






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__	:			Optional[Any]			   = logging.get_logger(__name__)

A__	:			Union[str, Any]			   = torch.device('cpu')






def   a			(						):






				'''simple docstring'''
				lowercase__    =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				lowercase__    =			Image.open(requests.get(lowerCamelCase_		,   stream=lowerCamelCase_				).raw				)
				return im






def   a			(						lowerCamelCase_				):






				'''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   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			dct.pop(lowerCamelCase_				)
				lowercase__    =			val






def   a			(						lowerCamelCase_				):






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






@torch.no_grad()
def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			SwiftFormerConfig()

				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				lowercase__    =			1000
				lowercase__    =			'''huggingface/label-files'''
				lowercase__    =			'''imagenet-1k-id2label.json'''
				lowercase__    =			json.load(open(hf_hub_download(lowerCamelCase_		,   lowerCamelCase_		,   repo_type='''dataset'''				)		,   '''r'''				)				)
				lowercase__    =			{int(lowerCamelCase_				): v for k, v in idalabel.items()}
				lowercase__    =			idalabel
				lowercase__    =			{v: k for k, v in idalabel.items()}

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

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

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

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

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

				lowercase__    =			create_rename_keys(lowerCamelCase_				)
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				)

				# load HuggingFace model
				lowercase__    =			SwiftFormerForImageClassification(lowerCamelCase_				).eval()
				hf_model.load_state_dict(lowerCamelCase_				)

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

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

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

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


if __name__ == "__main__":
							A__	:			Union[str, Any]			   = 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__	:			Dict			   = parser.parse_args()
							convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)












from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
							import timm


if is_torch_available():
							from torch import Tensor
class       _UpperCAmelCase					(       A__		,A__	):

				"""simple docstring"""




				lowercase__											=       """pixel_values"""
				lowercase__											=       False
				lowercase__											=       TimmBackboneConfig




				def __init__(     self						:    Tuple,  lowerCamelCase						:    List[str],  **lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								requires_backends(self,  '''timm'''  )
								super().__init__(lowerCamelCase  )
								lowercase__    =			config

								if config.backbone is None:
												raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''  )

								if config.backbone not in timm.list_models():
												raise ValueError(F"""backbone {config.backbone} is not supported by timm."""  )

								if hasattr(lowerCamelCase,  '''out_features'''  ) and config.out_features is not None:
												raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''  )

								lowercase__    =			getattr(lowerCamelCase,  '''use_pretrained_backbone''',  lowerCamelCase  )
								if pretrained is None:
												raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''  )

								# We just take the final layer by default. This matches the default for the transformers models.
								lowercase__    =			config.out_indices if getattr(lowerCamelCase,  '''out_indices''',  lowerCamelCase  ) is not None else (-1,)

								lowercase__    =			timm.create_model(
								    config.backbone,  pretrained=lowerCamelCase,  features_only=config.features_only,  in_chans=config.num_channels,  out_indices=lowerCamelCase,  **lowerCamelCase,  )
								# These are used to control the output of the model when called. If output_hidden_states is True, then
								# return_layers is modified to include all layers.
								lowercase__    =			self._backbone.return_layers
								lowercase__    =			{layer['''module''']: str(lowerCamelCase  ) for i, layer in enumerate(self._backbone.feature_info.info  )}
								super()._init_backbone(lowerCamelCase  )




				@classmethod
				def 				lowercase__   (     cls						:    List[str],  lowerCamelCase						:    List[str],  *lowerCamelCase						:    Optional[int],  **lowerCamelCase						:    List[Any]  ):

								'''simple docstring'''
								requires_backends(cls,  ['''vision''', '''timm''']  )
								from ...models.timm_backbone import TimmBackboneConfig

								lowercase__    =			kwargs.pop('''config''',  TimmBackboneConfig()  )

								lowercase__    =			kwargs.pop('''use_timm_backbone''',  lowerCamelCase  )
								if not use_timm:
												raise ValueError('''use_timm_backbone must be True for timm backbones'''  )

								lowercase__    =			kwargs.pop('''num_channels''',  config.num_channels  )
								lowercase__    =			kwargs.pop('''features_only''',  config.features_only  )
								lowercase__    =			kwargs.pop('''use_pretrained_backbone''',  config.use_pretrained_backbone  )
								lowercase__    =			kwargs.pop('''out_indices''',  config.out_indices  )
								lowercase__    =			TimmBackboneConfig(
								    backbone=lowerCamelCase,  num_channels=lowerCamelCase,  features_only=lowerCamelCase,  use_pretrained_backbone=lowerCamelCase,  out_indices=lowerCamelCase,  )
								return super()._from_config(lowerCamelCase,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    Optional[Any]  ):

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    Optional[int]=None,  lowerCamelCase						:    List[Any]=None,  lowerCamelCase						:    int=None,  **lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			return_dict if return_dict is not None else self.config.use_return_dict
								lowercase__    =			(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								lowercase__    =			output_attentions if output_attentions is not None else self.config.output_attentions

								if output_attentions:
												raise ValueError('''Cannot output attentions for timm backbones at the moment'''  )

								if output_hidden_states:
												# We modify the return layers to include all the stages of the backbone
												lowercase__    =			self._all_layers
												lowercase__    =			self._backbone(lowerCamelCase,  **lowerCamelCase  )
												lowercase__    =			self._return_layers
												lowercase__    =			tuple(hidden_states[i] for i in self.out_indices  )
								else:
												lowercase__    =			self._backbone(lowerCamelCase,  **lowerCamelCase  )
												lowercase__    =			None

								lowercase__    =			tuple(lowerCamelCase  )
								lowercase__    =			tuple(lowerCamelCase  ) if hidden_states is not None else None

								if not return_dict:
												lowercase__    =			(feature_maps,)
												if output_hidden_states:
																lowercase__    =			output + (hidden_states,)
												return output

								return BackboneOutput(feature_maps=lowerCamelCase,  hidden_states=lowerCamelCase,  attentions=lowerCamelCase  )




