



'''simple docstring'''

import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


_A				:    List[Any]     						=logging.get_logger(__name__)

_A				:    Any     						=OrderedDict(
    [
        ('''align''', '''EfficientNetImageProcessor'''),
        ('''beit''', '''BeitImageProcessor'''),
        ('''bit''', '''BitImageProcessor'''),
        ('''blip''', '''BlipImageProcessor'''),
        ('''blip-2''', '''BlipImageProcessor'''),
        ('''bridgetower''', '''BridgeTowerImageProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPImageProcessor'''),
        ('''clip''', '''CLIPImageProcessor'''),
        ('''clipseg''', '''ViTImageProcessor'''),
        ('''conditional_detr''', '''ConditionalDetrImageProcessor'''),
        ('''convnext''', '''ConvNextImageProcessor'''),
        ('''convnextv2''', '''ConvNextImageProcessor'''),
        ('''cvt''', '''ConvNextImageProcessor'''),
        ('''data2vec-vision''', '''BeitImageProcessor'''),
        ('''deformable_detr''', '''DeformableDetrImageProcessor'''),
        ('''deit''', '''DeiTImageProcessor'''),
        ('''deta''', '''DetaImageProcessor'''),
        ('''detr''', '''DetrImageProcessor'''),
        ('''dinat''', '''ViTImageProcessor'''),
        ('''donut-swin''', '''DonutImageProcessor'''),
        ('''dpt''', '''DPTImageProcessor'''),
        ('''efficientformer''', '''EfficientFormerImageProcessor'''),
        ('''efficientnet''', '''EfficientNetImageProcessor'''),
        ('''flava''', '''FlavaImageProcessor'''),
        ('''focalnet''', '''BitImageProcessor'''),
        ('''git''', '''CLIPImageProcessor'''),
        ('''glpn''', '''GLPNImageProcessor'''),
        ('''groupvit''', '''CLIPImageProcessor'''),
        ('''imagegpt''', '''ImageGPTImageProcessor'''),
        ('''instructblip''', '''BlipImageProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2ImageProcessor'''),
        ('''layoutlmv3''', '''LayoutLMv3ImageProcessor'''),
        ('''levit''', '''LevitImageProcessor'''),
        ('''mask2former''', '''Mask2FormerImageProcessor'''),
        ('''maskformer''', '''MaskFormerImageProcessor'''),
        ('''mgp-str''', '''ViTImageProcessor'''),
        ('''mobilenet_v1''', '''MobileNetV1ImageProcessor'''),
        ('''mobilenet_v2''', '''MobileNetV2ImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevitv2''', '''MobileViTImageProcessor'''),
        ('''nat''', '''ViTImageProcessor'''),
        ('''oneformer''', '''OneFormerImageProcessor'''),
        ('''owlvit''', '''OwlViTImageProcessor'''),
        ('''perceiver''', '''PerceiverImageProcessor'''),
        ('''pix2struct''', '''Pix2StructImageProcessor'''),
        ('''poolformer''', '''PoolFormerImageProcessor'''),
        ('''regnet''', '''ConvNextImageProcessor'''),
        ('''resnet''', '''ConvNextImageProcessor'''),
        ('''sam''', '''SamImageProcessor'''),
        ('''segformer''', '''SegformerImageProcessor'''),
        ('''swiftformer''', '''ViTImageProcessor'''),
        ('''swin''', '''ViTImageProcessor'''),
        ('''swin2sr''', '''Swin2SRImageProcessor'''),
        ('''swinv2''', '''ViTImageProcessor'''),
        ('''table-transformer''', '''DetrImageProcessor'''),
        ('''timesformer''', '''VideoMAEImageProcessor'''),
        ('''tvlt''', '''TvltImageProcessor'''),
        ('''upernet''', '''SegformerImageProcessor'''),
        ('''van''', '''ConvNextImageProcessor'''),
        ('''videomae''', '''VideoMAEImageProcessor'''),
        ('''vilt''', '''ViltImageProcessor'''),
        ('''vit''', '''ViTImageProcessor'''),
        ('''vit_hybrid''', '''ViTHybridImageProcessor'''),
        ('''vit_mae''', '''ViTImageProcessor'''),
        ('''vit_msn''', '''ViTImageProcessor'''),
        ('''xclip''', '''CLIPImageProcessor'''),
        ('''yolos''', '''YolosImageProcessor'''),
    ]
)

_A				:    List[Any]     						=_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)







def 						__UpperCamelCase				(							_lowercase					) ->			str:
	for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
		if class_name in extractors:
			_lowercase					:       Optional[Any]       =		model_type_to_module_name(_lowercase					)

			_lowercase					:       Tuple       =		importlib.import_module(f'''.{module_name}''',				'transformers.models'					)
			try:
				return getattr(_lowercase,				_lowercase					)
			except AttributeError:
				continue

	for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
		if getattr(_lowercase,				'__name__',				_lowercase					) == class_name:
			return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
	_lowercase					:       Optional[Any]       =		importlib.import_module('transformers'					)
	if hasattr(_lowercase,				_lowercase					):
		return getattr(_lowercase,				_lowercase					)

	return None







def 						__UpperCamelCase				(							_lowercase,				_lowercase = None,				_lowercase = False,				_lowercase = False,				_lowercase = None,				_lowercase = None,				_lowercase = None,				_lowercase = False,				**_lowercase,				) ->			List[Any]:
	_lowercase					:       List[Any]       =		get_file_from_repo(
	    _lowercase,				_lowercase,				cache_dir=_lowercase,				force_download=_lowercase,				resume_download=_lowercase,				proxies=_lowercase,				use_auth_token=_lowercase,				revision=_lowercase,				local_files_only=_lowercase,				)
	if resolved_config_file is None:
		logger.info(
		    'Could not locate the image processor configuration file, will try to use the model config instead.'					)
		return {}

	with open(_lowercase,				encoding='utf-8'					) as reader:
		return json.load(_lowercase					)






class 		lowerCamelCase__      :




					'''simple docstring'''



					def __init__(  self				:	Union[str, Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						raise EnvironmentError(
						    'AutoImageProcessor is designed to be instantiated '
						    'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'					)




					@classmethod
					@replace_list_option_in_docstrings(UpperCamelCase_					)
					def 						__UpperCAmelCase     (  cls				:	Tuple     ,  UpperCamelCase_				:	str     ,  **UpperCamelCase_				:	List[Any]					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Any       =		kwargs.pop('config'     ,  UpperCamelCase_					)
						_lowercase					:       Any       =		kwargs.pop('trust_remote_code'     ,  UpperCamelCase_					)
						_lowercase					:       Dict       =		True

						_lowercase  ,    _lowercase					:       int       =		ImageProcessingMixin.get_image_processor_dict(UpperCamelCase_     ,  **UpperCamelCase_					)
						_lowercase					:       Any       =		config_dict.get('image_processor_type'     ,  UpperCamelCase_					)
						_lowercase					:       List[str]       =		None
						if "AutoImageProcessor" in config_dict.get('auto_map'     ,  {}					):
							_lowercase					:       Tuple       =		config_dict['auto_map']['AutoImageProcessor']

						# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
						# and if so, infer the image processor class from there.
						if image_processor_class is None and image_processor_auto_map is None:
							_lowercase					:       Tuple       =		config_dict.pop('feature_extractor_type'     ,  UpperCamelCase_					)
							if feature_extractor_class is not None:
								logger.warning(
								    'Could not find image processor class in the image processor config or the model config. Loading'
								    ' based on pattern matching with the model\'s feature extractor configuration.'					)
								_lowercase					:       List[str]       =		feature_extractor_class.replace('FeatureExtractor'     ,  'ImageProcessor'					)
							if "AutoFeatureExtractor" in config_dict.get('auto_map'     ,  {}					):
								_lowercase					:       Tuple       =		config_dict['auto_map']['AutoFeatureExtractor']
								_lowercase					:       Dict       =		feature_extractor_auto_map.replace('FeatureExtractor'     ,  'ImageProcessor'					)
								logger.warning(
								    'Could not find image processor auto map in the image processor config or the model config.'
								    ' Loading based on pattern matching with the model\'s feature extractor configuration.'					)

        # If we don't find the image processor class in the image processor config, let's try the model config.
						if image_processor_class is None and image_processor_auto_map is None:
							if not isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
								_lowercase					:       str       =		AutoConfig.from_pretrained(UpperCamelCase_     ,  **UpperCamelCase_					)
							# It could be in `config.image_processor_type``
							_lowercase					:       str       =		getattr(UpperCamelCase_     ,  'image_processor_type'     ,  UpperCamelCase_					)
							if hasattr(UpperCamelCase_     ,  'auto_map'					) and "AutoImageProcessor" in config.auto_map:
								_lowercase					:       Tuple       =		config.auto_map['AutoImageProcessor']

						if image_processor_class is not None:
							_lowercase					:       List[str]       =		image_processor_class_from_name(UpperCamelCase_					)

						_lowercase					:       List[Any]       =		image_processor_auto_map is not None
						_lowercase					:       Union[str, Any]       =		image_processor_class is not None or type(UpperCamelCase_					) in IMAGE_PROCESSOR_MAPPING
						_lowercase					:       Tuple       =		resolve_trust_remote_code(
						    UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)

						if has_remote_code and trust_remote_code:
							_lowercase					:       Dict       =		get_class_from_dynamic_module(
							    UpperCamelCase_     ,  UpperCamelCase_     ,  **UpperCamelCase_					)
							_lowercase					:       Tuple       =		kwargs.pop('code_revision'     ,  UpperCamelCase_					)
							if os.path.isdir(UpperCamelCase_					):
								image_processor_class.register_for_auto_class()
							return image_processor_class.from_dict(UpperCamelCase_     ,  **UpperCamelCase_					)
						elif image_processor_class is not None:
							return image_processor_class.from_dict(UpperCamelCase_     ,  **UpperCamelCase_					)
						# Last try: we use the IMAGE_PROCESSOR_MAPPING.
						elif type(UpperCamelCase_					) in IMAGE_PROCESSOR_MAPPING:
							_lowercase					:       Union[str, Any]       =		IMAGE_PROCESSOR_MAPPING[type(UpperCamelCase_					)]
							return image_processor_class.from_dict(UpperCamelCase_     ,  **UpperCamelCase_					)

						raise ValueError(
						    F'''Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '''
						    F'''`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '''
						    F'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()					)}'''					)




					@staticmethod
					def 						__UpperCAmelCase     (  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	int					)  ->       List[str]:



						'''simple docstring'''





						IMAGE_PROCESSOR_MAPPING.register(UpperCamelCase_     ,  UpperCamelCase_					)





'''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)
