


import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
				import bitsandbytes as bnb

from copy import deepcopy


_lowercase							:   Tuple						=						logging.getLogger(__name__)





def 	_lowerCAmelCase					( UpperCamelCase__:  torch.nn.Module				,       UpperCamelCase__:  BnbQuantizationConfig				,       UpperCamelCase__:  Union[str, os.PathLike] = None				,       UpperCamelCase__:  Optional[Dict[str, Union[int, str, torch.device]]] = None				,       UpperCamelCase__:  Optional[List[str]] = None				,       UpperCamelCase__:  Optional[Dict[Union[int, str], Union[int, str]]] = None				,       UpperCamelCase__:  Optional[Union[str, os.PathLike]] = None				,       UpperCamelCase__:  bool = False				,       )			-> List[Any]:





	"""simple docstring"""







	A             =					bnb_quantization_config.load_in_abit
	A             =					bnb_quantization_config.load_in_abit

	if load_in_abit and not is_abit_bnb_available():
		raise ImportError(
		    """You have a version of `bitsandbytes` that is not compatible with 8bit quantization,"""
		    """ make sure you have the latest version of `bitsandbytes` installed."""    )
	if load_in_abit and not is_abit_bnb_available():
		raise ValueError(
		    """You have a version of `bitsandbytes` that is not compatible with 4bit quantization,"""
		    """make sure you have the latest version of `bitsandbytes` installed."""    )

	A             =					[]
	# custom device map
	if isinstance(UpperCamelCase__				,       UpperCamelCase__    ) and len(device_map.keys()    ) > 1:
		A             =					[key for key, value in device_map.items() if value in ["""disk""", """cpu"""]]

	# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
	if bnb_quantization_config.skip_modules is None:
		A             =					get_keys_to_not_convert(UpperCamelCase__    )

	# add cpu modules to skip modules only for 4-bit modules
	if load_in_abit:
		bnb_quantization_config.skip_modules.extend(UpperCamelCase__    )
	A             =					bnb_quantization_config.skip_modules

	# We add the modules we want to keep in full precision
	if bnb_quantization_config.keep_in_fpaa_modules is None:
		A             =					[]
	A             =					bnb_quantization_config.keep_in_fpaa_modules
	modules_to_not_convert.extend(UpperCamelCase__    )

	# compatibility with peft
	A             =					load_in_abit
	A             =					load_in_abit

	A             =					get_parameter_device(UpperCamelCase__    )
	if model_device.type != "meta":
		# quantization of an already loaded model
		logger.warning(
		    """It is not recommended to quantize a loaded model. """
		    """The model should be instantiated under the `init_empty_weights` context manager."""    )
		A             =					replace_with_bnb_layers(UpperCamelCase__				,       UpperCamelCase__				,       modules_to_not_convert=UpperCamelCase__    )
		# convert param to the right dtype
		A             =					bnb_quantization_config.torch_dtype
		for name, param in model.state_dict().items():
			if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules    ):
				param.to(torch.floataa    )
				if param.dtype != torch.floataa:
					A             =					name.replace(""".weight"""				,       """"""    ).replace(""".bias"""				,       """"""    )
					A             =					getattr(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__    )
					if param is not None:
						param.to(torch.floataa    )
			elif torch.is_floating_point(UpperCamelCase__    ):
				param.to(UpperCamelCase__    )
		if model_device.type == "cuda":
			# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
			model.cuda(torch.cuda.current_device()    )
			torch.cuda.empty_cache()
		elif torch.cuda.is_available():
			model.to(torch.cuda.current_device()    )
		else:
			raise RuntimeError("""No GPU found. A GPU is needed for quantization."""    )
		logger.info(
		    f'The model device type is {model_device.type}. However, cuda is needed for quantization.'
		    """We move the model to cuda."""    )
		return model

	elif weights_location is None:
		raise RuntimeError(
		    f'`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} '    )

	else:
		with init_empty_weights():
			A             =					replace_with_bnb_layers(
			    UpperCamelCase__				,       UpperCamelCase__				,       modules_to_not_convert=UpperCamelCase__    )

		A             =					get_quantized_model_device_map(
		    UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       max_memory=UpperCamelCase__				,       no_split_module_classes=UpperCamelCase__				,       )
		if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
			A             =					True

		A             =					any(x in list(device_map.values()    ) for x in ["""cpu""", """disk"""]    )

		load_checkpoint_in_model(
		    UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       dtype=bnb_quantization_config.torch_dtype				,       offload_folder=UpperCamelCase__				,       offload_state_dict=UpperCamelCase__				,       keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules				,       offload_abit_bnb=load_in_abit and offload				,       )
		return dispatch_model(UpperCamelCase__				,       device_map=UpperCamelCase__				,       offload_dir=UpperCamelCase__    )





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[int]				,       UpperCamelCase__:  str				,       UpperCamelCase__:  str=None				,       UpperCamelCase__:  str=None				,       UpperCamelCase__:  Any=None    )			-> int:





	"""simple docstring"""







	if device_map is None:
		if torch.cuda.is_available():
			A             =					{"""""": torch.cuda.current_device()}
		else:
			raise RuntimeError("""No GPU found. A GPU is needed for quantization."""    )
		logger.info("""The device_map was not initialized.""" """Setting device_map to `{'':torch.cuda.current_device()}`."""    )

	if isinstance(UpperCamelCase__				,       UpperCamelCase__    ):
		if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
			raise ValueError(
			    """If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or """
			    """'sequential'."""    )

		A             =					{}
		special_dtypes.update(
		    {
		        name: bnb_quantization_config.torch_dtype
		        for name, _ in model.named_parameters()
		        if any(m in name for m in bnb_quantization_config.skip_modules    )
		    }    )
		special_dtypes.update(
		    {
		        name: torch.floataa
		        for name, _ in model.named_parameters()
		        if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules    )
		    }    )

		A             =					{}
		A             =					special_dtypes
		A             =					no_split_module_classes
		A             =					bnb_quantization_config.target_dtype

		# get max_memory for each device.
		if device_map != "sequential":
			A             =					get_balanced_memory(
			    UpperCamelCase__				,       low_zero=(device_map == """balanced_low_0""")				,       max_memory=UpperCamelCase__				,       **UpperCamelCase__				,       )

		A             =					max_memory
		A             =					infer_auto_device_map(UpperCamelCase__				,       **UpperCamelCase__    )

	if isinstance(UpperCamelCase__				,       UpperCamelCase__    ):
		# check if don't have any quantized module on the cpu
		A             =					bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

		A             =					{
		    key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
		}
		for device in ["cpu", "disk"]:
			if device in device_map_without_some_modules.values():
				if bnb_quantization_config.load_in_abit:
					raise ValueError(
					    """
                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
                        these modules in `torch_dtype`, you need to pass a custom `device_map` to
                        `load_and_quantize_model`. Check
                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
                        for more details.
                        """    )
				else:
					logger.info(
					    """Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit"""    )
		del device_map_without_some_modules
	return device_map





def 	_lowerCAmelCase					( UpperCamelCase__:  str				,       UpperCamelCase__:  Optional[int]				,       UpperCamelCase__:  List[str]=None				,       UpperCamelCase__:  Optional[Any]=None    )			-> List[Any]:





	"""simple docstring"""







	if modules_to_not_convert is None:
		A             =					[]

	A       ,      A             =					_replace_with_bnb_layers(
	    UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__    )
	if not has_been_replaced:
		logger.warning(
		    """You are loading your model in 8bit or 4bit but no linear modules were found in your model."""
		    """ this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers."""
		    """ Please double check your model architecture, or submit an issue on github if you think this is"""
		    """ a bug."""    )
	return model





def 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  List[str]				,       UpperCamelCase__:  List[Any]=None				,       UpperCamelCase__:  Optional[int]=None				,       )			-> str:





	"""simple docstring"""







	A             =					False
	for name, module in model.named_children():
		if current_key_name is None:
			A             =					[]
		current_key_name.append(UpperCamelCase__    )
		if isinstance(UpperCamelCase__				,       nn.Linear    ) and name not in modules_to_not_convert:
			# Check if the current key is not in the `modules_to_not_convert`
			A             =					""".""".join(UpperCamelCase__    )
			A             =					True
			for key in modules_to_not_convert:
				if (
				    (key in current_key_name_str) and (key + "." in current_key_name_str)
				) or key == current_key_name_str:
					A             =					False
					break
			if proceed:
				# Load bnb module with empty weight and replace ``nn.Linear` module
				if bnb_quantization_config.load_in_abit:
					A             =					bnb.nn.LinearabitLt(
					    module.in_features				,       module.out_features				,       module.bias is not None				,       has_fpaa_weights=UpperCamelCase__				,       threshold=bnb_quantization_config.llm_inta_threshold				,       )
				elif bnb_quantization_config.load_in_abit:
					A             =					bnb.nn.Linearabit(
					    module.in_features				,       module.out_features				,       module.bias is not None				,       bnb_quantization_config.bnb_abit_compute_dtype				,       compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant				,       quant_type=bnb_quantization_config.bnb_abit_quant_type				,       )
				else:
					raise ValueError("""load_in_8bit and load_in_4bit can't be both False"""    )
				A             =					module.weight.data
				if module.bias is not None:
					A             =					module.bias.data
				bnb_module.requires_grad_(UpperCamelCase__    )
				setattr(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__    )
				A             =					True
		if len(list(module.children()    )    ) > 0:
			A       ,      A             =					_replace_with_bnb_layers(
			    UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__    )
			A             =					has_been_replaced | _has_been_replaced
		# Remove the last key for recursion
		current_key_name.pop(-1    )
	return model, has_been_replaced





def 	_lowerCAmelCase					( UpperCamelCase__:  Union[str, Any]    )			-> Optional[int]:





	"""simple docstring"""







	with init_empty_weights():
		A             =					deepcopy(UpperCamelCase__    )  # this has 0 cost since it is done inside `init_empty_weights` context manager`

	A             =					find_tied_parameters(UpperCamelCase__    )
	# For compatibility with Accelerate < 0.18
	if isinstance(UpperCamelCase__				,       UpperCamelCase__    ):
		A             =					sum(list(tied_params.values()    )				,       []    ) + list(tied_params.keys()    )
	else:
		A             =					sum(UpperCamelCase__				,       []    )
	A             =					len(UpperCamelCase__    ) > 0

	# Check if it is a base model
	A             =					False
	if hasattr(UpperCamelCase__				,       """base_model_prefix"""    ):
		A             =					not hasattr(UpperCamelCase__				,       model.base_model_prefix    )

	# Ignore this for base models (BertModel, GPT2Model, etc.)
	if (not has_tied_params) and is_base_model:
		return []

	# otherwise they have an attached head
	A             =					list(model.named_children()    )
	A             =					[list_modules[-1][0]]

	# add last module together with tied weights
	A             =					set(UpperCamelCase__    ) - set(UpperCamelCase__    )
	A             =					list(set(UpperCamelCase__    )    ) + list(UpperCamelCase__    )

	# remove ".weight" from the keys
	A             =					[""".weight""", """.bias"""]
	A             =					[]
	for name in list_untouched:
		for name_to_remove in names_to_remove:
			if name_to_remove in name:
				A             =					name.replace(UpperCamelCase__				,       """"""    )
		filtered_module_names.append(UpperCamelCase__    )

	return filtered_module_names





def 	_lowerCAmelCase					( UpperCamelCase__:  Dict    )			-> Union[str, Any]:





	"""simple docstring"""







	for m in model.modules():
		if isinstance(UpperCamelCase__				,       bnb.nn.Linearabit    ):
			return True
	return False





def 	_lowerCAmelCase					( UpperCamelCase__:  nn.Module    )			-> List[Any]:





	"""simple docstring"""







	return next(parameter.parameters()    ).device





def 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  Optional[Any]				,       UpperCamelCase__:  str				,       UpperCamelCase__:  str				,       UpperCamelCase__:  Optional[Any]				,       UpperCamelCase__:  List[str]				,       UpperCamelCase__:  str    )			-> Optional[Any]:





	"""simple docstring"""







	if fpaa_statistics is None:
		set_module_tensor_to_device(UpperCamelCase__				,       UpperCamelCase__				,       0				,       dtype=UpperCamelCase__				,       value=UpperCamelCase__    )
		A             =					param_name
		A             =					model
		if "." in tensor_name:
			A             =					tensor_name.split("""."""    )
			for split in splits[:-1]:
				A             =					getattr(UpperCamelCase__				,       UpperCamelCase__    )
				if new_module is None:
					raise ValueError(f'{module} has no attribute {split}.'    )
				A             =					new_module
			A             =					splits[-1]
		# offload weights
		A             =					False
		offload_weight(module._parameters[tensor_name]				,       UpperCamelCase__				,       UpperCamelCase__				,       index=UpperCamelCase__    )
		if hasattr(module._parameters[tensor_name]				,       """SCB"""    ):
			offload_weight(
			    module._parameters[tensor_name].SCB				,       param_name.replace("""weight"""				,       """SCB"""    )				,       UpperCamelCase__				,       index=UpperCamelCase__				,       )
	else:
		offload_weight(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       index=UpperCamelCase__    )
		offload_weight(UpperCamelCase__				,       param_name.replace("""weight"""				,       """SCB"""    )				,       UpperCamelCase__				,       index=UpperCamelCase__    )

	set_module_tensor_to_device(UpperCamelCase__				,       UpperCamelCase__				,       """meta"""				,       dtype=UpperCamelCase__				,       value=torch.empty(*param.size()    )    )







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__:  Any    )			-> Optional[int]:





	"""simple docstring"""







	A             =					SwinaSRConfig()

	if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
		A             =					4
	elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
		A             =					4
		A             =					48
		A             =					"""pixelshuffle_aux"""
	elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
		A             =					[6, 6, 6, 6]
		A             =					60
		A             =					[6, 6, 6, 6]
		A             =					"""pixelshuffledirect"""
	elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
		A             =					4
		A             =					"""nearest+conv"""
	elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
		A             =					1
		A             =					1
		A             =					1_26
		A             =					7
		A             =					2_55.0
		A             =					""""""

	return config





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]				,       UpperCamelCase__:  Dict    )			-> Optional[Any]:





	"""simple docstring"""







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

	if name == "norm.weight":
		A             =					"""layernorm.weight"""
	if name == "norm.bias":
		A             =					"""layernorm.bias"""

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

	return name





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[int]				,       UpperCamelCase__:  Union[str, Any]    )			-> Tuple:





	"""simple docstring"""







	for key in orig_state_dict.copy().keys():
		A             =					orig_state_dict.pop(UpperCamelCase__    )

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

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

	return orig_state_dict





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  List[Any]    )			-> str:





	"""simple docstring"""







	A             =					get_config(UpperCamelCase__    )
	A             =					SwinaSRForImageSuperResolution(UpperCamelCase__    )
	model.eval()

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

	A             =					1_26 if """Jpeg""" in checkpoint_url else 2_56
	A             =					Compose(
	    [
	        Resize((image_size, image_size)    ),
	        ToTensor(),
	        Normalize(mean=[0.4_85, 0.4_56, 0.4_06]				,       std=[0.2_29, 0.2_24, 0.2_25]    ),
	    ]    )
	A             =					transforms(UpperCamelCase__    ).unsqueeze(0    )

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

	A             =					model(UpperCamelCase__    )

	# assert values
	if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
		A             =					torch.Size([1, 3, 5_12, 5_12]    )
		A             =					torch.tensor(
		    [[-0.70_87, -0.71_38, -0.67_21], [-0.83_40, -0.80_95, -0.72_98], [-0.91_49, -0.84_14, -0.79_40]]    )
	elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
		A             =					torch.Size([1, 3, 10_24, 10_24]    )
		A             =					torch.tensor(
		    [[-0.77_75, -0.81_05, -0.89_33], [-0.77_64, -0.83_56, -0.92_25], [-0.79_76, -0.86_86, -0.95_79]]    )
	elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
		# TODO values didn't match exactly here
		A             =					torch.Size([1, 3, 10_24, 10_24]    )
		A             =					torch.tensor(
		    [[-0.80_35, -0.75_04, -0.74_91], [-0.85_38, -0.81_24, -0.77_82], [-0.88_04, -0.86_51, -0.84_93]]    )
	elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
		A             =					torch.Size([1, 3, 5_12, 5_12]    )
		A             =					torch.tensor(
		    [[-0.76_69, -0.86_62, -0.87_67], [-0.88_10, -0.99_62, -0.98_20], [-0.93_40, -1.03_22, -1.11_49]]    )
	elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
		A             =					torch.Size([1, 3, 10_24, 10_24]    )
		A             =					torch.tensor(
		    [[-0.52_38, -0.55_57, -0.63_21], [-0.60_16, -0.59_03, -0.63_91], [-0.62_44, -0.63_34, -0.68_89]]    )

	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!"""    )

	A             =					{
	    """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"""
	    ),
	}
	A             =					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__":
				_lowercase							:   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.")

				_lowercase							:   Tuple						=						parser.parse_args()
				convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



