






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DetrImageProcessor
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def __init__(      self		:			int       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Optional[int]=7       ,				lowerCamelCase		:			int=3       ,				lowerCamelCase		:			Any=30       ,				lowerCamelCase		:			Union[str, Any]=400       ,				lowerCamelCase		:			List[str]=True       ,				lowerCamelCase		:			Union[str, Any]=None       ,				lowerCamelCase		:			List[Any]=True       ,				lowerCamelCase		:			Optional[Any]=1 / 255       ,				lowerCamelCase		:			Union[str, Any]=True       ,				lowerCamelCase		:			Tuple=[0.5, 0.5, 0.5]       ,				lowerCamelCase		:			Union[str, Any]=[0.5, 0.5, 0.5]       ,				lowerCamelCase		:			Any=True       ,				)						->	str:


         """simple docstring"""
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         _UpperCAmelCase							=    size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    min_resolution
         _UpperCAmelCase							=    max_resolution
         _UpperCAmelCase							=    do_resize
         _UpperCAmelCase							=    size
         _UpperCAmelCase							=    do_rescale
         _UpperCAmelCase							=    rescale_factor
         _UpperCAmelCase							=    do_normalize
         _UpperCAmelCase							=    image_mean
         _UpperCAmelCase							=    image_std
         _UpperCAmelCase							=    do_pad


  def  lowerCamelCase       (      self		:			str							)						->	str:


         """simple docstring"""
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_pad": self.do_pad,
         }





  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Optional[Any]=False							)						->	str:


         """simple docstring"""
         if not batched:
                _UpperCAmelCase							=    image_inputs[0]
                if isinstance(lowerCamelCase       ,				Image.Image							):
                       _UpperCAmelCase	,			_UpperCAmelCase							=    image.size
                else:
                       _UpperCAmelCase	,			_UpperCAmelCase							=    image.shape[1], image.shape[2]
                if w < h:
                       _UpperCAmelCase							=    int(self.size["""shortest_edge"""] * h / w							)
                       _UpperCAmelCase							=    self.size["""shortest_edge"""]
                elif w > h:
                       _UpperCAmelCase							=    self.size["""shortest_edge"""]
                       _UpperCAmelCase							=    int(self.size["""shortest_edge"""] * w / h							)
                else:
                       _UpperCAmelCase							=    self.size["""shortest_edge"""]
                       _UpperCAmelCase							=    self.size["""shortest_edge"""]

         else:
                _UpperCAmelCase							=    []
                for image in image_inputs:
                       _UpperCAmelCase	,			_UpperCAmelCase							=    self.get_expected_values([image]							)
                       expected_values.append((expected_height, expected_width)							)
                _UpperCAmelCase							=    max(lowerCamelCase       ,				key=lambda lowerCamelCase							: item[0]							)[0]
                _UpperCAmelCase							=    max(lowerCamelCase       ,				key=lambda lowerCamelCase							: item[1]							)[1]

         return expected_height, expected_width



@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  DetrImageProcessor if is_vision_available() else None


  def  lowerCamelCase       (      self		:			Tuple							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    DetrImageProcessingTester(self							)


  @property
  def  lowerCamelCase       (      self		:			Dict							)						->	Tuple:


         """simple docstring"""
         return self.image_processor_tester.prepare_image_processor_dict()


  def  lowerCamelCase       (      self		:			Any							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""image_mean"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""image_std"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_normalize"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_rescale"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""rescale_factor"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_resize"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""size"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_pad"""							)							)


  def  lowerCamelCase       (      self		:			Any							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class.from_dict(self.image_processor_dict							)
         self.assertEqual(image_processor.size       ,				{"""shortest_edge""": 18, """longest_edge""": 1333}							)
         self.assertEqual(image_processor.do_pad       ,				lowerCamelCase							)

         _UpperCAmelCase							=    self.image_processing_class.from_dict(
             self.image_processor_dict       ,				size=42       ,				max_size=84       ,				pad_and_return_pixel_mask=lowerCamelCase							)
         self.assertEqual(image_processor.size       ,				{"""shortest_edge""": 42, """longest_edge""": 84}							)
         self.assertEqual(image_processor.do_pad       ,				lowerCamelCase							)


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


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			Any							)						->	Tuple:


         """simple docstring"""
         # Initialize image_processing
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random PIL images
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				Image.Image							)

         # Test not batched input
         _UpperCAmelCase							=    image_processing(image_inputs[0]       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processor_tester.get_expected_values(lowerCamelCase							)

         self.assertEqual(
             encoded_images.shape       ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,				)

         # Test batched
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processor_tester.get_expected_values(lowerCamelCase       ,				batched=lowerCamelCase							)

         _UpperCAmelCase							=    image_processing(lowerCamelCase       ,				return_tensors="""pt"""							).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,				)


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


         """simple docstring"""
         # Initialize image_processing
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=lowerCamelCase       ,				numpify=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				np.ndarray							)

         # Test not batched input
         _UpperCAmelCase							=    image_processing(image_inputs[0]       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processor_tester.get_expected_values(lowerCamelCase							)

         self.assertEqual(
             encoded_images.shape       ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,				)

         # Test batched
         _UpperCAmelCase							=    image_processing(lowerCamelCase       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processor_tester.get_expected_values(lowerCamelCase       ,				batched=lowerCamelCase							)

         self.assertEqual(
             encoded_images.shape       ,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,				)


  def  lowerCamelCase       (      self		:			List[str]							)						->	Any:


         """simple docstring"""
         # Initialize image_processing
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=lowerCamelCase       ,				torchify=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				torch.Tensor							)

         # Test not batched input
         _UpperCAmelCase							=    image_processing(image_inputs[0]       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processor_tester.get_expected_values(lowerCamelCase							)

         self.assertEqual(
             encoded_images.shape       ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,				)

         # Test batched
         _UpperCAmelCase							=    image_processing(lowerCamelCase       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processor_tester.get_expected_values(lowerCamelCase       ,				batched=lowerCamelCase							)

         self.assertEqual(
             encoded_images.shape       ,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,				)


  @slow
  def  lowerCamelCase       (      self		:			Tuple							)						->	Union[str, Any]:


         """simple docstring"""
         # prepare image and target
         _UpperCAmelCase							=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
         with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""       ,				"""r"""							) as f:
                _UpperCAmelCase							=    json.loads(f.read()							)

         _UpperCAmelCase							=    {"""image_id""": 3_9769, """annotations""": target}

         # encode them
         _UpperCAmelCase							=    DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""							)
         _UpperCAmelCase							=    image_processing(images=lowerCamelCase       ,				annotations=lowerCamelCase       ,				return_tensors="""pt"""							)

         # verify pixel values
         _UpperCAmelCase							=    torch.Size([1, 3, 800, 1066]							)
         self.assertEqual(encoding["""pixel_values"""].shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    torch.tensor([0.2796, 0.3138, 0.3481]							)
         self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,				lowerCamelCase       ,				atol=1E-4							)							)

         # verify area
         _UpperCAmelCase							=    torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,				lowerCamelCase							)							)
         # verify boxes
         _UpperCAmelCase							=    torch.Size([6, 4]							)
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,				lowerCamelCase							)
         _UpperCAmelCase							=    torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,				lowerCamelCase       ,				atol=1E-3							)							)
         # verify image_id
         _UpperCAmelCase							=    torch.tensor([3_9769]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,				lowerCamelCase							)							)
         # verify is_crowd
         _UpperCAmelCase							=    torch.tensor([0, 0, 0, 0, 0, 0]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,				lowerCamelCase							)							)
         # verify class_labels
         _UpperCAmelCase							=    torch.tensor([75, 75, 63, 65, 17, 17]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,				lowerCamelCase							)							)
         # verify orig_size
         _UpperCAmelCase							=    torch.tensor([480, 640]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,				lowerCamelCase							)							)
         # verify size
         _UpperCAmelCase							=    torch.tensor([800, 1066]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,				lowerCamelCase							)							)





  @slow
  def  lowerCamelCase       (      self		:			List[Any]							)						->	Dict:


         """simple docstring"""
         # prepare image, target and masks_path
         _UpperCAmelCase							=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
         with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""       ,				"""r"""							) as f:
                _UpperCAmelCase							=    json.loads(f.read()							)

         _UpperCAmelCase							=    {"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

         _UpperCAmelCase							=    pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

         # encode them
         _UpperCAmelCase							=    DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""							)
         _UpperCAmelCase							=    image_processing(images=lowerCamelCase       ,				annotations=lowerCamelCase       ,				masks_path=lowerCamelCase       ,				return_tensors="""pt"""							)

         # verify pixel values
         _UpperCAmelCase							=    torch.Size([1, 3, 800, 1066]							)
         self.assertEqual(encoding["""pixel_values"""].shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    torch.tensor([0.2796, 0.3138, 0.3481]							)
         self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,				lowerCamelCase       ,				atol=1E-4							)							)

         # verify area
         _UpperCAmelCase							=    torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,				lowerCamelCase							)							)
         # verify boxes
         _UpperCAmelCase							=    torch.Size([6, 4]							)
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,				lowerCamelCase							)
         _UpperCAmelCase							=    torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,				lowerCamelCase       ,				atol=1E-3							)							)
         # verify image_id
         _UpperCAmelCase							=    torch.tensor([3_9769]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,				lowerCamelCase							)							)
         # verify is_crowd
         _UpperCAmelCase							=    torch.tensor([0, 0, 0, 0, 0, 0]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,				lowerCamelCase							)							)
         # verify class_labels
         _UpperCAmelCase							=    torch.tensor([17, 17, 63, 75, 75, 93]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,				lowerCamelCase							)							)
         # verify masks
         _UpperCAmelCase							=    82_2873
         self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()       ,				lowerCamelCase							)
         # verify orig_size
         _UpperCAmelCase							=    torch.tensor([480, 640]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,				lowerCamelCase							)							)
         # verify size
         _UpperCAmelCase							=    torch.tensor([800, 1066]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,				lowerCamelCase							)							)







from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )				->						List[str]:
       for param, grad_param in zip(model_a.parameters()   ,      model_b.parameters() ):
              if not param.requires_grad:
                     continue
              if not did_step:
                     # Grads should not be in sync
                     assert (
                         torch.allclose(param.grad   ,      grad_param.grad ) is False
                     ), f"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
              else:
                     # Grads should be in sync
                     assert (
                         torch.allclose(param.grad   ,      grad_param.grad ) is True
                     ), f"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case=True )				->						Optional[int]:
       model.train()
       _UpperCAmelCase							=    model(__snake_case )
       _UpperCAmelCase							=    F.mse_loss(__snake_case   ,      target.to(output.device ) )
       if not do_backward:
              loss /= accelerator.gradient_accumulation_steps
              loss.backward()
       else:
              accelerator.backward(__snake_case )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case=False )				->						Optional[int]:
       set_seed(4_2 )
       _UpperCAmelCase							=    RegressionModel()
       _UpperCAmelCase							=    deepcopy(__snake_case )
       _UpperCAmelCase							=    RegressionDataset(length=8_0 )
       _UpperCAmelCase							=    DataLoader(__snake_case   ,      batch_size=1_6 )
       model.to(accelerator.device )
       if sched:
              _UpperCAmelCase							=    AdamW(params=model.parameters()   ,      lr=1E-3 )
              _UpperCAmelCase							=    AdamW(params=ddp_model.parameters()   ,      lr=1E-3 )
              _UpperCAmelCase							=    LambdaLR(__snake_case   ,      lr_lambda=lambda __snake_case : epoch**0.65 )
              _UpperCAmelCase							=    LambdaLR(__snake_case   ,      lr_lambda=lambda __snake_case : epoch**0.65 )
       # Make a copy of `model`
       if sched:
              _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.prepare(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
       else:
              _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.prepare(__snake_case   ,      __snake_case )
       if sched:
              return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
       return model, ddp_model, dataloader




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Dict:
       # Test when on a single CPU or GPU that the context manager does nothing
       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_training_setup(__snake_case )
       # Use a single batch
       _UpperCAmelCase	,			_UpperCAmelCase							=    next(iter(__snake_case ) ).values()
       for iteration in range(3 ):
              # Gather the distributed inputs and targs for the base model
              _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.gather((ddp_input, ddp_target) )
              _UpperCAmelCase	,			_UpperCAmelCase							=    input.to(accelerator.device ), target.to(accelerator.device )
              # Perform our initial ground truth step in non "DDP"
              step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(__snake_case ):
                            step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              else:
                     # Sync grads
                     step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )

              # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
              check_model_parameters(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              for param, ddp_param in zip(model.parameters()   ,      ddp_model.parameters() ):
                     if not param.requires_grad:
                            continue
                     assert torch.allclose(
                         param.grad   ,      ddp_param.grad ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

              # Shuffle ddp_input on each iteration
              torch.manual_seed(1_3_3_7 + iteration )
              _UpperCAmelCase							=    ddp_input[torch.randperm(len(__snake_case ) )]




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						int:
       # Test on distributed setup that context manager behaves properly
       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_training_setup(__snake_case )
       # Use a single batch
       _UpperCAmelCase	,			_UpperCAmelCase							=    next(iter(__snake_case ) ).values()
       for iteration in range(3 ):
              # Gather the distributed inputs and targs for the base model
              _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.gather((ddp_input, ddp_target) )
              _UpperCAmelCase	,			_UpperCAmelCase							=    input.to(accelerator.device ), target.to(accelerator.device )
              # Perform our initial ground truth step in non "DDP"
              step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(__snake_case ):
                            step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              else:
                     # Sync grads
                     step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )

              # DDP model and model should only be in sync when not (iteration % 2 == 0)
              for param, ddp_param in zip(model.parameters()   ,      ddp_model.parameters() ):
                     if not param.requires_grad:
                            continue
                     if iteration % 2 == 0:
                            # Grads should not be in sync
                            assert (
                                torch.allclose(param.grad   ,      ddp_param.grad ) is False
                            ), f"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
                     else:
                            # Grads should be in sync
                            assert (
                                torch.allclose(param.grad   ,      ddp_param.grad ) is True
                            ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
              torch.manual_seed(1_3_3_7 + iteration )
              _UpperCAmelCase							=    ddp_input[torch.randperm(len(__snake_case ) )]




def       _SCREAMING_SNAKE_CASE (							__snake_case=False   ,      __snake_case=False )				->						Any:
       _UpperCAmelCase							=    Accelerator(
           split_batches=__snake_case   ,      dispatch_batches=__snake_case   ,      gradient_accumulation_steps=2 )
       # Test that context manager behaves properly
       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_training_setup(__snake_case )
       for iteration, batch in enumerate(__snake_case ):
              _UpperCAmelCase	,			_UpperCAmelCase							=    batch.values()
              # Gather the distributed inputs and targs for the base model
              _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.gather((ddp_input, ddp_target) )
              _UpperCAmelCase	,			_UpperCAmelCase							=    input.to(accelerator.device ), target.to(accelerator.device )
              # Perform our initial ground truth step in non "DDP"
              step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              # Do "gradient accumulation" (noop)
              with accelerator.accumulate(__snake_case ):
                     step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )

              # DDP model and model should only be in sync when not (iteration % 2 == 0)
              for param, ddp_param in zip(model.parameters()   ,      ddp_model.parameters() ):
                     if not param.requires_grad:
                            continue
                     if ((iteration + 1) % 2 == 0) or (iteration == len(__snake_case ) - 1):
                            # Grads should be in sync
                            assert (
                                torch.allclose(param.grad   ,      ddp_param.grad ) is True
                            ), f"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
                     else:
                            # Grads should not be in sync
                            assert (
                                torch.allclose(param.grad   ,      ddp_param.grad ) is False
                            ), f"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
              torch.manual_seed(1_3_3_7 + iteration )
              _UpperCAmelCase							=    ddp_input[torch.randperm(len(__snake_case ) )]
       GradientState._reset_state()




def       _SCREAMING_SNAKE_CASE (							__snake_case=False   ,      __snake_case=False )				->						Dict:
       _UpperCAmelCase							=    Accelerator(
           split_batches=__snake_case   ,      dispatch_batches=__snake_case   ,      gradient_accumulation_steps=2 )
       # Test that context manager behaves properly
       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_training_setup(__snake_case   ,      __snake_case )
       for iteration, batch in enumerate(__snake_case ):
              _UpperCAmelCase	,			_UpperCAmelCase							=    batch.values()
              # Gather the distributed inputs and targs for the base model
              _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.gather((ddp_input, ddp_target) )
              _UpperCAmelCase	,			_UpperCAmelCase							=    input.to(accelerator.device ), target.to(accelerator.device )
              # Perform our initial ground truth step in non "DDP"
              model.train()
              ddp_model.train()
              step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              opt.step()

              if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__snake_case )):
                     if split_batches:
                            sched.step()
                     else:
                            for _ in range(accelerator.num_processes ):
                                   sched.step()
              opt.zero_grad()
              # Perform gradient accumulation under wrapper
              with accelerator.accumulate(__snake_case ):
                     step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
                     ddp_opt.step()
                     ddp_sched.step()
                     ddp_opt.zero_grad()

              # Learning rates should be the same
              assert (
                  opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
              ), f"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
              _UpperCAmelCase							=    (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__snake_case ))
              if accelerator.num_processes > 1:
                     check_model_parameters(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              # Shuffle ddp_input on each iteration
              torch.manual_seed(1_3_3_7 + iteration )
       GradientState._reset_state()




def       _SCREAMING_SNAKE_CASE (							)				->						Union[str, Any]:
       _UpperCAmelCase							=    Accelerator()

       _UpperCAmelCase							=    RegressionDataset(length=8_0 )
       _UpperCAmelCase							=    DataLoader(__snake_case   ,      batch_size=1_6 )
       _UpperCAmelCase							=    RegressionDataset(length=9_6 )
       _UpperCAmelCase							=    DataLoader(__snake_case   ,      batch_size=1_6 )
       _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.prepare(__snake_case   ,      __snake_case )
       assert accelerator.gradient_state.active_dataloader is None
       for iteration, _ in enumerate(__snake_case ):
              assert id(accelerator.gradient_state.active_dataloader ) == id(__snake_case )
              if iteration < len(__snake_case ) - 1:
                     assert not accelerator.gradient_state.end_of_dataloader
                     if iteration == 1:
                            for batch_num, _ in enumerate(__snake_case ):
                                   assert id(accelerator.gradient_state.active_dataloader ) == id(__snake_case )
                                   if batch_num < len(__snake_case ) - 1:
                                          assert not accelerator.gradient_state.end_of_dataloader
                                   else:
                                          assert accelerator.gradient_state.end_of_dataloader
              else:
                     assert accelerator.gradient_state.end_of_dataloader
       assert accelerator.gradient_state.active_dataloader is None




def       _SCREAMING_SNAKE_CASE (							)				->						List[Any]:
       _UpperCAmelCase							=    Accelerator()
       _UpperCAmelCase							=    accelerator.state
       if state.local_process_index == 0:
              print("""**Test `accumulate` gradient accumulation with dataloader break**""" )
       test_dataloader_break()
       if state.distributed_type == DistributedType.NO:
              if state.local_process_index == 0:
                     print("""**Test NOOP `no_sync` context manager**""" )
              test_noop_sync(__snake_case )
       if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
              if state.local_process_index == 0:
                     print("""**Test Distributed `no_sync` context manager**""" )
              test_distributed_sync(__snake_case )
       if state.distributed_type == DistributedType.MULTI_GPU:
              for split_batch in [True, False]:
                     for dispatch_batches in [True, False]:
                            if state.local_process_index == 0:
                                   print(
                                       """**Test `accumulate` gradient accumulation, """   ,      f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""   ,      )
                            test_gradient_accumulation(__snake_case   ,      __snake_case )

    # Currently will break on torch 2.0 +, need to investigate why
       if is_torch_version("""<"""   ,      """2.0""" ) or state.distributed_type == DistributedType.NO:
              if state.local_process_index == 0:
                     print(
                         """**Test `accumulate` gradient accumulation with optimizer and scheduler, """   ,      """`split_batches=False`, `dispatch_batches=False`**"""   ,      )
              test_gradient_accumulation_with_opt_and_scheduler()
              if state.distributed_type == DistributedType.MULTI_GPU:
                     for split_batch in [True, False]:
                            for dispatch_batches in [True, False]:
                                   if not split_batch and not dispatch_batches:
                                          continue
                                   if state.local_process_index == 0:
                                          print(
                                              """**Test `accumulate` gradient accumulation with optimizer and scheduler, """   ,      f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""   ,      )
                                   test_gradient_accumulation_with_opt_and_scheduler(__snake_case   ,      __snake_case )




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Tuple:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
  main()