



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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			for key in orig_state_dict.copy().keys():
						lowercase__					  =				orig_state_dict.pop(SCREAMING_SNAKE_CASE_  )

						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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				get_config(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				SwinaSRForImageSuperResolution(SCREAMING_SNAKE_CASE_  )
			model.eval()

			lowercase__					  =				torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_						,  map_location="cpu"  )
			lowercase__					  =				convert_state_dict(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			lowercase__  ,       lowercase__					  =				model.load_state_dict(SCREAMING_SNAKE_CASE_						,  strict=SCREAMING_SNAKE_CASE_  )

			if len(SCREAMING_SNAKE_CASE_  ) > 0:
						raise ValueError("Missing keys when converting: {}".format(SCREAMING_SNAKE_CASE_  )  )
			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(SCREAMING_SNAKE_CASE_						,  stream=SCREAMING_SNAKE_CASE_  ).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(SCREAMING_SNAKE_CASE_  ).unsqueeze(0  )

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

			lowercase__					  =				model(SCREAMING_SNAKE_CASE_  )

			# 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]						,  SCREAMING_SNAKE_CASE_						,  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(SCREAMING_SNAKE_CASE_  )
						print(f'''Saving image processor to {pytorch_dump_folder_path}'''  )
						processor.save_pretrained(SCREAMING_SNAKE_CASE_  )

			if push_to_hub:
						model.push_to_hub(f'''caidas/{model_name}'''  )
						processor.push_to_hub(f'''caidas/{model_name}'''  )


if __name__ == "__main__":
							lowercase_								=     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.""")

							lowercase_								=     parser.parse_args()
							convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class 				_snake_case			(					unittest.TestCase):




				def __init__(						self					:						Dict,					__lowercase					:						int,					__lowercase					:						Union[str, Any]=7,					__lowercase					:						Union[str, Any]=3,					__lowercase					:						Any=18,					__lowercase					:						Union[str, Any]=30,					__lowercase					:						Any=400,					__lowercase					:						List[str]=True,					__lowercase					:						Dict=None,					__lowercase					:						List[str]=True,					__lowercase					:						int=False,					__lowercase					:						Union[str, Any]=True,					__lowercase					:						str=True,					__lowercase					:						Optional[int]=[0.5, 0.5, 0.5],					__lowercase					:						List[Any]=[0.5, 0.5, 0.5],					):
							lowercase__					  =				parent
							lowercase__					  =				batch_size
							lowercase__					  =				num_channels
							lowercase__					  =				image_size
							lowercase__					  =				min_resolution
							lowercase__					  =				max_resolution
							lowercase__					  =				do_resize
							lowercase__					  =				size if size is not None else {"height": 18, "width": 20}
							lowercase__					  =				do_thumbnail
							lowercase__					  =				do_align_axis
							lowercase__					  =				do_pad
							lowercase__					  =				do_normalize
							lowercase__					  =				image_mean
							lowercase__					  =				image_std




				def 							A__      (						self					:						Optional[Any]						):
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_thumbnail": self.do_thumbnail,
							    "do_align_long_axis": self.do_align_axis,
							    "do_pad": self.do_pad,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							}
@require_torch
@require_vision
class 				_snake_case			(					lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Optional[int]     		=DonutImageProcessor if is_vision_available() else None




				def 							A__      (						self					:						str						):
							lowercase__					  =				DonutImageProcessingTester(self						)




				@property
				def 							A__      (						self					:						List[str]						):
							return self.image_processor_tester.prepare_image_processor_dict()




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(__lowercase,					"do_resize"						)						)
							self.assertTrue(hasattr(__lowercase,					"size"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_thumbnail"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_align_long_axis"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_pad"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_normalize"						)						)
							self.assertTrue(hasattr(__lowercase,					"image_mean"						)						)
							self.assertTrue(hasattr(__lowercase,					"image_std"						)						)




				def 							A__      (						self					:						str						):
							lowercase__					  =				self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size,					{"height": 18, "width": 20}						)

							lowercase__					  =				self.image_processing_class.from_dict(self.image_processor_dict,					size=42						)
							self.assertEqual(image_processor.size,					{"height": 42, "width": 42}						)

							# Previous config had dimensions in (width, height) order
							lowercase__					  =				self.image_processing_class.from_dict(self.image_processor_dict,					size=(42, 84)						)
							self.assertEqual(image_processor.size,					{"height": 84, "width": 42}						)




				def 							A__      (						self					:						List[str]						):
							pass




				@is_flaky()
				def 							A__      (						self					:						Dict						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					Image.Image						)

							# Test not batched input
							lowercase__					  =				image_processing(image_inputs[0],					return_tensors="pt"						).pixel_values
							self.assertEqual(
							    encoded_images.shape,					(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)

							# Test batched
							lowercase__					  =				image_processing(__lowercase,					return_tensors="pt"						).pixel_values
							self.assertEqual(
							    encoded_images.shape,					(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)




				@is_flaky()
				def 							A__      (						self					:						Optional[Any]						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase,					numpify=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					np.ndarray						)

							# Test not batched input
							lowercase__					  =				image_processing(image_inputs[0],					return_tensors="pt"						).pixel_values
							self.assertEqual(
							    encoded_images.shape,					(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)

							# Test batched
							lowercase__					  =				image_processing(__lowercase,					return_tensors="pt"						).pixel_values
							self.assertEqual(
							    encoded_images.shape,					(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)




				@is_flaky()
				def 							A__      (						self					:						Tuple						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase,					torchify=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					torch.Tensor						)

							# Test not batched input
							lowercase__					  =				image_processing(image_inputs[0],					return_tensors="pt"						).pixel_values
							self.assertEqual(
							    encoded_images.shape,					(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)

							# Test batched
							lowercase__					  =				image_processing(__lowercase,					return_tensors="pt"						).pixel_values
							self.assertEqual(
							    encoded_images.shape,					(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)




