


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


snake_case							:	Tuple					      =				logging.get_logger(__name__)

enable_full_determinism()







class 	snake_case_       (lowerCamelCase_     ,						lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   str   				=      UNetaDModel
					UpperCAmelCase__ :   str   				=      '''sample'''
					@property
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   List[str]:
												a__					=					4
												a__					=					3
												a__					=					(32, 32)

												a__					=					floats_tensor((batch_size, num_channels) + sizes				).to(__snake_case				)
												a__					=					torch.tensor([10]				).to(__snake_case				)

												return {"sample": noise, "timestep": time_step}
					@property
					def 			lowerCamelCase__(				self       :Tuple				) ->   Tuple:
												return (3, 32, 32)
					@property
					def 			lowerCamelCase__(				self       :List[str]				) ->   Optional[Any]:
												return (3, 32, 32)



					def 			lowerCamelCase__(				self       :str				) ->   Tuple:
												a__					=					{
												    '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,
												}
												a__					=					self.dummy_input
												return init_dict, inputs_dict







class 	snake_case_       (lowerCamelCase_     ,						lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   int   				=      UNetaDModel
					UpperCAmelCase__ :   Any   				=      '''sample'''
					@property
					def 			lowerCamelCase__(				self       :Dict				) ->   List[str]:
												a__					=					4
												a__					=					4
												a__					=					(32, 32)

												a__					=					floats_tensor((batch_size, num_channels) + sizes				).to(__snake_case				)
												a__					=					torch.tensor([10]				).to(__snake_case				)

												return {"sample": noise, "timestep": time_step}
					@property
					def 			lowerCamelCase__(				self       :Any				) ->   str:
												return (4, 32, 32)
					@property
					def 			lowerCamelCase__(				self       :Any				) ->   Dict:
												return (4, 32, 32)
					def 			lowerCamelCase__(				self       :int				) ->   int:
												a__					=					{
												    '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'),
												}
												a__					=					self.dummy_input
												return init_dict, inputs_dict
					def 			lowerCamelCase__(				self       :str				) ->   Any:
												a__				, a__					=					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				)
												a__					=					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				) ->   Optional[int]:
												a__				, a__					=					UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'      ,output_loading_info=__snake_case				)
												model.to(__snake_case				)
												a__					=					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       :Union[str, Any]				) ->   int:
												# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
												a__				, a__					=					UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'      ,output_loading_info=__snake_case				)
												model_accelerate.to(__snake_case				)
												model_accelerate.eval()

												a__					=					torch.randn(
												    1      ,model_accelerate.config.in_channels      ,model_accelerate.config.sample_size      ,model_accelerate.config.sample_size      ,generator=torch.manual_seed(0				)      ,)
												a__					=					noise.to(__snake_case				)
												a__					=					torch.tensor([10] * noise.shape[0]				).to(__snake_case				)

												a__					=					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()

												a__				, a__					=					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()
												a__					=					model_normal_load(__snake_case      ,__snake_case				)['sample']

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



					def 			lowerCamelCase__(				self       :str				) ->   Union[str, Any]:
												a__					=					UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'				)
												model.eval()
												model.to(__snake_case				)

												a__					=					torch.randn(
												    1      ,model.config.in_channels      ,model.config.sample_size      ,model.config.sample_size      ,generator=torch.manual_seed(0				)      ,)
												a__					=					noise.to(__snake_case				)
												a__					=					torch.tensor([10] * noise.shape[0]				).to(__snake_case				)

												with torch.no_grad():
																			a__					=					model(__snake_case      ,__snake_case				).sample

												a__					=					output[0, -1, -3:, -3:].flatten().cpu()
												# fmt: off
												a__					=					torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00]				)
												# fmt: on

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







class 	snake_case_       (lowerCamelCase_     ,						lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   Dict   				=      UNetaDModel
					UpperCAmelCase__ :   Optional[Any]   				=      '''sample'''
					@property
					def 			lowerCamelCase__(				self       :Optional[Any]      ,__snake_case       :List[Any]=(32, 32)				) ->   Optional[int]:
												a__					=					4
												a__					=					3

												a__					=					floats_tensor((batch_size, num_channels) + sizes				).to(__snake_case				)
												a__					=					torch.tensor(batch_size * [10]				).to(dtype=torch.intaa      ,device=__snake_case				)

												return {"sample": noise, "timestep": time_step}
					@property
					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[int]:
												return (3, 32, 32)
					@property
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Optional[int]:
												return (3, 32, 32)
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   List[str]:
												a__					=					{
												    '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',
												    ],
												}
												a__					=					self.dummy_input
												return init_dict, inputs_dict
					@slow
					def 			lowerCamelCase__(				self       :str				) ->   Tuple:
												a__				, a__					=					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				)
												a__					=					self.dummy_input
												a__					=					floats_tensor((4, 3) + (2_56, 2_56)				).to(__snake_case				)
												a__					=					noise
												a__					=					model(**__snake_case				)

												assert image is not None, "Make sure output is not None"
					@slow
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Dict:
												a__					=					UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'				)
												model.to(__snake_case				)

												a__					=					4
												a__					=					3
												a__					=					(2_56, 2_56)

												a__					=					torch.ones((batch_size, num_channels) + sizes				).to(__snake_case				)
												a__					=					torch.tensor(batch_size * [1E-4]				).to(__snake_case				)

												with torch.no_grad():
																			a__					=					model(__snake_case      ,__snake_case				).sample

												a__					=					output[0, -3:, -3:, -1].flatten().cpu()
												# fmt: off
												a__					=					torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08]				)
												# fmt: on

												self.assertTrue(torch_all_close(__snake_case      ,__snake_case      ,rtol=1E-2				)				)
					def 			lowerCamelCase__(				self       :Dict				) ->   int:
												a__					=					UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'				)
												model.to(__snake_case				)

												a__					=					4
												a__					=					3
												a__					=					(32, 32)

												a__					=					torch.ones((batch_size, num_channels) + sizes				).to(__snake_case				)
												a__					=					torch.tensor(batch_size * [1E-4]				).to(__snake_case				)

												with torch.no_grad():
																			a__					=					model(__snake_case      ,__snake_case				).sample

												a__					=					output[0, -3:, -3:, -1].flatten().cpu()
												# fmt: off
												a__					=					torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56]				)
												# fmt: on

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



					def 			lowerCamelCase__(				self       :int				) ->   str:
												# not required for this model
												pass








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()
snake_case							:	Union[str, Any]					      =				logging.get_logger(__name__)

snake_case							:	Tuple					      =				torch.device('''cpu''')






def 							__lowercase       (			):
							a__					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
							a__					=					Image.open(requests.get(__lowerCAmelCase ,   stream=__lowerCAmelCase       ).raw       )
							return im






def 							__lowercase       (			__lowerCAmelCase							:    List[str]       ):
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]       )

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]       )

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]       )

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]       )






def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    str       ):
							a__					=					dct.pop(__lowerCAmelCase       )
							a__					=					val






def 							__lowercase       (			__lowerCAmelCase							:    int       ):
							a__					=					[]
							for k in state_dict.keys():
														a__					=					k
														if ".pwconv" in k:
																					a__					=					k_new.replace('.pwconv' ,   '.point_wise_conv'       )
														if ".dwconv" in k:
																					a__					=					k_new.replace('.dwconv' ,   '.depth_wise_conv'       )
														if ".Proj." in k:
																					a__					=					k_new.replace('.Proj.' ,   '.proj.'       )
														if "patch_embed" in k_new:
																					a__					=					k_new.replace('patch_embed' ,   'swiftformer.patch_embed.patch_embedding'       )
														if "network" in k_new:
																					a__					=					k_new.split('.'       )
																					if ls[2].isdigit():
																												a__					=					'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]       )
																					else:
																												a__					=					k_new.replace('network' ,   'swiftformer.encoder.network'       )
														rename_keys.append((k, k_new)       )
							return rename_keys






@torch.no_grad()
def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    str       ):
							a__					=					SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							a__					=					1_0_0_0
							a__					=					'huggingface/label-files'
							a__					=					'imagenet-1k-id2label.json'
							a__					=					json.load(open(hf_hub_download(__lowerCAmelCase ,   __lowerCAmelCase ,   repo_type='dataset'       ) ,   'r'       )       )
							a__					=					{int(__lowerCAmelCase       ): v for k, v in idalabel.items()}
							a__					=					idalabel
							a__					=					{v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														a__					=					[3, 3, 6, 4]
														a__					=					[4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														a__					=					[3, 3, 9, 6]
														a__					=					[4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														a__					=					[4, 3, 1_0, 5]
														a__					=					[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														a__					=					[4, 4, 1_2, 6]
														a__					=					[6_4, 1_2_8, 3_2_0, 5_1_2]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith('https'       ):
																					a__					=					torch.hub.load_state_dict_from_url(__lowerCAmelCase ,   map_location='cpu' ,   check_hash=__lowerCAmelCase       )
														else:
																					a__					=					torch.load(__lowerCAmelCase ,   map_location='cpu'       )
							a__					=					checkpoint

							a__					=					create_rename_keys(__lowerCAmelCase       )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )

							# load HuggingFace model
							a__					=					SwiftFormerForImageClassification(__lowerCAmelCase       ).eval()
							hf_model.load_state_dict(__lowerCAmelCase       )

							# prepare test inputs
							a__					=					prepare_img()
							a__					=					ViTImageProcessor.from_pretrained('preprocessor_config'       )
							a__					=					processor(images=__lowerCAmelCase ,   return_tensors='pt'       )

							# compare outputs from both models
							a__					=					get_expected_output(__lowerCAmelCase       )
							a__					=					hf_model(inputs['pixel_values']       ).logits

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]       )
							assert torch.allclose(hf_logits[0, 0:5] ,   __lowerCAmelCase ,   atol=1E-3       )

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


if __name__ == "__main__":
						snake_case							:	int					      =				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.''')

						snake_case							:	Optional[int]					      =				parser.parse_args()
						convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)




