

import logging
import os
import threading
import time


try:
						import warnings
except ImportError:
						_lowerCAmelCase					:			List[Any]  				=			None

try:
						import msvcrt
except ImportError:
						_lowerCAmelCase					:			Optional[Any]  				=			None

try:
						import fcntl
except ImportError:
						_lowerCAmelCase					:			str  				=			None


# Backward compatibility
# ------------------------------------------------
try:
						TimeoutError
except NameError:
						_lowerCAmelCase					:			List[str]  				=			OSError


# Data
# ------------------------------------------------
_lowerCAmelCase					:			Any  				=			[
    "Timeout",
    "BaseFileLock",
    "WindowsFileLock",
    "UnixFileLock",
    "SoftFileLock",
    "FileLock",
]

_lowerCAmelCase					:			Optional[Any]  				=			"3.0.12"


_lowerCAmelCase					:			List[str]  				=			None






def        UpperCamelCase_(							):




			"""simple docstring"""






			global _logger
			__a        =_logger or logging.getLogger(__name__	)
			return _logger






class 	__magic_name__    (			lowerCAmelCase_      ):





			def __init__(		self					,  __snake_case					)   -> Dict:




						'''simple docstring'''
						__a        =lock_file
						return None





			def __str__(		self					)   -> str:




						'''simple docstring'''
						__a        =f'The file lock \'{self.lock_file}\' could not be acquired.'
						return temp






class 	__magic_name__    :





			def __init__(		self					,  __snake_case					)   -> int:




						'''simple docstring'''
						__a        =lock
						return None





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




						'''simple docstring'''
						return self.lock





			def __exit__(		self					,  __snake_case					,  __snake_case					,  __snake_case					)   -> Optional[int]:




						'''simple docstring'''
						self.lock.release()
						return None






class 	__magic_name__    :





			def __init__(		self					,  __snake_case					,  __snake_case=-1					,  __snake_case=None					)   -> List[str]:




						'''simple docstring'''
						__a        =max_filename_length if max_filename_length is not None else 255
						# Hash the filename if it's too long
						__a        =self.hash_filename_if_too_long(__snake_case					,  __snake_case					)
						# The path to the lock file.
						__a        =lock_file

						# The file descriptor for the *_lock_file* as it is returned by the
						# os.open() function.
						# This file lock is only NOT None, if the object currently holds the
						# lock.
						__a        =None

						# The default timeout value.
						__a        =timeout

						# We use this lock primarily for the lock counter.
						__a        =threading.Lock()

						# The lock counter is used for implementing the nested locking
						# mechanism. Whenever the lock is acquired, the counter is increased and
						# the lock is only released, when this value is 0 again.
						__a        =0
						return None





			@property
			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						return self._lock_file





			@property
			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						return self._timeout





			@timeout.setter
			def 		__magic_name__    (		self					,  __snake_case					)   -> Optional[int]:




						'''simple docstring'''
						__a        =float(__snake_case					)
						return None





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						raise NotImplementedError()





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						raise NotImplementedError()





			@property
			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						return self._lock_file_fd is not None





			def 		__magic_name__    (		self					,  __snake_case=None					,  __snake_case=0.05					)   -> Any:




						'''simple docstring'''
						# Use the default timeout, if no timeout is provided.
						if timeout is None:
									__a        =self.timeout

						# Increment the number right at the beginning.
						# We can still undo it, if something fails.
						with self._thread_lock:
									self._lock_counter += 1

						__a        =id(self					)
						__a        =self._lock_file
						__a        =time.time()
						try:
									while True:
												with self._thread_lock:
															if not self.is_locked:
																		logger().debug(f'Attempting to acquire lock {lock_id} on {lock_filename}'					)
																		self._acquire()

												if self.is_locked:
															logger().debug(f'Lock {lock_id} acquired on {lock_filename}'					)
															break
												elif timeout >= 0 and time.time() - start_time > timeout:
															logger().debug(f'Timeout on acquiring lock {lock_id} on {lock_filename}'					)
															raise Timeout(self._lock_file					)
												else:
															logger().debug(
															    f'Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...'					)
															time.sleep(__snake_case					)
						except:  # noqa
									# Something did go wrong, so decrement the counter.
									with self._thread_lock:
												__a        =max(0					,  self._lock_counter - 1					)

									raise
						return _Acquire_ReturnProxy(lock=self					)





			def 		__magic_name__    (		self					,  __snake_case=False					)   -> int:




						'''simple docstring'''
						with self._thread_lock:
									if self.is_locked:
												self._lock_counter -= 1

												if self._lock_counter == 0 or force:
															__a        =id(self					)
															__a        =self._lock_file

															logger().debug(f'Attempting to release lock {lock_id} on {lock_filename}'					)
															self._release()
															__a        =0
															logger().debug(f'Lock {lock_id} released on {lock_filename}'					)

						return None





			def __enter__(		self					)   -> List[str]:




						'''simple docstring'''
						self.acquire()
						return self





			def __exit__(		self					,  __snake_case					,  __snake_case					,  __snake_case					)   -> Dict:




						'''simple docstring'''
						self.release()
						return None





			def __del__(		self					)   -> int:




						'''simple docstring'''
						self.release(force=__snake_case					)
						return None





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					)   -> str:




						'''simple docstring'''
						__a        =os.path.basename(__snake_case					)
						if len(__snake_case					) > max_length and max_length > 0:
									__a        =os.path.dirname(__snake_case					)
									__a        =str(hash(__snake_case					)					)
									__a        =filename[: max_length - len(__snake_case					) - 8] + '...' + hashed_filename + '.lock'
									return os.path.join(__snake_case					,  __snake_case					)
						else:
									return path






class 	__magic_name__    (			lowerCAmelCase_      ):





			def __init__(		self					,  __snake_case					,  __snake_case=-1					,  __snake_case=None					)   -> Dict:




						'''simple docstring'''
						from .file_utils import relative_to_absolute_path

						super().__init__(__snake_case					,  timeout=__snake_case					,  max_filename_length=__snake_case					)
						__a        ='\\\\?\\' + relative_to_absolute_path(self.lock_file					)





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						__a        =os.O_RDWR | os.O_CREAT | os.O_TRUNC

						try:
									__a        =os.open(self._lock_file					,  __snake_case					)
						except OSError:
									pass
						else:
									try:
												msvcrt.locking(__snake_case					,  msvcrt.LK_NBLCK					,  1					)
									except OSError:
												os.close(__snake_case					)
									else:
												__a        =fd
						return None





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




						'''simple docstring'''
						__a        =self._lock_file_fd
						__a        =None
						msvcrt.locking(__snake_case					,  msvcrt.LK_UNLCK					,  1					)
						os.close(__snake_case					)

						try:
									os.remove(self._lock_file					)
						# Probably another instance of the application
						# that acquired the file lock.
						except OSError:
									pass
						return None






class 	__magic_name__    (			lowerCAmelCase_      ):





			def __init__(		self					,  __snake_case					,  __snake_case=-1					,  __snake_case=None					)   -> Tuple:




						'''simple docstring'''
						__a        =os.statvfs(os.path.dirname(__snake_case					)					).f_namemax
						super().__init__(__snake_case					,  timeout=__snake_case					,  max_filename_length=__snake_case					)





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						__a        =os.O_RDWR | os.O_CREAT | os.O_TRUNC
						__a        =os.open(self._lock_file					,  __snake_case					)

						try:
									fcntl.flock(__snake_case					,  fcntl.LOCK_EX | fcntl.LOCK_NB					)
						except OSError:
									os.close(__snake_case					)
						else:
									__a        =fd
						return None





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						# Do not remove the lockfile:
						#
						#   https://github.com/benediktschmitt/py-filelock/issues/31
						#   https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
						__a        =self._lock_file_fd
						__a        =None
						fcntl.flock(__snake_case					,  fcntl.LOCK_UN					)
						os.close(__snake_case					)
						return None






class 	__magic_name__    (			lowerCAmelCase_      ):





			def 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						__a        =os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
						try:
									__a        =os.open(self._lock_file					,  __snake_case					)
						except OSError:
									pass
						else:
									__a        =fd
						return None





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						os.close(self._lock_file_fd					)
						__a        =None

						try:
									os.remove(self._lock_file					)
						# The file is already deleted and that's what we want.
						except OSError:
									pass
						return None


_lowerCAmelCase					:			str  				=			None

if msvcrt:
						_lowerCAmelCase					:			Union[str, Any]  				=			WindowsFileLock
elif fcntl:
						_lowerCAmelCase					:			Dict  				=			UnixFileLock
else:
						_lowerCAmelCase					:			List[str]  				=			SoftFileLock

						if warnings is not None:
												warnings.warn("only soft file lock is available")









import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowerCAmelCase					:			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",
}

_lowerCAmelCase					:			List[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",
}

_lowerCAmelCase					:			str  				=			{
    "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					:			List[str]  				=			{
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_lowerCAmelCase					:			Optional[Any]  				=			{
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_lowerCAmelCase					:			List[str]  				=			{
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}






def        UpperCamelCase_(							_snake_case   :  str	):




			"""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        UpperCamelCase_(							_snake_case   :  Tuple       ,     _snake_case   :  List[str]       ,     _snake_case   :  Any       ,     _snake_case   :  str       ,     _snake_case   :  Union[str, Any]=False	):




			"""simple docstring"""






			__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_(							_snake_case   :  Dict       ,     _snake_case   :  Union[str, Any]       ,     _snake_case   :  Union[str, Any]       ,     _snake_case   :  Optional[Any]       ,     _snake_case   :  Optional[int]=None	):




			"""simple docstring"""






			__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_(							_snake_case   :  str       ,     _snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     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(_snake_case	):
						__a        =channels_list[i]
						__a        =current_channels != prev_channels
						if layer_type == "ResnetDownsampleBlock2D":
									for j in range(_snake_case	):
												__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(_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	):
												__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(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     has_skip=_snake_case	)
												__a        =F'down_blocks.{i}.attentions.{j}'
												__a        =F'input_blocks.{current_layer}.1'
												__a        =convert_attention(
												    _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
												current_layer += 1

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

						__a        =current_channels

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

			__a        =0
			__a        =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	):
												__a        =F'up_blocks.{i}.resnets.{j}'
												__a        =F'output_blocks.{current_layer}.0'
												__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     has_skip=_snake_case	)
												current_layer += 1

									if i != len(_snake_case	) - 1:
												__a        =F'up_blocks.{i}.upsamplers.0'
												__a        =F'output_blocks.{current_layer-1}.1'
												__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
						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(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     has_skip=_snake_case	)
												__a        =F'up_blocks.{i}.attentions.{j}'
												__a        =F'output_blocks.{current_layer}.1'
												__a        =convert_attention(
												    _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
												current_layer += 1

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

			__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					:			Optional[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					:			Optional[Any]  				=			strabool(args.class_cond)

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

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

						if not args.class_cond:
												_lowerCAmelCase					:			List[Any]  				=			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					:			Optional[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)):
												_lowerCAmelCase					:			List[str]  				=			CT_LSUN_256_SCHEDULER_CONFIG
						else:
												raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

						_lowerCAmelCase					:			Any  				=			CMStochasticIterativeScheduler(**scheduler_config)

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






