
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  lowerCAmelCase__			(  )				->		str:
			lowerCAmelCase__      :      int         =		'http://images.cocodataset.org/val2017/000000039769.jpg'
			lowerCAmelCase__      :      Tuple         =		Image.open(requests.get(_SCREAMING_SNAKE_CASE       ,							stream=_SCREAMING_SNAKE_CASE     ).raw     )
			return im
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		str:
			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  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		List[str]:
			lowerCAmelCase__      :      int         =		dct.pop(_SCREAMING_SNAKE_CASE     )
			lowerCAmelCase__      :      Tuple         =		val
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Dict:
			lowerCAmelCase__      :      Dict         =		[]
			for k in state_dict.keys():
						lowerCAmelCase__      :      List[Any]         =		k
						if ".pwconv" in k:
									lowerCAmelCase__      :      List[Any]         =		k_new.replace('.pwconv'       ,							'.point_wise_conv'     )
						if ".dwconv" in k:
									lowerCAmelCase__      :      Dict         =		k_new.replace('.dwconv'       ,							'.depth_wise_conv'     )
						if ".Proj." in k:
									lowerCAmelCase__      :      Optional[int]         =		k_new.replace('.Proj.'       ,							'.proj.'     )
						if "patch_embed" in k_new:
									lowerCAmelCase__      :      List[Any]         =		k_new.replace('patch_embed'       ,							'swiftformer.patch_embed.patch_embedding'     )
						if "network" in k_new:
									lowerCAmelCase__      :      Union[str, Any]         =		k_new.split('.'     )
									if ls[2].isdigit():
												lowerCAmelCase__      :      Union[str, Any]         =		'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]     )
									else:
												lowerCAmelCase__      :      Union[str, Any]         =		k_new.replace('network'       ,							'swiftformer.encoder.network'     )
						rename_keys.append((k, k_new)     )
			return rename_keys
@torch.no_grad()
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Optional[Any]:
			lowerCAmelCase__      :      Union[str, Any]         =		SwiftFormerConfig()

			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			lowerCAmelCase__      :      List[str]         =		1_000
			lowerCAmelCase__      :      Tuple         =		'huggingface/label-files'
			lowerCAmelCase__      :      str         =		'imagenet-1k-id2label.json'
			lowerCAmelCase__      :      Dict         =		json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							repo_type='dataset'     )       ,							'r'     )     )
			lowerCAmelCase__      :      Optional[int]         =		{int(_SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
			lowerCAmelCase__      :      Any         =		idalabel
			lowerCAmelCase__      :      str         =		{v: k for k, v in idalabel.items()}

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

			elif swiftformer_name == "swiftformer_s":
						lowerCAmelCase__      :      Dict         =		[3, 3, 9, 6]
						lowerCAmelCase__      :      List[str]         =		[48, 64, 168, 224]

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

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

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

			lowerCAmelCase__      :      Dict         =		create_rename_keys(_SCREAMING_SNAKE_CASE     )
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE     )

			# load HuggingFace model
			lowerCAmelCase__      :      Tuple         =		SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE     ).eval()
			hf_model.load_state_dict(_SCREAMING_SNAKE_CASE     )

			# prepare test inputs
			lowerCAmelCase__      :      Tuple         =		prepare_img()
			lowerCAmelCase__      :      str         =		ViTImageProcessor.from_pretrained('preprocessor_config'     )
			lowerCAmelCase__      :      Tuple         =		processor(images=_SCREAMING_SNAKE_CASE       ,							return_tensors='pt'     )

			# compare outputs from both models
			lowerCAmelCase__      :      List[Any]         =		get_expected_output(_SCREAMING_SNAKE_CASE     )
			lowerCAmelCase__      :      Dict         =		hf_model(inputs['pixel_values']     ).logits

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

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


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)



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowercase	:						str       =	{
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						str       =	[
	    'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecAudioForAudioFrameClassification',
	    'Data2VecAudioForCTC',
	    'Data2VecAudioForSequenceClassification',
	    'Data2VecAudioForXVector',
	    'Data2VecAudioModel',
	    'Data2VecAudioPreTrainedModel',
	]
	__lowercase	:						Tuple       =	[
	    'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecTextForCausalLM',
	    'Data2VecTextForMaskedLM',
	    'Data2VecTextForMultipleChoice',
	    'Data2VecTextForQuestionAnswering',
	    'Data2VecTextForSequenceClassification',
	    'Data2VecTextForTokenClassification',
	    'Data2VecTextModel',
	    'Data2VecTextPreTrainedModel',
	]
	__lowercase	:						Dict       =	[
	    'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecVisionForImageClassification',
	    'Data2VecVisionForMaskedImageModeling',
	    'Data2VecVisionForSemanticSegmentation',
	    'Data2VecVisionModel',
	    'Data2VecVisionPreTrainedModel',
	]

if is_tf_available():
	__lowercase	:						Optional[Any]       =	[
	    'TFData2VecVisionForImageClassification',
	    'TFData2VecVisionForSemanticSegmentation',
	    'TFData2VecVisionModel',
	    'TFData2VecVisionPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
	from .configuration_dataavec_text import (
	    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DataaVecTextConfig,
	    DataaVecTextOnnxConfig,
	)
	from .configuration_dataavec_vision import (
	    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DataaVecVisionConfig,
	    DataaVecVisionOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_dataavec_audio import (
		    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecAudioForAudioFrameClassification,
		    DataaVecAudioForCTC,
		    DataaVecAudioForSequenceClassification,
		    DataaVecAudioForXVector,
		    DataaVecAudioModel,
		    DataaVecAudioPreTrainedModel,
		)
		from .modeling_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecTextForCausalLM,
		    DataaVecTextForMaskedLM,
		    DataaVecTextForMultipleChoice,
		    DataaVecTextForQuestionAnswering,
		    DataaVecTextForSequenceClassification,
		    DataaVecTextForTokenClassification,
		    DataaVecTextModel,
		    DataaVecTextPreTrainedModel,
		)
		from .modeling_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecVisionForImageClassification,
		    DataaVecVisionForMaskedImageModeling,
		    DataaVecVisionForSemanticSegmentation,
		    DataaVecVisionModel,
		    DataaVecVisionPreTrainedModel,
		)
	if is_tf_available():
		from .modeling_tf_dataavec_vision import (
		    TFDataaVecVisionForImageClassification,
		    TFDataaVecVisionForSemanticSegmentation,
		    TFDataaVecVisionModel,
		    TFDataaVecVisionPreTrainedModel,
		)

else:
	import sys

	__lowercase	:						int       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



