


import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification






def   _lowercase(					__a				:						Dict     ):
							a__      =SwinConfig()
							a__      =swin_name.split('_'     )

							a__      =name_split[1]
							a__      =int(name_split[4]     )
							a__      =int(name_split[3][-1]     )

							if model_size == "tiny":
														a__      =96
														a__      =(2, 2, 6, 2)
														a__      =(3, 6, 12, 24)
							elif model_size == "small":
														a__      =96
														a__      =(2, 2, 18, 2)
														a__      =(3, 6, 12, 24)
							elif model_size == "base":
														a__      =128
														a__      =(2, 2, 18, 2)
														a__      =(4, 8, 16, 32)
							else:
														a__      =192
														a__      =(2, 2, 18, 2)
														a__      =(6, 12, 24, 48)

							if "in22k" in swin_name:
														a__      =2_1841
							else:
														a__      =1000
														a__      ='huggingface/label-files'
														a__      ='imagenet-1k-id2label.json'
														a__      =json.load(open(hf_hub_download(__a    ,     __a    ,     repo_type='dataset'     )    ,     'r'     )     )
														a__      ={int(__a     ): v for k, v in idalabel.items()}
														a__      =idalabel
														a__      ={v: k for k, v in idalabel.items()}

							a__      =img_size
							a__      =num_classes
							a__      =embed_dim
							a__      =depths
							a__      =num_heads
							a__      =window_size

							return config






def   _lowercase(					__a				:						Any     ):
							if "patch_embed.proj" in name:
														a__      =name.replace('patch_embed.proj'    ,     'embeddings.patch_embeddings.projection'     )
							if "patch_embed.norm" in name:
														a__      =name.replace('patch_embed.norm'    ,     'embeddings.norm'     )
							if "layers" in name:
														a__      ='encoder.' + name
							if "attn.proj" in name:
														a__      =name.replace('attn.proj'    ,     'attention.output.dense'     )
							if "attn" in name:
														a__      =name.replace('attn'    ,     'attention.self'     )
							if "norm1" in name:
														a__      =name.replace('norm1'    ,     'layernorm_before'     )
							if "norm2" in name:
														a__      =name.replace('norm2'    ,     'layernorm_after'     )
							if "mlp.fc1" in name:
														a__      =name.replace('mlp.fc1'    ,     'intermediate.dense'     )
							if "mlp.fc2" in name:
														a__      =name.replace('mlp.fc2'    ,     'output.dense'     )

							if name == "norm.weight":
														a__      ='layernorm.weight'
							if name == "norm.bias":
														a__      ='layernorm.bias'

							if "head" in name:
														a__      =name.replace('head'    ,     'classifier'     )
							else:
														a__      ='swin.' + name

							return name






def   _lowercase(					__a				:						List[str]    ,     __a				:						List[str]     ):
							for key in orig_state_dict.copy().keys():
														a__      =orig_state_dict.pop(__a     )

														if "mask" in key:
																					continue
														elif "qkv" in key:
																					a__      =key.split('.'     )
																					a__      =int(key_split[1]     )
																					a__      =int(key_split[3]     )
																					a__      =model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																					if "weight" in key:
																												a__      =val[:dim, :]
																												a__      =val[
																												    dim : dim * 2, :
																												]
																												a__      =val[-dim:, :]
																					else:
																												a__      =val[
																												    :dim
																												]
																												a__      =val[
																												    dim : dim * 2
																												]
																												a__      =val[
																												    -dim:
																												]
														else:
																					a__      =val

							return orig_state_dict






def   _lowercase(					__a				:						List[str]    ,     __a				:						List[Any]     ):
							a__      =timm.create_model(__a    ,     pretrained=__a     )
							timm_model.eval()

							a__      =get_swin_config(__a     )
							a__      =SwinForImageClassification(__a     )
							model.eval()

							a__      =convert_state_dict(timm_model.state_dict()    ,     __a     )
							model.load_state_dict(__a     )

							a__      ='http://images.cocodataset.org/val2017/000000039769.jpg'

							a__      =AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'    ,     '-'     )     )     )
							a__      =Image.open(requests.get(__a    ,     stream=__a     ).raw     )
							a__      =image_processor(images=__a    ,     return_tensors='pt'     )

							a__      =timm_model(inputs['pixel_values']     )
							a__      =model(**__a     ).logits

							assert torch.allclose(__a    ,     __a    ,     atol=1e-3     )

							print(f"""Saving model {swin_name} to {pytorch_dump_folder_path}"""     )
							model.save_pretrained(__a     )

							print(f"""Saving image processor to {pytorch_dump_folder_path}"""     )
							image_processor.save_pretrained(__a     )


if __name__ == "__main__":
	_lowerCAmelCase:						Any   				=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--swin_name',
	    default='swin_tiny_patch4_window7_224',
	    type=str,
	    help='Name of the Swin timm model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)

	_lowerCAmelCase:						Tuple   				=       parser.parse_args()
	convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)





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

import numpy as np

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


if is_torch_available():
	import torch


_lowerCAmelCase:						List[Any]   				=       logging.get_logger(__name__)




class       lowercase_					(lowercase__   ):
				snake_case	    =['pixel_values']





				def __init__(				self       ,  lowercase_ = True       ,  lowercase_ = None       ,  lowercase_ = PILImageResampling.BILINEAR       ,  lowercase_ = True       ,  lowercase_ = None       ,  lowercase_ = True       ,  lowercase_ = 1 / 255       ,  lowercase_ = True       ,  lowercase_ = None       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> None:
											super().__init__(**lowercase_)
											a__      =size if size is not None else {'shortest_edge': 256}
											a__      =get_size_dict(lowercase_       ,  default_to_square=lowercase_)
											a__      =crop_size if crop_size is not None else {'height': 224, 'width': 224}
											a__      =get_size_dict(lowercase_       ,  param_name='crop_size')
											a__      =do_resize
											a__      =size
											a__      =resample
											a__      =do_center_crop
											a__      =crop_size
											a__      =do_rescale
											a__      =rescale_factor
											a__      =do_normalize
											a__      =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											a__      =image_std if image_std is not None else IMAGENET_STANDARD_STD





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_ = PILImageResampling.BICUBIC       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> np.ndarray:
											a__      =get_size_dict(lowercase_       ,  default_to_square=lowercase_)
											if "shortest_edge" not in size:
																		raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""")
											a__      =get_resize_output_image_size(lowercase_       ,  size=size['shortest_edge']       ,  default_to_square=lowercase_)
											return resize(lowercase_       ,  size=lowercase_       ,  resample=lowercase_       ,  data_format=lowercase_       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> np.ndarray:
											a__      =get_size_dict(lowercase_)
											if "height" not in size or "width" not in size:
																		raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}""")
											return center_crop(lowercase_       ,  size=(size['height'], size['width'])       ,  data_format=lowercase_       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_ = None       ,  **lowercase_)  -> np.ndarray:
											return rescale(lowercase_       ,  scale=lowercase_       ,  data_format=lowercase_       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> np.ndarray:
											return normalize(lowercase_       ,  mean=lowercase_       ,  std=lowercase_       ,  data_format=lowercase_       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = ChannelDimension.FIRST       ,  **lowercase_       ,  )  -> Tuple:
											a__      =do_resize if do_resize is not None else self.do_resize
											a__      =size if size is not None else self.size
											a__      =get_size_dict(lowercase_       ,  default_to_square=lowercase_)
											a__      =resample if resample is not None else self.resample
											a__      =do_center_crop if do_center_crop is not None else self.do_center_crop
											a__      =crop_size if crop_size is not None else self.crop_size
											a__      =get_size_dict(lowercase_       ,  param_name='crop_size')
											a__      =do_rescale if do_rescale is not None else self.do_rescale
											a__      =rescale_factor if rescale_factor is not None else self.rescale_factor
											a__      =do_normalize if do_normalize is not None else self.do_normalize
											a__      =image_mean if image_mean is not None else self.image_mean
											a__      =image_std if image_std is not None else self.image_std

											a__      =make_list_of_images(lowercase_)

											if not valid_images(lowercase_):
																		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:
																		raise ValueError('Size must be specified if do_resize is True.')

											if do_center_crop and crop_size is None:
																		raise ValueError('Crop size must be specified if do_center_crop 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.
											a__      =[to_numpy_array(lowercase_) for image in images]

											if do_resize:
																		a__      =[self.resize(image=lowercase_       ,  size=lowercase_       ,  resample=lowercase_) for image in images]

											if do_center_crop:
																		a__      =[self.center_crop(image=lowercase_       ,  size=lowercase_) for image in images]

											if do_rescale:
																		a__      =[self.rescale(image=lowercase_       ,  scale=lowercase_) for image in images]

											if do_normalize:
																		a__      =[self.normalize(image=lowercase_       ,  mean=lowercase_       ,  std=lowercase_) for image in images]

											a__      =[to_channel_dimension_format(lowercase_       ,  lowercase_) for image in images]

											a__      ={'pixel_values': images}
											return BatchFeature(data=lowercase_       ,  tensor_type=lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = None)  -> str:
											a__      =outputs.logits

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

																		if is_torch_tensor(lowercase_):
																									a__      =target_sizes.numpy()

																		a__      =[]

																		for idx in range(len(lowercase_)):
																									a__      =torch.nn.functional.interpolate(
																									    logits[idx].unsqueeze(dim=0)       ,  size=target_sizes[idx]       ,  mode='bilinear'       ,  align_corners=lowercase_)
																									a__      =resized_logits[0].argmax(dim=0)
																									semantic_segmentation.append(lowercase_)
											else:
																		a__      =logits.argmax(dim=1)
																		a__      =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

											return semantic_segmentation

