






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()
UpperCAmelCase_ :						Optional[int]      =      logging.get_logger(__name__)

UpperCAmelCase_ :						List[str]      =      torch.device('''cpu''')

def    SCREAMING_SNAKE_CASE_							(							)		->			List[Any]:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	UpperCamelCase     :Any          =	Image.open(requests.get(__magic_name__	,			stream=__magic_name__					).raw					)
	return im

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Tuple					)		->			Dict:






	"""simple docstring"""



	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    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  str	,			__magic_name__					:  Dict	,			__magic_name__					:  Union[str, Any]					)		->			Tuple:






	"""simple docstring"""



	UpperCamelCase     :List[Any]          =	dct.pop(__magic_name__					)
	UpperCamelCase     :Optional[Any]          =	val

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Tuple					)		->			Optional[int]:






	"""simple docstring"""



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

@torch.no_grad()
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[Any]	,			__magic_name__					:  Dict	,			__magic_name__					:  List[str]					)		->			Optional[int]:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	SwiftFormerConfig()

	# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
	UpperCamelCase     :str          =	1000
	UpperCamelCase     :Optional[int]          =	"""huggingface/label-files"""
	UpperCamelCase     :int          =	"""imagenet-1k-id2label.json"""
	UpperCamelCase     :List[Any]          =	json.load(open(hf_hub_download(__magic_name__	,			__magic_name__	,			repo_type="""dataset"""					)	,			"""r"""					)					)
	UpperCamelCase     :Optional[Any]          =	{int(__magic_name__					): v for k, v in idalabel.items()}
	UpperCamelCase     :Any          =	idalabel
	UpperCamelCase     :Dict          =	{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     :Union[str, Any]          =	[48, 56, 112, 220]

	elif swiftformer_name == "swiftformer_s":
		UpperCamelCase     :Optional[Any]          =	[3, 3, 9, 6]
		UpperCamelCase     :Dict          =	[48, 64, 168, 224]

	elif swiftformer_name == "swiftformer_l1":
		UpperCamelCase     :Optional[int]          =	[4, 3, 10, 5]
		UpperCamelCase     :str          =	[48, 96, 192, 384]

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

	# load state_dict of original model, remove and rename some keys
	if original_ckpt:
		if original_ckpt.startswith("""https"""					):
			UpperCamelCase     :Optional[Any]          =	torch.hub.load_state_dict_from_url(__magic_name__	,			map_location="""cpu"""	,			check_hash=__magic_name__					)
		else:
			UpperCamelCase     :List[str]          =	torch.load(__magic_name__	,			map_location="""cpu"""					)
	UpperCamelCase     :Optional[int]          =	checkpoint

	UpperCamelCase     :Optional[int]          =	create_rename_keys(__magic_name__					)
	for rename_key_src, rename_key_dest in rename_keys:
		rename_key(__magic_name__	,			__magic_name__	,			__magic_name__					)

	# load HuggingFace model
	UpperCamelCase     :Tuple          =	SwiftFormerForImageClassification(__magic_name__					).eval()
	hf_model.load_state_dict(__magic_name__					)

	# prepare test inputs
	UpperCamelCase     :List[Any]          =	prepare_img()
	UpperCamelCase     :int          =	ViTImageProcessor.from_pretrained("""preprocessor_config"""					)
	UpperCamelCase     :int          =	processor(images=__magic_name__	,			return_tensors="""pt"""					)

	# compare outputs from both models
	UpperCamelCase     :str          =	get_expected_output(__magic_name__					)
	UpperCamelCase     :Optional[int]          =	hf_model(inputs["""pixel_values"""]					).logits

	assert hf_logits.shape == torch.Size([1, 1000]					)
	assert torch.allclose(hf_logits[0, 0:5]	,			__magic_name__	,			atol=1E-3					)

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


if __name__ == "__main__":
	UpperCAmelCase_ :						Optional[int]      =      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.''')

	UpperCAmelCase_ :						Union[str, Any]      =      parser.parse_args()
	convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)








from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					List[Any] ,  __lowerCamelCase				:					Callable ,  __lowerCamelCase				:					Optional[Features] = None ,  __lowerCamelCase				:					str = None ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					Optional[dict] = None ,  __lowerCamelCase				:					Optional[int] = None ,  **__lowerCamelCase				:					List[Any] ,  ):
		super().__init__(
		    features=__lowerCamelCase ,  cache_dir=__lowerCamelCase ,  keep_in_memory=__lowerCamelCase ,  streaming=__lowerCamelCase ,  num_proc=__lowerCamelCase ,  **__lowerCamelCase ,  )
		UpperCamelCase     :Union[str, Any]          =	Generator(
		    cache_dir=__lowerCamelCase ,  features=__lowerCamelCase ,  generator=__lowerCamelCase ,  gen_kwargs=__lowerCamelCase ,  **__lowerCamelCase ,  )





	def 						_A (		self				:					List[str]	):
		# Build iterable dataset
		if self.streaming:
			UpperCamelCase     :Any          =	self.builder.as_streaming_dataset(split="""train"""	)
		# Build regular (map-style) dataset
		else:
			UpperCamelCase     :Tuple          =	None
			UpperCamelCase     :Dict          =	None
			UpperCamelCase     :Dict          =	None
			UpperCamelCase     :List[str]          =	None

			self.builder.download_and_prepare(
			    download_config=__lowerCamelCase ,  download_mode=__lowerCamelCase ,  verification_mode=__lowerCamelCase ,  base_path=__lowerCamelCase ,  num_proc=self.num_proc ,  )
			UpperCamelCase     :Tuple          =	self.builder.as_dataset(
			    split="""train""" ,  verification_mode=__lowerCamelCase ,  in_memory=self.keep_in_memory	)
		return dataset
