



from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def 						__UpperCamelCase				(							_lowercase					) ->			Dict:
	return DownloadCommand(args.model,				args.cache_dir,				args.force,				args.trust_remote_code					)






class 		lowerCamelCase__      (						_a     ):




					'''simple docstring'''



					@staticmethod
					def 						__UpperCAmelCase     (  UpperCamelCase_				:	Optional[int]					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       int       =		parser.add_parser('download'					)
						download_parser.add_argument(
						    '--cache-dir'     ,  type=snake_case_     ,  default=snake_case_     ,  help='Path to location to store the models'					)
						download_parser.add_argument(
						    '--force'     ,  action='store_true'     ,  help='Force the model to be download even if already in cache-dir'					)
						download_parser.add_argument(
						    '--trust-remote-code'     ,  action='store_true'     ,  help='Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'     ,  )
						download_parser.add_argument('model'     ,  type=snake_case_     ,  help='Name of the model to download'					)
						download_parser.set_defaults(func=snake_case_					)




					def __init__(  self				:	int     ,  UpperCamelCase_				:	Tuple     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Tuple					)  ->       str:



						'''simple docstring'''





						_lowercase					:       Tuple       =		model
						_lowercase					:       Optional[int]       =		cache
						_lowercase					:       Any       =		force
						_lowercase					:       Dict       =		trust_remote_code




					def 						__UpperCAmelCase     (  self				:	str					)  ->       List[Any]:



						'''simple docstring'''





						from ..models.auto import AutoModel, AutoTokenizer

						AutoModel.from_pretrained(
						    self._model     ,  cache_dir=self._cache     ,  force_download=self._force     ,  trust_remote_code=self._trust_remote_code					)
						AutoTokenizer.from_pretrained(
						    self._model     ,  cache_dir=self._cache     ,  force_download=self._force     ,  trust_remote_code=self._trust_remote_code					)





'''simple docstring'''

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 						__UpperCamelCase				(							_lowercase					) ->			Tuple:
	_lowercase					:       Tuple       =		SwinaSRConfig()

	if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
		_lowercase					:       Optional[Any]       =		4
	elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
		_lowercase					:       Tuple       =		4
		_lowercase					:       Union[str, Any]       =		48
		_lowercase					:       Any       =		'pixelshuffle_aux'
	elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
		_lowercase					:       Dict       =		[6, 6, 6, 6]
		_lowercase					:       Optional[int]       =		60
		_lowercase					:       List[str]       =		[6, 6, 6, 6]
		_lowercase					:       Dict       =		'pixelshuffledirect'
	elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
		_lowercase					:       str       =		4
		_lowercase					:       str       =		'nearest+conv'
	elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
		_lowercase					:       str       =		1
		_lowercase					:       Tuple       =		1
		_lowercase					:       Dict       =		126
		_lowercase					:       Optional[int]       =		7
		_lowercase					:       List[Any]       =		2_5_5.0
		_lowercase					:       Tuple       =		''

	return config







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

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

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

	return name







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			List[str]:
	for key in orig_state_dict.copy().keys():
		_lowercase					:       int       =		orig_state_dict.pop(_lowercase					)

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

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

	return orig_state_dict







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			Union[str, Any]:
	_lowercase					:       Optional[Any]       =		get_config(_lowercase					)
	_lowercase					:       Union[str, Any]       =		SwinaSRForImageSuperResolution(_lowercase					)
	model.eval()

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

	_lowercase					:       Tuple       =		126 if 'Jpeg' in checkpoint_url else 256
	_lowercase					:       List[str]       =		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]					),
	    ]					)
	_lowercase					:       Optional[Any]       =		transforms(_lowercase					).unsqueeze(0					)

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

	_lowercase					:       Optional[int]       =		model(_lowercase					)

	# assert values
	if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
		_lowercase					:       Any       =		torch.Size([1, 3, 512, 512]					)
		_lowercase					:       Tuple       =		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:
		_lowercase					:       Optional[Any]       =		torch.Size([1, 3, 1024, 1024]					)
		_lowercase					:       int       =		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
		_lowercase					:       Optional[int]       =		torch.Size([1, 3, 1024, 1024]					)
		_lowercase					:       Dict       =		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:
		_lowercase					:       List[str]       =		torch.Size([1, 3, 512, 512]					)
		_lowercase					:       int       =		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:
		_lowercase					:       Any       =		torch.Size([1, 3, 1024, 1024]					)
		_lowercase					:       Union[str, Any]       =		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!'					)

	_lowercase					:       List[str]       =		{
	    '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					:       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(_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__":
		_A				:    Dict     						=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.''')

		_A				:    int     						=parser.parse_args()
		convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
