


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(	lowercase__	)						->	List[Any]:

		'''simple docstring'''







		__lowercase=			SwinaSRConfig()

		if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
				__lowercase=			4
		elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
				__lowercase=			4
				__lowercase=			4_8
				__lowercase=			'pixelshuffle_aux'
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				__lowercase=			[6, 6, 6, 6]
				__lowercase=			6_0
				__lowercase=			[6, 6, 6, 6]
				__lowercase=			'pixelshuffledirect'
		elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
				__lowercase=			4
				__lowercase=			'nearest+conv'
		elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
				__lowercase=			1
				__lowercase=			1
				__lowercase=			1_2_6
				__lowercase=			7
				__lowercase=			255.0
				__lowercase=			''

		return config







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Optional[int]:

		'''simple docstring'''







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

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

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

		return name







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	List[str]:

		'''simple docstring'''







		for key in orig_state_dict.copy().keys():
				__lowercase=			orig_state_dict.pop(lowercase__	)

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

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

		return orig_state_dict







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	List[str]:

		'''simple docstring'''







		__lowercase=			get_config(lowercase__	)
		__lowercase=			SwinaSRForImageSuperResolution(lowercase__	)
		model.eval()

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

		__lowercase=			1_2_6 if 'Jpeg' in checkpoint_url else 2_5_6
		__lowercase=			Compose(
		    [
		        Resize((image_size, image_size)	),
		        ToTensor(),
		        Normalize(mean=[0.485, 0.456, 0.406]   , std=[0.229, 0.224, 0.225]	),
		    ]	)
		__lowercase=			transforms(lowercase__	).unsqueeze(0	)

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

		__lowercase=			model(lowercase__	)

		# assert values
		if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
				__lowercase=			torch.Size([1, 3, 5_1_2, 5_1_2]	)
				__lowercase=			torch.tensor(
				    [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]	)
		elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
				__lowercase=			torch.Size([1, 3, 1_0_2_4, 1_0_2_4]	)
				__lowercase=			torch.tensor(
				    [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]	)
		elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
				# TODO values didn't match exactly here
				__lowercase=			torch.Size([1, 3, 1_0_2_4, 1_0_2_4]	)
				__lowercase=			torch.tensor(
				    [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]	)
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				__lowercase=			torch.Size([1, 3, 5_1_2, 5_1_2]	)
				__lowercase=			torch.tensor(
				    [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]	)
		elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
				__lowercase=			torch.Size([1, 3, 1_0_2_4, 1_0_2_4]	)
				__lowercase=			torch.tensor(
				    [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]	)

		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=			{
		    '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=			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           =		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)










import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
lowerCAmelCase           =		[
    '''kernels/rwkv/wkv_cuda.cu''',
    '''kernels/rwkv/wkv_op.cpp''',
    '''kernels/deformable_detr/ms_deform_attn.h''',
    '''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
    '''models/graphormer/algos_graphormer.pyx''',
]







def        _lowerCamelCase(	lowercase__	)						->	str:

		'''simple docstring'''







		for file in FILES_TO_FIND:
				if not (transformers_path / file).exists():
						return False
		return True


if __name__ == "__main__":
	lowerCAmelCase           =		argparse.ArgumentParser()
	parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
	lowerCAmelCase           =		parser.parse_args()
	if args.check_lib:
		lowerCAmelCase           =		importlib.import_module('''transformers''')
		lowerCAmelCase           =		Path(transformers_module.__file__).parent
	else:
		lowerCAmelCase           =		Path.cwd() / '''build/lib/transformers'''
	if not test_custom_files_are_present(transformers_path):
		raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')






