






"""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							(	lowerCamelCase__      :       Union[str, Any]    )							->      Optional[int]:
							lowerCamelCase_			:				Tuple										=SwinConfig()
							lowerCamelCase_			:				List[str]										=swin_name.split("_"    )

							lowerCamelCase_			:				List[str]										=name_split[1]
							lowerCamelCase_			:				Any										=int(name_split[4]    )
							lowerCamelCase_			:				List[Any]										=int(name_split[3][-1]    )

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

							if "in22k" in swin_name:
														lowerCamelCase_			:				str										=21_841
							else:
														lowerCamelCase_			:				Any										=1_000
														lowerCamelCase_			:				str										="huggingface/label-files"
														lowerCamelCase_			:				Tuple										="imagenet-1k-id2label.json"
														lowerCamelCase_			:				Union[str, Any]										=json.load(open(hf_hub_download(lowerCamelCase__							,		lowerCamelCase__							,		repo_type="dataset"    )							,		"r"    )    )
														lowerCamelCase_			:				List[Any]										={int(lowerCamelCase__    ): v for k, v in idalabel.items()}
														lowerCamelCase_			:				str										=idalabel
														lowerCamelCase_			:				int										={v: k for k, v in idalabel.items()}

							lowerCamelCase_			:				Dict										=img_size
							lowerCamelCase_			:				List[Any]										=num_classes
							lowerCamelCase_			:				Optional[Any]										=embed_dim
							lowerCamelCase_			:				Optional[Any]										=depths
							lowerCamelCase_			:				List[Any]										=num_heads
							lowerCamelCase_			:				List[str]										=window_size

							return config

def 					_snake_case							(	lowerCamelCase__      :       Union[str, Any]    )							->      str:




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

							if name == "norm.weight":
														lowerCamelCase_			:				List[str]										="layernorm.weight"
							if name == "norm.bias":
														lowerCamelCase_			:				str										="layernorm.bias"

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

							return name

def 					_snake_case							(	lowerCamelCase__      :       Union[str, Any]							,		lowerCamelCase__      :       Dict    )							->      Dict:




							for key in orig_state_dict.copy().keys():
														lowerCamelCase_			:				Optional[Any]										=orig_state_dict.pop(lowerCamelCase__    )

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

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

							return orig_state_dict

def 					_snake_case							(	lowerCamelCase__      :       Optional[Any]							,		lowerCamelCase__      :       Tuple    )							->      List[str]:
							lowerCamelCase_			:				int										=timm.create_model(lowerCamelCase__							,		pretrained=lowerCamelCase__    )
							timm_model.eval()

							lowerCamelCase_			:				Optional[int]										=get_swin_config(lowerCamelCase__    )
							lowerCamelCase_			:				Optional[int]										=SwinForImageClassification(lowerCamelCase__    )
							model.eval()

							lowerCamelCase_			:				Any										=convert_state_dict(timm_model.state_dict()							,		lowerCamelCase__    )
							model.load_state_dict(lowerCamelCase__    )

							lowerCamelCase_			:				Any										="http://images.cocodataset.org/val2017/000000039769.jpg"

							lowerCamelCase_			:				Optional[int]										=AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"							,		"-"    )    )    )
							lowerCamelCase_			:				int										=Image.open(requests.get(lowerCamelCase__							,		stream=lowerCamelCase__    ).raw    )
							lowerCamelCase_			:				Optional[int]										=image_processor(images=lowerCamelCase__							,		return_tensors="pt"    )

							lowerCamelCase_			:				Dict										=timm_model(inputs["pixel_values"]    )
							lowerCamelCase_			:				Dict										=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__":
						A__			:							Optional[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.'
						)

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







import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)



class 						_UpperCAmelCase      (     unittest.TestCase							):






		def _snake_case		(						self				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						["a", "b", "c"]

				# Defaults to last layer if both are None
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Dict				     =						get_aligned_output_features_output_indices(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase)
				self.assertEqual(UpperCAmelCase   ,  ["c"])
				self.assertEqual(UpperCAmelCase   ,  [2])

				# Out indices set to match out features
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Tuple				     =						get_aligned_output_features_output_indices(["a", "c"]   ,  UpperCAmelCase   ,  UpperCAmelCase)
				self.assertEqual(UpperCAmelCase   ,  ["a", "c"])
				self.assertEqual(UpperCAmelCase   ,  [0, 2])

				# Out features set to match out indices
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:int				     =						get_aligned_output_features_output_indices(UpperCAmelCase   ,  [0, 2]   ,  UpperCAmelCase)
				self.assertEqual(UpperCAmelCase   ,  ["a", "c"])
				self.assertEqual(UpperCAmelCase   ,  [0, 2])

				# Out features selected from negative indices
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Any				     =						get_aligned_output_features_output_indices(UpperCAmelCase   ,  [-3, -1]   ,  UpperCAmelCase)
				self.assertEqual(UpperCAmelCase   ,  ["a", "c"])
				self.assertEqual(UpperCAmelCase   ,  [-3, -1])






		def _snake_case		(						self				:     str):
				# Stage names must be set
				with self.assertRaises(UpperCAmelCase):
						verify_out_features_out_indices(["a", "b"]   ,  (0, 1)   ,  UpperCAmelCase)

				# Out features must be a list
				with self.assertRaises(UpperCAmelCase):
						verify_out_features_out_indices(("a", "b")   ,  (0, 1)   ,  ["a", "b"])

				# Out features must be a subset of stage names
				with self.assertRaises(UpperCAmelCase):
						verify_out_features_out_indices(["a", "b"]   ,  (0, 1)   ,  ["a"])

				# Out indices must be a list or tuple
				with self.assertRaises(UpperCAmelCase):
						verify_out_features_out_indices(UpperCAmelCase   ,  0   ,  ["a", "b"])

				# Out indices must be a subset of stage names
				with self.assertRaises(UpperCAmelCase):
						verify_out_features_out_indices(UpperCAmelCase   ,  (0, 1)   ,  ["a"])

				# Out features and out indices must be the same length
				with self.assertRaises(UpperCAmelCase):
						verify_out_features_out_indices(["a", "b"]   ,  (0,)   ,  ["a", "b", "c"])

				# Out features should match out indices
				with self.assertRaises(UpperCAmelCase):
						verify_out_features_out_indices(["a", "b"]   ,  (0, 2)   ,  ["a", "b", "c"])

				# Out features and out indices should be in order
				with self.assertRaises(UpperCAmelCase):
						verify_out_features_out_indices(["b", "a"]   ,  (0, 1)   ,  ["a", "b"])

				# Check passes with valid inputs
				verify_out_features_out_indices(["a", "b", "d"]   ,  (0, 1, -1)   ,  ["a", "b", "c", "d"])







		def _snake_case		(						self				:     Tuple):
				SCREAMING_SNAKE_CASE_				:int				     =						BackboneMixin()

				SCREAMING_SNAKE_CASE_				:List[str]				     =						["a", "b", "c"]
				SCREAMING_SNAKE_CASE_				:str				     =						["a", "c"]
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[0, 2]

				# Check that the output features and indices are set correctly
				self.assertEqual(backbone.out_features   ,  ["a", "c"])
				self.assertEqual(backbone.out_indices   ,  [0, 2])

				# Check out features and indices are updated correctly
				SCREAMING_SNAKE_CASE_				:Tuple				     =						["a", "b"]
				self.assertEqual(backbone.out_features   ,  ["a", "b"])
				self.assertEqual(backbone.out_indices   ,  [0, 1])

				SCREAMING_SNAKE_CASE_				:List[str]				     =						[-3, -1]
				self.assertEqual(backbone.out_features   ,  ["a", "c"])
				self.assertEqual(backbone.out_indices   ,  [-3, -1])






