





import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu

class    lowerCAmelCase							(					nn.Module			):
 def __init__(      self  :      Dict	)			->	Tuple:
     super().__init__()
     lowerCamelCase__    :      List[str]					 =		nn.Linear(3			,     4	)
     lowerCamelCase__    :      Optional[Any]					 =		nn.BatchNormad(4	)
     lowerCamelCase__    :      Dict					 =		nn.Linear(4			,     5	)







 def      A_	(      self  :      int			,     UpperCAmelCase  :      Dict	)			->	Optional[Any]:
     return self.lineara(self.batchnorm(self.lineara(lowercase_	)	)	)


class    lowerCAmelCase							(					UpperCamelCase__			):
 def      A_	(      self  :      Optional[Any]			,     UpperCAmelCase  :      Union[str, Any]			,     *UpperCAmelCase  :      Dict			,     **UpperCAmelCase  :      str	)			->	int:
     return (args[0] + 1,) + args[1:], kwargs


class    lowerCAmelCase							(					UpperCamelCase__			):
 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      Dict			,     UpperCAmelCase  :      List[str]	)			->	int:
     return output + 1


class    lowerCAmelCase							(					unittest.TestCase			):
 def      A_	(      self  :      int	)			->	Union[str, Any]:
     lowerCamelCase__    :      List[Any]					 =		ModelForTest()
     lowerCamelCase__    :      int					 =		ModelHook()

     add_hook_to_module(lowercase_			,     lowercase_	)
     self.assertEqual(test_model._hf_hook			,     lowercase_	)
     self.assertTrue(hasattr(lowercase_			,     '_old_forward'	)	)

     # Check adding the hook did not change the name or the signature
     self.assertEqual(test_model.forward.__name__			,     'forward'	)
     self.assertListEqual(list(inspect.signature(test_model.forward	).parameters	)			,     ['x']	)

     remove_hook_from_module(lowercase_	)
     self.assertFalse(hasattr(lowercase_			,     '_hf_hook'	)	)
     self.assertFalse(hasattr(lowercase_			,     '_old_forward'	)	)
 def      A_	(      self  :      Optional[Any]	)			->	str:
     lowerCamelCase__    :      Optional[int]					 =		ModelForTest()
     lowerCamelCase__    :      List[Any]					 =		ModelHook()

     add_hook_to_module(lowercase_			,     lowercase_	)
     add_hook_to_module(lowercase_			,     lowercase_			,     append=lowercase_	)

     self.assertEqual(isinstance(test_model._hf_hook			,     lowercase_	)			,     lowercase_	)
     self.assertEqual(len(test_model._hf_hook.hooks	)			,     2	)
     self.assertTrue(hasattr(lowercase_			,     '_old_forward'	)	)

     # Check adding the hook did not change the name or the signature
     self.assertEqual(test_model.forward.__name__			,     'forward'	)
     self.assertListEqual(list(inspect.signature(test_model.forward	).parameters	)			,     ['x']	)

     remove_hook_from_module(lowercase_	)
     self.assertFalse(hasattr(lowercase_			,     '_hf_hook'	)	)
     self.assertFalse(hasattr(lowercase_			,     '_old_forward'	)	)
 def      A_	(      self  :      Any	)			->	Optional[Any]:
     lowerCamelCase__    :      int					 =		ModelForTest()
     lowerCamelCase__    :      Union[str, Any]					 =		torch.randn(2			,     3	)
     lowerCamelCase__    :      Optional[int]					 =		test_model(x + 1	)
     lowerCamelCase__    :      Optional[Any]					 =		test_model(x + 2	)

     lowerCamelCase__    :      List[str]					 =		PreForwardHook()
     add_hook_to_module(lowercase_			,     lowercase_	)
     lowerCamelCase__    :      List[Any]					 =		test_model(lowercase_	)
     self.assertTrue(torch.allclose(lowercase_			,     lowercase_			,     atol=1e-5	)	)

     # Attaching a hook to a model when it already has one replaces, does not chain
     lowerCamelCase__    :      Optional[Any]					 =		PreForwardHook()
     add_hook_to_module(lowercase_			,     lowercase_	)
     lowerCamelCase__    :      Tuple					 =		test_model(lowercase_	)
     self.assertTrue(torch.allclose(lowercase_			,     lowercase_			,     atol=1e-5	)	)

     # You need to use the sequential hook to chain two or more hooks
     lowerCamelCase__    :      Tuple					 =		SequentialHook(PreForwardHook()			,     PreForwardHook()	)
     add_hook_to_module(lowercase_			,     lowercase_	)

     lowerCamelCase__    :      List[Any]					 =		test_model(lowercase_	)
     assert torch.allclose(lowercase_			,     lowercase_			,     atol=1e-5	)
 def      A_	(      self  :      int	)			->	Optional[int]:
     lowerCamelCase__    :      Optional[Any]					 =		ModelForTest()
     lowerCamelCase__    :      int					 =		torch.randn(2			,     3	)
     lowerCamelCase__    :      Any					 =		test_model(lowercase_	)

     lowerCamelCase__    :      Optional[Any]					 =		PostForwardHook()
     add_hook_to_module(lowercase_			,     lowercase_	)
     lowerCamelCase__    :      List[str]					 =		test_model(lowercase_	)
     self.assertTrue(torch.allclose(lowercase_			,     output + 1			,     atol=1e-5	)	)

     # Attaching a hook to a model when it already has one replaces, does not chain
     lowerCamelCase__    :      Union[str, Any]					 =		PostForwardHook()
     add_hook_to_module(lowercase_			,     lowercase_	)
     lowerCamelCase__    :      Dict					 =		test_model(lowercase_	)
     self.assertTrue(torch.allclose(lowercase_			,     output + 1			,     atol=1e-5	)	)

     # You need to use the sequential hook to chain two or more hooks
     lowerCamelCase__    :      int					 =		SequentialHook(PostForwardHook()			,     PostForwardHook()	)
     add_hook_to_module(lowercase_			,     lowercase_	)

     lowerCamelCase__    :      Union[str, Any]					 =		test_model(lowercase_	)
     assert torch.allclose(lowercase_			,     output + 2			,     atol=1e-5	)
 def      A_	(      self  :      Any	)			->	Dict:
     lowerCamelCase__    :      Optional[Any]					 =		ModelForTest()
     lowerCamelCase__    :      Union[str, Any]					 =		torch.randn(2			,     3	)
     lowerCamelCase__    :      Union[str, Any]					 =		test_model(lowercase_	)

     lowerCamelCase__    :      Union[str, Any]					 =		PostForwardHook()
     add_hook_to_module(lowercase_			,     lowercase_	)
     lowerCamelCase__    :      Optional[Any]					 =		test_model(lowercase_	)
     self.assertTrue(torch.allclose(lowercase_			,     output + 1	)	)
     self.assertTrue(outputa.requires_grad	)

     lowerCamelCase__    :      str					 =		True
     lowerCamelCase__    :      Union[str, Any]					 =		test_model(lowercase_	)
     self.assertFalse(outputa.requires_grad	)
 @require_multi_gpu
 def      A_	(      self  :      Tuple	)			->	str:
     lowerCamelCase__    :      List[Any]					 =		ModelForTest()
     # Everything is on CPU
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.batchnorm.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)

     # This will move each submodule on different devices
     add_hook_to_module(model.lineara			,     AlignDevicesHook(execution_device=0	)	)
     add_hook_to_module(model.batchnorm			,     AlignDevicesHook(execution_device=0	)	)
     add_hook_to_module(model.lineara			,     AlignDevicesHook(execution_device=1	)	)

     self.assertEqual(model.lineara.weight.device			,     torch.device(0	)	)
     self.assertEqual(model.batchnorm.weight.device			,     torch.device(0	)	)
     self.assertEqual(model.batchnorm.running_mean.device			,     torch.device(0	)	)
     self.assertEqual(model.lineara.weight.device			,     torch.device(1	)	)

     # We can still make a forward pass. The input does not need to be on any particular device
     lowerCamelCase__    :      Optional[Any]					 =		torch.randn(2			,     3	)
     lowerCamelCase__    :      Optional[int]					 =		model(lowercase_	)
     self.assertEqual(output.device			,     torch.device(1	)	)

     # We can add a general hook to put back output on same device as input.
     add_hook_to_module(lowercase_			,     AlignDevicesHook(io_same_device=lowercase_	)	)
     lowerCamelCase__    :      str					 =		torch.randn(2			,     3	).to(0	)
     lowerCamelCase__    :      int					 =		model(lowercase_	)
     self.assertEqual(output.device			,     torch.device(0	)	)
 def      A_	(      self  :      List[str]	)			->	str:
     lowerCamelCase__    :      List[Any]					 =		ModelForTest()

     # Everything is on CPU
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.batchnorm.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)

     # This will move each submodule on different devices
     lowerCamelCase__    :      List[str]					 =		{'execution_device': 0 if torch.cuda.is_available() else 'cpu', 'offload': True}

     add_hook_to_module(model.lineara			,     AlignDevicesHook(**lowercase_	)	)
     add_hook_to_module(model.batchnorm			,     AlignDevicesHook(**lowercase_	)	)
     add_hook_to_module(model.lineara			,     AlignDevicesHook(**lowercase_	)	)

     # Parameters have been offloaded, so on the meta device
     self.assertEqual(model.lineara.weight.device			,     torch.device('meta'	)	)
     self.assertEqual(model.batchnorm.weight.device			,     torch.device('meta'	)	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('meta'	)	)
     # Buffers are not included in the offload by default, so are on the execution device
     lowerCamelCase__    :      Optional[int]					 =		torch.device(hook_kwargs['execution_device']	)
     self.assertEqual(model.batchnorm.running_mean.device			,     lowercase_	)

     lowerCamelCase__    :      Optional[Any]					 =		torch.randn(2			,     3	)
     lowerCamelCase__    :      List[Any]					 =		model(lowercase_	)
     self.assertEqual(output.device			,     lowercase_	)

     # Removing hooks loads back the weights in the model.
     remove_hook_from_module(model.lineara	)
     remove_hook_from_module(model.batchnorm	)
     remove_hook_from_module(model.lineara	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.batchnorm.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)

     # Now test with buffers included in the offload
     lowerCamelCase__    :      List[Any]					 =		{
         'execution_device': 0 if torch.cuda.is_available() else 'cpu',
         'offload': True,
         'offload_buffers': True,
     }

     add_hook_to_module(model.lineara			,     AlignDevicesHook(**lowercase_	)	)
     add_hook_to_module(model.batchnorm			,     AlignDevicesHook(**lowercase_	)	)
     add_hook_to_module(model.lineara			,     AlignDevicesHook(**lowercase_	)	)

     # Parameters have been offloaded, so on the meta device, buffers included
     self.assertEqual(model.lineara.weight.device			,     torch.device('meta'	)	)
     self.assertEqual(model.batchnorm.weight.device			,     torch.device('meta'	)	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('meta'	)	)
     self.assertEqual(model.batchnorm.running_mean.device			,     torch.device('meta'	)	)

     lowerCamelCase__    :      Any					 =		torch.randn(2			,     3	)
     lowerCamelCase__    :      List[Any]					 =		model(lowercase_	)
     self.assertEqual(output.device			,     lowercase_	)

     # Removing hooks loads back the weights in the model.
     remove_hook_from_module(model.lineara	)
     remove_hook_from_module(model.batchnorm	)
     remove_hook_from_module(model.lineara	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.batchnorm.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)
 def      A_	(      self  :      Dict	)			->	List[str]:
     lowerCamelCase__    :      List[str]					 =		ModelForTest()

     # Everything is on CPU
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.batchnorm.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)

     # This will move each submodule on different devices
     lowerCamelCase__    :      Optional[int]					 =		0 if torch.cuda.is_available() else 'cpu'
     attach_align_device_hook(lowercase_			,     execution_device=lowercase_			,     offload=lowercase_	)

     # Parameters have been offloaded, so on the meta device
     self.assertEqual(model.lineara.weight.device			,     torch.device('meta'	)	)
     self.assertEqual(model.batchnorm.weight.device			,     torch.device('meta'	)	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('meta'	)	)
     # Buffers are not included in the offload by default, so are on the execution device
     lowerCamelCase__    :      List[str]					 =		torch.device(lowercase_	)
     self.assertEqual(model.batchnorm.running_mean.device			,     lowercase_	)

     lowerCamelCase__    :      str					 =		torch.randn(2			,     3	)
     lowerCamelCase__    :      Dict					 =		model(lowercase_	)
     self.assertEqual(output.device			,     lowercase_	)

     # Removing hooks loads back the weights in the model.
     remove_hook_from_submodules(lowercase_	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.batchnorm.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)

     # Now test with buffers included in the offload
     attach_align_device_hook(lowercase_			,     execution_device=lowercase_			,     offload=lowercase_			,     offload_buffers=lowercase_	)

     # Parameters have been offloaded, so on the meta device, buffers included
     self.assertEqual(model.lineara.weight.device			,     torch.device('meta'	)	)
     self.assertEqual(model.batchnorm.weight.device			,     torch.device('meta'	)	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('meta'	)	)
     self.assertEqual(model.batchnorm.running_mean.device			,     torch.device('meta'	)	)

     lowerCamelCase__    :      List[Any]					 =		torch.randn(2			,     3	)
     lowerCamelCase__    :      List[str]					 =		model(lowercase_	)
     self.assertEqual(output.device			,     lowercase_	)

     # Removing hooks loads back the weights in the model.
     remove_hook_from_submodules(lowercase_	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.batchnorm.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)







 def      A_	(      self  :      Optional[Any]	)			->	Any:
     lowerCamelCase__    :      Optional[Any]					 =		ModelForTest()

     # Everything is on CPU
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.batchnorm.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)

     # This will move each submodule on different devices
     lowerCamelCase__    :      int					 =		0 if torch.cuda.is_available() else 'cpu'
     attach_align_device_hook(
         lowercase_			,     execution_device=lowercase_			,     offload=lowercase_			,     weights_map=model.state_dict()	)

     # Parameters have been offloaded, so on the meta device
     self.assertEqual(model.lineara.weight.device			,     torch.device('meta'	)	)
     self.assertEqual(model.batchnorm.weight.device			,     torch.device('meta'	)	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('meta'	)	)
     # Buffers are not included in the offload by default, so are on the execution device
     lowerCamelCase__    :      Union[str, Any]					 =		torch.device(lowercase_	)
     self.assertEqual(model.batchnorm.running_mean.device			,     lowercase_	)

     lowerCamelCase__    :      List[str]					 =		torch.randn(2			,     3	)
     lowerCamelCase__    :      int					 =		model(lowercase_	)
     self.assertEqual(output.device			,     lowercase_	)

     # Removing hooks loads back the weights in the model.
     remove_hook_from_submodules(lowercase_	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.batchnorm.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)

     # Now test with buffers included in the offload
     attach_align_device_hook(
         lowercase_			,     execution_device=lowercase_			,     offload=lowercase_			,     weights_map=model.state_dict()			,     offload_buffers=lowercase_			,     )

     # Parameters have been offloaded, so on the meta device, buffers included
     self.assertEqual(model.lineara.weight.device			,     torch.device('meta'	)	)
     self.assertEqual(model.batchnorm.weight.device			,     torch.device('meta'	)	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('meta'	)	)
     self.assertEqual(model.batchnorm.running_mean.device			,     torch.device('meta'	)	)

     lowerCamelCase__    :      Optional[int]					 =		torch.randn(2			,     3	)
     lowerCamelCase__    :      str					 =		model(lowercase_	)
     self.assertEqual(output.device			,     lowercase_	)

     # Removing hooks loads back the weights in the model.
     remove_hook_from_submodules(lowercase_	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.batchnorm.weight.device			,     torch.device('cpu'	)	)
     self.assertEqual(model.lineara.weight.device			,     torch.device('cpu'	)	)












'''simple docstring'''

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCAmelCase							:	List[str]			={
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1000,
    "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",
}

__lowerCAmelCase							:	Dict			={
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1000,
    "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",
}

__lowerCAmelCase							:	Union[str, Any]			={
    "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",
}

__lowerCAmelCase							:	str			={
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__lowerCAmelCase							:	Tuple			={
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__lowerCAmelCase							:	Dict			={
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}





def 						UpperCamelCase				(							_lowerCamelCase					:					Tuple       ):
    if isinstance(_lowerCamelCase						,       _lowerCamelCase       ):
        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 						UpperCamelCase				(							_lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Dict						,       _lowerCamelCase					:					Optional[int]=False       ):
    A__				=			checkpoint[F"{old_prefix}.in_layers.0.weight"]
    A__				=			checkpoint[F"{old_prefix}.in_layers.0.bias"]
    A__				=			checkpoint[F"{old_prefix}.in_layers.2.weight"]
    A__				=			checkpoint[F"{old_prefix}.in_layers.2.bias"]
    A__				=			checkpoint[F"{old_prefix}.emb_layers.1.weight"]
    A__				=			checkpoint[F"{old_prefix}.emb_layers.1.bias"]
    A__				=			checkpoint[F"{old_prefix}.out_layers.0.weight"]
    A__				=			checkpoint[F"{old_prefix}.out_layers.0.bias"]
    A__				=			checkpoint[F"{old_prefix}.out_layers.3.weight"]
    A__				=			checkpoint[F"{old_prefix}.out_layers.3.bias"]

    if has_skip:
        A__				=			checkpoint[F"{old_prefix}.skip_connection.weight"]
        A__				=			checkpoint[F"{old_prefix}.skip_connection.bias"]

    return new_checkpoint





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					Any						,       _lowerCamelCase					:					Tuple						,       _lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					List[Any]=None       ):
    A__,	A__,	A__				=			checkpoint[F"{old_prefix}.qkv.weight"].chunk(3						,       dim=0       )
    A__,	A__,	A__				=			checkpoint[F"{old_prefix}.qkv.bias"].chunk(3						,       dim=0       )

    A__				=			checkpoint[F"{old_prefix}.norm.weight"]
    A__				=			checkpoint[F"{old_prefix}.norm.bias"]

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

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

    return new_checkpoint





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					List[str]       ):
    A__				=			torch.load(_lowerCamelCase						,       map_location="cpu"       )
    A__				=			{}

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

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

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

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

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

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

        if i != len(_lowerCamelCase       ) - 1:
            A__				=			F"down_blocks.{i}.downsamplers.0"
            A__				=			F"input_blocks.{current_layer}.0"
            A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
            current_layer += 1

        A__				=			current_channels

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

    A__				=			0
    A__				=			unet_config["up_block_types"]

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

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

            if i != len(_lowerCamelCase       ) - 1:
                A__				=			F"up_blocks.{i}.upsamplers.0"
                A__				=			F"output_blocks.{current_layer-1}.2"
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )

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

    return new_checkpoint


if __name__ == "__main__":
  __lowerCAmelCase							:	List[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							:	Optional[Any]			=parser.parse_args()
  __lowerCAmelCase							:	List[Any]			=strabool(args.class_cond)

  __lowerCAmelCase							:	List[str]			=os.path.basename(args.unet_path)
  print(f"""Checkpoint: {ckpt_name}""")

  # Get U-Net config
  if "imagenet64" in ckpt_name:
    __lowerCAmelCase							:	List[str]			=IMAGENET_64_UNET_CONFIG
  elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
    __lowerCAmelCase							:	List[str]			=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							:	Dict			=None

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

  __lowerCAmelCase							:	Dict			=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							:	List[str]			=CD_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
    __lowerCAmelCase							:	Dict			=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							:	Dict			=CT_LSUN_256_SCHEDULER_CONFIG
  else:
    raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

  __lowerCAmelCase							:	Dict			=CMStochasticIterativeScheduler(**scheduler_config)

  __lowerCAmelCase							:	str			=ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
  consistency_model.save_pretrained(args.dump_path)



