






import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 RegNetForImageClassification, RegNetModel
		from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor



class 				__UpperCAmelCase		:







			def __init__(     self:  Tuple				,     UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  Union[str, Any]=3				,     UpperCAmelCase_:  Optional[int]=32				,     UpperCAmelCase_:  Tuple=3				,     UpperCAmelCase_:  int=10				,     UpperCAmelCase_:  int=[10, 20, 30, 40]				,     UpperCAmelCase_:  Union[str, Any]=[1, 1, 2, 1]				,     UpperCAmelCase_:  Dict=True				,     UpperCAmelCase_:  Dict=True				,     UpperCAmelCase_:  Optional[Any]="relu"				,     UpperCAmelCase_:  int=3				,     UpperCAmelCase_:  List[str]=None				,     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  parent
									_SCREAMING_SNAKE_CASE     				=  batch_size
									_SCREAMING_SNAKE_CASE     				=  image_size
									_SCREAMING_SNAKE_CASE     				=  num_channels
									_SCREAMING_SNAKE_CASE     				=  embeddings_size
									_SCREAMING_SNAKE_CASE     				=  hidden_sizes
									_SCREAMING_SNAKE_CASE     				=  depths
									_SCREAMING_SNAKE_CASE     				=  is_training
									_SCREAMING_SNAKE_CASE     				=  use_labels
									_SCREAMING_SNAKE_CASE     				=  hidden_act
									_SCREAMING_SNAKE_CASE     				=  num_labels
									_SCREAMING_SNAKE_CASE     				=  scope
									_SCREAMING_SNAKE_CASE     				=  len(UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




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

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

									_SCREAMING_SNAKE_CASE     				=  self.get_config()

									return config, pixel_values, labels







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									return RegNetConfig(
									    num_channels=self.num_channels				,     embeddings_size=self.embeddings_size				,     hidden_sizes=self.hidden_sizes				,     depths=self.depths				,     hidden_act=self.hidden_act				,     num_labels=self.num_labels				,     )







			def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  Optional[Any]				,     UpperCAmelCase_:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  RegNetModel(config=UpperCAmelCase_					)
									model.to(UpperCAmelCase_					)
									model.eval()
									_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_					)
									# expected last hidden states: B, C, H // 32, W // 32
									self.parent.assertEqual(
									    result.last_hidden_state.shape				,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,     )







			def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  List[Any]				,     UpperCAmelCase_:  Any				,     UpperCAmelCase_:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.num_labels
									_SCREAMING_SNAKE_CASE     				=  RegNetForImageClassification(UpperCAmelCase_					)
									model.to(UpperCAmelCase_					)
									model.eval()
									_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     labels=UpperCAmelCase_					)
									self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels)					)







			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.prepare_config_and_inputs()
									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  config_and_inputs
									_SCREAMING_SNAKE_CASE     				=  {"""pixel_values""": pixel_values}
									return config, inputs_dict



@require_torch
class 				__UpperCAmelCase		(_UpperCAmelCase	,_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				Dict									=							(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
			__snake_case    :				Tuple									=							(
			    {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
			    if is_torch_available()
			    else {}
			)

			__snake_case    :				Optional[int]									=							False
			__snake_case    :				Optional[int]									=							False
			__snake_case    :				int									=							False
			__snake_case    :				Optional[Any]									=							False







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  RegNetModelTester(self					)
									_SCREAMING_SNAKE_CASE     				=  ConfigTester(self				,     config_class=UpperCAmelCase_				,     has_text_modality=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									return







			@unittest.skip(reason="""RegNet does not use inputs_embeds"""					)
			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									pass







			@unittest.skip(reason="""RegNet does not support input and output embeddings"""					)
			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									pass







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  self.model_tester.prepare_config_and_inputs_for_common()

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

															_SCREAMING_SNAKE_CASE     				=  ["""pixel_values"""]
															self.assertListEqual(arg_names[:1]				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE     				=  model_class(config=UpperCAmelCase_					)
															for name, module in model.named_modules():
																					if isinstance(UpperCAmelCase_				,     (nn.BatchNormad, nn.GroupNorm)					):
																											self.assertTrue(
																											    torch.all(module.weight == 1					)				,     msg=F'Parameter {name} of model {model_class} seems not properly initialized'				,     )
																											self.assertTrue(
																											    torch.all(module.bias == 0					)				,     msg=F'Parameter {name} of model {model_class} seems not properly initialized'				,     )







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									def check_hidden_states_output(UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  Union[str, Any]					):
															_SCREAMING_SNAKE_CASE     				=  model_class(UpperCAmelCase_					)
															model.to(UpperCAmelCase_					)
															model.eval()

															with torch.no_grad():
																					_SCREAMING_SNAKE_CASE     				=  model(**self._prepare_for_class(UpperCAmelCase_				,     UpperCAmelCase_					)					)

															_SCREAMING_SNAKE_CASE     				=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															_SCREAMING_SNAKE_CASE     				=  self.model_tester.num_stages
															self.assertEqual(len(UpperCAmelCase_					)				,     expected_num_stages + 1					)

															# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]					)				,     [self.model_tester.image_size // 2, self.model_tester.image_size // 2]				,     )

									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  self.model_tester.prepare_config_and_inputs_for_common()
									_SCREAMING_SNAKE_CASE     				=  ["""basic""", """bottleneck"""]
									for model_class in self.all_model_classes:
															for layer_type in layers_type:
																					_SCREAMING_SNAKE_CASE     				=  layer_type
																					_SCREAMING_SNAKE_CASE     				=  True
																					check_hidden_states_output(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_					)

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

																					check_hidden_states_output(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_					)







			@slow
			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE     				=  RegNetModel.from_pretrained(UpperCAmelCase_					)
															self.assertIsNotNone(UpperCAmelCase_					)

def 	__lowerCamelCase      (		)							->  Union[str, Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
						return image




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







			@cached_property
			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									return (
									    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
									    if is_vision_available()
									    else None
									)







			@slow
			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  self.default_image_processor
									_SCREAMING_SNAKE_CASE     				=  prepare_img()
									_SCREAMING_SNAKE_CASE     				=  image_processor(images=UpperCAmelCase_				,     return_tensors="""pt"""					).to(UpperCAmelCase_					)

									# forward pass
									with torch.no_grad():
															_SCREAMING_SNAKE_CASE     				=  model(**UpperCAmelCase_					)

									# verify the logits
									_SCREAMING_SNAKE_CASE     				=  torch.Size((1, 1_000)					)
									self.assertEqual(outputs.logits.shape				,     UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  torch.tensor([-0.41_80, -1.50_51, -3.48_36]					).to(UpperCAmelCase_					)

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









import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


UpperCamelCase			    =     {
    '''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''',
}

UpperCamelCase			    =     {
    '''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''',
}

UpperCamelCase			    =     {
    '''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''',
}

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

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

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

def 	__lowerCamelCase      (		snake_case__		)							->  Optional[Any]:






						"""simple docstring"""





						if isinstance(snake_case__	,snake_case__		):
												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      (		snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__=False		)							->  Optional[int]:






						"""simple docstring"""





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

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

						return new_checkpoint

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__=None		)							->  Optional[int]:






						"""simple docstring"""





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

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

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

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

						return new_checkpoint

def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  torch.load(snake_case__	,map_location="""cpu"""		)
						_SCREAMING_SNAKE_CASE     				=  {}

						_SCREAMING_SNAKE_CASE     				=  checkpoint["""time_embed.0.weight"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""time_embed.0.bias"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""time_embed.2.weight"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""time_embed.2.bias"""]

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

						_SCREAMING_SNAKE_CASE     				=  checkpoint["""input_blocks.0.0.weight"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""input_blocks.0.0.bias"""]

						_SCREAMING_SNAKE_CASE     				=  unet_config["""down_block_types"""]
						_SCREAMING_SNAKE_CASE     				=  unet_config["""layers_per_block"""]
						_SCREAMING_SNAKE_CASE     				=  unet_config["""attention_head_dim"""]
						_SCREAMING_SNAKE_CASE     				=  unet_config["""block_out_channels"""]
						_SCREAMING_SNAKE_CASE     				=  1
						_SCREAMING_SNAKE_CASE     				=  channels_list[0]

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

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

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

												_SCREAMING_SNAKE_CASE     				=  current_channels

						# hardcoded the mid-block for now
						_SCREAMING_SNAKE_CASE     				=  """mid_block.resnets.0"""
						_SCREAMING_SNAKE_CASE     				=  """middle_block.0"""
						_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  """mid_block.attentions.0"""
						_SCREAMING_SNAKE_CASE     				=  """middle_block.1"""
						_SCREAMING_SNAKE_CASE     				=  convert_attention(snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  """mid_block.resnets.1"""
						_SCREAMING_SNAKE_CASE     				=  """middle_block.2"""
						_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  0
						_SCREAMING_SNAKE_CASE     				=  unet_config["""up_block_types"""]

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

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

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

						_SCREAMING_SNAKE_CASE     				=  checkpoint["""out.0.weight"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""out.0.bias"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""out.2.weight"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""out.2.bias"""]

						return new_checkpoint


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

		UpperCamelCase			    =     parser.parse_args()
		UpperCamelCase			    =     strabool(args.class_cond)

		UpperCamelCase			    =     os.path.basename(args.unet_path)
		print(f"Checkpoint: {ckpt_name}")

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

		if not args.class_cond:
				UpperCamelCase			    =     None

		UpperCamelCase			    =     con_pt_to_diffuser(args.unet_path, unet_config)

		UpperCamelCase			    =     UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				UpperCamelCase			    =     CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				UpperCamelCase			    =     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)):
				UpperCamelCase			    =     CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

		UpperCamelCase			    =     CMStochasticIterativeScheduler(**scheduler_config)

		UpperCamelCase			    =     ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)

