
"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A__ :       Optional[Any]				= logging.get_logger(__name__)

A__ :       str				= torch.device('cpu')




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Optional[int]   			=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _lowercase:      int   			=  Image.open(requests.get(_UpperCamelCase			,					stream=_UpperCamelCase    ).raw    )
  return im




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  if swiftformer_name == "swiftformer_xs":
    return torch.tensor([-2.17_03e00, 2.11_07e00, -2.08_11e00, 8.86_85e-01, 2.43_60e-01]    )

  elif swiftformer_name == "swiftformer_s":
    return torch.tensor([3.96_36e-01, 2.34_78e-01, -1.69_63e00, -1.73_81e00, -8.63_37e-01]    )

  elif swiftformer_name == "swiftformer_l1":
    return torch.tensor([-4.27_68e-01, -4.74_29e-01, -1.08_97e00, -1.02_48e00, 3.55_23e-02]    )

  elif swiftformer_name == "swiftformer_l3":
    return torch.tensor([-2.53_30e-01, 2.42_11e-01, -6.01_85e-01, -8.27_89e-01, -6.04_46e-02]    )




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Optional[Any]   			=  dct.pop(_UpperCamelCase    )
  _lowercase:      List[str]   			=  val




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      List[Any]   			=  []
  for k in state_dict.keys():
    _lowercase:      Dict   			=  k
    if ".pwconv" in k:
      _lowercase:      List[str]   			=  k_new.replace('''.pwconv'''			,					'''.point_wise_conv'''    )
    if ".dwconv" in k:
      _lowercase:      List[Any]   			=  k_new.replace('''.dwconv'''			,					'''.depth_wise_conv'''    )
    if ".Proj." in k:
      _lowercase:      Any   			=  k_new.replace('''.Proj.'''			,					'''.proj.'''    )
    if "patch_embed" in k_new:
      _lowercase:      Optional[Any]   			=  k_new.replace('''patch_embed'''			,					'''swiftformer.patch_embed.patch_embedding'''    )
    if "network" in k_new:
      _lowercase:      Tuple   			=  k_new.split('''.'''    )
      if ls[2].isdigit():
        _lowercase:      Tuple   			=  '''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]    )
      else:
        _lowercase:      List[str]   			=  k_new.replace('''network'''			,					'''swiftformer.encoder.network'''    )
    rename_keys.append((k, k_new)    )
  return rename_keys




@torch.no_grad()
def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  _lowercase:      Any   			=  1_000
  _lowercase:      Any   			=  '''huggingface/label-files'''
  _lowercase:      Tuple   			=  '''imagenet-1k-id2label.json'''
  _lowercase:      List[Any]   			=  json.load(open(hf_hub_download(_UpperCamelCase			,					_UpperCamelCase			,					repo_type='''dataset'''    )			,					'''r'''    )    )
  _lowercase:      Any   			=  {int(_UpperCamelCase    ): v for k, v in idalabel.items()}
  _lowercase:      Dict   			=  idalabel
  _lowercase:      Union[str, Any]   			=  {v: k for k, v in idalabel.items()}

  # size of the architecture
  if swiftformer_name == "swiftformer_xs":
    _lowercase:      List[Any]   			=  [3, 3, 6, 4]
    _lowercase:      List[Any]   			=  [48, 56, 112, 220]

  elif swiftformer_name == "swiftformer_s":
    _lowercase:      Union[str, Any]   			=  [3, 3, 9, 6]
    _lowercase:      Union[str, Any]   			=  [48, 64, 168, 224]

  elif swiftformer_name == "swiftformer_l1":
    _lowercase:      Tuple   			=  [4, 3, 10, 5]
    _lowercase:      Any   			=  [48, 96, 192, 384]

  elif swiftformer_name == "swiftformer_l3":
    _lowercase:      Optional[int]   			=  [4, 4, 12, 6]
    _lowercase:      str   			=  [64, 128, 320, 512]

  # load state_dict of original model, remove and rename some keys
  if original_ckpt:
    if original_ckpt.startswith('''https'''    ):
      _lowercase:      Optional[int]   			=  torch.hub.load_state_dict_from_url(_UpperCamelCase			,					map_location='''cpu'''			,					check_hash=_UpperCamelCase    )
    else:
      _lowercase:      Tuple   			=  torch.load(_UpperCamelCase			,					map_location='''cpu'''    )
  _lowercase:      Dict   			=  checkpoint

  _lowercase:      Dict   			=  create_rename_keys(_UpperCamelCase    )
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    )

  # load HuggingFace model
  _lowercase:      Optional[Any]   			=  SwiftFormerForImageClassification(_UpperCamelCase    ).eval()
  hf_model.load_state_dict(_UpperCamelCase    )

  # prepare test inputs
  _lowercase:      str   			=  prepare_img()
  _lowercase:      Dict   			=  ViTImageProcessor.from_pretrained('''preprocessor_config'''    )
  _lowercase:      Dict   			=  processor(images=_UpperCamelCase			,					return_tensors='''pt'''    )

  # compare outputs from both models
  _lowercase:      List[str]   			=  get_expected_output(_UpperCamelCase    )
  _lowercase:      Tuple   			=  hf_model(inputs['''pixel_values''']    ).logits

  assert hf_logits.shape == torch.Size([1, 1_000]    )
  assert torch.allclose(hf_logits[0, 0:5]			,					_UpperCamelCase			,					atol=1e-3    )

  Path(_UpperCamelCase    ).mkdir(exist_ok=_UpperCamelCase    )
  print(f'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''    )
  hf_model.save_pretrained(_UpperCamelCase    )


if __name__ == "__main__":
  A__ :       Union[str, Any]				= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--swiftformer_name',
      default='swiftformer_xs',
      choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
      type=str,
      help='Name of the SwiftFormer model you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default='./converted_outputs/',
      type=str,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')

  A__ :       List[str]				= parser.parse_args()
  convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)


"""simple docstring"""




import argparse
import os

# New Code #
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.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A__ :       List[Any]				= 1_6
A__ :       Union[str, Any]				= 3_2




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase = 16    ):






  """simple docstring"""



  _lowercase:      Any   			=  AutoTokenizer.from_pretrained('''bert-base-cased'''    )
  _lowercase:      List[str]   			=  load_dataset('''glue'''			,					'''mrpc'''    )

  def tokenize_function(_UpperCamelCase    ):
    # max_length=None => use the model max length (it's actually the default)
    _lowercase:      Union[str, Any]   			=  tokenizer(examples['''sentence1''']			,					examples['''sentence2''']			,					truncation=_UpperCamelCase			,					max_length=_UpperCamelCase    )
    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:      Tuple   			=  datasets.map(
        _UpperCamelCase			,					batched=_UpperCamelCase			,					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[Any]   			=  tokenized_datasets.rename_column('''label'''			,					'''labels'''    )

  def collate_fn(_UpperCamelCase    ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _lowercase:      Union[str, Any]   			=  128 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:      int   			=  16
    elif accelerator.mixed_precision != "no":
      _lowercase:      Tuple   			=  8
    else:
      _lowercase:      str   			=  None

    return tokenizer.pad(
        _UpperCamelCase			,					padding='''longest'''			,					max_length=_UpperCamelCase			,					pad_to_multiple_of=_UpperCamelCase			,					return_tensors='''pt'''			,					)

  # Instantiate dataloaders.
  _lowercase:      List[Any]   			=  DataLoader(
      tokenized_datasets['''train''']			,					shuffle=_UpperCamelCase			,					collate_fn=_UpperCamelCase			,					batch_size=_UpperCamelCase    )
  _lowercase:      int   			=  DataLoader(
      tokenized_datasets['''validation''']			,					shuffle=_UpperCamelCase			,					collate_fn=_UpperCamelCase			,					batch_size=_UpperCamelCase    )

  return train_dataloader, eval_dataloader


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

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




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,					_UpperCamelCase    ) == "1":
    _lowercase:      Tuple   			=  2
  # Initialize accelerator
  _lowercase:      Optional[Any]   			=  Accelerator(cpu=args.cpu			,					mixed_precision=args.mixed_precision    )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowercase:      Optional[Any]   			=  config['''lr''']
  _lowercase:      Tuple   			=  int(config['''num_epochs''']    )
  _lowercase:      Any   			=  int(config['''seed''']    )
  _lowercase:      List[Any]   			=  int(config['''batch_size''']    )

  _lowercase:      Optional[int]   			=  evaluate.load('''glue'''			,					'''mrpc'''    )

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=_UpperCamelCase    )
  def inner_training_loop(_UpperCamelCase    ):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(_UpperCamelCase    )

    # 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=_UpperCamelCase    )

    # 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:      List[Any]   			=  model.to(accelerator.device    )

    # Instantiate optimizer
    _lowercase:      List[str]   			=  AdamW(params=model.parameters()			,					lr=_UpperCamelCase    )
    _lowercase			,					_lowercase:      Tuple   			=  get_dataloaders(_UpperCamelCase			,					_UpperCamelCase    )

    # Instantiate scheduler
    _lowercase:      str   			=  get_linear_schedule_with_warmup(
        optimizer=_UpperCamelCase			,					num_warmup_steps=100			,					num_training_steps=(len(_UpperCamelCase    ) * 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:      Union[str, Any]   			=  accelerator.prepare(
        _UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    )

    # Now we train the model
    for epoch in range(_UpperCamelCase    ):
      model.train()
      for step, batch in enumerate(_UpperCamelCase    ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device    )
        _lowercase:      Tuple   			=  model(**_UpperCamelCase    )
        _lowercase:      Union[str, Any]   			=  outputs.loss
        accelerator.backward(_UpperCamelCase    )
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

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

      _lowercase:      int   			=  metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''			,					_UpperCamelCase    )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Dict   			=  argparse.ArgumentParser(description='''Simple example of training script.'''    )
  parser.add_argument(
      '''--mixed_precision'''			,					type=_UpperCamelCase			,					default=_UpperCamelCase			,					choices=['''no''', '''fp16''', '''bf16''', '''fp8''']			,					help='''Whether to use mixed precision. Choose'''
      '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
      '''and an Nvidia Ampere GPU.'''			,					)
  parser.add_argument('''--cpu'''			,					action='''store_true'''			,					help='''If passed, will train on the CPU.'''    )
  _lowercase:      Tuple   			=  parser.parse_args()
  _lowercase:      str   			=  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(_UpperCamelCase			,					_UpperCamelCase    )


if __name__ == "__main__":
  main()
