






"""simple docstring"""


import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
		from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import DPTImageProcessor




class 	__UpperCamelCase    :
					def __init__(       self       :  List[str]    ,   UpperCAmelCase       :  Tuple    ,   UpperCAmelCase       :  Any=2    ,   UpperCAmelCase       :  int=32    ,   UpperCAmelCase       :  List[Any]=16    ,   UpperCAmelCase       :  Union[str, Any]=3    ,   UpperCAmelCase       :  Tuple=True    ,   UpperCAmelCase       :  Any=True    ,   UpperCAmelCase       :  List[Any]=32    ,   UpperCAmelCase       :  Dict=4    ,   UpperCAmelCase       :  int=[0, 1, 2, 3]    ,   UpperCAmelCase       :  int=4    ,   UpperCAmelCase       :  List[str]=37    ,   UpperCAmelCase       :  List[str]="gelu"    ,   UpperCAmelCase       :  List[Any]=0.1    ,   UpperCAmelCase       :  Tuple=0.1    ,   UpperCAmelCase       :  int=0.0_2    ,   UpperCAmelCase       :  Tuple=3    ,   UpperCAmelCase       :  Any=False    ,   UpperCAmelCase       :  Dict=None    ,   )  ->					Any:
											lowerCAmelCase      :str				  = parent
											lowerCAmelCase      :int				  = batch_size
											lowerCAmelCase      :List[str]				  = image_size
											lowerCAmelCase      :Tuple				  = patch_size
											lowerCAmelCase      :Dict				  = num_channels
											lowerCAmelCase      :Optional[int]				  = is_training
											lowerCAmelCase      :str				  = use_labels
											lowerCAmelCase      :List[str]				  = hidden_size
											lowerCAmelCase      :Dict				  = num_hidden_layers
											lowerCAmelCase      :Optional[Any]				  = backbone_out_indices
											lowerCAmelCase      :List[str]				  = num_attention_heads
											lowerCAmelCase      :int				  = intermediate_size
											lowerCAmelCase      :Dict				  = hidden_act
											lowerCAmelCase      :Union[str, Any]				  = hidden_dropout_prob
											lowerCAmelCase      :Tuple				  = attention_probs_dropout_prob
											lowerCAmelCase      :int				  = initializer_range
											lowerCAmelCase      :str				  = num_labels
											lowerCAmelCase      :Tuple				  = scope
											lowerCAmelCase      :str				  = is_hybrid
											# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
											lowerCAmelCase      :List[Any]				  = (image_size // patch_size) ** 2
											lowerCAmelCase      :List[str]				  = num_patches + 1
					def 					UpperCAmelCase__   (       self       :  Tuple      )  ->					Any:
											lowerCAmelCase      :Any				  = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

											lowerCAmelCase      :Any				  = None
											if self.use_labels:
																	lowerCAmelCase      :Union[str, Any]				  = ids_tensor([self.batch_size, self.image_size, self.image_size]    ,   self.num_labels      )

											lowerCAmelCase      :List[str]				  = self.get_config()

											return config, pixel_values, labels
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					str:
											return DPTConfig(
											    image_size=self.image_size    ,   patch_size=self.patch_size    ,   num_channels=self.num_channels    ,   hidden_size=self.hidden_size    ,   num_hidden_layers=self.num_hidden_layers    ,   backbone_out_indices=self.backbone_out_indices    ,   num_attention_heads=self.num_attention_heads    ,   intermediate_size=self.intermediate_size    ,   hidden_act=self.hidden_act    ,   hidden_dropout_prob=self.hidden_dropout_prob    ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,   is_decoder=UpperCAmelCase    ,   initializer_range=self.initializer_range    ,   is_hybrid=self.is_hybrid    ,   )
					def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  List[Any]    ,   UpperCAmelCase       :  Optional[int]    ,   UpperCAmelCase       :  Union[str, Any]      )  ->					List[str]:
											lowerCAmelCase      :Union[str, Any]				  = DPTModel(config=UpperCAmelCase      )
											model.to(UpperCAmelCase      )
											model.eval()
											lowerCAmelCase      :Optional[int]				  = model(UpperCAmelCase      )
											self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)      )
					def 					UpperCAmelCase__   (       self       :  List[Any]    ,   UpperCAmelCase       :  Union[str, Any]    ,   UpperCAmelCase       :  Union[str, Any]    ,   UpperCAmelCase       :  Optional[Any]      )  ->					Any:
											lowerCAmelCase      :Optional[Any]				  = self.num_labels
											lowerCAmelCase      :Union[str, Any]				  = DPTForDepthEstimation(UpperCAmelCase      )
											model.to(UpperCAmelCase      )
											model.eval()
											lowerCAmelCase      :str				  = model(UpperCAmelCase      )
											self.parent.assertEqual(result.predicted_depth.shape    ,   (self.batch_size, self.image_size, self.image_size)      )
					def 					UpperCAmelCase__   (       self       :  Optional[int]    ,   UpperCAmelCase       :  Dict    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  Any      )  ->					Any:
											lowerCAmelCase      :List[Any]				  = self.num_labels
											lowerCAmelCase      :Any				  = DPTForSemanticSegmentation(UpperCAmelCase      )
											model.to(UpperCAmelCase      )
											model.eval()
											lowerCAmelCase      :Optional[int]				  = model(UpperCAmelCase    ,   labels=UpperCAmelCase      )
											self.parent.assertEqual(
											    result.logits.shape    ,   (self.batch_size, self.num_labels, self.image_size, self.image_size)      )
					def 					UpperCAmelCase__   (       self       :  List[Any]      )  ->					List[str]:
											lowerCAmelCase      :Union[str, Any]				  = self.prepare_config_and_inputs()
											lowerCAmelCase  ,   lowerCAmelCase  ,   lowerCAmelCase      :List[str]				  = config_and_inputs
											lowerCAmelCase      :Any				  = {'pixel_values': pixel_values}
											return config, inputs_dict




@require_torch
class 	__UpperCamelCase    (    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    unittest.TestCase	):
					lowercase_						:							Dict			=     (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
					lowercase_						:							Tuple			=     (
					    {
					        """depth-estimation""": DPTForDepthEstimation,
					        """feature-extraction""": DPTModel,
					        """image-segmentation""": DPTForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					lowercase_						:							int			=     False
					lowercase_						:							Optional[int]			=     False
					lowercase_						:							Tuple			=     False
					def 					UpperCAmelCase__   (       self       :  Dict      )  ->					int:
											lowerCAmelCase      :List[str]				  = DPTModelTester(self      )
											lowerCAmelCase      :Optional[Any]				  = ConfigTester(self    ,   config_class=UpperCAmelCase    ,   has_text_modality=UpperCAmelCase    ,   hidden_size=37      )
					def 					UpperCAmelCase__   (       self       :  List[str]      )  ->					int:
											self.config_tester.run_common_tests()
					@unittest.skip(reason='DPT does not use inputs_embeds'      )
					def 					UpperCAmelCase__   (       self       :  Tuple      )  ->					Any:
											pass
					def 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					str:
											lowerCAmelCase  ,   lowerCAmelCase      :Optional[Any]				  = self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	lowerCAmelCase      :Optional[Any]				  = model_class(UpperCAmelCase      )
																	self.assertIsInstance(model.get_input_embeddings()    ,   (nn.Module)      )
																	lowerCAmelCase      :Dict				  = model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(UpperCAmelCase    ,   nn.Linear      )      )
					def 					UpperCAmelCase__   (       self       :  Optional[int]      )  ->					Optional[Any]:
											lowerCAmelCase  ,   lowerCAmelCase      :int				  = self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowerCAmelCase      :int				  = ['pixel_values']
																	self.assertListEqual(arg_names[:1]    ,   UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  int      )  ->					Tuple:
											lowerCAmelCase      :Dict				  = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					List[str]:
											lowerCAmelCase      :Any				  = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_depth_estimation(*UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  str      )  ->					Tuple:
											lowerCAmelCase      :List[str]				  = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Any      )  ->					Tuple:



											for model_class in self.all_model_classes:
																	if model_class.__name__ == "DPTForDepthEstimation":
																							continue

																	lowerCAmelCase  ,   lowerCAmelCase      :Any				  = self.model_tester.prepare_config_and_inputs_for_common()
																	lowerCAmelCase      :str				  = True

																	if model_class in get_values(UpperCAmelCase      ):
																							continue

																	lowerCAmelCase      :Any				  = model_class(UpperCAmelCase      )
																	model.to(UpperCAmelCase      )
																	model.train()
																	lowerCAmelCase      :Any				  = self._prepare_for_class(UpperCAmelCase    ,   UpperCAmelCase    ,   return_labels=UpperCAmelCase      )
																	lowerCAmelCase      :List[str]				  = model(**UpperCAmelCase      ).loss
																	loss.backward()
					def 					UpperCAmelCase__   (       self       :  str      )  ->					Tuple:



											for model_class in self.all_model_classes:
																	if model_class.__name__ == "DPTForDepthEstimation":
																							continue

																	lowerCAmelCase  ,   lowerCAmelCase      :Any				  = self.model_tester.prepare_config_and_inputs_for_common()
																	lowerCAmelCase      :List[str]				  = False
																	lowerCAmelCase      :Optional[Any]				  = True

																	if model_class in get_values(UpperCAmelCase      ) or not model_class.supports_gradient_checkpointing:
																							continue
																	lowerCAmelCase      :int				  = model_class(UpperCAmelCase      )
																	model.to(UpperCAmelCase      )
																	model.gradient_checkpointing_enable()
																	model.train()
																	lowerCAmelCase      :str				  = self._prepare_for_class(UpperCAmelCase    ,   UpperCAmelCase    ,   return_labels=UpperCAmelCase      )
																	lowerCAmelCase      :List[Any]				  = model(**UpperCAmelCase      ).loss
																	loss.backward()
					def 					UpperCAmelCase__   (       self       :  List[Any]      )  ->					int:
											lowerCAmelCase  ,   lowerCAmelCase      :List[Any]				  = self.model_tester.prepare_config_and_inputs_for_common()

											lowerCAmelCase      :List[str]				  = _config_zero_init(UpperCAmelCase      )
											for model_class in self.all_model_classes:
																	lowerCAmelCase      :Tuple				  = model_class(config=UpperCAmelCase      )
																	# Skip the check for the backbone
																	lowerCAmelCase      :List[Any]				  = []
																	for name, module in model.named_modules():
																							if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																													lowerCAmelCase      :str				  = [f"""{name}.{key}""" for key in module.state_dict().keys()]
																													break

																	for name, param in model.named_parameters():
																							if param.requires_grad:
																													if name in backbone_params:
																																			continue
																													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"""    ,   )
					@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
					def 					UpperCAmelCase__   (       self       :  List[Any]      )  ->					Optional[int]:
											pass
					@slow
					def 					UpperCAmelCase__   (       self       :  str      )  ->					str:



											for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowerCAmelCase      :Any				  = DPTModel.from_pretrained(UpperCAmelCase      )
																	self.assertIsNotNone(UpperCAmelCase      )


def 					UpperCAmelCase			(				)     ->   List[str]:





						'''simple docstring'''






						lowerCAmelCase      :Dict				  = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
						return image







@require_torch
@require_vision
@slow
class 	__UpperCamelCase    (    unittest.TestCase	):
					def 					UpperCAmelCase__   (       self       :  str      )  ->					List[Any]:
											lowerCAmelCase      :int				  = DPTImageProcessor.from_pretrained('Intel/dpt-large'      )
											lowerCAmelCase      :Dict				  = DPTForDepthEstimation.from_pretrained('Intel/dpt-large'      ).to(UpperCAmelCase      )

											lowerCAmelCase      :Tuple				  = prepare_img()
											lowerCAmelCase      :int				  = image_processor(images=UpperCAmelCase    ,   return_tensors='pt'      ).to(UpperCAmelCase      )

											# forward pass
											with torch.no_grad():
																	lowerCAmelCase      :Optional[int]				  = model(**UpperCAmelCase      )
																	lowerCAmelCase      :Tuple				  = outputs.predicted_depth

											# verify the predicted depth
											lowerCAmelCase      :Any				  = torch.Size((1, 384, 384)      )
											self.assertEqual(predicted_depth.shape    ,   UpperCAmelCase      )

											lowerCAmelCase      :Optional[Any]				  = torch.tensor(
											    [[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]      ).to(UpperCAmelCase      )

											self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]    ,   UpperCAmelCase    ,   atol=1e-4      )      )
					def 					UpperCAmelCase__   (       self       :  Dict      )  ->					Union[str, Any]:
											lowerCAmelCase      :Optional[Any]				  = DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'      )
											lowerCAmelCase      :Any				  = DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'      ).to(UpperCAmelCase      )

											lowerCAmelCase      :Dict				  = prepare_img()
											lowerCAmelCase      :int				  = image_processor(images=UpperCAmelCase    ,   return_tensors='pt'      ).to(UpperCAmelCase      )

											# forward pass
											with torch.no_grad():
																	lowerCAmelCase      :List[str]				  = model(**UpperCAmelCase      )

											# verify the logits
											lowerCAmelCase      :Dict				  = torch.Size((1, 150, 480, 480)      )
											self.assertEqual(outputs.logits.shape    ,   UpperCAmelCase      )

											lowerCAmelCase      :Optional[Any]				  = torch.tensor(
											    [[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]      ).to(UpperCAmelCase      )

											self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]    ,   UpperCAmelCase    ,   atol=1e-4      )      )
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					Optional[int]:
											lowerCAmelCase      :str				  = DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'      )
											lowerCAmelCase      :Optional[int]				  = DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'      ).to(UpperCAmelCase      )

											lowerCAmelCase      :Optional[Any]				  = prepare_img()
											lowerCAmelCase      :Optional[Any]				  = image_processor(images=UpperCAmelCase    ,   return_tensors='pt'      ).to(UpperCAmelCase      )

											# forward pass
											with torch.no_grad():
																	lowerCAmelCase      :List[str]				  = model(**UpperCAmelCase      )

											lowerCAmelCase      :int				  = outputs.logits.detach().cpu()

											lowerCAmelCase      :Tuple				  = image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase    ,   target_sizes=[(500, 300)]      )
											lowerCAmelCase      :List[Any]				  = torch.Size((500, 300)      )
											self.assertEqual(segmentation[0].shape    ,   UpperCAmelCase      )

											lowerCAmelCase      :Optional[int]				  = image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase      )
											lowerCAmelCase      :Dict				  = torch.Size((480, 480)      )
											self.assertEqual(segmentation[0].shape    ,   UpperCAmelCase      )







"""simple docstring"""


import argparse
import json

import torch

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


def 					UpperCAmelCase			(				a__    ,			a__=1   ):





						'''simple docstring'''






						if n_shave_prefix_segments >= 0:
												return ".".join(path.split('.'   )[n_shave_prefix_segments:]   )
						else:
												return ".".join(path.split('.'   )[:n_shave_prefix_segments]   )


def 					UpperCAmelCase			(				a__    ,			a__=0   ):





						'''simple docstring'''






						lowerCAmelCase      :int				  = []
						for old_item in old_list:
												lowerCAmelCase      :Union[str, Any]				  = old_item
												lowerCAmelCase      :str				  = new_item.replace('block.'    ,			'resnets.'   )
												lowerCAmelCase      :int				  = new_item.replace('conv_shorcut'    ,			'conv1'   )
												lowerCAmelCase      :int				  = new_item.replace('in_shortcut'    ,			'conv_shortcut'   )
												lowerCAmelCase      :Union[str, Any]				  = new_item.replace('temb_proj'    ,			'time_emb_proj'   )

												lowerCAmelCase      :List[str]				  = shave_segments(a__    ,			n_shave_prefix_segments=a__   )

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

						return mapping


def 					UpperCAmelCase			(				a__    ,			a__=0    ,			a__=False   ):





						'''simple docstring'''






						lowerCAmelCase      :Tuple				  = []
						for old_item in old_list:
												lowerCAmelCase      :int				  = old_item

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

												lowerCAmelCase      :Optional[Any]				  = new_item.replace('proj_out'    ,			'proj_attn'   )
												lowerCAmelCase      :List[str]				  = new_item.replace('norm'    ,			'group_norm'   )

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

						return mapping


def 					UpperCAmelCase			(				a__    ,			a__    ,			a__    ,			a__=None    ,			a__=None    ,			a__=None   ):





						'''simple docstring'''






						assert isinstance(a__    ,			a__   ), "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():
																		lowerCAmelCase      :Union[str, Any]				  = old_checkpoint[path]
																		lowerCAmelCase      :List[Any]				  = old_tensor.shape[0] // 3

																		lowerCAmelCase      :int				  = (-1, channels) if len(old_tensor.shape   ) == 3 else (-1)

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

																		lowerCAmelCase      :Tuple				  = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]   )
																		lowerCAmelCase  ,   lowerCAmelCase  ,   lowerCAmelCase      :Optional[Any]				  = old_tensor.split(channels // num_heads    ,			dim=1   )

																		lowerCAmelCase      :str				  = query.reshape(a__   ).squeeze()
																		lowerCAmelCase      :Dict				  = key.reshape(a__   ).squeeze()
																		lowerCAmelCase      :Union[str, Any]				  = value.reshape(a__   ).squeeze()

						for path in paths:
												lowerCAmelCase      :Union[str, Any]				  = path['new']

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												lowerCAmelCase      :Dict				  = new_path.replace('down.'    ,			'down_blocks.'   )
												lowerCAmelCase      :int				  = new_path.replace('up.'    ,			'up_blocks.'   )

												if additional_replacements is not None:
																		for replacement in additional_replacements:
																								lowerCAmelCase      :Dict				  = new_path.replace(replacement['old']    ,			replacement['new']   )

												if "attentions" in new_path:
																		lowerCAmelCase      :Tuple				  = old_checkpoint[path['old']].squeeze()
												else:
																		lowerCAmelCase      :Optional[int]				  = old_checkpoint[path['old']]


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[Any]				  = {}

						lowerCAmelCase      :int				  = checkpoint['temb.dense.0.weight']
						lowerCAmelCase      :Union[str, Any]				  = checkpoint['temb.dense.0.bias']
						lowerCAmelCase      :str				  = checkpoint['temb.dense.1.weight']
						lowerCAmelCase      :int				  = checkpoint['temb.dense.1.bias']

						lowerCAmelCase      :Dict				  = checkpoint['norm_out.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['norm_out.bias']

						lowerCAmelCase      :Optional[int]				  = checkpoint['conv_in.weight']
						lowerCAmelCase      :List[Any]				  = checkpoint['conv_in.bias']
						lowerCAmelCase      :List[Any]				  = checkpoint['conv_out.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['conv_out.bias']

						lowerCAmelCase      :Tuple				  = len({'.'.join(layer.split('.'   )[:2]   ) for layer in checkpoint if 'down' in layer}   )
						lowerCAmelCase      :Dict				  = {
						    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(a__   )
						}

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

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

												if any('downsample' in layer for layer in down_blocks[i]   ):
																		lowerCAmelCase      :Tuple				  = checkpoint[
																		    F"""down.{i}.downsample.op.weight"""
																		]
																		lowerCAmelCase      :Optional[Any]				  = 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]   ):
																		lowerCAmelCase      :List[Any]				  = len(
																		    {'.'.join(shave_segments(a__    ,			2   ).split('.'   )[:2]   ) for layer in down_blocks[i] if 'block' in layer}   )
																		lowerCAmelCase      :Optional[Any]				  = {
																		    layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
																		    for layer_id in range(a__   )
																		}

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

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

																		if num_attn > 0:
																								for j in range(config['layers_per_block']   ):
																														lowerCAmelCase      :Optional[Any]				  = renew_attention_paths(attns[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__    ,			config=a__   )

						lowerCAmelCase      :Optional[int]				  = [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase      :Optional[int]				  = [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase      :str				  = [key for key in checkpoint if 'mid.attn_1' in key]

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

						lowerCAmelCase      :Union[str, Any]				  = renew_resnet_paths(a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]    ,			)

						lowerCAmelCase      :List[str]				  = renew_attention_paths(a__    ,			in_mid=a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]    ,			)

						for i in range(a__   ):
												lowerCAmelCase      :Optional[Any]				  = num_up_blocks - 1 - i

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

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

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

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

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

						lowerCAmelCase      :Tuple				  = {k.replace('mid_new_2'    ,			'mid_block'   ): v for k, v in new_checkpoint.items()}
						return new_checkpoint


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :List[Any]				  = {}

						lowerCAmelCase      :int				  = checkpoint['encoder.norm_out.weight']
						lowerCAmelCase      :Union[str, Any]				  = checkpoint['encoder.norm_out.bias']

						lowerCAmelCase      :str				  = checkpoint['encoder.conv_in.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['encoder.conv_in.bias']
						lowerCAmelCase      :Dict				  = checkpoint['encoder.conv_out.weight']
						lowerCAmelCase      :Tuple				  = checkpoint['encoder.conv_out.bias']

						lowerCAmelCase      :Dict				  = checkpoint['decoder.norm_out.weight']
						lowerCAmelCase      :List[str]				  = checkpoint['decoder.norm_out.bias']

						lowerCAmelCase      :str				  = checkpoint['decoder.conv_in.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['decoder.conv_in.bias']
						lowerCAmelCase      :Union[str, Any]				  = checkpoint['decoder.conv_out.weight']
						lowerCAmelCase      :int				  = checkpoint['decoder.conv_out.bias']

						lowerCAmelCase      :List[str]				  = len({'.'.join(layer.split('.'   )[:3]   ) for layer in checkpoint if 'down' in layer}   )
						lowerCAmelCase      :int				  = {
						    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(a__   )
						}

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

						for i in range(a__   ):
												lowerCAmelCase      :Tuple				  = (i - 1) // (config['layers_per_block'] + 1)

												if any('downsample' in layer for layer in down_blocks[i]   ):
																		lowerCAmelCase      :List[Any]				  = checkpoint[
																		    F"""encoder.down.{i}.downsample.conv.weight"""
																		]
																		lowerCAmelCase      :Optional[int]				  = checkpoint[
																		    F"""encoder.down.{i}.downsample.conv.bias"""
																		]

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

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

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

																		if num_attn > 0:
																								for j in range(config['layers_per_block']   ):
																														lowerCAmelCase      :Any				  = renew_attention_paths(attns[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__    ,			config=a__   )

						lowerCAmelCase      :List[Any]				  = [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase      :Tuple				  = [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase      :List[Any]				  = [key for key in checkpoint if 'mid.attn_1' in key]

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

						lowerCAmelCase      :List[str]				  = renew_resnet_paths(a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]    ,			)

						lowerCAmelCase      :List[str]				  = renew_attention_paths(a__    ,			in_mid=a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]    ,			)

						for i in range(a__   ):
												lowerCAmelCase      :Optional[int]				  = num_up_blocks - 1 - i

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

												if any('block' in layer for layer in up_blocks[i]   ):
																		lowerCAmelCase      :Dict				  = len(
																		    {'.'.join(shave_segments(a__    ,			3   ).split('.'   )[:3]   ) for layer in up_blocks[i] if 'block' in layer}   )
																		lowerCAmelCase      :Optional[Any]				  = {
																		    layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(a__   )
																		}

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

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

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

						lowerCAmelCase      :Optional[Any]				  = {k.replace('mid_new_2'    ,			'mid_block'   ): v for k, v in new_checkpoint.items()}
						lowerCAmelCase      :Optional[int]				  = checkpoint['quant_conv.weight']
						lowerCAmelCase      :Tuple				  = checkpoint['quant_conv.bias']
						if "quantize.embedding.weight" in checkpoint:
												lowerCAmelCase      :Optional[Any]				  = checkpoint['quantize.embedding.weight']
						lowerCAmelCase      :Any				  = checkpoint['post_quant_conv.weight']
						lowerCAmelCase      :str				  = checkpoint['post_quant_conv.bias']

						return new_checkpoint


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE      =      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.')

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

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

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

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

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

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

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