




"""simple docstring"""






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   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[str]					=					SwinConfig()
				_lowerCamelCase  :    List[Any]					=					swin_name.split('_'	)

				_lowerCamelCase  :    Any					=					name_split[1]
				_lowerCamelCase  :    Tuple					=					int(name_split[4]	)
				_lowerCamelCase  :    int					=					int(name_split[3][-1]	)

				if model_size == "tiny":
								_lowerCamelCase  :    List[Any]					=					96
								_lowerCamelCase  :    Any					=					(2, 2, 6, 2)
								_lowerCamelCase  :    Union[str, Any]					=					(3, 6, 12, 24)
				elif model_size == "small":
								_lowerCamelCase  :    Union[str, Any]					=					96
								_lowerCamelCase  :    List[str]					=					(2, 2, 18, 2)
								_lowerCamelCase  :    int					=					(3, 6, 12, 24)
				elif model_size == "base":
								_lowerCamelCase  :    List[str]					=					128
								_lowerCamelCase  :    Optional[int]					=					(2, 2, 18, 2)
								_lowerCamelCase  :    Optional[int]					=					(4, 8, 16, 32)
				else:
								_lowerCamelCase  :    Any					=					192
								_lowerCamelCase  :    Optional[Any]					=					(2, 2, 18, 2)
								_lowerCamelCase  :    Union[str, Any]					=					(6, 12, 24, 48)

				if "in22k" in swin_name:
								_lowerCamelCase  :    List[Any]					=					21841
				else:
								_lowerCamelCase  :    Any					=					1000
								_lowerCamelCase  :    Dict					=					'huggingface/label-files'
								_lowerCamelCase  :    Optional[Any]					=					'imagenet-1k-id2label.json'
								_lowerCamelCase  :    int					=					json.load(open(hf_hub_download(lowercase__							,					lowercase__							,					repo_type='dataset'	)							,					'r'	)	)
								_lowerCamelCase  :    Union[str, Any]					=					{int(lowercase__	): v for k, v in idalabel.items()}
								_lowerCamelCase  :    List[str]					=					idalabel
								_lowerCamelCase  :    Optional[int]					=					{v: k for k, v in idalabel.items()}

				_lowerCamelCase  :    Optional[int]					=					img_size
				_lowerCamelCase  :    List[Any]					=					num_classes
				_lowerCamelCase  :    Optional[Any]					=					embed_dim
				_lowerCamelCase  :    Tuple					=					depths
				_lowerCamelCase  :    Optional[Any]					=					num_heads
				_lowerCamelCase  :    Optional[Any]					=					window_size

				return config







def   _snake_case    (       lowercase__	):
				if "patch_embed.proj" in name:
								_lowerCamelCase  :    Tuple					=					name.replace('patch_embed.proj'							,					'embeddings.patch_embeddings.projection'	)
				if "patch_embed.norm" in name:
								_lowerCamelCase  :    Union[str, Any]					=					name.replace('patch_embed.norm'							,					'embeddings.norm'	)
				if "layers" in name:
								_lowerCamelCase  :    Union[str, Any]					=					'encoder.' + name
				if "attn.proj" in name:
								_lowerCamelCase  :    Any					=					name.replace('attn.proj'							,					'attention.output.dense'	)
				if "attn" in name:
								_lowerCamelCase  :    Any					=					name.replace('attn'							,					'attention.self'	)
				if "norm1" in name:
								_lowerCamelCase  :    Optional[Any]					=					name.replace('norm1'							,					'layernorm_before'	)
				if "norm2" in name:
								_lowerCamelCase  :    Optional[int]					=					name.replace('norm2'							,					'layernorm_after'	)
				if "mlp.fc1" in name:
								_lowerCamelCase  :    List[Any]					=					name.replace('mlp.fc1'							,					'intermediate.dense'	)
				if "mlp.fc2" in name:
								_lowerCamelCase  :    Optional[int]					=					name.replace('mlp.fc2'							,					'output.dense'	)

				if name == "norm.weight":
								_lowerCamelCase  :    Any					=					'layernorm.weight'
				if name == "norm.bias":
								_lowerCamelCase  :    int					=					'layernorm.bias'

				if "head" in name:
								_lowerCamelCase  :    Optional[Any]					=					name.replace('head'							,					'classifier'	)
				else:
								_lowerCamelCase  :    List[Any]					=					'swin.' + name

				return name







def   _snake_case    (       lowercase__							,					lowercase__	):
				for key in orig_state_dict.copy().keys():
								_lowerCamelCase  :    int					=					orig_state_dict.pop(lowercase__	)

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

												if "weight" in key:
																_lowerCamelCase  :    Dict					=					val[:dim, :]
																_lowerCamelCase  :    List[str]					=					val[
																    dim : dim * 2, :
																]
																_lowerCamelCase  :    str					=					val[-dim:, :]
												else:
																_lowerCamelCase  :    List[Any]					=					val[
																    :dim
																]
																_lowerCamelCase  :    int					=					val[
																    dim : dim * 2
																]
																_lowerCamelCase  :    List[str]					=					val[
																    -dim:
																]
								else:
												_lowerCamelCase  :    List[Any]					=					val

				return orig_state_dict







def   _snake_case    (       lowercase__							,					lowercase__	):
				_lowerCamelCase  :    Union[str, Any]					=					timm.create_model(lowercase__							,					pretrained=lowercase__	)
				timm_model.eval()

				_lowerCamelCase  :    Optional[Any]					=					get_swin_config(lowercase__	)
				_lowerCamelCase  :    Any					=					SwinForImageClassification(lowercase__	)
				model.eval()

				_lowerCamelCase  :    Tuple					=					convert_state_dict(timm_model.state_dict()							,					lowercase__	)
				model.load_state_dict(lowercase__	)

				_lowerCamelCase  :    Optional[int]					=					'http://images.cocodataset.org/val2017/000000039769.jpg'

				_lowerCamelCase  :    int					=					AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'							,					'-'	)	)	)
				_lowerCamelCase  :    Any					=					Image.open(requests.get(lowercase__							,					stream=lowercase__	).raw	)
				_lowerCamelCase  :    Dict					=					image_processor(images=lowercase__							,					return_tensors='pt'	)

				_lowerCamelCase  :    List[str]					=					timm_model(inputs['pixel_values']	)
				_lowerCamelCase  :    int					=					model(**lowercase__	).logits

				assert torch.allclose(lowercase__							,					lowercase__							,					atol=1E-3	)

				print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''	)
				model.save_pretrained(lowercase__	)

				print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
				image_processor.save_pretrained(lowercase__	)


if __name__ == "__main__":
			lowercase__  		=		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."""
			)

			lowercase__  		=		parser.parse_args()
			convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)





"""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


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """facebook/deit-base-distilled-patch16-224""": (
        """https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"""
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """deit"""
			def __init__(						self						,   lowercase=768						,   lowercase=12						,   lowercase=12						,   lowercase=3072						,   lowercase="gelu"						,   lowercase=0.0						,   lowercase=0.0						,   lowercase=0.02						,   lowercase=1E-12						,   lowercase=224						,   lowercase=16						,   lowercase=3						,   lowercase=True						,   lowercase=16						,   **lowercase						,   ):
							super().__init__(**lowercase						)

							_lowerCamelCase  :    Optional[Any]					=					hidden_size
							_lowerCamelCase  :    List[str]					=					num_hidden_layers
							_lowerCamelCase  :    Tuple					=					num_attention_heads
							_lowerCamelCase  :    Any					=					intermediate_size
							_lowerCamelCase  :    int					=					hidden_act
							_lowerCamelCase  :    Any					=					hidden_dropout_prob
							_lowerCamelCase  :    str					=					attention_probs_dropout_prob
							_lowerCamelCase  :    str					=					initializer_range
							_lowerCamelCase  :    Union[str, Any]					=					layer_norm_eps
							_lowerCamelCase  :    Optional[Any]					=					image_size
							_lowerCamelCase  :    Optional[int]					=					patch_size
							_lowerCamelCase  :    Dict					=					num_channels
							_lowerCamelCase  :    Dict					=					qkv_bias
							_lowerCamelCase  :    Dict					=					encoder_stride






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    version.parse("""1.11"""		)
			@property
			def       A_					(						self						):
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]						)






			@property
			def       A_					(						self						):
							return 1E-4