




import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class 		UpperCAmelCase   (				unittest.TestCase			):







						'''simple docstring'''





						lowerCAmelCase_     		=   JukeboxTokenizer
						lowerCAmelCase_     		=   {
						    '''artist''': '''Zac Brown Band''',
						    '''genres''': '''Country''',
						    '''lyrics''': '''I met a traveller from an antique land,
        Who said "Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        ''',
						}






						@require_torch
						def        snake_case__						(						self							:     Union[str, Any]						):






							"""simple docstring"""






							import torch

							snake_case_		    =     JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics"						)
							snake_case_		    =     tokenizer(**self.metas						)["input_ids"]
							# fmt: off
							snake_case_		    =     [
							    torch.tensor([[
							        0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
							        76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
							        44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
							        47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
							        76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
							        30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
							        27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
							        45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
							        41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
							        19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
							        76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
							        76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
							        64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
							        30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
							        27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
							        34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
							        27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
							        41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
							        76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
							        44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
							        76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
							        32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
							        40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
							        20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
							        45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
							        31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
							        45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
							        76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
							        34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
							        31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
							        40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
							        38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
							        78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
							        76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
							        41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
							        27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
							        46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
							        76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
							        41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
							        46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
							        41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
							        78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
							        40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
							        27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
							        76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
							        76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
							        41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
							        76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
							        27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
							        78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
							        34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
							        44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
							        40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
							        78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
							        46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
							        38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
							        40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
							        27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
							        20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
							        76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
							        76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
							        76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
							        76, 76]]						),
							    torch.tensor([[0, 0, 0, 10_69, 11]]						),
							    torch.tensor([[0, 0, 0, 10_69, 11]]						),
							]
							# fmt: on
							self.assertTrue(torch.allclose(tokens[0]       ,	EXPECTED_OUTPUT[0]						)						)
							self.assertTrue(torch.allclose(tokens[1]       ,	EXPECTED_OUTPUT[1]						)						)
							self.assertTrue(torch.allclose(tokens[2]       ,	EXPECTED_OUTPUT[2]						)						)






						@require_torch
						def        snake_case__						(						self							:     List[Any]						):






							"""simple docstring"""






							import torch

							snake_case_		    =     JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics"						)
							snake_case_		    =     tokenizer(**self.metas						)["input_ids"]
							# fmt: off
							snake_case_		    =     [
							    torch.tensor([[
							        0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
							        31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
							        31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
							        40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
							        79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
							        77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
							        27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
							        37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
							        32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
							        77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
							        77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
							        77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
							        46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
							        77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
							        77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
							        77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
							        77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
							        64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
							        40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
							        40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
							        38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
							        31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
							        41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
							        77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
							        46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
							        41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
							        31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
							        31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
							        23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
							        44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
							        31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
							        38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
							        40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
							        77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
							        27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
							        31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
							        34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
							        31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
							        1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
							        31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
							        45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
							        31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
							        77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
							        15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
							        11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
							        45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
							        41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
							        44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
							        46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
							        27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
							        77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
							        35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
							        77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
							        31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
							        77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
							        41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
							        77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
							        40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
							        77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
							        77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
							        27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
							        77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
							        77, 77, 77, 77, 77, 77]]						),
							    torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]						),
							    torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]						),
							]
							# fmt: on
							self.assertTrue(torch.allclose(tokens[0]       ,	EXPECTED_OUTPUT[0]						)						)
							self.assertTrue(torch.allclose(tokens[1]       ,	EXPECTED_OUTPUT[1]						)						)
							self.assertTrue(torch.allclose(tokens[2]       ,	EXPECTED_OUTPUT[2]						)						)






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

lowercase__				:     Optional[int] 		=						[
    # (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"),
]

lowercase__				:     List[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"),
]

lowercase__				:     Optional[Any] 		=						[]
# 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
				lowercase__				:     str 		=						f'''down_blocks.{i}.resnets.{j}.'''
				lowercase__				:     Union[str, Any] 		=						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
						lowercase__				:     Tuple 		=						f'''down_blocks.{i}.attentions.{j}.'''
						lowercase__				:     Dict 		=						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
				lowercase__				:     List[Any] 		=						f'''up_blocks.{i}.resnets.{j}.'''
				lowercase__				:     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
						lowercase__				:     List[str] 		=						f'''up_blocks.{i}.attentions.{j}.'''
						lowercase__				:     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
				lowercase__				:     List[str] 		=						f'''down_blocks.{i}.downsamplers.0.conv.'''
				lowercase__				:     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
				lowercase__				:     Optional[int] 		=						f'''up_blocks.{i}.upsamplers.0.'''
				lowercase__				:     int 		=						f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
				unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowercase__				:     Union[str, Any] 		=						"mid_block.attentions.0."
lowercase__				:     List[Any] 		=						"middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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


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

lowercase__				:     Dict 		=						[
    # (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):
				lowercase__				:     Any 		=						f'''encoder.down_blocks.{i}.resnets.{j}.'''
				lowercase__				:     List[Any] 		=						f'''encoder.down.{i}.block.{j}.'''
				vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

		if i < 3:
				lowercase__				:     Optional[int] 		=						f'''down_blocks.{i}.downsamplers.0.'''
				lowercase__				:     Tuple 		=						f'''down.{i}.downsample.'''
				vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

				lowercase__				:     List[str] 		=						f'''up_blocks.{i}.upsamplers.0.'''
				lowercase__				:     Optional[int] 		=						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):
				lowercase__				:     int 		=						f'''decoder.up_blocks.{i}.resnets.{j}.'''
				lowercase__				:     Union[str, Any] 		=						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):
		lowercase__				:     Dict 		=						f'''mid_block.resnets.{i}.'''
		lowercase__				:     int 		=						f'''mid.block_{i+1}.'''
		vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     {k: k for k in vae_state_dict.keys()}
	for k, v in mapping.items():
		for sd_part, hf_part in vae_conversion_map:
			snake_case_		    =     v.replace(_A     ,	_A							)
		snake_case_		    =     v
	for k, v in mapping.items():
		if "attentions" in k:
			for sd_part, hf_part in vae_conversion_map_attn:
				snake_case_		    =     v.replace(_A     ,	_A							)
			snake_case_		    =     v
	snake_case_		    =     {v: vae_state_dict[k] for k, v in mapping.items()}
	snake_case_		    =     ["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"							)
				snake_case_		    =     reshape_weight_for_sd(_A							)
	return new_state_dict


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


lowercase__				:     int 		=						[
    # (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"),
]
lowercase__				:     Dict 		=						{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowercase__				:     Tuple 		=						re.compile("|".join(protected.keys()))

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


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     {}
	snake_case_		    =     {}
	snake_case_		    =     {}
	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"							)
		):
			snake_case_		    =     k[: -len(".q_proj.weight"							)]
			snake_case_		    =     k[-len("q_proj.weight"							)]
			if k_pre not in capture_qkv_weight:
				snake_case_		    =     [None, None, None]
			snake_case_		    =     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"							)
		):
			snake_case_		    =     k[: -len(".q_proj.bias"							)]
			snake_case_		    =     k[-len("q_proj.bias"							)]
			if k_pre not in capture_qkv_bias:
				snake_case_		    =     [None, None, None]
			snake_case_		    =     v
			continue

		snake_case_		    =     textenc_pattern.sub(lambda _A							: protected[re.escape(m.group(0							)							)]     ,	_A							)
		snake_case_		    =     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"							)
		snake_case_		    =     textenc_pattern.sub(lambda _A							: protected[re.escape(m.group(0							)							)]     ,	_A							)
		snake_case_		    =     torch.cat(_A							)

	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"							)
		snake_case_		    =     textenc_pattern.sub(lambda _A							: protected[re.escape(m.group(0							)							)]     ,	_A							)
		snake_case_		    =     torch.cat(_A							)

	return new_state_dict


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return text_enc_dict


if __name__ == "__main__":
		lowercase__				:     int 		=						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."
		)

		lowercase__				:     Dict 		=						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
		lowercase__				:     Tuple 		=						osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
		lowercase__				:     int 		=						osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
		lowercase__				:     Any 		=						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):
				lowercase__				:     str 		=						load_file(unet_path, device="cpu")
		else:
				lowercase__				:     Optional[Any] 		=						osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
				lowercase__				:     Any 		=						torch.load(unet_path, map_location="cpu")

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

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

		# Convert the UNet model
		lowercase__				:     Dict 		=						convert_unet_state_dict(unet_state_dict)
		lowercase__				:     Any 		=						{"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

		# Convert the VAE model
		lowercase__				:     Dict 		=						convert_vae_state_dict(vae_state_dict)
		lowercase__				:     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
		lowercase__				:     Optional[Any] 		=						"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
				lowercase__				:     Any 		=						{"transformer." + k: v for k, v in text_enc_dict.items()}
				lowercase__				:     List[Any] 		=						convert_text_enc_state_dict_vaa(text_enc_dict)
				lowercase__				:     List[str] 		=						{"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
		else:
				lowercase__				:     Tuple 		=						convert_text_enc_state_dict(text_enc_dict)
				lowercase__				:     Any 		=						{"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

		# Put together new checkpoint
		lowercase__				:     Tuple 		=						{**unet_state_dict, **vae_state_dict, **text_enc_dict}
		if args.half:
				lowercase__				:     Any 		=						{k: v.half() for k, v in state_dict.items()}

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