import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							SwinaSRConfig()

				if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
								__lowercase 			=							4
				elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
								__lowercase 			=							4
								__lowercase 			=							48
								__lowercase 			=							'''pixelshuffle_aux'''
				elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
								__lowercase 			=							[6, 6, 6, 6]
								__lowercase 			=							60
								__lowercase 			=							[6, 6, 6, 6]
								__lowercase 			=							'''pixelshuffledirect'''
				elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
								__lowercase 			=							4
								__lowercase 			=							'''nearest+conv'''
				elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
								__lowercase 			=							1
								__lowercase 			=							1
								__lowercase 			=							126
								__lowercase 			=							7
								__lowercase 			=							255.0
								__lowercase 			=							''''''

				return config





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				if "patch_embed.proj" in name and "layers" not in name:
								__lowercase 			=							name.replace('''patch_embed.proj'''							,			'''embeddings.patch_embeddings.projection'''							)
				if "patch_embed.norm" in name:
								__lowercase 			=							name.replace('''patch_embed.norm'''							,			'''embeddings.patch_embeddings.layernorm'''							)
				if "layers" in name:
								__lowercase 			=							name.replace('''layers'''							,			'''encoder.stages'''							)
				if "residual_group.blocks" in name:
								__lowercase 			=							name.replace('''residual_group.blocks'''							,			'''layers'''							)
				if "attn.proj" in name:
								__lowercase 			=							name.replace('''attn.proj'''							,			'''attention.output.dense'''							)
				if "attn" in name:
								__lowercase 			=							name.replace('''attn'''							,			'''attention.self'''							)
				if "norm1" in name:
								__lowercase 			=							name.replace('''norm1'''							,			'''layernorm_before'''							)
				if "norm2" in name:
								__lowercase 			=							name.replace('''norm2'''							,			'''layernorm_after'''							)
				if "mlp.fc1" in name:
								__lowercase 			=							name.replace('''mlp.fc1'''							,			'''intermediate.dense'''							)
				if "mlp.fc2" in name:
								__lowercase 			=							name.replace('''mlp.fc2'''							,			'''output.dense'''							)
				if "q_bias" in name:
								__lowercase 			=							name.replace('''q_bias'''							,			'''query.bias'''							)
				if "k_bias" in name:
								__lowercase 			=							name.replace('''k_bias'''							,			'''key.bias'''							)
				if "v_bias" in name:
								__lowercase 			=							name.replace('''v_bias'''							,			'''value.bias'''							)
				if "cpb_mlp" in name:
								__lowercase 			=							name.replace('''cpb_mlp'''							,			'''continuous_position_bias_mlp'''							)
				if "patch_embed.proj" in name:
								__lowercase 			=							name.replace('''patch_embed.proj'''							,			'''patch_embed.projection'''							)

				if name == "norm.weight":
								__lowercase 			=							'''layernorm.weight'''
				if name == "norm.bias":
								__lowercase 			=							'''layernorm.bias'''

				if "conv_first" in name:
								__lowercase 			=							name.replace('''conv_first'''							,			'''first_convolution'''							)

				if (
				    "upsample" in name
				    or "conv_before_upsample" in name
				    or "conv_bicubic" in name
				    or "conv_up" in name
				    or "conv_hr" in name
				    or "conv_last" in name
				    or "aux" in name
				):
								# heads
								if "conv_last" in name:
												__lowercase 			=							name.replace('''conv_last'''							,			'''final_convolution'''							)
								if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
												if "conv_before_upsample.0" in name:
																__lowercase 			=							name.replace('''conv_before_upsample.0'''							,			'''conv_before_upsample'''							)
												if "upsample.0" in name:
																__lowercase 			=							name.replace('''upsample.0'''							,			'''upsample.convolution_0'''							)
												if "upsample.2" in name:
																__lowercase 			=							name.replace('''upsample.2'''							,			'''upsample.convolution_1'''							)
												__lowercase 			=							'''upsample.''' + name
								elif config.upsampler == "pixelshuffledirect":
												__lowercase 			=							name.replace('''upsample.0.weight'''							,			'''upsample.conv.weight'''							)
												__lowercase 			=							name.replace('''upsample.0.bias'''							,			'''upsample.conv.bias'''							)
								else:
												pass
				else:
								__lowercase 			=							'''swin2sr.''' + name

				return name





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				for key in orig_state_dict.copy().keys():
								__lowercase 			=							orig_state_dict.pop(lowercase							)

								if "qkv" in key:
												__lowercase 			=							key.split('''.'''							)
												__lowercase 			=							int(key_split[1]							)
												__lowercase 			=							int(key_split[4]							)
												__lowercase 			=							config.embed_dim

												if "weight" in key:
																__lowercase 			=							val[:dim, :]
																__lowercase 			=							val[dim : dim * 2, :]
																__lowercase 			=							val[-dim:, :]
												else:
																__lowercase 			=							val[:dim]
																__lowercase 			=							val[dim : dim * 2]
																__lowercase 			=							val[-dim:]
												pass
								else:
												__lowercase 			=							val

				return orig_state_dict





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							get_config(lowercase							)
				__lowercase 			=							SwinaSRForImageSuperResolution(lowercase							)
				model.eval()

				__lowercase 			=							torch.hub.load_state_dict_from_url(lowercase							,			map_location='''cpu'''							)
				__lowercase 			=							convert_state_dict(lowercase							,			lowercase							)
				__lowercase			,     __lowercase 			=							model.load_state_dict(lowercase							,			strict=lowercase							)

				if len(lowercase							) > 0:
								raise ValueError('''Missing keys when converting: {}'''.format(lowercase							)							)
				for key in unexpected_keys:
								if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
												raise ValueError(F"Unexpected key {key} in state_dict"							)

    # verify values
				__lowercase 			=							'''https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'''
				__lowercase 			=							Image.open(requests.get(lowercase							,			stream=lowercase							).raw							).convert('''RGB'''							)
				__lowercase 			=							SwinaSRImageProcessor()
				# pixel_values = processor(image, return_tensors="pt").pixel_values

				__lowercase 			=							126 if '''Jpeg''' in checkpoint_url else 256
				__lowercase 			=							Compose(
				    [
				        Resize((image_size, image_size)							),
				        ToTensor(),
				        Normalize(mean=[0.485, 0.456, 0.406]							,			std=[0.229, 0.224, 0.225]							),
				    ]							)
				__lowercase 			=							transforms(lowercase							).unsqueeze(0							)

				if config.num_channels == 1:
								__lowercase 			=							pixel_values[:, 0, :, :].unsqueeze(1							)

				__lowercase 			=							model(lowercase							)

				# assert values
				if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
								__lowercase 			=							torch.Size([1, 3, 512, 512]							)
								__lowercase 			=							torch.tensor(
								    [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]							)
				elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
								__lowercase 			=							torch.Size([1, 3, 1024, 1024]							)
								__lowercase 			=							torch.tensor(
								    [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]							)
				elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
								# TODO values didn't match exactly here
								__lowercase 			=							torch.Size([1, 3, 1024, 1024]							)
								__lowercase 			=							torch.tensor(
								    [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]							)
				elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
								__lowercase 			=							torch.Size([1, 3, 512, 512]							)
								__lowercase 			=							torch.tensor(
								    [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]							)
				elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
								__lowercase 			=							torch.Size([1, 3, 1024, 1024]							)
								__lowercase 			=							torch.tensor(
								    [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]							)

				assert (
				    outputs.reconstruction.shape == expected_shape
				), F"Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"
				assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]							,			lowercase							,			atol=1E-3							)
				print('''Looks ok!'''							)

				__lowercase 			=							{
				    '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''': (
				        '''swin2SR-classical-sr-x2-64'''
				    ),
				    '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth''': (
				        '''swin2SR-classical-sr-x4-64'''
				    ),
				    '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth''': (
				        '''swin2SR-compressed-sr-x4-48'''
				    ),
				    '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth''': (
				        '''swin2SR-lightweight-x2-64'''
				    ),
				    '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth''': (
				        '''swin2SR-realworld-sr-x4-64-bsrgan-psnr'''
				    ),
				}
				__lowercase 			=							url_to_name[checkpoint_url]

				if pytorch_dump_folder_path is not None:
								print(F"Saving model {model_name} to {pytorch_dump_folder_path}"							)
								model.save_pretrained(lowercase							)
								print(F"Saving image processor to {pytorch_dump_folder_path}"							)
								processor.save_pretrained(lowercase							)

				if push_to_hub:
								model.push_to_hub(F"caidas/{model_name}"							)
								processor.push_to_hub(F"caidas/{model_name}"							)


if __name__ == "__main__":
		__a  :     int       =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--checkpoint_url""",
		    default="""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""",
		    type=str,
		    help="""URL of the original Swin2SR checkpoint you'd like to convert.""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
		)
		parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the converted model to the hub.""")

		__a  :     List[Any]       =  parser.parse_args()
		convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
import gc
import threading
import time

import psutil
import torch




class    _UpperCamelCase      :


			"""simple docstring"""






			def __init__(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							psutil.Process()
							__lowercase 			=							False





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							-1

							while True:
											__lowercase 			=							max(self.process.memory_info().rss      ,   self.cpu_memory_peak						)

											# can't sleep or will not catch the peak right (this comment is here on purpose)
											if not self.peak_monitoring:
															break





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							True
							__lowercase 			=							threading.Thread(target=self.peak_monitor						)
							__lowercase 			=							True
							self.thread.start()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							False
							self.thread.join()
							return self.cpu_memory_peak


__a  :     List[str]       =  PeakCPUMemory()





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							{'''time''': time.time()}

				gc.collect()
				torch.cuda.empty_cache()

				# CPU mem
				__lowercase 			=							psutil.Process().memory_info().rss
				cpu_peak_tracker.start()

				# GPU mem
				for i in range(torch.cuda.device_count()							):
								__lowercase 			=							torch.cuda.memory_allocated(lowercase							)
				torch.cuda.reset_peak_memory_stats()

				return measures





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							{'''time''': time.time() - start_measures['''time''']}

				gc.collect()
				torch.cuda.empty_cache()

				# CPU mem
				__lowercase 			=							(psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
				__lowercase 			=							(cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

				# GPU mem
				for i in range(torch.cuda.device_count()							):
								__lowercase 			=							(torch.cuda.memory_allocated(lowercase							) - start_measures[str(lowercase							)]) / 2**20
								__lowercase 			=							(torch.cuda.max_memory_allocated(lowercase							) - start_measures[str(lowercase							)]) / 2**20

				return measures





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				print(F"{description}:"							)
				print(F"- Time: {measures['time']:.2f}s"							)
				for i in range(torch.cuda.device_count()							):
								print(F"- GPU {i} allocated: {measures[str(lowercase							)]:.2f}MiB"							)
								__lowercase 			=							measures[F"{i}-peak"]
								print(F"- GPU {i} peak: {peak:.2f}MiB"							)
				print(F"- CPU RAM allocated: {measures['cpu']:.2f}MiB"							)
				print(F"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"							)