




"""simple docstring"""







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


__A   :  Dict					=      logging.getLogger(__name__)
__A   :  Any					=      list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__A   :  Optional[Any]					=      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class       lowerCAmelCase__       :


				"""simple docstring"""





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



				def       snake_case    (     self					:	Tuple						):
						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       lowerCAmelCase__       :


				"""simple docstring"""





				__UpperCAmelCase :     Optional[str]       =      field(
				    default=lowerCAmelCase_  ,			metadata={"help": "The name of the dataset to use (via the datasets library)."}    )
				__UpperCAmelCase :     Optional[str]       =      field(
				    default=lowerCAmelCase_  ,			metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}    )
				__UpperCAmelCase :     Optional[str]       =      field(default=lowerCAmelCase_  ,			metadata={"help": "The input training data file (a text file)."}    )
				__UpperCAmelCase :     Optional[str]       =      field(
				    default=lowerCAmelCase_  ,			metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}  ,			)
				__UpperCAmelCase :     Optional[str]       =      field(
				    default=lowerCAmelCase_  ,			metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}  ,			)
				__UpperCAmelCase :     Optional[str]       =      field(
				    default=lowerCAmelCase_  ,			metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}  ,			)
				__UpperCAmelCase :     bool       =      field(
				    default=lowerCAmelCase_  ,			metadata={"help": "Overwrite the cached training and evaluation sets"}    )
				__UpperCAmelCase :     Optional[int]       =      field(
				    default=5  ,			metadata={
				        "help": "The percentage of the train set used as validation set in case there's no validation split"
				    }  ,			)
				__UpperCAmelCase :     Optional[int]       =      field(
				    default=lowerCAmelCase_  ,			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."
				        )
				    }  ,			)
				__UpperCAmelCase :     Optional[int]       =      field(
				    default=lowerCAmelCase_  ,			metadata={"help": "The number of processes to use for the preprocessing."}  ,			)
				__UpperCAmelCase :     float       =      field(
				    default=0.15  ,			metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}    )
				__UpperCAmelCase :     bool       =      field(
				    default=lowerCAmelCase_  ,			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					:	int						):
						if self.train_file is not None:
								__lowercase				:		Optional[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:
								__lowercase				:		Optional[int]												=				self.validation_file.split("."						)[-1]
								assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->List[str]:






		"""simple docstring"""


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

		__lowercase				:		Tuple												=				{c: dataset[c] for c in dataset.column_names}
		__lowercase				:		List[str]												=				refs
		return Dataset.from_dict(_lowerCamelCase				)

def 						snake_case__       (					)			->List[str]:






		"""simple docstring"""


		__lowercase				:		List[str]												=				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.
				__lowercase						,__lowercase						,__lowercase				:		Tuple												=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
		else:
				__lowercase						,__lowercase						,__lowercase				:		List[str]												=				parser.parse_args_into_dataclasses()

		# Detecting last checkpoint.
		__lowercase				:		List[str]												=				None
		if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
				__lowercase				:		Union[str, Any]												=				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",  _lowerCamelCase				)

		# 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.
				__lowercase				:		List[str]												=				load_dataset(data_args.dataset_name,  data_args.dataset_config_name				)
				if "validation" not in datasets.keys():
						__lowercase				:		Optional[int]												=				load_dataset(
						    data_args.dataset_name,  data_args.dataset_config_name,  split=F'train[:{data_args.validation_split_percentage}%]',  )
						__lowercase				:		Tuple												=				load_dataset(
						    data_args.dataset_name,  data_args.dataset_config_name,  split=F'train[{data_args.validation_split_percentage}%:]',  )
		else:
				__lowercase				:		Optional[Any]												=				{}
				if data_args.train_file is not None:
						__lowercase				:		Tuple												=				data_args.train_file
				if data_args.validation_file is not None:
						__lowercase				:		Optional[int]												=				data_args.validation_file
				__lowercase				:		Optional[Any]												=				data_args.train_file.split("."				)[-1]
				if extension == "txt":
						__lowercase				:		Union[str, Any]												=				"text"
				__lowercase				:		Dict												=				load_dataset(_lowerCamelCase,  data_files=_lowerCamelCase				)
		# 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.
		__lowercase				:		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:
				__lowercase				:		Tuple												=				AutoConfig.from_pretrained(model_args.config_name,  **_lowerCamelCase				)
		elif model_args.model_name_or_path:
				__lowercase				:		Union[str, Any]												=				AutoConfig.from_pretrained(model_args.model_name_or_path,  **_lowerCamelCase				)
		else:
				__lowercase				:		List[Any]												=				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}'				)

		__lowercase				:		Union[str, Any]												=				{
		    "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:
				__lowercase				:		Optional[Any]												=				AutoTokenizer.from_pretrained(model_args.tokenizer_name,  **_lowerCamelCase				)
		elif model_args.model_name_or_path:
				__lowercase				:		List[Any]												=				AutoTokenizer.from_pretrained(model_args.model_name_or_path,  **_lowerCamelCase				)
		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:
				__lowercase				:		List[Any]												=				AutoModelForMaskedLM.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,  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"				)
				__lowercase				:		Optional[int]												=				AutoModelForMaskedLM.from_config(_lowerCamelCase				)

		model.resize_token_embeddings(len(_lowerCamelCase				)				)

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

		__lowercase				:		List[str]												=				"max_length" if data_args.pad_to_max_length else False

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

		__lowercase				:		Optional[int]												=				datasets.map(
		    _lowerCamelCase,  batched=_lowerCamelCase,  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:
				__lowercase				:		Dict												=				add_chinese_references(tokenized_datasets["train"],  data_args.train_ref_file				)
		if data_args.validation_ref_file is not None:
				__lowercase				:		List[str]												=				add_chinese_references(
				    tokenized_datasets["validation"],  data_args.validation_ref_file				)
		# If we have ref files, need to avoid it removed by trainer
		__lowercase				:		Optional[int]												=				data_args.train_ref_file or data_args.validation_ref_file
		if has_ref:
				__lowercase				:		List[str]												=				False

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

		# Initialize our Trainer
		__lowercase				:		Any												=				Trainer(
		    model=_lowerCamelCase,  args=_lowerCamelCase,  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=_lowerCamelCase,  data_collator=_lowerCamelCase,  )

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

				__lowercase				:		List[Any]												=				os.path.join(training_args.output_dir,  "train_results.txt"				)
				if trainer.is_world_process_zero():
						with open(_lowerCamelCase,  "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
		__lowercase				:		Tuple												=				{}
		if training_args.do_eval:
				logger.info("*** Evaluate ***"				)

				__lowercase				:		Union[str, Any]												=				trainer.evaluate()

				__lowercase				:		str												=				math.exp(eval_output["eval_loss"]				)
				__lowercase				:		Optional[Any]												=				perplexity

				__lowercase				:		Union[str, Any]												=				os.path.join(training_args.output_dir,  "eval_results_mlm_wwm.txt"				)
				if trainer.is_world_process_zero():
						with open(_lowerCamelCase,  "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 						snake_case__       (					_lowerCamelCase				)			->Union[str, Any]:






		"""simple docstring"""


		main()


if __name__ == "__main__":
						main()






"""simple docstring"""







import os
import sys
import unittest


__A   :  Optional[Any]					=      os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__A   :  Optional[Any]					=      os.path.join(git_repo_path, 'src', 'diffusers')




class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				def       snake_case    (     self					:	str						):
						__lowercase				:		int												=				find_backend("    if not is_torch_available():"						)
						self.assertEqual(lowercase__    ,   "torch"						)

						# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
						# self.assertEqual(backend_with_underscore, "tensorflow_text")

						__lowercase				:		int												=				find_backend("    if not (is_torch_available() and is_transformers_available()):"						)
						self.assertEqual(lowercase__    ,   "torch_and_transformers"						)

						# double_backend_with_underscore = find_backend(
						#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
						# )
						# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

						__lowercase				:		str												=				find_backend(
						    "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"						)
						self.assertEqual(lowercase__    ,   "torch_and_transformers_and_onnx"						)



				def       snake_case    (     self					:	Any						):
						__lowercase				:		List[Any]												=				read_init()
						# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
						self.assertIn("torch"    ,   lowercase__						)
						self.assertIn("torch_and_transformers"    ,   lowercase__						)
						self.assertIn("flax_and_transformers"    ,   lowercase__						)
						self.assertIn("torch_and_transformers_and_onnx"    ,   lowercase__						)

						# Likewise, we can't assert on the exact content of a key
						self.assertIn("UNet2DModel"    ,   objects["torch"]						)
						self.assertIn("FlaxUNet2DConditionModel"    ,   objects["flax"]						)
						self.assertIn("StableDiffusionPipeline"    ,   objects["torch_and_transformers"]						)
						self.assertIn("FlaxStableDiffusionPipeline"    ,   objects["flax_and_transformers"]						)
						self.assertIn("LMSDiscreteScheduler"    ,   objects["torch_and_scipy"]						)
						self.assertIn("OnnxStableDiffusionPipeline"    ,   objects["torch_and_transformers_and_onnx"]						)



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Tuple												=				create_dummy_object("CONSTANT"    ,   "'torch'"						)
						self.assertEqual(lowercase__    ,   "\nCONSTANT = None\n"						)

						__lowercase				:		Union[str, Any]												=				create_dummy_object("function"    ,   "'torch'"						)
						self.assertEqual(
						    lowercase__    ,   "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"						)

						__lowercase				:		Tuple												=				"\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
						__lowercase				:		Dict												=				create_dummy_object("FakeClass"    ,   "'torch'"						)
						self.assertEqual(lowercase__    ,   lowercase__						)



				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		Optional[int]												=				"# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
						__lowercase				:		List[str]												=				create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}						)
						self.assertEqual(dummy_files["torch"]    ,   lowercase__						)
