



'''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()
snake_case_ :	Tuple         =  logging.get_logger(__name__)

snake_case_ :	Dict         =  torch.device('cpu')






def A__    (     ):
						_UpperCamelCase					:		str 				=   'http://images.cocodataset.org/val2017/000000039769.jpg'
						_UpperCamelCase					:		Dict 				=   Image.open(requests.get(UpperCAmelCase_  ,  stream=UpperCAmelCase_							).raw							)
						return im






def A__    (     UpperCAmelCase_							):
						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__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		Dict 				=   dct.pop(UpperCAmelCase_							)
						_UpperCamelCase					:		str 				=   val






def A__    (     UpperCAmelCase_							):
						_UpperCamelCase					:		str 				=   []
						for k in state_dict.keys():
												_UpperCamelCase					:		str 				=   k
												if ".pwconv" in k:
																		_UpperCamelCase					:		Dict 				=   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					:		List[str] 				=   k_new.replace('patch_embed'  ,  'swiftformer.patch_embed.patch_embedding'							)
												if "network" in k_new:
																		_UpperCamelCase					:		int 				=   k_new.split('.'							)
																		if ls[2].isdigit():
																								_UpperCamelCase					:		Tuple 				=   '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 A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		str 				=   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					:		Optional[Any] 				=   'imagenet-1k-id2label.json'
						_UpperCamelCase					:		Union[str, Any] 				=   json.load(open(hf_hub_download(UpperCAmelCase_  ,  UpperCAmelCase_  ,  repo_type='dataset'							)  ,  'r'							)							)
						_UpperCamelCase					:		Dict 				=   {int(UpperCAmelCase_							): v for k, v in idalabel.items()}
						_UpperCamelCase					:		Any 				=   idalabel
						_UpperCamelCase					:		Any 				=   {v: k for k, v in idalabel.items()}

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

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

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

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

						_UpperCamelCase					:		Dict 				=   create_rename_keys(UpperCAmelCase_							)
						for rename_key_src, rename_key_dest in rename_keys:
												rename_key(UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)

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

						# prepare test inputs
						_UpperCamelCase					:		List[str] 				=   prepare_img()
						_UpperCamelCase					:		str 				=   ViTImageProcessor.from_pretrained('preprocessor_config'							)
						_UpperCamelCase					:		Optional[int] 				=   processor(images=UpperCAmelCase_  ,  return_tensors='pt'							)

						# compare outputs from both models
						_UpperCamelCase					:		Tuple 				=   get_expected_output(UpperCAmelCase_							)
						_UpperCamelCase					:		str 				=   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]  ,  UpperCAmelCase_  ,  atol=1E-3							)

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


if __name__ == "__main__":
					snake_case_ :	str         =  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.')

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







import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
						import torch

if is_vision_available():
						import PIL


_snake_case		=  logging.get_logger(__name__)






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							def constraint_to_multiple_of(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=0				, _lowerCamelCase=None  ):
														_lowerCAmelCase     :							Tuple    =    round(val / multiple  ) * multiple

														if max_val is not None and x > max_val:
																					_lowerCAmelCase     :							Optional[int]    =    math.floor(val / multiple  ) * multiple

														if x < min_val:
																					_lowerCAmelCase     :							List[str]    =    math.ceil(val / multiple  ) * multiple

														return x

							_lowerCAmelCase     :							Union[str, Any]    =    (output_size, output_size) if isinstance(_lowerCamelCase				, _lowerCamelCase  ) else output_size

							_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    get_image_size(_lowerCamelCase  )
							_lowerCAmelCase   ,    _lowerCAmelCase     :							Any    =    output_size

							# determine new height and width
							_lowerCAmelCase     :							List[Any]    =    output_height / input_height
							_lowerCAmelCase     :							Any    =    output_width / input_width

							if keep_aspect_ratio:
														# scale as little as possible
														if abs(1 - scale_width  ) < abs(1 - scale_height  ):
																					# fit width
																					_lowerCAmelCase     :							Union[str, Any]    =    scale_width
														else:
																					# fit height
																					_lowerCAmelCase     :							Union[str, Any]    =    scale_height

							_lowerCAmelCase     :							List[str]    =    constraint_to_multiple_of(scale_height * input_height				, multiple=_lowerCamelCase  )
							_lowerCAmelCase     :							Dict    =    constraint_to_multiple_of(scale_width * input_width				, multiple=_lowerCamelCase  )

							return (new_height, new_width)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		['pixel_values']







							def __init__(   self,			__a = True,			__a = None,			__a = PILImageResampling.BILINEAR,			__a = False,			__a = 1,			__a = True,			__a = 1 / 255,			__a = True,			__a = None,			__a = None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a)
														_lowerCAmelCase     :							Any    =    size if size is not None else {"height": 384, "width": 384}
														_lowerCAmelCase     :							Optional[int]    =    get_size_dict(__a)
														_lowerCAmelCase     :							Optional[Any]    =    do_resize
														_lowerCAmelCase     :							Dict    =    size
														_lowerCAmelCase     :							Any    =    keep_aspect_ratio
														_lowerCAmelCase     :							str    =    ensure_multiple_of
														_lowerCAmelCase     :							str    =    resample
														_lowerCAmelCase     :							Dict    =    do_rescale
														_lowerCAmelCase     :							Optional[int]    =    rescale_factor
														_lowerCAmelCase     :							Dict    =    do_normalize
														_lowerCAmelCase     :							Dict    =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														_lowerCAmelCase     :							int    =    image_std if image_std is not None else IMAGENET_STANDARD_STD







							def    snake_case__      (   self,			__a,			__a,			__a = False,			__a = 1,			__a = PILImageResampling.BICUBIC,			__a = None,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    get_size_dict(__a)
														if "height" not in size or "width" not in size:
																					raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}")
														_lowerCAmelCase     :							List[Any]    =    get_resize_output_image_size(
														    __a,			output_size=(size["height"], size["width"]),			keep_aspect_ratio=__a,			multiple=__a,			)
														return resize(__a,			size=__a,			resample=__a,			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

														return rescale(__a,			scale=__a,			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

														return normalize(__a,			mean=__a,			std=__a,			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = ChannelDimension.FIRST,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    do_resize if do_resize is not None else self.do_resize
														_lowerCAmelCase     :							List[Any]    =    size if size is not None else self.size
														_lowerCAmelCase     :							str    =    get_size_dict(__a)
														_lowerCAmelCase     :							Dict    =    keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
														_lowerCAmelCase     :							Any    =    ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
														_lowerCAmelCase     :							int    =    resample if resample is not None else self.resample
														_lowerCAmelCase     :							Union[str, Any]    =    do_rescale if do_rescale is not None else self.do_rescale
														_lowerCAmelCase     :							Tuple    =    rescale_factor if rescale_factor is not None else self.rescale_factor
														_lowerCAmelCase     :							List[str]    =    do_normalize if do_normalize is not None else self.do_normalize
														_lowerCAmelCase     :							Dict    =    image_mean if image_mean is not None else self.image_mean
														_lowerCAmelCase     :							List[str]    =    image_std if image_std is not None else self.image_std

														_lowerCAmelCase     :							Optional[Any]    =    make_list_of_images(__a)

														if not valid_images(__a):
																					raise ValueError(
																					    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																					    "torch.Tensor, tf.Tensor or jax.ndarray.")

														if do_resize and size is None or resample is None:
																					raise ValueError("Size and resample must be specified if do_resize is True.")

														if do_rescale and rescale_factor is None:
																					raise ValueError("Rescale factor must be specified if do_rescale is True.")

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError("Image mean and std must be specified if do_normalize is True.")

														# All transformations expect numpy arrays.
														_lowerCAmelCase     :							List[Any]    =    [to_numpy_array(__a) for image in images]

														if do_resize:
																					_lowerCAmelCase     :							Any    =    [self.resize(image=__a,			size=__a,			resample=__a) for image in images]

														if do_rescale:
																					_lowerCAmelCase     :							List[str]    =    [self.rescale(image=__a,			scale=__a) for image in images]

														if do_normalize:
																					_lowerCAmelCase     :							Dict    =    [self.normalize(image=__a,			mean=__a,			std=__a) for image in images]

														_lowerCAmelCase     :							List[str]    =    [to_channel_dimension_format(__a,			__a) for image in images]

														_lowerCAmelCase     :							Optional[Any]    =    {"pixel_values": images}
														return BatchFeature(data=__a,			tensor_type=__a)







							def    snake_case__      (   self,			__a,			__a = None):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    outputs.logits

														# Resize logits and compute semantic segmentation maps
														if target_sizes is not None:
																					if len(__a) != len(__a):
																												raise ValueError(
																												    "Make sure that you pass in as many target sizes as the batch dimension of the logits")

																					if is_torch_tensor(__a):
																												_lowerCAmelCase     :							List[Any]    =    target_sizes.numpy()

																					_lowerCAmelCase     :							Dict    =    []

																					for idx in range(len(__a)):
																												_lowerCAmelCase     :							int    =    torch.nn.functional.interpolate(
																												    logits[idx].unsqueeze(dim=0),			size=target_sizes[idx],			mode="bilinear",			align_corners=__a)
																												_lowerCAmelCase     :							int    =    resized_logits[0].argmax(dim=0)
																												semantic_segmentation.append(__a)
														else:
																					_lowerCAmelCase     :							Dict    =    logits.argmax(dim=1)
																					_lowerCAmelCase     :							str    =    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

														return semantic_segmentation
