code: 
  
  
  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__				,  A__				,  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: 
  
  
  import argparse
  import copy
  import json
  import multiprocessing
  import os
  import time
  
  from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
  from huggingface_hub import ModelFilter, hf_api
  
  import transformers
  from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
  from transformers.image_processing_utils import BaseImageProcessor
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							)			->							Tuple:
  				A__     :							List[Any]   				=set()
  				# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
  				for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
  								A__     :							Optional[int]   				=getattr(transformers.models.auto,	snake_case_,	snake_case_	)
  								if module is None:
  												continue
  								# all mappings in a single auto modeling file
  								A__     :							Optional[int]   				=[
  								    x
  								    for x in dir(snake_case_	)
  								    if x.endswith('''_MAPPING_NAMES'''	)
  								    and (x.startswith('''MODEL_'''	) or x.startswith('''TF_MODEL_'''	) or x.startswith('''FLAX_MODEL_'''	))
  								]
  								for name in mapping_names:
  												A__     :							Any   				=getattr(snake_case_,	snake_case_	)
  												if mapping is not None:
  																for v in mapping.values():
  																				if isinstance(snake_case_,	(list, tuple)	):
  																								model_names.update(snake_case_	)
  																				elif isinstance(snake_case_,	snake_case_	):
  																								model_names.add(snake_case_	)
  
  				return sorted(snake_case_	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							)			->							Optional[Any]:
  				# All model names defined in auto mappings
  				A__     :							int   				=set(get_all_model_names()	)
  
  				with open('''tests/utils/tiny_model_summary.json'''	) as fp:
  								A__     :							Union[str, Any]   				=json.load(snake_case_	)
  				A__     :							int   				=set()
  				for model_base_name in tiny_model_info:
  								tiny_models_names.update(tiny_model_info[model_base_name]['''model_classes''']	)
  
  				# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
  				A__     :							Optional[Any]   				=model_names.difference(snake_case_	)
  				for model_name in copy.copy(snake_case_	):
  								if not model_name.startswith('''TF'''	) and f'TF{model_name}' in not_on_hub:
  												tiny_models_names.remove(snake_case_	)
  								elif model_name.startswith('''TF'''	) and model_name[2:] in not_on_hub:
  												tiny_models_names.remove(snake_case_	)
  
  				return sorted(snake_case_	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Tuple:
  				A__     :							Dict   				=COMPOSITE_MODELS.values()
  
  				# All tiny model base names on Hub
  				A__     :							Union[str, Any]   				=get_all_model_names()
  				A__     :							Optional[Any]   				=hf_api.list_models(
  				    filter=ModelFilter(
  				        author='''hf-internal-testing''',	)	)
  				A__     :							str   				=set()
  				for x in models:
  								A__     :							List[Any]   				=x.modelId
  								A__				,  A__     :							List[str]   				=model.split('''/'''	)
  								if not model.startswith('''tiny-random-'''	):
  												continue
  								A__     :							str   				=model.replace('''tiny-random-''',	''''''	)
  								if not model[0].isupper():
  												continue
  								if model not in model_names and model not in special_models:
  												continue
  								_models.add(snake_case_	)
  
  				A__     :							Dict   				=sorted(_models	)
  				# All tiny model names on Hub
  				A__     :							Optional[int]   				={}
  				for model in models:
  								A__     :							Optional[int]   				=f'hf-internal-testing/tiny-random-{model}'
  								A__     :							str   				=model.split('''-'''	)[0]
  								try:
  												A__     :							int   				=hf_api.repo_info(snake_case_	)
  												A__     :							Optional[int]   				={
  												    '''tokenizer_classes''': set(),
  												    '''processor_classes''': set(),
  												    '''model_classes''': set(),
  												    '''sha''': repo_info.sha,
  												}
  								except Exception:
  												continue
  								try:
  												time.sleep(1	)
  												A__     :							List[str]   				=AutoTokenizer.from_pretrained(snake_case_	)
  												content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__	)
  								except Exception:
  												pass
  								try:
  												time.sleep(1	)
  												A__     :							int   				=AutoTokenizer.from_pretrained(snake_case_,	use_fast=snake_case_	)
  												content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__	)
  								except Exception:
  												pass
  								try:
  												time.sleep(1	)
  												A__     :							int   				=AutoImageProcessor.from_pretrained(snake_case_	)
  												content["processor_classes"].add(img_p.__class__.__name__	)
  								except Exception:
  												pass
  								try:
  												time.sleep(1	)
  												A__     :							Optional[int]   				=AutoFeatureExtractor.from_pretrained(snake_case_	)
  												if not isinstance(snake_case_,	snake_case_	):
  																content["processor_classes"].add(feat_p.__class__.__name__	)
  								except Exception:
  												pass
  								try:
  												time.sleep(1	)
  												A__     :							Tuple   				=getattr(snake_case_,	snake_case_	)
  												A__     :							Tuple   				=model_class.from_pretrained(snake_case_	)
  												content["model_classes"].add(m.__class__.__name__	)
  								except Exception:
  												pass
  								try:
  												time.sleep(1	)
  												A__     :							int   				=getattr(snake_case_,	f'TF{model}'	)
  												A__     :							Union[str, Any]   				=model_class.from_pretrained(snake_case_	)
  												content["model_classes"].add(m.__class__.__name__	)
  								except Exception:
  												pass
  
  								A__     :							List[str]   				=sorted(content['''tokenizer_classes''']	)
  								A__     :							Tuple   				=sorted(content['''processor_classes''']	)
  								A__     :							Optional[Any]   				=sorted(content['''model_classes''']	)
  
  								A__     :							List[Any]   				=content
  								with open(os.path.join(snake_case_,	'''hub_tiny_model_summary.json'''	),	'''w'''	) as fp:
  												json.dump(snake_case_,	snake_case_,	ensure_ascii=snake_case_,	indent=4	)
  
  
  if __name__ == "__main__":
  						__lowerCamelCase       :       Any				   =			argparse.ArgumentParser()
  						parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
  						__lowerCamelCase       :       int				   =			parser.parse_args()
  
  						# This has to be `spawn` to avoid hanging forever!
  						multiprocessing.set_start_method("spawn")
  
  						__lowerCamelCase       :       Any				   =			"tiny_models"
  						__lowerCamelCase       :       Optional[int]				   =			True
  						__lowerCamelCase       :       int				   =			None
  						__lowerCamelCase       :       str				   =			get_tiny_model_names_from_repo()
  						__lowerCamelCase       :       List[Any]				   =			True
  						__lowerCamelCase       :       List[Any]				   =			True
  						__lowerCamelCase       :       Optional[Any]				   =			"hf-internal-testing"
  
  						create_tiny_models(
  						    output_path,
  						    all,
  						    model_types,
  						    models_to_skip,
  						    no_check,
  						    upload,
  						    organization,
  						    token=os.environ.get("TOKEN", None),
  						    num_workers=args.num_workers,
  						)
  
  
  
  