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

SCREAMING_SNAKE_CASE_    					=						torch.device('''cpu''')




def     lowercase__						(					) ->      Optional[int]:






    """simple docstring"""




    UpperCAmelCase									=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase									=					Image.open(requests.get(a_ ,				stream=a_ ).raw )
    return im




def     lowercase__						(					lowerCAmelCase				:					Any ) ->      Tuple:






    """simple docstring"""




    if swiftformer_name == "swiftformer_xs":
        return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1] )

    elif swiftformer_name == "swiftformer_s":
        return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1] )

    elif swiftformer_name == "swiftformer_l1":
        return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2] )

    elif swiftformer_name == "swiftformer_l3":
        return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2] )




def     lowercase__						(					lowerCAmelCase				:					Any ,				lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Any ) ->      Optional[Any]:






    """simple docstring"""




    UpperCAmelCase									=					dct.pop(a_ )
    UpperCAmelCase									=					val




def     lowercase__						(					lowerCAmelCase				:					List[str] ) ->      List[str]:






    """simple docstring"""




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




@torch.no_grad()
def     lowercase__						(					lowerCAmelCase				:					str ,				lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					int ) ->      Any:






    """simple docstring"""




    UpperCAmelCase									=					SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    UpperCAmelCase									=					1_000
    UpperCAmelCase									=					'''huggingface/label-files'''
    UpperCAmelCase									=					'''imagenet-1k-id2label.json'''
    UpperCAmelCase									=					json.load(open(hf_hub_download(a_ ,				a_ ,				repo_type='dataset' ) ,				'r' ) )
    UpperCAmelCase									=					{int(a_ ): v for k, v in idalabel.items()}
    UpperCAmelCase									=					idalabel
    UpperCAmelCase									=					{v: k for k, v in idalabel.items()}

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

    elif swiftformer_name == "swiftformer_s":
        UpperCAmelCase									=					[3, 3, 9, 6]
        UpperCAmelCase									=					[48, 64, 168, 224]

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

    elif swiftformer_name == "swiftformer_l3":
        UpperCAmelCase									=					[4, 4, 12, 6]
        UpperCAmelCase									=					[64, 128, 320, 512]

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

    UpperCAmelCase									=					create_rename_keys(a_ )
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(a_ ,				a_ ,				a_ )

    # load HuggingFace model
    UpperCAmelCase									=					SwiftFormerForImageClassification(a_ ).eval()
    hf_model.load_state_dict(a_ )

    # prepare test inputs
    UpperCAmelCase									=					prepare_img()
    UpperCAmelCase									=					ViTImageProcessor.from_pretrained('preprocessor_config' )
    UpperCAmelCase									=					processor(images=a_ ,				return_tensors='pt' )

    # compare outputs from both models
    UpperCAmelCase									=					get_expected_output(a_ )
    UpperCAmelCase									=					hf_model(inputs['pixel_values'] ).logits

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

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


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

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





"""simple docstring"""

import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							str							      =	""
      __SCREAMING_SNAKE_CASE   :							str							      =	(
          None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
      )
      __SCREAMING_SNAKE_CASE   :							str							      =	None  # compression type in fsspec. ex: "gzip"
      __SCREAMING_SNAKE_CASE   :							str							      =	None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz




      def __init__(							self   ,       lowercase_ = ""   ,       lowercase_ = None   ,       lowercase_ = None   ,       **lowercase_							)						->					str:
          super().__init__(self   ,       **lowercase_							)
          # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
          UpperCAmelCase									=					fsspec.open(
              lowercase_   ,       mode='rb'   ,       protocol=lowercase_   ,       compression=self.compression   ,       client_kwargs={
                  'requote_redirect_url': False,  # see https://github.com/huggingface/datasets/pull/5459
                  'trust_env': True,  # Enable reading proxy env variables.
                  **(target_options or {}).pop('client_kwargs'   ,       {}							),  # To avoid issues if it was already passed.
              }   ,       **(target_options or {})   ,       )
          UpperCAmelCase									=					os.path.basename(self.file.path.split('::'							)[0]							)
          UpperCAmelCase									=					(
              self.compressed_name[: self.compressed_name.rindex('.'							)]
              if '.' in self.compressed_name
              else self.compressed_name
          )
          UpperCAmelCase									=					None




      @classmethod
      def     a_      (							cls   ,       lowercase_							)						->					Union[str, Any]:
          # compressed file paths are always relative to the archive root
          return super()._strip_protocol(lowercase_							).lstrip('/'							)




      def     a_      (							self							)						->					int:
          if self.dir_cache is None:
              UpperCAmelCase									=					{**self.file.fs.info(self.file.path							), 'name': self.uncompressed_name}
              UpperCAmelCase									=					{f['name']: f}




      def     a_      (							self   ,       lowercase_							)						->					Any:
          return self.file.open().read()




      def     a_      (							self   ,       lowercase_   ,       lowercase_ = "rb"   ,       lowercase_=None   ,       lowercase_=True   ,       lowercase_=None   ,       **lowercase_   ,       )						->					Optional[Any]:
          UpperCAmelCase									=					self._strip_protocol(lowercase_							)
          if mode != "rb":
              raise ValueError(F"Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"							)
          return self.file.open()

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	"bz2"
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	"bz2"
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	".bz2"

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	"gzip"
      __SCREAMING_SNAKE_CASE   :							Any							      =	"gzip"
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	".gz"

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	"lz4"
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	"lz4"
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	".lz4"

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	"xz"
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	"xz"
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	".xz"

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	"zstd"
      __SCREAMING_SNAKE_CASE   :							int							      =	"zstd"
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	".zst"




      def __init__(							self   ,       lowercase_   ,       lowercase_ = "rb"   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = DEFAULT_BLOCK_SIZE   ,       **lowercase_   ,       )						->					str:
          super().__init__(
              fo=lowercase_   ,       mode=lowercase_   ,       target_protocol=lowercase_   ,       target_options=lowercase_   ,       block_size=lowercase_   ,       **lowercase_   ,       )
          # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
          #
          # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
          #     out.close = close
          # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
          #
          # see https://github.com/intake/filesystem_spec/issues/725
          UpperCAmelCase									=					self.file.__enter__
          class      _UpperCAmelCase							:




                def __init__(							self   ,       lowercase_							)						->					List[Any]:
                    UpperCAmelCase									=					file_




                def __enter__(							self							)						->					List[str]:
                    self._file.__enter__()
                    return self




                def __exit__(							self   ,       *lowercase_   ,       **lowercase_							)						->					List[str]:
                    self._file.__exit__(*lowercase_   ,       **lowercase_							)




                def __iter__(							self							)						->					Optional[int]:
                    return iter(self._file							)




                def     a_      (							self							)						->					Tuple:
                    return next(self._file							)




                def __getattr__(							self   ,       lowercase_							)						->					Optional[int]:
                    return getattr(self._file   ,       lowercase_							)

          def fixed_enter(*lowercase_   ,       **lowercase_							):
              return WrappedFile(_enter(*lowercase_   ,       **lowercase_							)							)

          UpperCAmelCase									=					fixed_enter



