

'''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 						SCREAMING_SNAKE_CASE(  )    ->    Optional[Any]:
	UpperCAmelCase_		:      List[str]       	=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	UpperCAmelCase_		:      Optional[Any]       	=					Image.open(requests.get(snake_case_   ,stream=snake_case_  ).raw  )
	return im







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Tuple:
	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(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Optional[int]:
	UpperCAmelCase_		:      Tuple       	=					dct.pop(snake_case_  )
	UpperCAmelCase_		:      Optional[int]       	=					val







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    int:
	UpperCAmelCase_		:      int       	=					[]
	for k in state_dict.keys():
		UpperCAmelCase_		:      Dict       	=					k
		if ".pwconv" in k:
			UpperCAmelCase_		:      Dict       	=					k_new.replace('.pwconv'   ,'.point_wise_conv'  )
		if ".dwconv" in k:
			UpperCAmelCase_		:      int       	=					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_		:      str       	=					k_new.replace('patch_embed'   ,'swiftformer.patch_embed.patch_embedding'  )
		if "network" in k_new:
			UpperCAmelCase_		:      Optional[int]       	=					k_new.split('.'  )
			if ls[2].isdigit():
				UpperCAmelCase_		:      Any       	=					"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]  )
			else:
				UpperCAmelCase_		:      int       	=					k_new.replace('network'   ,'swiftformer.encoder.network'  )
		rename_keys.append((k, k_new)  )
	return rename_keys







@torch.no_grad()
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    int:
	UpperCAmelCase_		:      List[Any]       	=					SwiftFormerConfig()

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

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

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

	elif swiftformer_name == "swiftformer_l1":
		UpperCAmelCase_		:      Optional[Any]       	=					[4, 3, 1_0, 5]
		UpperCAmelCase_		:      Dict       	=					[4_8, 9_6, 1_9_2, 3_8_4]

	elif swiftformer_name == "swiftformer_l3":
		UpperCAmelCase_		:      List[str]       	=					[4, 4, 1_2, 6]
		UpperCAmelCase_		:      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_		:      Any       	=					torch.hub.load_state_dict_from_url(snake_case_   ,map_location='cpu'   ,check_hash=snake_case_  )
		else:
			UpperCAmelCase_		:      Union[str, Any]       	=					torch.load(snake_case_   ,map_location='cpu'  )
	UpperCAmelCase_		:      List[str]       	=					checkpoint

	UpperCAmelCase_		:      List[Any]       	=					create_rename_keys(snake_case_  )
	for rename_key_src, rename_key_dest in rename_keys:
		rename_key(snake_case_   ,snake_case_   ,snake_case_  )

	# load HuggingFace model
	UpperCAmelCase_		:      Union[str, Any]       	=					SwiftFormerForImageClassification(snake_case_  ).eval()
	hf_model.load_state_dict(snake_case_  )

	# prepare test inputs
	UpperCAmelCase_		:      Tuple       	=					prepare_img()
	UpperCAmelCase_		:      Dict       	=					ViTImageProcessor.from_pretrained('preprocessor_config'  )
	UpperCAmelCase_		:      Optional[Any]       	=					processor(images=snake_case_   ,return_tensors='pt'  )

	# compare outputs from both models
	UpperCAmelCase_		:      Optional[int]       	=					get_expected_output(snake_case_  )
	UpperCAmelCase_		:      Union[str, Any]       	=					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]   ,snake_case_   ,atol=1e-3  )

	Path(snake_case_  ).mkdir(exist_ok=snake_case_  )
	print(f"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""  )
	hf_model.save_pretrained(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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}



class 					lowercase						(  a_,					a_ ):
							_lowerCamelCase		:      Optional[int]=   "swin"

							_lowerCamelCase		:      Dict=   {
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(			self    ,			_snake_case=224    ,			_snake_case=4    ,			_snake_case=3    ,			_snake_case=96    ,			_snake_case=[2, 2, 6, 2]    ,			_snake_case=[3, 6, 12, 24]    ,			_snake_case=7    ,			_snake_case=4.0    ,			_snake_case=True    ,			_snake_case=0.0    ,			_snake_case=0.0    ,			_snake_case=0.1    ,			_snake_case="gelu"    ,			_snake_case=False    ,			_snake_case=0.02    ,			_snake_case=1e-5    ,			_snake_case=32    ,			_snake_case=None    ,			_snake_case=None    ,			**_snake_case    ,			)							->				Tuple:
								super().__init__(**_snake_case)

								UpperCAmelCase_		:      Any       	=					image_size
								UpperCAmelCase_		:      Dict       	=					patch_size
								UpperCAmelCase_		:      int       	=					num_channels
								UpperCAmelCase_		:      Tuple       	=					embed_dim
								UpperCAmelCase_		:      Dict       	=					depths
								UpperCAmelCase_		:      Dict       	=					len(_snake_case)
								UpperCAmelCase_		:      List[Any]       	=					num_heads
								UpperCAmelCase_		:      Optional[int]       	=					window_size
								UpperCAmelCase_		:      Any       	=					mlp_ratio
								UpperCAmelCase_		:      Any       	=					qkv_bias
								UpperCAmelCase_		:      str       	=					hidden_dropout_prob
								UpperCAmelCase_		:      Optional[int]       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      Dict       	=					drop_path_rate
								UpperCAmelCase_		:      int       	=					hidden_act
								UpperCAmelCase_		:      Tuple       	=					use_absolute_embeddings
								UpperCAmelCase_		:      Optional[int]       	=					layer_norm_eps
								UpperCAmelCase_		:      Optional[int]       	=					initializer_range
								UpperCAmelCase_		:      Optional[Any]       	=					encoder_stride
								# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								UpperCAmelCase_		:      Union[str, Any]       	=					int(embed_dim * 2 ** (len(_snake_case) - 1))
								UpperCAmelCase_		:      Tuple       	=					['stem'] + [F"""stage{idx}""" for idx in range(1    ,			len(_snake_case) + 1)]
								UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					get_aligned_output_features_output_indices(
								    out_features=_snake_case    ,			out_indices=_snake_case    ,			stage_names=self.stage_names)



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Tuple=   version.parse("1.11" )


							@property
							def 						_snake_case							(			self)							->				Mapping[str, Mapping[int, str]]:
								return OrderedDict(
								    [
								        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
								    ])





							@property
							def 						_snake_case							(			self)							->				float:
								return 1e-4






