

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_			      =      logging.getLogger(__name__)
lowerCAmelCase_			      =      list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowerCAmelCase_			      =      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class       snake_case_  :







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Optional[str]		     =							field(
			    default=__A      ,			metadata={
			        "help": (
			            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
			        )
			    }      ,			)
			SCREAMING_SNAKE_CASE	:  Optional[str]		     =							field(
			    default=__A      ,			metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(__A   )}      ,			)
			SCREAMING_SNAKE_CASE	:  Optional[str]		     =							field(
			    default=__A      ,			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"
			        )
			    }      ,			)
			SCREAMING_SNAKE_CASE	:  Optional[str]		     =							field(
			    default=__A      ,			metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
			SCREAMING_SNAKE_CASE	:  Optional[str]		     =							field(
			    default=__A      ,			metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
			SCREAMING_SNAKE_CASE	:  Optional[str]		     =							field(
			    default=__A      ,			metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}      ,			)
			SCREAMING_SNAKE_CASE	:  bool		     =							field(
			    default=__A      ,			metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}      ,			)
			SCREAMING_SNAKE_CASE	:  str		     =							field(
			    default="main"      ,			metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}      ,			)
			SCREAMING_SNAKE_CASE	:  bool		     =							field(
			    default=__A      ,			metadata={
			        "help": (
			            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
			            "with private models)."
			        )
			    }      ,			)







			def 							snake_case__(	self					:							Optional[Any]				) ->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       snake_case_  :







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Optional[str]		     =							field(
			    default=__A      ,			metadata={"help": "The name of the dataset to use (via the datasets library)."}   )
			SCREAMING_SNAKE_CASE	:  Optional[str]		     =							field(
			    default=__A      ,			metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}   )
			SCREAMING_SNAKE_CASE	:  Optional[str]		     =							field(default=__A      ,			metadata={"help": "The input training data file (a text file)."}   )
			SCREAMING_SNAKE_CASE	:  Optional[str]		     =							field(
			    default=__A      ,			metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}      ,			)
			SCREAMING_SNAKE_CASE	:  Optional[str]		     =							field(
			    default=__A      ,			metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}      ,			)
			SCREAMING_SNAKE_CASE	:  Optional[str]		     =							field(
			    default=__A      ,			metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}      ,			)
			SCREAMING_SNAKE_CASE	:  bool		     =							field(
			    default=__A      ,			metadata={"help": "Overwrite the cached training and evaluation sets"}   )
			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							field(
			    default=5      ,			metadata={
			        "help": "The percentage of the train set used as validation set in case there's no validation split"
			    }      ,			)
			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							field(
			    default=__A      ,			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."
			        )
			    }      ,			)
			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							field(
			    default=__A      ,			metadata={"help": "The number of processes to use for the preprocessing."}      ,			)
			SCREAMING_SNAKE_CASE	:  float		     =							field(
			    default=0.15      ,			metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}   )
			SCREAMING_SNAKE_CASE	:  bool		     =							field(
			    default=__A      ,			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 							snake_case__(	self					:							Tuple				) ->Optional[int]:
									if self.train_file is not None:
															snake_case_  =      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_  =      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   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						with open(SCREAMING_SNAKE_CASE__    ,						'''r'''    ,						encoding='''utf-8'''			) as f:
												snake_case_  =      [json.loads(SCREAMING_SNAKE_CASE__			) for line in f.read().splitlines() if (len(SCREAMING_SNAKE_CASE__			) > 0 and not line.isspace())]
						assert len(SCREAMING_SNAKE_CASE__			) == len(SCREAMING_SNAKE_CASE__			)

						snake_case_  =      {c: dataset[c] for c in dataset.column_names}
						snake_case_  =      refs
						return Dataset.from_dict(SCREAMING_SNAKE_CASE__			)


def     __SCREAMING_SNAKE_CASE   ():
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.

						snake_case_  =      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_  =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
						else:
												snake_case_,       snake_case_,       snake_case_  =      parser.parse_args_into_dataclasses()

						# Detecting last checkpoint.
						snake_case_  =      None
						if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
												snake_case_  =      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'''    ,						SCREAMING_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_  =      load_dataset(data_args.dataset_name    ,						data_args.dataset_config_name			)
												if "validation" not in datasets.keys():
																		snake_case_  =      load_dataset(
																		    data_args.dataset_name    ,						data_args.dataset_config_name    ,						split=F'''train[:{data_args.validation_split_percentage}%]'''    ,						)
																		snake_case_  =      load_dataset(
																		    data_args.dataset_name    ,						data_args.dataset_config_name    ,						split=F'''train[{data_args.validation_split_percentage}%:]'''    ,						)
						else:
												snake_case_  =      {}
												if data_args.train_file is not None:
																		snake_case_  =      data_args.train_file
												if data_args.validation_file is not None:
																		snake_case_  =      data_args.validation_file
												snake_case_  =      data_args.train_file.split('''.'''			)[-1]
												if extension == "txt":
																		snake_case_  =      '''text'''
												snake_case_  =      load_dataset(SCREAMING_SNAKE_CASE__    ,						data_files=SCREAMING_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_  =      {
						    '''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_  =      AutoConfig.from_pretrained(model_args.config_name    ,						**SCREAMING_SNAKE_CASE__			)
						elif model_args.model_name_or_path:
												snake_case_  =      AutoConfig.from_pretrained(model_args.model_name_or_path    ,						**SCREAMING_SNAKE_CASE__			)
						else:
												snake_case_  =      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_  =      {
						    '''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_  =      AutoTokenizer.from_pretrained(model_args.tokenizer_name    ,						**SCREAMING_SNAKE_CASE__			)
						elif model_args.model_name_or_path:
												snake_case_  =      AutoTokenizer.from_pretrained(model_args.model_name_or_path    ,						**SCREAMING_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_  =      AutoModelForMaskedLM.from_pretrained(
												    model_args.model_name_or_path    ,						from_tf=bool('''.ckpt''' in model_args.model_name_or_path			)    ,						config=SCREAMING_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_  =      AutoModelForMaskedLM.from_config(SCREAMING_SNAKE_CASE__			)

						model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE__			)			)

						# Preprocessing the datasets.
						# First we tokenize all the texts.
						if training_args.do_train:
												snake_case_  =      datasets['''train'''].column_names
						else:
												snake_case_  =      datasets['''validation'''].column_names
						snake_case_  =      '''text''' if '''text''' in column_names else column_names[0]

						snake_case_  =      '''max_length''' if data_args.pad_to_max_length else False

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

						snake_case_  =      datasets.map(
						    SCREAMING_SNAKE_CASE__    ,						batched=SCREAMING_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_  =      add_chinese_references(tokenized_datasets['''train''']    ,						data_args.train_ref_file			)
						if data_args.validation_ref_file is not None:
												snake_case_  =      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_  =      data_args.train_ref_file or data_args.validation_ref_file
						if has_ref:
												snake_case_  =      False

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

						# Initialize our Trainer
						snake_case_  =      Trainer(
						    model=SCREAMING_SNAKE_CASE__    ,						args=SCREAMING_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=SCREAMING_SNAKE_CASE__    ,						data_collator=SCREAMING_SNAKE_CASE__    ,						)

						# Training
						if training_args.do_train:
												if last_checkpoint is not None:
																		snake_case_  =      last_checkpoint
												elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path			):
																		snake_case_  =      model_args.model_name_or_path
												else:
																		snake_case_  =      None
												snake_case_  =      trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE__			)
												trainer.save_model()  # Saves the tokenizer too for easy upload

												snake_case_  =      os.path.join(training_args.output_dir    ,						'''train_results.txt'''			)
												if trainer.is_world_process_zero():
																		with open(SCREAMING_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_  =      {}
						if training_args.do_eval:
												logger.info('''*** Evaluate ***'''			)

												snake_case_  =      trainer.evaluate()

												snake_case_  =      math.exp(eval_output['''eval_loss''']			)
												snake_case_  =      perplexity

												snake_case_  =      os.path.join(training_args.output_dir    ,						'''eval_results_mlm_wwm.txt'''			)
												if trainer.is_world_process_zero():
																		with open(SCREAMING_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   (SCREAMING_SNAKE_CASE__			):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
					main()



"""simple docstring"""


from ..utils import DummyObject, requires_backends
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       int							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Union[str, Any]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       int							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[int]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[Any]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[Any]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[Any]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       int							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[int]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[str]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )


def _lowerCamelCase      (						*_UpperCamelCase       ,					**_UpperCamelCase ):

			'''simple docstring'''




			requires_backends(_UpperCamelCase       ,					["torch"] )


def _lowerCamelCase      (						*_UpperCamelCase       ,					**_UpperCamelCase ):

			'''simple docstring'''




			requires_backends(_UpperCamelCase       ,					["torch"] )


def _lowerCamelCase      (						*_UpperCamelCase       ,					**_UpperCamelCase ):

			'''simple docstring'''




			requires_backends(_UpperCamelCase       ,					["torch"] )


def _lowerCamelCase      (						*_UpperCamelCase       ,					**_UpperCamelCase ):

			'''simple docstring'''




			requires_backends(_UpperCamelCase       ,					["torch"] )


def _lowerCamelCase      (						*_UpperCamelCase       ,					**_UpperCamelCase ):

			'''simple docstring'''




			requires_backends(_UpperCamelCase       ,					["torch"] )


def _lowerCamelCase      (						*_UpperCamelCase       ,					**_UpperCamelCase ):

			'''simple docstring'''




			requires_backends(_UpperCamelCase       ,					["torch"] )


def _lowerCamelCase      (						*_UpperCamelCase       ,					**_UpperCamelCase ):

			'''simple docstring'''




			requires_backends(_UpperCamelCase       ,					["torch"] )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Union[str, Any]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Tuple							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       int							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[Any]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[str]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Any							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[int]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Any							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[Any]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[Any]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[Any]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[str]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Dict							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[str]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[Any]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[str]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       int							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Tuple							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Union[str, Any]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[Any]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[Any]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[str]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Tuple							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       int							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Any							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Any							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[int]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Tuple							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[str]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Dict							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Union[str, Any]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[str]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Union[str, Any]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[int]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[int]							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )
class     _UpperCamelCase    (  metaclass=lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Any							 =["""torch"""]

							def __init__(	self       ,  *__a       ,  **__a      ):
										requires_backends(self       ,  ["torch"]      )

							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )



							@classmethod
							def      snake_case							(	cls       ,  *__a       ,  **__a      ):
										requires_backends(cls       ,  ["torch"]      )




