

"""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 			__a					:





       """simple docstring"""


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





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

        lowerCAmelCase__   :				Optional[int] 			=				None
        if self.use_labels:
         lowerCAmelCase__   :				Tuple 			=				ids_tensor([self.batch_size]			,					self.type_sequence_label_size   )

        lowerCAmelCase__   :				List[str] 			=				self.get_config()

        return config, pixel_values, labels






       def 			SCREAMING_SNAKE_CASE_	(   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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				self.prepare_config_and_inputs()
        (
            (
            lowerCAmelCase__
        )			,						(
            lowerCAmelCase__
        )			,						(
            lowerCAmelCase__
        )			,						
        )   :				Union[str, Any] 			=				config_and_inputs
        lowerCAmelCase__   :				List[Any] 			=				{"pixel_values": pixel_values}
        return config, inputs_dict






@require_torch
class 			__a					(						__magic_name__       ,	__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      str    =						(UperNetForSemanticSegmentation,) if is_torch_available() else ()
       __UpperCamelCase					:      int    =						{'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
       __UpperCamelCase					:      Optional[int]    =						False
       __UpperCamelCase					:      Dict    =						False
       __UpperCamelCase					:      List[str]    =						False
       __UpperCamelCase					:      Optional[int]    =						False
       __UpperCamelCase					:      int    =						False
       __UpperCamelCase					:      Tuple    =						False






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				UperNetModelTester(self   )
        lowerCAmelCase__   :				Optional[Any] 			=				ConfigTester(self			,					config_class=snake_case			,					has_text_modality=snake_case			,					hidden_size=37   )






       def 			SCREAMING_SNAKE_CASE_	(   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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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

        for model_class in self.all_model_classes:
         lowerCAmelCase__   :				Optional[Any] 			=				model_class(snake_case   )
         lowerCAmelCase__   :				Dict 			=				inspect.signature(model.forward   )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCAmelCase__   :				Optional[int] 			=				[*signature.parameters.keys()]

         lowerCAmelCase__   :				Optional[int] 			=				["pixel_values"]
         self.assertListEqual(arg_names[:1]			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*snake_case   )






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





        """simple docstring"""

        pass






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





        """simple docstring"""

        pass






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





        """simple docstring"""

        pass






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





        """simple docstring"""

        pass






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





        """simple docstring"""

        pass






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        def check_hidden_states_output(snake_case			,					snake_case			,					snake_case   ):
         lowerCAmelCase__   :				Union[str, Any] 			=				model_class(snake_case   )
         model.to(snake_case   )
         model.eval()

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

         lowerCAmelCase__   :				Union[str, Any] 			=				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         lowerCAmelCase__   :				Any 			=				self.model_tester.num_stages
         self.assertEqual(len(snake_case   )			,					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]			,					)

        lowerCAmelCase__			,						lowerCAmelCase__   :				List[Any] 			=				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCAmelCase__   :				Optional[Any] 			=				True
         check_hidden_states_output(snake_case			,					snake_case			,					snake_case   )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowerCAmelCase__   :				Union[str, Any] 			=				True

         check_hidden_states_output(snake_case			,					snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__			,						lowerCAmelCase__   :				Optional[int] 			=				self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase__   :				List[Any] 			=				_config_zero_init(snake_case   )
        lowerCAmelCase__   :				List[Any] 			=				_config_zero_init(configs_no_init.backbone_config   )
        for model_class in self.all_model_classes:
         lowerCAmelCase__   :				str 			=				model_class(config=snake_case   )
         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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        pass






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCAmelCase__   :				str 			=				UperNetForSemanticSegmentation.from_pretrained(snake_case   )
         self.assertIsNotNone(snake_case   )




def       SCREAMING_SNAKE_CASE							( )			->						str:
 lowerCAmelCase__   :				Optional[int] 			=				hf_hub_download(
     repo_id="hf-internal-testing/fixtures_ade20k"					,					repo_type="dataset"					,					filename="ADE_val_00000001.jpg"   )
 lowerCAmelCase__   :				Union[str, Any] 			=				Image.open(lowercase__   ).convert("RGB"   )
 return image






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





       """simple docstring"""


       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				AutoImageProcessor.from_pretrained("openmmlab/upernet-swin-tiny"   )
        lowerCAmelCase__   :				Dict 			=				UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-swin-tiny"   ).to(snake_case   )

        lowerCAmelCase__   :				Dict 			=				prepare_img()
        lowerCAmelCase__   :				int 			=				processor(images=snake_case			,					return_tensors="pt"   ).to(snake_case   )

        with torch.no_grad():
         lowerCAmelCase__   :				Optional[int] 			=				model(**snake_case   )

        lowerCAmelCase__   :				List[str] 			=				torch.Size((1, model.config.num_labels, 512, 512)   )
        self.assertEqual(outputs.logits.shape			,					snake_case   )

        lowerCAmelCase__   :				Optional[Any] 			=				torch.tensor(
            [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]   ).to(snake_case   )
        self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]			,					snake_case			,					atol=1e-4   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-tiny"   )
        lowerCAmelCase__   :				Optional[int] 			=				UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-tiny"   ).to(snake_case   )

        lowerCAmelCase__   :				str 			=				prepare_img()
        lowerCAmelCase__   :				Union[str, Any] 			=				processor(images=snake_case			,					return_tensors="pt"   ).to(snake_case   )

        with torch.no_grad():
         lowerCAmelCase__   :				int 			=				model(**snake_case   )

        lowerCAmelCase__   :				str 			=				torch.Size((1, model.config.num_labels, 512, 512)   )
        self.assertEqual(outputs.logits.shape			,					snake_case   )

        lowerCAmelCase__   :				Union[str, Any] 			=				torch.tensor(
            [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]   ).to(snake_case   )
        self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]			,					snake_case			,					atol=1e-4   )   )









"""simple docstring"""

import argparse
import gc
import json
import os

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


_UpperCamelCase		     =    1_6
_UpperCamelCase		     =    3_2



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						str:
 return int(x / 2**2_0   )






class 			__a					:





       """simple docstring"""


       def __enter__(   self   ):





        """simple docstring"""

        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        lowerCAmelCase__   :				str 			=				torch.cuda.memory_allocated()
        return self






       def __exit__(   self			,					*snake_case   ):





        """simple docstring"""

        gc.collect()
        torch.cuda.empty_cache()
        lowerCAmelCase__   :				List[str] 			=				torch.cuda.memory_allocated()
        lowerCAmelCase__   :				Optional[int] 			=				torch.cuda.max_memory_allocated()
        lowerCAmelCase__   :				List[str] 			=				bamb(self.end - self.begin   )
        lowerCAmelCase__   :				Union[str, Any] 			=				bamb(self.peak - self.begin   )
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__ = 1_6					,					lowercase__ = "bert-base-cased"					,					lowercase__ = 3_2_0					,					lowercase__ = 1_6_0					,					)			->						str:
 lowerCAmelCase__   :				Union[str, Any] 			=				AutoTokenizer.from_pretrained(lowercase__   )
 lowerCAmelCase__   :				Optional[Any] 			=				load_dataset(
     "glue"					,					"mrpc"					,					split={"train": F"""train[:{n_train}]""", "validation": F"""validation[:{n_val}]"""}   )

 def tokenize_function(lowercase__   ):
  # max_length=None => use the model max length (it's actually the default)
  lowerCAmelCase__   :				str 			=				tokenizer(examples["sentence1"]					,					examples["sentence2"]					,					truncation=lowercase__					,					max_length=lowercase__   )
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 lowerCAmelCase__   :				str 			=				datasets.map(
     lowercase__					,					batched=lowercase__					,					remove_columns=["idx", "sentence1", "sentence2"]					,					load_from_cache_file=lowercase__   )

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

 def collate_fn(lowercase__   ):
  # 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(lowercase__					,					padding="max_length"					,					max_length=1_2_8					,					return_tensors="pt"   )
  return tokenizer.pad(lowercase__					,					padding="longest"					,					return_tensors="pt"   )

 # Instantiate dataloaders.
 lowerCAmelCase__   :				Tuple 			=				DataLoader(
     tokenized_datasets["train"]					,					shuffle=lowercase__					,					collate_fn=lowercase__					,					batch_size=lowercase__   )
 lowerCAmelCase__   :				Optional[Any] 			=				DataLoader(
     tokenized_datasets["validation"]					,					shuffle=lowercase__					,					collate_fn=lowercase__					,					batch_size=lowercase__   )

 return train_dataloader, eval_dataloader



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Dict:
 # Initialize accelerator
 lowerCAmelCase__   :				Optional[Any] 			=				Accelerator()

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

 set_seed(lowercase__   )
 lowerCAmelCase__			,						lowerCAmelCase__   :				Optional[Any] 			=				get_dataloaders(lowercase__					,					lowercase__					,					lowercase__					,					args.n_train					,					args.n_val   )

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 lowerCAmelCase__   :				Any 			=				AutoModelForSequenceClassification.from_pretrained(lowercase__					,					return_dict=lowercase__   )

 # Instantiate optimizer
 lowerCAmelCase__   :				List[str] 			=				(
     AdamW
     if accelerator.state.deepspeed_plugin is None
     or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
     else DummyOptim
 )
 lowerCAmelCase__   :				Any 			=				optimizer_cls(params=model.parameters()					,					lr=lowercase__   )

 if accelerator.state.deepspeed_plugin is not None:
  lowerCAmelCase__   :				List[str] 			=				accelerator.state.deepspeed_plugin.deepspeed_config[
      "gradient_accumulation_steps"
  ]
 else:
  lowerCAmelCase__   :				Dict 			=				1
 lowerCAmelCase__   :				List[str] 			=				(len(lowercase__   ) * num_epochs) // gradient_accumulation_steps

 # Instantiate scheduler
 if (
     accelerator.state.deepspeed_plugin is None
     or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
 ):
  lowerCAmelCase__   :				Any 			=				get_linear_schedule_with_warmup(
      optimizer=lowercase__					,					num_warmup_steps=0					,					num_training_steps=lowercase__					,					)
 else:
  lowerCAmelCase__   :				Optional[int] 			=				DummyScheduler(lowercase__					,					total_num_steps=lowercase__					,					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.
 lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				Dict 			=				accelerator.prepare(
     lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__   )

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

 # Now we train the model
 lowerCAmelCase__   :				List[Any] 			=				{}
 for epoch in range(lowercase__					,					lowercase__   ):
  with TorchTracemalloc() as tracemalloc:
   model.train()
   for step, batch in enumerate(lowercase__   ):
    lowerCAmelCase__   :				Optional[Any] 			=				model(**lowercase__   )
    lowerCAmelCase__   :				Optional[int] 			=				outputs.loss
    lowerCAmelCase__   :				Any 			=				loss / gradient_accumulation_steps
    accelerator.backward(lowercase__   )
    if step % gradient_accumulation_steps == 0:
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()

    overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin   )   )   )
  accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used   )   )
  accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked   )   )
  accelerator.print(
      "Total Peak Memory consumed during the train (max): {}".format(
          tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
  lowerCAmelCase__   :				Optional[int] 			=				tracemalloc.peaked + bamb(tracemalloc.begin   )
  if args.peak_memory_upper_bound is not None:
   assert (
       train_total_peak_memory[F"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
   ), "Peak memory usage exceeded the upper bound"

 accelerator.wait_for_everyone()
 if accelerator.is_main_process:
  with open(os.path.join(args.output_dir					,					"peak_memory_utilization.json"   )					,					"w"   ) as f:
   json.dump(lowercase__					,					lowercase__   )



def       SCREAMING_SNAKE_CASE							( )			->						Optional[Any]:
 lowerCAmelCase__   :				int 			=				argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."   )
 parser.add_argument(
     "--model_name_or_path"					,					type=lowercase__					,					default="bert-base-cased"					,					help="Path to pretrained model or model identifier from huggingface.co/models."					,					required=lowercase__					,					)
 parser.add_argument(
     "--output_dir"					,					type=lowercase__					,					default="."					,					help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."					,					)
 parser.add_argument(
     "--peak_memory_upper_bound"					,					type=lowercase__					,					default=lowercase__					,					help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."					,					)
 parser.add_argument(
     "--n_train"					,					type=lowercase__					,					default=3_2_0					,					help="Number of training examples to use."					,					)
 parser.add_argument(
     "--n_val"					,					type=lowercase__					,					default=1_6_0					,					help="Number of validation examples to use."					,					)
 parser.add_argument(
     "--num_epochs"					,					type=lowercase__					,					default=1					,					help="Number of train epochs."					,					)
 lowerCAmelCase__   :				Optional[int] 			=				parser.parse_args()
 lowerCAmelCase__   :				Dict 			=				{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
 training_function(lowercase__					,					lowercase__   )


if __name__ == "__main__":
    main()






