
import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase					   =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCamelCase					   =[]
for i in range(6):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
      )
      rename_keys.append(
          (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
      )
      rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
      rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
      rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
      rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
      rename_keys.append(
          (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
      )
      rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
      rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
      rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
      # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
      rename_keys.append(
          (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
      )
      rename_keys.append(
          (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
      )
      rename_keys.append(
          (
              f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
              f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
          )
      )
      rename_keys.append(
          (
              f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
              f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
          )
      )
      rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
      rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
      rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
      rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
      rename_keys.append(
          (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
      )
      rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
      rename_keys.append(
          (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
      )
      rename_keys.append(
          (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
      )
      rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
      rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
        ("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
    ]
)





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 lowerCamelCase		:	int							=      state_dict.pop(lowerCamelCase							)
 lowerCamelCase		:	List[str]							=      val





def     _a					(						lowerCamelCase							):
 lowerCamelCase		:	str							=      OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   lowerCamelCase		:	Tuple							=      key.replace("""backbone.0.body""",	"""backbone.conv_encoder.model"""							)
   lowerCamelCase		:	Optional[Any]							=      value
  else:
   lowerCamelCase		:	Optional[int]							=      value

 return new_state_dict





def     _a					(						lowerCamelCase							):
 lowerCamelCase		:	str							=      """"""

 # first: transformer encoder
 for i in range(6							):
  # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
  lowerCamelCase		:	List[Any]							=      state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''							)
  lowerCamelCase		:	Optional[int]							=      state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''							)
  # next, add query, keys and values (in that order) to the state dict
  lowerCamelCase		:	Dict							=      in_proj_weight[:256, :]
  lowerCamelCase		:	Tuple							=      in_proj_bias[:256]
  lowerCamelCase		:	Optional[int]							=      in_proj_weight[256:512, :]
  lowerCamelCase		:	Any							=      in_proj_bias[256:512]
  lowerCamelCase		:	Optional[Any]							=      in_proj_weight[-256:, :]
  lowerCamelCase		:	int							=      in_proj_bias[-256:]
 # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
 for i in range(6							):
  # read in weights + bias of input projection layer of self-attention
  lowerCamelCase		:	List[str]							=      state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''							)
  lowerCamelCase		:	Tuple							=      state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''							)
  # next, add query, keys and values (in that order) to the state dict
  lowerCamelCase		:	List[Any]							=      in_proj_weight[:256, :]
  lowerCamelCase		:	Tuple							=      in_proj_bias[:256]
  lowerCamelCase		:	Tuple							=      in_proj_weight[256:512, :]
  lowerCamelCase		:	Any							=      in_proj_bias[256:512]
  lowerCamelCase		:	Optional[Any]							=      in_proj_weight[-256:, :]
  lowerCamelCase		:	Any							=      in_proj_bias[-256:]
  # read in weights + bias of input projection layer of cross-attention
  lowerCamelCase		:	Optional[Any]							=      state_dict.pop(
      F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''							)
  lowerCamelCase		:	str							=      state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''							)
  # next, add query, keys and values (in that order) of cross-attention to the state dict
  lowerCamelCase		:	Any							=      in_proj_weight_cross_attn[:256, :]
  lowerCamelCase		:	List[str]							=      in_proj_bias_cross_attn[:256]
  lowerCamelCase		:	str							=      in_proj_weight_cross_attn[256:512, :]
  lowerCamelCase		:	Any							=      in_proj_bias_cross_attn[256:512]
  lowerCamelCase		:	Dict							=      in_proj_weight_cross_attn[-256:, :]
  lowerCamelCase		:	Union[str, Any]							=      in_proj_bias_cross_attn[-256:]





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 lowerCamelCase			,    lowerCamelCase		:	Optional[int]							=      image.size
 lowerCamelCase		:	str							=      max(lowerCamelCase,	lowerCamelCase							)
 lowerCamelCase		:	Optional[int]							=      800 if """detection""" in checkpoint_url else 1000
 lowerCamelCase		:	int							=      target_max_size / current_max_size
 lowerCamelCase		:	Optional[int]							=      image.resize((int(round(scale * width							)							), int(round(scale * height							)							))							)

 return resized_image





def     _a					(						lowerCamelCase							):
 lowerCamelCase		:	str							=      F.to_tensor(lowerCamelCase							)
 lowerCamelCase		:	Optional[Any]							=      F.normalize(lowerCamelCase,	mean=[0.4_8_5, 0.4_5_6, 0.4_0_6],	std=[0.2_2_9, 0.2_2_4, 0.2_2_5]							)
 return image





@torch.no_grad()
def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 logger.info("""Converting model..."""							)

 # load original state dict
 lowerCamelCase		:	Union[str, Any]							=      torch.hub.load_state_dict_from_url(lowerCamelCase,	map_location="""cpu"""							)
 # rename keys
 for src, dest in rename_keys:
  rename_key(lowerCamelCase,	lowerCamelCase,	lowerCamelCase							)
 lowerCamelCase		:	Union[str, Any]							=      rename_backbone_keys(lowerCamelCase							)
 # query, key and value matrices need special treatment
 read_in_q_k_v(lowerCamelCase							)
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 lowerCamelCase		:	str							=      """model."""
 for key in state_dict.copy().keys():
  if not key.startswith("""class_labels_classifier"""							) and not key.startswith("""bbox_predictor"""							):
   lowerCamelCase		:	List[str]							=      state_dict.pop(lowerCamelCase							)
   lowerCamelCase		:	Dict							=      val
    # create HuggingFace model and load state dict
 lowerCamelCase		:	Optional[Any]							=      TableTransformerConfig(
     backbone="""resnet18""",	mask_loss_coefficient=1,	dice_loss_coefficient=1,	ce_loss_coefficient=1,	bbox_loss_coefficient=5,	giou_loss_coefficient=2,	eos_coefficient=0.4,	class_cost=1,	bbox_cost=5,	giou_cost=2,	)

 if "detection" in checkpoint_url:
  lowerCamelCase		:	Tuple							=      15
  lowerCamelCase		:	Any							=      2
  lowerCamelCase		:	Dict							=      {0: """table""", 1: """table rotated"""}
  lowerCamelCase		:	Union[str, Any]							=      idalabel
  lowerCamelCase		:	Tuple							=      {v: k for k, v in idalabel.items()}
 else:
  lowerCamelCase		:	Any							=      125
  lowerCamelCase		:	Union[str, Any]							=      6
  lowerCamelCase		:	Dict							=      {
      0: """table""",
      1: """table column""",
      2: """table row""",
      3: """table column header""",
      4: """table projected row header""",
      5: """table spanning cell""",
  }
  lowerCamelCase		:	List[str]							=      idalabel
  lowerCamelCase		:	Dict							=      {v: k for k, v in idalabel.items()}

 lowerCamelCase		:	Union[str, Any]							=      DetrImageProcessor(
     format="""coco_detection""",	max_size=800 if """detection""" in checkpoint_url else 1000							)
 lowerCamelCase		:	Optional[Any]							=      TableTransformerForObjectDetection(lowerCamelCase							)
 model.load_state_dict(lowerCamelCase							)
 model.eval()

 # verify our conversion
 lowerCamelCase		:	List[str]							=      """example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
 lowerCamelCase		:	Optional[Any]							=      hf_hub_download(repo_id="""nielsr/example-pdf""",	repo_type="""dataset""",	filename=lowerCamelCase							)
 lowerCamelCase		:	Optional[Any]							=      Image.open(lowerCamelCase							).convert("""RGB"""							)
 lowerCamelCase		:	List[Any]							=      normalize(resize(lowerCamelCase,	lowerCamelCase							)							).unsqueeze(0							)

 lowerCamelCase		:	Tuple							=      model(lowerCamelCase							)

 if "detection" in checkpoint_url:
  lowerCamelCase		:	Optional[Any]							=      (1, 15, 3)
  lowerCamelCase		:	str							=      torch.tensor(
      [[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]]							)
  lowerCamelCase		:	Tuple							=      torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]							)

 else:
  lowerCamelCase		:	List[str]							=      (1, 125, 7)
  lowerCamelCase		:	List[Any]							=      torch.tensor(
      [[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]]							)
  lowerCamelCase		:	List[Any]							=      torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]							)

 assert outputs.logits.shape == expected_shape
 assert torch.allclose(outputs.logits[0, :3, :3],	lowerCamelCase,	atol=1e-4							)
 assert torch.allclose(outputs.pred_boxes[0, :3, :3],	lowerCamelCase,	atol=1e-4							)
 print("""Looks ok!"""							)

 if pytorch_dump_folder_path is not None:
  # Save model and image processor
  logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''							)
  Path(lowerCamelCase							).mkdir(exist_ok=lowerCamelCase							)
  model.save_pretrained(lowerCamelCase							)
  image_processor.save_pretrained(lowerCamelCase							)

 if push_to_hub:
  # Push model to HF hub
  logger.info("""Pushing model to the hub..."""							)
  lowerCamelCase		:	Any							=      (
      """microsoft/table-transformer-detection"""
      if """detection""" in checkpoint_url
      else """microsoft/table-transformer-structure-recognition"""
  )
  model.push_to_hub(lowerCamelCase							)
  image_processor.push_to_hub(lowerCamelCase							)


if __name__ == "__main__":
      _lowerCamelCase					   =argparse.ArgumentParser()

      parser.add_argument(
          """--checkpoint_url""",
          default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
          type=str,
          choices=[
              """https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
              """https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
          ],
          help="""URL of the Table Transformer checkpoint you'd like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
      )
      parser.add_argument(
          """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
      )
      _lowerCamelCase					   =parser.parse_args()
      convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






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     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 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     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase=True							):
 model.train()
 lowerCamelCase		:	Dict							=      model(lowerCamelCase							)
 lowerCamelCase		:	Any							=      F.mse_loss(lowerCamelCase,	target.to(output.device							)							)
 if not do_backward:
  loss /= accelerator.gradient_accumulation_steps
  loss.backward()
 else:
  accelerator.backward(lowerCamelCase							)





def     _a					(						lowerCamelCase,	lowerCamelCase=False							):
 set_seed(42							)
 lowerCamelCase		:	Tuple							=      RegressionModel()
 lowerCamelCase		:	Any							=      deepcopy(lowerCamelCase							)
 lowerCamelCase		:	Any							=      RegressionDataset(length=80							)
 lowerCamelCase		:	Dict							=      DataLoader(lowerCamelCase,	batch_size=16							)
 model.to(accelerator.device							)
 if sched:
  lowerCamelCase		:	int							=      AdamW(params=model.parameters(),	lr=1e-3							)
  lowerCamelCase		:	Optional[Any]							=      AdamW(params=ddp_model.parameters(),	lr=1e-3							)
  lowerCamelCase		:	str							=      LambdaLR(lowerCamelCase,	lr_lambda=lambda lowerCamelCase							: epoch**0.6_5							)
  lowerCamelCase		:	Tuple							=      LambdaLR(lowerCamelCase,	lr_lambda=lambda lowerCamelCase							: epoch**0.6_5							)
 # Make a copy of `model`
 if sched:
  lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Optional[int]							=      accelerator.prepare(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							)
 else:
  lowerCamelCase			,    lowerCamelCase		:	List[Any]							=      accelerator.prepare(lowerCamelCase,	lowerCamelCase							)
 if sched:
  return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
 return model, ddp_model, dataloader





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

  # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
  check_model_parameters(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							)
  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(1337 + iteration							)
  lowerCamelCase		:	List[Any]							=      ddp_input[torch.randperm(len(lowerCamelCase							)							)]





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

  # 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(1337 + iteration							)
  lowerCamelCase		:	Optional[Any]							=      ddp_input[torch.randperm(len(lowerCamelCase							)							)]





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

  # 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(lowerCamelCase							) - 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(1337 + iteration							)
  lowerCamelCase		:	Any							=      ddp_input[torch.randperm(len(lowerCamelCase							)							)]
 GradientState._reset_state()





def     _a					(						lowerCamelCase=False,	lowerCamelCase=False							):
 lowerCamelCase		:	List[Any]							=      Accelerator(
     split_batches=lowerCamelCase,	dispatch_batches=lowerCamelCase,	gradient_accumulation_steps=2							)
 # Test that context manager behaves properly
 lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	List[Any]							=      get_training_setup(lowerCamelCase,	lowerCamelCase							)
 for iteration, batch in enumerate(lowerCamelCase							):
  lowerCamelCase			,    lowerCamelCase		:	Union[str, Any]							=      batch.values()
  # Gather the distributed inputs and targs for the base model
  lowerCamelCase			,    lowerCamelCase		:	Optional[Any]							=      accelerator.gather((ddp_input, ddp_target)							)
  lowerCamelCase			,    lowerCamelCase		:	Tuple							=      input.to(accelerator.device							), target.to(accelerator.device							)
  # Perform our initial ground truth step in non "DDP"
  model.train()
  ddp_model.train()
  step_model(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							)
  opt.step()

  if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowerCamelCase							)):
   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(lowerCamelCase							):
   step_model(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							)
   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'''
  lowerCamelCase		:	Union[str, Any]							=      (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowerCamelCase							))
  if accelerator.num_processes > 1:
   check_model_parameters(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							)
  # Shuffle ddp_input on each iteration
  torch.manual_seed(1337 + iteration							)
 GradientState._reset_state()





def     _a					(						):
 lowerCamelCase		:	int							=      Accelerator()

 lowerCamelCase		:	Optional[Any]							=      RegressionDataset(length=80							)
 lowerCamelCase		:	List[str]							=      DataLoader(lowerCamelCase,	batch_size=16							)
 lowerCamelCase		:	int							=      RegressionDataset(length=96							)
 lowerCamelCase		:	Optional[int]							=      DataLoader(lowerCamelCase,	batch_size=16							)
 lowerCamelCase			,    lowerCamelCase		:	Optional[Any]							=      accelerator.prepare(lowerCamelCase,	lowerCamelCase							)
 assert accelerator.gradient_state.active_dataloader is None
 for iteration, _ in enumerate(lowerCamelCase							):
  assert id(accelerator.gradient_state.active_dataloader							) == id(lowerCamelCase							)
  if iteration < len(lowerCamelCase							) - 1:
   assert not accelerator.gradient_state.end_of_dataloader
   if iteration == 1:
    for batch_num, _ in enumerate(lowerCamelCase							):
     assert id(accelerator.gradient_state.active_dataloader							) == id(lowerCamelCase							)
     if batch_num < len(lowerCamelCase							) - 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     _a					(						):
 lowerCamelCase		:	List[Any]							=      Accelerator()
 lowerCamelCase		:	int							=      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(lowerCamelCase							)
 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(lowerCamelCase							)
 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(lowerCamelCase,	lowerCamelCase							)

    # 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(lowerCamelCase,	lowerCamelCase							)





def     _a					(						lowerCamelCase							):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
      main()




