






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					(					lowerCAmelCase__				):
				A__							 =       SwinaSRConfig()

				if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
								A__							 =       4
				elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
								A__							 =       4
								A__							 =       48
								A__							 =       """pixelshuffle_aux"""
				elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
								A__							 =       [6, 6, 6, 6]
								A__							 =       60
								A__							 =       [6, 6, 6, 6]
								A__							 =       """pixelshuffledirect"""
				elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
								A__							 =       4
								A__							 =       """nearest+conv"""
				elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
								A__							 =       1
								A__							 =       1
								A__							 =       126
								A__							 =       7
								A__							 =       2_5_5.0
								A__							 =       """"""

				return config



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

				if name == "norm.weight":
								A__							 =       """layernorm.weight"""
				if name == "norm.bias":
								A__							 =       """layernorm.bias"""

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

				return name



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				for key in orig_state_dict.copy().keys():
								A__							 =       orig_state_dict.pop(__lowerCAmelCase				)

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

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

				return orig_state_dict



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       get_config(__lowerCAmelCase				)
				A__							 =       SwinaSRForImageSuperResolution(__lowerCAmelCase				)
				model.eval()

				A__							 =       torch.hub.load_state_dict_from_url(__lowerCAmelCase       ,map_location='cpu'				)
				A__							 =       convert_state_dict(__lowerCAmelCase       ,__lowerCAmelCase				)
				A__							 =       model.load_state_dict(__lowerCAmelCase       ,strict=__lowerCAmelCase				)

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

				A__							 =       126 if """Jpeg""" in checkpoint_url else 256
				A__							 =       Compose(
				    [
				        Resize((image_size, image_size)				),
				        ToTensor(),
				        Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]       ,std=[0.2_2_9, 0.2_2_4, 0.2_2_5]				),
				    ]				)
				A__							 =       transforms(__lowerCAmelCase				).unsqueeze(0				)

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

				A__							 =       model(__lowerCAmelCase				)

				# assert values
				if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
								A__							 =       torch.Size([1, 3, 512, 512]				)
								A__							 =       torch.tensor(
								    [[-0.7_0_8_7, -0.7_1_3_8, -0.6_7_2_1], [-0.8_3_4_0, -0.8_0_9_5, -0.7_2_9_8], [-0.9_1_4_9, -0.8_4_1_4, -0.7_9_4_0]]				)
				elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
								A__							 =       torch.Size([1, 3, 1024, 1024]				)
								A__							 =       torch.tensor(
								    [[-0.7_7_7_5, -0.8_1_0_5, -0.8_9_3_3], [-0.7_7_6_4, -0.8_3_5_6, -0.9_2_2_5], [-0.7_9_7_6, -0.8_6_8_6, -0.9_5_7_9]]				)
				elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
								# TODO values didn't match exactly here
								A__							 =       torch.Size([1, 3, 1024, 1024]				)
								A__							 =       torch.tensor(
								    [[-0.8_0_3_5, -0.7_5_0_4, -0.7_4_9_1], [-0.8_5_3_8, -0.8_1_2_4, -0.7_7_8_2], [-0.8_8_0_4, -0.8_6_5_1, -0.8_4_9_3]]				)
				elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
								A__							 =       torch.Size([1, 3, 512, 512]				)
								A__							 =       torch.tensor(
								    [[-0.7_6_6_9, -0.8_6_6_2, -0.8_7_6_7], [-0.8_8_1_0, -0.9_9_6_2, -0.9_8_2_0], [-0.9_3_4_0, -1.0_3_2_2, -1.1_1_4_9]]				)
				elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
								A__							 =       torch.Size([1, 3, 1024, 1024]				)
								A__							 =       torch.tensor(
								    [[-0.5_2_3_8, -0.5_5_5_7, -0.6_3_2_1], [-0.6_0_1_6, -0.5_9_0_3, -0.6_3_9_1], [-0.6_2_4_4, -0.6_3_3_4, -0.6_8_8_9]]				)

				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]       ,__lowerCAmelCase       ,atol=1E-3				)
				print('Looks ok!'				)

				A__							 =       {
				    """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"""
				    ),
				}
				A__							 =       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(__lowerCAmelCase				)
								print(f'''Saving image processor to {pytorch_dump_folder_path}'''				)
								processor.save_pretrained(__lowerCAmelCase				)

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE						:						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.''')

					SCREAMING_SNAKE_CASE						:						List[Any]					   =       parser.parse_args()
					convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""






from ...processing_utils import ProcessorMixin





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					["""image_processor""", """feature_extractor"""]
	SCREAMING_SNAKE_CASE_:							Optional[int]   			=					"""TvltImageProcessor"""
	SCREAMING_SNAKE_CASE_:							Optional[int]   			=					"""TvltFeatureExtractor"""


	def __init__(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					super().__init__(image_processor=__a  ,					feature_extractor=__a						)

					A__							 =       image_processor
					A__							 =       feature_extractor


	def __call__(      self  ,					__a=None  ,					__a=None  ,					__a=None  ,					__a=None  ,					__a=False  ,					__a=False  ,					*__a  ,					**__a  ,					):


					"""simple docstring"""

					if images is None and audio is None:
									raise ValueError('You need to specify either an `images` or `audio` input to process.'						)

					A__							 =       None
					if images is not None:
									A__							 =       self.image_processor(__a  ,					mask_pixel=__a  ,					*__a  ,					**__a						)
					if images_mixed is not None:
									A__							 =       self.image_processor(__a  ,					is_mixed=__a  ,					*__a  ,					**__a						)
					if audio is not None:
									A__							 =       self.feature_extractor(
									    __a  ,					*__a  ,					sampling_rate=__a  ,					mask_audio=__a  ,					**__a						)

					A__							 =       {}
					if audio is not None:
									output_dict.update(__a						)
					if images is not None:
									output_dict.update(__a						)
					if images_mixed_dict is not None:
									output_dict.update(__a						)
					return output_dict






	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.image_processor.model_input_names
					A__							 =       self.feature_extractor.model_input_names
					return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names						)						)


