






"""simple docstring"""
import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE







class      UpperCamelCase_			(__A	):
	__magic_name__       =							''''''
	__magic_name__       =							(
	    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
	)
	__magic_name__       =							None  # compression type in fsspec. ex: "gzip"
	__magic_name__       =							None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz





	def __init__( self   :					List[Any]      ,      lowerCAmelCase_   :					str = ""      ,      lowerCAmelCase_   :					Optional[str] = None      ,      lowerCAmelCase_   :					Optional[dict] = None      ,      **lowerCAmelCase_   :					List[str]		)		->     List[Any]:
				super().__init__(self      ,      **lowerCAmelCase_		)
				# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
				UpperCAmelCase_	:					Dict     	=   fsspec.open(
				    lowerCAmelCase_      ,      mode="rb"      ,      protocol=lowerCAmelCase_      ,      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_	:					Optional[int]     	=   os.path.basename(self.file.path.split("::"		)[0]		)
				UpperCAmelCase_	:					Any     	=   (
				    self.compressed_name[: self.compressed_name.rindex("."		)]
				    if "." in self.compressed_name
				    else self.compressed_name
				)
				UpperCAmelCase_	:					Any     	=   None





	@classmethod
	def 	_SCREAMING_SNAKE_CASE ( cls   :					Optional[Any]      ,      lowerCAmelCase_   :					List[str]		)		->     Any:
				# compressed file paths are always relative to the archive root
				return super()._strip_protocol(lowerCAmelCase_		).lstrip("/"		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     List[str]:
				if self.dir_cache is None:
							UpperCAmelCase_	:					Optional[Any]     	=   {**self.file.fs.info(self.file.path		), "name": self.uncompressed_name}
							UpperCAmelCase_	:					str     	=   {f["name"]: f}





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					str		)		->     Any:
				return self.file.open().read()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str = "rb"      ,      lowerCAmelCase_   :					List[str]=None      ,      lowerCAmelCase_   :					List[str]=True      ,      lowerCAmelCase_   :					int=None      ,      **lowerCAmelCase_   :					Tuple      ,      )		->     Tuple:
				UpperCAmelCase_	:					List[str]     	=   self._strip_protocol(lowerCAmelCase_		)
				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_			(__A	):
	__magic_name__       =							'''bz2'''
	__magic_name__       =							'''bz2'''
	__magic_name__       =							'''.bz2'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''gzip'''
	__magic_name__       =							'''gzip'''
	__magic_name__       =							'''.gz'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''lz4'''
	__magic_name__       =							'''lz4'''
	__magic_name__       =							'''.lz4'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''xz'''
	__magic_name__       =							'''xz'''
	__magic_name__       =							'''.xz'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''zstd'''
	__magic_name__       =							'''zstd'''
	__magic_name__       =							'''.zst'''





	def __init__( self   :					Tuple      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str = "rb"      ,      lowerCAmelCase_   :					Optional[str] = None      ,      lowerCAmelCase_   :					Optional[dict] = None      ,      lowerCAmelCase_   :					int = DEFAULT_BLOCK_SIZE      ,      **lowerCAmelCase_   :					List[Any]      ,      )		->     Dict:
				super().__init__(
				    fo=lowerCAmelCase_      ,      mode=lowerCAmelCase_      ,      target_protocol=lowerCAmelCase_      ,      target_options=lowerCAmelCase_      ,      block_size=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )
				# 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_	:					Optional[Any]     	=   self.file.__enter__







				class      UpperCamelCase_			:





					def __init__( self   :					Tuple      ,      lowerCAmelCase_   :					List[Any]		)		->     List[Any]:
								UpperCAmelCase_	:					Optional[int]     	=   file_





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





					def __exit__( self   :					Union[str, Any]      ,      *lowerCAmelCase_   :					int      ,      **lowerCAmelCase_   :					int		)		->     Optional[int]:
								self._file.__exit__(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





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





					def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     int:
								return next(self._file		)





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

				def fixed_enter(*lowerCAmelCase_   :					int      ,      **lowerCAmelCase_   :					Optional[Any]		):
							return WrappedFile(_enter(*lowerCAmelCase_      ,      **lowerCAmelCase_		)		)

				UpperCAmelCase_	:					List[Any]     	=   fixed_enter











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

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

def 							snake_case (  ):
			UpperCAmelCase_	:					str     	=   "http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCAmelCase_	:					str     	=   Image.open(requests.get(A__      ,stream=A__		).raw		)
			return im

def 							snake_case (  A__		):
			if swiftformer_name == "swiftformer_xs":
						return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]		)

			elif swiftformer_name == "swiftformer_s":
						return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]		)

			elif swiftformer_name == "swiftformer_l1":
						return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]		)

			elif swiftformer_name == "swiftformer_l3":
						return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]		)

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Tuple     	=   dct.pop(A__		)
			UpperCAmelCase_	:					Optional[Any]     	=   val

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

@torch.no_grad()
def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Optional[int]     	=   SwiftFormerConfig()

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

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

			elif swiftformer_name == "swiftformer_s":
						UpperCAmelCase_	:					Optional[Any]     	=   [3, 3, 9, 6]
						UpperCAmelCase_	:					Optional[Any]     	=   [48, 64, 1_68, 2_24]

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

			elif swiftformer_name == "swiftformer_l3":
						UpperCAmelCase_	:					Dict     	=   [4, 4, 12, 6]
						UpperCAmelCase_	:					Optional[int]     	=   [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"		):
									UpperCAmelCase_	:					List[Any]     	=   torch.hub.load_state_dict_from_url(A__      ,map_location="cpu"      ,check_hash=A__		)
						else:
									UpperCAmelCase_	:					Any     	=   torch.load(A__      ,map_location="cpu"		)
			UpperCAmelCase_	:					List[str]     	=   checkpoint

			UpperCAmelCase_	:					Dict     	=   create_rename_keys(A__		)
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(A__      ,A__      ,A__		)

			# load HuggingFace model
			UpperCAmelCase_	:					Optional[int]     	=   SwiftFormerForImageClassification(A__		).eval()
			hf_model.load_state_dict(A__		)

			# prepare test inputs
			UpperCAmelCase_	:					Tuple     	=   prepare_img()
			UpperCAmelCase_	:					int     	=   ViTImageProcessor.from_pretrained("preprocessor_config"		)
			UpperCAmelCase_	:					int     	=   processor(images=A__      ,return_tensors="pt"		)

			# compare outputs from both models
			UpperCAmelCase_	:					List[Any]     	=   get_expected_output(A__		)
			UpperCAmelCase_	:					int     	=   hf_model(inputs["pixel_values"]		).logits

			assert hf_logits.shape == torch.Size([1, 10_00]		)
			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__":
			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)



