





import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase=False					)						->		str:






				"""simple docstring"""


				try:
								snake_case_		:       Any           =    os.environ[key]
				except KeyError:
								# KEY isn't set, default to `default`.
								snake_case_		:       Dict           =    default
				else:
								# KEY is set, convert it to True or False.
								try:
												snake_case_		:       Tuple           =    strtobool(lowerCamelCase__					)
								except ValueError:
												# More values are supported, but let's keep the message simple.
												raise ValueError(f'''If set, {key} must be yes or no.'''					)
				return _value


lowerCAmelCase_        =							parse_flag_from_env('''RUN_SLOW''', default=False)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		Any:






				"""simple docstring"""


				return unittest.skip('''Test was skipped'''					)(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		Dict:






				"""simple docstring"""


				return unittest.skipUnless(_run_slow_tests					,							'''test is slow'''					)(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		int:






				"""simple docstring"""


				return unittest.skipUnless(not torch.cuda.is_available()					,							'''test requires only a CPU'''					)(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		Optional[int]:






				"""simple docstring"""


				return unittest.skipUnless(torch.cuda.is_available()					,							'''test requires a GPU'''					)(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		List[str]:






				"""simple docstring"""


				return unittest.skipUnless(is_xpu_available()					,							'''test requires a XPU'''					)(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		List[str]:






				"""simple docstring"""


				return unittest.skipUnless(is_mps_available()					,							'''test requires a `mps` backend support in `torch`'''					)(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		Dict:






				"""simple docstring"""


				return unittest.skipUnless(
				    is_transformers_available() and is_datasets_available()					,							'''test requires the Hugging Face suite'''					)(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		str:






				"""simple docstring"""


				return unittest.skipUnless(is_bnb_available()					,							'''test requires the bitsandbytes library'''					)(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		List[Any]:






				"""simple docstring"""


				return unittest.skipUnless(is_tpu_available()					,							'''test requires TPU'''					)(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		List[Any]:






				"""simple docstring"""


				return unittest.skipUnless(torch.cuda.device_count() == 1					,							'''test requires a GPU'''					)(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		Tuple:






				"""simple docstring"""


				return unittest.skipUnless(torch.xpu.device_count() == 1					,							'''test requires a XPU'''					)(lowerCamelCase__					)






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






				"""simple docstring"""


				return unittest.skipUnless(torch.cuda.device_count() > 1					,							'''test requires multiple GPUs'''					)(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		Dict:






				"""simple docstring"""


				return unittest.skipUnless(torch.xpu.device_count() > 1					,							'''test requires multiple XPUs'''					)(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		str:






				"""simple docstring"""


				return unittest.skipUnless(is_safetensors_available()					,							'''test requires safetensors'''					)(lowerCamelCase__					)






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






				"""simple docstring"""


				return unittest.skipUnless(is_deepspeed_available()					,							'''test requires DeepSpeed'''					)(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		str:






				"""simple docstring"""


				return unittest.skipUnless(is_torch_version('''>='''					,							'''1.12.0'''					)					,							'''test requires torch version >= 1.12.0'''					)(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase=None					,							_UpperCamelCase=None					)						->		int:






				"""simple docstring"""


				if test_case is None:
								return partial(lowerCamelCase__					,							version=lowerCamelCase__					)
				return unittest.skipUnless(is_torch_version('''>='''					,							lowerCamelCase__					)					,							f'''test requires torch version >= {version}'''					)(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		List[Any]:






				"""simple docstring"""


				return unittest.skipUnless(is_tensorboard_available()					,							'''test requires Tensorboard'''					)(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		int:






				"""simple docstring"""


				return unittest.skipUnless(is_wandb_available()					,							'''test requires wandb'''					)(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		Optional[Any]:






				"""simple docstring"""


				return unittest.skipUnless(is_comet_ml_available()					,							'''test requires comet_ml'''					)(lowerCamelCase__					)


lowerCAmelCase_        =							(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		str:






				"""simple docstring"""


				return unittest.skipUnless(
				    _atleast_one_tracker_available					,							'''test requires at least one tracker to be available and for `comet_ml` to not be installed'''					,							)(lowerCamelCase__					)



class    __lowerCAmelCase			(     unittest.TestCase	):
			lowerCamelCase_	:	Union[str, Any]			     = True






			@classmethod
			def      lowerCamelCase    (cls       )	->  int:


							'''simple docstring'''



							snake_case_		:       Optional[Any]           =    tempfile.mkdtemp()






			@classmethod
			def      lowerCamelCase    (cls       )	->  int:


							'''simple docstring'''



							if os.path.exists(cls.tmpdir       ):
											shutil.rmtree(cls.tmpdir       )






			def      lowerCamelCase    (self       )	->  Tuple:


							'''simple docstring'''



							if self.clear_on_setup:
											for path in Path(self.tmpdir       ).glob('''**/*'''       ):
															if path.is_file():
																			path.unlink()
															elif path.is_dir():
																			shutil.rmtree(__magic_name__       )




class    __lowerCAmelCase			(     unittest.TestCase	):






			def      lowerCamelCase    (self       )	->  List[Any]:


							'''simple docstring'''



							super().tearDown()
							# Reset the state of the AcceleratorState singleton.
							AcceleratorState._reset_state()
							PartialState._reset_state()




class    __lowerCAmelCase			(     unittest.TestCase	):






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


							'''simple docstring'''



							snake_case_		:       List[Any]           =    mocks if isinstance(__magic_name__   ,				(tuple, list)       ) else [mocks]
							for m in self.mocks:
											m.start()
											self.addCleanup(m.stop       )






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		int:






				"""simple docstring"""


				snake_case_		:       Any           =    AcceleratorState()
				snake_case_		:       List[Any]           =    tensor[None].clone().to(state.device					)
				snake_case_		:       Union[str, Any]           =    gather(lowerCamelCase__					).cpu()
				snake_case_		:       Optional[int]           =    tensor[0].cpu()
				for i in range(tensors.shape[0]					):
								if not torch.equal(tensors[i]					,							lowerCamelCase__					):
												return False
				return True



class    __lowerCAmelCase			:






			def __init__(self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__       )	->  Any:


							'''simple docstring'''



							snake_case_		:       Optional[int]           =    returncode
							snake_case_		:       int           =    stdout
							snake_case_		:       Optional[int]           =    stderr






async def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		List[Any]:






				"""simple docstring"""


				while True:
								snake_case_		:       Any           =    await stream.readline()
								if line:
												callback(lowerCamelCase__					)
								else:
												break






async def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase=None					,							_UpperCamelCase=None					,							_UpperCamelCase=None					,							_UpperCamelCase=False					,							_UpperCamelCase=False					)						->		Optional[int]:






				"""simple docstring"""


				if echo:
								print('''\nRunning: '''					,							''' '''.join(lowerCamelCase__					)					)

				snake_case_		:       str           =    await asyncio.create_subprocess_exec(
				    cmd[0]					,							*cmd[1:]					,							stdin=lowerCamelCase__					,							stdout=asyncio.subprocess.PIPE					,							stderr=asyncio.subprocess.PIPE					,							env=lowerCamelCase__					,							)

				# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
				# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
				#
				# If it starts hanging, will need to switch to the following code. The problem is that no data
				# will be seen until it's done and if it hangs for example there will be no debug info.
				# out, err = await p.communicate()
				# return _RunOutput(p.returncode, out, err)

				snake_case_		:       Optional[Any]           =    []
				snake_case_		:       List[Any]           =    []

				def tee(_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase=""					):
								snake_case_		:       Tuple           =    line.decode('''utf-8'''					).rstrip()
								sink.append(lowerCamelCase__					)
								if not quiet:
												print(lowerCamelCase__					,							lowerCamelCase__					,							file=lowerCamelCase__					)

    # XXX: the timeout doesn't seem to make any difference here
				await asyncio.wait(
				    [
				        asyncio.create_task(_read_stream(p.stdout					,							lambda _UpperCamelCase					: tee(lowerCamelCase__					,							lowerCamelCase__					,							sys.stdout					,							label='''stdout:'''					)					)					),
				        asyncio.create_task(_read_stream(p.stderr					,							lambda _UpperCamelCase					: tee(lowerCamelCase__					,							lowerCamelCase__					,							sys.stderr					,							label='''stderr:'''					)					)					),
				    ]					,							timeout=lowerCamelCase__					,							)
				return _RunOutput(await p.wait()					,							lowerCamelCase__					,							lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase=None					,							_UpperCamelCase=None					,							_UpperCamelCase=180					,							_UpperCamelCase=False					,							_UpperCamelCase=True					)						->		Optional[Any]:






				"""simple docstring"""


				snake_case_		:       str           =    asyncio.get_event_loop()
				snake_case_		:       Union[str, Any]           =    loop.run_until_complete(
				    _stream_subprocess(lowerCamelCase__					,							env=lowerCamelCase__					,							stdin=lowerCamelCase__					,							timeout=lowerCamelCase__					,							quiet=lowerCamelCase__					,							echo=lowerCamelCase__					)					)

				snake_case_		:       Any           =    ''' '''.join(lowerCamelCase__					)
				if result.returncode > 0:
								snake_case_		:       Optional[int]           =    '''\n'''.join(result.stderr					)
								raise RuntimeError(
								    f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
								    f'''The combined stderr from workers follows:\n{stderr}'''					)

				return result



class    __lowerCAmelCase			(     a_	):
			pass






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase=False					)						->		List[str]:






				"""simple docstring"""


				try:
								snake_case_		:       List[str]           =    subprocess.check_output(lowerCamelCase__					,							stderr=subprocess.STDOUT					)
								if return_stdout:
												if hasattr(lowerCamelCase__					,							'''decode'''					):
																snake_case_		:       Optional[int]           =    output.decode('''utf-8'''					)
												return output
				except subprocess.CalledProcessError as e:
								raise SubprocessCallException(
								    f'''Command `{" ".join(lowerCamelCase__					)}` failed with the following error:\n\n{e.output.decode()}'''					) from e




import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

UpperCAmelCase   :    Tuple     =		[
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

UpperCAmelCase   :    Any     =		[
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

UpperCAmelCase   :    int     =		[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
		# loop over downblocks/upblocks

		for j in range(2):
				# loop over resnets/attentions for downblocks
				UpperCAmelCase   :    Any     =		f"""down_blocks.{i}.resnets.{j}."""
				UpperCAmelCase   :    Dict     =		f"""input_blocks.{3*i + j + 1}.0."""
				unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

				if i < 3:
						# no attention layers in down_blocks.3
						UpperCAmelCase   :    List[Any]     =		f"""down_blocks.{i}.attentions.{j}."""
						UpperCAmelCase   :    Optional[int]     =		f"""input_blocks.{3*i + j + 1}.1."""
						unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

		for j in range(3):
				# loop over resnets/attentions for upblocks
				UpperCAmelCase   :    List[Any]     =		f"""up_blocks.{i}.resnets.{j}."""
				UpperCAmelCase   :    int     =		f"""output_blocks.{3*i + j}.0."""
				unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

				if i > 0:
						# no attention layers in up_blocks.0
						UpperCAmelCase   :    Optional[Any]     =		f"""up_blocks.{i}.attentions.{j}."""
						UpperCAmelCase   :    Tuple     =		f"""output_blocks.{3*i + j}.1."""
						unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

		if i < 3:
				# no downsample in down_blocks.3
				UpperCAmelCase   :    int     =		f"""down_blocks.{i}.downsamplers.0.conv."""
				UpperCAmelCase   :    Union[str, Any]     =		f"""input_blocks.{3*(i+1)}.0.op."""
				unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

				# no upsample in up_blocks.3
				UpperCAmelCase   :    Optional[Any]     =		f"""up_blocks.{i}.upsamplers.0."""
				UpperCAmelCase   :    str     =		f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
				unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

UpperCAmelCase   :    str     =		"mid_block.attentions.0."
UpperCAmelCase   :    int     =		"middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
		UpperCAmelCase   :    int     =		f"""mid_block.resnets.{j}."""
		UpperCAmelCase   :    List[str]     =		f"""middle_block.{2*j}."""
		unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




			lowerCamelCase    		=  {k: k for k in unet_state_dict.keys()}
			for sd_name, hf_name in unet_conversion_map:
						lowerCamelCase    		=  sd_name
			for k, v in mapping.items():
						if "resnets" in k:
									for sd_part, hf_part in unet_conversion_map_resnet:
												lowerCamelCase    		=  v.replace(lowerCamelCase__  ,							lowerCamelCase__      )
									lowerCamelCase    		=  v
			for k, v in mapping.items():
						for sd_part, hf_part in unet_conversion_map_layer:
									lowerCamelCase    		=  v.replace(lowerCamelCase__  ,							lowerCamelCase__      )
						lowerCamelCase    		=  v
			lowerCamelCase    		=  {v: unet_state_dict[k] for k, v in mapping.items()}
			return new_state_dict


# ================#
# VAE Conversion #
# ================#

UpperCAmelCase   :    int     =		[
    # (stable-diffusion, HF Diffusers)
    ("nin_shortcut", "conv_shortcut"),
    ("norm_out", "conv_norm_out"),
    ("mid.attn_1.", "mid_block.attentions.0."),
]

for i in range(4):
		# down_blocks have two resnets
		for j in range(2):
				UpperCAmelCase   :    List[Any]     =		f"""encoder.down_blocks.{i}.resnets.{j}."""
				UpperCAmelCase   :    int     =		f"""encoder.down.{i}.block.{j}."""
				vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

		if i < 3:
				UpperCAmelCase   :    Any     =		f"""down_blocks.{i}.downsamplers.0."""
				UpperCAmelCase   :    Tuple     =		f"""down.{i}.downsample."""
				vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

				UpperCAmelCase   :    List[Any]     =		f"""up_blocks.{i}.upsamplers.0."""
				UpperCAmelCase   :    List[str]     =		f"""up.{3-i}.upsample."""
				vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

		# up_blocks have three resnets
		# also, up blocks in hf are numbered in reverse from sd
		for j in range(3):
				UpperCAmelCase   :    Any     =		f"""decoder.up_blocks.{i}.resnets.{j}."""
				UpperCAmelCase   :    str     =		f"""decoder.up.{3-i}.block.{j}."""
				vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
		UpperCAmelCase   :    Dict     =		f"""mid_block.resnets.{i}."""
		UpperCAmelCase   :    Union[str, Any]     =		f"""mid.block_{i+1}."""
		vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


UpperCAmelCase   :    Tuple     =		[
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[int]      ):





			'''simple docstring'''




			return w.reshape(*w.shape  ,							1  ,							1      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			lowerCamelCase    		=  {k: k for k in vae_state_dict.keys()}
			for k, v in mapping.items():
						for sd_part, hf_part in vae_conversion_map:
									lowerCamelCase    		=  v.replace(lowerCamelCase__  ,							lowerCamelCase__      )
						lowerCamelCase    		=  v
			for k, v in mapping.items():
						if "attentions" in k:
									for sd_part, hf_part in vae_conversion_map_attn:
												lowerCamelCase    		=  v.replace(lowerCamelCase__  ,							lowerCamelCase__      )
									lowerCamelCase    		=  v
			lowerCamelCase    		=  {v: vae_state_dict[k] for k, v in mapping.items()}
			lowerCamelCase    		=  ["""q""", """k""", """v""", """proj_out"""]
			for k, v in new_state_dict.items():
						for weight_name in weights_to_convert:
									if f'mid.attn_1.{weight_name}.weight' in k:
												print(f'Reshaping {k} for SD format'      )
												lowerCamelCase    		=  reshape_weight_for_sd(lowerCamelCase__      )
			return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


UpperCAmelCase   :    Union[str, Any]     =		[
    # (stable-diffusion, HF Diffusers)
    ("resblocks.", "text_model.encoder.layers."),
    ("ln_1", "layer_norm1"),
    ("ln_2", "layer_norm2"),
    (".c_fc.", ".fc1."),
    (".c_proj.", ".fc2."),
    (".attn", ".self_attn"),
    ("ln_final.", "transformer.text_model.final_layer_norm."),
    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
UpperCAmelCase   :    str     =		{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
UpperCAmelCase   :    List[Any]     =		re.compile("|".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
UpperCAmelCase   :    int     =		{"q": 0, "k": 1, "v": 2}






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			lowerCamelCase    		=  {}
			lowerCamelCase    		=  {}
			lowerCamelCase    		=  {}
			for k, v in text_enc_dict.items():
						if (
						    k.endswith(""".self_attn.q_proj.weight"""      )
						    or k.endswith(""".self_attn.k_proj.weight"""      )
						    or k.endswith(""".self_attn.v_proj.weight"""      )
						):
									lowerCamelCase    		=  k[: -len(""".q_proj.weight"""      )]
									lowerCamelCase    		=  k[-len("""q_proj.weight"""      )]
									if k_pre not in capture_qkv_weight:
												lowerCamelCase    		=  [None, None, None]
									lowerCamelCase    		=  v
									continue

						if (
						    k.endswith(""".self_attn.q_proj.bias"""      )
						    or k.endswith(""".self_attn.k_proj.bias"""      )
						    or k.endswith(""".self_attn.v_proj.bias"""      )
						):
									lowerCamelCase    		=  k[: -len(""".q_proj.bias"""      )]
									lowerCamelCase    		=  k[-len("""q_proj.bias"""      )]
									if k_pre not in capture_qkv_bias:
												lowerCamelCase    		=  [None, None, None]
									lowerCamelCase    		=  v
									continue

						lowerCamelCase    		=  textenc_pattern.sub(lambda lowerCamelCase__      : protected[re.escape(m.group(0      )      )]  ,							lowerCamelCase__      )
						lowerCamelCase    		=  v

			for k_pre, tensors in capture_qkv_weight.items():
						if None in tensors:
									raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""      )
						lowerCamelCase    		=  textenc_pattern.sub(lambda lowerCamelCase__      : protected[re.escape(m.group(0      )      )]  ,							lowerCamelCase__      )
						lowerCamelCase    		=  torch.cat(lowerCamelCase__      )

			for k_pre, tensors in capture_qkv_bias.items():
						if None in tensors:
									raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""      )
						lowerCamelCase    		=  textenc_pattern.sub(lambda lowerCamelCase__      : protected[re.escape(m.group(0      )      )]  ,							lowerCamelCase__      )
						lowerCamelCase    		=  torch.cat(lowerCamelCase__      )

			return new_state_dict






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			return text_enc_dict


if __name__ == "__main__":
		UpperCAmelCase   :    List[str]     =		argparse.ArgumentParser()

		parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
		parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
		parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
		parser.add_argument(
		    "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
		)

		UpperCAmelCase   :    List[str]     =		parser.parse_args()

		assert args.model_path is not None, "Must provide a model path!"

		assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

		# Path for safetensors
		UpperCAmelCase   :    int     =		osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
		UpperCAmelCase   :    Dict     =		osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
		UpperCAmelCase   :    Dict     =		osp.join(args.model_path, "text_encoder", "model.safetensors")

		# Load models from safetensors if it exists, if it doesn't pytorch
		if osp.exists(unet_path):
				UpperCAmelCase   :    Tuple     =		load_file(unet_path, device="cpu")
		else:
				UpperCAmelCase   :    Tuple     =		osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
				UpperCAmelCase   :    List[Any]     =		torch.load(unet_path, map_location="cpu")

		if osp.exists(vae_path):
				UpperCAmelCase   :    Any     =		load_file(vae_path, device="cpu")
		else:
				UpperCAmelCase   :    Dict     =		osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
				UpperCAmelCase   :    Dict     =		torch.load(vae_path, map_location="cpu")

		if osp.exists(text_enc_path):
				UpperCAmelCase   :    str     =		load_file(text_enc_path, device="cpu")
		else:
				UpperCAmelCase   :    Optional[int]     =		osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
				UpperCAmelCase   :    str     =		torch.load(text_enc_path, map_location="cpu")

		# Convert the UNet model
		UpperCAmelCase   :    List[str]     =		convert_unet_state_dict(unet_state_dict)
		UpperCAmelCase   :    List[str]     =		{"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

		# Convert the VAE model
		UpperCAmelCase   :    Optional[Any]     =		convert_vae_state_dict(vae_state_dict)
		UpperCAmelCase   :    Union[str, Any]     =		{"first_stage_model." + k: v for k, v in vae_state_dict.items()}

		# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
		UpperCAmelCase   :    str     =		"text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict

		if is_vaa_model:
				# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
				UpperCAmelCase   :    Optional[Any]     =		{"transformer." + k: v for k, v in text_enc_dict.items()}
				UpperCAmelCase   :    List[str]     =		convert_text_enc_state_dict_vaa(text_enc_dict)
				UpperCAmelCase   :    Optional[Any]     =		{"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
		else:
				UpperCAmelCase   :    int     =		convert_text_enc_state_dict(text_enc_dict)
				UpperCAmelCase   :    Optional[Any]     =		{"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

		# Put together new checkpoint
		UpperCAmelCase   :    Dict     =		{**unet_state_dict, **vae_state_dict, **text_enc_dict}
		if args.half:
				UpperCAmelCase   :    List[Any]     =		{k: v.half() for k, v in state_dict.items()}

		if args.use_safetensors:
				save_file(state_dict, args.checkpoint_path)
		else:
				UpperCAmelCase   :    Union[str, Any]     =		{"state_dict": state_dict}
				torch.save(state_dict, args.checkpoint_path)

