code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import logging
  import os
  from dataclasses import dataclass, field
  from typing import Dict, List, Optional
  
  import numpy as np
  import torch
  from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels
  
  import transformers
  from transformers import (
      AutoConfig,
      AutoModelForSequenceClassification,
      AutoTokenizer,
      HfArgumentParser,
      Trainer,
      TrainingArguments,
      default_data_collator,
      set_seed,
  )
  from transformers.trainer_utils import is_main_process
  
  
  __lowerCamelCase       :       Dict				   =			logging.getLogger(__name__)
  
  
  
  @dataclass
  class 			a      :
  		__lowercase													=		field(
  		    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
  		__lowercase													=		field(
  		    default=UpperCamelCase_      ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
  		__lowercase													=		field(
  		    default=UpperCamelCase_      ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
  		__lowercase													=		field(
  		    default=UpperCamelCase_      ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,)
  
  
  
  
  
  
  
  @dataclass
  class 			a      :
  		__lowercase													=		field(
  		    metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()   )}   )
  		__lowercase													=		field(
  		    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}   )
  		__lowercase													=		field(
  		    default=128      ,metadata={
  		        """help""": (
  		            """The maximum total input sequence length after tokenization. Sequences longer """
  		            """than this will be truncated, sequences shorter will be padded."""
  		        )
  		    }      ,)
  		__lowercase													=		field(
  		    default=UpperCamelCase_      ,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )
  
  
  
  
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Dict[str, torch.Tensor]:
  				A__     :							str   				=default_data_collator(snake_case_	)
  				A__     :							Optional[Any]   				=batch.pop('''pairID''',	snake_case_	)
  				return batch
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							)			->							Optional[int]:
  				# 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.
  
  				A__     :							Union[str, Any]   				=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
  				A__     :							Tuple   				=parser.parse_args_into_dataclasses()
  
  				if (
  				    os.path.exists(training_args.output_dir	)
  				    and os.listdir(training_args.output_dir	)
  				    and training_args.do_train
  				    and not training_args.overwrite_output_dir
  				):
  								raise ValueError(
  								    f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
  								    ''' --overwrite_output_dir to overcome.'''	)
  
  				# Setup logging
  				logging.basicConfig(
  				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',	datefmt='''%m/%d/%Y %H:%M:%S''',	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,	)
  				logger.warning(
  				    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''',	training_args.local_rank,	training_args.device,	training_args.n_gpu,	bool(training_args.local_rank != -1	),	training_args.fpaa,	)
  				# Set the verbosity to info of the Transformers logger (on main process only):
  				if is_main_process(training_args.local_rank	):
  								transformers.utils.logging.set_verbosity_info()
  								transformers.utils.logging.enable_default_handler()
  								transformers.utils.logging.enable_explicit_format()
  				logger.info('''Training/evaluation parameters %s''',	snake_case_	)
  
  				# Set seed
  				set_seed(training_args.seed	)
  
  				try:
  								A__     :							Optional[int]   				=hans_tasks_num_labels[data_args.task_name]
  				except KeyError:
  								raise ValueError('''Task not found: %s''' % (data_args.task_name)	)
  
  				# Load pretrained model and tokenizer
  				#
  				# Distributed training:
  				# The .from_pretrained methods guarantee that only one local process can concurrently
  				# download model & vocab.
  
  				A__     :							str   				=AutoConfig.from_pretrained(
  				    model_args.config_name if model_args.config_name else model_args.model_name_or_path,	num_labels=snake_case_,	finetuning_task=data_args.task_name,	cache_dir=model_args.cache_dir,	)
  				A__     :							Union[str, Any]   				=AutoTokenizer.from_pretrained(
  				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,	cache_dir=model_args.cache_dir,	)
  				A__     :							int   				=AutoModelForSequenceClassification.from_pretrained(
  				    model_args.model_name_or_path,	from_tf=bool('''.ckpt''' in model_args.model_name_or_path	),	config=snake_case_,	cache_dir=model_args.cache_dir,	)
  
  				# Get datasets
  				A__     :							List[Any]   				=(
  				    HansDataset(
  				        data_dir=data_args.data_dir,	tokenizer=snake_case_,	task=data_args.task_name,	max_seq_length=data_args.max_seq_length,	overwrite_cache=data_args.overwrite_cache,	)
  				    if training_args.do_train
  				    else None
  				)
  				A__     :							List[str]   				=(
  				    HansDataset(
  				        data_dir=data_args.data_dir,	tokenizer=snake_case_,	task=data_args.task_name,	max_seq_length=data_args.max_seq_length,	overwrite_cache=data_args.overwrite_cache,	evaluate=snake_case_,	)
  				    if training_args.do_eval
  				    else None
  				)
  
  				# Initialize our Trainer
  				A__     :							Optional[Any]   				=Trainer(
  				    model=snake_case_,	args=snake_case_,	train_dataset=snake_case_,	eval_dataset=snake_case_,	data_collator=snake_case_,	)
  
  				# Training
  				if training_args.do_train:
  								trainer.train(
  								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
  								trainer.save_model()
  								# For convenience, we also re-save the tokenizer to the same directory,
  								# so that you can share your model easily on huggingface.co/models =)
  								if trainer.is_world_master():
  												tokenizer.save_pretrained(training_args.output_dir	)
  
      # Evaluation
  				if training_args.do_eval:
  								logger.info('''*** Evaluate ***'''	)
  
  								A__     :							Optional[Any]   				=trainer.predict(snake_case_	)
  								A__     :							Dict   				=output.predictions
  								A__     :							Optional[int]   				=np.argmax(snake_case_,	axis=1	)
  
  								A__     :							Dict   				=[ex.pairID for ex in eval_dataset]
  								A__     :							Tuple   				=os.path.join(training_args.output_dir,	'''hans_predictions.txt'''	)
  								A__     :							List[Any]   				=eval_dataset.get_labels()
  								if trainer.is_world_master():
  												with open(snake_case_,	'''w'''	) as writer:
  																writer.write('''pairID,gold_label\n'''	)
  																for pid, pred in zip(snake_case_,	snake_case_	):
  																				writer.write('''ex''' + str(snake_case_	) + ''',''' + label_list[int(snake_case_	)] + '''\n'''	)
  
  								trainer._log(output.metrics	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Union[str, Any]:
  				# For xla_spawn (TPUs)
  				main()
  
  
  if __name__ == "__main__":
  						main()
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  @dataclass
  class 			a      (      UpperCamelCase_   ):
  		__lowercase													=		{
  		    """sld_warmup_steps""": 15,
  		    """sld_guidance_scale""": 20,
  		    """sld_threshold""": 0.0,
  		    """sld_momentum_scale""": 0.0,
  		    """sld_mom_beta""": 0.0,
  		}
  		__lowercase													=		{
  		    """sld_warmup_steps""": 10,
  		    """sld_guidance_scale""": 1_000,
  		    """sld_threshold""": 0.01,
  		    """sld_momentum_scale""": 0.3,
  		    """sld_mom_beta""": 0.4,
  		}
  		__lowercase													=		{
  		    """sld_warmup_steps""": 7,
  		    """sld_guidance_scale""": 2_000,
  		    """sld_threshold""": 0.0_25,
  		    """sld_momentum_scale""": 0.5,
  		    """sld_mom_beta""": 0.7,
  		}
  		__lowercase													=		{
  		    """sld_warmup_steps""": 0,
  		    """sld_guidance_scale""": 5_000,
  		    """sld_threshold""": 1.0,
  		    """sld_momentum_scale""": 0.5,
  		    """sld_mom_beta""": 0.7,
  		}
  
  
  
  
  
  
  
  @dataclass
  class 			a      (      UpperCamelCase_   ):
  		__lowercase													=		42
  		__lowercase													=		42
  		__lowercase													=		42
  		__lowercase													=		42
  
  
  if is_transformers_available() and is_torch_available():
  						from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
  						from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
  