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
  
  
  SCREAMING_SNAKE_CASE : List[Any]          =    logging.getLogger(__name__)
  
  
  
  
  
  @dataclass
  class 					snake_case__  :
  	UpperCAmelCase   :							str         = field(
  	    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}       )
  	UpperCAmelCase   :							Optional[str]         = field(
  	    default=__A      ,    metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}       )
  	UpperCAmelCase   :							Optional[str]         = field(
  	    default=__A      ,    metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}       )
  	UpperCAmelCase   :							Optional[str]         = field(
  	    default=__A      ,    metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,    )
  
  
  
  
  
  @dataclass
  class 					snake_case__  :
  	UpperCAmelCase   :							str         = field(
  	    metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()       )}       )
  	UpperCAmelCase   :							str         = field(
  	    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}       )
  	UpperCAmelCase   :							int         = field(
  	    default=1_28      ,    metadata={
  	        """help""": (
  	            """The maximum total input sequence length after tokenization. Sequences longer """
  	            """than this will be truncated, sequences shorter will be padded."""
  	        )
  	    }      ,    )
  	UpperCAmelCase   :							bool         = field(
  	    default=__A      ,    metadata={"""help""": """Overwrite the cached training and evaluation sets"""}       )
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: List[InputFeatures]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Optional[int]			=	default_data_collator(SCREAMING_SNAKE_CASE_      )
  							a_       :   Optional[int]			=	batch.pop("""pairID"""	,	SCREAMING_SNAKE_CASE_      )
  							return batch
  
  
  
  
  
  def 						_lowerCamelCase		(							):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   List[str]			=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
  							a_       :   Optional[int]			=	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"""	,	SCREAMING_SNAKE_CASE_      )
  
  							# Set seed
  							set_seed(training_args.seed      )
  
  							try:
  														a_       :   Dict			=	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_       :   Optional[Any]			=	AutoConfig.from_pretrained(
  							    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,	num_labels=SCREAMING_SNAKE_CASE_	,	finetuning_task=data_args.task_name	,	cache_dir=model_args.cache_dir	,	)
  							a_       :   int			=	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_       :   Tuple			=	AutoModelForSequenceClassification.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	,	)
  
  							# Get datasets
  							a_       :   Optional[int]			=	(
  							    HansDataset(
  							        data_dir=data_args.data_dir	,	tokenizer=SCREAMING_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_       :   Dict			=	(
  							    HansDataset(
  							        data_dir=data_args.data_dir	,	tokenizer=SCREAMING_SNAKE_CASE_	,	task=data_args.task_name	,	max_seq_length=data_args.max_seq_length	,	overwrite_cache=data_args.overwrite_cache	,	evaluate=SCREAMING_SNAKE_CASE_	,	)
  							    if training_args.do_eval
  							    else None
  							)
  
  							# Initialize our Trainer
  							a_       :   Any			=	Trainer(
  							    model=SCREAMING_SNAKE_CASE_	,	args=SCREAMING_SNAKE_CASE_	,	train_dataset=SCREAMING_SNAKE_CASE_	,	eval_dataset=SCREAMING_SNAKE_CASE_	,	data_collator=SCREAMING_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_       :   Any			=	trainer.predict(SCREAMING_SNAKE_CASE_      )
  														a_       :   str			=	output.predictions
  														a_       :   str			=	np.argmax(SCREAMING_SNAKE_CASE_	,	axis=1      )
  
  														a_       :   Optional[int]			=	[ex.pairID for ex in eval_dataset]
  														a_       :   int			=	os.path.join(training_args.output_dir	,	"""hans_predictions.txt"""      )
  														a_       :   Any			=	eval_dataset.get_labels()
  														if trainer.is_world_master():
  																					with open(SCREAMING_SNAKE_CASE_	,	"""w"""      ) as writer:
  																												writer.write("""pairID,gold_label\n"""      )
  																												for pid, pred in zip(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      ):
  																																			writer.write("""ex""" + str(SCREAMING_SNAKE_CASE_      ) + """,""" + label_list[int(SCREAMING_SNAKE_CASE_      )] + """\n"""      )
  
  														trainer._log(output.metrics      )
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Dict      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							main()
  
  
  if __name__ == "__main__":
  	main()
  
  
  
style_context: 
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  SCREAMING_SNAKE_CASE : Dict          =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE : str          =    {
      "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
      "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
      "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
      "apple/deeplabv3-mobilevit-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
      ),
      "apple/deeplabv3-mobilevit-x-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
      ),
      "apple/deeplabv3-mobilevit-xx-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
      ),
      # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
  }
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							List[str]         = """mobilevit"""
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_=3				,    UpperCamelCase_=256				,    UpperCamelCase_=2				,    UpperCamelCase_=[144, 192, 240]				,    UpperCamelCase_=[16, 32, 64, 96, 128, 160, 640]				,    UpperCamelCase_=4				,    UpperCamelCase_=2.0				,    UpperCamelCase_=4.0				,    UpperCamelCase_="silu"				,    UpperCamelCase_=3				,    UpperCamelCase_=32				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.0				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.02				,    UpperCamelCase_=1e-5				,    UpperCamelCase_=True				,    UpperCamelCase_=256				,    UpperCamelCase_=[6, 12, 18]				,    UpperCamelCase_=0.1				,    UpperCamelCase_=255				,    **UpperCamelCase_				,    )		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								super().__init__(**UpperCamelCase_							)
  
  								a_       :   Optional[int]			=	num_channels
  								a_       :   Optional[Any]			=	image_size
  								a_       :   int			=	patch_size
  								a_       :   List[str]			=	hidden_sizes
  								a_       :   Optional[int]			=	neck_hidden_sizes
  								a_       :   List[Any]			=	num_attention_heads
  								a_       :   Union[str, Any]			=	mlp_ratio
  								a_       :   List[str]			=	expand_ratio
  								a_       :   str			=	hidden_act
  								a_       :   Union[str, Any]			=	conv_kernel_size
  								a_       :   Optional[Any]			=	output_stride
  								a_       :   Union[str, Any]			=	hidden_dropout_prob
  								a_       :   List[Any]			=	attention_probs_dropout_prob
  								a_       :   List[Any]			=	classifier_dropout_prob
  								a_       :   Union[str, Any]			=	initializer_range
  								a_       :   List[Any]			=	layer_norm_eps
  								a_       :   str			=	qkv_bias
  
  								# decode head attributes for semantic segmentation
  								a_       :   Optional[Any]			=	aspp_out_channels
  								a_       :   Any			=	atrous_rates
  								a_       :   Union[str, Any]			=	aspp_dropout_prob
  								a_       :   Any			=	semantic_loss_ignore_index
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							Optional[int]         = version.parse("""1.11"""       )
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]							)
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if self.task == "image-classification":
  															return OrderedDict([("""logits""", {0: """batch"""})]							)
  								else:
  															return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]							)
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			float:
  								"""simple docstring"""
  
  
  
  
  
  
  								return 1e-4
  
  
  