






'''simple docstring'''





import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A        =			logging.getLogger(__name__)


A        =			list(MODEL_WITH_LM_HEAD_MAPPING.keys())
A        =			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				Union[str, Any]				    =						field(
		    default=SCREAMING_SNAKE_CASE__ ,	metadata={
		        """help""": (
		            """The model checkpoint for weights initialization. Leave None if you want to train a model from"""
		            """ scratch."""
		        )
		    } ,	)
		UpperCAmelCase__      :				Dict				    =						field(
		    default=SCREAMING_SNAKE_CASE__ ,	metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(SCREAMING_SNAKE_CASE__						)} ,	)
		UpperCAmelCase__      :				Union[str, Any]				    =						field(
		    default=SCREAMING_SNAKE_CASE__ ,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}						)
		UpperCAmelCase__      :				Any				    =						field(
		    default=SCREAMING_SNAKE_CASE__ ,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}						)
		UpperCAmelCase__      :				str				    =						field(
		    default=SCREAMING_SNAKE_CASE__ ,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} ,	)






@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				Any				    =						field(
		    default=SCREAMING_SNAKE_CASE__ ,	metadata={"""help""": """The input training data file (a text file)."""}						)
		UpperCAmelCase__      :				Optional[Any]				    =						field(
		    default=SCREAMING_SNAKE_CASE__ ,	metadata={
		        """help""": (
		            """The input training data files (multiple files in glob format). """
		            """Very often splitting large files to smaller files can prevent tokenizer going out of memory"""
		        )
		    } ,	)
		UpperCAmelCase__      :				Union[str, Any]				    =						field(
		    default=SCREAMING_SNAKE_CASE__ ,	metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} ,	)
		UpperCAmelCase__      :				Dict				    =						field(
		    default=SCREAMING_SNAKE_CASE__ ,	metadata={"""help""": """An optional input train ref data file for whole word mask in Chinese."""} ,	)
		UpperCAmelCase__      :				Optional[Any]				    =						field(
		    default=SCREAMING_SNAKE_CASE__ ,	metadata={"""help""": """An optional input eval ref data file for whole word mask in Chinese."""} ,	)
		UpperCAmelCase__      :				List[Any]				    =						field(
		    default=SCREAMING_SNAKE_CASE__ ,	metadata={"""help""": """Whether distinct lines of text in the dataset are to be handled as distinct sequences."""} ,	)

		UpperCAmelCase__      :				Any				    =						field(
		    default=SCREAMING_SNAKE_CASE__ ,	metadata={"""help""": """Train with masked-language modeling loss instead of language modeling."""}						)
		UpperCAmelCase__      :				Optional[Any]				    =						field(default=SCREAMING_SNAKE_CASE__ ,	metadata={"""help""": """Whether ot not to use whole word mask."""}						)
		UpperCAmelCase__      :				int				    =						field(
		    default=0.15 ,	metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}						)
		UpperCAmelCase__      :				str				    =						field(
		    default=1 / 6 ,	metadata={
		        """help""": (
		            """Ratio of length of a span of masked tokens to surrounding context length for permutation language"""
		            """ modeling."""
		        )
		    } ,	)
		UpperCAmelCase__      :				Optional[Any]				    =						field(
		    default=5 ,	metadata={"""help""": """Maximum length of a span of masked tokens for permutation language modeling."""}						)

		UpperCAmelCase__      :				Union[str, Any]				    =						field(
		    default=-1 ,	metadata={
		        """help""": (
		            """Optional input sequence length after tokenization."""
		            """The training dataset will be truncated in block of this size for training."""
		            """Default to the model max input length for single sentence inputs (take into account special tokens)."""
		        )
		    } ,	)
		UpperCAmelCase__      :				List[Any]				    =						field(
		    default=SCREAMING_SNAKE_CASE__ ,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""}						)






def 					_lowerCamelCase(			lowerCAmelCase__				:		Dict    ,    lowerCAmelCase__				:		Union[str, Any]    ,    lowerCAmelCase__				:		List[Any] = False    ,    lowerCAmelCase__				:		List[str] = None    ,    ):





				'''simple docstring'''







				def _dataset(lowerCAmelCase__				:		Dict    ,    lowerCAmelCase__				:		List[str]=None      ):
								if args.line_by_line:
												if ref_path is not None:
																if not args.whole_word_mask or not args.mlm:
																				raise ValueError('You need to set world whole masking and mlm to True for Chinese Whole Word Mask'      )
																return LineByLineWithRefDataset(
																    tokenizer=lowerCAmelCase__    ,    file_path=lowerCAmelCase__    ,    block_size=args.block_size    ,    ref_path=lowerCAmelCase__    ,    )

												return LineByLineTextDataset(tokenizer=lowerCAmelCase__    ,    file_path=lowerCAmelCase__    ,    block_size=args.block_size      )
								else:
												return TextDataset(
												    tokenizer=lowerCAmelCase__    ,    file_path=lowerCAmelCase__    ,    block_size=args.block_size    ,    overwrite_cache=args.overwrite_cache    ,    cache_dir=lowerCAmelCase__    ,    )

				if evaluate:
								return _dataset(args.eval_data_file    ,    args.eval_ref_file      )
				elif args.train_data_files:
								return ConcatDataset([_dataset(lowerCAmelCase__      ) for f in glob(args.train_data_files      )]      )
				else:
								return _dataset(args.train_data_file    ,    args.train_ref_file      )



def 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
				SCREAMING_SNAKE_CASE_   :	List[str]													=   parser.parse_args_into_dataclasses()

				if data_args.eval_data_file is None and training_args.do_eval:
								raise ValueError(
								    'Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '
								    'or remove the --do_eval argument.'      )
				if (
				    os.path.exists(training_args.output_dir      )
				    and os.listdir(training_args.output_dir      )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
								    ' --overwrite_output_dir to overcome.'      )

				# Setup logging
				logging.basicConfig(
				    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'    ,    datefmt='%m/%d/%Y %H:%M:%S'    ,    level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN    ,    )
				logger.warning(
				    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'    ,    training_args.local_rank    ,    training_args.device    ,    training_args.n_gpu    ,    bool(training_args.local_rank != -1      )    ,    training_args.fpaa    ,    )
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank      ):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info('Training/evaluation parameters %s'    ,    lowerCAmelCase__      )

				# Set seed
				set_seed(training_args.seed      )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				if model_args.config_name:
								SCREAMING_SNAKE_CASE_   :	List[str]													=   AutoConfig.from_pretrained(model_args.config_name    ,    cache_dir=model_args.cache_dir      )
				elif model_args.model_name_or_path:
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   AutoConfig.from_pretrained(model_args.model_name_or_path    ,    cache_dir=model_args.cache_dir      )
				else:
								SCREAMING_SNAKE_CASE_   :	List[str]													=   CONFIG_MAPPING[model_args.model_type]()
								logger.warning('You are instantiating a new config instance from scratch.'      )

				if model_args.tokenizer_name:
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AutoTokenizer.from_pretrained(model_args.tokenizer_name    ,    cache_dir=model_args.cache_dir      )
				elif model_args.model_name_or_path:
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   AutoTokenizer.from_pretrained(model_args.model_name_or_path    ,    cache_dir=model_args.cache_dir      )
				else:
								raise ValueError(
								    'You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'
								    ' script, save it,and load it from here, using --tokenizer_name'      )

				if model_args.model_name_or_path:
								SCREAMING_SNAKE_CASE_   :	Dict													=   AutoModelWithLMHead.from_pretrained(
								    model_args.model_name_or_path    ,    from_tf=bool('.ckpt' in model_args.model_name_or_path      )    ,    config=lowerCAmelCase__    ,    cache_dir=model_args.cache_dir    ,    )
				else:
								logger.info('Training new model from scratch'      )
								SCREAMING_SNAKE_CASE_   :	List[str]													=   AutoModelWithLMHead.from_config(lowerCAmelCase__      )

				model.resize_token_embeddings(len(lowerCAmelCase__      )      )

				if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
								raise ValueError(
								    'BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'
								    '--mlm flag (masked language modeling).'      )

				if data_args.block_size <= 0:
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tokenizer.max_len
								# Our input block size will be the max possible for the model
				else:
								SCREAMING_SNAKE_CASE_   :	Dict													=   min(data_args.block_size    ,    tokenizer.max_len      )

				# Get datasets

				SCREAMING_SNAKE_CASE_   :	Tuple													=   (
				    get_dataset(lowerCAmelCase__    ,    tokenizer=lowerCAmelCase__    ,    cache_dir=model_args.cache_dir      ) if training_args.do_train else None
				)
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   (
				    get_dataset(lowerCAmelCase__    ,    tokenizer=lowerCAmelCase__    ,    evaluate=lowerCAmelCase__    ,    cache_dir=model_args.cache_dir      )
				    if training_args.do_eval
				    else None
				)
				if config.model_type == "xlnet":
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   DataCollatorForPermutationLanguageModeling(
								    tokenizer=lowerCAmelCase__    ,    plm_probability=data_args.plm_probability    ,    max_span_length=data_args.max_span_length    ,    )
				else:
								if data_args.mlm and data_args.whole_word_mask:
												SCREAMING_SNAKE_CASE_   :	Any													=   DataCollatorForWholeWordMask(
												    tokenizer=lowerCAmelCase__    ,    mlm_probability=data_args.mlm_probability      )
								else:
												SCREAMING_SNAKE_CASE_   :	Optional[int]													=   DataCollatorForLanguageModeling(
												    tokenizer=lowerCAmelCase__    ,    mlm=data_args.mlm    ,    mlm_probability=data_args.mlm_probability      )

    # Initialize our Trainer
				SCREAMING_SNAKE_CASE_   :	Tuple													=   Trainer(
				    model=lowerCAmelCase__    ,    args=lowerCAmelCase__    ,    data_collator=lowerCAmelCase__    ,    train_dataset=lowerCAmelCase__    ,    eval_dataset=lowerCAmelCase__    ,    prediction_loss_only=lowerCAmelCase__    ,    )

				# Training
				if training_args.do_train:
								SCREAMING_SNAKE_CASE_   :	Dict													=   (
								    model_args.model_name_or_path
								    if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path      )
								    else None
								)
								trainer.train(model_path=lowerCAmelCase__      )
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_master():
												tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
				SCREAMING_SNAKE_CASE_   :	Any													=   {}
				if training_args.do_eval:
								logger.info('*** Evaluate ***'      )

								SCREAMING_SNAKE_CASE_   :	List[str]													=   trainer.evaluate()

								SCREAMING_SNAKE_CASE_   :	List[Any]													=   math.exp(eval_output['eval_loss']      )
								SCREAMING_SNAKE_CASE_   :	str													=   {"perplexity": perplexity}

								SCREAMING_SNAKE_CASE_   :	Any													=   os.path.join(training_args.output_dir    ,    'eval_results_lm.txt'      )
								if trainer.is_world_master():
												with open(lowerCAmelCase__    ,    'w'      ) as writer:
																logger.info('***** Eval results *****'      )
																for key in sorted(result.keys()      ):
																				logger.info('  %s = %s'    ,    lowerCAmelCase__    ,    str(result[key]      )      )
																				writer.write('%s = %s\n' % (key, str(result[key]      ))      )

								results.update(lowerCAmelCase__      )

				return results



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				main()


if __name__ == "__main__":
				main()







import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

A        =			[
    # (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'),
]

A        =			[
    # (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'),
]

A        =			[]
# 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
								A        =			f"""down_blocks.{i}.resnets.{j}."""
								A        =			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
												A        =			f"""down_blocks.{i}.attentions.{j}."""
												A        =			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
								A        =			f"""up_blocks.{i}.resnets.{j}."""
								A        =			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
												A        =			f"""up_blocks.{i}.attentions.{j}."""
												A        =			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
								A        =			f"""down_blocks.{i}.downsamplers.0.conv."""
								A        =			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
								A        =			f"""up_blocks.{i}.upsamplers.0."""
								A        =			f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
								unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

A        =			'mid_block.attentions.0.'
A        =			'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
				A        =			f"""mid_block.resnets.{j}."""
				A        =			f"""middle_block.{2*j}."""
				unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))



def 					_lowerCamelCase(			lowerCAmelCase__				:		Union[str, Any]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   sd_name
				for k, v in mapping.items():
								if "resnets" in k:
												for sd_part, hf_part in unet_conversion_map_resnet:
																SCREAMING_SNAKE_CASE_   :	int													=   v.replace(lowerCAmelCase__    ,    lowerCAmelCase__      )
												SCREAMING_SNAKE_CASE_   :	List[str]													=   v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												SCREAMING_SNAKE_CASE_   :	str													=   v.replace(lowerCAmelCase__    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Tuple													=   v
				SCREAMING_SNAKE_CASE_   :	str													=   {v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


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

A        =			[
    # (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):
								A        =			f"""encoder.down_blocks.{i}.resnets.{j}."""
								A        =			f"""encoder.down.{i}.block.{j}."""
								vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

								A        =			f"""up_blocks.{i}.upsamplers.0."""
								A        =			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):
								A        =			f"""decoder.up_blocks.{i}.resnets.{j}."""
								A        =			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):
				A        =			f"""mid_block.resnets.{i}."""
				A        =			f"""mid.block_{i+1}."""
				vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


A        =			[
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







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



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Dict													=   {k: k for k in vae_state_dict.keys()}
				for k, v in mapping.items():
								for sd_part, hf_part in vae_conversion_map:
												SCREAMING_SNAKE_CASE_   :	Any													=   v.replace(lowerCAmelCase__    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	int													=   v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																SCREAMING_SNAKE_CASE_   :	Dict													=   v.replace(lowerCAmelCase__    ,    lowerCAmelCase__      )
												SCREAMING_SNAKE_CASE_   :	Tuple													=   v
				SCREAMING_SNAKE_CASE_   :	Dict													=   {v: vae_state_dict[k] for k, v in mapping.items()}
				SCREAMING_SNAKE_CASE_   :	int													=   ['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'''      )
																SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   reshape_weight_for_sd(lowerCAmelCase__      )
				return new_state_dict


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


A        =			[
    # (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'),
]
A        =			{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
A        =			re.compile('|'.join(protected.keys()))

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



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[Any]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   {}
				SCREAMING_SNAKE_CASE_   :	List[str]													=   {}
				SCREAMING_SNAKE_CASE_   :	Any													=   {}
				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'      )
								):
												SCREAMING_SNAKE_CASE_   :	int													=   k[: -len('.q_proj.weight'      )]
												SCREAMING_SNAKE_CASE_   :	List[str]													=   k[-len('q_proj.weight'      )]
												if k_pre not in capture_qkv_weight:
																SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [None, None, None]
												SCREAMING_SNAKE_CASE_   :	int													=   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'      )
								):
												SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   k[: -len('.q_proj.bias'      )]
												SCREAMING_SNAKE_CASE_   :	str													=   k[-len('q_proj.bias'      )]
												if k_pre not in capture_qkv_bias:
																SCREAMING_SNAKE_CASE_   :	Tuple													=   [None, None, None]
												SCREAMING_SNAKE_CASE_   :	Any													=   v
												continue

								SCREAMING_SNAKE_CASE_   :	Tuple													=   textenc_pattern.sub(lambda lowerCAmelCase__      : protected[re.escape(m.group(0      )      )]    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Any													=   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'      )
								SCREAMING_SNAKE_CASE_   :	Dict													=   textenc_pattern.sub(lambda lowerCAmelCase__      : protected[re.escape(m.group(0      )      )]    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   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'      )
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   textenc_pattern.sub(lambda lowerCAmelCase__      : protected[re.escape(m.group(0      )      )]    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.cat(lowerCAmelCase__      )

				return new_state_dict



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]      ):





				'''simple docstring'''







				return text_enc_dict


if __name__ == "__main__":
				A        =			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.'
				)

				A        =			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
				A        =			osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
				A        =			osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
				A        =			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):
								A        =			load_file(unet_path, device='cpu')
				else:
								A        =			osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
								A        =			torch.load(unet_path, map_location='cpu')

				if osp.exists(vae_path):
								A        =			load_file(vae_path, device='cpu')
				else:
								A        =			osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
								A        =			torch.load(vae_path, map_location='cpu')

				if osp.exists(text_enc_path):
								A        =			load_file(text_enc_path, device='cpu')
				else:
								A        =			osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
								A        =			torch.load(text_enc_path, map_location='cpu')

				# Convert the UNet model
				A        =			convert_unet_state_dict(unet_state_dict)
				A        =			{'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

				# Convert the VAE model
				A        =			convert_vae_state_dict(vae_state_dict)
				A        =			{'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
				A        =			'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
								A        =			{'transformer.' + k: v for k, v in text_enc_dict.items()}
								A        =			convert_text_enc_state_dict_vaa(text_enc_dict)
								A        =			{'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
				else:
								A        =			convert_text_enc_state_dict(text_enc_dict)
								A        =			{'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

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

				if args.use_safetensors:
								save_file(state_dict, args.checkpoint_path)
				else:
								A        =			{'state_dict': state_dict}
								torch.save(state_dict, args.checkpoint_path)