






"""simple docstring"""




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_snake_case          =   logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-classification/requirements.txt')

_snake_case          =   list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_snake_case          =   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  with open(UpperCamelCase__     ,   """rb"""				) as f:
    _a  :       Union[str, Any]             =	Image.open(UpperCamelCase__				)
    return im.convert("""RGB"""				)






@dataclass
class        UpperCamelCase			:
 UpperCamelCase					:       Optional[str]			=					field(
     default=snake_case_		,     metadata={
         '''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
     }		,     )
 UpperCamelCase					:       Optional[str]			=					field(
     default=snake_case_		,     metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}      )
 UpperCamelCase					:       Optional[str]			=					field(default=snake_case_		,     metadata={'''help''': '''A folder containing the training data.'''}      )
 UpperCamelCase					:       Optional[str]			=					field(default=snake_case_		,     metadata={'''help''': '''A folder containing the validation data.'''}      )
 UpperCamelCase					:       Optional[float]			=					field(
     default=0.1_5		,     metadata={'''help''': '''Percent to split off of train for validation.'''}      )
 UpperCamelCase					:       Optional[int]			=					field(
     default=snake_case_		,     metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of training examples to this '''
             '''value if set.'''
         )
     }		,     )
 UpperCamelCase					:       Optional[int]			=					field(
     default=snake_case_		,     metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
             '''value if set.'''
         )
     }		,     )


 def _lowercase	(     self		:						Union[str, Any]							)					->       List[str]:
   if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
     raise ValueError(
         """You must specify either a dataset name from the hub or a train and/or validation directory."""							)







@dataclass
class        UpperCamelCase			:
 UpperCamelCase					:       str			=					field(
     default='''google/vit-base-patch16-224-in21k'''		,     metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}		,     )
 UpperCamelCase					:       Optional[str]			=					field(
     default=snake_case_		,     metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(snake_case_      )}		,     )
 UpperCamelCase					:       Optional[str]			=					field(
     default=snake_case_		,     metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}      )
 UpperCamelCase					:       Optional[str]			=					field(
     default=snake_case_		,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}      )
 UpperCamelCase					:       str			=					field(
     default='''main'''		,     metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}		,     )
 UpperCamelCase					:       str			=					field(default=snake_case_		,     metadata={'''help''': '''Name or path of preprocessor config.'''}      )
 UpperCamelCase					:       bool			=					field(
     default=snake_case_		,     metadata={
         '''help''': (
             '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
             '''with private models).'''
         )
     }		,     )
 UpperCamelCase					:       bool			=					field(
     default=snake_case_		,     metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}		,     )







def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Tuple             =	torch.stack([example["""pixel_values"""] for example in examples]				)
  _a  :       int             =	torch.tensor([example["""labels"""] for example in examples]				)
  return {"pixel_values": pixel_values, "labels": labels}

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.

  _a  :       Optional[Any]             =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
  if len(sys.argv				) == 2 and sys.argv[1].endswith(""".json"""				):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    _a      ,  _a      ,  _a  :       List[str]             =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
  else:
    _a      ,  _a      ,  _a  :       Any             =	parser.parse_args_into_dataclasses()

  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry("""run_image_classification"""     ,   UpperCamelCase__     ,   UpperCamelCase__				)

  # Setup logging
  logging.basicConfig(
      format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""     ,   datefmt="""%m/%d/%Y %H:%M:%S"""     ,   handlers=[logging.StreamHandler(sys.stdout				)]     ,   )

  if training_args.should_log:
    # The default of training_args.log_level is passive, so we set log level at info here to have that default.
    transformers.utils.logging.set_verbosity_info()

  _a  :       Dict             =	training_args.get_process_log_level()
  logger.setLevel(UpperCamelCase__				)
  transformers.utils.logging.set_verbosity(UpperCamelCase__				)
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()

  # Log on each process the small summary:
  logger.warning(
      F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
      + F"""distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}"""				)
  logger.info(F"""Training/evaluation parameters {training_args}"""				)

  # Detecting last checkpoint.
  _a  :       Optional[Any]             =	None
  if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
    _a  :       List[str]             =	get_last_checkpoint(training_args.output_dir				)
    if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
      raise ValueError(
          F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
          """Use --overwrite_output_dir to overcome."""				)
    elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
      logger.info(
          F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
          """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""				)

    # Set seed before initializing model.
  set_seed(training_args.seed				)

  # Initialize our dataset and prepare it for the 'image-classification' task.
  if data_args.dataset_name is not None:
    _a  :       List[str]             =	load_dataset(
        data_args.dataset_name     ,   data_args.dataset_config_name     ,   cache_dir=model_args.cache_dir     ,   task="""image-classification"""     ,   use_auth_token=True if model_args.use_auth_token else None     ,   )
  else:
    _a  :       Any             =	{}
    if data_args.train_dir is not None:
      _a  :       Union[str, Any]             =	os.path.join(data_args.train_dir     ,   """**"""				)
    if data_args.validation_dir is not None:
      _a  :       List[Any]             =	os.path.join(data_args.validation_dir     ,   """**"""				)
    _a  :       Dict             =	load_dataset(
        """imagefolder"""     ,   data_files=UpperCamelCase__     ,   cache_dir=model_args.cache_dir     ,   task="""image-classification"""     ,   )

  # If we don't have a validation split, split off a percentage of train as validation.
  _a  :       Optional[int]             =	None if """validation""" in dataset.keys() else data_args.train_val_split
  if isinstance(data_args.train_val_split     ,   UpperCamelCase__				) and data_args.train_val_split > 0.0:
    _a  :       int             =	dataset["""train"""].train_test_split(data_args.train_val_split				)
    _a  :       List[Any]             =	split["""train"""]
    _a  :       Any             =	split["""test"""]

  # Prepare label mappings.
  # We'll include these in the model's config to get human readable labels in the Inference API.
  _a  :       Dict             =	dataset["""train"""].features["""labels"""].names
  _a      ,  _a  :       List[Any]             =	{}, {}
  for i, label in enumerate(UpperCamelCase__				):
    _a  :       Optional[int]             =	str(UpperCamelCase__				)
    _a  :       List[Any]             =	label

  # Load the accuracy metric from the datasets package
  _a  :       str             =	evaluate.load("""accuracy"""				)

  # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
  # predictions and label_ids field) and has to return a dictionary string to float.
  def compute_metrics(UpperCamelCase__				):
    return metric.compute(predictions=np.argmax(p.predictions     ,   axis=1				)     ,   references=p.label_ids				)

  _a  :       Optional[Any]             =	AutoConfig.from_pretrained(
      model_args.config_name or model_args.model_name_or_path     ,   num_labels=len(UpperCamelCase__				)     ,   labelaid=UpperCamelCase__     ,   idalabel=UpperCamelCase__     ,   finetuning_task="""image-classification"""     ,   cache_dir=model_args.cache_dir     ,   revision=model_args.model_revision     ,   use_auth_token=True if model_args.use_auth_token else None     ,   )
  _a  :       Union[str, Any]             =	AutoModelForImageClassification.from_pretrained(
      model_args.model_name_or_path     ,   from_tf=bool(""".ckpt""" in model_args.model_name_or_path				)     ,   config=UpperCamelCase__     ,   cache_dir=model_args.cache_dir     ,   revision=model_args.model_revision     ,   use_auth_token=True if model_args.use_auth_token else None     ,   ignore_mismatched_sizes=model_args.ignore_mismatched_sizes     ,   )
  _a  :       Tuple             =	AutoImageProcessor.from_pretrained(
      model_args.image_processor_name or model_args.model_name_or_path     ,   cache_dir=model_args.cache_dir     ,   revision=model_args.model_revision     ,   use_auth_token=True if model_args.use_auth_token else None     ,   )

  # Define torchvision transforms to be applied to each image.
  if "shortest_edge" in image_processor.size:
    _a  :       Optional[int]             =	image_processor.size["""shortest_edge"""]
  else:
    _a  :       Tuple             =	(image_processor.size["""height"""], image_processor.size["""width"""])
  _a  :       Dict             =	Normalize(mean=image_processor.image_mean     ,   std=image_processor.image_std				)
  _a  :       Any             =	Compose(
      [
          RandomResizedCrop(UpperCamelCase__				),
          RandomHorizontalFlip(),
          ToTensor(),
          normalize,
      ]				)
  _a  :       Union[str, Any]             =	Compose(
      [
          Resize(UpperCamelCase__				),
          CenterCrop(UpperCamelCase__				),
          ToTensor(),
          normalize,
      ]				)

  def train_transforms(UpperCamelCase__				):
    _a  :       Any             =	[
        _train_transforms(pil_img.convert("""RGB"""				)				) for pil_img in example_batch["""image"""]
    ]
    return example_batch

  def val_transforms(UpperCamelCase__				):
    _a  :       Tuple             =	[_val_transforms(pil_img.convert("""RGB"""				)				) for pil_img in example_batch["""image"""]]
    return example_batch

  if training_args.do_train:
    if "train" not in dataset:
      raise ValueError("""--do_train requires a train dataset"""				)
    if data_args.max_train_samples is not None:
      _a  :       str             =	(
          dataset["""train"""].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
      )
    # Set the training transforms
    dataset["train"].set_transform(UpperCamelCase__				)

  if training_args.do_eval:
    if "validation" not in dataset:
      raise ValueError("""--do_eval requires a validation dataset"""				)
    if data_args.max_eval_samples is not None:
      _a  :       Optional[Any]             =	(
          dataset["""validation"""].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
      )
    # Set the validation transforms
    dataset["validation"].set_transform(UpperCamelCase__				)

  # Initalize our trainer
  _a  :       Tuple             =	Trainer(
      model=UpperCamelCase__     ,   args=UpperCamelCase__     ,   train_dataset=dataset["""train"""] if training_args.do_train else None     ,   eval_dataset=dataset["""validation"""] if training_args.do_eval else None     ,   compute_metrics=UpperCamelCase__     ,   tokenizer=UpperCamelCase__     ,   data_collator=UpperCamelCase__     ,   )

  # Training
  if training_args.do_train:
    _a  :       List[str]             =	None
    if training_args.resume_from_checkpoint is not None:
      _a  :       str             =	training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      _a  :       Any             =	last_checkpoint
    _a  :       Any             =	trainer.train(resume_from_checkpoint=UpperCamelCase__				)
    trainer.save_model()
    trainer.log_metrics("""train"""     ,   train_result.metrics				)
    trainer.save_metrics("""train"""     ,   train_result.metrics				)
    trainer.save_state()

  # Evaluation
  if training_args.do_eval:
    _a  :       Optional[Any]             =	trainer.evaluate()
    trainer.log_metrics("""eval"""     ,   UpperCamelCase__				)
    trainer.save_metrics("""eval"""     ,   UpperCamelCase__				)

  # Write model card and (optionally) push to hub
  _a  :       List[Any]             =	{
      """finetuned_from""": model_args.model_name_or_path,
      """tasks""": """image-classification""",
      """dataset""": data_args.dataset_name,
      """tags""": ["""image-classification""", """vision"""],
  }
  if training_args.push_to_hub:
    trainer.push_to_hub(**UpperCamelCase__				)
  else:
    trainer.create_model_card(**UpperCamelCase__				)


if __name__ == "__main__":
 main()









"""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()
_snake_case          =   logging.get_logger(__name__)

_snake_case          =   torch.device('cpu')

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  _a  :       int             =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  _a  :       Dict             =	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'''
  _a  :       Any             =	dct.pop(UpperCamelCase__				)
  _a  :       Dict             =	val

def 			lowerCAmelCase__     (     UpperCamelCase__				):



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

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  _a  :       Optional[int]             =	1_0_0_0
  _a  :       Optional[Any]             =	"""huggingface/label-files"""
  _a  :       Optional[Any]             =	"""imagenet-1k-id2label.json"""
  _a  :       List[str]             =	json.load(open(hf_hub_download(UpperCamelCase__     ,   UpperCamelCase__     ,   repo_type="""dataset"""				)     ,   """r"""				)				)
  _a  :       Optional[Any]             =	{int(UpperCamelCase__				): v for k, v in idalabel.items()}
  _a  :       Dict             =	idalabel
  _a  :       Optional[int]             =	{v: k for k, v in idalabel.items()}

  # size of the architecture
  if swiftformer_name == "swiftformer_xs":
    _a  :       Any             =	[3, 3, 6, 4]
    _a  :       int             =	[4_8, 5_6, 1_1_2, 2_2_0]

  elif swiftformer_name == "swiftformer_s":
    _a  :       Any             =	[3, 3, 9, 6]
    _a  :       List[str]             =	[4_8, 6_4, 1_6_8, 2_2_4]

  elif swiftformer_name == "swiftformer_l1":
    _a  :       List[Any]             =	[4, 3, 1_0, 5]
    _a  :       Optional[int]             =	[4_8, 9_6, 1_9_2, 3_8_4]

  elif swiftformer_name == "swiftformer_l3":
    _a  :       List[Any]             =	[4, 4, 1_2, 6]
    _a  :       Optional[Any]             =	[6_4, 1_2_8, 3_2_0, 5_1_2]

  # load state_dict of original model, remove and rename some keys
  if original_ckpt:
    if original_ckpt.startswith("""https"""				):
      _a  :       Tuple             =	torch.hub.load_state_dict_from_url(UpperCamelCase__     ,   map_location="""cpu"""     ,   check_hash=UpperCamelCase__				)
    else:
      _a  :       Dict             =	torch.load(UpperCamelCase__     ,   map_location="""cpu"""				)
  _a  :       int             =	checkpoint

  _a  :       Optional[Any]             =	create_rename_keys(UpperCamelCase__				)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)

  # load HuggingFace model
  _a  :       Any             =	SwiftFormerForImageClassification(UpperCamelCase__				).eval()
  hf_model.load_state_dict(UpperCamelCase__				)

  # prepare test inputs
  _a  :       Any             =	prepare_img()
  _a  :       Union[str, Any]             =	ViTImageProcessor.from_pretrained("""preprocessor_config"""				)
  _a  :       Optional[int]             =	processor(images=UpperCamelCase__     ,   return_tensors="""pt"""				)

  # compare outputs from both models
  _a  :       Dict             =	get_expected_output(UpperCamelCase__				)
  _a  :       int             =	hf_model(inputs["""pixel_values"""]				).logits

  assert hf_logits.shape == torch.Size([1, 1_0_0_0]				)
  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__":
 _snake_case          =   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.')

 _snake_case          =   parser.parse_args()
 convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)

