





import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_        =							logging.get_logger(__name__)

lowerCAmelCase_        =							"▁"

lowerCAmelCase_        =							{
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowerCAmelCase_        =							{
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

lowerCAmelCase_        =							{
    "facebook/m2m100_418M": 1_0_2_4,
}

# fmt: off
lowerCAmelCase_        =							{
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}



class    __lowerCAmelCase			(     a_	):
			lowerCamelCase_	:	List[str]			     = VOCAB_FILES_NAMES
			lowerCamelCase_	:	Optional[int]			     = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_	:	Tuple			     = PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_	:	Optional[int]			     = ["input_ids", "attention_mask"]

			lowerCamelCase_	:	List[int]			     = []
			lowerCamelCase_	:	List[int]			     = []






			def __init__(self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__="<s>"   ,				__magic_name__="</s>"   ,				__magic_name__="</s>"   ,				__magic_name__="<pad>"   ,				__magic_name__="<unk>"   ,				__magic_name__="m2m100"   ,				__magic_name__ = None   ,				__magic_name__=8   ,				**__magic_name__   ,				)	->  None:


							'''simple docstring'''



							snake_case_		:       str           =    {} if sp_model_kwargs is None else sp_model_kwargs

							snake_case_		:       Union[str, Any]           =    language_codes
							snake_case_		:       int           =    FAIRSEQ_LANGUAGE_CODES[language_codes]
							snake_case_		:       str           =    {lang_code: F'''__{lang_code}__''' for lang_code in fairseq_language_code}

							snake_case_		:       int           =    kwargs.get('''additional_special_tokens'''   ,				[]       )
							kwargs["additional_special_tokens"] += [
							    self.get_lang_token(__magic_name__       )
							    for lang_code in fairseq_language_code
							    if self.get_lang_token(__magic_name__       ) not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    src_lang=__magic_name__   ,				tgt_lang=__magic_name__   ,				bos_token=__magic_name__   ,				eos_token=__magic_name__   ,				sep_token=__magic_name__   ,				unk_token=__magic_name__   ,				pad_token=__magic_name__   ,				language_codes=__magic_name__   ,				sp_model_kwargs=self.sp_model_kwargs   ,				num_madeup_words=__magic_name__   ,				**__magic_name__   ,				)

							snake_case_		:       Dict           =    vocab_file
							snake_case_		:       Tuple           =    load_json(__magic_name__       )
							snake_case_		:       str           =    {v: k for k, v in self.encoder.items()}
							snake_case_		:       Optional[int]           =    spm_file
							snake_case_		:       int           =    load_spm(__magic_name__   ,				self.sp_model_kwargs       )

							snake_case_		:       Tuple           =    len(self.encoder       )

							snake_case_		:       Optional[int]           =    {
							    self.get_lang_token(__magic_name__       ): self.encoder_size + i for i, lang_code in enumerate(__magic_name__       )
							}
							snake_case_		:       Union[str, Any]           =    {lang_code: self.encoder_size + i for i, lang_code in enumerate(__magic_name__       )}
							snake_case_		:       Dict           =    {v: k for k, v in self.lang_token_to_id.items()}

							snake_case_		:       Optional[Any]           =    src_lang if src_lang is not None else '''en'''
							snake_case_		:       Tuple           =    tgt_lang
							snake_case_		:       Optional[int]           =    self.get_lang_id(self._src_lang       )
							self.set_src_lang_special_tokens(self._src_lang       )

							snake_case_		:       Optional[int]           =    num_madeup_words






			@property
			def      lowerCamelCase    (self       )	->  int:


							'''simple docstring'''



							return len(self.encoder       ) + len(self.lang_token_to_id       )






			@property
			def      lowerCamelCase    (self       )	->  str:


							'''simple docstring'''



							return self._src_lang






			@src_lang.setter
			def      lowerCamelCase    (self   ,				__magic_name__       )	->  None:


							'''simple docstring'''



							snake_case_		:       Tuple           =    new_src_lang
							self.set_src_lang_special_tokens(self._src_lang       )






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


							'''simple docstring'''



							return self.sp_model.encode(__magic_name__   ,				out_type=__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Any:


							'''simple docstring'''



							if token in self.lang_token_to_id:
											return self.lang_token_to_id[token]
							return self.encoder.get(__magic_name__   ,				self.encoder[self.unk_token]       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  str:


							'''simple docstring'''



							if index in self.id_to_lang_token:
											return self.id_to_lang_token[index]
							return self.decoder.get(__magic_name__   ,				self.unk_token       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  int:


							'''simple docstring'''



							snake_case_		:       Dict           =    []
							snake_case_		:       Tuple           =    ''''''
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															out_string += self.sp_model.decode(__magic_name__       ) + token
															snake_case_		:       Dict           =    []
											else:
															current_sub_tokens.append(__magic_name__       )
							out_string += self.sp_model.decode(__magic_name__       )
							return out_string.strip()






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = None   ,				__magic_name__ = False       )	->  List[int]:


							'''simple docstring'''



							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=__magic_name__   ,				token_ids_a=__magic_name__   ,				already_has_special_tokens=__magic_name__       )

							snake_case_		:       Optional[Any]           =    [1] * len(self.prefix_tokens       )
							snake_case_		:       Tuple           =    [1] * len(self.suffix_tokens       )
							if token_ids_a is None:
											return prefix_ones + ([0] * len(__magic_name__       )) + suffix_ones
							return prefix_ones + ([0] * len(__magic_name__       )) + ([0] * len(__magic_name__       )) + suffix_ones






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = None       )	->  List[int]:


							'''simple docstring'''



							if token_ids_a is None:
											return self.prefix_tokens + token_ids_a + self.suffix_tokens
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       str           =    {self.convert_ids_to_tokens(__magic_name__       ): i for i in range(self.vocab_size       )}
							vocab.update(self.added_tokens_encoder       )
							return vocab






			def __getstate__(self       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       Dict           =    self.__dict__.copy()
							snake_case_		:       Any           =    None
							return state






			def __setstate__(self   ,				__magic_name__       )	->  None:


							'''simple docstring'''



							snake_case_		:       Union[str, Any]           =    d

							# for backward compatibility
							if not hasattr(self   ,				'''sp_model_kwargs'''       ):
											snake_case_		:       List[str]           =    {}

							snake_case_		:       Any           =    load_spm(self.spm_file   ,				self.sp_model_kwargs       )






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = None       )	->  Tuple[str]:


							'''simple docstring'''



							snake_case_		:       Union[str, Any]           =    Path(__magic_name__       )
							if not save_dir.is_dir():
											raise OSError(F'''{save_directory} should be a directory'''       )
							snake_case_		:       Any           =    save_dir / (
							    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
							)
							snake_case_		:       Union[str, Any]           =    save_dir / (
							    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
							)

							save_json(self.encoder   ,				__magic_name__       )

							if os.path.abspath(self.spm_file       ) != os.path.abspath(__magic_name__       ) and os.path.isfile(self.spm_file       ):
											copyfile(self.spm_file   ,				__magic_name__       )
							elif not os.path.isfile(self.spm_file       ):
											with open(__magic_name__   ,				'''wb'''       ) as fi:
															snake_case_		:       int           =    self.sp_model.serialized_model_proto()
															fi.write(__magic_name__       )

							return (str(__magic_name__       ), str(__magic_name__       ))






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = "en"   ,				__magic_name__ = None   ,				__magic_name__ = "ro"   ,				**__magic_name__   ,				)	->  BatchEncoding:


							'''simple docstring'''



							snake_case_		:       Any           =    src_lang
							snake_case_		:       Dict           =    tgt_lang
							self.set_src_lang_special_tokens(self.src_lang       )
							return super().prepare_seqaseq_batch(__magic_name__   ,				__magic_name__   ,				**__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				**__magic_name__       )	->  Tuple:


							'''simple docstring'''



							if src_lang is None or tgt_lang is None:
											raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''       )
							snake_case_		:       List[str]           =    src_lang
							snake_case_		:       Any           =    self(__magic_name__   ,				add_special_tokens=__magic_name__   ,				**__magic_name__       )
							snake_case_		:       List[Any]           =    self.get_lang_id(__magic_name__       )
							snake_case_		:       List[Any]           =    tgt_lang_id
							return inputs






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


							'''simple docstring'''



							self.set_src_lang_special_tokens(self.src_lang       )






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


							'''simple docstring'''



							self.set_tgt_lang_special_tokens(self.tgt_lang       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  None:


							'''simple docstring'''



							snake_case_		:       Optional[int]           =    self.get_lang_token(__magic_name__       )
							snake_case_		:       List[str]           =    self.lang_token_to_id[lang_token]
							snake_case_		:       Tuple           =    [self.cur_lang_id]
							snake_case_		:       int           =    [self.eos_token_id]






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  None:


							'''simple docstring'''



							snake_case_		:       Optional[Any]           =    self.get_lang_token(__magic_name__       )
							snake_case_		:       str           =    self.lang_token_to_id[lang_token]
							snake_case_		:       Tuple           =    [self.cur_lang_id]
							snake_case_		:       Any           =    [self.eos_token_id]






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  str:


							'''simple docstring'''



							return self.lang_code_to_token[lang]






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  int:


							'''simple docstring'''



							snake_case_		:       Dict           =    self.get_lang_token(__magic_name__       )
							return self.lang_token_to_id[lang_token]






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		Dict:






				"""simple docstring"""


				snake_case_		:       Dict           =    sentencepiece.SentencePieceProcessor(**lowerCamelCase__					)
				spm.Load(str(lowerCamelCase__					)					)
				return spm






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		str:






				"""simple docstring"""


				with open(lowerCamelCase__					,							'''r'''					) as f:
								return json.load(lowerCamelCase__					)






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		Tuple:






				"""simple docstring"""


				with open(lowerCamelCase__					,							'''w'''					) as f:
								json.dump(lowerCamelCase__					,							lowerCamelCase__					,							indent=2					)




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)

