






'''simple docstring'''







import inspect
import unittest

from transformers import ViTMSNConfig
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_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import ViTMSNForImageClassification, ViTMSNModel
			from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor

class _lowercase     :




							def __init__(							self:    List[str]      ,							UpperCamelCase__:    Tuple      ,							UpperCamelCase__:    Optional[int]=13      ,							UpperCamelCase__:    Dict=30      ,							UpperCamelCase__:    List[Any]=2      ,							UpperCamelCase__:    Tuple=3      ,							UpperCamelCase__:    Optional[Any]=True      ,							UpperCamelCase__:    str=True      ,							UpperCamelCase__:    str=32      ,							UpperCamelCase__:    str=5      ,							UpperCamelCase__:    str=4      ,							UpperCamelCase__:    Any=37      ,							UpperCamelCase__:    Optional[int]="gelu"      ,							UpperCamelCase__:    Dict=0.1      ,							UpperCamelCase__:    List[str]=0.1      ,							UpperCamelCase__:    Dict=10      ,							UpperCamelCase__:    Optional[Any]=0.02      ,							UpperCamelCase__:    List[str]=None      ,							):
														lowerCamelCase__ :     Union[str, Any]	  =	parent
														lowerCamelCase__ :     List[str]	  =	batch_size
														lowerCamelCase__ :     str	  =	image_size
														lowerCamelCase__ :     Optional[int]	  =	patch_size
														lowerCamelCase__ :     Union[str, Any]	  =	num_channels
														lowerCamelCase__ :     str	  =	is_training
														lowerCamelCase__ :     Tuple	  =	use_labels
														lowerCamelCase__ :     List[str]	  =	hidden_size
														lowerCamelCase__ :     Dict	  =	num_hidden_layers
														lowerCamelCase__ :     List[str]	  =	num_attention_heads
														lowerCamelCase__ :     int	  =	intermediate_size
														lowerCamelCase__ :     List[str]	  =	hidden_act
														lowerCamelCase__ :     Optional[Any]	  =	hidden_dropout_prob
														lowerCamelCase__ :     Dict	  =	attention_probs_dropout_prob
														lowerCamelCase__ :     str	  =	type_sequence_label_size
														lowerCamelCase__ :     Optional[int]	  =	initializer_range
														lowerCamelCase__ :     Any	  =	scope

														# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
														lowerCamelCase__ :     int	  =	(image_size // patch_size) ** 2
														lowerCamelCase__ :     Optional[int]	  =	num_patches + 1




							def 		lowerCamelCase_    (							self:    List[str]					):
														lowerCamelCase__ :     Dict	  =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

														lowerCamelCase__ :     Tuple	  =	self.get_config()

														return config, pixel_values, labels




							def 		lowerCamelCase_    (							self:    str					):
														return ViTMSNConfig(
														    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      ,							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      ,							initializer_range=self.initializer_range      ,							)




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    int      ,							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    Union[str, Any]					):
														lowerCamelCase__ :     List[str]	  =	ViTMSNModel(config=UpperCamelCase__					)
														model.to(UpperCamelCase__					)
														model.eval()
														lowerCamelCase__ :     List[str]	  =	model(UpperCamelCase__					)
														self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, self.seq_length, self.hidden_size)					)




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    Tuple      ,							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    List[Any]					):
														lowerCamelCase__ :     Tuple	  =	self.type_sequence_label_size
														lowerCamelCase__ :     int	  =	ViTMSNForImageClassification(UpperCamelCase__					)
														model.to(UpperCamelCase__					)
														model.eval()
														lowerCamelCase__ :     Tuple	  =	model(UpperCamelCase__      ,							labels=UpperCamelCase__					)
														print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}"""					)
														print("""Labels: {labels}"""					)
														self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.type_sequence_label_size)					)

														# test greyscale images
														lowerCamelCase__ :     Optional[int]	  =	1
														lowerCamelCase__ :     List[str]	  =	ViTMSNForImageClassification(UpperCamelCase__					)
														model.to(UpperCamelCase__					)
														model.eval()

														lowerCamelCase__ :     Optional[int]	  =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
														lowerCamelCase__ :     Dict	  =	model(UpperCamelCase__					)
														self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.type_sequence_label_size)					)




							def 		lowerCamelCase_    (							self:    List[Any]					):
														lowerCamelCase__ :     Optional[Any]	  =	self.prepare_config_and_inputs()
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	config_and_inputs
														lowerCamelCase__ :     str	  =	{"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_torch
class _lowercase     (    _lowercase    ,				_lowercase    ,				unittest.TestCase		):
							a    				=	(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
							a    				=	(
							    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
							    if is_torch_available()
							    else {}
							)

							a    				=	False
							a    				=	False
							a    				=	False
							a    				=	False




							def 		lowerCamelCase_    (							self:    int					):
														lowerCamelCase__ :     Optional[int]	  =	ViTMSNModelTester(self					)
														lowerCamelCase__ :     int	  =	ConfigTester(self      ,							config_class=UpperCamelCase__      ,							has_text_modality=UpperCamelCase__      ,							hidden_size=37					)




							def 		lowerCamelCase_    (							self:    int					):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""ViTMSN does not use inputs_embeds"""					)
							def 		lowerCamelCase_    (							self:    List[str]					):
														pass




							def 		lowerCamelCase_    (							self:    Any					):
														lowerCamelCase__  ,		lowerCamelCase__ :     Dict	  =	self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__ :     List[str]	  =	model_class(UpperCamelCase__					)
																					self.assertIsInstance(model.get_input_embeddings()      ,							(nn.Module)					)
																					lowerCamelCase__ :     List[str]	  =	model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(UpperCamelCase__      ,							nn.Linear					)					)




							def 		lowerCamelCase_    (							self:    List[str]					):
														lowerCamelCase__  ,		lowerCamelCase__ :     Union[str, Any]	  =	self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__ :     int	  =	model_class(UpperCamelCase__					)
																					lowerCamelCase__ :     str	  =	inspect.signature(model.forward					)
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__ :     Union[str, Any]	  =	[*signature.parameters.keys()]

																					lowerCamelCase__ :     List[str]	  =	["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]      ,							UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														lowerCamelCase__ :     Optional[int]	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     Tuple	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__					)




							@slow
							def 		lowerCamelCase_    (							self:    Union[str, Any]					):
														for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__ :     int	  =	ViTMSNModel.from_pretrained(UpperCamelCase__					)
																					self.assertIsNotNone(UpperCamelCase__					)





def 			SCREAMING_SNAKE_CASE_	()				->	List[str]:
							lowerCamelCase__ :     Dict	  =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
							return image



@require_torch
@require_vision
class _lowercase     (    unittest.TestCase		):




							@cached_property
							def 		lowerCamelCase_    (							self:    List[str]					):
														return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""					) if is_vision_available() else None




							@slow
							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														torch.manual_seed(2					)
														lowerCamelCase__ :     List[Any]	  =	ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""					).to(UpperCamelCase__					)

														lowerCamelCase__ :     Optional[Any]	  =	self.default_image_processor
														lowerCamelCase__ :     List[Any]	  =	prepare_img()
														lowerCamelCase__ :     int	  =	image_processor(images=UpperCamelCase__      ,							return_tensors="""pt"""					).to(UpperCamelCase__					)

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__ :     str	  =	model(**UpperCamelCase__					)

														# verify the logits
														lowerCamelCase__ :     Tuple	  =	torch.Size((1, 1_000)					)
														self.assertEqual(outputs.logits.shape      ,							UpperCamelCase__					)

														lowerCamelCase__ :     List[Any]	  =	torch.tensor([-0.0_803, -0.4_454, -0.2_375]					).to(UpperCamelCase__					)

														self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,							UpperCamelCase__      ,							atol=1e-4					)					)









'''simple docstring'''







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_A     :      str   					={
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [32, 64],
    '''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''',
}

_A     :      Union[str, Any]   					={
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4],
    '''attention_head_dim''': 64,
    '''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''',
}

_A     :      Dict   					={
    '''sample_size''': 256,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    '''attention_head_dim''': 64,
    '''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''',
}

_A     :      Dict   					={
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_A     :      str   					={
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_A     :      int   					={
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							if isinstance(UpperCamelCase    ,      UpperCamelCase				):
														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 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=False				)				->	Any:
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.0.weight''']
							lowerCamelCase__ :     int	  =	checkpoint[f'''{old_prefix}.in_layers.0.bias''']
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.2.weight''']
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.2.bias''']
							lowerCamelCase__ :     Optional[Any]	  =	checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
							lowerCamelCase__ :     Optional[int]	  =	checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
							lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.out_layers.0.weight''']
							lowerCamelCase__ :     Tuple	  =	checkpoint[f'''{old_prefix}.out_layers.0.bias''']
							lowerCamelCase__ :     str	  =	checkpoint[f'''{old_prefix}.out_layers.3.weight''']
							lowerCamelCase__ :     int	  =	checkpoint[f'''{old_prefix}.out_layers.3.bias''']

							if has_skip:
														lowerCamelCase__ :     Tuple	  =	checkpoint[f'''{old_prefix}.skip_connection.weight''']
														lowerCamelCase__ :     List[Any]	  =	checkpoint[f'''{old_prefix}.skip_connection.bias''']

							return new_checkpoint





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=None				)				->	str:
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3    ,      dim=0				)
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     str	  =	checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3    ,      dim=0				)

							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.norm.weight''']
							lowerCamelCase__ :     Optional[int]	  =	checkpoint[f'''{old_prefix}.norm.bias''']

							lowerCamelCase__ :     List[Any]	  =	weight_q.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     List[Any]	  =	bias_q.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Any	  =	weight_k.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Optional[Any]	  =	bias_k.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Dict	  =	weight_v.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Union[str, Any]	  =	bias_v.squeeze(-1				).squeeze(-1				)

							lowerCamelCase__ :     Optional[Any]	  =	(
							    checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1				).squeeze(-1				)
							)
							lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1				).squeeze(-1				)

							return new_checkpoint





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	List[Any]:
							lowerCamelCase__ :     str	  =	torch.load(UpperCamelCase    ,      map_location="""cpu"""				)
							lowerCamelCase__ :     Optional[int]	  =	{}

							lowerCamelCase__ :     Optional[int]	  =	checkpoint["""time_embed.0.weight"""]
							lowerCamelCase__ :     List[Any]	  =	checkpoint["""time_embed.0.bias"""]
							lowerCamelCase__ :     int	  =	checkpoint["""time_embed.2.weight"""]
							lowerCamelCase__ :     Optional[Any]	  =	checkpoint["""time_embed.2.bias"""]

							if unet_config["num_class_embeds"] is not None:
														lowerCamelCase__ :     Optional[Any]	  =	checkpoint["""label_emb.weight"""]

							lowerCamelCase__ :     Tuple	  =	checkpoint["""input_blocks.0.0.weight"""]
							lowerCamelCase__ :     List[str]	  =	checkpoint["""input_blocks.0.0.bias"""]

							lowerCamelCase__ :     Optional[Any]	  =	unet_config["""down_block_types"""]
							lowerCamelCase__ :     Any	  =	unet_config["""layers_per_block"""]
							lowerCamelCase__ :     Any	  =	unet_config["""attention_head_dim"""]
							lowerCamelCase__ :     List[Any]	  =	unet_config["""block_out_channels"""]
							lowerCamelCase__ :     str	  =	1
							lowerCamelCase__ :     str	  =	channels_list[0]

							for i, layer_type in enumerate(UpperCamelCase				):
														lowerCamelCase__ :     List[Any]	  =	channels_list[i]
														lowerCamelCase__ :     List[Any]	  =	current_channels != prev_channels
														if layer_type == "ResnetDownsampleBlock2D":
																					for j in range(UpperCamelCase				):
																												lowerCamelCase__ :     int	  =	f'''down_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     Dict	  =	f'''input_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     Tuple	  =	True if j == 0 and downsample_block_has_skip else False
																												lowerCamelCase__ :     List[Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												current_layer += 1

														elif layer_type == "AttnDownBlock2D":
																					for j in range(UpperCamelCase				):
																												lowerCamelCase__ :     Tuple	  =	f'''down_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     Optional[Any]	  =	f'''input_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     str	  =	True if j == 0 and downsample_block_has_skip else False
																												lowerCamelCase__ :     Union[str, Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												lowerCamelCase__ :     Any	  =	f'''down_blocks.{i}.attentions.{j}'''
																												lowerCamelCase__ :     Dict	  =	f'''input_blocks.{current_layer}.1'''
																												lowerCamelCase__ :     Tuple	  =	convert_attention(
																												    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																												current_layer += 1

														if i != len(UpperCamelCase				) - 1:
																					lowerCamelCase__ :     Tuple	  =	f'''down_blocks.{i}.downsamplers.0'''
																					lowerCamelCase__ :     str	  =	f'''input_blocks.{current_layer}.0'''
																					lowerCamelCase__ :     Union[str, Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																					current_layer += 1

														lowerCamelCase__ :     Union[str, Any]	  =	current_channels

							# hardcoded the mid-block for now
							lowerCamelCase__ :     Any	  =	"""mid_block.resnets.0"""
							lowerCamelCase__ :     Optional[Any]	  =	"""middle_block.0"""
							lowerCamelCase__ :     int	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
							lowerCamelCase__ :     List[Any]	  =	"""mid_block.attentions.0"""
							lowerCamelCase__ :     Dict	  =	"""middle_block.1"""
							lowerCamelCase__ :     int	  =	convert_attention(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
							lowerCamelCase__ :     Any	  =	"""mid_block.resnets.1"""
							lowerCamelCase__ :     Tuple	  =	"""middle_block.2"""
							lowerCamelCase__ :     int	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							lowerCamelCase__ :     Union[str, Any]	  =	0
							lowerCamelCase__ :     Any	  =	unet_config["""up_block_types"""]

							for i, layer_type in enumerate(UpperCamelCase				):
														if layer_type == "ResnetUpsampleBlock2D":
																					for j in range(layers_per_block + 1				):
																												lowerCamelCase__ :     int	  =	f'''up_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     Optional[Any]	  =	f'''output_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     Any	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												current_layer += 1

																					if i != len(UpperCamelCase				) - 1:
																												lowerCamelCase__ :     Dict	  =	f'''up_blocks.{i}.upsamplers.0'''
																												lowerCamelCase__ :     List[str]	  =	f'''output_blocks.{current_layer-1}.1'''
																												lowerCamelCase__ :     Optional[Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
														elif layer_type == "AttnUpBlock2D":
																					for j in range(layers_per_block + 1				):
																												lowerCamelCase__ :     str	  =	f'''up_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     List[Any]	  =	f'''output_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     Optional[Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												lowerCamelCase__ :     Optional[Any]	  =	f'''up_blocks.{i}.attentions.{j}'''
																												lowerCamelCase__ :     Any	  =	f'''output_blocks.{current_layer}.1'''
																												lowerCamelCase__ :     Optional[int]	  =	convert_attention(
																												    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																												current_layer += 1

																					if i != len(UpperCamelCase				) - 1:
																												lowerCamelCase__ :     Tuple	  =	f'''up_blocks.{i}.upsamplers.0'''
																												lowerCamelCase__ :     Tuple	  =	f'''output_blocks.{current_layer-1}.2'''
																												lowerCamelCase__ :     List[str]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							lowerCamelCase__ :     Dict	  =	checkpoint["""out.0.weight"""]
							lowerCamelCase__ :     Dict	  =	checkpoint["""out.0.bias"""]
							lowerCamelCase__ :     Dict	  =	checkpoint["""out.2.weight"""]
							lowerCamelCase__ :     Tuple	  =	checkpoint["""out.2.bias"""]

							return new_checkpoint


if __name__ == "__main__":
			_A     :      Tuple   					=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.''')

			_A     :      Tuple   					=parser.parse_args()
			_A     :      Optional[int]   					=strabool(args.class_cond)

			_A     :      List[str]   					=os.path.basename(args.unet_path)
			print(F'Checkpoint: {ckpt_name}')

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

			if not args.class_cond:
						_A     :      str   					=None

			_A     :      Optional[int]   					=con_pt_to_diffuser(args.unet_path, unet_config)

			_A     :      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:
						_A     :      Tuple   					=CD_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
						_A     :      int   					=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)):
						_A     :      Union[str, Any]   					=CT_LSUN_256_SCHEDULER_CONFIG
			else:
						raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

			_A     :      str   					=CMStochasticIterativeScheduler(**scheduler_config)

			_A     :      Optional[Any]   					=ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
			consistency_model.save_pretrained(args.dump_path)

