



'''simple docstring'''





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 	lowerCamelCase__ :


						def __init__( self						:  Any      ,       _a						:  List[Any]      ,       _a						:  List[Any]=1_3      ,       _a						:  Optional[int]=3_2      ,       _a						:  str=2      ,       _a						:  List[Any]=3      ,       _a						:  str=1_6      ,       _a						:  Tuple=[1, 2, 1]      ,       _a						:  Union[str, Any]=[2, 2, 4]      ,       _a						:  List[str]=2      ,       _a						:  int=2.0      ,       _a						:  Union[str, Any]=True      ,       _a						:  Optional[Any]=0.0      ,       _a						:  List[str]=0.0      ,       _a						:  Union[str, Any]=0.1      ,       _a						:  Dict="gelu"      ,       _a						:  Optional[Any]=False      ,       _a						:  List[str]=True      ,       _a						:  Any=0.0_2      ,       _a						:  Union[str, Any]=1e-5      ,       _a						:  int=True      ,       _a						:  Optional[Any]=None      ,       _a						:  str=True      ,       _a						:  List[Any]=1_0      ,       _a						:  str=8      ,       _a						:  Optional[Any]=["stage1", "stage2"]      ,       _a						:  Any=[1, 2]      ,       ):
													a__:       Tuple											=parent
													a__:       Optional[int]											=batch_size
													a__:       Any											=image_size
													a__:       List[str]											=patch_size
													a__:       Union[str, Any]											=num_channels
													a__:       str											=embed_dim
													a__:       Optional[int]											=depths
													a__:       List[Any]											=num_heads
													a__:       Optional[Any]											=window_size
													a__:       Optional[Any]											=mlp_ratio
													a__:       Optional[int]											=qkv_bias
													a__:       Any											=hidden_dropout_prob
													a__:       Dict											=attention_probs_dropout_prob
													a__:       Any											=drop_path_rate
													a__:       Tuple											=hidden_act
													a__:       Optional[Any]											=use_absolute_embeddings
													a__:       List[Any]											=patch_norm
													a__:       Tuple											=layer_norm_eps
													a__:       str											=initializer_range
													a__:       List[str]											=is_training
													a__:       str											=scope
													a__:       Union[str, Any]											=use_labels
													a__:       int											=type_sequence_label_size
													a__:       int											=encoder_stride
													a__:       List[Any]											=out_features
													a__:       Optional[Any]											=out_indices


						def   _lowerCamelCase			( self						:  str			):
													a__:       Union[str, Any]											=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

													a__:       Optional[Any]											=None
													if self.use_labels:
																				a__:       int											=ids_tensor([self.batch_size]      ,       self.type_sequence_label_size			)

													a__:       Any											=self.get_config()

													return config, pixel_values, labels


						def   _lowerCamelCase			( self						:  Any			):
													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   _lowerCamelCase			( self						:  str      ,       _a						:  Any      ,       _a						:  str      ,       _a						:  Any			):
													a__:       Any											=SwinModel(config=_a			)
													model.to(_a			)
													model.eval()
													a__:       Optional[Any]											=model(_a			)

													a__:       Tuple											=((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths			) - 1))
													a__:       List[str]											=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   _lowerCamelCase			( self						:  List[Any]      ,       _a						:  Optional[int]      ,       _a						:  List[Any]      ,       _a						:  Dict			):
													a__:       Tuple											=SwinBackbone(config=_a			)
													model.to(_a			)
													model.eval()
													a__:       List[Any]											=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], 1_6, 1_6]			)

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

													# verify backbone works with out_features=None
													a__:       int											=None
													a__:       str											=SwinBackbone(config=_a			)
													model.to(_a			)
													model.eval()
													a__:       Dict											=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   _lowerCamelCase			( self						:  Tuple      ,       _a						:  List[str]      ,       _a						:  List[str]      ,       _a						:  int			):
													a__:       List[str]											=SwinForMaskedImageModeling(config=_a			)
													model.to(_a			)
													model.eval()
													a__:       Dict											=model(_a			)
													self.parent.assertEqual(
													    result.logits.shape      ,       (self.batch_size, self.num_channels, self.image_size, self.image_size)			)

													# test greyscale images
													a__:       Union[str, Any]											=1
													a__:       int											=SwinForMaskedImageModeling(_a			)
													model.to(_a			)
													model.eval()

													a__:       int											=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
													a__:       List[str]											=model(_a			)
													self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, 1, self.image_size, self.image_size)			)


						def   _lowerCamelCase			( self						:  Optional[Any]      ,       _a						:  List[str]      ,       _a						:  Optional[Any]      ,       _a						:  List[Any]			):
													a__:       List[Any]											=self.type_sequence_label_size
													a__:       Union[str, Any]											=SwinForImageClassification(_a			)
													model.to(_a			)
													model.eval()
													a__:       Dict											=model(_a      ,       labels=_a			)
													self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)			)

													# test greyscale images
													a__:       Union[str, Any]											=1
													a__:       Optional[Any]											=SwinForImageClassification(_a			)
													model.to(_a			)
													model.eval()

													a__:       List[Any]											=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
													a__:       Optional[Any]											=model(_a			)
													self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)			)







						def   _lowerCamelCase			( self						:  Any			):
													a__:       Dict											=self.prepare_config_and_inputs()
													(
													    a__			
													):       Tuple											=config_and_inputs
													a__:       Dict											={"pixel_values": pixel_values}
													return config, inputs_dict


@require_torch
class 	lowerCamelCase__ (		_a				,					_a				,					unittest.TestCase   ):
						_lowerCAmelCase								= (
						    (
						        SwinModel,
						        SwinBackbone,
						        SwinForImageClassification,
						        SwinForMaskedImageModeling,
						    )
						    if is_torch_available()
						    else ()
						)
						_lowerCAmelCase								= (
						    {'''feature-extraction''': SwinModel, '''image-classification''': SwinForImageClassification}
						    if is_torch_available()
						    else {}
						)
						_lowerCAmelCase								= True

						_lowerCAmelCase								= False
						_lowerCAmelCase								= False
						_lowerCAmelCase								= False


						def   _lowerCamelCase			( self						:  Optional[Any]			):
													a__:       str											=SwinModelTester(self			)
													a__:       Optional[int]											=ConfigTester(self      ,       config_class=_a      ,       embed_dim=3_7			)


						def   _lowerCamelCase			( self						:  Optional[Any]			):
													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   _lowerCamelCase			( self						:  Union[str, Any]			):
													return


						def   _lowerCamelCase			( self						:  Optional[int]			):
													a__:       List[str]											=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   _lowerCamelCase			( self						:  List[str]			):
													pass


						def   _lowerCamelCase			( self						:  List[Any]			):
													super().test_training_gradient_checkpointing()


						def   _lowerCamelCase			( self						:  str			):
													a__:       Dict											=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_backbone(*_a			)


						def   _lowerCamelCase			( self						:  Tuple			):
													a__:       str											=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_image_modeling(*_a			)


						def   _lowerCamelCase			( self						:  List[str]			):
													a__:       Dict											=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   _lowerCamelCase			( self						:  int			):
													pass


						@unittest.skip(reason="Swin Transformer does not use feedforward chunking"			)
						def   _lowerCamelCase			( self						:  List[str]			):
													pass


						def   _lowerCamelCase			( self						:  str			):
													a__:       Optional[Any]											=self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				a__:       Union[str, Any]											=model_class(_a			)
																				self.assertIsInstance(model.get_input_embeddings()      ,       (nn.Module)			)
																				a__:       int											=model.get_output_embeddings()
																				self.assertTrue(x is None or isinstance(_a      ,       nn.Linear			)			)


						def   _lowerCamelCase			( self						:  Optional[Any]			):
													a__:       Union[str, Any]											=self.model_tester.prepare_config_and_inputs_for_common()

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

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


						def   _lowerCamelCase			( self						:  List[Any]			):
													a__:       List[str]											=self.model_tester.prepare_config_and_inputs_for_common()
													a__:       List[str]											=True

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

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

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

																				# also another +1 for reshaped_hidden_states
																				a__:       Optional[Any]											=1 if model_class.__name__ == "SwinBackbone" else 2
																				self.assertEqual(out_len + added_hidden_states      ,       len(_a			)			)

																				a__:       List[str]											=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   _lowerCamelCase			( self						:  Optional[int]      ,       _a						:  Tuple      ,       _a						:  str      ,       _a						:  Any      ,       _a						:  List[str]			):
													a__:       List[Any]											=model_class(_a			)
													model.to(_a			)
													model.eval()

													with torch.no_grad():
																				a__:       Optional[int]											=model(**self._prepare_for_class(_a      ,       _a			)			)

													a__:       Tuple											=outputs.hidden_states

													a__:       Optional[int]											=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
													a__:       int											=(
													    config.patch_size
													    if isinstance(config.patch_size      ,       collections.abc.Iterable			)
													    else (config.patch_size, config.patch_size)
													)

													a__:       List[str]											=(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":
																				a__:       Any											=outputs.reshaped_hidden_states
																				self.assertEqual(len(_a			)      ,       _a			)

																				a__:       Tuple											=reshaped_hidden_states[0].shape
																				a__:       Tuple											=(
																				    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   _lowerCamelCase			( self						:  List[Any]			):
													a__:       List[str]											=self.model_tester.prepare_config_and_inputs_for_common()

													a__:       Dict											=(
													    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:
																				a__:       Any											=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"]
																				a__:       List[Any]											=True

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


						def   _lowerCamelCase			( self						:  Dict			):
													a__:       Dict											=self.model_tester.prepare_config_and_inputs_for_common()
													a__:       int											=3

													a__:       List[Any]											=(
													    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)
													)
													a__:       Optional[int]											=(
													    config.patch_size
													    if isinstance(config.patch_size      ,       collections.abc.Iterable			)
													    else (config.patch_size, config.patch_size)
													)

													a__:       List[Any]											=image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
													a__:       List[str]											=image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

													for model_class in self.all_model_classes:
																				a__:       Dict											=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"]
																				a__:       Optional[int]											=True
																				self.check_hidden_states_output(_a      ,       _a      ,       _a      ,       (padded_height, padded_width)			)


						@slow
						def   _lowerCamelCase			( self						:  List[Any]			):
													for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				a__:       Optional[int]											=SwinModel.from_pretrained(_a			)
																				self.assertIsNotNone(_a			)







						def   _lowerCamelCase			( self						:  Tuple			):
													a__:       int											=self.model_tester.prepare_config_and_inputs_for_common()

													a__:       Dict											=_config_zero_init(_a			)
													for model_class in self.all_model_classes:
																				a__:       Optional[int]											=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 	lowerCamelCase__ (		unittest.TestCase   ):


						@cached_property
						def   _lowerCamelCase			( self						:  int			):
													return (
													    AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"			)
													    if is_vision_available()
													    else None
													)







						@slow
						def   _lowerCamelCase			( self						:  List[Any]			):
													a__:       Tuple											=SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"			).to(_a			)
													a__:       Dict											=self.default_image_processor

													a__:       Any											=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
													a__:       Optional[Any]											=image_processor(images=_a      ,       return_tensors="pt"			).to(_a			)

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

													# verify the logits
													a__:       Optional[int]											=torch.Size((1, 1_0_0_0)			)
													self.assertEqual(outputs.logits.shape      ,       _a			)
													a__:       Any											=torch.tensor([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]			).to(_a			)
													self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,       _a      ,       atol=1e-4			)			)


@require_torch
class 	lowerCamelCase__ (		unittest.TestCase				,					_a   ):
						_lowerCAmelCase								= (SwinBackbone,) if is_torch_available() else ()
						_lowerCAmelCase								= SwinConfig


						def   _lowerCamelCase			( self						:  int			):
													a__:       Tuple											=SwinModelTester(self			)










import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel





def 						__lowerCamelCase       (      __magic_name__	:  int     , __magic_name__	:  Optional[Any]=1						):
							if n_shave_prefix_segments >= 0:
														return ".".join(path.split("."						)[n_shave_prefix_segments:]						)
							else:
														return ".".join(path.split("."						)[:n_shave_prefix_segments]						)





def 						__lowerCamelCase       (      __magic_name__	:  Optional[Any]     , __magic_name__	:  Tuple=0						):
							a__:       Dict											=[]
							for old_item in old_list:
														a__:       Tuple											=old_item
														a__:       Any											=new_item.replace("block."     , "resnets."						)
														a__:       Optional[int]											=new_item.replace("conv_shorcut"     , "conv1"						)
														a__:       Dict											=new_item.replace("in_shortcut"     , "conv_shortcut"						)
														a__:       Optional[int]											=new_item.replace("temb_proj"     , "time_emb_proj"						)

														a__:       Optional[int]											=shave_segments(__magic_name__     , n_shave_prefix_segments=__magic_name__						)

														mapping.append({"old": old_item, "new": new_item}						)

							return mapping





def 						__lowerCamelCase       (      __magic_name__	:  Union[str, Any]     , __magic_name__	:  Optional[int]=0     , __magic_name__	:  Dict=False						):
							a__:       Optional[int]											=[]
							for old_item in old_list:
														a__:       Dict											=old_item

														# In `model.mid`, the layer is called `attn`.
														if not in_mid:
																					a__:       Optional[int]											=new_item.replace("attn"     , "attentions"						)
														a__:       Dict											=new_item.replace(".k."     , ".key."						)
														a__:       Dict											=new_item.replace(".v."     , ".value."						)
														a__:       str											=new_item.replace(".q."     , ".query."						)

														a__:       Union[str, Any]											=new_item.replace("proj_out"     , "proj_attn"						)
														a__:       Tuple											=new_item.replace("norm"     , "group_norm"						)

														a__:       Any											=shave_segments(__magic_name__     , n_shave_prefix_segments=__magic_name__						)
														mapping.append({"old": old_item, "new": new_item}						)

							return mapping





def 						__lowerCamelCase       (      __magic_name__	:  Tuple     , __magic_name__	:  Tuple     , __magic_name__	:  int     , __magic_name__	:  Union[str, Any]=None     , __magic_name__	:  Tuple=None     , __magic_name__	:  Union[str, Any]=None						):
							assert isinstance(__magic_name__     , __magic_name__						), "Paths should be a list of dicts containing 'old' and 'new' keys."

							if attention_paths_to_split is not None:
														if config is None:
																					raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'."						)

														for path, path_map in attention_paths_to_split.items():
																					a__:       Any											=old_checkpoint[path]
																					a__:       List[Any]											=old_tensor.shape[0] // 3

																					a__:       List[Any]											=(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

																					a__:       Optional[int]											=old_tensor.shape[0] // config.get("num_head_channels"     , 1						) // 3

																					a__:       List[str]											=old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
																					a__							,			a__							,			a__:       Any											=old_tensor.split(channels // num_heads     , dim=1						)

																					a__:       Any											=query.reshape(__magic_name__						).squeeze()
																					a__:       Optional[Any]											=key.reshape(__magic_name__						).squeeze()
																					a__:       List[Any]											=value.reshape(__magic_name__						).squeeze()

							for path in paths:
														a__:       Optional[Any]											=path["new"]

														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

														a__:       Any											=new_path.replace("down."     , "down_blocks."						)
														a__:       Union[str, Any]											=new_path.replace("up."     , "up_blocks."						)

														if additional_replacements is not None:
																					for replacement in additional_replacements:
																												a__:       Union[str, Any]											=new_path.replace(replacement["old"]     , replacement["new"]						)

														if "attentions" in new_path:
																					a__:       int											=old_checkpoint[path["old"]].squeeze()
														else:
																					a__:       int											=old_checkpoint[path["old"]]





def 						__lowerCamelCase       (      __magic_name__	:  Any     , __magic_name__	:  List[str]						):
							a__:       Tuple											={}

							a__:       Optional[Any]											=checkpoint["temb.dense.0.weight"]
							a__:       List[str]											=checkpoint["temb.dense.0.bias"]
							a__:       Dict											=checkpoint["temb.dense.1.weight"]
							a__:       int											=checkpoint["temb.dense.1.bias"]

							a__:       Union[str, Any]											=checkpoint["norm_out.weight"]
							a__:       Tuple											=checkpoint["norm_out.bias"]

							a__:       Dict											=checkpoint["conv_in.weight"]
							a__:       Any											=checkpoint["conv_in.bias"]
							a__:       List[Any]											=checkpoint["conv_out.weight"]
							a__:       Tuple											=checkpoint["conv_out.bias"]

							a__:       Optional[int]											=len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "down" in layer}						)
							a__:       int											={
							    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(__magic_name__						)
							}

							a__:       Any											=len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "up" in layer}						)
							a__:       List[str]											={layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(__magic_name__						)}

							for i in range(__magic_name__						):
														a__:       Dict											=(i - 1) // (config["layers_per_block"] + 1)

														if any("downsample" in layer for layer in down_blocks[i]						):
																					a__:       List[str]											=checkpoint[
																					    F"down.{i}.downsample.op.weight"
																					]
																					a__:       int											=checkpoint[F"down.{i}.downsample.op.bias"]
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

														if any("block" in layer for layer in down_blocks[i]						):
																					a__:       List[Any]											=len(
																					    {".".join(shave_segments(__magic_name__     , 2						).split("."						)[:2]						) for layer in down_blocks[i] if "block" in layer}						)
																					a__:       Optional[int]											={
																					    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																					    for layer_id in range(__magic_name__						)
																					}

																					if num_blocks > 0:
																												for j in range(config["layers_per_block"]						):
																																			a__:       List[Any]											=renew_resnet_paths(blocks[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__						)

														if any("attn" in layer for layer in down_blocks[i]						):
																					a__:       str											=len(
																					    {".".join(shave_segments(__magic_name__     , 2						).split("."						)[:2]						) for layer in down_blocks[i] if "attn" in layer}						)
																					a__:       str											={
																					    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																					    for layer_id in range(__magic_name__						)
																					}

																					if num_attn > 0:
																												for j in range(config["layers_per_block"]						):
																																			a__:       Tuple											=renew_attention_paths(attns[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , config=__magic_name__						)

							a__:       Union[str, Any]											=[key for key in checkpoint if "mid.block_1" in key]
							a__:       Dict											=[key for key in checkpoint if "mid.block_2" in key]
							a__:       List[str]											=[key for key in checkpoint if "mid.attn_1" in key]

							# Mid new 2
							a__:       Any											=renew_resnet_paths(__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]     , )

							a__:       Optional[Any]											=renew_resnet_paths(__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]     , )

							a__:       Tuple											=renew_attention_paths(__magic_name__     , in_mid=__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]     , )

							for i in range(__magic_name__						):
														a__:       Union[str, Any]											=num_up_blocks - 1 - i

														if any("upsample" in layer for layer in up_blocks[i]						):
																					a__:       Tuple											=checkpoint[
																					    F"up.{i}.upsample.conv.weight"
																					]
																					a__:       Any											=checkpoint[F"up.{i}.upsample.conv.bias"]

														if any("block" in layer for layer in up_blocks[i]						):
																					a__:       Dict											=len(
																					    {".".join(shave_segments(__magic_name__     , 2						).split("."						)[:2]						) for layer in up_blocks[i] if "block" in layer}						)
																					a__:       Dict											={
																					    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(__magic_name__						)
																					}

																					if num_blocks > 0:
																												for j in range(config["layers_per_block"] + 1						):
																																			a__:       Optional[int]											={"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
																																			a__:       Union[str, Any]											=renew_resnet_paths(blocks[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[replace_indices]						)

														if any("attn" in layer for layer in up_blocks[i]						):
																					a__:       Union[str, Any]											=len(
																					    {".".join(shave_segments(__magic_name__     , 2						).split("."						)[:2]						) for layer in up_blocks[i] if "attn" in layer}						)
																					a__:       List[Any]											={
																					    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(__magic_name__						)
																					}

																					if num_attn > 0:
																												for j in range(config["layers_per_block"] + 1						):
																																			a__:       List[str]											={"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
																																			a__:       List[str]											=renew_attention_paths(attns[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[replace_indices]						)

							a__:       Optional[int]											={k.replace("mid_new_2"     , "mid_block"						): v for k, v in new_checkpoint.items()}
							return new_checkpoint





def 						__lowerCamelCase       (      __magic_name__	:  str     , __magic_name__	:  Optional[Any]						):
							a__:       List[str]											={}

							a__:       Optional[Any]											=checkpoint["encoder.norm_out.weight"]
							a__:       Any											=checkpoint["encoder.norm_out.bias"]

							a__:       List[str]											=checkpoint["encoder.conv_in.weight"]
							a__:       List[str]											=checkpoint["encoder.conv_in.bias"]
							a__:       Union[str, Any]											=checkpoint["encoder.conv_out.weight"]
							a__:       Optional[Any]											=checkpoint["encoder.conv_out.bias"]

							a__:       List[Any]											=checkpoint["decoder.norm_out.weight"]
							a__:       Tuple											=checkpoint["decoder.norm_out.bias"]

							a__:       Optional[int]											=checkpoint["decoder.conv_in.weight"]
							a__:       str											=checkpoint["decoder.conv_in.bias"]
							a__:       Tuple											=checkpoint["decoder.conv_out.weight"]
							a__:       Dict											=checkpoint["decoder.conv_out.bias"]

							a__:       Optional[int]											=len({".".join(layer.split("."						)[:3]						) for layer in checkpoint if "down" in layer}						)
							a__:       Optional[Any]											={
							    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(__magic_name__						)
							}

							a__:       int											=len({".".join(layer.split("."						)[:3]						) for layer in checkpoint if "up" in layer}						)
							a__:       Optional[int]											={layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(__magic_name__						)}

							for i in range(__magic_name__						):
														a__:       Union[str, Any]											=(i - 1) // (config["layers_per_block"] + 1)

														if any("downsample" in layer for layer in down_blocks[i]						):
																					a__:       List[str]											=checkpoint[
																					    F"encoder.down.{i}.downsample.conv.weight"
																					]
																					a__:       Union[str, Any]											=checkpoint[
																					    F"encoder.down.{i}.downsample.conv.bias"
																					]

														if any("block" in layer for layer in down_blocks[i]						):
																					a__:       Union[str, Any]											=len(
																					    {".".join(shave_segments(__magic_name__     , 3						).split("."						)[:3]						) for layer in down_blocks[i] if "block" in layer}						)
																					a__:       List[str]											={
																					    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																					    for layer_id in range(__magic_name__						)
																					}

																					if num_blocks > 0:
																												for j in range(config["layers_per_block"]						):
																																			a__:       Tuple											=renew_resnet_paths(blocks[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__						)

														if any("attn" in layer for layer in down_blocks[i]						):
																					a__:       int											=len(
																					    {".".join(shave_segments(__magic_name__     , 3						).split("."						)[:3]						) for layer in down_blocks[i] if "attn" in layer}						)
																					a__:       int											={
																					    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																					    for layer_id in range(__magic_name__						)
																					}

																					if num_attn > 0:
																												for j in range(config["layers_per_block"]						):
																																			a__:       str											=renew_attention_paths(attns[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , config=__magic_name__						)

							a__:       List[Any]											=[key for key in checkpoint if "mid.block_1" in key]
							a__:       int											=[key for key in checkpoint if "mid.block_2" in key]
							a__:       List[str]											=[key for key in checkpoint if "mid.attn_1" in key]

							# Mid new 2
							a__:       Optional[Any]											=renew_resnet_paths(__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]     , )

							a__:       int											=renew_resnet_paths(__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]     , )

							a__:       Any											=renew_attention_paths(__magic_name__     , in_mid=__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]     , )

							for i in range(__magic_name__						):
														a__:       List[Any]											=num_up_blocks - 1 - i

														if any("upsample" in layer for layer in up_blocks[i]						):
																					a__:       Optional[Any]											=checkpoint[
																					    F"decoder.up.{i}.upsample.conv.weight"
																					]
																					a__:       Any											=checkpoint[
																					    F"decoder.up.{i}.upsample.conv.bias"
																					]

														if any("block" in layer for layer in up_blocks[i]						):
																					a__:       List[str]											=len(
																					    {".".join(shave_segments(__magic_name__     , 3						).split("."						)[:3]						) for layer in up_blocks[i] if "block" in layer}						)
																					a__:       Union[str, Any]											={
																					    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(__magic_name__						)
																					}

																					if num_blocks > 0:
																												for j in range(config["layers_per_block"] + 1						):
																																			a__:       Union[str, Any]											={"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
																																			a__:       Optional[Any]											=renew_resnet_paths(blocks[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[replace_indices]						)

														if any("attn" in layer for layer in up_blocks[i]						):
																					a__:       List[str]											=len(
																					    {".".join(shave_segments(__magic_name__     , 3						).split("."						)[:3]						) for layer in up_blocks[i] if "attn" in layer}						)
																					a__:       Optional[int]											={
																					    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(__magic_name__						)
																					}

																					if num_attn > 0:
																												for j in range(config["layers_per_block"] + 1						):
																																			a__:       Dict											={"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
																																			a__:       int											=renew_attention_paths(attns[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[replace_indices]						)

							a__:       List[Any]											={k.replace("mid_new_2"     , "mid_block"						): v for k, v in new_checkpoint.items()}
							a__:       List[Any]											=checkpoint["quant_conv.weight"]
							a__:       Tuple											=checkpoint["quant_conv.bias"]
							if "quantize.embedding.weight" in checkpoint:
														a__:       Dict											=checkpoint["quantize.embedding.weight"]
							a__:       Union[str, Any]											=checkpoint["post_quant_conv.weight"]
							a__:       str											=checkpoint["post_quant_conv.bias"]

							return new_checkpoint


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

		parser.add_argument(
		    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
		)

		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help='''The config json file corresponding to the architecture.''',
		)

		parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

		__UpperCAmelCase						  =     parser.parse_args()
		__UpperCAmelCase						  =     torch.load(args.checkpoint_path)

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

		# unet case
		__UpperCAmelCase						  =     {key.split('''.''')[0] for key in checkpoint.keys()}
		if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
				__UpperCAmelCase						  =     convert_vq_autoenc_checkpoint(checkpoint, config)
		else:
				__UpperCAmelCase						  =     convert_ddpm_checkpoint(checkpoint, config)

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

		if config["_class_name"] == "VQModel":
				__UpperCAmelCase						  =     VQModel(**config)
				model.load_state_dict(converted_checkpoint)
				model.save_pretrained(args.dump_path)
		elif config["_class_name"] == "AutoencoderKL":
				__UpperCAmelCase						  =     AutoencoderKL(**config)
				model.load_state_dict(converted_checkpoint)
				model.save_pretrained(args.dump_path)
		else:
				__UpperCAmelCase						  =     UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				__UpperCAmelCase						  =     DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

				__UpperCAmelCase						  =     DDPMPipeline(unet=model, scheduler=scheduler)
				pipe.save_pretrained(args.dump_path)





