




import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


__lowerCamelCase      :    List[str]   		=   logging.getLogger(__name__)
__lowerCamelCase      :    str   		=   list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__lowerCamelCase      :    int   		=   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					a_			=	field(
					    default=UpperCamelCase__				,     metadata={
					        "help": (
					            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
					        )
					    }				,     )
					a_			=	field(
					    default=UpperCamelCase__				,     metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(UpperCamelCase__		)}				,     )
					a_			=	field(
					    default=UpperCamelCase__				,     metadata={
					        "help": (
					            "Override some existing default config settings when a model is trained from scratch. Example: "
					            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
					        )
					    }				,     )
					a_			=	field(
					    default=UpperCamelCase__				,     metadata={"help": "Pretrained config name or path if not the same as model_name"}		)
					a_			=	field(
					    default=UpperCamelCase__				,     metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}		)
					a_			=	field(
					    default=UpperCamelCase__				,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}				,     )
					a_			=	field(
					    default=UpperCamelCase__				,     metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}				,     )
					a_			=	field(
					    default="main"				,     metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}				,     )
					a_			=	field(
					    default=UpperCamelCase__				,     metadata={
					        "help": (
					            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
					            "with private models)."
					        )
					    }				,     )



					def  _lowercase  (      self  :						Any ):
						if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
							raise ValueError(
							    "--config_overrides can't be used in combination with --config_name or --model_name_or_path" )



@dataclass
class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					a_			=	field(
					    default=UpperCamelCase__				,     metadata={"help": "The name of the dataset to use (via the datasets library)."}		)
					a_			=	field(
					    default=UpperCamelCase__				,     metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}		)
					a_			=	field(default=UpperCamelCase__				,     metadata={"help": "The input training data file (a text file)."}		)
					a_			=	field(
					    default=UpperCamelCase__				,     metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}				,     )
					a_			=	field(
					    default=UpperCamelCase__				,     metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}				,     )
					a_			=	field(
					    default=UpperCamelCase__				,     metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}				,     )
					a_			=	field(
					    default=UpperCamelCase__				,     metadata={"help": "Overwrite the cached training and evaluation sets"}		)
					a_			=	field(
					    default=5				,     metadata={
					        "help": "The percentage of the train set used as validation set in case there's no validation split"
					    }				,     )
					a_			=	field(
					    default=UpperCamelCase__				,     metadata={
					        "help": (
					            "The maximum total input sequence length after tokenization. Sequences longer "
					            "than this will be truncated. Default to the max input length of the model."
					        )
					    }				,     )
					a_			=	field(
					    default=UpperCamelCase__				,     metadata={"help": "The number of processes to use for the preprocessing."}				,     )
					a_			=	field(
					    default=0.15				,     metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}		)
					a_			=	field(
					    default=UpperCamelCase__				,     metadata={
					        "help": (
					            "Whether to pad all samples to `max_seq_length`. "
					            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
					        )
					    }				,     )



					def  _lowercase  (      self  :						List[Any] ):
						if self.train_file is not None:
							snake_case__				:   List[Any]     =						self.train_file.split("." )[-1]
							assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
						if self.validation_file is not None:
							snake_case__				:   Tuple     =						self.validation_file.split("." )[-1]
							assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[Any]  ,						snake_case_      :							str     ):




	with open(snake_case_  ,						"r"  ,						encoding="utf-8"     ) as f:
		snake_case__				:   List[Any]     =						[json.loads(snake_case_     ) for line in f.read().splitlines() if (len(snake_case_     ) > 0 and not line.isspace())]
	assert len(snake_case_     ) == len(snake_case_     )

	snake_case__				:   List[Any]     =						{c: dataset[c] for c in dataset.column_names}
	snake_case__				:   int     =						refs
	return Dataset.from_dict(snake_case_     )







def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Optional[Any]     =						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
	if len(sys.argv     ) == 2 and sys.argv[1].endswith(".json"     ):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		snake_case__,		snake_case__,		snake_case__				:   Any     =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
	else:
		snake_case__,		snake_case__,		snake_case__				:   Optional[Any]     =						parser.parse_args_into_dataclasses()

	# Detecting last checkpoint.
	snake_case__				:   List[str]     =						None
	if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
		snake_case__				:   Tuple     =						get_last_checkpoint(training_args.output_dir     )
		if last_checkpoint is None and len(os.listdir(training_args.output_dir     )     ) > 0:
			raise ValueError(
			    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
			    "Use --overwrite_output_dir to overcome."     )
		elif last_checkpoint is not None:
			logger.info(
			    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
			    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."     )

    # Setup logging
	logging.basicConfig(
	    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"  ,						datefmt="%m/%d/%Y %H:%M:%S"  ,						handlers=[logging.StreamHandler(sys.stdout     )]  ,						)
	logger.setLevel(logging.INFO if is_main_process(training_args.local_rank     ) else logging.WARN     )

	# Log on each process the small summary:
	logger.warning(
	    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
	    + F'''distributed training: {bool(training_args.local_rank != -1     )}, 16-bits training: {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"  ,						snake_case_     )

	# Set seed before initializing model.
	set_seed(training_args.seed     )

	# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
	# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
	# (the dataset will be downloaded automatically from the datasets Hub).
	#
	# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
	# 'text' is found. You can easily tweak this behavior (see below).
	#
	# In distributed training, the load_dataset function guarantee that only one local process can concurrently
	# download the dataset.
	if data_args.dataset_name is not None:
		# Downloading and loading a dataset from the hub.
		snake_case__				:   Dict     =						load_dataset(data_args.dataset_name  ,						data_args.dataset_config_name     )
		if "validation" not in datasets.keys():
			snake_case__				:   Optional[Any]     =						load_dataset(
			    data_args.dataset_name  ,						data_args.dataset_config_name  ,						split=F'''train[:{data_args.validation_split_percentage}%]'''  ,						)
			snake_case__				:   Optional[Any]     =						load_dataset(
			    data_args.dataset_name  ,						data_args.dataset_config_name  ,						split=F'''train[{data_args.validation_split_percentage}%:]'''  ,						)
	else:
		snake_case__				:   List[str]     =						{}
		if data_args.train_file is not None:
			snake_case__				:   Any     =						data_args.train_file
		if data_args.validation_file is not None:
			snake_case__				:   List[str]     =						data_args.validation_file
		snake_case__				:   Union[str, Any]     =						data_args.train_file.split("."     )[-1]
		if extension == "txt":
			snake_case__				:   List[Any]     =						"text"
		snake_case__				:   Any     =						load_dataset(snake_case_  ,						data_files=snake_case_     )
	# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
	# https://huggingface.co/docs/datasets/loading_datasets.html.

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.
	snake_case__				:   Optional[Any]     =						{
	    "cache_dir": model_args.cache_dir,
	    "revision": model_args.model_revision,
	    "use_auth_token": True if model_args.use_auth_token else None,
	}
	if model_args.config_name:
		snake_case__				:   List[str]     =						AutoConfig.from_pretrained(model_args.config_name  ,						**snake_case_     )
	elif model_args.model_name_or_path:
		snake_case__				:   List[str]     =						AutoConfig.from_pretrained(model_args.model_name_or_path  ,						**snake_case_     )
	else:
		snake_case__				:   str     =						CONFIG_MAPPING[model_args.model_type]()
		logger.warning("You are instantiating a new config instance from scratch."     )
		if model_args.config_overrides is not None:
			logger.info(F'''Overriding config: {model_args.config_overrides}'''     )
			config.update_from_string(model_args.config_overrides     )
			logger.info(F'''New config: {config}'''     )

	snake_case__				:   int     =						{
	    "cache_dir": model_args.cache_dir,
	    "use_fast": model_args.use_fast_tokenizer,
	    "revision": model_args.model_revision,
	    "use_auth_token": True if model_args.use_auth_token else None,
	}
	if model_args.tokenizer_name:
		snake_case__				:   Tuple     =						AutoTokenizer.from_pretrained(model_args.tokenizer_name  ,						**snake_case_     )
	elif model_args.model_name_or_path:
		snake_case__				:   str     =						AutoTokenizer.from_pretrained(model_args.model_name_or_path  ,						**snake_case_     )
	else:
		raise ValueError(
		    "You are instantiating a new tokenizer from scratch. This is not supported by this script."
		    "You can do it from another script, save it, and load it from here, using --tokenizer_name."     )

	if model_args.model_name_or_path:
		snake_case__				:   int     =						AutoModelForMaskedLM.from_pretrained(
		    model_args.model_name_or_path  ,						from_tf=bool(".ckpt" in model_args.model_name_or_path     )  ,						config=snake_case_  ,						cache_dir=model_args.cache_dir  ,						revision=model_args.model_revision  ,						use_auth_token=True if model_args.use_auth_token else None  ,						)
	else:
		logger.info("Training new model from scratch"     )
		snake_case__				:   Optional[Any]     =						AutoModelForMaskedLM.from_config(snake_case_     )

	model.resize_token_embeddings(len(snake_case_     )     )

	# Preprocessing the datasets.
	# First we tokenize all the texts.
	if training_args.do_train:
		snake_case__				:   Optional[int]     =						datasets["train"].column_names
	else:
		snake_case__				:   Tuple     =						datasets["validation"].column_names
	snake_case__				:   Optional[Any]     =						"text" if "text" in column_names else column_names[0]

	snake_case__				:   Dict     =						"max_length" if data_args.pad_to_max_length else False

	def tokenize_function(snake_case_      :							str     ):
		# Remove empty lines
		snake_case__				:   Optional[Any]     =						[line for line in examples["text"] if len(snake_case_     ) > 0 and not line.isspace()]
		return tokenizer(examples["text"]  ,						padding=snake_case_  ,						truncation=snake_case_  ,						max_length=data_args.max_seq_length     )

	snake_case__				:   Any     =						datasets.map(
	    snake_case_  ,						batched=snake_case_  ,						num_proc=data_args.preprocessing_num_workers  ,						remove_columns=[text_column_name]  ,						load_from_cache_file=not data_args.overwrite_cache  ,						)

	# Add the chinese references if provided
	if data_args.train_ref_file is not None:
		snake_case__				:   List[Any]     =						add_chinese_references(tokenized_datasets["train"]  ,						data_args.train_ref_file     )
	if data_args.validation_ref_file is not None:
		snake_case__				:   List[Any]     =						add_chinese_references(
		    tokenized_datasets["validation"]  ,						data_args.validation_ref_file     )
	# If we have ref files, need to avoid it removed by trainer
	snake_case__				:   int     =						data_args.train_ref_file or data_args.validation_ref_file
	if has_ref:
		snake_case__				:   Tuple     =						False

	# Data collator
	# This one will take care of randomly masking the tokens.
	snake_case__				:   Any     =						DataCollatorForWholeWordMask(tokenizer=snake_case_  ,						mlm_probability=data_args.mlm_probability     )

	# Initialize our Trainer
	snake_case__				:   Union[str, Any]     =						Trainer(
	    model=snake_case_  ,						args=snake_case_  ,						train_dataset=tokenized_datasets["train"] if training_args.do_train else None  ,						eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None  ,						tokenizer=snake_case_  ,						data_collator=snake_case_  ,						)

	# Training
	if training_args.do_train:
		if last_checkpoint is not None:
			snake_case__				:   Optional[Any]     =						last_checkpoint
		elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path     ):
			snake_case__				:   str     =						model_args.model_name_or_path
		else:
			snake_case__				:   Union[str, Any]     =						None
		snake_case__				:   Tuple     =						trainer.train(resume_from_checkpoint=snake_case_     )
		trainer.save_model()  # Saves the tokenizer too for easy upload

		snake_case__				:   Any     =						os.path.join(training_args.output_dir  ,						"train_results.txt"     )
		if trainer.is_world_process_zero():
			with open(snake_case_  ,						"w"     ) as writer:
				logger.info("***** Train results *****"     )
				for key, value in sorted(train_result.metrics.items()     ):
					logger.info(F'''  {key} = {value}'''     )
					writer.write(F'''{key} = {value}\n'''     )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
			trainer.state.save_to_json(os.path.join(training_args.output_dir  ,						"trainer_state.json"     )     )

    # Evaluation
	snake_case__				:   Dict     =						{}
	if training_args.do_eval:
		logger.info("*** Evaluate ***"     )

		snake_case__				:   Optional[Any]     =						trainer.evaluate()

		snake_case__				:   Any     =						math.exp(eval_output["eval_loss"]     )
		snake_case__				:   Dict     =						perplexity

		snake_case__				:   Tuple     =						os.path.join(training_args.output_dir  ,						"eval_results_mlm_wwm.txt"     )
		if trainer.is_world_process_zero():
			with open(snake_case_  ,						"w"     ) as writer:
				logger.info("***** Eval results *****"     )
				for key, value in sorted(results.items()     ):
					logger.info(F'''  {key} = {value}'''     )
					writer.write(F'''{key} = {value}\n'''     )

	return results







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]     ):
	main()


if __name__ == "__main__":
	main()











import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCamelCase      :    Union[str, Any]   		=   logging.get_logger(__name__)

__lowerCamelCase      :    Dict   		=   {
    """Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"instructblip_vision_model"



					def __init__(      self  :						List[Any]							,	__A  :						Dict=1_4_0_8							,	__A  :						Tuple=6_1_4_4							,	__A  :						str=3_9							,	__A  :						int=1_6							,	__A  :						str=2_2_4							,	__A  :						Any=1_4							,	__A  :						Dict="gelu"							,	__A  :						List[Any]=1e-6							,	__A  :						Any=0.0							,	__A  :						List[Any]=1e-1_0							,	__A  :						Union[str, Any]=True							,	**__A  :						Tuple							,	):
						super().__init__(**__A )

						snake_case__				:   List[str]     =						hidden_size
						snake_case__				:   Optional[int]     =						intermediate_size
						snake_case__				:   List[str]     =						num_hidden_layers
						snake_case__				:   List[Any]     =						num_attention_heads
						snake_case__				:   str     =						patch_size
						snake_case__				:   int     =						image_size
						snake_case__				:   int     =						initializer_range
						snake_case__				:   Optional[int]     =						attention_dropout
						snake_case__				:   str     =						layer_norm_eps
						snake_case__				:   Optional[Any]     =						hidden_act
						snake_case__				:   Tuple     =						qkv_bias






					@classmethod
					def  _lowercase  (      cls  :						List[str]							,	__A  :						Union[str, os.PathLike]							,	**__A  :						Optional[Any] ):
						cls._set_token_in_kwargs(__A )

						snake_case__,		snake_case__				:   str     =						cls.get_config_dict(__A							,	**__A )

						# get the vision config dict if we are loading from InstructBlipConfig
						if config_dict.get("model_type" ) == "instructblip":
							snake_case__				:   Union[str, Any]     =						config_dict["vision_config"]

						if "model_type" in config_dict and hasattr(cls							,	"model_type" ) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
							    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )

						return cls.from_dict(__A							,	**__A )



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"instructblip_qformer"



					def __init__(      self  :						Any							,	__A  :						Union[str, Any]=3_0_5_2_2							,	__A  :						Union[str, Any]=7_6_8							,	__A  :						Optional[int]=1_2							,	__A  :						Dict=1_2							,	__A  :						Dict=3_0_7_2							,	__A  :						List[str]="gelu"							,	__A  :						Union[str, Any]=0.1							,	__A  :						Tuple=0.1							,	__A  :						Any=5_1_2							,	__A  :						Optional[int]=0.0_2							,	__A  :						List[str]=1e-1_2							,	__A  :						Any=0							,	__A  :						Optional[Any]="absolute"							,	__A  :						str=2							,	__A  :						Any=1_4_0_8							,	**__A  :						List[str]							,	):
						super().__init__(pad_token_id=__A							,	**__A )

						snake_case__				:   Dict     =						vocab_size
						snake_case__				:   Optional[int]     =						hidden_size
						snake_case__				:   Optional[Any]     =						num_hidden_layers
						snake_case__				:   str     =						num_attention_heads
						snake_case__				:   int     =						hidden_act
						snake_case__				:   Optional[Any]     =						intermediate_size
						snake_case__				:   Union[str, Any]     =						hidden_dropout_prob
						snake_case__				:   List[Any]     =						attention_probs_dropout_prob
						snake_case__				:   List[Any]     =						max_position_embeddings
						snake_case__				:   int     =						initializer_range
						snake_case__				:   Dict     =						layer_norm_eps
						snake_case__				:   str     =						position_embedding_type
						snake_case__				:   Dict     =						cross_attention_frequency
						snake_case__				:   List[str]     =						encoder_hidden_size






					@classmethod
					def  _lowercase  (      cls  :						List[Any]							,	__A  :						Union[str, os.PathLike]							,	**__A  :						Optional[int] ):
						cls._set_token_in_kwargs(__A )

						snake_case__,		snake_case__				:   Tuple     =						cls.get_config_dict(__A							,	**__A )

						# get the qformer config dict if we are loading from InstructBlipConfig
						if config_dict.get("model_type" ) == "instructblip":
							snake_case__				:   List[Any]     =						config_dict["qformer_config"]

						if "model_type" in config_dict and hasattr(cls							,	"model_type" ) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
							    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )

						return cls.from_dict(__A							,	**__A )



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"instructblip"
					a_			=	True



					def __init__(      self  :						List[str]							,	__A  :						Optional[Any]=None							,	__A  :						Tuple=None							,	__A  :						Optional[int]=None							,	__A  :						Optional[Any]=3_2							,	**__A  :						Optional[int] ):
						super().__init__(**__A )

						if vision_config is None:
							snake_case__				:   Any     =						{}
							logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values." )

						if qformer_config is None:
							snake_case__				:   Optional[Any]     =						{}
							logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values." )

						if text_config is None:
							snake_case__				:   Optional[int]     =						{}
							logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)." )

						snake_case__				:   List[Any]     =						InstructBlipVisionConfig(**__A )
						snake_case__				:   Union[str, Any]     =						InstructBlipQFormerConfig(**__A )
						snake_case__				:   Dict     =						text_config["model_type"] if "model_type" in text_config else "opt"
						snake_case__				:   List[Any]     =						CONFIG_MAPPING[text_model_type](**__A )

						snake_case__				:   Union[str, Any]     =						self.text_config.tie_word_embeddings
						snake_case__				:   Tuple     =						self.text_config.is_encoder_decoder

						snake_case__				:   str     =						num_query_tokens
						snake_case__				:   Dict     =						self.vision_config.hidden_size
						snake_case__				:   List[Any]     =						self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
						snake_case__				:   int     =						1.0
						snake_case__				:   Optional[int]     =						0.0_2



					@classmethod
					def  _lowercase  (      cls  :						List[str]							,	__A  :						InstructBlipVisionConfig							,	__A  :						InstructBlipQFormerConfig							,	__A  :						PretrainedConfig							,	**__A  :						int							,	):
						return cls(
						    vision_config=vision_config.to_dict()							,	qformer_config=qformer_config.to_dict()							,	text_config=text_config.to_dict()							,	**__A							,	)






					def  _lowercase  (      self  :						Optional[int] ):
						snake_case__				:   Any     =						copy.deepcopy(self.__dict__ )
						snake_case__				:   Optional[Any]     =						self.vision_config.to_dict()
						snake_case__				:   List[str]     =						self.qformer_config.to_dict()
						snake_case__				:   List[Any]     =						self.text_config.to_dict()
						snake_case__				:   List[Any]     =						self.__class__.model_type
						return output



