


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_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= SwinaSRConfig()

		if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
				lowerCAmelCase__       :      List[Any]   					= 4
		elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
				lowerCAmelCase__       :      str   					= 4
				lowerCAmelCase__       :      Any   					= 48
				lowerCAmelCase__       :      str   					= '''pixelshuffle_aux'''
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				lowerCAmelCase__       :      Union[str, Any]   					= [6, 6, 6, 6]
				lowerCAmelCase__       :      Tuple   					= 60
				lowerCAmelCase__       :      Dict   					= [6, 6, 6, 6]
				lowerCAmelCase__       :      Dict   					= '''pixelshuffledirect'''
		elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
				lowerCAmelCase__       :      Optional[int]   					= 4
				lowerCAmelCase__       :      Union[str, Any]   					= '''nearest+conv'''
		elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
				lowerCAmelCase__       :      List[str]   					= 1
				lowerCAmelCase__       :      Any   					= 1
				lowerCAmelCase__       :      str   					= 126
				lowerCAmelCase__       :      Any   					= 7
				lowerCAmelCase__       :      Any   					= 2_55.0
				lowerCAmelCase__       :      int   					= ''''''

		return config






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






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

		if name == "norm.weight":
				lowerCAmelCase__       :      Union[str, Any]   					= '''layernorm.weight'''
		if name == "norm.bias":
				lowerCAmelCase__       :      Optional[Any]   					= '''layernorm.bias'''

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

		return name






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		for key in orig_state_dict.copy().keys():
				lowerCAmelCase__       :      Any   					= orig_state_dict.pop(_a	)

				if "qkv" in key:
						lowerCAmelCase__       :      List[Any]   					= key.split('''.'''	)
						lowerCAmelCase__       :      List[Any]   					= int(key_split[1]	)
						lowerCAmelCase__       :      Optional[int]   					= int(key_split[4]	)
						lowerCAmelCase__       :      str   					= config.embed_dim

						if "weight" in key:
								lowerCAmelCase__       :      Tuple   					= val[:dim, :]
								lowerCAmelCase__       :      str   					= val[dim : dim * 2, :]
								lowerCAmelCase__       :      Tuple   					= val[-dim:, :]
						else:
								lowerCAmelCase__       :      Optional[Any]   					= val[:dim]
								lowerCAmelCase__       :      Dict   					= val[dim : dim * 2]
								lowerCAmelCase__       :      str   					= val[-dim:]
						pass
				else:
						lowerCAmelCase__       :      Optional[Any]   					= val

		return orig_state_dict






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      int   					= get_config(_a	)
		lowerCAmelCase__       :      str   					= SwinaSRForImageSuperResolution(_a	)
		model.eval()

		lowerCAmelCase__       :      Optional[Any]   					= torch.hub.load_state_dict_from_url(_a     ,       map_location='''cpu'''	)
		lowerCAmelCase__       :      Any   					= convert_state_dict(_a     ,       _a	)
		lowerCAmelCase__					,     lowerCAmelCase__       :      Any   					= 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
		lowerCAmelCase__       :      int   					= '''https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'''
		lowerCAmelCase__       :      List[Any]   					= Image.open(requests.get(_a     ,       stream=_a	).raw	).convert('''RGB'''	)
		lowerCAmelCase__       :      Optional[int]   					= SwinaSRImageProcessor()
		# pixel_values = processor(image, return_tensors="pt").pixel_values

		lowerCAmelCase__       :      Optional[int]   					= 126 if '''Jpeg''' in checkpoint_url else 256
		lowerCAmelCase__       :      Any   					= Compose(
		    [
		        Resize((image_size, image_size)	),
		        ToTensor(),
		        Normalize(mean=[0.4_85, 0.4_56, 0.4_06]     ,       std=[0.2_29, 0.2_24, 0.2_25]	),
		    ]	)
		lowerCAmelCase__       :      int   					= transforms(_a	).unsqueeze(0	)

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

		lowerCAmelCase__       :      Optional[Any]   					= model(_a	)

		# assert values
		if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
				lowerCAmelCase__       :      int   					= torch.Size([1, 3, 512, 512]	)
				lowerCAmelCase__       :      Union[str, Any]   					= torch.tensor(
				    [[-0.70_87, -0.71_38, -0.67_21], [-0.83_40, -0.80_95, -0.72_98], [-0.91_49, -0.84_14, -0.79_40]]	)
		elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
				lowerCAmelCase__       :      Union[str, Any]   					= torch.Size([1, 3, 1_024, 1_024]	)
				lowerCAmelCase__       :      List[str]   					= torch.tensor(
				    [[-0.77_75, -0.81_05, -0.89_33], [-0.77_64, -0.83_56, -0.92_25], [-0.79_76, -0.86_86, -0.95_79]]	)
		elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
				# TODO values didn't match exactly here
				lowerCAmelCase__       :      Optional[int]   					= torch.Size([1, 3, 1_024, 1_024]	)
				lowerCAmelCase__       :      str   					= torch.tensor(
				    [[-0.80_35, -0.75_04, -0.74_91], [-0.85_38, -0.81_24, -0.77_82], [-0.88_04, -0.86_51, -0.84_93]]	)
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				lowerCAmelCase__       :      List[str]   					= torch.Size([1, 3, 512, 512]	)
				lowerCAmelCase__       :      Optional[int]   					= torch.tensor(
				    [[-0.76_69, -0.86_62, -0.87_67], [-0.88_10, -0.99_62, -0.98_20], [-0.93_40, -1.03_22, -1.11_49]]	)
		elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
				lowerCAmelCase__       :      List[str]   					= torch.Size([1, 3, 1_024, 1_024]	)
				lowerCAmelCase__       :      int   					= torch.tensor(
				    [[-0.52_38, -0.55_57, -0.63_21], [-0.60_16, -0.59_03, -0.63_91], [-0.62_44, -0.63_34, -0.68_89]]	)

		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!'''	)

		lowerCAmelCase__       :      List[Any]   					= {
		    '''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'''
		    ),
		}
		lowerCAmelCase__       :      Optional[int]   					= 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__":
		lowerCamelCase						=		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.''')

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








from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''facebook/dpr-ctx_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-ctx_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
    ),
}





class  _a							(    _lowercase):
	_a  :  List[Any]				   =							'''dpr'''




	def __init__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		List[str]=3_0522		, _SCREAMING_SNAKE_CASE						:		Optional[int]=768		, _SCREAMING_SNAKE_CASE						:		List[str]=12		, _SCREAMING_SNAKE_CASE						:		Tuple=12		, _SCREAMING_SNAKE_CASE						:		str=3072		, _SCREAMING_SNAKE_CASE						:		Optional[Any]="gelu"		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0.1		, _SCREAMING_SNAKE_CASE						:		List[str]=512		, _SCREAMING_SNAKE_CASE						:		int=2		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=0.02		, _SCREAMING_SNAKE_CASE						:		Tuple=1E-12		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=0		, _SCREAMING_SNAKE_CASE						:		List[str]="absolute"		, _SCREAMING_SNAKE_CASE						:		int = 0		, **_SCREAMING_SNAKE_CASE						:		Optional[Any]		, )->	Optional[int]:
			super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      Union[str, Any]   					= vocab_size
			lowerCAmelCase__       :      Tuple   					= hidden_size
			lowerCAmelCase__       :      Union[str, Any]   					= num_hidden_layers
			lowerCAmelCase__       :      List[Any]   					= num_attention_heads
			lowerCAmelCase__       :      Optional[int]   					= hidden_act
			lowerCAmelCase__       :      Optional[Any]   					= intermediate_size
			lowerCAmelCase__       :      Optional[Any]   					= hidden_dropout_prob
			lowerCAmelCase__       :      int   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      str   					= max_position_embeddings
			lowerCAmelCase__       :      List[Any]   					= type_vocab_size
			lowerCAmelCase__       :      Optional[int]   					= initializer_range
			lowerCAmelCase__       :      List[str]   					= layer_norm_eps
			lowerCAmelCase__       :      Dict   					= projection_dim
			lowerCAmelCase__       :      int   					= position_embedding_type




