
import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__          =		logging.get_logger(__name__)

lowerCamelCase__          =		torch.device("""cpu""")
def  lowerCAmelCase__			(  )				->		Tuple:
			lowerCAmelCase__      :      Union[str, Any]         =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			lowerCAmelCase__      :      Tuple         =		Image.open(requests.get(_lowercase       ,							stream=_lowercase     ).raw     )
			return im
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		int:


			if swiftformer_name == "swiftformer_xs":
						return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]     )

			elif swiftformer_name == "swiftformer_s":
						return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]     )

			elif swiftformer_name == "swiftformer_l1":
						return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]     )

			elif swiftformer_name == "swiftformer_l3":
						return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]     )
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Any:
			lowerCAmelCase__      :      Optional[int]         =		dct.pop(_lowercase     )
			lowerCAmelCase__      :      List[str]         =		val
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		int:
			lowerCAmelCase__      :      List[str]         =		[]
			for k in state_dict.keys():
						lowerCAmelCase__      :      str         =		k
						if ".pwconv" in k:
									lowerCAmelCase__      :      List[Any]         =		k_new.replace('.pwconv'       ,							'.point_wise_conv'     )
						if ".dwconv" in k:
									lowerCAmelCase__      :      int         =		k_new.replace('.dwconv'       ,							'.depth_wise_conv'     )
						if ".Proj." in k:
									lowerCAmelCase__      :      Dict         =		k_new.replace('.Proj.'       ,							'.proj.'     )
						if "patch_embed" in k_new:
									lowerCAmelCase__      :      Union[str, Any]         =		k_new.replace('patch_embed'       ,							'swiftformer.patch_embed.patch_embedding'     )
						if "network" in k_new:
									lowerCAmelCase__      :      Any         =		k_new.split('.'     )
									if ls[2].isdigit():
												lowerCAmelCase__      :      Any         =		"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]     )
									else:
												lowerCAmelCase__      :      Tuple         =		k_new.replace('network'       ,							'swiftformer.encoder.network'     )
						rename_keys.append((k, k_new)     )
			return rename_keys
@torch.no_grad()
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Any:
			lowerCAmelCase__      :      Tuple         =		SwiftFormerConfig()

			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			lowerCAmelCase__      :      Optional[int]         =		1_000
			lowerCAmelCase__      :      Dict         =		"""huggingface/label-files"""
			lowerCAmelCase__      :      Tuple         =		"""imagenet-1k-id2label.json"""
			lowerCAmelCase__      :      List[Any]         =		json.load(open(hf_hub_download(_lowercase       ,							_lowercase       ,							repo_type='dataset'     )       ,							'r'     )     )
			lowerCAmelCase__      :      int         =		{int(_lowercase     ): v for k, v in idalabel.items()}
			lowerCAmelCase__      :      str         =		idalabel
			lowerCAmelCase__      :      int         =		{v: k for k, v in idalabel.items()}

			# size of the architecture
			if swiftformer_name == "swiftformer_xs":
						lowerCAmelCase__      :      Dict         =		[3, 3, 6, 4]
						lowerCAmelCase__      :      str         =		[48, 56, 112, 220]

			elif swiftformer_name == "swiftformer_s":
						lowerCAmelCase__      :      Union[str, Any]         =		[3, 3, 9, 6]
						lowerCAmelCase__      :      Any         =		[48, 64, 168, 224]

			elif swiftformer_name == "swiftformer_l1":
						lowerCAmelCase__      :      Any         =		[4, 3, 10, 5]
						lowerCAmelCase__      :      Union[str, Any]         =		[48, 96, 192, 384]

			elif swiftformer_name == "swiftformer_l3":
						lowerCAmelCase__      :      str         =		[4, 4, 12, 6]
						lowerCAmelCase__      :      List[str]         =		[64, 128, 320, 512]

			# load state_dict of original model, remove and rename some keys
			if original_ckpt:
						if original_ckpt.startswith('https'     ):
									lowerCAmelCase__      :      str         =		torch.hub.load_state_dict_from_url(_lowercase       ,							map_location='cpu'       ,							check_hash=_lowercase     )
						else:
									lowerCAmelCase__      :      List[str]         =		torch.load(_lowercase       ,							map_location='cpu'     )
			lowerCAmelCase__      :      Union[str, Any]         =		checkpoint

			lowerCAmelCase__      :      Union[str, Any]         =		create_rename_keys(_lowercase     )
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(_lowercase       ,							_lowercase       ,							_lowercase     )

			# load HuggingFace model
			lowerCAmelCase__      :      Optional[int]         =		SwiftFormerForImageClassification(_lowercase     ).eval()
			hf_model.load_state_dict(_lowercase     )

			# prepare test inputs
			lowerCAmelCase__      :      Any         =		prepare_img()
			lowerCAmelCase__      :      Optional[int]         =		ViTImageProcessor.from_pretrained('preprocessor_config'     )
			lowerCAmelCase__      :      Optional[int]         =		processor(images=_lowercase       ,							return_tensors='pt'     )

			# compare outputs from both models
			lowerCAmelCase__      :      List[Any]         =		get_expected_output(_lowercase     )
			lowerCAmelCase__      :      int         =		hf_model(inputs['pixel_values']     ).logits

			assert hf_logits.shape == torch.Size([1, 1_000]     )
			assert torch.allclose(hf_logits[0, 0:5]       ,							_lowercase       ,							atol=1e-3     )

			Path(_lowercase     ).mkdir(exist_ok=_lowercase     )
			print(F'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''     )
			hf_model.save_pretrained(_lowercase     )


if __name__ == "__main__":
					lowerCamelCase__          =		argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--swiftformer_name""",
					    default="""swiftformer_xs""",
					    choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
					    type=str,
					    help="""Name of the SwiftFormer model you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default="""./converted_outputs/""",
					    type=str,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

					lowerCamelCase__          =		parser.parse_args()
					convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)




"""simple docstring"""
import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


__a      =				logging.get_logger(__name__)

enable_full_determinism()
class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[Any]						=     UNetaDModel
					_A		:			Union[str, Any]						=     """sample"""






					@property
					def    lowerCAmelCase_				(       self:							str						)   ->	Tuple:
											snake_case_ :List[str]						=			4
											snake_case_ :Tuple						=			3
											snake_case_ :Optional[Any]						=			(32, 32)

											snake_case_ :str						=			floats_tensor((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :Union[str, Any]						=			torch.tensor([10]						).to(snake_case						)

											return {"sample": noise, "timestep": time_step}






					@property
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Dict:
											return (3, 32, 32)






					@property
					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[int]:
											return (3, 32, 32)






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Dict:
											snake_case_ :Any						=			{
											    """block_out_channels""": (32, 64),
											    """down_block_types""": ("""DownBlock2D""", """AttnDownBlock2D"""),
											    """up_block_types""": ("""AttnUpBlock2D""", """UpBlock2D"""),
											    """attention_head_dim""": 3,
											    """out_channels""": 3,
											    """in_channels""": 3,
											    """layers_per_block""": 2,
											    """sample_size""": 32,
											}
											snake_case_ :Tuple						=			self.dummy_input
											return init_dict, inputs_dict

class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[str]						=     UNetaDModel
					_A		:			Union[str, Any]						=     """sample"""






					@property
					def    lowerCAmelCase_				(       self:							str						)   ->	str:
											snake_case_ :List[str]						=			4
											snake_case_ :Optional[int]						=			4
											snake_case_ :int						=			(32, 32)

											snake_case_ :Any						=			floats_tensor((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :List[Any]						=			torch.tensor([10]						).to(snake_case						)

											return {"sample": noise, "timestep": time_step}






					@property
					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[int]:
											return (4, 32, 32)






					@property
					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	int:
											return (4, 32, 32)






					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	List[Any]:
											snake_case_ :Dict						=			{
											    """sample_size""": 32,
											    """in_channels""": 4,
											    """out_channels""": 4,
											    """layers_per_block""": 2,
											    """block_out_channels""": (32, 64),
											    """attention_head_dim""": 32,
											    """down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
											    """up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
											}
											snake_case_ :List[str]						=			self.dummy_input
											return init_dict, inputs_dict






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[Any]:
											snake_case_,       snake_case_ :List[str]						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						)

											self.assertIsNotNone(snake_case						)
											self.assertEqual(len(loading_info["""missing_keys"""]						)						,     0						)

											model.to(snake_case						)
											snake_case_ :List[str]						=			model(**self.dummy_input						).sample

											assert image is not None, "Make sure output is not None"






					@unittest.skipIf(torch_device != """cuda"""						,     """This test is supposed to run on GPU"""						)
					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Dict:
											snake_case_,       snake_case_ :Union[str, Any]						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						)
											model.to(snake_case						)
											snake_case_ :Union[str, Any]						=			model(**self.dummy_input						).sample

											assert image is not None, "Make sure output is not None"






					@unittest.skipIf(torch_device != """cuda"""						,     """This test is supposed to run on GPU"""						)
					def    lowerCAmelCase_				(       self:							str						)   ->	Any:
											# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
											snake_case_,       snake_case_ :List[str]						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						)
											model_accelerate.to(snake_case						)
											model_accelerate.eval()

											snake_case_ :List[Any]						=			torch.randn(
											    1						,     model_accelerate.config.in_channels						,     model_accelerate.config.sample_size						,     model_accelerate.config.sample_size						,     generator=torch.manual_seed(0						)						,     )
											snake_case_ :int						=			noise.to(snake_case						)
											snake_case_ :str						=			torch.tensor([10] * noise.shape[0]						).to(snake_case						)

											snake_case_ :Optional[int]						=			model_accelerate(snake_case						,     snake_case						)["""sample"""]

											# two models don't need to stay in the device at the same time
											del model_accelerate
											torch.cuda.empty_cache()
											gc.collect()

											snake_case_,       snake_case_ :str						=			UNetaDModel.from_pretrained(
											    """fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						,     low_cpu_mem_usage=snake_case						)
											model_normal_load.to(snake_case						)
											model_normal_load.eval()
											snake_case_ :int						=			model_normal_load(snake_case						,     snake_case						)["""sample"""]

											assert torch_all_close(snake_case						,     snake_case						,     rtol=1E-3						)






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Any:
											snake_case_ :Tuple						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						)
											model.eval()
											model.to(snake_case						)

											snake_case_ :Optional[int]						=			torch.randn(
											    1						,     model.config.in_channels						,     model.config.sample_size						,     model.config.sample_size						,     generator=torch.manual_seed(0						)						,     )
											snake_case_ :int						=			noise.to(snake_case						)
											snake_case_ :List[Any]						=			torch.tensor([10] * noise.shape[0]						).to(snake_case						)

											with torch.no_grad():
																	snake_case_ :Union[str, Any]						=			model(snake_case						,     snake_case						).sample

											snake_case_ :Optional[int]						=			output[0, -1, -3:, -3:].flatten().cpu()
											# fmt: off
											snake_case_ :Dict						=			torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0]						)
											# fmt: on

											self.assertTrue(torch_all_close(snake_case						,     snake_case						,     rtol=1E-3						)						)

class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[Any]						=     UNetaDModel
					_A		:			List[Any]						=     """sample"""






					@property
					def    lowerCAmelCase_				(       self:							Union[str, Any]						,     snake_case:							int=(32, 32)						)   ->	Tuple:
											snake_case_ :Union[str, Any]						=			4
											snake_case_ :Any						=			3

											snake_case_ :int						=			floats_tensor((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :Any						=			torch.tensor(batch_size * [10]						).to(dtype=torch.intaa						,     device=snake_case						)

											return {"sample": noise, "timestep": time_step}






					@property
					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	Any:
											return (3, 32, 32)






					@property
					def    lowerCAmelCase_				(       self:							int						)   ->	Tuple:
											return (3, 32, 32)






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Tuple:
											snake_case_ :List[Any]						=			{
											    """block_out_channels""": [32, 64, 64, 64],
											    """in_channels""": 3,
											    """layers_per_block""": 1,
											    """out_channels""": 3,
											    """time_embedding_type""": """fourier""",
											    """norm_eps""": 1E-6,
											    """mid_block_scale_factor""": math.sqrt(2.0						),
											    """norm_num_groups""": None,
											    """down_block_types""": [
											        """SkipDownBlock2D""",
											        """AttnSkipDownBlock2D""",
											        """SkipDownBlock2D""",
											        """SkipDownBlock2D""",
											    ],
											    """up_block_types""": [
											        """SkipUpBlock2D""",
											        """SkipUpBlock2D""",
											        """AttnSkipUpBlock2D""",
											        """SkipUpBlock2D""",
											    ],
											}
											snake_case_ :int						=			self.dummy_input
											return init_dict, inputs_dict






					@slow
					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	List[Any]:
											snake_case_,       snake_case_ :List[Any]						=			UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""						,     output_loading_info=snake_case						)
											self.assertIsNotNone(snake_case						)
											self.assertEqual(len(loading_info["""missing_keys"""]						)						,     0						)

											model.to(snake_case						)
											snake_case_ :Any						=			self.dummy_input
											snake_case_ :int						=			floats_tensor((4, 3) + (256, 256)						).to(snake_case						)
											snake_case_ :int						=			noise
											snake_case_ :int						=			model(**snake_case						)

											assert image is not None, "Make sure output is not None"






					@slow
					def    lowerCAmelCase_				(       self:							str						)   ->	Dict:
											snake_case_ :Dict						=			UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""						)
											model.to(snake_case						)

											snake_case_ :List[str]						=			4
											snake_case_ :Optional[int]						=			3
											snake_case_ :List[str]						=			(256, 256)

											snake_case_ :Tuple						=			torch.ones((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :str						=			torch.tensor(batch_size * [1E-4]						).to(snake_case						)

											with torch.no_grad():
																	snake_case_ :Dict						=			model(snake_case						,     snake_case						).sample

											snake_case_ :int						=			output[0, -3:, -3:, -1].flatten().cpu()
											# fmt: off
											snake_case_ :Optional[Any]						=			torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8]						)
											# fmt: on

											self.assertTrue(torch_all_close(snake_case						,     snake_case						,     rtol=1E-2						)						)






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	List[Any]:
											snake_case_ :Optional[Any]						=			UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""						)
											model.to(snake_case						)

											snake_case_ :Optional[int]						=			4
											snake_case_ :Optional[Any]						=			3
											snake_case_ :Optional[Any]						=			(32, 32)

											snake_case_ :Dict						=			torch.ones((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :Any						=			torch.tensor(batch_size * [1E-4]						).to(snake_case						)

											with torch.no_grad():
																	snake_case_ :str						=			model(snake_case						,     snake_case						).sample

											snake_case_ :int						=			output[0, -3:, -3:, -1].flatten().cpu()
											# fmt: off
											snake_case_ :int						=			torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6]						)
											# fmt: on

											self.assertTrue(torch_all_close(snake_case						,     snake_case						,     rtol=1E-2						)						)






					def    lowerCAmelCase_				(       self:							Dict						)   ->	Optional[Any]:
											# not required for this model
											pass
