'''simple docstring'''


import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

       if is_vision_available():
              from transformers import MaskaFormerImageProcessor

if is_vision_available():
       from PIL import Image





class       _a    :


  """simple docstring"""

  def __init__(		self      :       str   ,				lowercase_      :       str   ,				lowercase_      :       str=2   ,				lowercase_      :       List[str]=True   ,				lowercase_      :       Tuple=False   ,				lowercase_      :       int=10   ,				lowercase_      :       int=3   ,				lowercase_      :       Any=32 * 8   ,				lowercase_      :       Dict=32 * 8   ,				lowercase_      :       Optional[int]=4   ,				lowercase_      :       Optional[int]=64   ,				):







    '''simple docstring'''


    lowercase_     =       parent
    lowercase_     =       batch_size
    lowercase_     =       is_training
    lowercase_     =       use_auxiliary_loss
    lowercase_     =       num_queries
    lowercase_     =       num_channels
    lowercase_     =       min_size
    lowercase_     =       max_size
    lowercase_     =       num_labels
    lowercase_     =       hidden_dim
    lowercase_     =       hidden_dim




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]      ).to(
        lowercase_      )

    lowercase_     =       torch.ones([self.batch_size, self.min_size, self.max_size]   ,				device=lowercase_      )

    lowercase_     =       (
        torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]   ,				device=lowercase_      ) > 0.5
    ).float()
    lowercase_     =       (torch.rand((self.batch_size, self.num_labels)   ,				device=lowercase_      ) > 0.5).long()

    lowercase_     =       self.get_config()
    return config, pixel_values, pixel_mask, mask_labels, class_labels




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       MaskaFormerConfig(
        hidden_size=self.hidden_dim   ,				)
    lowercase_     =       self.num_queries
    lowercase_     =       self.num_labels

    lowercase_     =       [1, 1, 1, 1]
    lowercase_     =       self.num_channels

    lowercase_     =       64
    lowercase_     =       128
    lowercase_     =       self.hidden_dim
    lowercase_     =       self.hidden_dim
    lowercase_     =       self.hidden_dim
    return config




  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       self.prepare_config_and_inputs()
    lowercase_     =       {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
    return config, inputs_dict




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       Tuple   ,				lowercase_      :       Union[str, Any]      ):







    '''simple docstring'''


    lowercase_     =       output.encoder_hidden_states
    lowercase_     =       output.pixel_decoder_hidden_states
    lowercase_     =       output.transformer_decoder_hidden_states

    self.parent.assertTrue(len(lowercase_      )   ,				len(config.backbone_config.depths      )      )
    self.parent.assertTrue(len(lowercase_      )   ,				len(config.backbone_config.depths      )      )
    self.parent.assertTrue(len(lowercase_      )   ,				config.decoder_layers      )




  def 		lowerCamelCase__  (		self      :       List[str]   ,				lowercase_      :       int   ,				lowercase_      :       Optional[int]   ,				lowercase_      :       Optional[int]   ,				lowercase_      :       int=False      ):







    '''simple docstring'''


    with torch.no_grad():
      lowercase_     =       MaskaFormerModel(config=lowercase_      )
      model.to(lowercase_      )
      model.eval()

      lowercase_     =       model(pixel_values=lowercase_   ,				pixel_mask=lowercase_      )
      lowercase_     =       model(lowercase_   ,				output_hidden_states=lowercase_      )

    self.parent.assertEqual(
        output.transformer_decoder_last_hidden_state.shape   ,				(self.batch_size, self.num_queries, self.hidden_dim)   ,				)
    # let's ensure the other two hidden state exists
    self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None      )
    self.parent.assertTrue(output.encoder_last_hidden_state is not None      )

    if output_hidden_states:
      self.check_output_hidden_state(lowercase_   ,				lowercase_      )





  def 		lowerCamelCase__  (		self      :       Tuple   ,				lowercase_      :       Tuple   ,				lowercase_      :       Optional[Any]   ,				lowercase_      :       List[str]   ,				lowercase_      :       Union[str, Any]   ,				lowercase_      :       Optional[Any]      ):







    '''simple docstring'''


    lowercase_     =       MaskaFormerForUniversalSegmentation(config=lowercase_      )
    model.to(lowercase_      )
    model.eval()

    def comm_check_on_output(lowercase_      :       List[str]      ):
      # let's still check that all the required stuff is there
      self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None      )
      self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None      )
      self.parent.assertTrue(result.encoder_last_hidden_state is not None      )
      # okay, now we need to check the logits shape
      # due to the encoder compression, masks have a //4 spatial size
      self.parent.assertEqual(
          result.masks_queries_logits.shape   ,				(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)   ,				)
      # + 1 for null class
      self.parent.assertEqual(
          result.class_queries_logits.shape   ,				(self.batch_size, self.num_queries, self.num_labels + 1)      )

    with torch.no_grad():
      lowercase_     =       model(pixel_values=lowercase_   ,				pixel_mask=lowercase_      )
      lowercase_     =       model(lowercase_      )

      comm_check_on_output(lowercase_      )

      lowercase_     =       model(
          pixel_values=lowercase_   ,				pixel_mask=lowercase_   ,				mask_labels=lowercase_   ,				class_labels=lowercase_      )

    comm_check_on_output(lowercase_      )

    self.parent.assertTrue(result.loss is not None      )
    self.parent.assertEqual(result.loss.shape   ,				torch.Size([1]      )      )






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


  """simple docstring"""

  A_    							=						(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
  A_    							=						{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

  A_    							=						False
  A_    							=						False
  A_    							=						False
  A_    							=						False




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       MaskaFormerModelTester(self      )
    lowercase_     =       ConfigTester(self   ,				config_class=lowercase_   ,				has_text_modality=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


    self.config_tester.run_common_tests()




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    lowercase_     ,	lowercase_     =       self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.create_and_check_maskaformer_model(lowercase_   ,				**lowercase_   ,				output_hidden_states=lowercase_      )




  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowercase_      )




  @unittest.skip(reason="""Mask2Former does not use inputs_embeds"""      )
  def 		lowerCamelCase__  (		self      :       Union[str, Any]      ):







    '''simple docstring'''


    pass




  @unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""      )
  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    pass




  @unittest.skip(reason="""Mask2Former is not a generative model"""      )
  def 		lowerCamelCase__  (		self      :       List[Any]      ):







    '''simple docstring'''


    pass




  @unittest.skip(reason="""Mask2Former does not use token embeddings"""      )
  def 		lowerCamelCase__  (		self      :       List[Any]      ):







    '''simple docstring'''


    pass




  @require_torch_multi_gpu
  @unittest.skip(
      reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""      )
  def 		lowerCamelCase__  (		self      :       List[Any]      ):







    '''simple docstring'''


    pass




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







    '''simple docstring'''


    pass




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    lowercase_     ,	lowercase_     =       self.model_tester.prepare_config_and_inputs_for_common()

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

      lowercase_     =       ["""pixel_values"""]
      self.assertListEqual(arg_names[:1]   ,				lowercase_      )




  @slow
  def 		lowerCamelCase__  (		self      :       List[Any]      ):







    '''simple docstring'''


    for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
      lowercase_     =       MaskaFormerModel.from_pretrained(lowercase_      )
      self.assertIsNotNone(lowercase_      )




  def 		lowerCamelCase__  (		self      :       Union[str, Any]      ):







    '''simple docstring'''


    lowercase_     =       (self.model_tester.min_size,) * 2
    lowercase_     =       {
        """pixel_values""": torch.randn((2, 3, *size)   ,				device=lowercase_      ),
        """mask_labels""": torch.randn((2, 10, *size)   ,				device=lowercase_      ),
        """class_labels""": torch.zeros(2   ,				10   ,				device=lowercase_      ).long(),
    }
    lowercase_     =       self.model_tester.get_config()

    lowercase_     =       MaskaFormerForUniversalSegmentation(lowercase_      ).to(lowercase_      )
    lowercase_     =       model(**lowercase_      )
    self.assertTrue(outputs.loss is not None      )




  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    lowercase_     ,	lowercase_     =       self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.create_and_check_maskaformer_model(lowercase_   ,				**lowercase_   ,				output_hidden_states=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    lowercase_     ,	lowercase_     =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      lowercase_     =       model_class(lowercase_      ).to(lowercase_      )
      lowercase_     =       model(**lowercase_   ,				output_attentions=lowercase_      )
      self.assertTrue(outputs.attentions is not None      )




  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    if not self.model_tester.is_training:
      return

    lowercase_     =       self.all_model_classes[1]
    lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       self.model_tester.prepare_config_and_inputs()

    lowercase_     =       model_class(lowercase_      )
    model.to(lowercase_      )
    model.train()

    lowercase_     =       model(lowercase_   ,				mask_labels=lowercase_   ,				class_labels=lowercase_      ).loss
    loss.backward()





  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    lowercase_     =       self.all_model_classes[1]
    lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       self.model_tester.prepare_config_and_inputs()
    lowercase_     =       True
    lowercase_     =       True

    lowercase_     =       model_class(lowercase_      ).to(lowercase_      )
    model.train()

    lowercase_     =       model(lowercase_   ,				mask_labels=lowercase_   ,				class_labels=lowercase_      )

    lowercase_     =       outputs.encoder_hidden_states[0]
    encoder_hidden_states.retain_grad()

    lowercase_     =       outputs.pixel_decoder_hidden_states[0]
    pixel_decoder_hidden_states.retain_grad()

    lowercase_     =       outputs.transformer_decoder_hidden_states[0]
    transformer_decoder_hidden_states.retain_grad()

    lowercase_     =       outputs.attentions[0]
    attentions.retain_grad()

    outputs.loss.backward(retain_graph=lowercase_      )

    self.assertIsNotNone(encoder_hidden_states.grad      )
    self.assertIsNotNone(pixel_decoder_hidden_states.grad      )
    self.assertIsNotNone(transformer_decoder_hidden_states.grad      )
    self.assertIsNotNone(attentions.grad      )






__snake_case						  =		1e-4

def   A_      (       )			->List[str]:
  lowercase_     =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
  return image





@require_vision
@slow
class       _a    (		unittest.TestCase      ):


  """simple docstring"""

  @cached_property
  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    return "facebook/mask2former-swin-small-coco-instance"




  @cached_property
  def 		lowerCamelCase__  (		self      :       List[Any]      ):







    '''simple docstring'''


    return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints      ) if is_vision_available() else None




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       MaskaFormerModel.from_pretrained(self.model_checkpoints      ).to(lowercase_      )
    lowercase_     =       self.default_image_processor
    lowercase_     =       prepare_img()
    lowercase_     =       image_processor(lowercase_   ,				return_tensors="""pt"""      ).to(lowercase_      )
    lowercase_     =       inputs["""pixel_values"""].shape
    # check size is divisible by 32
    self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0      )
    # check size
    self.assertEqual(lowercase_   ,				(1, 3, 384, 384)      )

    with torch.no_grad():
      lowercase_     =       model(**lowercase_      )

    lowercase_     =       torch.tensor(
        [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]      ).to(lowercase_      )
    self.assertTrue(
        torch.allclose(
            outputs.encoder_last_hidden_state[0, 0, :3, :3]   ,				lowercase_   ,				atol=lowercase_      )      )

    lowercase_     =       torch.tensor(
        [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]      ).to(lowercase_      )
    self.assertTrue(
        torch.allclose(
            outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]   ,				lowercase_   ,				atol=lowercase_      )      )

    lowercase_     =       torch.tensor(
        [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]      ).to(lowercase_      )
    self.assertTrue(
        torch.allclose(
            outputs.transformer_decoder_last_hidden_state[0, :3, :3]   ,				lowercase_   ,				atol=lowercase_      )      )




  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    lowercase_     =       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(lowercase_      ).eval()
    lowercase_     =       self.default_image_processor
    lowercase_     =       prepare_img()
    lowercase_     =       image_processor(lowercase_   ,				return_tensors="""pt"""      ).to(lowercase_      )
    lowercase_     =       inputs["""pixel_values"""].shape
    # check size is divisible by 32
    self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0      )
    # check size
    self.assertEqual(lowercase_   ,				(1, 3, 384, 384)      )

    with torch.no_grad():
      lowercase_     =       model(**lowercase_      )
    # masks_queries_logits
    lowercase_     =       outputs.masks_queries_logits
    self.assertEqual(
        masks_queries_logits.shape   ,				(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)      )
    lowercase_     =       [
        [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
        [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
        [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
    ]
    lowercase_     =       torch.tensor(lowercase_      ).to(lowercase_      )
    self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]   ,				lowercase_   ,				atol=lowercase_      )      )
    # class_queries_logits
    lowercase_     =       outputs.class_queries_logits
    self.assertEqual(class_queries_logits.shape   ,				(1, model.config.num_queries, model.config.num_labels + 1)      )
    lowercase_     =       torch.tensor(
        [
            [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
            [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
            [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
        ]      ).to(lowercase_      )
    self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]   ,				lowercase_   ,				atol=lowercase_      )      )





  def 		lowerCamelCase__  (		self      :       Union[str, Any]      ):







    '''simple docstring'''


    lowercase_     =       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(lowercase_      ).eval()
    lowercase_     =       self.default_image_processor

    lowercase_     =       image_processor(
        [np.zeros((3, 800, 1_333)      ), np.zeros((3, 800, 1_333)      )]   ,				segmentation_maps=[np.zeros((384, 384)      ).astype(np.floataa      ), np.zeros((384, 384)      ).astype(np.floataa      )]   ,				return_tensors="""pt"""   ,				)

    lowercase_     =       inputs["""pixel_values"""].to(lowercase_      )
    lowercase_     =       [el.to(lowercase_      ) for el in inputs["""mask_labels"""]]
    lowercase_     =       [el.to(lowercase_      ) for el in inputs["""class_labels"""]]

    with torch.no_grad():
      lowercase_     =       model(**lowercase_      )

    self.assertTrue(outputs.loss is not None      )


'''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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

__snake_case						  =		16
__snake_case						  =		32

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ = 16						)			->str:
  lowercase_     =       AutoTokenizer.from_pretrained("""bert-base-cased"""						)
  lowercase_     =       load_dataset("""glue""" ,	"""mrpc"""						)

  def tokenize_function(SCREAMING_SNAKE_CASE_						):
    # max_length=None => use the model max length (it's actually the default)
    lowercase_     =       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
  # starting with the main process first:
  with accelerator.main_process_first():
    lowercase_     =       datasets.map(
        SCREAMING_SNAKE_CASE_ ,	batched=SCREAMING_SNAKE_CASE_ ,	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
  lowercase_     =       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.
    lowercase_     =       1_28 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      lowercase_     =       16
    elif accelerator.mixed_precision != "no":
      lowercase_     =       8
    else:
      lowercase_     =       None

    return tokenizer.pad(
        SCREAMING_SNAKE_CASE_ ,	padding="""longest""" ,	max_length=SCREAMING_SNAKE_CASE_ ,	pad_to_multiple_of=SCREAMING_SNAKE_CASE_ ,	return_tensors="""pt""" ,	)

  # Instantiate dataloaders.
  lowercase_     =       DataLoader(
      tokenized_datasets["""train"""] ,	shuffle=SCREAMING_SNAKE_CASE_ ,	collate_fn=SCREAMING_SNAKE_CASE_ ,	batch_size=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       DataLoader(
      tokenized_datasets["""validation"""] ,	shuffle=SCREAMING_SNAKE_CASE_ ,	collate_fn=SCREAMING_SNAKE_CASE_ ,	batch_size=SCREAMING_SNAKE_CASE_						)

  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

       __snake_case						  =		mocked_dataloaders  # noqa: F811

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Dict:
  # For testing only
  if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,	SCREAMING_SNAKE_CASE_						) == "1":
    lowercase_     =       2
  # Initialize Accelerator

  # New Code #
  # We pass in "all" to `log_with` to grab all available trackers in the environment
  # Note: If using a custom `Tracker` class, should be passed in here such as:
  # >>> log_with = ["all", MyCustomTrackerClassInstance()]
  if args.with_tracking:
    lowercase_     =       Accelerator(
        cpu=args.cpu ,	mixed_precision=args.mixed_precision ,	log_with="""all""" ,	project_dir=args.project_dir						)
  else:
    lowercase_     =       Accelerator(cpu=args.cpu ,	mixed_precision=args.mixed_precision						)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowercase_     =       config["""lr"""]
  lowercase_     =       int(config["""num_epochs"""]						)
  lowercase_     =       int(config["""seed"""]						)
  lowercase_     =       int(config["""batch_size"""]						)
  set_seed(SCREAMING_SNAKE_CASE_						)

  lowercase_     ,	lowercase_     =       get_dataloaders(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  lowercase_     =       evaluate.load("""glue""" ,	"""mrpc"""						)

  # If the batch size is too big we use gradient accumulation
  lowercase_     =       1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    lowercase_     =       batch_size // MAX_GPU_BATCH_SIZE
    lowercase_     =       MAX_GPU_BATCH_SIZE

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowercase_     =       AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,	return_dict=SCREAMING_SNAKE_CASE_						)

  # 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).
  lowercase_     =       model.to(accelerator.device						)

  # Instantiate optimizer
  lowercase_     =       AdamW(params=model.parameters() ,	lr=SCREAMING_SNAKE_CASE_						)

  # Instantiate scheduler
  lowercase_     =       get_linear_schedule_with_warmup(
      optimizer=SCREAMING_SNAKE_CASE_ ,	num_warmup_steps=1_00 ,	num_training_steps=(len(SCREAMING_SNAKE_CASE_						) * num_epochs) // gradient_accumulation_steps ,	)

  # 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.
  lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       accelerator.prepare(
      SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

  # New Code #
  # We need to initialize the trackers we use. Overall configurations can also be stored
  if args.with_tracking:
    lowercase_     =       os.path.split(SCREAMING_SNAKE_CASE_						)[-1].split("""."""						)[0]
    accelerator.init_trackers(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

  # Now we train the model
  for epoch in range(SCREAMING_SNAKE_CASE_						):
    model.train()
    # New Code #
    # For our tracking example, we will log the total loss of each epoch
    if args.with_tracking:
      lowercase_     =       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						)
      lowercase_     =       model(**SCREAMING_SNAKE_CASE_						)
      lowercase_     =       outputs.loss
      # New Code #
      if args.with_tracking:
        total_loss += loss.detach().float()
      lowercase_     =       loss / gradient_accumulation_steps
      accelerator.backward(SCREAMING_SNAKE_CASE_						)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

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

    lowercase_     =       metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f"""epoch {epoch}:""" ,	SCREAMING_SNAKE_CASE_						)

    # New Code #
    # To actually log, we call `Accelerator.log`
    # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
    if args.with_tracking:
      accelerator.log(
          {
              """accuracy""": eval_metric["""accuracy"""],
              """f1""": eval_metric["""f1"""],
              """train_loss""": total_loss.item() / len(SCREAMING_SNAKE_CASE_						),
              """epoch""": epoch,
          } ,	step=SCREAMING_SNAKE_CASE_ ,	)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
  if args.with_tracking:
    accelerator.end_training()

def   A_      (       )			->Union[str, Any]:
  lowercase_     =       argparse.ArgumentParser(description="""Simple example of training script."""						)
  parser.add_argument(
      """--mixed_precision""" ,	type=SCREAMING_SNAKE_CASE_ ,	default=SCREAMING_SNAKE_CASE_ ,	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.""" ,	)
  parser.add_argument("""--cpu""" ,	action="""store_true""" ,	help="""If passed, will train on the CPU."""						)
  parser.add_argument(
      """--with_tracking""" ,	action="""store_true""" ,	help="""Whether to load in all available experiment trackers from the environment and use them for logging.""" ,	)
  parser.add_argument(
      """--project_dir""" ,	type=SCREAMING_SNAKE_CASE_ ,	default="""logs""" ,	help="""Location on where to store experiment tracking logs` and relevent project information""" ,	)
  lowercase_     =       parser.parse_args()
  lowercase_     =       {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
       main()

