import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TFEfficientFormerForImageClassification,
         TFEfficientFormerForImageClassificationWithTeacher,
         TFEfficientFormerModel,
     )
     from transformers.models.efficientformer.modeling_tf_efficientformer import (
         TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
     )


if is_vision_available():
     from PIL import Image

     from transformers import EfficientFormerImageProcessor
class SCREAMING_SNAKE_CASE_		:







   def __init__(     self						:						Any   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						Union[str, Any] = 13   ,   lowerCamelCase_						:						str = 64   ,   lowerCamelCase_						:						Union[str, Any] = 2   ,   lowerCamelCase_						:						Any = 3   ,   lowerCamelCase_						:						Tuple = 3   ,   lowerCamelCase_						:						Optional[int] = True   ,   lowerCamelCase_						:						List[Any] = True   ,   lowerCamelCase_						:						str = 128   ,   lowerCamelCase_						:						int=[16, 32, 64, 128]   ,   lowerCamelCase_						:						str = 7   ,   lowerCamelCase_						:						Tuple = 4   ,   lowerCamelCase_						:						Any = 37   ,   lowerCamelCase_						:						Dict = "gelu"   ,   lowerCamelCase_						:						str = 0.1   ,   lowerCamelCase_						:						Any = 0.1   ,   lowerCamelCase_						:						Optional[Any] = 10   ,   lowerCamelCase_						:						Tuple = 0.0_2   ,   lowerCamelCase_						:						Union[str, Any] = 2   ,   lowerCamelCase_						:						str = 1   ,   lowerCamelCase_						:						str = 128   ,   lowerCamelCase_						:						Union[str, Any] = [2, 2, 2, 2]   ,   lowerCamelCase_						:						Optional[int] = 2   ,   lowerCamelCase_						:						str = 2   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      parent
    UpperCamelCase			  =      batch_size
    UpperCamelCase			  =      image_size
    UpperCamelCase			  =      patch_size
    UpperCamelCase			  =      num_channels
    UpperCamelCase			  =      is_training
    UpperCamelCase			  =      use_labels
    UpperCamelCase			  =      hidden_size
    UpperCamelCase			  =      num_hidden_layers
    UpperCamelCase			  =      num_attention_heads
    UpperCamelCase			  =      intermediate_size
    UpperCamelCase			  =      hidden_act
    UpperCamelCase			  =      hidden_dropout_prob
    UpperCamelCase			  =      attention_probs_dropout_prob
    UpperCamelCase			  =      type_sequence_label_size
    UpperCamelCase			  =      initializer_range
    UpperCamelCase			  =      encoder_stride
    UpperCamelCase			  =      num_attention_outputs
    UpperCamelCase			  =      embed_dim
    UpperCamelCase			  =      embed_dim + 1
    UpperCamelCase			  =      resolution
    UpperCamelCase			  =      depths
    UpperCamelCase			  =      hidden_sizes
    UpperCamelCase			  =      dim
    UpperCamelCase			  =      mlp_expansion_ratio







   def 						lowerCamelCase_	(     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 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    return EfficientFormerConfig(
        image_size=self.image_size   ,   patch_size=self.patch_size   ,   num_channels=self.num_channels   ,   hidden_size=self.hidden_size   ,   num_hidden_layers=self.num_hidden_layers   ,   num_attention_heads=self.num_attention_heads   ,   intermediate_size=self.intermediate_size   ,   hidden_act=self.hidden_act   ,   hidden_dropout_prob=self.hidden_dropout_prob   ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,   is_decoder=a_   ,   initializer_range=self.initializer_range   ,   encoder_stride=self.encoder_stride   ,   resolution=self.resolution   ,   depths=self.depths   ,   hidden_sizes=self.hidden_sizes   ,   dim=self.dim   ,   mlp_expansion_ratio=self.mlp_expansion_ratio   ,   )







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      TFEfficientFormerModel(config=a_				)
    UpperCamelCase			  =      model(a_   ,   training=a_				)
    self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.type_sequence_label_size
    UpperCamelCase			  =      TFEfficientFormerForImageClassification(a_				)
    UpperCamelCase			  =      model(a_   ,   labels=a_   ,   training=a_				)
    self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.type_sequence_label_size)				)

    # test greyscale images
    UpperCamelCase			  =      1
    UpperCamelCase			  =      TFEfficientFormerForImageClassification(a_				)

    UpperCamelCase			  =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
    UpperCamelCase			  =      model(a_   ,   labels=a_				)
    self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.type_sequence_label_size)				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


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





@require_tf
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				(
       (
           TFEfficientFormerModel,
           TFEfficientFormerForImageClassificationWithTeacher,
           TFEfficientFormerForImageClassification,
       )
       if is_tf_available()
       else ()
   )
   __lowerCAmelCase           =				(
       {
           """feature-extraction""": TFEfficientFormerModel,
           """image-classification""": (
               TFEfficientFormerForImageClassification,
               TFEfficientFormerForImageClassificationWithTeacher,
           ),
       }
       if is_tf_available()
       else {}
   )

   __lowerCAmelCase           =				False

   __lowerCAmelCase           =				False
   __lowerCAmelCase           =				False
   __lowerCAmelCase           =				False
   __lowerCAmelCase           =				False







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      TFEfficientFormerModelTester(self				)
    UpperCamelCase			  =      ConfigTester(
        self   ,   config_class=a_   ,   has_text_modality=a_   ,   hidden_size=37				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    self.config_tester.run_common_tests()







   @unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""				)
   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    pass







   @unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""				)
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    pass







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs_for_common()

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

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







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    def check_hidden_states_output(lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						Optional[Any]				):
     UpperCamelCase			  =      model_class(a_				)

     UpperCamelCase			  =      model(**self._prepare_for_class(a_   ,   a_				)   ,   training=a_				)
     UpperCamelCase			  =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

     UpperCamelCase			  =      getattr(
         self.model_tester   ,   """expected_num_hidden_layers"""   ,   self.model_tester.num_hidden_layers + 1				)
     self.assertEqual(len(a_				)   ,   a_				)

     if hasattr(self.model_tester   ,   """encoder_seq_length"""				):
      UpperCamelCase			  =      self.model_tester.encoder_seq_length
      if hasattr(self.model_tester   ,   """chunk_length"""				) and self.model_tester.chunk_length > 1:
       UpperCamelCase			  =      seq_length * self.model_tester.chunk_length
     else:
      UpperCamelCase			  =      self.model_tester.seq_length

     self.assertListEqual(
         list(hidden_states[-1].shape[-2:]				)   ,   [seq_length, self.model_tester.hidden_size]   ,   )

     if config.is_encoder_decoder:
      UpperCamelCase			  =      outputs.decoder_hidden_states

      self.asseretIsInstance(a_   ,   (list, tuple)				)
      self.assertEqual(len(a_				)   ,   a_				)
      UpperCamelCase			  =      getattr(self.model_tester   ,   """seq_length"""   ,   a_				)
      UpperCamelCase			  =      getattr(self.model_tester   ,   """decoder_seq_length"""   ,   a_				)

      self.assertListEqual(
          list(hidden_states[-1].shape[-2:]				)   ,   [decoder_seq_length, self.model_tester.hidden_size]   ,   )

    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     UpperCamelCase			  =      True
     check_hidden_states_output(a_   ,   a_   ,   a_				)

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

     check_hidden_states_output(a_   ,   a_   ,   a_				)







   def 						lowerCamelCase_	(     self						:						str   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						int=False				):





    """simple docstring"""


    UpperCamelCase			  =      super()._prepare_for_class(a_   ,   a_   ,   return_labels=a_				)

    if return_labels:
     if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
      del inputs_dict["labels"]

    return inputs_dict







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a_				)







   @unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""				)
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_image_modeling(*a_				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*a_				)







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





    """simple docstring"""


    for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCamelCase			  =      TFEfficientFormerModel.from_pretrained(a_				)
     self.assertIsNotNone(a_				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs_for_common()
    UpperCamelCase			  =      True

    UpperCamelCase			  =      getattr(self.model_tester   ,   """seq_length"""   ,   a_				)
    UpperCamelCase			  =      getattr(self.model_tester   ,   """encoder_seq_length"""   ,   a_				)
    UpperCamelCase			  =      getattr(self.model_tester   ,   """key_length"""   ,   a_				)
    UpperCamelCase			  =      getattr(self.model_tester   ,   """chunk_length"""   ,   a_				)

    if chunk_length is not None and hasattr(self.model_tester   ,   """num_hashes"""				):
     UpperCamelCase			  =      encoder_seq_length * self.model_tester.num_hashes

    for model_class in self.all_model_classes:
     UpperCamelCase			  =      True
     UpperCamelCase			  =      False
     UpperCamelCase			  =      True
     UpperCamelCase			  =      model_class(a_				)

     UpperCamelCase			  =      model(**self._prepare_for_class(a_   ,   a_				)   ,   training=a_				)
     UpperCamelCase			  =      outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
     self.assertEqual(len(a_				)   ,   self.model_tester.num_attention_outputs				)

     # check that output_attentions also work using config
     del inputs_dict["output_attentions"]
     UpperCamelCase			  =      True
     UpperCamelCase			  =      model_class(a_				)
     UpperCamelCase			  =      model(**self._prepare_for_class(a_   ,   a_				)   ,   training=a_				)

     UpperCamelCase			  =      outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
     self.assertEqual(len(a_				)   ,   self.model_tester.num_attention_outputs				)

     if chunk_length is not None:
      self.assertListEqual(
          list(attentions[0].shape[-4:]				)   ,   [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]   ,   )
     else:
      self.assertListEqual(
          list(attentions[0].shape[-3:]				)   ,   [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]   ,   )







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     # Prepare our model
     UpperCamelCase			  =      model_class(a_				)
     # These are maximally general inputs for the model, with multiple None dimensions
     # Hopefully this will catch any conditionals that fail for flexible shapes
     UpperCamelCase			  =      {
         key: tf.keras.Input(shape=val.shape[1:]   ,   dtype=val.dtype   ,   name=a_				)
         for key, val in model.input_signature.items()
         if key in model.dummy_inputs
     }
     UpperCamelCase			  =      model(a_				)
     self.assertTrue(outputs_dict is not None				)





def 			lowercase(	)		->	Union[str, Any]:







 '''simple docstring'''
 UpperCamelCase			  =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
 return image


@require_tf
@require_vision
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







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





    """simple docstring"""


    return (
        EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""				)
        if is_vision_available()
        else None
    )







   @slow
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""				)
    UpperCamelCase			  =      self.default_image_processor
    UpperCamelCase			  =      prepare_img()
    UpperCamelCase			  =      image_processor(images=a_   ,   return_tensors="""tf"""				)
    # forward pass
    UpperCamelCase			  =      model(**a_   ,   training=a_				)
    # verify the logits
    UpperCamelCase			  =      tf.TensorShape((1, 1000)				)
    self.assertEqual(outputs.logits.shape   ,   a_				)
    UpperCamelCase			  =      tf.constant([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]				)
    self.assertTrue(np.allclose(outputs.logits[0, :3]   ,   a_   ,   atol=1E-4				)				)







   @slow
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
        """snap-research/efficientformer-l1-300"""				)
    UpperCamelCase			  =      self.default_image_processor
    UpperCamelCase			  =      prepare_img()
    UpperCamelCase			  =      image_processor(images=a_   ,   return_tensors="""tf"""				)
    # forward pass
    UpperCamelCase			  =      model(**a_   ,   training=a_				)
    # verify the logits
    UpperCamelCase			  =      tf.TensorShape((1, 1000)				)
    self.assertEqual(outputs.logits.shape   ,   a_				)
    UpperCamelCase			  =      tf.constant([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]				)
    self.assertTrue(np.allclose(outputs.logits[0, :3]   ,   a_   ,   atol=1E-4				)				)




'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


lowerCAmelCase   : Tuple             =				1_6
lowerCAmelCase   : Optional[int]             =				3_2




def 	_A  (       A							,A = 1_6							)  ->				Union[str, Any]:
	lowercase						:		List[Any]   					=		AutoTokenizer.from_pretrained("bert-base-cased"							)
	lowercase						:		Any   					=		load_dataset("glue"							,"mrpc"							)

	def tokenize_function(A							):
		# max_length=None => use the model max length (it's actually the default)
		lowercase						:		List[str]   					=		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():
		lowercase						:		List[str]   					=		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
	lowercase						:		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.
		lowercase						:		Tuple   					=		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":
			lowercase						:		Any   					=		1_6
		elif accelerator.mixed_precision != "no":
			lowercase						:		List[Any]   					=		8
		else:
			lowercase						:		List[Any]   					=		None

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

	# Instantiate dataloaders.
	lowercase						:		Any   					=		DataLoader(
	    tokenized_datasets["train"]							,shuffle=A							,collate_fn=A							,batch_size=A							)
	lowercase						:		List[str]   					=		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

					lowerCAmelCase   : List[Any]             =				mocked_dataloaders  # noqa: F811




def 	_A  (       A							,A							)  ->				str:
	# For testing only
	if os.environ.get("TESTING_MOCKED_DATALOADERS"							,A							) == "1":
		lowercase						:		Optional[Any]   					=		2
	# New Code #
	lowercase						:		List[str]   					=		int(args.gradient_accumulation_steps							)
	lowercase						:		Union[str, Any]   					=		int(args.local_sgd_steps							)
	# Initialize accelerator
	lowercase						:		Union[str, Any]   					=		Accelerator(
	    cpu=args.cpu							,mixed_precision=args.mixed_precision							,gradient_accumulation_steps=A							)
	if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
		raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"							)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	lowercase						:		Dict   					=		config["lr"]
	lowercase						:		List[Any]   					=		int(config["num_epochs"]							)
	lowercase						:		int   					=		int(config["seed"]							)
	lowercase						:		Dict   					=		int(config["batch_size"]							)

	lowercase						:		List[str]   					=		evaluate.load("glue"							,"mrpc"							)

	set_seed(A							)
	lowercase					,		lowercase						:		Any   					=		get_dataloaders(A							,A							)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	lowercase						:		Optional[int]   					=		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).
	lowercase						:		Optional[int]   					=		model.to(accelerator.device							)

	# Instantiate optimizer
	lowercase						:		Tuple   					=		AdamW(params=model.parameters()							,lr=A							)

	# Instantiate scheduler
	lowercase						:		Union[str, Any]   					=		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.
	lowercase					,		lowercase					,		lowercase					,		lowercase					,		lowercase						:		Tuple   					=		accelerator.prepare(
	    A							,A							,A							,A							,A							)

	# Now we train the model
	for epoch in range(A							):
		model.train()
		with LocalSGD(
		    accelerator=A							,model=A							,local_sgd_steps=A							,enabled=local_sgd_steps is not None							) as local_sgd:
			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							):
					lowercase						:		Any   					=		model(**A							)
					lowercase						:		Dict   					=		output.loss
					accelerator.backward(A							)
					optimizer.step()
					lr_scheduler.step()
					optimizer.zero_grad()
					# LocalSGD-specific line
					local_sgd.step()

		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():
				lowercase						:		Dict   					=		model(**A							)
			lowercase						:		Tuple   					=		outputs.logits.argmax(dim=-1							)
			lowercase					,		lowercase						:		str   					=		accelerator.gather_for_metrics((predictions, batch["labels"])							)
			metric.add_batch(
			    predictions=A							,references=A							,)

		lowercase						:		Any   					=		metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'''epoch {epoch}:'''							,A							)




def 	_A  (       )  ->				Dict:
	lowercase						:		Optional[int]   					=		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(
	    "--local_sgd_steps"							,type=A							,default=8							,help="Number of local SGD steps or None to disable local SGD"							)
	parser.add_argument("--cpu"							,action="store_true"							,help="If passed, will train on the CPU."							)
	lowercase						:		Optional[Any]   					=		parser.parse_args()
	lowercase						:		Union[str, Any]   					=		{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
	training_function(A							,A							)


if __name__ == "__main__":
					main()


