelCase_   ,       auxiliary_loss_weight=0.4   ,       auxiliary_in_channels=40   ,       auxiliary_channels=256   ,       auxiliary_num_convs=1   ,       auxiliary_concat_input=UpperCamelCase_   ,       loss_ignore_index=255   ,       num_labels=self.num_labels   ,       )




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			UperNetForSemanticSegmentation(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__       :Optional[Any]							 =			model(UpperCamelCase_							)
							self.parent.assertEqual(
							    result.logits.shape   ,       (self.batch_size, self.num_labels, self.image_size, self.image_size)							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			self.prepare_config_and_inputs()
							(
							    (
							    UpperCamelCase__
							) ,    (
							    UpperCamelCase__
							) ,    (
							    UpperCamelCase__
							) ,    
							)       :Tuple							 =			config_and_inputs
							UpperCamelCase__       :Any							 =			{'''pixel_values''': pixel_values}
							return config, inputs_dict



@require_torch
class        lowercase     (							A__     ,		A__     ,		unittest.TestCase ):

		"""simple docstring"""

		_a   			=    (UperNetForSemanticSegmentation,) if is_torch_available() else ()
		_a   			=    {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
		_a   			=    False
		_a   			=    False
		_a   			=    False
		_a   			=    False
		_a   			=    False
		_a   			=    False




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			UperNetModelTester(self							)
							UpperCamelCase__       :str							 =			ConfigTester(self   ,       config_class=UpperCamelCase_   ,       has_text_modality=UpperCamelCase_   ,       hidden_size=37							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							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    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__ ,    UpperCamelCase__       :Union[str, Any]							 =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												UpperCamelCase__       :Union[str, Any]							 =			model_class(UpperCamelCase_							)
												UpperCamelCase__       :List[Any]							 =			inspect.signature(model.forward							)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												UpperCamelCase__       :Union[str, Any]							 =			[*signature.parameters.keys()]

												UpperCamelCase__       :Dict							 =			['''pixel_values''']
												self.assertListEqual(arg_names[:1]   ,       UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[int]							 =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase_							)




		@unittest.skip(reason='''UperNet does not use inputs_embeds'''							)
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							pass




		@unittest.skip(reason='''UperNet does not support input and output embeddings'''							)
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							pass




		@unittest.skip(reason='''UperNet does not have a base model'''							)
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							pass




		@unittest.skip(reason='''UperNet does not have a base model'''							)
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							pass




		@require_torch_multi_gpu
		@unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''							)
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							pass




		@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''							)
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							pass




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							def check_hidden_states_output(UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							):
												UpperCamelCase__       :List[str]							 =			model_class(UpperCamelCase_							)
												model.to(UpperCamelCase_							)
												model.eval()

												with torch.no_grad():
																	UpperCamelCase__       :List[str]							 =			model(**self._prepare_for_class(UpperCamelCase_   ,       UpperCamelCase_							)							)

												UpperCamelCase__       :str							 =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												UpperCamelCase__       :int							 =			self.model_tester.num_stages
												self.assertEqual(len(UpperCamelCase_							)   ,       expected_num_stages + 1							)

												# ConvNext'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__       :Optional[Any]							 =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												UpperCamelCase__       :int							 =			True
												check_hidden_states_output(UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							)

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

												check_hidden_states_output(UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__ ,    UpperCamelCase__       :Optional[Any]							 =			self.model_tester.prepare_config_and_inputs_for_common()

							UpperCamelCase__       :Dict							 =			_config_zero_init(UpperCamelCase_							)
							UpperCamelCase__       :Optional[int]							 =			_config_zero_init(configs_no_init.backbone_config							)
							for model_class in self.all_model_classes:
												UpperCamelCase__       :Dict							 =			model_class(config=UpperCamelCase_							)
												for name, param in model.named_parameters():
																	if param.requires_grad:
																						self.assertIn(
																						    ((param.data.mean() * 1e9).round() / 1e9).item()   ,       [0.0, 1.0]   ,       msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''   ,       )




		@unittest.skip(reason='''UperNet does not have tied weights'''							)
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							pass




		@slow
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCamelCase__       :Optional[int]							 =			UperNetForSemanticSegmentation.from_pretrained(UpperCamelCase_							)
												self.assertIsNotNone(UpperCamelCase_							)



def       a				(      )  ->     List[str]:



					'''simple docstring'''






					UpperCamelCase__       :List[str]							 =			hf_hub_download(
					    repo_id='''hf-internal-testing/fixtures_ade20k'''			, repo_type='''dataset'''			, filename='''ADE_val_00000001.jpg'''       )
					UpperCamelCase__       :Optional[int]							 =			Image.open(__a       ).convert('''RGB'''       )
					return image







@require_torch
@require_vision
@slow
class        lowercase     (							unittest.TestCase ):

		"""simple docstring"""

		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny'''							)
							UpperCamelCase__       :Dict							 =			UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny'''							).to(UpperCamelCase_							)

							UpperCamelCase__       :Union[str, Any]							 =			prepare_img()
							UpperCamelCase__       :List[str]							 =			processor(images=UpperCamelCase_   ,       return_tensors='''pt'''							).to(UpperCamelCase_							)

							with torch.no_grad():
												UpperCamelCase__       :Any							 =			model(**UpperCamelCase_							)

							UpperCamelCase__       :Tuple							 =			torch.Size((1, model.config.num_labels, 512, 512)							)
							self.assertEqual(outputs.logits.shape   ,       UpperCamelCase_							)

							UpperCamelCase__       :Optional[Any]							 =			torch.tensor(
							    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]							).to(UpperCamelCase_							)
							self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]   ,       UpperCamelCase_   ,       atol=1e-4							)							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny'''							)
							UpperCamelCase__       :Optional[int]							 =			UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny'''							).to(UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			prepare_img()
							UpperCamelCase__       :List[Any]							 =			processor(images=UpperCamelCase_   ,       return_tensors='''pt'''							).to(UpperCamelCase_							)

							with torch.no_grad():
												UpperCamelCase__       :str							 =			model(**UpperCamelCase_							)

							UpperCamelCase__       :Optional[int]							 =			torch.Size((1, model.config.num_labels, 512, 512)							)
							self.assertEqual(outputs.logits.shape   ,       UpperCamelCase_							)

							UpperCamelCase__       :Any							 =			torch.tensor(
							    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]							).to(UpperCamelCase_							)
							self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]   ,       UpperCamelCase_   ,       atol=1e-4							)							)



'''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


__snake_case											=					16
__snake_case											=					32


def       a				(      __a			, __a = 16			, __a = "bert-base-cased"       )  ->     Any:



					'''simple docstring'''






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

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

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					UpperCamelCase__       :Optional[int]							 =			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.
										if accelerator.distributed_type == DistributedType.TPU:
															return tokenizer.pad(__a			, padding='''max_length'''			, max_length=128			, return_tensors='''pt'''       )
										return tokenizer.pad(__a			, padding='''longest'''			, return_tensors='''pt'''       )

					# Instantiate dataloaders.
					UpperCamelCase__       :Dict							 =			DataLoader(
					    tokenized_datasets['''train''']			, shuffle=__a			, collate_fn=__a			, batch_size=__a       )
					UpperCamelCase__       :str							 =			DataLoader(
					    tokenized_datasets['''validation''']			, shuffle=__a			, collate_fn=__a			, batch_size=__a       )

					return train_dataloader, eval_dataloader


def       a				(      __a			, __a			, __a			, __a       )  ->     str:



					'''simple docstring'''






					model.eval()
					UpperCamelCase__       :List[str]							 =			0
					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():
															UpperCamelCase__       :int							 =			model(**__a       )
										UpperCamelCase__       :Tuple							 =			outputs.logits.argmax(dim=-1       )
										# It is slightly faster to call this once, than multiple times
										UpperCamelCase__ ,    UpperCamelCase__       :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(__a       ) - 1:
																				UpperCamelCase__       :Union[str, Any]							 =			predictions[: len(eval_dataloader.dataset       ) - samples_seen]
																				UpperCamelCase__       :List[str]							 =			references[: len(eval_dataloader.dataset       ) - samples_seen]
															else:
																				samples_seen += references.shape[0]
										metric.add_batch(
										    predictions=__a			, references=__a			, )

					UpperCamelCase__       :Union[str, Any]							 =			metric.compute()
					return eval_metric["accuracy"]


def       a				(      __a			, __a       )  ->     List[Any]:



					'''simple docstring'''






					UpperCamelCase__       :Any							 =			Accelerator()

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

					set_seed(__a       )
					UpperCamelCase__ ,    UpperCamelCase__       :Any							 =			get_dataloaders(__a			, __a			, __a       )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCamelCase__       :Union[str, Any]							 =			AutoModelForSequenceClassification.from_pretrained(__a			, return_dict=__a       )

					# Instantiate optimizer
					UpperCamelCase__       :Any							 =			(
					    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=__a       )

					if accelerator.state.deepspeed_plugin is not None:
										UpperCamelCase__       :Tuple							 =			accelerator.state.deepspeed_plugin.deepspeed_config[
										    '''gradient_accumulation_steps'''
										]
					else:
										UpperCamelCase__       :Dict							 =			1
					UpperCamelCase__       :Tuple							 =			(len(__a       ) * 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__       :Any							 =			get_linear_schedule_with_warmup(
										    optimizer=__a			, num_warmup_steps=0			, num_training_steps=__a			, )
					else:
										UpperCamelCase__       :Any							 =			DummyScheduler(__a			, total_num_steps=__a			, 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__       :Dict							 =			accelerator.prepare(
					    __a			, __a			, __a			, __a			, __a       )

					# We need to keep track of how many total steps we have iterated over
					UpperCamelCase__       :Tuple							 =			0
					# We also need to keep track of the stating epoch so files are named properly
					UpperCamelCase__       :Optional[Any]							 =			0
					UpperCamelCase__       :Optional[int]							 =			evaluate.load('''glue'''			, '''mrpc'''       )
					UpperCamelCase__       :List[Any]							 =			num_epochs

					if args.partial_train_epoch is not None:
										UpperCamelCase__       :Optional[Any]							 =			args.partial_train_epoch

					if args.resume_from_checkpoint:
										accelerator.load_state(args.resume_from_checkpoint       )
										UpperCamelCase__       :Dict							 =			args.resume_from_checkpoint.split('''epoch_'''       )[1]
										UpperCamelCase__       :Tuple							 =			''''''
										for char in epoch_string:
															if char.isdigit():
																				state_epoch_num += char
															else:
																				break
										UpperCamelCase__       :Any							 =			int(__a       ) + 1
										UpperCamelCase__       :Dict							 =			evaluation_loop(__a			, __a			, __a			, __a       )
										accelerator.print('''resumed checkpoint performance:'''			, __a       )
										accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''			, lr_scheduler.get_lr()[0]       )
										accelerator.print('''resumed optimizers\'s lr:'''			, optimizer.param_groups[0]['''lr''']       )
										with open(os.path.join(args.output_dir			, f'''state_{starting_epoch-1}.json'''       )			, '''r'''       ) as f:
															UpperCamelCase__       :Optional[int]							 =			json.load(__a       )
															assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["lr"] == lr_scheduler.get_lr()[0]
															), "Scheduler learning rate mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
															), "Optimizer learning rate mismatch, loading from checkpoint failed"
															assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
															return

    # Now we train the model
					UpperCamelCase__       :Optional[Any]							 =			{}
					for epoch in range(__a			, __a       ):
										model.train()
										for step, batch in enumerate(__a       ):
															UpperCamelCase__       :Optional[int]							 =			model(**__a       )
															UpperCamelCase__       :Optional[int]							 =			outputs.loss
															UpperCamelCase__       :str							 =			loss / gradient_accumulation_steps
															accelerator.backward(__a       )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1
										UpperCamelCase__       :Union[str, Any]							 =			f'''epoch_{epoch}'''
										UpperCamelCase__       :List[Any]							 =			os.path.join(args.output_dir			, __a       )
										accelerator.save_state(__a       )
										UpperCamelCase__       :List[Any]							 =			evaluation_loop(__a			, __a			, __a			, __a       )
										UpperCamelCase__       :int							 =			accuracy
										UpperCamelCase__       :List[Any]							 =			lr_scheduler.get_lr()[0]
										UpperCamelCase__       :Any							 =			optimizer.param_groups[0]['''lr''']
										UpperCamelCase__       :int							 =			epoch
										UpperCamelCase__       :Tuple							 =			overall_step
										accelerator.print(f'''epoch {epoch}:'''			, __a       )

										accelerator.wait_for_everyone()
										if accelerator.is_main_process:
															with open(os.path.join(args.output_dir			, f'''state_{epoch}.json'''       )			, '''w'''       ) as f:
																				json.dump(__a			, __a       )


def       a				(      )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :List[Any]							 =			argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''       )
					parser.add_argument(
					    '''--model_name_or_path'''			, type=__a			, default='''bert-base-cased'''			, help='''Path to pretrained model or model identifier from huggingface.co/models.'''			, required=__a			, )
					parser.add_argument(
					    '''--output_dir'''			, type=__a			, default='''.'''			, help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''			, )
					parser.add_argument(
					    '''--resume_from_checkpoint'''			, type=__a			, default=__a			, help='''If the training should continue from a checkpoint folder.'''			, )
					parser.add_argument(
					    '''--partial_train_epoch'''			, type=__a			, default=__a			, help='''If passed, the training will stop after this number of epochs.'''			, )
					parser.add_argument(
					    '''--num_epochs'''			, type=__a			, default=2			, help='''Number of train epochs.'''			, )
					UpperCamelCase__       :Optional[int]							 =			parser.parse_args()
					UpperCamelCase__       :List[str]							 =			{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

					training_function(__a			, __a       )


if __name__ == "__main__":
		main()