



"""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


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



@dataclass
class    _lowerCAmelCase    :





		"""simple docstring"""






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





		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								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"""






		_lowerCamelCase			  =       field(
		    default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}		)
		_lowerCamelCase			  =       field(
		    default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}		)
		_lowerCamelCase			  =       field(default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''The input training data file (a text file).'''}		)
		_lowerCamelCase			  =       field(
		    default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}  ,				)
		_lowerCamelCase			  =       field(
		    default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}  ,				)
		_lowerCamelCase			  =       field(
		    default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}  ,				)
		_lowerCamelCase			  =       field(
		    default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)
		_lowerCamelCase			  =       field(
		    default=5  ,				metadata={
		        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
		    }  ,				)
		_lowerCamelCase			  =       field(
		    default=SCREAMING_SNAKE_CASE__  ,				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.'''
		        )
		    }  ,				)
		_lowerCamelCase			  =       field(
		    default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''The number of processes to use for the preprocessing.'''}  ,				)
		_lowerCamelCase			  =       field(
		    default=0.15  ,				metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}		)
		_lowerCamelCase			  =       field(
		    default=SCREAMING_SNAKE_CASE__  ,				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    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

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




def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[Any]					, __UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






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

						snake_case_   : str						=			{c: dataset[c] for c in dataset.column_names}
						snake_case_   : Dict						=			refs
						return Dataset.from_dict(__UpperCamelCase    )
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






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

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

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

						snake_case_   : 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:
												snake_case_   : Union[str, Any]						=			AutoTokenizer.from_pretrained(model_args.tokenizer_name					, **__UpperCamelCase    )
						elif model_args.model_name_or_path:
												snake_case_   : Optional[Any]						=			AutoTokenizer.from_pretrained(model_args.model_name_or_path					, **__UpperCamelCase    )
						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_   : Optional[int]						=			AutoModelForMaskedLM.from_pretrained(
												    model_args.model_name_or_path					, from_tf=bool(""".ckpt""" in model_args.model_name_or_path    )					, config=__UpperCamelCase					, 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_   : Dict						=			AutoModelForMaskedLM.from_config(__UpperCamelCase    )

						model.resize_token_embeddings(len(__UpperCamelCase    )    )

						# 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_   : List[str]						=			datasets["""validation"""].column_names
						snake_case_   : List[str]						=			"""text""" if """text""" in column_names else column_names[0]

						snake_case_   : Optional[Any]						=			"""max_length""" if data_args.pad_to_max_length else False

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

						snake_case_   : Optional[int]						=			datasets.map(
						    __UpperCamelCase					, batched=__UpperCamelCase					, 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_   : Union[str, Any]						=			add_chinese_references(tokenized_datasets["""train"""]					, data_args.train_ref_file    )
						if data_args.validation_ref_file is not None:
												snake_case_   : Optional[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_   : List[str]						=			data_args.train_ref_file or data_args.validation_ref_file
						if has_ref:
												snake_case_   : Union[str, Any]						=			False

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

						# Initialize our Trainer
						snake_case_   : Optional[int]						=			Trainer(
						    model=__UpperCamelCase					, args=__UpperCamelCase					, 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=__UpperCamelCase					, data_collator=__UpperCamelCase					, )

						# Training
						if training_args.do_train:
												if last_checkpoint is not None:
																		snake_case_   : 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_   : List[str]						=			model_args.model_name_or_path
												else:
																		snake_case_   : Optional[int]						=			None
												snake_case_   : Any						=			trainer.train(resume_from_checkpoint=__UpperCamelCase    )
												trainer.save_model()  # Saves the tokenizer too for easy upload

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

												snake_case_   : str						=			trainer.evaluate()

												snake_case_   : Tuple						=			math.exp(eval_output["""eval_loss"""]    )
												snake_case_   : int						=			perplexity

												snake_case_   : str						=			os.path.join(training_args.output_dir					, """eval_results_mlm_wwm.txt"""    )
												if trainer.is_world_process_zero():
																		with open(__UpperCamelCase					, """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 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						main()


if __name__ == "__main__":
					main()






"""simple docstring"""





import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


__lowerCAmelCase       :					Optional[int]      					=					argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
__lowerCAmelCase       :					Optional[Any]      					=					parser.parse_args()


__lowerCAmelCase       :					Dict      					=					'''cpu'''
__lowerCAmelCase       :					Optional[Any]      					=					'''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''

__lowerCAmelCase       :					Tuple      					=					'''path-to-your-trained-model'''
__lowerCAmelCase       :					List[Any]      					=					StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
					__lowerCAmelCase       :					Optional[int]      					=					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
__lowerCAmelCase       :					List[Any]      					=					pipe.to(device)

# to channels last
__lowerCAmelCase       :					Optional[Any]      					=					pipe.unet.to(memory_format=torch.channels_last)
__lowerCAmelCase       :					List[str]      					=					pipe.vae.to(memory_format=torch.channels_last)
__lowerCAmelCase       :					Optional[Any]      					=					pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
					__lowerCAmelCase       :					Dict      					=					pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
__lowerCAmelCase       :					Tuple      					=					torch.randn(2, 4, 64, 64)
__lowerCAmelCase       :					Any      					=					torch.rand(1) * 999
__lowerCAmelCase       :					List[str]      					=					torch.randn(2, 77, 768)
__lowerCAmelCase       :					Optional[int]      					=					(sample, timestep, encoder_hidden_status)
try:
					__lowerCAmelCase       :					List[Any]      					=					ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
					__lowerCAmelCase       :					Optional[Any]      					=					ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
__lowerCAmelCase       :					Any      					=					ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
__lowerCAmelCase       :					int      					=					ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
					__lowerCAmelCase       :					Union[str, Any]      					=					ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
__lowerCAmelCase       :					List[str]      					=					666
__lowerCAmelCase       :					Optional[int]      					=					torch.Generator(device).manual_seed(seed)
__lowerCAmelCase       :					List[Any]      					=					{'''generator''': generator}
if args.steps is not None:
					__lowerCAmelCase       :					Any      					=					args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
					__lowerCAmelCase       :					str      					=					pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('''generated.png''')

