


import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_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 MaskFormerSwinBackbone
    from transformers.models.maskformer import MaskFormerSwinModel


class     lowercase__  :






  def __init__(					self      :					Tuple		,						_lowercase      :					Tuple		,						_lowercase      :					List[str]=13		,						_lowercase      :					str=32		,						_lowercase      :					int=2		,						_lowercase      :					Union[str, Any]=3		,						_lowercase      :					Optional[Any]=16		,						_lowercase      :					Any=[1, 2, 1]		,						_lowercase      :					List[Any]=[2, 2, 4]		,						_lowercase      :					str=2		,						_lowercase      :					Any=2.0		,						_lowercase      :					List[Any]=True		,						_lowercase      :					str=0.0		,						_lowercase      :					str=0.0		,						_lowercase      :					Optional[int]=0.1		,						_lowercase      :					List[Any]="gelu"		,						_lowercase      :					str=False		,						_lowercase      :					str=True		,						_lowercase      :					List[Any]=0.0_2		,						_lowercase      :					Any=1E-5		,						_lowercase      :					Optional[Any]=True		,						_lowercase      :					Any=None		,						_lowercase      :					Union[str, Any]=True		,						_lowercase      :					List[str]=10		,						_lowercase      :					Union[str, Any]=8		,						_lowercase      :					Union[str, Any]=["stage1", "stage2", "stage3"]		,						_lowercase      :					Tuple=[1, 2, 3]		,						):




         """simple docstring"""







         UpperCAmelCase__										=     parent
         UpperCAmelCase__										=     batch_size
         UpperCAmelCase__										=     image_size
         UpperCAmelCase__										=     patch_size
         UpperCAmelCase__										=     num_channels
         UpperCAmelCase__										=     embed_dim
         UpperCAmelCase__										=     depths
         UpperCAmelCase__										=     num_heads
         UpperCAmelCase__										=     window_size
         UpperCAmelCase__										=     mlp_ratio
         UpperCAmelCase__										=     qkv_bias
         UpperCAmelCase__										=     hidden_dropout_prob
         UpperCAmelCase__										=     attention_probs_dropout_prob
         UpperCAmelCase__										=     drop_path_rate
         UpperCAmelCase__										=     hidden_act
         UpperCAmelCase__										=     use_absolute_embeddings
         UpperCAmelCase__										=     patch_norm
         UpperCAmelCase__										=     layer_norm_eps
         UpperCAmelCase__										=     initializer_range
         UpperCAmelCase__										=     is_training
         UpperCAmelCase__										=     scope
         UpperCAmelCase__										=     use_labels
         UpperCAmelCase__										=     type_sequence_label_size
         UpperCAmelCase__										=     encoder_stride
         UpperCAmelCase__										=     out_features
         UpperCAmelCase__										=     out_indices






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




         """simple docstring"""







         UpperCAmelCase__										=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         UpperCAmelCase__										=     None
         if self.use_labels:
                UpperCAmelCase__										=     ids_tensor([self.batch_size]		,						self.type_sequence_label_size )

         UpperCAmelCase__										=     self.get_config()

         return config, pixel_values, labels






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         return MaskFormerSwinConfig(
             image_size=self.image_size		,						patch_size=self.patch_size		,						num_channels=self.num_channels		,						embed_dim=self.embed_dim		,						depths=self.depths		,						num_heads=self.num_heads		,						window_size=self.window_size		,						mlp_ratio=self.mlp_ratio		,						qkv_bias=self.qkv_bias		,						hidden_dropout_prob=self.hidden_dropout_prob		,						attention_probs_dropout_prob=self.attention_probs_dropout_prob		,						drop_path_rate=self.drop_path_rate		,						hidden_act=self.hidden_act		,						use_absolute_embeddings=self.use_absolute_embeddings		,						path_norm=self.patch_norm		,						layer_norm_eps=self.layer_norm_eps		,						initializer_range=self.initializer_range		,						encoder_stride=self.encoder_stride		,						out_features=self.out_features		,						out_indices=self.out_indices		,						)






  def 			_UpperCAmelCase  (					self      :					str		,						_lowercase      :					List[str]		,						_lowercase      :					int		,						_lowercase      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     MaskFormerSwinModel(config=_lowercase )
         model.to(_lowercase )
         model.eval()
         UpperCAmelCase__										=     model(_lowercase )

         UpperCAmelCase__										=     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
         UpperCAmelCase__										=     int(config.embed_dim * 2 ** (len(config.depths ) - 1) )

         self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, expected_seq_len, expected_dim) )






  def 			_UpperCAmelCase  (					self      :					Tuple		,						_lowercase      :					str		,						_lowercase      :					str		,						_lowercase      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     MaskFormerSwinBackbone(config=_lowercase )
         model.to(_lowercase )
         model.eval()
         UpperCAmelCase__										=     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 )		,						[13, 16, 16, 16] )

         # verify channels
         self.parent.assertEqual(len(model.channels )		,						len(config.out_features ) )
         self.parent.assertListEqual(model.channels		,						[16, 32, 64] )

         # verify ValueError
         with self.parent.assertRaises(_lowercase ):
                UpperCAmelCase__										=     ["stem"]
                UpperCAmelCase__										=     MaskFormerSwinBackbone(config=_lowercase )






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




         """simple docstring"""







         UpperCAmelCase__										=     self.prepare_config_and_inputs()
         UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     config_and_inputs
         UpperCAmelCase__										=     {"pixel_values": pixel_values}
         return config, inputs_dict




@require_torch
class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  (
      (
          MaskFormerSwinModel,
          MaskFormerSwinBackbone,
      )
      if is_torch_available()
      else ()
  )
  A__=  {'feature-extraction': MaskFormerSwinModel} if is_torch_available() else {}
  A__=  False
  A__=  False
  A__=  False
  A__=  False
  A__=  False






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     MaskFormerSwinModelTester(self )
         UpperCAmelCase__										=     ConfigTester(self		,						config_class=_lowercase		,						embed_dim=37 )






  @require_torch_multi_gpu
  @unittest.skip(
      reason=(
          "`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"
          " `nn.DataParallel`"
      ) )
  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         pass






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """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 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         return






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowercase )






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*_lowercase )






  @unittest.skip("Swin does not use inputs_embeds" )
  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         pass






  @unittest.skip("Swin does not support feedforward chunking" )
  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         pass






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                UpperCAmelCase__										=     model_class(_lowercase )
                self.assertIsInstance(model.get_input_embeddings()		,						(nn.Module) )
                UpperCAmelCase__										=     model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(_lowercase		,						nn.Linear ) )






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs_for_common()

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

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






  @unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions" )
  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         pass






  @unittest.skip(reason="MaskFormerSwin is only used as an internal backbone" )
  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         pass






  def 			_UpperCAmelCase  (					self      :					int		,						_lowercase      :					Dict		,						_lowercase      :					Optional[Any]		,						_lowercase      :					List[str]		,						_lowercase      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     model_class(_lowercase )
         model.to(_lowercase )
         model.eval()

         with torch.no_grad():
                UpperCAmelCase__										=     model(**self._prepare_for_class(_lowercase		,						_lowercase ) )

         UpperCAmelCase__										=     outputs.hidden_states

         UpperCAmelCase__										=     getattr(
             self.model_tester		,						"expected_num_hidden_layers"		,						len(self.model_tester.depths ) + 1 )
         self.assertEqual(len(_lowercase )		,						_lowercase )

         # Swin has a different seq_length
         UpperCAmelCase__										=     (
             config.patch_size
             if isinstance(config.patch_size		,						collections.abc.Iterable )
             else (config.patch_size, config.patch_size)
         )

         UpperCAmelCase__										=     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

         self.assertListEqual(
             list(hidden_states[0].shape[-2:] )		,						[num_patches, self.model_tester.embed_dim]		,						)






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs_for_common()

         UpperCAmelCase__										=     (
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size		,						collections.abc.Iterable )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )

         for model_class in self.all_model_classes:
                UpperCAmelCase__										=     True
                self.check_hidden_states_output(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase )

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                UpperCAmelCase__										=     True

                self.check_hidden_states_output(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase__										=     3

         UpperCAmelCase__										=     (
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size		,						collections.abc.Iterable )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )
         UpperCAmelCase__										=     (
             config.patch_size
             if isinstance(config.patch_size		,						collections.abc.Iterable )
             else (config.patch_size, config.patch_size)
         )

         UpperCAmelCase__										=     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         UpperCAmelCase__										=     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes:
                UpperCAmelCase__										=     True
                self.check_hidden_states_output(_lowercase		,						_lowercase		,						_lowercase		,						(padded_height, padded_width) )

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                UpperCAmelCase__										=     True
                self.check_hidden_states_output(_lowercase		,						_lowercase		,						_lowercase		,						(padded_height, padded_width) )






  @unittest.skip(reason="MaskFormerSwin doesn't have pretrained checkpoints" )
  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         pass






  @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin" )
  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         pass






  @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin" )
  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         pass






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs_for_common()

         def set_nan_tensor_to_zero(_lowercase      :					List[Any] ):
                UpperCAmelCase__										=     0
                return t

         def check_equivalence(_lowercase      :					int		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Tuple		,						_lowercase      :					List[str]={} ):
                with torch.no_grad():
                       UpperCAmelCase__										=     model(**_lowercase		,						return_dict=_lowercase		,						**_lowercase )
                       UpperCAmelCase__										=     model(**_lowercase		,						return_dict=_lowercase		,						**_lowercase ).to_tuple()

                       def recursive_check(_lowercase      :					List[str]		,						_lowercase      :					List[Any] ):
                              if isinstance(_lowercase		,						(List, Tuple) ):
                                     for tuple_iterable_value, dict_iterable_value in zip(_lowercase		,						_lowercase ):
                                            recursive_check(_lowercase		,						_lowercase )
                              elif isinstance(_lowercase		,						_lowercase ):
                                     for tuple_iterable_value, dict_iterable_value in zip(
                                         tuple_object.values()		,						dict_object.values() ):
                                            recursive_check(_lowercase		,						_lowercase )
                              elif tuple_object is None:
                                     return
                              else:
                                     self.assertTrue(
                                         torch.allclose(
                                             set_nan_tensor_to_zero(_lowercase )		,						set_nan_tensor_to_zero(_lowercase )		,						atol=1E-5 )		,						msg=(
                                             "Tuple and dict output are not equal. Difference:"
                                             F""" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:"""
                                             F""" {torch.isnan(_lowercase ).any()} and `inf`: {torch.isinf(_lowercase )}. Dict has"""
                                             F""" `nan`: {torch.isnan(_lowercase ).any()} and `inf`: {torch.isinf(_lowercase )}."""
                                         )		,						)

                       recursive_check(_lowercase		,						_lowercase )

         for model_class in self.all_model_classes:
                UpperCAmelCase__										=     model_class(_lowercase )
                model.to(_lowercase )
                model.eval()

                UpperCAmelCase__										=     self._prepare_for_class(_lowercase		,						_lowercase )
                UpperCAmelCase__										=     self._prepare_for_class(_lowercase		,						_lowercase )
                check_equivalence(_lowercase		,						_lowercase		,						_lowercase )

                UpperCAmelCase__										=     self._prepare_for_class(_lowercase		,						_lowercase		,						return_labels=_lowercase )
                UpperCAmelCase__										=     self._prepare_for_class(_lowercase		,						_lowercase		,						return_labels=_lowercase )
                check_equivalence(_lowercase		,						_lowercase		,						_lowercase )

                UpperCAmelCase__										=     self._prepare_for_class(_lowercase		,						_lowercase )
                UpperCAmelCase__										=     self._prepare_for_class(_lowercase		,						_lowercase )
                check_equivalence(_lowercase		,						_lowercase		,						_lowercase		,						{"output_hidden_states": True} )

                UpperCAmelCase__										=     self._prepare_for_class(_lowercase		,						_lowercase		,						return_labels=_lowercase )
                UpperCAmelCase__										=     self._prepare_for_class(_lowercase		,						_lowercase		,						return_labels=_lowercase )
                check_equivalence(_lowercase		,						_lowercase		,						_lowercase		,						{"output_hidden_states": True} )




@require_torch
class     lowercase__  (					unittest.TestCase					,							__SCREAMING_SNAKE_CASE		):
  A__=  (MaskFormerSwinBackbone,) if is_torch_available() else ()
  A__=  MaskFormerSwinConfig






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     MaskFormerSwinModelTester(self )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase__										=     inputs_dict["pixel_values"].shape[0]

         for backbone_class in self.all_model_classes:
                UpperCAmelCase__										=     backbone_class(_lowercase )
                backbone.to(_lowercase )
                backbone.eval()

                UpperCAmelCase__										=     backbone(**_lowercase )

                # Test default outputs and verify feature maps
                self.assertIsInstance(outputs.feature_maps		,						_lowercase )
                self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
                for feature_map, n_channels in zip(outputs.feature_maps		,						backbone.channels ):
                       self.assertTrue(feature_map.shape[:2]		,						(batch_size, n_channels) )
                self.assertIsNone(outputs.hidden_states )
                self.assertIsNone(outputs.attentions )

                # Test output_hidden_states=True
                UpperCAmelCase__										=     backbone(**_lowercase		,						output_hidden_states=_lowercase )
                self.assertIsNotNone(outputs.hidden_states )
                self.assertTrue(len(outputs.hidden_states )		,						len(backbone.stage_names ) )
                # We skip the stem layer
                for hidden_states, n_channels in zip(outputs.hidden_states[1:]		,						backbone.channels ):
                       for hidden_state in hidden_states:
                              # Hidden states are in the format (batch_size, (height * width), n_channels)
                              UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     hidden_state.shape
                              self.assertTrue((h_batch_size, h_n_channels)		,						(batch_size, n_channels) )

            # Test output_attentions=True
                if self.has_attentions:
                       UpperCAmelCase__										=     backbone(**_lowercase		,						output_attentions=_lowercase )
                       self.assertIsNotNone(outputs.attentions )









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
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A      = 16
A      = 32





def __UpperCAmelCase       (		__A ,						__A = 1_6			)						->					Tuple:



       '''simple docstring'''





       UpperCAmelCase__										=     AutoTokenizer.from_pretrained("bert-base-cased"			)
       UpperCAmelCase__										=     load_dataset("glue" ,						"mrpc"			)

       def tokenize_function(__A			):
              # max_length=None => use the model max length (it's actually the default)
              UpperCAmelCase__										=     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
       # starting with the main process first:
       with accelerator.main_process_first():
              UpperCAmelCase__										=     datasets.map(
                  __A ,						batched=__A ,						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
       UpperCAmelCase__										=     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.
              UpperCAmelCase__										=     1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     UpperCAmelCase__										=     1_6
              elif accelerator.mixed_precision != "no":
                     UpperCAmelCase__										=     8
              else:
                     UpperCAmelCase__										=     None

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

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

       return train_dataloader, eval_dataloader


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

    A      = mocked_dataloaders  # noqa: F811





def __UpperCAmelCase       (		__A ,						__A			)						->					Dict:



       '''simple docstring'''





       if os.environ.get("TESTING_MOCKED_DATALOADERS" ,						__A			) == "1":
              UpperCAmelCase__										=     2
       # New Code #
       UpperCAmelCase__										=     int(args.gradient_accumulation_steps			)
       # Initialize accelerator
       UpperCAmelCase__										=     Accelerator(
           cpu=args.cpu ,						mixed_precision=args.mixed_precision ,						gradient_accumulation_steps=__A			)
       if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
              raise NotImplementedError(
                  "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"			)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       UpperCAmelCase__										=     config["lr"]
       UpperCAmelCase__										=     int(config["num_epochs"]			)
       UpperCAmelCase__										=     int(config["seed"]			)
       UpperCAmelCase__										=     int(config["batch_size"]			)

       UpperCAmelCase__										=     evaluate.load("glue" ,						"mrpc"			)

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

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

       # Instantiate optimizer
       UpperCAmelCase__										=     AdamW(params=model.parameters() ,						lr=__A			)

       # Instantiate scheduler
       UpperCAmelCase__										=     get_linear_schedule_with_warmup(
           optimizer=__A ,						num_warmup_steps=1_0_0 ,						num_training_steps=(len(__A			) * num_epochs) ,						)

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     accelerator.prepare(
           __A ,						__A ,						__A ,						__A ,						__A			)

       # Now we train the model
       for epoch in range(__A			):
              model.train()
              for step, batch in enumerate(__A			):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device			)
                     # New code #
                     # We use the new `accumulate` context manager to perform gradient accumulation
                     # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                     with accelerator.accumulate(__A			):
                            UpperCAmelCase__										=     model(**__A			)
                            UpperCAmelCase__										=     output.loss
                            accelerator.backward(__A			)
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              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__										=     model(**__A			)
                     UpperCAmelCase__										=     outputs.logits.argmax(dim=-1			)
                     UpperCAmelCase__						,			UpperCAmelCase__										=     accelerator.gather_for_metrics((predictions, batch["labels"])			)
                     metric.add_batch(
                         predictions=__A ,						references=__A ,						)

              UpperCAmelCase__										=     metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:""" ,						__A			)





def __UpperCAmelCase       (		)						->					Dict:



       '''simple docstring'''





       UpperCAmelCase__										=     argparse.ArgumentParser(description="Simple example of training script."			)
       parser.add_argument(
           "--mixed_precision" ,						type=__A ,						default=__A ,						choices=["no", "fp16", "bf16", "fp8"] ,						help="Whether to use mixed precision. Choose"
           "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
           "and an Nvidia Ampere GPU." ,						)
       # New Code #
       parser.add_argument(
           "--gradient_accumulation_steps" ,						type=__A ,						default=1 ,						help="The number of minibatches to be ran before gradients are accumulated." ,						)
       parser.add_argument("--cpu" ,						action="store_true" ,						help="If passed, will train on the CPU."			)
       UpperCAmelCase__										=     parser.parse_args()
       UpperCAmelCase__										=     {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
       training_function(__A ,						__A			)


if __name__ == "__main__":
    main()





