import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   int:
							__UpperCAmelCase						:			Any         =   UNetaDModel(**snake_case__							)
							__UpperCAmelCase						:			int         =   checkpoint["all_modules.0.W"].data
							__UpperCAmelCase						:			Any         =   checkpoint["all_modules.0.W"].data
							__UpperCAmelCase						:			Optional[int]         =   checkpoint["all_modules.1.weight"].data
							__UpperCAmelCase						:			Optional[Any]         =   checkpoint["all_modules.1.bias"].data

							__UpperCAmelCase						:			str         =   checkpoint["all_modules.2.weight"].data
							__UpperCAmelCase						:			Dict         =   checkpoint["all_modules.2.bias"].data

							__UpperCAmelCase						:			Any         =   checkpoint["all_modules.3.weight"].data
							__UpperCAmelCase						:			Optional[int]         =   checkpoint["all_modules.3.bias"].data

							__UpperCAmelCase						:			Any         =   checkpoint[list(checkpoint.keys()							)[-4]].data
							__UpperCAmelCase						:			int         =   checkpoint[list(checkpoint.keys()							)[-3]].data
							__UpperCAmelCase						:			List[Any]         =   checkpoint[list(checkpoint.keys()							)[-2]].data
							__UpperCAmelCase						:			int         =   checkpoint[list(checkpoint.keys()							)[-1]].data

							__UpperCAmelCase						:			Optional[Any]         =   4

							def set_attention_weights(snake_case__,    snake_case__,    snake_case__							):
														__UpperCAmelCase						:			int         =   old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
														__UpperCAmelCase						:			Union[str, Any]         =   old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
														__UpperCAmelCase						:			Optional[Any]         =   old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

														__UpperCAmelCase						:			List[Any]         =   old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
														__UpperCAmelCase						:			Union[str, Any]         =   old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
														__UpperCAmelCase						:			Any         =   old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

														__UpperCAmelCase						:			List[Any]         =   old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
														__UpperCAmelCase						:			List[str]         =   old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

														__UpperCAmelCase						:			Dict         =   old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
														__UpperCAmelCase						:			Optional[Any]         =   old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

							def set_resnet_weights(snake_case__,    snake_case__,    snake_case__							):
														__UpperCAmelCase						:			Optional[int]         =   old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
														__UpperCAmelCase						:			List[str]         =   old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
														__UpperCAmelCase						:			str         =   old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
														__UpperCAmelCase						:			Optional[Any]         =   old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

														__UpperCAmelCase						:			Tuple         =   old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
														__UpperCAmelCase						:			List[Any]         =   old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
														__UpperCAmelCase						:			int         =   old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
														__UpperCAmelCase						:			Dict         =   old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

														__UpperCAmelCase						:			Optional[int]         =   old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
														__UpperCAmelCase						:			Any         =   old_checkpoint[f'''all_modules.{index}.Dense_0.bias'''].data

														if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
																					__UpperCAmelCase						:			List[Any]         =   old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
																					__UpperCAmelCase						:			int         =   old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

							for i, block in enumerate(new_model_architecture.downsample_blocks							):
														__UpperCAmelCase						:			str         =   hasattr(snake_case__,    "attentions"							)
														for j in range(len(block.resnets							)							):
																					set_resnet_weights(block.resnets[j],    snake_case__,    snake_case__							)
																					module_index += 1
																					if has_attentions:
																												set_attention_weights(block.attentions[j],    snake_case__,    snake_case__							)
																												module_index += 1

														if hasattr(snake_case__,    "downsamplers"							) and block.downsamplers is not None:
																					set_resnet_weights(block.resnet_down,    snake_case__,    snake_case__							)
																					module_index += 1
																					__UpperCAmelCase						:			Dict         =   checkpoint[f'''all_modules.{module_index}.Conv_0.weight'''].data
																					__UpperCAmelCase						:			Union[str, Any]         =   checkpoint[f'''all_modules.{module_index}.Conv_0.bias'''].data
																					module_index += 1

							set_resnet_weights(new_model_architecture.mid_block.resnets[0],    snake_case__,    snake_case__							)
							module_index += 1
							set_attention_weights(new_model_architecture.mid_block.attentions[0],    snake_case__,    snake_case__							)
							module_index += 1
							set_resnet_weights(new_model_architecture.mid_block.resnets[1],    snake_case__,    snake_case__							)
							module_index += 1

							for i, block in enumerate(new_model_architecture.up_blocks							):
														__UpperCAmelCase						:			str         =   hasattr(snake_case__,    "attentions"							)
														for j in range(len(block.resnets							)							):
																					set_resnet_weights(block.resnets[j],    snake_case__,    snake_case__							)
																					module_index += 1
														if has_attentions:
																					set_attention_weights(
																					    block.attentions[0],    snake_case__,    snake_case__							)  # why can there only be a single attention layer for up?
																					module_index += 1

														if hasattr(snake_case__,    "resnet_up"							) and block.resnet_up is not None:
																					__UpperCAmelCase						:			int         =   checkpoint[f'''all_modules.{module_index}.weight'''].data
																					__UpperCAmelCase						:			int         =   checkpoint[f'''all_modules.{module_index}.bias'''].data
																					module_index += 1
																					__UpperCAmelCase						:			Union[str, Any]         =   checkpoint[f'''all_modules.{module_index}.weight'''].data
																					__UpperCAmelCase						:			Optional[Any]         =   checkpoint[f'''all_modules.{module_index}.bias'''].data
																					module_index += 1
																					set_resnet_weights(block.resnet_up,    snake_case__,    snake_case__							)
																					module_index += 1

							__UpperCAmelCase						:			str         =   checkpoint[f'''all_modules.{module_index}.weight'''].data
							__UpperCAmelCase						:			Optional[int]         =   checkpoint[f'''all_modules.{module_index}.bias'''].data
							module_index += 1
							__UpperCAmelCase						:			str         =   checkpoint[f'''all_modules.{module_index}.weight'''].data
							__UpperCAmelCase						:			str         =   checkpoint[f'''all_modules.{module_index}.bias'''].data

							return new_model_architecture.state_dict()


if __name__ == "__main__":
	_snake_case			=						argparse.ArgumentParser()

	parser.add_argument(
	    '''--checkpoint_path''',
	    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin''',
	    type=str,
	    required=False,
	    help='''Path to the checkpoint to convert.''',
	)

	parser.add_argument(
	    '''--config_file''',
	    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/config.json''',
	    type=str,
	    required=False,
	    help='''The config json file corresponding to the architecture.''',
	)

	parser.add_argument(
	    '''--dump_path''',
	    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt''',
	    type=str,
	    required=False,
	    help='''Path to the output model.''',
	)

	_snake_case			=						parser.parse_args()

	_snake_case			=						torch.load(args.checkpoint_path, map_location='''cpu''')

	with open(args.config_file) as f:
		_snake_case			=						json.loads(f.read())

	_snake_case			=						convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

	if "sde" in config:
		del config["sde"]

	_snake_case			=						UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		_snake_case			=						ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

		_snake_case			=						ScoreSdeVePipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)





import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
	from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
	from PIL import Image

	from transformers import SwinaSRImageProcessor




class 					_snake_case						:
					def __init__(  self:				Tuple	,  __lowerCamelCase:				Tuple	,  __lowerCamelCase:				Union[str, Any]=13	,  __lowerCamelCase:				str=32	,  __lowerCamelCase:				Dict=1	,  __lowerCamelCase:				List[Any]=3	,  __lowerCamelCase:				List[Any]=16	,  __lowerCamelCase:				Any=[1, 2, 1]	,  __lowerCamelCase:				List[str]=[2, 2, 4]	,  __lowerCamelCase:				Tuple=2	,  __lowerCamelCase:				Tuple=2.0	,  __lowerCamelCase:				Optional[Any]=True	,  __lowerCamelCase:				List[str]=0.0	,  __lowerCamelCase:				str=0.0	,  __lowerCamelCase:				List[str]=0.1	,  __lowerCamelCase:				Optional[Any]="gelu"	,  __lowerCamelCase:				List[str]=False	,  __lowerCamelCase:				int=True	,  __lowerCamelCase:				List[str]=0.02	,  __lowerCamelCase:				Dict=1e-5	,  __lowerCamelCase:				Dict=True	,  __lowerCamelCase:				List[Any]=None	,  __lowerCamelCase:				Tuple=False	,  __lowerCamelCase:				Optional[Any]=2	,  )    ->				Union[str, Any]:
												__UpperCAmelCase						:			Any         =   parent
												__UpperCAmelCase						:			Optional[int]         =   batch_size
												__UpperCAmelCase						:			Union[str, Any]         =   image_size
												__UpperCAmelCase						:			int         =   patch_size
												__UpperCAmelCase						:			str         =   num_channels
												__UpperCAmelCase						:			Any         =   embed_dim
												__UpperCAmelCase						:			str         =   depths
												__UpperCAmelCase						:			Optional[int]         =   num_heads
												__UpperCAmelCase						:			Any         =   window_size
												__UpperCAmelCase						:			Tuple         =   mlp_ratio
												__UpperCAmelCase						:			List[str]         =   qkv_bias
												__UpperCAmelCase						:			int         =   hidden_dropout_prob
												__UpperCAmelCase						:			Dict         =   attention_probs_dropout_prob
												__UpperCAmelCase						:			Union[str, Any]         =   drop_path_rate
												__UpperCAmelCase						:			Union[str, Any]         =   hidden_act
												__UpperCAmelCase						:			Optional[int]         =   use_absolute_embeddings
												__UpperCAmelCase						:			Any         =   patch_norm
												__UpperCAmelCase						:			str         =   layer_norm_eps
												__UpperCAmelCase						:			Any         =   initializer_range
												__UpperCAmelCase						:			List[str]         =   is_training
												__UpperCAmelCase						:			Union[str, Any]         =   scope
												__UpperCAmelCase						:			Optional[int]         =   use_labels
												__UpperCAmelCase						:			int         =   upscale

												# here we set some attributes to make tests pass
												__UpperCAmelCase						:			Union[str, Any]         =   len(__lowerCamelCase     )
												__UpperCAmelCase						:			Optional[Any]         =   embed_dim
												__UpperCAmelCase						:			List[Any]         =   (image_size // patch_size) ** 2
					def    _lowerCamelCase (  self:				List[Any]     )    ->				List[Any]:
												__UpperCAmelCase						:			Any         =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

												__UpperCAmelCase						:			Union[str, Any]         =   None
												if self.use_labels:
																			__UpperCAmelCase						:			Any         =   ids_tensor([self.batch_size]	,  self.type_sequence_label_size     )

												__UpperCAmelCase						:			Optional[Any]         =   self.get_config()

												return config, pixel_values, labels
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Tuple:
												return SwinaSRConfig(
												    image_size=self.image_size	,  patch_size=self.patch_size	,  num_channels=self.num_channels	,  embed_dim=self.embed_dim	,  depths=self.depths	,  num_heads=self.num_heads	,  window_size=self.window_size	,  mlp_ratio=self.mlp_ratio	,  qkv_bias=self.qkv_bias	,  hidden_dropout_prob=self.hidden_dropout_prob	,  attention_probs_dropout_prob=self.attention_probs_dropout_prob	,  drop_path_rate=self.drop_path_rate	,  hidden_act=self.hidden_act	,  use_absolute_embeddings=self.use_absolute_embeddings	,  path_norm=self.patch_norm	,  layer_norm_eps=self.layer_norm_eps	,  initializer_range=self.initializer_range	,  upscale=self.upscale	,  )
					def    _lowerCamelCase (  self:				List[Any]	,  __lowerCamelCase:				List[Any]	,  __lowerCamelCase:				Any	,  __lowerCamelCase:				List[Any]     )    ->				Optional[int]:
												__UpperCAmelCase						:			str         =   SwinaSRModel(config=__lowerCamelCase     )
												model.to(__lowerCamelCase     )
												model.eval()
												__UpperCAmelCase						:			List[str]         =   model(__lowerCamelCase     )

												self.parent.assertEqual(
												    result.last_hidden_state.shape	,  (self.batch_size, self.embed_dim, self.image_size, self.image_size)     )
					def    _lowerCamelCase (  self:				Union[str, Any]	,  __lowerCamelCase:				Optional[int]	,  __lowerCamelCase:				Tuple	,  __lowerCamelCase:				int     )    ->				Union[str, Any]:
												__UpperCAmelCase						:			Dict         =   SwinaSRForImageSuperResolution(__lowerCamelCase     )
												model.to(__lowerCamelCase     )
												model.eval()
												__UpperCAmelCase						:			List[str]         =   model(__lowerCamelCase     )

												__UpperCAmelCase						:			Optional[Any]         =   self.image_size * self.upscale
												self.parent.assertEqual(
												    result.reconstruction.shape	,  (self.batch_size, self.num_channels, expected_image_size, expected_image_size)     )





					def    _lowerCamelCase (  self:				Any     )    ->				Dict:
												__UpperCAmelCase						:			List[Any]         =   self.prepare_config_and_inputs()
												__UpperCAmelCase    ,	__UpperCAmelCase    ,	__UpperCAmelCase						:			Any         =   config_and_inputs
												__UpperCAmelCase						:			Dict         =   {"pixel_values": pixel_values}
												return config, inputs_dict




@require_torch
class 					_snake_case						(						_lowercase					,      _lowercase					,      unittest.TestCase				):
					lowerCamelCase__:		List[str]  =     (SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
					lowerCamelCase__:		List[Any]  =     {"feature-extraction": SwinaSRModel} if is_torch_available() else {}

					lowerCamelCase__:		List[Any]  =     False
					lowerCamelCase__:		Any  =     False
					lowerCamelCase__:		int  =     False
					lowerCamelCase__:		Optional[Any]  =     False
					lowerCamelCase__:		str  =     False
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Union[str, Any]:
												__UpperCAmelCase						:			Union[str, Any]         =   SwinaSRModelTester(self     )
												__UpperCAmelCase						:			Optional[int]         =   ConfigTester(self	,  config_class=__lowerCamelCase	,  embed_dim=37     )
					def    _lowerCamelCase (  self:				List[str]     )    ->				List[Any]:
												self.config_tester.create_and_test_config_to_json_string()
												self.config_tester.create_and_test_config_to_json_file()
												self.config_tester.create_and_test_config_from_and_save_pretrained()
												self.config_tester.create_and_test_config_with_num_labels()
												self.config_tester.check_config_can_be_init_without_params()
												self.config_tester.check_config_arguments_init()
					def    _lowerCamelCase (  self:				Union[str, Any]     )    ->				Optional[Any]:
												__UpperCAmelCase						:			Optional[int]         =   self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__lowerCamelCase     )
					def    _lowerCamelCase (  self:				Optional[Any]     )    ->				List[str]:
												__UpperCAmelCase						:			Union[str, Any]         =   self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_super_resolution(*__lowerCamelCase     )
					@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."     )
					def    _lowerCamelCase (  self:				str     )    ->				List[Any]:
												pass
					@unittest.skip(reason="Swin2SR does not use inputs_embeds"     )
					def    _lowerCamelCase (  self:				str     )    ->				Optional[Any]:
												pass
					@unittest.skip(reason="Swin2SR does not support training yet"     )
					def    _lowerCamelCase (  self:				int     )    ->				Dict:
												pass
					@unittest.skip(reason="Swin2SR does not support training yet"     )
					def    _lowerCamelCase (  self:				List[str]     )    ->				Optional[Any]:
												pass
					def    _lowerCamelCase (  self:				Optional[Any]     )    ->				Optional[int]:
												__UpperCAmelCase    ,	__UpperCAmelCase						:			Union[str, Any]         =   self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			__UpperCAmelCase						:			Dict         =   model_class(__lowerCamelCase     )
																			self.assertIsInstance(model.get_input_embeddings()	,  (nn.Module)     )
																			__UpperCAmelCase						:			Union[str, Any]         =   model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(__lowerCamelCase	,  nn.Linear     )     )
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Union[str, Any]:
												__UpperCAmelCase    ,	__UpperCAmelCase						:			Dict         =   self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			__UpperCAmelCase						:			List[Any]         =   model_class(__lowerCamelCase     )
																			__UpperCAmelCase						:			Dict         =   inspect.signature(model.forward     )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			__UpperCAmelCase						:			int         =   [*signature.parameters.keys()]

																			__UpperCAmelCase						:			int         =   ["pixel_values"]
																			self.assertListEqual(arg_names[:1]	,  __lowerCamelCase     )
					@slow
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Union[str, Any]:
												for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__UpperCAmelCase						:			Union[str, Any]         =   SwinaSRModel.from_pretrained(__lowerCamelCase     )
																			self.assertIsNotNone(__lowerCamelCase     )
					def    _lowerCamelCase (  self:				Dict     )    ->				Optional[Any]:
												__UpperCAmelCase    ,	__UpperCAmelCase						:			Any         =   self.model_tester.prepare_config_and_inputs_for_common()

												__UpperCAmelCase						:			Union[str, Any]         =   _config_zero_init(__lowerCamelCase     )
												for model_class in self.all_model_classes:
																			__UpperCAmelCase						:			Tuple         =   model_class(config=__lowerCamelCase     )
																			for name, param in model.named_parameters():
																										if "logit_scale" in name:
																																	continue
																										if param.requires_grad:
																																	self.assertIn(
																																	    ((param.data.mean() * 1e9).round() / 1e9).item()	,  [0.0, 1.0]	,  msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''	,  )





					def    _lowerCamelCase (  self:				Optional[int]     )    ->				List[Any]:
												__UpperCAmelCase    ,	__UpperCAmelCase						:			int         =   self.model_tester.prepare_config_and_inputs_for_common()
												__UpperCAmelCase						:			List[Any]         =   True

												for model_class in self.all_model_classes:
																			__UpperCAmelCase						:			Optional[int]         =   True
																			__UpperCAmelCase						:			str         =   False
																			__UpperCAmelCase						:			int         =   True
																			__UpperCAmelCase						:			Dict         =   model_class(__lowerCamelCase     )
																			model.to(__lowerCamelCase     )
																			model.eval()
																			with torch.no_grad():
																										__UpperCAmelCase						:			Optional[int]         =   model(**self._prepare_for_class(__lowerCamelCase	,  __lowerCamelCase     )     )
																			__UpperCAmelCase						:			Any         =   outputs.attentions
																			__UpperCAmelCase						:			Union[str, Any]         =   len(self.model_tester.depths     )
																			self.assertEqual(len(__lowerCamelCase     )	,  __lowerCamelCase     )

																			# check that output_attentions also work using config
																			del inputs_dict["output_attentions"]
																			__UpperCAmelCase						:			List[str]         =   True
																			__UpperCAmelCase						:			Any         =   config.window_size**2
																			__UpperCAmelCase						:			List[str]         =   model_class(__lowerCamelCase     )
																			model.to(__lowerCamelCase     )
																			model.eval()
																			with torch.no_grad():
																										__UpperCAmelCase						:			int         =   model(**self._prepare_for_class(__lowerCamelCase	,  __lowerCamelCase     )     )
																			__UpperCAmelCase						:			List[Any]         =   outputs.attentions
																			self.assertEqual(len(__lowerCamelCase     )	,  __lowerCamelCase     )

																			self.assertListEqual(
																			    list(attentions[0].shape[-3:]     )	,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	,  )
																			__UpperCAmelCase						:			List[str]         =   len(__lowerCamelCase     )

																			# Check attention is always last and order is fine
																			__UpperCAmelCase						:			Tuple         =   True
																			__UpperCAmelCase						:			List[str]         =   True
																			__UpperCAmelCase						:			List[Any]         =   model_class(__lowerCamelCase     )
																			model.to(__lowerCamelCase     )
																			model.eval()
																			with torch.no_grad():
																										__UpperCAmelCase						:			Optional[int]         =   model(**self._prepare_for_class(__lowerCamelCase	,  __lowerCamelCase     )     )

																			self.assertEqual(out_len + 1	,  len(__lowerCamelCase     )     )

																			__UpperCAmelCase						:			List[Any]         =   outputs.attentions

																			self.assertEqual(len(__lowerCamelCase     )	,  __lowerCamelCase     )

																			self.assertListEqual(
																			    list(self_attentions[0].shape[-3:]     )	,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	,  )




@require_vision
@require_torch
@slow
class 					_snake_case						(						unittest.TestCase				):
					def    _lowerCamelCase (  self:				Optional[Any]     )    ->				Optional[int]:
												__UpperCAmelCase						:			List[str]         =   SwinaSRImageProcessor()
												__UpperCAmelCase						:			int         =   SwinaSRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64"     ).to(__lowerCamelCase     )

												__UpperCAmelCase						:			Union[str, Any]         =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
												__UpperCAmelCase						:			Dict         =   processor(images=__lowerCamelCase	,  return_tensors="pt"     ).to(__lowerCamelCase     )

												# forward pass
												with torch.no_grad():
																			__UpperCAmelCase						:			int         =   model(**__lowerCamelCase     )

												# verify the logits
												__UpperCAmelCase						:			str         =   torch.Size([1, 3, 9_76, 12_96]     )
												self.assertEqual(outputs.reconstruction.shape	,  __lowerCamelCase     )
												__UpperCAmelCase						:			Union[str, Any]         =   torch.tensor(
												    [[0.54_58, 0.55_46, 0.56_38], [0.55_26, 0.55_65, 0.56_51], [0.53_96, 0.54_26, 0.56_21]]     ).to(__lowerCamelCase     )
												self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]	,  __lowerCamelCase	,  atol=1e-4     )     )




