


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 						__a :
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=32     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=3     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=32     ,       _SCREAMING_SNAKE_CASE=4     ,       _SCREAMING_SNAKE_CASE=[0, 1, 2, 3]     ,       _SCREAMING_SNAKE_CASE=4     ,       _SCREAMING_SNAKE_CASE=37     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=3     ,       _SCREAMING_SNAKE_CASE=[1, 384, 24, 24]     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=None     ,       )					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     parent
									_UpperCAmelCase											=     batch_size
									_UpperCAmelCase											=     image_size
									_UpperCAmelCase											=     patch_size
									_UpperCAmelCase											=     num_channels
									_UpperCAmelCase											=     is_training
									_UpperCAmelCase											=     use_labels
									_UpperCAmelCase											=     hidden_size
									_UpperCAmelCase											=     num_hidden_layers
									_UpperCAmelCase											=     backbone_out_indices
									_UpperCAmelCase											=     num_attention_heads
									_UpperCAmelCase											=     intermediate_size
									_UpperCAmelCase											=     hidden_act
									_UpperCAmelCase											=     hidden_dropout_prob
									_UpperCAmelCase											=     attention_probs_dropout_prob
									_UpperCAmelCase											=     initializer_range
									_UpperCAmelCase											=     num_labels
									_UpperCAmelCase											=     backbone_featmap_shape
									_UpperCAmelCase											=     scope
									_UpperCAmelCase											=     is_hybrid
									# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
									_UpperCAmelCase											=     (image_size // patch_size) ** 2
									_UpperCAmelCase											=     num_patches + 1
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_UpperCAmelCase											=     None
									if self.use_labels:
																_UpperCAmelCase											=     ids_tensor([self.batch_size, self.image_size, self.image_size]     ,       self.num_labels		)

									_UpperCAmelCase											=     self.get_config()

									return config, pixel_values, labels
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     {
									    'global_padding': 'same',
									    'layer_type': 'bottleneck',
									    'depths': [3, 4, 9],
									    'out_features': ['stage1', 'stage2', 'stage3'],
									    'embedding_dynamic_padding': True,
									    'hidden_sizes': [96, 192, 384, 768],
									    'num_groups': 2,
									}

									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=_SCREAMING_SNAKE_CASE     ,       initializer_range=self.initializer_range     ,       is_hybrid=self.is_hybrid     ,       backbone_config=_SCREAMING_SNAKE_CASE     ,       backbone_featmap_shape=self.backbone_featmap_shape     ,       )
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     DPTModel(config=_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.num_labels
									_UpperCAmelCase											=     DPTForDepthEstimation(_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(result.predicted_depth.shape     ,       (self.batch_size, self.image_size, self.image_size)		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.num_labels
									_UpperCAmelCase											=     DPTForSemanticSegmentation(_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(
									    result.logits.shape     ,       (self.batch_size, self.num_labels, self.image_size, self.image_size)		)






		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.prepare_config_and_inputs()
									_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     config_and_inputs
									_UpperCAmelCase											=     {'pixel_values': pixel_values}
									return config, inputs_dict



@require_torch
class 						__a (							UpperCAmelCase       , UpperCAmelCase       , unittest.TestCase     ):
		_a      :    List[str]           =      (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
		_a      :    List[Any]           =      (
		    {
		        'depth-estimation': DPTForDepthEstimation,
		        'feature-extraction': DPTModel,
		        'image-segmentation': DPTForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		_a      :    Any           =      False
		_a      :    Optional[Any]           =      False
		_a      :    Union[str, Any]           =      False
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     DPTModelTester(self		)
									_UpperCAmelCase											=     ConfigTester(self     ,       config_class=_SCREAMING_SNAKE_CASE     ,       has_text_modality=_SCREAMING_SNAKE_CASE     ,       hidden_size=37		)
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									self.config_tester.run_common_tests()
		@unittest.skip(reason='DPT does not use inputs_embeds'		)
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									pass
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()

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




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()

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

																_UpperCAmelCase											=     ['pixel_values']
																self.assertListEqual(arg_names[:1]     ,       _SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_depth_estimation(*_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





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

																_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()
																_UpperCAmelCase											=     True

																if model_class in get_values(_SCREAMING_SNAKE_CASE		):
																							continue

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




									"""simple docstring"""





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

																_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()
																_UpperCAmelCase											=     False
																_UpperCAmelCase											=     True

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




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()

									_UpperCAmelCase											=     _config_zero_init(_SCREAMING_SNAKE_CASE		)
									for model_class in self.all_model_classes:
																_UpperCAmelCase											=     model_class(config=_SCREAMING_SNAKE_CASE		)
																# Skip the check for the backbone
																_UpperCAmelCase											=     []
																for name, module in model.named_modules():
																							if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																														_UpperCAmelCase											=     [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		)					->			Optional[Any]:




									"""simple docstring"""





									pass
		@slow
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
																_UpperCAmelCase											=     DPTModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
																self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)






		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()
									_UpperCAmelCase											=     'add'
									with self.assertRaises(_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase											=     DPTForDepthEstimation(_SCREAMING_SNAKE_CASE		)



def 			lowerCAmelCase__					(     )    ->						str:


							'''simple docstring'''





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







@require_torch
@require_vision
@slow
class 						__a (							unittest.TestCase     ):
		def       UpperCAmelCase__							(     self		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'		)
									_UpperCAmelCase											=     DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'		).to(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     prepare_img()
									_UpperCAmelCase											=     image_processor(images=_SCREAMING_SNAKE_CASE     ,       return_tensors='pt'		).to(_SCREAMING_SNAKE_CASE		)

									# forward pass
									with torch.no_grad():
																_UpperCAmelCase											=     model(**_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     outputs.predicted_depth

									# verify the predicted depth
									_UpperCAmelCase											=     torch.Size((1, 384, 384)		)
									self.assertEqual(predicted_depth.shape     ,       _SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     torch.tensor(
									    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]		).to(_SCREAMING_SNAKE_CASE		)

									self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100     ,       _SCREAMING_SNAKE_CASE     ,       atol=1e-4		)		)




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowerCAmelCase__    :List[Any]       =				{
    '''sample_size''': 3_2,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_0_0_0,
    '''block_out_channels''': [3_2, 6_4],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

lowerCAmelCase__    :List[str]       =				{
    '''sample_size''': 6_4,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_0_0_0,
    '''block_out_channels''': [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    '''attention_head_dim''': 6_4,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

lowerCAmelCase__    :Any       =				{
    '''sample_size''': 2_5_6,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    '''attention_head_dim''': 6_4,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

lowerCAmelCase__    :Optional[int]       =				{
    '''num_train_timesteps''': 4_0,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

lowerCAmelCase__    :Optional[int]       =				{
    '''num_train_timesteps''': 2_0_1,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

lowerCAmelCase__    :Any       =				{
    '''num_train_timesteps''': 1_5_1,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

def 			lowerCAmelCase__					(     a__:						Optional[int]			)    ->						int:


							'''simple docstring'''





							if isinstance(a__     ,    a__			):
														return v
							if v.lower() in ("yes", "true", "t", "y", "1"):
														return True
							elif v.lower() in ("no", "false", "f", "n", "0"):
														return False
							else:
														raise argparse.ArgumentTypeError('boolean value expected'			)

def 			lowerCAmelCase__					(     a__:						Dict     ,    a__:						int     ,    a__:						Union[str, Any]     ,    a__:						Dict     ,    a__:						Optional[int]=False			)    ->						Dict:


							'''simple docstring'''





							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.in_layers.0.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.in_layers.0.bias''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.in_layers.2.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.in_layers.2.bias''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.out_layers.0.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.out_layers.0.bias''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.out_layers.3.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.out_layers.3.bias''']

							if has_skip:
														_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.skip_connection.weight''']
														_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.skip_connection.bias''']

							return new_checkpoint

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any     ,    a__:						List[str]     ,    a__:						List[Any]     ,    a__:						List[str]=None			)    ->						Optional[Any]:


							'''simple docstring'''





							_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3     ,    dim=0			)
							_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3     ,    dim=0			)

							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.norm.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.norm.bias''']

							_UpperCAmelCase											=     weight_q.squeeze(-1			).squeeze(-1			)
							_UpperCAmelCase											=     bias_q.squeeze(-1			).squeeze(-1			)
							_UpperCAmelCase											=     weight_k.squeeze(-1			).squeeze(-1			)
							_UpperCAmelCase											=     bias_k.squeeze(-1			).squeeze(-1			)
							_UpperCAmelCase											=     weight_v.squeeze(-1			).squeeze(-1			)
							_UpperCAmelCase											=     bias_v.squeeze(-1			).squeeze(-1			)

							_UpperCAmelCase											=     (
							    checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1			).squeeze(-1			)
							)
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1			).squeeze(-1			)

							return new_checkpoint

def 			lowerCAmelCase__					(     a__:						str     ,    a__:						Any			)    ->						Tuple:


							'''simple docstring'''





							_UpperCAmelCase											=     torch.load(a__     ,    map_location='cpu'			)
							_UpperCAmelCase											=     {}

							_UpperCAmelCase											=     checkpoint['time_embed.0.weight']
							_UpperCAmelCase											=     checkpoint['time_embed.0.bias']
							_UpperCAmelCase											=     checkpoint['time_embed.2.weight']
							_UpperCAmelCase											=     checkpoint['time_embed.2.bias']

							if unet_config["num_class_embeds"] is not None:
														_UpperCAmelCase											=     checkpoint['label_emb.weight']

							_UpperCAmelCase											=     checkpoint['input_blocks.0.0.weight']
							_UpperCAmelCase											=     checkpoint['input_blocks.0.0.bias']

							_UpperCAmelCase											=     unet_config['down_block_types']
							_UpperCAmelCase											=     unet_config['layers_per_block']
							_UpperCAmelCase											=     unet_config['attention_head_dim']
							_UpperCAmelCase											=     unet_config['block_out_channels']
							_UpperCAmelCase											=     1
							_UpperCAmelCase											=     channels_list[0]

							for i, layer_type in enumerate(a__			):
														_UpperCAmelCase											=     channels_list[i]
														_UpperCAmelCase											=     current_channels != prev_channels
														if layer_type == "ResnetDownsampleBlock2D":
																					for j in range(a__			):
																												_UpperCAmelCase											=     F'''down_blocks.{i}.resnets.{j}'''
																												_UpperCAmelCase											=     F'''input_blocks.{current_layer}.0'''
																												_UpperCAmelCase											=     True if j == 0 and downsample_block_has_skip else False
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__     ,    has_skip=a__			)
																												current_layer += 1

														elif layer_type == "AttnDownBlock2D":
																					for j in range(a__			):
																												_UpperCAmelCase											=     F'''down_blocks.{i}.resnets.{j}'''
																												_UpperCAmelCase											=     F'''input_blocks.{current_layer}.0'''
																												_UpperCAmelCase											=     True if j == 0 and downsample_block_has_skip else False
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__     ,    has_skip=a__			)
																												_UpperCAmelCase											=     F'''down_blocks.{i}.attentions.{j}'''
																												_UpperCAmelCase											=     F'''input_blocks.{current_layer}.1'''
																												_UpperCAmelCase											=     convert_attention(
																												    a__     ,    a__     ,    a__     ,    a__     ,    a__			)
																												current_layer += 1

														if i != len(a__			) - 1:
																					_UpperCAmelCase											=     F'''down_blocks.{i}.downsamplers.0'''
																					_UpperCAmelCase											=     F'''input_blocks.{current_layer}.0'''
																					_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)
																					current_layer += 1

														_UpperCAmelCase											=     current_channels

							# hardcoded the mid-block for now
							_UpperCAmelCase											=     'mid_block.resnets.0'
							_UpperCAmelCase											=     'middle_block.0'
							_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)
							_UpperCAmelCase											=     'mid_block.attentions.0'
							_UpperCAmelCase											=     'middle_block.1'
							_UpperCAmelCase											=     convert_attention(a__     ,    a__     ,    a__     ,    a__     ,    a__			)
							_UpperCAmelCase											=     'mid_block.resnets.1'
							_UpperCAmelCase											=     'middle_block.2'
							_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)

							_UpperCAmelCase											=     0
							_UpperCAmelCase											=     unet_config['up_block_types']

							for i, layer_type in enumerate(a__			):
														if layer_type == "ResnetUpsampleBlock2D":
																					for j in range(layers_per_block + 1			):
																												_UpperCAmelCase											=     F'''up_blocks.{i}.resnets.{j}'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer}.0'''
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__     ,    has_skip=a__			)
																												current_layer += 1

																					if i != len(a__			) - 1:
																												_UpperCAmelCase											=     F'''up_blocks.{i}.upsamplers.0'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer-1}.1'''
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)
														elif layer_type == "AttnUpBlock2D":
																					for j in range(layers_per_block + 1			):
																												_UpperCAmelCase											=     F'''up_blocks.{i}.resnets.{j}'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer}.0'''
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__     ,    has_skip=a__			)
																												_UpperCAmelCase											=     F'''up_blocks.{i}.attentions.{j}'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer}.1'''
																												_UpperCAmelCase											=     convert_attention(
																												    a__     ,    a__     ,    a__     ,    a__     ,    a__			)
																												current_layer += 1

																					if i != len(a__			) - 1:
																												_UpperCAmelCase											=     F'''up_blocks.{i}.upsamplers.0'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer-1}.2'''
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)

							_UpperCAmelCase											=     checkpoint['out.0.weight']
							_UpperCAmelCase											=     checkpoint['out.0.bias']
							_UpperCAmelCase											=     checkpoint['out.2.weight']
							_UpperCAmelCase											=     checkpoint['out.2.bias']

							return new_checkpoint


if __name__ == "__main__":
		lowerCAmelCase__    :Union[str, Any]       =				argparse.ArgumentParser()

		parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
		parser.add_argument(
		    '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
		)
		parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

		lowerCAmelCase__    :Dict       =				parser.parse_args()
		lowerCAmelCase__    :List[str]       =				strabool(args.class_cond)

		lowerCAmelCase__    :Any       =				os.path.basename(args.unet_path)
		print(f'''Checkpoint: {ckpt_name}''')

		# Get U-Net config
		if "imagenet64" in ckpt_name:
				lowerCAmelCase__    :Union[str, Any]       =				IMAGENET_64_UNET_CONFIG
		elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				lowerCAmelCase__    :Tuple       =				LSUN_256_UNET_CONFIG
		elif "test" in ckpt_name:
				lowerCAmelCase__    :Any       =				TEST_UNET_CONFIG
		else:
				raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

		if not args.class_cond:
				lowerCAmelCase__    :str       =				None

		lowerCAmelCase__    :Tuple       =				con_pt_to_diffuser(args.unet_path, unet_config)

		lowerCAmelCase__    :Optional[int]       =				UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				lowerCAmelCase__    :int       =				CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				lowerCAmelCase__    :Union[str, Any]       =				CT_IMAGENET_64_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				lowerCAmelCase__    :Tuple       =				CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

		lowerCAmelCase__    :Any       =				CMStochasticIterativeScheduler(**scheduler_config)

		lowerCAmelCase__    :Union[str, Any]       =				ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)
