





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       A__(		__lowerCAmelCase ):
				_snake_case :			Optional[int]  						=   SwinConfig()
				_snake_case :			Union[str, Any]  						=   swin_name.split('_' )

				_snake_case :			Dict  						=   name_split[1]
				_snake_case :			Optional[Any]  						=   int(name_split[4] )
				_snake_case :			Union[str, Any]  						=   int(name_split[3][-1] )

				if model_size == "tiny":
								_snake_case :			str  						=   96
								_snake_case :			List[Any]  						=   (2, 2, 6, 2)
								_snake_case :			str  						=   (3, 6, 12, 24)
				elif model_size == "small":
								_snake_case :			Optional[Any]  						=   96
								_snake_case :			Dict  						=   (2, 2, 18, 2)
								_snake_case :			Tuple  						=   (3, 6, 12, 24)
				elif model_size == "base":
								_snake_case :			Union[str, Any]  						=   1_28
								_snake_case :			List[str]  						=   (2, 2, 18, 2)
								_snake_case :			int  						=   (4, 8, 16, 32)
				else:
								_snake_case :			Any  						=   1_92
								_snake_case :			str  						=   (2, 2, 18, 2)
								_snake_case :			Any  						=   (6, 12, 24, 48)

				if "in22k" in swin_name:
								_snake_case :			Dict  						=   2_18_41
				else:
								_snake_case :			Optional[int]  						=   10_00
								_snake_case :			Dict  						=   'huggingface/label-files'
								_snake_case :			Any  						=   'imagenet-1k-id2label.json'
								_snake_case :			int  						=   json.load(open(hf_hub_download(_lowerCamelCase	,			_lowerCamelCase	,			repo_type='dataset' )	,			'r' ) )
								_snake_case :			Dict  						=   {int(_lowerCamelCase ): v for k, v in idalabel.items()}
								_snake_case :			Dict  						=   idalabel
								_snake_case :			Dict  						=   {v: k for k, v in idalabel.items()}

				_snake_case :			Any  						=   img_size
				_snake_case :			Dict  						=   num_classes
				_snake_case :			List[Any]  						=   embed_dim
				_snake_case :			Dict  						=   depths
				_snake_case :			Optional[Any]  						=   num_heads
				_snake_case :			Tuple  						=   window_size

				return config

def       A__(		__lowerCAmelCase ):



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

				if name == "norm.weight":
								_snake_case :			Any  						=   'layernorm.weight'
				if name == "norm.bias":
								_snake_case :			Union[str, Any]  						=   'layernorm.bias'

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

				return name

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):



				for key in orig_state_dict.copy().keys():
								_snake_case :			Any  						=   orig_state_dict.pop(_lowerCamelCase )

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

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

				return orig_state_dict

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			int  						=   timm.create_model(_lowerCamelCase	,			pretrained=_lowerCamelCase )
				timm_model.eval()

				_snake_case :			Optional[int]  						=   get_swin_config(_lowerCamelCase )
				_snake_case :			Union[str, Any]  						=   SwinForImageClassification(_lowerCamelCase )
				model.eval()

				_snake_case :			List[str]  						=   convert_state_dict(timm_model.state_dict()	,			_lowerCamelCase )
				model.load_state_dict(_lowerCamelCase )

				_snake_case :			List[Any]  						=   'http://images.cocodataset.org/val2017/000000039769.jpg'

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

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

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

				print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}''' )
				model.save_pretrained(_lowerCamelCase )

				print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
				image_processor.save_pretrained(_lowerCamelCase )


if __name__ == "__main__":
		UpperCamelCase_		:				Union[str, 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.'''
		)

		UpperCamelCase_		:				Dict				=						parser.parse_args()
		convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)













import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 			lowercase				(							a_		,			a_		,			a_     ):



						"""simple docstring"""

						@register_to_config
						def __init__(    self			:		Any ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		float ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		str ,				lowerCamelCase_			:		bool = False ,				):





										'''simple docstring'''







										super().__init__()

										_snake_case :			Optional[int]  						=   nn.Embedding(lowerCamelCase_ ,				lowerCamelCase_					)

										_snake_case :			List[str]  						=   nn.Embedding(lowerCamelCase_ ,				lowerCamelCase_					)
										_snake_case :			Union[str, Any]  						=   False

										_snake_case :			Tuple  						=   nn.Dropout(p=lowerCamelCase_					)

										_snake_case :			Union[str, Any]  						=   TaConfig(
										    vocab_size=lowerCamelCase_ ,				d_model=lowerCamelCase_ ,				num_heads=lowerCamelCase_ ,				d_kv=lowerCamelCase_ ,				d_ff=lowerCamelCase_ ,				dropout_rate=lowerCamelCase_ ,				feed_forward_proj=lowerCamelCase_ ,				is_decoder=lowerCamelCase_ ,				is_encoder_decoder=lowerCamelCase_ ,				)

										_snake_case :			Union[str, Any]  						=   nn.ModuleList()
										for lyr_num in range(lowerCamelCase_					):
														_snake_case :			Any  						=   TaBlock(lowerCamelCase_					)
														self.encoders.append(lowerCamelCase_					)

										_snake_case :			Tuple  						=   TaLayerNorm(lowerCamelCase_					)
										_snake_case :			List[str]  						=   nn.Dropout(p=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		Tuple					):





										'''simple docstring'''







										_snake_case :			Any  						=   self.token_embedder(lowerCamelCase_					)

										_snake_case :			List[Any]  						=   encoder_input_tokens.shape[1]
										_snake_case :			Any  						=   torch.arange(lowerCamelCase_ ,				device=encoder_input_tokens.device					)
										x += self.position_encoding(lowerCamelCase_					)

										_snake_case :			Tuple  						=   self.dropout_pre(lowerCamelCase_					)

										# inverted the attention mask
										_snake_case :			Dict  						=   encoder_input_tokens.size()
										_snake_case :			Optional[int]  						=   self.get_extended_attention_mask(lowerCamelCase_ ,				lowerCamelCase_					)

										for lyr in self.encoders:
														_snake_case :			str  						=   lyr(lowerCamelCase_ ,				lowerCamelCase_					)[0]
										_snake_case :			Any  						=   self.layer_norm(lowerCamelCase_					)

										return self.dropout_post(lowerCamelCase_					), encoder_inputs_mask






