



'''simple docstring'''




import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 			UpperCamelCase_ (							__magic_name__      ,     unittest.TestCase			):
	lowercase          = LongformerTokenizer
	lowercase          = True
	lowercase          = LongformerTokenizerFast
	lowercase          = True





	def  _lowercase(  self    )			->			List[Any]:
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								UpperCAmelCase     :		List[str]							     =		[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """\u0120""",
								    """\u0120l""",
								    """\u0120n""",
								    """\u0120lo""",
								    """\u0120low""",
								    """er""",
								    """\u0120lowest""",
								    """\u0120newer""",
								    """\u0120wider""",
								    """<unk>""",
								]
								UpperCAmelCase     :		int							     =		dict(zip(A		,		range(len(A    )    )    )    )
								UpperCAmelCase     :		Any							     =		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
								UpperCAmelCase     :		Dict							     =		{"""unk_token""": """<unk>"""}

								UpperCAmelCase     :		int							     =		os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES["""vocab_file"""]    )
								UpperCAmelCase     :		Optional[int]							     =		os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES["""merges_file"""]    )
								with open(self.vocab_file		,		"""w"""		,		encoding="""utf-8"""    ) as fp:
															fp.write(json.dumps(A    ) + """\n"""    )
								with open(self.merges_file		,		"""w"""		,		encoding="""utf-8"""    ) as fp:
															fp.write("""\n""".join(A    )    )





	def  _lowercase(  self		,		**A    )			->			Optional[Any]:
								kwargs.update(self.special_tokens_map    )
								return self.tokenizer_class.from_pretrained(self.tmpdirname		,		**A    )





	def  _lowercase(  self		,		**A    )			->			int:
								kwargs.update(self.special_tokens_map    )
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,		**A    )





	def  _lowercase(  self		,		A    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		"""lower newer"""
								UpperCAmelCase     :		Optional[int]							     =		"""lower newer"""
								return input_text, output_text





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Tuple							     =		self.tokenizer_class(self.vocab_file		,		self.merges_file		,		**self.special_tokens_map    )
								UpperCAmelCase     :		Dict							     =		"""lower newer"""
								UpperCAmelCase     :		int							     =		["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
								UpperCAmelCase     :		Tuple							     =		tokenizer.tokenize(A    )  # , add_prefix_space=True)
								self.assertListEqual(A		,		A    )

								UpperCAmelCase     :		Any							     =		tokens + [tokenizer.unk_token]
								UpperCAmelCase     :		Tuple							     =		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(A    )		,		A    )





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		str							     =		self.get_tokenizer()

								self.assertListEqual(tokenizer.encode("""Hello world!"""		,		add_special_tokens=A    )		,		[0, 31414, 232, 328, 2]    )
								self.assertListEqual(
								    tokenizer.encode("""Hello world! cécé herlolip 418"""		,		add_special_tokens=A    )		,		[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]		,		)





	@slow
	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Any							     =		self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""    )

								UpperCAmelCase     :		List[Any]							     =		tokenizer.encode("""sequence builders"""		,		add_special_tokens=A    )
								UpperCAmelCase     :		Optional[Any]							     =		tokenizer.encode("""multi-sequence build"""		,		add_special_tokens=A    )

								UpperCAmelCase     :		List[str]							     =		tokenizer.encode(
								    """sequence builders"""		,		add_special_tokens=A		,		add_prefix_space=A    )
								UpperCAmelCase     :		List[str]							     =		tokenizer.encode(
								    """sequence builders"""		,		"""multi-sequence build"""		,		add_special_tokens=A		,		add_prefix_space=A    )

								UpperCAmelCase     :		Union[str, Any]							     =		tokenizer.build_inputs_with_special_tokens(A    )
								UpperCAmelCase     :		Any							     =		tokenizer.build_inputs_with_special_tokens(A		,		A    )

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode





	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		str							     =		self.get_tokenizer()

								UpperCAmelCase     :		List[Any]							     =		"""Encode this sequence."""
								UpperCAmelCase     :		List[str]							     =		tokenizer.byte_encoder[""" """.encode("""utf-8"""    )[0]]

								# Testing encoder arguments
								UpperCAmelCase     :		Union[str, Any]							     =		tokenizer.encode(A		,		add_special_tokens=A		,		add_prefix_space=A    )
								UpperCAmelCase     :		Dict							     =		tokenizer.convert_ids_to_tokens(encoded[0]    )[0]
								self.assertNotEqual(A		,		A    )

								UpperCAmelCase     :		Tuple							     =		tokenizer.encode(A		,		add_special_tokens=A		,		add_prefix_space=A    )
								UpperCAmelCase     :		int							     =		tokenizer.convert_ids_to_tokens(encoded[0]    )[0]
								self.assertEqual(A		,		A    )

								tokenizer.add_special_tokens({"""bos_token""": """<s>"""}    )
								UpperCAmelCase     :		int							     =		tokenizer.encode(A		,		add_special_tokens=A    )
								UpperCAmelCase     :		List[Any]							     =		tokenizer.convert_ids_to_tokens(encoded[1]    )[0]
								self.assertNotEqual(A		,		A    )

								# Testing spaces after special tokens
								UpperCAmelCase     :		Union[str, Any]							     =		"""<mask>"""
								tokenizer.add_special_tokens(
								    {"""mask_token""": AddedToken(A		,		lstrip=A		,		rstrip=A    )}    )  # mask token has a left space
								UpperCAmelCase     :		str							     =		tokenizer.convert_tokens_to_ids(A    )

								UpperCAmelCase     :		Union[str, Any]							     =		"""Encode <mask> sequence"""
								UpperCAmelCase     :		Union[str, Any]							     =		"""Encode <mask>sequence"""

								UpperCAmelCase     :		Union[str, Any]							     =		tokenizer.encode(A    )
								UpperCAmelCase     :		Union[str, Any]							     =		encoded.index(A    )
								UpperCAmelCase     :		List[str]							     =		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]    )[0]
								self.assertEqual(A		,		A    )

								UpperCAmelCase     :		Tuple							     =		tokenizer.encode(A    )
								UpperCAmelCase     :		Optional[int]							     =		encoded.index(A    )
								UpperCAmelCase     :		Optional[int]							     =		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]    )[0]
								self.assertNotEqual(A		,		A    )





	def  _lowercase(  self    )			->			Optional[int]:
								pass





	def  _lowercase(  self    )			->			Any:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
																						UpperCAmelCase     :		str							     =		self.rust_tokenizer_class.from_pretrained(A		,		**A    )
																						UpperCAmelCase     :		int							     =		self.tokenizer_class.from_pretrained(A		,		**A    )
																						UpperCAmelCase     :		Dict							     =		"""A, <mask> AllenNLP sentence."""
																						UpperCAmelCase     :		Dict							     =		tokenizer_r.encode_plus(A		,		add_special_tokens=A		,		return_token_type_ids=A    )
																						UpperCAmelCase     :		Tuple							     =		tokenizer_p.encode_plus(A		,		add_special_tokens=A		,		return_token_type_ids=A    )

																						# token_type_ids should put 0 everywhere
																						self.assertEqual(sum(tokens_r["""token_type_ids"""]    )		,		sum(tokens_p["""token_type_ids"""]    )    )

																						# attention_mask should put 1 everywhere, so sum over length should be 1
																						self.assertEqual(
																						    sum(tokens_r["""attention_mask"""]    ) / len(tokens_r["""attention_mask"""]    )		,		sum(tokens_p["""attention_mask"""]    ) / len(tokens_p["""attention_mask"""]    )		,		)

																						UpperCAmelCase     :		List[Any]							     =		tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]    )
																						UpperCAmelCase     :		int							     =		tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]    )

																						# Rust correctly handles the space before the mask while python doesnt
																						self.assertSequenceEqual(tokens_p["""input_ids"""]		,		[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]    )
																						self.assertSequenceEqual(tokens_r["""input_ids"""]		,		[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]    )

																						self.assertSequenceEqual(
																						    A		,		["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )
																						self.assertSequenceEqual(
																						    A		,		["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )





	def  _lowercase(  self    )			->			List[Any]:
								for trim_offsets, add_prefix_space in itertools.product([True, False]		,		repeat=2    ):
															UpperCAmelCase     :		Optional[int]							     =		self.rust_tokenizer_class.from_pretrained(
															    self.tmpdirname		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )

															UpperCAmelCase     :		Optional[int]							     =		json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()    )
															UpperCAmelCase     :		Optional[int]							     =		json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()    )

															self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]		,		A    )

															self.assertEqual(post_processor_state["""add_prefix_space"""]		,		A    )
															self.assertEqual(post_processor_state["""trim_offsets"""]		,		A    )






	def  _lowercase(  self    )			->			Optional[Any]:
								# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
								# `trim_offsets`
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
																						UpperCAmelCase     :		Union[str, Any]							     =		"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																						UpperCAmelCase     :		int							     =		f'''{text_of_1_token} {text_of_1_token}'''

																						UpperCAmelCase     :		List[Any]							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		str							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(len(A    ) + 1, len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Optional[Any]							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		Dict							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(len(A    ) + 1, len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		int							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		List[Any]							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(len(A    ), len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Any							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		str							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(len(A    ), len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Optional[Any]							     =		f''' {text}'''

																						# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																						#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																						# )
																						# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																						# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																						# self.assertEqual(
																						#     encoding.offset_mapping[1],
																						#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																						# )

																						UpperCAmelCase     :		Any							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		str							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(1, 1 + len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(1 + len(A    ) + 1, 1 + len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Optional[int]							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		Union[str, Any]							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, 1 + len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(1 + len(A    ), 1 + len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Union[str, Any]							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		Optional[int]							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, 1 + len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(1 + len(A    ), 1 + len(A    ) + 1 + len(A    ))		,		)






'''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   :			List[Any]					  =    logging.get_logger(__name__)

a   :			Union[str, Any]					  =    torch.device("""cpu""")



def       __lowerCamelCase  (					)      ->			Any:
							UpperCAmelCase     :		Optional[Any]							     =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase     :		Dict							     =		Image.open(requests.get(_lowercase	,				stream=_lowercase		).raw		)
							return im



def       __lowerCamelCase  (					_lowercase		)      ->			Dict:
							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       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			str:
							UpperCAmelCase     :		Union[str, Any]							     =		dct.pop(_lowercase		)
							UpperCAmelCase     :		str							     =		val



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



@torch.no_grad()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		Optional[Any]							     =		SwiftFormerConfig()

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

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

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

							elif swiftformer_name == "swiftformer_l1":
														UpperCAmelCase     :		List[Any]							     =		[4, 3, 1_0, 5]
														UpperCAmelCase     :		Union[str, Any]							     =		[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														UpperCAmelCase     :		Any							     =		[4, 4, 1_2, 6]
														UpperCAmelCase     :		List[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"""		):
																					UpperCAmelCase     :		Dict							     =		torch.hub.load_state_dict_from_url(_lowercase	,				map_location="""cpu"""	,				check_hash=_lowercase		)
														else:
																					UpperCAmelCase     :		Dict							     =		torch.load(_lowercase	,				map_location="""cpu"""		)
							UpperCAmelCase     :		str							     =		checkpoint

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

							# load HuggingFace model
							UpperCAmelCase     :		str							     =		SwiftFormerForImageClassification(_lowercase		).eval()
							hf_model.load_state_dict(_lowercase		)

							# prepare test inputs
							UpperCAmelCase     :		Any							     =		prepare_img()
							UpperCAmelCase     :		List[Any]							     =		ViTImageProcessor.from_pretrained("""preprocessor_config"""		)
							UpperCAmelCase     :		List[str]							     =		processor(images=_lowercase	,				return_tensors="""pt"""		)

							# compare outputs from both models
							UpperCAmelCase     :		List[str]							     =		get_expected_output(_lowercase		)
							UpperCAmelCase     :		Dict							     =		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]	,				_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__":
							a   :			Tuple					  =    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   :			Union[str, Any]					  =    parser.parse_args()
							convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)

