

"""simple docstring"""






import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification



def 	lowercase_						(     _lowercase							:		List[str]						):




					'''simple docstring'''






					UpperCAmelCase      : Tuple   =					SwinvaConfig()
					UpperCAmelCase      : int   =					swinva_name.split("_"						)

					UpperCAmelCase      : Union[str, Any]   =					name_split[1]
					if "to" in name_split[3]:
										UpperCAmelCase      : Tuple   =					int(name_split[3][-3:]						)
					else:
										UpperCAmelCase      : List[Any]   =					int(name_split[3]						)
					if "to" in name_split[2]:
										UpperCAmelCase      : Optional[int]   =					int(name_split[2][-2:]						)
					else:
										UpperCAmelCase      : str   =					int(name_split[2][6:]						)

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

					if "to" in swinva_name:
										UpperCAmelCase      : int   =					(12, 12, 12, 6)

					if ("22k" in swinva_name) and ("to" not in swinva_name):
										UpperCAmelCase      : Union[str, Any]   =					2_18_41
										UpperCAmelCase      : Optional[Any]   =					'''huggingface/label-files'''
										UpperCAmelCase      : Tuple   =					'''imagenet-22k-id2label.json'''
										UpperCAmelCase      : Tuple   =					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__				,   repo_type="dataset"						)				,   "r"						)						)
										UpperCAmelCase      : str   =					{int(SCREAMING_SNAKE_CASE__						): v for k, v in idalabel.items()}
										UpperCAmelCase      : Optional[int]   =					idalabel
										UpperCAmelCase      : Optional[int]   =					{v: k for k, v in idalabel.items()}

					else:
										UpperCAmelCase      : str   =					10_00
										UpperCAmelCase      : List[Any]   =					'''huggingface/label-files'''
										UpperCAmelCase      : List[Any]   =					'''imagenet-1k-id2label.json'''
										UpperCAmelCase      : Dict   =					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__				,   repo_type="dataset"						)				,   "r"						)						)
										UpperCAmelCase      : Any   =					{int(SCREAMING_SNAKE_CASE__						): v for k, v in idalabel.items()}
										UpperCAmelCase      : List[Any]   =					idalabel
										UpperCAmelCase      : Union[str, Any]   =					{v: k for k, v in idalabel.items()}

					UpperCAmelCase      : int   =					img_size
					UpperCAmelCase      : int   =					num_classes
					UpperCAmelCase      : int   =					embed_dim
					UpperCAmelCase      : int   =					depths
					UpperCAmelCase      : List[str]   =					num_heads
					UpperCAmelCase      : Tuple   =					window_size

					return config



def 	lowercase_						(     _lowercase							:		Optional[int]						):




					'''simple docstring'''






					if "patch_embed.proj" in name:
										UpperCAmelCase      : str   =					name.replace("patch_embed.proj"				,   "embeddings.patch_embeddings.projection"						)
					if "patch_embed.norm" in name:
										UpperCAmelCase      : Optional[int]   =					name.replace("patch_embed.norm"				,   "embeddings.norm"						)
					if "layers" in name:
										UpperCAmelCase      : Dict   =					'''encoder.''' + name
					if "attn.proj" in name:
										UpperCAmelCase      : Any   =					name.replace("attn.proj"				,   "attention.output.dense"						)
					if "attn" in name:
										UpperCAmelCase      : Any   =					name.replace("attn"				,   "attention.self"						)
					if "norm1" in name:
										UpperCAmelCase      : str   =					name.replace("norm1"				,   "layernorm_before"						)
					if "norm2" in name:
										UpperCAmelCase      : Dict   =					name.replace("norm2"				,   "layernorm_after"						)
					if "mlp.fc1" in name:
										UpperCAmelCase      : Union[str, Any]   =					name.replace("mlp.fc1"				,   "intermediate.dense"						)
					if "mlp.fc2" in name:
										UpperCAmelCase      : List[str]   =					name.replace("mlp.fc2"				,   "output.dense"						)
					if "q_bias" in name:
										UpperCAmelCase      : List[Any]   =					name.replace("q_bias"				,   "query.bias"						)
					if "k_bias" in name:
										UpperCAmelCase      : Dict   =					name.replace("k_bias"				,   "key.bias"						)
					if "v_bias" in name:
										UpperCAmelCase      : Optional[int]   =					name.replace("v_bias"				,   "value.bias"						)
					if "cpb_mlp" in name:
										UpperCAmelCase      : List[str]   =					name.replace("cpb_mlp"				,   "continuous_position_bias_mlp"						)
					if name == "norm.weight":
										UpperCAmelCase      : Union[str, Any]   =					'''layernorm.weight'''
					if name == "norm.bias":
										UpperCAmelCase      : int   =					'''layernorm.bias'''

					if "head" in name:
										UpperCAmelCase      : str   =					name.replace("head"				,   "classifier"						)
					else:
										UpperCAmelCase      : Dict   =					'''swinv2.''' + name

					return name



def 	lowercase_						(     _lowercase							:		Tuple				,   _lowercase							:		Any						):




					'''simple docstring'''






					for key in orig_state_dict.copy().keys():
										UpperCAmelCase      : str   =					orig_state_dict.pop(SCREAMING_SNAKE_CASE__						)

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

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

					return orig_state_dict



def 	lowercase_						(     _lowercase							:		List[str]				,   _lowercase							:		List[Any]						):




					'''simple docstring'''






					UpperCAmelCase      : Optional[Any]   =					timm.create_model(SCREAMING_SNAKE_CASE__				,   pretrained=SCREAMING_SNAKE_CASE__						)
					timm_model.eval()

					UpperCAmelCase      : str   =					get_swinva_config(SCREAMING_SNAKE_CASE__						)
					UpperCAmelCase      : List[Any]   =					SwinvaForImageClassification(SCREAMING_SNAKE_CASE__						)
					model.eval()

					UpperCAmelCase      : Optional[int]   =					convert_state_dict(timm_model.state_dict()				,   SCREAMING_SNAKE_CASE__						)
					model.load_state_dict(SCREAMING_SNAKE_CASE__						)

					UpperCAmelCase      : Union[str, Any]   =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''

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

					UpperCAmelCase      : Union[str, Any]   =					timm_model(inputs["pixel_values"]						)
					UpperCAmelCase      : List[str]   =					model(**SCREAMING_SNAKE_CASE__						).logits

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

					print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""						)
					model.save_pretrained(SCREAMING_SNAKE_CASE__						)

					print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
					image_processor.save_pretrained(SCREAMING_SNAKE_CASE__						)

					model.push_to_hub(
					    repo_path_or_name=Path(SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__						)				,   organization="nandwalritik"				,   commit_message="Add model"				,   )


if __name__ == "__main__":
	snake_case_ : Optional[Any]   =							argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--swinv2_name""",
	    default="""swinv2_tiny_patch4_window8_256""",
	    type=str,
	    help="""Name of the Swinv2 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."""
	)

	snake_case_ : List[Any]   =							parser.parse_args()
	convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)




'''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


lowercase__						       =      logging.get_logger(__name__)

enable_full_determinism()






class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Any     			=						4
				snake_case  :       List[Any]     			=						3
				snake_case  :       str     			=						(32, 32)

				snake_case  :       Optional[Any]     			=						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       Tuple     			=						torch.tensor([10]      ).to(UpperCamelCase__      )

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



		@property
		def       lowerCAmelCase				(      self				:			Any      )						->			Union[str, Any]:






				"""simple docstring"""







				return (3, 32, 32)



		@property
		def       lowerCAmelCase				(      self				:			str      )						->			Dict:






				"""simple docstring"""







				return (3, 32, 32)



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			int:






				"""simple docstring"""







				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  :       Dict     			=						self.dummy_input
				return init_dict, inputs_dict







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



		@property
		def       lowerCAmelCase				(      self				:			Any      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       str     			=						4
				snake_case  :       Tuple     			=						4
				snake_case  :       str     			=						(32, 32)

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

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



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				return (4, 32, 32)



		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			int:






				"""simple docstring"""







				return (4, 32, 32)



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						{
				    '''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  :       str     			=						self.dummy_input
				return init_dict, inputs_dict



		def       lowerCAmelCase				(      self				:			int      )						->			str:






				"""simple docstring"""







				snake_case			,snake_case  :       int     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )

				self.assertIsNotNone(UpperCamelCase__      )
				self.assertEqual(len(loading_info['''missing_keys''']      )  ,	0      )

				model.to(UpperCamelCase__      )
				snake_case  :       List[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				:			int      )						->			Dict:






				"""simple docstring"""







				snake_case			,snake_case  :       str     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )
				model.to(UpperCamelCase__      )
				snake_case  :       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				:			Any      )						->			str:






				"""simple docstring"""







				snake_case			,snake_case  :       Any     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )
				model_accelerate.to(UpperCamelCase__      )
				model_accelerate.eval()

				snake_case  :       Optional[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(UpperCamelCase__      )
				snake_case  :       List[str]     			=						torch.tensor([10] * noise.shape[0]      ).to(UpperCamelCase__      )

				snake_case  :       Union[str, Any]     			=						model_accelerate(UpperCamelCase__  ,	UpperCamelCase__      )['''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  :       List[Any]     			=						UNetaDModel.from_pretrained(
				    '''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__  ,	low_cpu_mem_usage=UpperCamelCase__      )
				model_normal_load.to(UpperCamelCase__      )
				model_normal_load.eval()
				snake_case  :       Union[str, Any]     			=						model_normal_load(UpperCamelCase__  ,	UpperCamelCase__      )['''sample''']

				assert torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-3      )



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       int     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''      )
				model.eval()
				model.to(UpperCamelCase__      )

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

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

				snake_case  :       int     			=						output[0, -1, -3:, -3:].flatten().cpu()
				# fmt: off
				snake_case  :       Tuple     			=						torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-3      )      )







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



		@property
		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			List[Any]=(32, 32)      )						->			Any:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						4
				snake_case  :       Tuple     			=						3

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

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



		@property
		def       lowerCAmelCase				(      self				:			List[str]      )						->			List[Any]:






				"""simple docstring"""







				return (3, 32, 32)



		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Dict:






				"""simple docstring"""







				return (3, 32, 32)



		def       lowerCAmelCase				(      self				:			int      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Dict     			=						{
				    '''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  :       Dict     			=						self.dummy_input
				return init_dict, inputs_dict



		@slow
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			int:






				"""simple docstring"""







				snake_case			,snake_case  :       str     			=						UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''  ,	output_loading_info=UpperCamelCase__      )
				self.assertIsNotNone(UpperCamelCase__      )
				self.assertEqual(len(loading_info['''missing_keys''']      )  ,	0      )

				model.to(UpperCamelCase__      )
				snake_case  :       str     			=						self.dummy_input
				snake_case  :       Dict     			=						floats_tensor((4, 3) + (256, 256)      ).to(UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						noise
				snake_case  :       List[Any]     			=						model(**UpperCamelCase__      )

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



		@slow
		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			str:






				"""simple docstring"""







				snake_case  :       int     			=						UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''      )
				model.to(UpperCamelCase__      )

				snake_case  :       Dict     			=						4
				snake_case  :       Optional[int]     			=						3
				snake_case  :       Tuple     			=						(256, 256)

				snake_case  :       Optional[int]     			=						torch.ones((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       List[str]     			=						torch.tensor(batch_size * [1e-4]      ).to(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       List[str]     			=						model(UpperCamelCase__  ,	UpperCamelCase__      ).sample

				snake_case  :       Dict     			=						output[0, -3:, -3:, -1].flatten().cpu()
				# fmt: off
				snake_case  :       Optional[int]     			=						torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-2      )      )



		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Any:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''      )
				model.to(UpperCamelCase__      )

				snake_case  :       Optional[Any]     			=						4
				snake_case  :       List[Any]     			=						3
				snake_case  :       Union[str, Any]     			=						(32, 32)

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

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

				snake_case  :       Optional[Any]     			=						output[0, -3:, -3:, -1].flatten().cpu()
				# fmt: off
				snake_case  :       int     			=						torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-2      )      )



		def       lowerCAmelCase				(      self				:			List[str]      )						->			List[Any]:






				"""simple docstring"""







				pass


