




"""simple docstring"""






import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class lowerCAmelCase__	(     lowercase,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			CodeGenTokenizer
			lowerCamelCase__      =			CodeGenTokenizerFast
			lowerCamelCase__      =			True
			lowerCamelCase__      =			{"""add_prefix_space""": True}
			lowerCamelCase__      =			False
			def 					A_    (  self  ):
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							_lowerCamelCase     :			Optional[int]    =    [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							    '<|endoftext|>',
							]
							_lowerCamelCase     :			Optional[int]    =    dict(zip(lowercase  ,      range(len(lowercase  )  )  )  )
							_lowerCamelCase     :			List[Any]    =    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							_lowerCamelCase     :			Union[str, Any]    =    {'unk_token': '<unk>'}

							_lowerCamelCase     :			Dict    =    os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES['vocab_file']  )
							_lowerCamelCase     :			Tuple    =    os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES['merges_file']  )
							with open(self.vocab_file  ,      'w'  ,      encoding='utf-8'  ) as fp:
											fp.write(json.dumps(lowercase  ) + '\n'  )
							with open(self.merges_file  ,      'w'  ,      encoding='utf-8'  ) as fp:
											fp.write('\n'.join(lowercase  )  )
			def 					A_    (  self  ,      **lowercase  ):
							kwargs.update(self.special_tokens_map  )
							return CodeGenTokenizer.from_pretrained(self.tmpdirname  ,      **lowercase  )
			def 					A_    (  self  ,      **lowercase  ):
							kwargs.update(self.special_tokens_map  )
							return CodeGenTokenizerFast.from_pretrained(self.tmpdirname  ,      **lowercase  )
			def 					A_    (  self  ,      lowercase  ):
							_lowerCamelCase     :			List[Any]    =    'lower newer'
							_lowerCamelCase     :			Union[str, Any]    =    'lower newer'
							return input_text, output_text
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    CodeGenTokenizer(self.vocab_file  ,      self.merges_file  ,      **self.special_tokens_map  )
							_lowerCamelCase     :			Optional[Any]    =    'lower newer'
							_lowerCamelCase     :			Any    =    ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
							_lowerCamelCase     :			Dict    =    tokenizer.tokenize(lowercase  ,      add_prefix_space=lowercase  )
							self.assertListEqual(lowercase  ,      lowercase  )

							_lowerCamelCase     :			str    =    tokens + [tokenizer.unk_token]
							_lowerCamelCase     :			str    =    [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase  )  ,      lowercase  )
			def 					A_    (  self  ):
							if not self.test_rust_tokenizer:
											return

							_lowerCamelCase     :			int    =    self.get_tokenizer()
							_lowerCamelCase     :			int    =    self.get_rust_tokenizer(add_prefix_space=lowercase  )

							_lowerCamelCase     :			Any    =    'lower newer'

							# Testing tokenization
							_lowerCamelCase     :			Any    =    tokenizer.tokenize(lowercase  ,      add_prefix_space=lowercase  )
							_lowerCamelCase     :			str    =    rust_tokenizer.tokenize(lowercase  )
							self.assertListEqual(lowercase  ,      lowercase  )

							# Testing conversion to ids without special tokens
							_lowerCamelCase     :			int    =    tokenizer.encode(lowercase  ,      add_special_tokens=lowercase  ,      add_prefix_space=lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    rust_tokenizer.encode(lowercase  ,      add_special_tokens=lowercase  )
							self.assertListEqual(lowercase  ,      lowercase  )

							# Testing conversion to ids with special tokens
							_lowerCamelCase     :			Tuple    =    self.get_rust_tokenizer(add_prefix_space=lowercase  )
							_lowerCamelCase     :			List[Any]    =    tokenizer.encode(lowercase  ,      add_prefix_space=lowercase  )
							_lowerCamelCase     :			Union[str, Any]    =    rust_tokenizer.encode(lowercase  )
							self.assertListEqual(lowercase  ,      lowercase  )

							# Testing the unknown token
							_lowerCamelCase     :			Any    =    tokens + [rust_tokenizer.unk_token]
							_lowerCamelCase     :			Union[str, Any]    =    [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowercase  )  ,      lowercase  )
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							# It's very difficult to mix/test pretokenization with byte-level
							# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
							pass
			def 					A_    (  self  ,      lowercase=15  ):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
															_lowerCamelCase     :			Dict    =    self.rust_tokenizer_class.from_pretrained(lowercase  ,      **lowercase  )

															# Simple input
															_lowerCamelCase     :			Optional[Any]    =    'This is a simple input'
															_lowerCamelCase     :			Tuple    =    ['This is a simple input 1', 'This is a simple input 2']
															_lowerCamelCase     :			List[str]    =    ('This is a simple input', 'This is a pair')
															_lowerCamelCase     :			Optional[int]    =    [
															    ('This is a simple input 1', 'This is a simple input 2'),
															    ('This is a simple pair 1', 'This is a simple pair 2'),
															]

															# Simple input tests
															self.assertRaises(lowercase  ,      tokenizer_r.encode  ,      lowercase  ,      max_length=lowercase  ,      padding='max_length'  )

															# Simple input
															self.assertRaises(lowercase  ,      tokenizer_r.encode_plus  ,      lowercase  ,      max_length=lowercase  ,      padding='max_length'  )

															# Simple input
															self.assertRaises(
															    lowercase  ,      tokenizer_r.batch_encode_plus  ,      lowercase  ,      max_length=lowercase  ,      padding='max_length'  ,      )

															# Pair input
															self.assertRaises(lowercase  ,      tokenizer_r.encode  ,      lowercase  ,      max_length=lowercase  ,      padding='max_length'  )

															# Pair input
															self.assertRaises(lowercase  ,      tokenizer_r.encode_plus  ,      lowercase  ,      max_length=lowercase  ,      padding='max_length'  )

															# Pair input
															self.assertRaises(
															    lowercase  ,      tokenizer_r.batch_encode_plus  ,      lowercase  ,      max_length=lowercase  ,      padding='max_length'  ,      )
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    CodeGenTokenizer.from_pretrained(self.tmpdirname  ,      pad_token='<pad>'  )

							# Simple input
							_lowerCamelCase     :			Union[str, Any]    =    'This is a simple input'
							_lowerCamelCase     :			str    =    ['This is a simple input looooooooong', 'This is a simple input']
							_lowerCamelCase     :			Optional[Any]    =    ('This is a simple input', 'This is a pair')
							_lowerCamelCase     :			Any    =    [
							    ('This is a simple input loooooong', 'This is a simple input'),
							    ('This is a simple pair loooooong', 'This is a simple pair'),
							]

							_lowerCamelCase     :			Tuple    =    tokenizer.pad_token_id

							_lowerCamelCase     :			Optional[Any]    =    tokenizer(lowercase  ,      padding='max_length'  ,      max_length=30  ,      return_tensors='np'  )
							_lowerCamelCase     :			List[Any]    =    tokenizer(lowercase  ,      padding=lowercase  ,      truncate=lowercase  ,      return_tensors='np'  )
							_lowerCamelCase     :			str    =    tokenizer(*lowercase  ,      padding='max_length'  ,      max_length=60  ,      return_tensors='np'  )
							_lowerCamelCase     :			str    =    tokenizer(lowercase  ,      padding=lowercase  ,      truncate=lowercase  ,      return_tensors='np'  )

							# s
							# test single string max_length padding
							self.assertEqual(out_s['input_ids'].shape[-1]  ,      30  )
							self.assertTrue(pad_token_id in out_s['input_ids']  )
							self.assertTrue(0 in out_s['attention_mask']  )

							# s2
							# test automatic padding
							self.assertEqual(out_sa['input_ids'].shape[-1]  ,      33  )
							# long slice doesn't have padding
							self.assertFalse(pad_token_id in out_sa['input_ids'][0]  )
							self.assertFalse(0 in out_sa['attention_mask'][0]  )
							# short slice does have padding
							self.assertTrue(pad_token_id in out_sa['input_ids'][1]  )
							self.assertTrue(0 in out_sa['attention_mask'][1]  )

							# p
							# test single pair max_length padding
							self.assertEqual(out_p['input_ids'].shape[-1]  ,      60  )
							self.assertTrue(pad_token_id in out_p['input_ids']  )
							self.assertTrue(0 in out_p['attention_mask']  )

							# p2
							# test automatic padding pair
							self.assertEqual(out_pa['input_ids'].shape[-1]  ,      52  )
							# long slice pair doesn't have padding
							self.assertFalse(pad_token_id in out_pa['input_ids'][0]  )
							self.assertFalse(0 in out_pa['attention_mask'][0]  )
							# short slice pair does have padding
							self.assertTrue(pad_token_id in out_pa['input_ids'][1]  )
							self.assertTrue(0 in out_pa['attention_mask'][1]  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    '$$$'
							_lowerCamelCase     :			Union[str, Any]    =    CodeGenTokenizer.from_pretrained(self.tmpdirname  ,      bos_token=lowercase  ,      add_bos_token=lowercase  )

							_lowerCamelCase     :			Union[str, Any]    =    'This is a simple input'
							_lowerCamelCase     :			Dict    =    ['This is a simple input 1', 'This is a simple input 2']

							_lowerCamelCase     :			List[str]    =    tokenizer.bos_token_id

							_lowerCamelCase     :			List[Any]    =    tokenizer(lowercase  )
							_lowerCamelCase     :			int    =    tokenizer(lowercase  )

							self.assertEqual(out_s.input_ids[0]  ,      lowercase  )
							self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids  )  )

							_lowerCamelCase     :			Any    =    tokenizer.decode(out_s.input_ids  )
							_lowerCamelCase     :			str    =    tokenizer.batch_decode(out_sa.input_ids  )

							self.assertEqual(decode_s.split()[0]  ,      lowercase  )
							self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa  )  )
			@slow
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[Any]    =    CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono'  )

							_lowerCamelCase     :			Optional[int]    =    '\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'
							_lowerCamelCase     :			str    =    '\nif len_a > len_b:      result = a\nelse:      result = b'

							_lowerCamelCase     :			Optional[Any]    =    tokenizer.encode(lowercase  )
							_lowerCamelCase     :			str    =    ['^#', re.escape('<|endoftext|>'  ), '^\'\'\'', '^"""', '\n\n\n']
							_lowerCamelCase     :			str    =    tokenizer.decode(lowercase  ,      truncate_before_pattern=lowercase  )
							self.assertEqual(lowercase  ,      lowercase  )






			def 					A_    (  self  ):
							pass





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)
