






"""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 (  A__		):
			UpperCAmelCase_	:					str     	=   SwinConfig()
			UpperCAmelCase_	:					str     	=   swin_name.split("_"		)

			UpperCAmelCase_	:					int     	=   name_split[1]
			UpperCAmelCase_	:					int     	=   int(name_split[4]		)
			UpperCAmelCase_	:					Optional[int]     	=   int(name_split[3][-1]		)

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

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

			UpperCAmelCase_	:					List[str]     	=   img_size
			UpperCAmelCase_	:					Union[str, Any]     	=   num_classes
			UpperCAmelCase_	:					Any     	=   embed_dim
			UpperCAmelCase_	:					List[Any]     	=   depths
			UpperCAmelCase_	:					Optional[int]     	=   num_heads
			UpperCAmelCase_	:					List[str]     	=   window_size

			return config

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

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

			if "head" in name:
						UpperCAmelCase_	:					Tuple     	=   name.replace("head"      ,"classifier"		)
			else:
						UpperCAmelCase_	:					Union[str, Any]     	=   "swin." + name

			return name

def 							snake_case (  A__      ,A__		):
			for key in orig_state_dict.copy().keys():
						UpperCAmelCase_	:					Union[str, Any]     	=   orig_state_dict.pop(A__		)

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

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

			return orig_state_dict

def 							snake_case (  A__      ,A__		):
			UpperCAmelCase_	:					str     	=   timm.create_model(A__      ,pretrained=A__		)
			timm_model.eval()

			UpperCAmelCase_	:					Tuple     	=   get_swin_config(A__		)
			UpperCAmelCase_	:					Union[str, Any]     	=   SwinForImageClassification(A__		)
			model.eval()

			UpperCAmelCase_	:					Tuple     	=   convert_state_dict(timm_model.state_dict()      ,A__		)
			model.load_state_dict(A__		)

			UpperCAmelCase_	:					int     	=   "http://images.cocodataset.org/val2017/000000039769.jpg"

			UpperCAmelCase_	:					Optional[Any]     	=   AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"      ,"-"		)		)		)
			UpperCAmelCase_	:					List[str]     	=   Image.open(requests.get(A__      ,stream=A__		).raw		)
			UpperCAmelCase_	:					Optional[Any]     	=   image_processor(images=A__      ,return_tensors="pt"		)

			UpperCAmelCase_	:					Optional[int]     	=   timm_model(inputs["pixel_values"]		)
			UpperCAmelCase_	:					Union[str, Any]     	=   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_							      = 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_							      = parser.parse_args()
			convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)











"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor







class      UpperCamelCase_			(unittest.TestCase	):





	def __init__( self   :					Optional[int]      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					List[str]=13      ,      lowerCAmelCase_   :					Union[str, Any]=3      ,      lowerCAmelCase_   :					Union[str, Any]=224      ,      lowerCAmelCase_   :					List[Any]=30      ,      lowerCAmelCase_   :					Any=400      ,      lowerCAmelCase_   :					Any=True      ,      lowerCAmelCase_   :					Dict=None      ,      lowerCAmelCase_   :					int=True      ,      lowerCAmelCase_   :					Any=[0.5, 0.5, 0.5]      ,      lowerCAmelCase_   :					str=[0.5, 0.5, 0.5]      ,      )		->     Dict:
				UpperCAmelCase_	:					int     	=   size if size is not None else {"height": 18, "width": 18}
				UpperCAmelCase_	:					List[Any]     	=   parent
				UpperCAmelCase_	:					int     	=   batch_size
				UpperCAmelCase_	:					Optional[int]     	=   num_channels
				UpperCAmelCase_	:					Dict     	=   image_size
				UpperCAmelCase_	:					Union[str, Any]     	=   min_resolution
				UpperCAmelCase_	:					List[str]     	=   max_resolution
				UpperCAmelCase_	:					Dict     	=   do_resize
				UpperCAmelCase_	:					Optional[int]     	=   size
				UpperCAmelCase_	:					List[str]     	=   do_normalize
				UpperCAmelCase_	:					List[Any]     	=   image_mean
				UpperCAmelCase_	:					Dict     	=   image_std





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Union[str, Any]:
				return {
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_normalize": self.do_normalize,
				    "do_resize": self.do_resize,
				    "size": self.size,
				}







@require_torch
@require_vision
class      UpperCamelCase_			(__A							,		unittest.TestCase	):
	__magic_name__       =							ViTImageProcessor if is_vision_available() else None





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Optional[Any]:
				UpperCAmelCase_	:					Optional[Any]     	=   EfficientFormerImageProcessorTester(self		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     List[Any]:
				return self.image_proc_tester.prepare_image_processor_dict()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     str:
				UpperCAmelCase_	:					Tuple     	=   self.image_processing_class(**self.image_processor_dict		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "image_mean"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "image_std"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "do_normalize"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "do_resize"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "size"		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     List[str]:
				pass





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     int:
				# Initialize image_processor
				UpperCAmelCase_	:					str     	=   self.image_processing_class(**self.image_processor_dict		)
				# create random PIL images
				UpperCAmelCase_	:					int     	=   prepare_image_inputs(self.image_proc_tester      ,      equal_resolution=lowerCAmelCase_		)
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase_      ,      Image.Image		)

				# Test not batched input
				UpperCAmelCase_	:					Optional[Any]     	=   image_processor(image_inputs[0]      ,      return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )      ,      )

				# Test batched
				UpperCAmelCase_	:					str     	=   image_processor(lowerCAmelCase_      ,      return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )      ,      )





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     List[Any]:
				# Initialize image_processor
				UpperCAmelCase_	:					List[str]     	=   self.image_processing_class(**self.image_processor_dict		)
				# create random numpy tensors
				UpperCAmelCase_	:					List[str]     	=   prepare_image_inputs(self.image_proc_tester      ,      equal_resolution=lowerCAmelCase_      ,      numpify=lowerCAmelCase_		)
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase_      ,      np.ndarray		)

				# Test not batched input
				UpperCAmelCase_	:					str     	=   image_processor(image_inputs[0]      ,      return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )      ,      )

				# Test batched
				UpperCAmelCase_	:					Dict     	=   image_processor(lowerCAmelCase_      ,      return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )      ,      )





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     List[Any]:
				# Initialize image_processor
				UpperCAmelCase_	:					Optional[Any]     	=   self.image_processing_class(**self.image_processor_dict		)
				# create random PyTorch tensors
				UpperCAmelCase_	:					Dict     	=   prepare_image_inputs(self.image_proc_tester      ,      equal_resolution=lowerCAmelCase_      ,      torchify=lowerCAmelCase_		)
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase_      ,      torch.Tensor		)

				# Test not batched input
				UpperCAmelCase_	:					Optional[int]     	=   image_processor(image_inputs[0]      ,      return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )      ,      )

				# Test batched
				UpperCAmelCase_	:					Optional[int]     	=   image_processor(lowerCAmelCase_      ,      return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )      ,      )



