
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_		(	__lowercase       :     List[str]			)			->  Optional[int]:
			_snake_case          =      SwinaSRConfig()

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

			return config






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

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

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

			return name






def 	a_		(	__lowercase       :     int	, __lowercase       :     int			)			->  int:
			for key in orig_state_dict.copy().keys():
						_snake_case          =      orig_state_dict.pop(__lowercase			)

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

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

			return orig_state_dict






def 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     List[str]	, __lowercase       :     Union[str, Any]			)			->  Optional[Any]:
			_snake_case          =      get_config(__lowercase			)
			_snake_case          =      SwinaSRForImageSuperResolution(__lowercase			)
			model.eval()

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

			_snake_case          =      126 if 'Jpeg' in checkpoint_url else 256
			_snake_case          =      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]			),
			    ]			)
			_snake_case          =      transforms(__lowercase			).unsqueeze(0			)

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

			_snake_case          =      model(__lowercase			)

			# assert values
			if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
						_snake_case          =      torch.Size([1, 3, 512, 512]			)
						_snake_case          =      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:
						_snake_case          =      torch.Size([1, 3, 1_024, 1_024]			)
						_snake_case          =      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
						_snake_case          =      torch.Size([1, 3, 1_024, 1_024]			)
						_snake_case          =      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:
						_snake_case          =      torch.Size([1, 3, 512, 512]			)
						_snake_case          =      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:
						_snake_case          =      torch.Size([1, 3, 1_024, 1_024]			)
						_snake_case          =      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]	, __lowercase	, atol=1E-3			)
			print('Looks ok!'			)

			_snake_case          =      {
			    '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          =      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__":
				_lowerCamelCase	:    List[str]          =      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	:    Union[str, Any]          =      parser.parse_args()
				convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 	a_		(	__lowercase       :     Dict			)			->  int:
			_snake_case          =      [
			    'encoder.version',
			    'decoder.version',
			    'model.encoder.version',
			    'model.decoder.version',
			    '_float_tensor',
			    'decoder.output_projection.weight',
			]
			for k in ignore_keys:
						state_dict.pop(__lowercase	, __lowercase			)






def 	a_		(	__lowercase       :     List[str]			)			->  str:
			_snake_case     , _snake_case          =      emb.weight.shape
			_snake_case          =      nn.Linear(__lowercase	, __lowercase	, bias=__lowercase			)
			_snake_case          =      emb.weight.data
			return lin_layer






def 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     Optional[Any]="facebook/mbart-large-en-ro"	, __lowercase       :     Union[str, Any]=False	, __lowercase       :     Optional[Any]=False			)			->  int:
			_snake_case          =      torch.load(__lowercase	, map_location='cpu'			)['model']
			remove_ignore_keys_(__lowercase			)
			_snake_case          =      state_dict['encoder.embed_tokens.weight'].shape[0]

			_snake_case          =      MBartConfig.from_pretrained(__lowercase	, vocab_size=__lowercase			)
			if mbart_aa and finetuned:
						_snake_case          =      'relu'

			_snake_case          =      state_dict['decoder.embed_tokens.weight']
			_snake_case          =      MBartForConditionalGeneration(__lowercase			)
			model.model.load_state_dict(__lowercase			)

			if finetuned:
						_snake_case          =      make_linear_from_emb(model.model.shared			)

			return model


if __name__ == "__main__":
			_lowerCamelCase	:    List[str]          =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
			)
			parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument(
			    '''--hf_config''',
			    default='''facebook/mbart-large-cc25''',
			    type=str,
			    help='''Which huggingface architecture to use: mbart-large''',
			)
			parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
			parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
			_lowerCamelCase	:    List[str]          =      parser.parse_args()
			_lowerCamelCase	:    Tuple          =      convert_fairseq_mbart_checkpoint_from_disk(
			    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
			)
			model.save_pretrained(args.pytorch_dump_folder_path)