





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       A__							(   SCREAMING_SNAKE_CASE__)       ->   int:
		__snake_case:						List[str]			  =						SwinaSRConfig()

		if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
				__snake_case:						int			  =						4
		elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
				__snake_case:						Union[str, Any]			  =						4
				__snake_case:						Tuple			  =						48
				__snake_case:						Any			  =						'''pixelshuffle_aux'''
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				__snake_case:						int			  =						[6, 6, 6, 6]
				__snake_case:						List[Any]			  =						60
				__snake_case:						Optional[int]			  =						[6, 6, 6, 6]
				__snake_case:						str			  =						'''pixelshuffledirect'''
		elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
				__snake_case:						List[str]			  =						4
				__snake_case:						Dict			  =						'''nearest+conv'''
		elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
				__snake_case:						Optional[Any]			  =						1
				__snake_case:						int			  =						1
				__snake_case:						str			  =						126
				__snake_case:						str			  =						7
				__snake_case:						List[str]			  =						255.0
				__snake_case:						List[str]			  =						''''''

		return config







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   Dict:



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

		if name == "norm.weight":
				__snake_case:						str			  =						'''layernorm.weight'''
		if name == "norm.bias":
				__snake_case:						str			  =						'''layernorm.bias'''

		if "conv_first" in name:
				__snake_case:						str			  =						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:
						__snake_case:						List[Any]			  =						name.replace("""conv_last""" , """final_convolution""")
				if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
						if "conv_before_upsample.0" in name:
								__snake_case:						Dict			  =						name.replace("""conv_before_upsample.0""" , """conv_before_upsample""")
						if "upsample.0" in name:
								__snake_case:						List[str]			  =						name.replace("""upsample.0""" , """upsample.convolution_0""")
						if "upsample.2" in name:
								__snake_case:						Optional[int]			  =						name.replace("""upsample.2""" , """upsample.convolution_1""")
						__snake_case:						Optional[Any]			  =						'''upsample.''' + name
				elif config.upsampler == "pixelshuffledirect":
						__snake_case:						Optional[int]			  =						name.replace("""upsample.0.weight""" , """upsample.conv.weight""")
						__snake_case:						Tuple			  =						name.replace("""upsample.0.bias""" , """upsample.conv.bias""")
				else:
						pass
		else:
				__snake_case:						str			  =						'''swin2sr.''' + name

		return name







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   Optional[int]:



		for key in orig_state_dict.copy().keys():
				__snake_case:						Tuple			  =						orig_state_dict.pop(a__)

				if "qkv" in key:
						__snake_case:						Dict			  =						key.split(""".""")
						__snake_case:						Optional[int]			  =						int(key_split[1])
						__snake_case:						Optional[int]			  =						int(key_split[4])
						__snake_case:						Optional[Any]			  =						config.embed_dim

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

		return orig_state_dict







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   str:
		__snake_case:						int			  =						get_config(a__)
		__snake_case:						List[Any]			  =						SwinaSRForImageSuperResolution(a__)
		model.eval()

		__snake_case:						Union[str, Any]			  =						torch.hub.load_state_dict_from_url(a__ , map_location="""cpu""")
		__snake_case:						Optional[Any]			  =						convert_state_dict(a__ , a__)
		__snake_case:						int			  =						model.load_state_dict(a__ , strict=a__)

		if len(a__) > 0:
				raise ValueError("""Missing keys when converting: {}""".format(a__))
		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
		__snake_case:						List[str]			  =						'''https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'''
		__snake_case:						Union[str, Any]			  =						Image.open(requests.get(a__ , stream=a__).raw).convert("""RGB""")
		__snake_case:						List[Any]			  =						SwinaSRImageProcessor()
		# pixel_values = processor(image, return_tensors="pt").pixel_values

		__snake_case:						Any			  =						126 if '''Jpeg''' in checkpoint_url else 256
		__snake_case:						List[Any]			  =						Compose(
		    [
		        Resize((image_size, image_size)),
		        ToTensor(),
		        Normalize(mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225]),
		    ])
		__snake_case:						Dict			  =						transforms(a__).unsqueeze(0)

		if config.num_channels == 1:
				__snake_case:						Optional[Any]			  =						pixel_values[:, 0, :, :].unsqueeze(1)

		__snake_case:						Optional[int]			  =						model(a__)

		# assert values
		if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
				__snake_case:						Union[str, Any]			  =						torch.Size([1, 3, 512, 512])
				__snake_case:						int			  =						torch.tensor(
				    [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]])
		elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
				__snake_case:						str			  =						torch.Size([1, 3, 1024, 1024])
				__snake_case:						Tuple			  =						torch.tensor(
				    [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]])
		elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
				# TODO values didn't match exactly here
				__snake_case:						Optional[Any]			  =						torch.Size([1, 3, 1024, 1024])
				__snake_case:						int			  =						torch.tensor(
				    [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]])
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				__snake_case:						Optional[Any]			  =						torch.Size([1, 3, 512, 512])
				__snake_case:						Union[str, Any]			  =						torch.tensor(
				    [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]])
		elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
				__snake_case:						Union[str, Any]			  =						torch.Size([1, 3, 1024, 1024])
				__snake_case:						Tuple			  =						torch.tensor(
				    [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]])

		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] , a__ , atol=1e-3)
		print("""Looks ok!""")

		__snake_case:						Optional[int]			  =						{
		    '''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'''
		    ),
		}
		__snake_case:						Optional[Any]			  =						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(a__)
				print(F'''Saving image processor to {pytorch_dump_folder_path}''')
				processor.save_pretrained(a__)

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


if __name__ == "__main__":
						__UpperCAmelCase	:       Optional[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.")

						__UpperCAmelCase	:       int    	=							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 diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       pass







@nightly
@require_onnxruntime
@require_torch_gpu
class    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )




       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Optional[int]			  =						ort.SessionOptions()
         __snake_case:						List[Any]			  =						False
         return options




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Any			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						List[str]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						int			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Any			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=10							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[Any]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Any			  =						np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3






       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Optional[int]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						Optional[int]			  =						LMSDiscreteScheduler.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    subfolder="""scheduler"""							,    revision="""onnx"""		)
         __snake_case:						List[Any]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    scheduler=A							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						Optional[int]			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Dict			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=20							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[str]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Union[str, Any]			  =						np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3






