





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()
_lowerCamelCase			       =      logging.get_logger(__name__)

_lowerCamelCase			       =      torch.device('cpu')




def    __UpperCAmelCase(      ):
       _lowerCamelCase							:     Optional[Any]	=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       _lowerCamelCase							:     List[str]	=		Image.open(requests.get(lowercase_ ,    stream=lowercase_      ).raw      )
       return im




def    __UpperCAmelCase(      lowercase_      ):
       if swiftformer_name == "swiftformer_xs":
              return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]      )

       elif swiftformer_name == "swiftformer_s":
              return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]      )

       elif swiftformer_name == "swiftformer_l1":
              return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]      )

       elif swiftformer_name == "swiftformer_l3":
              return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     int	=		dct.pop(lowercase_      )
       _lowerCamelCase							:     Tuple	=		val




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




@torch.no_grad()
def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     Any	=		SwiftFormerConfig()

       # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
       _lowerCamelCase							:     Union[str, Any]	=		10_00
       _lowerCamelCase							:     Tuple	=		'''huggingface/label-files'''
       _lowerCamelCase							:     Optional[int]	=		'''imagenet-1k-id2label.json'''
       _lowerCamelCase							:     int	=		json.load(open(hf_hub_download(lowercase_ ,    lowercase_ ,    repo_type='''dataset'''      ) ,    '''r'''      )      )
       _lowerCamelCase							:     str	=		{int(lowercase_      ): v for k, v in idalabel.items()}
       _lowerCamelCase							:     str	=		idalabel
       _lowerCamelCase							:     str	=		{v: k for k, v in idalabel.items()}

       # size of the architecture
       if swiftformer_name == "swiftformer_xs":
              _lowerCamelCase							:     int	=		[3, 3, 6, 4]
              _lowerCamelCase							:     List[str]	=		[48, 56, 1_12, 2_20]

       elif swiftformer_name == "swiftformer_s":
              _lowerCamelCase							:     List[str]	=		[3, 3, 9, 6]
              _lowerCamelCase							:     List[Any]	=		[48, 64, 1_68, 2_24]

       elif swiftformer_name == "swiftformer_l1":
              _lowerCamelCase							:     Optional[int]	=		[4, 3, 10, 5]
              _lowerCamelCase							:     Any	=		[48, 96, 1_92, 3_84]

       elif swiftformer_name == "swiftformer_l3":
              _lowerCamelCase							:     str	=		[4, 4, 12, 6]
              _lowerCamelCase							:     Dict	=		[64, 1_28, 3_20, 5_12]

       # load state_dict of original model, remove and rename some keys
       if original_ckpt:
              if original_ckpt.startswith('''https'''      ):
                     _lowerCamelCase							:     Any	=		torch.hub.load_state_dict_from_url(lowercase_ ,    map_location='''cpu''' ,    check_hash=lowercase_      )
              else:
                     _lowerCamelCase							:     Tuple	=		torch.load(lowercase_ ,    map_location='''cpu'''      )
       _lowerCamelCase							:     Dict	=		checkpoint

       _lowerCamelCase							:     int	=		create_rename_keys(lowercase_      )
       for rename_key_src, rename_key_dest in rename_keys:
              rename_key(lowercase_ ,    lowercase_ ,    lowercase_      )

       # load HuggingFace model
       _lowerCamelCase							:     int	=		SwiftFormerForImageClassification(lowercase_      ).eval()
       hf_model.load_state_dict(lowercase_      )

       # prepare test inputs
       _lowerCamelCase							:     int	=		prepare_img()
       _lowerCamelCase							:     Tuple	=		ViTImageProcessor.from_pretrained('''preprocessor_config'''      )
       _lowerCamelCase							:     Optional[Any]	=		processor(images=lowercase_ ,    return_tensors='''pt'''      )

       # compare outputs from both models
       _lowerCamelCase							:     Dict	=		get_expected_output(lowercase_      )
       _lowerCamelCase							:     Optional[Any]	=		hf_model(inputs['''pixel_values''']      ).logits

       assert hf_logits.shape == torch.Size([1, 10_00]      )
       assert torch.allclose(hf_logits[0, 0:5] ,    lowercase_ ,    atol=1e-3      )

       Path(lowercase_      ).mkdir(exist_ok=lowercase_      )
       print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""      )
       hf_model.save_pretrained(lowercase_      )


if __name__ == "__main__":
       _lowerCamelCase			       =      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.')

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








import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


_lowerCamelCase			       =      logging.get_logger(__name__)







@dataclass
class 			__A       :




      """simple docstring"""







      UpperCAmelCase__						=      field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()  )}  )
      UpperCAmelCase__						=      field(
          metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}  )
      UpperCAmelCase__						=      field(
          default=128 ,metadata={
              """help""": (
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )
          } ,)
      UpperCAmelCase__						=      field(
          default=lowerCamelCase__ ,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		self.task_name.lower()







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      """train"""
      UpperCAmelCase__						=      """dev"""
      UpperCAmelCase__						=      """test"""







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      42
      UpperCAmelCase__						=      42
      UpperCAmelCase__						=      42


      def __init__(		self      ,							a__      ,							a__      ,							a__ = None      ,							a__ = Split.train      ,							a__ = None      ,							):



             """simple docstring"""





             warnings.warn(
                 '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
                 '''library. You can have a look at this example script for pointers: '''
                 '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''      ,							a__      ,							)
             _lowerCamelCase							:     Optional[Any]	=		args
             _lowerCamelCase							:     Tuple	=		glue_processors[args.task_name]()
             _lowerCamelCase							:     Any	=		glue_output_modes[args.task_name]
             if isinstance(a__      ,							a__):
                    try:
                           _lowerCamelCase							:     List[Any]	=		Split[mode]
                    except KeyError:
                           raise KeyError('''mode is not a valid split name''')
        # Load data features from cache or dataset file
             _lowerCamelCase							:     Tuple	=		os.path.join(
                 cache_dir if cache_dir is not None else args.data_dir      ,							F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""      ,							)
             _lowerCamelCase							:     int	=		self.processor.get_labels()
             if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
                 "RobertaTokenizer",
                 "RobertaTokenizerFast",
                 "XLMRobertaTokenizer",
                 "BartTokenizer",
                 "BartTokenizerFast",
             ):
                    # HACK(label indices are swapped in RoBERTa pretrained model)
                    _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		label_list[2], label_list[1]
             _lowerCamelCase							:     str	=		label_list

             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
             _lowerCamelCase							:     Any	=		cached_features_file + '''.lock'''
             with FileLock(a__):
                    if os.path.exists(a__) and not args.overwrite_cache:
                           _lowerCamelCase							:     Any	=		time.time()
                           _lowerCamelCase							:     Optional[Any]	=		torch.load(a__)
                           logger.info(
                               F"""Loading features from cached file {cached_features_file} [took %.3f s]"""      ,							time.time() - start)
                    else:
                           logger.info(F"""Creating features from dataset file at {args.data_dir}""")

                           if mode == Split.dev:
                                  _lowerCamelCase							:     List[str]	=		self.processor.get_dev_examples(args.data_dir)
                           elif mode == Split.test:
                                  _lowerCamelCase							:     str	=		self.processor.get_test_examples(args.data_dir)
                           else:
                                  _lowerCamelCase							:     List[Any]	=		self.processor.get_train_examples(args.data_dir)
                           if limit_length is not None:
                                  _lowerCamelCase							:     List[Any]	=		examples[:limit_length]
                           _lowerCamelCase							:     List[str]	=		glue_convert_examples_to_features(
                               a__      ,							a__      ,							max_length=args.max_seq_length      ,							label_list=a__      ,							output_mode=self.output_mode      ,							)
                           _lowerCamelCase							:     int	=		time.time()
                           torch.save(self.features      ,							a__)
                           # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                           logger.info(
                               F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]""")


      def __len__(		self):



             """simple docstring"""





             return len(self.features)


      def __getitem__(		self      ,							a__):



             """simple docstring"""





             return self.features[i]



      def 				__snake_case			(		self):



             """simple docstring"""





             return self.label_list

