





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_						(    _UpperCamelCase			):






				"""simple docstring"""


				snake_case_						:   int           =  SwinaSRConfig()

				if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
								snake_case_						:   str           =  4
				elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
								snake_case_						:   str           =  4
								snake_case_						:   Tuple           =  48
								snake_case_						:   Union[str, Any]           =  '''pixelshuffle_aux'''
				elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
								snake_case_						:   Optional[Any]           =  [6, 6, 6, 6]
								snake_case_						:   Any           =  60
								snake_case_						:   List[str]           =  [6, 6, 6, 6]
								snake_case_						:   str           =  '''pixelshuffledirect'''
				elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
								snake_case_						:   Any           =  4
								snake_case_						:   Optional[int]           =  '''nearest+conv'''
				elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
								snake_case_						:   Optional[int]           =  1
								snake_case_						:   int           =  1
								snake_case_						:   str           =  126
								snake_case_						:   Optional[Any]           =  7
								snake_case_						:   List[Any]           =  255.0
								snake_case_						:   Tuple           =  ''''''

				return config






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			):






				"""simple docstring"""


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

				if name == "norm.weight":
								snake_case_						:   List[str]           =  '''layernorm.weight'''
				if name == "norm.bias":
								snake_case_						:   Optional[Any]           =  '''layernorm.bias'''

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

				return name






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			):






				"""simple docstring"""


				for key in orig_state_dict.copy().keys():
								snake_case_						:   List[str]           =  orig_state_dict.pop(_UpperCamelCase			)

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

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

				return orig_state_dict






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			):






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  get_config(_UpperCamelCase			)
				snake_case_						:   Dict           =  SwinaSRForImageSuperResolution(_UpperCamelCase			)
				model.eval()

				snake_case_						:   List[str]           =  torch.hub.load_state_dict_from_url(_UpperCamelCase			,    map_location='''cpu'''			)
				snake_case_						:   Any           =  convert_state_dict(_UpperCamelCase			,    _UpperCamelCase			)
				snake_case_						:   List[str]           =  model.load_state_dict(_UpperCamelCase			,    strict=_UpperCamelCase			)

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

				snake_case_						:   Any           =  126 if '''Jpeg''' in checkpoint_url else 256
				snake_case_						:   List[Any]           =  Compose(
				    [
				        Resize((image_size, image_size)			),
				        ToTensor(),
				        Normalize(mean=[0.485, 0.456, 0.406]			,    std=[0.229, 0.224, 0.225]			),
				    ]			)
				snake_case_						:   List[Any]           =  transforms(_UpperCamelCase			).unsqueeze(0			)

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

				snake_case_						:   Union[str, Any]           =  model(_UpperCamelCase			)

				# assert values
				if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
								snake_case_						:   Optional[Any]           =  torch.Size([1, 3, 512, 512]			)
								snake_case_						:   List[Any]           =  torch.tensor(
								    [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]]			)
				elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
								snake_case_						:   Dict           =  torch.Size([1, 3, 1_024, 1_024]			)
								snake_case_						:   str           =  torch.tensor(
								    [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]]			)
				elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
								# TODO values didn't match exactly here
								snake_case_						:   Union[str, Any]           =  torch.Size([1, 3, 1_024, 1_024]			)
								snake_case_						:   Dict           =  torch.tensor(
								    [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]]			)
				elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
								snake_case_						:   Any           =  torch.Size([1, 3, 512, 512]			)
								snake_case_						:   Dict           =  torch.tensor(
								    [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]]			)
				elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
								snake_case_						:   Dict           =  torch.Size([1, 3, 1_024, 1_024]			)
								snake_case_						:   Optional[int]           =  torch.tensor(
								    [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]]			)

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

				snake_case_						:   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'''
				    ),
				}
				snake_case_						:   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(_UpperCamelCase			)
								print(f'''Saving image processor to {pytorch_dump_folder_path}'''			)
								processor.save_pretrained(_UpperCamelCase			)

				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 os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Optional[Any]	      =				['''image_processor''', '''tokenizer''']
			lowerCamelCase_					:					List[Any]	      =				'''BlipImageProcessor'''
			lowerCamelCase_					:					Union[str, Any]	      =				'''AutoTokenizer'''






			def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							super().__init__(__magic_name__ ,							__magic_name__   )

							# add QFormer tokenizer
							snake_case_						:   Optional[Any]           =  qformer_tokenizer






			def __call__(self ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = True ,							__magic_name__ = False ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = 0 ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = True ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       BatchFeature:


							'''simple docstring'''



							if images is None and text is None:
											raise ValueError('''You have to specify at least images or text.'''   )

							snake_case_						:   Tuple           =  BatchFeature()

							if text is not None:
											snake_case_						:   Tuple           =  self.tokenizer(
											    text=__magic_name__ ,							add_special_tokens=__magic_name__ ,							padding=__magic_name__ ,							truncation=__magic_name__ ,							max_length=__magic_name__ ,							stride=__magic_name__ ,							pad_to_multiple_of=__magic_name__ ,							return_attention_mask=__magic_name__ ,							return_overflowing_tokens=__magic_name__ ,							return_special_tokens_mask=__magic_name__ ,							return_offsets_mapping=__magic_name__ ,							return_token_type_ids=__magic_name__ ,							return_length=__magic_name__ ,							verbose=__magic_name__ ,							return_tensors=__magic_name__ ,							**__magic_name__ ,							)
											encoding.update(__magic_name__   )
											snake_case_						:   Optional[Any]           =  self.qformer_tokenizer(
											    text=__magic_name__ ,							add_special_tokens=__magic_name__ ,							padding=__magic_name__ ,							truncation=__magic_name__ ,							max_length=__magic_name__ ,							stride=__magic_name__ ,							pad_to_multiple_of=__magic_name__ ,							return_attention_mask=__magic_name__ ,							return_overflowing_tokens=__magic_name__ ,							return_special_tokens_mask=__magic_name__ ,							return_offsets_mapping=__magic_name__ ,							return_token_type_ids=__magic_name__ ,							return_length=__magic_name__ ,							verbose=__magic_name__ ,							return_tensors=__magic_name__ ,							**__magic_name__ ,							)
											snake_case_						:   Optional[int]           =  qformer_text_encoding.pop('''input_ids'''   )
											snake_case_						:   Tuple           =  qformer_text_encoding.pop('''attention_mask'''   )

							if images is not None:
											snake_case_						:   Any           =  self.image_processor(__magic_name__ ,							return_tensors=__magic_name__   )
											encoding.update(__magic_name__   )

							return encoding






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							return self.tokenizer.batch_decode(*__magic_name__ ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



							return self.tokenizer.decode(*__magic_name__ ,							**__magic_name__   )






			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  self.tokenizer.model_input_names
							snake_case_						:   List[Any]           =  self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							**__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							if os.path.isfile(__magic_name__   ):
											raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''   )
							os.makedirs(__magic_name__ ,							exist_ok=__magic_name__   )
							snake_case_						:   Any           =  os.path.join(__magic_name__ ,							'''qformer_tokenizer'''   )
							self.qformer_tokenizer.save_pretrained(__magic_name__   )
							return super().save_pretrained(__magic_name__ ,							**__magic_name__   )






			@classmethod
			def       lowerCamelCase  (cls ,							__magic_name__ ,							**__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  AutoTokenizer.from_pretrained(__magic_name__ ,							subfolder='''qformer_tokenizer'''   )
							snake_case_						:   str           =  cls._get_arguments_from_pretrained(__magic_name__ ,							**__magic_name__   )
							args.append(__magic_name__   )
							return cls(*__magic_name__   )
