



import collections
import inspect
import unittest

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

from ...test_backbone_common import BackboneTesterMixin
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
							from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor
class  _snake_case	:




				def __init__(  self    :  Tuple,    __lowercase    :  Any,    __lowercase    :  List[Any]=13,    __lowercase    :  Optional[Any]=32,    __lowercase    :  List[Any]=2,    __lowercase    :  Tuple=3,    __lowercase    :  str=16,    __lowercase    :  List[Any]=[1, 2, 1],    __lowercase    :  int=[2, 2, 4],    __lowercase    :  int=2,    __lowercase    :  Any=2.0,    __lowercase    :  Tuple=True,    __lowercase    :  Tuple=0.0,    __lowercase    :  Optional[Any]=0.0,    __lowercase    :  Optional[int]=0.1,    __lowercase    :  Dict="gelu",    __lowercase    :  Optional[Any]=False,    __lowercase    :  List[Any]=True,    __lowercase    :  int=0.02,    __lowercase    :  Dict=1e-5,    __lowercase    :  Optional[Any]=True,    __lowercase    :  Optional[int]=None,    __lowercase    :  Dict=True,    __lowercase    :  List[str]=10,    __lowercase    :  Dict=8,    __lowercase    :  Any=["stage1", "stage2"],    __lowercase    :  Tuple=[1, 2],    ):
							lowercase__         = parent
							lowercase__         = batch_size
							lowercase__         = image_size
							lowercase__         = patch_size
							lowercase__         = num_channels
							lowercase__         = embed_dim
							lowercase__         = depths
							lowercase__         = num_heads
							lowercase__         = window_size
							lowercase__         = mlp_ratio
							lowercase__         = qkv_bias
							lowercase__         = hidden_dropout_prob
							lowercase__         = attention_probs_dropout_prob
							lowercase__         = drop_path_rate
							lowercase__         = hidden_act
							lowercase__         = use_absolute_embeddings
							lowercase__         = patch_norm
							lowercase__         = layer_norm_eps
							lowercase__         = initializer_range
							lowercase__         = is_training
							lowercase__         = scope
							lowercase__         = use_labels
							lowercase__         = type_sequence_label_size
							lowercase__         = encoder_stride
							lowercase__         = out_features
							lowercase__         = out_indices




				def 							A__					(  self    :  Dict  ):
							lowercase__         = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

							lowercase__         = None
							if self.use_labels:
										lowercase__         = ids_tensor([self.batch_size],    self.type_sequence_label_size  )

							lowercase__         = self.get_config()

							return config, pixel_values, labels




				def 							A__					(  self    :  List[str]  ):
							return SwinConfig(
							    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,    encoder_stride=self.encoder_stride,    out_features=self.out_features,    out_indices=self.out_indices,    )




				def 							A__					(  self    :  Tuple,    __lowercase    :  List[str],    __lowercase    :  Optional[int],    __lowercase    :  Optional[Any]  ):
							lowercase__         = SwinModel(config=_a  )
							model.to(_a  )
							model.eval()
							lowercase__         = model(_a  )

							lowercase__         = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
							lowercase__         = int(config.embed_dim * 2 ** (len(config.depths  ) - 1)  )

							self.parent.assertEqual(result.last_hidden_state.shape,    (self.batch_size, expected_seq_len, expected_dim)  )




				def 							A__					(  self    :  Optional[int],    __lowercase    :  Optional[Any],    __lowercase    :  Optional[int],    __lowercase    :  Tuple  ):
							lowercase__         = SwinBackbone(config=_a  )
							model.to(_a  )
							model.eval()
							lowercase__         = model(_a  )

							# verify hidden states
							self.parent.assertEqual(len(result.feature_maps  ),    len(config.out_features  )  )
							self.parent.assertListEqual(list(result.feature_maps[0].shape  ),    [self.batch_size, model.channels[0], 16, 16]  )

							# verify channels
							self.parent.assertEqual(len(model.channels  ),    len(config.out_features  )  )

							# verify backbone works with out_features=None
							lowercase__         = None
							lowercase__         = SwinBackbone(config=_a  )
							model.to(_a  )
							model.eval()
							lowercase__         = model(_a  )

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps  ),    1  )
							self.parent.assertListEqual(list(result.feature_maps[0].shape  ),    [self.batch_size, model.channels[-1], 4, 4]  )

							# verify channels
							self.parent.assertEqual(len(model.channels  ),    1  )




				def 							A__					(  self    :  List[str],    __lowercase    :  List[str],    __lowercase    :  str,    __lowercase    :  List[str]  ):
							lowercase__         = SwinForMaskedImageModeling(config=_a  )
							model.to(_a  )
							model.eval()
							lowercase__         = model(_a  )
							self.parent.assertEqual(
							    result.logits.shape,    (self.batch_size, self.num_channels, self.image_size, self.image_size)  )

							# test greyscale images
							lowercase__         = 1
							lowercase__         = SwinForMaskedImageModeling(_a  )
							model.to(_a  )
							model.eval()

							lowercase__         = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
							lowercase__         = model(_a  )
							self.parent.assertEqual(result.logits.shape,    (self.batch_size, 1, self.image_size, self.image_size)  )




				def 							A__					(  self    :  str,    __lowercase    :  Optional[Any],    __lowercase    :  Any,    __lowercase    :  Optional[Any]  ):
							lowercase__         = self.type_sequence_label_size
							lowercase__         = SwinForImageClassification(_a  )
							model.to(_a  )
							model.eval()
							lowercase__         = model(_a,    labels=_a  )
							self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.type_sequence_label_size)  )

							# test greyscale images
							lowercase__         = 1
							lowercase__         = SwinForImageClassification(_a  )
							model.to(_a  )
							model.eval()

							lowercase__         = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
							lowercase__         = model(_a  )
							self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.type_sequence_label_size)  )




				def 							A__					(  self    :  int  ):
							lowercase__         = self.prepare_config_and_inputs()
							(
							    (
							    lowercase__ 
							)     ,				(
							    lowercase__ 
							)     ,				(
							    lowercase__ 
							)     ,				 
							)         = config_and_inputs
							lowercase__         = {"pixel_values": pixel_values}
							return config, inputs_dict
@require_torch
class  _snake_case	(    UpperCamelCase__			,		UpperCamelCase__			,		unittest.TestCase):
				UpperCamelCase__ :    Union[str, Any]   						=(
				    (
				        SwinModel,
				        SwinBackbone,
				        SwinForImageClassification,
				        SwinForMaskedImageModeling,
				    )
				    if is_torch_available()
				    else ()
				)
				UpperCamelCase__ :    Dict   						=(
				    {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
				    if is_torch_available()
				    else {}
				)
				UpperCamelCase__ :    Dict   						=True

				UpperCamelCase__ :    Dict   						=False
				UpperCamelCase__ :    Union[str, Any]   						=False
				UpperCamelCase__ :    str   						=False




				def 							A__					(  self    :  str  ):
							lowercase__         = SwinModelTester(self  )
							lowercase__         = ConfigTester(self,    config_class=_a,    embed_dim=37  )




				def 							A__					(  self    :  Dict  ):
							self.create_and_test_config_common_properties()
							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 							A__					(  self    :  Union[str, Any]  ):
							return




				def 							A__					(  self    :  int  ):
							lowercase__         = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_a  )




				@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."  )
				def 							A__					(  self    :  Any  ):
							pass




				def 							A__					(  self    :  str  ):
							super().test_training_gradient_checkpointing()




				def 							A__					(  self    :  Optional[int]  ):
							lowercase__         = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*_a  )




				def 							A__					(  self    :  Optional[Any]  ):
							lowercase__         = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*_a  )




				def 							A__					(  self    :  Any  ):
							lowercase__         = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_a  )




				@unittest.skip(reason="Swin does not use inputs_embeds"  )
				def 							A__					(  self    :  Union[str, Any]  ):
							pass




				@unittest.skip(reason="Swin Transformer does not use feedforward chunking"  )
				def 							A__					(  self    :  Any  ):
							pass




				def 							A__					(  self    :  int  ):
							lowercase__     ,				lowercase__         = self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowercase__         = model_class(_a  )
										self.assertIsInstance(model.get_input_embeddings(),    (nn.Module)  )
										lowercase__         = model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(_a,    nn.Linear  )  )




				def 							A__					(  self    :  List[str]  ):
							lowercase__     ,				lowercase__         = self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowercase__         = model_class(_a  )
										lowercase__         = inspect.signature(model.forward  )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowercase__         = [*signature.parameters.keys()]

										lowercase__         = ["pixel_values"]
										self.assertListEqual(arg_names[:1],    _a  )




				def 							A__					(  self    :  Union[str, Any]  ):
							lowercase__     ,				lowercase__         = self.model_tester.prepare_config_and_inputs_for_common()
							lowercase__         = True

							for model_class in self.all_model_classes:
										lowercase__         = True
										lowercase__         = False
										lowercase__         = True
										lowercase__         = model_class(_a  )
										model.to(_a  )
										model.eval()
										with torch.no_grad():
													lowercase__         = model(**self._prepare_for_class(_a,    _a  )  )
										lowercase__         = outputs.attentions
										lowercase__         = len(self.model_tester.depths  )
										self.assertEqual(len(_a  ),    _a  )

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

										self.assertListEqual(
										    list(attentions[0].shape[-3:]  ),    [self.model_tester.num_heads[0], window_size_squared, window_size_squared],    )
										lowercase__         = len(_a  )

										# Check attention is always last and order is fine
										lowercase__         = True
										lowercase__         = True
										lowercase__         = model_class(_a  )
										model.to(_a  )
										model.eval()
										with torch.no_grad():
													lowercase__         = model(**self._prepare_for_class(_a,    _a  )  )

										# also another +1 for reshaped_hidden_states
										lowercase__         = 1 if model_class.__name__ == "SwinBackbone" else 2
										self.assertEqual(out_len + added_hidden_states,    len(_a  )  )

										lowercase__         = outputs.attentions

										self.assertEqual(len(_a  ),    _a  )

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




				def 							A__					(  self    :  Any,    __lowercase    :  int,    __lowercase    :  Dict,    __lowercase    :  Optional[Any],    __lowercase    :  Union[str, Any]  ):
							lowercase__         = model_class(_a  )
							model.to(_a  )
							model.eval()

							with torch.no_grad():
										lowercase__         = model(**self._prepare_for_class(_a,    _a  )  )

							lowercase__         = outputs.hidden_states

							lowercase__         = getattr(
							    self.model_tester,    "expected_num_hidden_layers",    len(self.model_tester.depths  ) + 1  )
							self.assertEqual(len(_a  ),    _a  )

							# Swin has a different seq_length
							lowercase__         = (
							    config.patch_size
							    if isinstance(config.patch_size,    collections.abc.Iterable  )
							    else (config.patch_size, config.patch_size)
							)

							lowercase__         = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]  ),    [num_patches, self.model_tester.embed_dim],    )

							if not model_class.__name__ == "SwinBackbone":
										lowercase__         = outputs.reshaped_hidden_states
										self.assertEqual(len(_a  ),    _a  )

										lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__         = reshaped_hidden_states[0].shape
										lowercase__         = (
										    reshaped_hidden_states[0].view(_a,    _a,    height * width  ).permute(0,    2,    1  )
										)
										self.assertListEqual(
										    list(reshaped_hidden_states.shape[-2:]  ),    [num_patches, self.model_tester.embed_dim],    )




				def 							A__					(  self    :  Optional[int]  ):
							lowercase__     ,				lowercase__         = self.model_tester.prepare_config_and_inputs_for_common()

							lowercase__         = (
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size,    collections.abc.Iterable  )
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)

							for model_class in self.all_model_classes:
										lowercase__         = True
										self.check_hidden_states_output(_a,    _a,    _a,    _a  )

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										lowercase__         = True

										self.check_hidden_states_output(_a,    _a,    _a,    _a  )




				def 							A__					(  self    :  str  ):
							lowercase__     ,				lowercase__         = self.model_tester.prepare_config_and_inputs_for_common()
							lowercase__         = 3

							lowercase__         = (
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size,    collections.abc.Iterable  )
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)
							lowercase__         = (
							    config.patch_size
							    if isinstance(config.patch_size,    collections.abc.Iterable  )
							    else (config.patch_size, config.patch_size)
							)

							lowercase__         = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							lowercase__         = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes:
										lowercase__         = True
										self.check_hidden_states_output(_a,    _a,    _a,    (padded_height, padded_width)  )

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										lowercase__         = True
										self.check_hidden_states_output(_a,    _a,    _a,    (padded_height, padded_width)  )




				@slow
				def 							A__					(  self    :  int  ):





							for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase__         = SwinModel.from_pretrained(_a  )
										self.assertIsNotNone(_a  )




				def 							A__					(  self    :  Optional[Any]  ):
							lowercase__     ,				lowercase__         = self.model_tester.prepare_config_and_inputs_for_common()

							lowercase__         = _config_zero_init(_a  )
							for model_class in self.all_model_classes:
										lowercase__         = model_class(config=_a  )
										for name, param in model.named_parameters():
													if "embeddings" not in name and 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''',    )
@require_vision
@require_torch
class  _snake_case	(    unittest.TestCase):




				@cached_property
				def 							A__					(  self    :  List[Any]  ):
							return (
							    AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"  )
							    if is_vision_available()
							    else None
							)




				@slow
				def 							A__					(  self    :  Optional[int]  ):
							lowercase__         = SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"  ).to(_a  )
							lowercase__         = self.default_image_processor

							lowercase__         = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
							lowercase__         = image_processor(images=_a,    return_tensors="pt"  ).to(_a  )

							# forward pass
							with torch.no_grad():
										lowercase__         = model(**_a  )

							# verify the logits
							lowercase__         = torch.Size((1, 1000)  )
							self.assertEqual(outputs.logits.shape,    _a  )
							lowercase__         = torch.tensor([-0.0948, -0.6454, -0.0921]  ).to(_a  )
							self.assertTrue(torch.allclose(outputs.logits[0, :3],    _a,    atol=1e-4  )  )
@require_torch
class  _snake_case	(    unittest.TestCase			,		UpperCamelCase__):
				UpperCamelCase__ :    int   						=(SwinBackbone,) if is_torch_available() else ()
				UpperCamelCase__ :    int   						=SwinConfig




				def 							A__					(  self    :  Tuple  ):
							lowercase__         = SwinModelTester(self  )








import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =UNetaDModel(**a__)
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.0.W'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.0.W'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.1.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.1.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.2.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.2.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.3.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.3.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-4]].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-3]].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-2]].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-1]].data

				_SCREAMING_SNAKE_CASE	     =4

				def set_attention_weights(a__		,a__		,a__):
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

				def set_resnet_weights(a__		,a__		,a__):
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =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:
												_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
												_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

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

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

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

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

								if hasattr(a__		,'''resnet_up''') and block.resnet_up is not None:
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
												module_index += 1
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
												module_index += 1
												set_resnet_weights(block.resnet_up		,a__		,a__)
												module_index += 1

				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
				module_index += 1
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data

				return new_model_architecture.state_dict()


if __name__ == "__main__":
							snake_case_  :	Tuple							  =							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_  :	List[str]							  =							parser.parse_args()

							snake_case_  :	List[Any]							  =							torch.load(args.checkpoint_path, map_location='''cpu''')

							with open(args.config_file) as f:
														snake_case_  :	List[str]							  =							json.loads(f.read())

							snake_case_  :	Optional[int]							  =							convert_ncsnpp_checkpoint(
							    checkpoint,
							    config,
							)

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

							snake_case_  :	Union[str, Any]							  =							UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

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

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