
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

lowerCamelCase__     :		str        = logging.getLogger(__name__)



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  Optional[int]	   =      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.'
         )
     }	,    )
 __lowerCAmelCase	:  bool	   =      field(
     default=UpperCAmelCase_	,    metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}				)
 __lowerCAmelCase	:  bool	   =      field(
     default=UpperCAmelCase_	,    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.'
         )
     }	,    )
 __lowerCAmelCase	:  Optional[int]	   =      field(
     default=UpperCAmelCase_	,    metadata={
         'help': (
             'For debugging purposes or quicker training, truncate the number of training examples to this '
             'value if set.'
         )
     }	,    )
 __lowerCAmelCase	:  Optional[int]	   =      field(
     default=UpperCAmelCase_	,    metadata={
         'help': (
             'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
             'value if set.'
         )
     }	,    )
 __lowerCAmelCase	:  Optional[int]	   =      field(
     default=UpperCAmelCase_	,    metadata={
         'help': (
             'For debugging purposes or quicker training, truncate the number of prediction examples to this '
             'value if set.'
         )
     }	,    )



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  str	   =      field(
     default=UpperCAmelCase_	,    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}				)
 __lowerCAmelCase	:  str	   =      field(
     default=UpperCAmelCase_	,    metadata={'help': 'Evaluation language. Also train language if `train_language` is set to None.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default=UpperCAmelCase_	,    metadata={'help': 'Train language if it is different from the evaluation language.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default=UpperCAmelCase_	,    metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default=UpperCAmelCase_	,    metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default=UpperCAmelCase_	,    metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,    )
 __lowerCAmelCase	:  Optional[bool]	   =      field(
     default=UpperCAmelCase_	,    metadata={'help': 'arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'}	,    )
 __lowerCAmelCase	:  bool	   =      field(
     default=UpperCAmelCase_	,    metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}	,    )
 __lowerCAmelCase	:  str	   =      field(
     default='main'	,    metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}	,    )
 __lowerCAmelCase	:  bool	   =      field(
     default=UpperCAmelCase_	,    metadata={
         'help': (
             'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
             'with private models).'
         )
     }	,    )
 __lowerCAmelCase	:  bool	   =      field(
     default=UpperCAmelCase_	,    metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}	,    )

def 		UpperCamelCase						(   )		->						Union[str, Any]:


  '''simple docstring'''






  lowercase__	: Union[str, Any]     		=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
  lowercase__		,     lowercase__		,     lowercase__	: Optional[int]     		=   parser.parse_args_into_dataclasses()

  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry("""run_xnli"""			,			lowercase_ )

  # 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 )]			,			)

  if training_args.should_log:
    # The default of training_args.log_level is passive, so we set log level at info here to have that default.
    transformers.utils.logging.set_verbosity_info()

  lowercase__	: int     		=   training_args.get_process_log_level()
  logger.setLevel(lowercase_ )
  datasets.utils.logging.set_verbosity(lowercase_ )
  transformers.utils.logging.set_verbosity(lowercase_ )
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()

  # 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}' )
  logger.info(F'Training/evaluation parameters {training_args}' )

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

    # Set seed before initializing model.
  set_seed(training_args.seed )

  # In distributed training, the load_dataset function guarantees that only one local process can concurrently
  # download the dataset.
  # Downloading and loading xnli dataset from the hub.
  if training_args.do_train:
    if model_args.train_language is None:
      lowercase__	: Union[str, Any]     		=   load_dataset(
          """xnli"""			,			model_args.language			,			split="""train"""			,			cache_dir=model_args.cache_dir			,			use_auth_token=True if model_args.use_auth_token else None			,			)
    else:
      lowercase__	: Dict     		=   load_dataset(
          """xnli"""			,			model_args.train_language			,			split="""train"""			,			cache_dir=model_args.cache_dir			,			use_auth_token=True if model_args.use_auth_token else None			,			)
    lowercase__	: List[str]     		=   train_dataset.features["""label"""].names

  if training_args.do_eval:
    lowercase__	: Optional[Any]     		=   load_dataset(
        """xnli"""			,			model_args.language			,			split="""validation"""			,			cache_dir=model_args.cache_dir			,			use_auth_token=True if model_args.use_auth_token else None			,			)
    lowercase__	: Tuple     		=   eval_dataset.features["""label"""].names

  if training_args.do_predict:
    lowercase__	: List[Any]     		=   load_dataset(
        """xnli"""			,			model_args.language			,			split="""test"""			,			cache_dir=model_args.cache_dir			,			use_auth_token=True if model_args.use_auth_token else None			,			)
    lowercase__	: Tuple     		=   predict_dataset.features["""label"""].names

  # Labels
  lowercase__	: List[str]     		=   len(lowercase_ )

  # Load pretrained model and tokenizer
  # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  lowercase__	: List[str]     		=   AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path			,			num_labels=lowercase_			,			idalabel={str(lowercase_ ): label for i, label in enumerate(lowercase_ )}			,			labelaid={label: i for i, label in enumerate(lowercase_ )}			,			finetuning_task="""xnli"""			,			cache_dir=model_args.cache_dir			,			revision=model_args.model_revision			,			use_auth_token=True if model_args.use_auth_token else None			,			)
  lowercase__	: str     		=   AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,			do_lower_case=model_args.do_lower_case			,			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			,			)
  lowercase__	: Optional[Any]     		=   AutoModelForSequenceClassification.from_pretrained(
      model_args.model_name_or_path			,			from_tf=bool(""".ckpt""" in model_args.model_name_or_path )			,			config=lowercase_			,			cache_dir=model_args.cache_dir			,			revision=model_args.model_revision			,			use_auth_token=True if model_args.use_auth_token else None			,			ignore_mismatched_sizes=model_args.ignore_mismatched_sizes			,			)

  # Preprocessing the datasets
  # Padding strategy
  if data_args.pad_to_max_length:
    lowercase__	: str     		=   """max_length"""
  else:
    # We will pad later, dynamically at batch creation, to the max sequence length in each batch
    lowercase__	: Any     		=   False

  def preprocess_function(lowercase_ ):
    # Tokenize the texts
    return tokenizer(
        examples["""premise"""]			,			examples["""hypothesis"""]			,			padding=lowercase_			,			max_length=data_args.max_seq_length			,			truncation=lowercase_			,			)

  if training_args.do_train:
    if data_args.max_train_samples is not None:
      lowercase__	: int     		=   min(len(lowercase_ )			,			data_args.max_train_samples )
      lowercase__	: Optional[Any]     		=   train_dataset.select(range(lowercase_ ) )
    with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
      lowercase__	: Any     		=   train_dataset.map(
          lowercase_			,			batched=lowercase_			,			load_from_cache_file=not data_args.overwrite_cache			,			desc="""Running tokenizer on train dataset"""			,			)
    # Log a few random samples from the training set:
    for index in random.sample(range(len(lowercase_ ) )			,			3 ):
      logger.info(F'Sample {index} of the training set: {train_dataset[index]}.' )

  if training_args.do_eval:
    if data_args.max_eval_samples is not None:
      lowercase__	: int     		=   min(len(lowercase_ )			,			data_args.max_eval_samples )
      lowercase__	: Union[str, Any]     		=   eval_dataset.select(range(lowercase_ ) )
    with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
      lowercase__	: Dict     		=   eval_dataset.map(
          lowercase_			,			batched=lowercase_			,			load_from_cache_file=not data_args.overwrite_cache			,			desc="""Running tokenizer on validation dataset"""			,			)

  if training_args.do_predict:
    if data_args.max_predict_samples is not None:
      lowercase__	: int     		=   min(len(lowercase_ )			,			data_args.max_predict_samples )
      lowercase__	: Optional[int]     		=   predict_dataset.select(range(lowercase_ ) )
    with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ):
      lowercase__	: List[Any]     		=   predict_dataset.map(
          lowercase_			,			batched=lowercase_			,			load_from_cache_file=not data_args.overwrite_cache			,			desc="""Running tokenizer on prediction dataset"""			,			)

    # Get the metric function
  lowercase__	: Union[str, Any]     		=   evaluate.load("""xnli""" )

  # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
  # predictions and label_ids field) and has to return a dictionary string to float.
  def compute_metrics(lowercase_ ):
    lowercase__	: str     		=   p.predictions[0] if isinstance(p.predictions			,			lowercase_ ) else p.predictions
    lowercase__	: Optional[int]     		=   np.argmax(lowercase_			,			axis=1 )
    return metric.compute(predictions=lowercase_			,			references=p.label_ids )

  # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
  if data_args.pad_to_max_length:
    lowercase__	: List[str]     		=   default_data_collator
  elif training_args.fpaa:
    lowercase__	: Any     		=   DataCollatorWithPadding(lowercase_			,			pad_to_multiple_of=8 )
  else:
    lowercase__	: Optional[Any]     		=   None

  # Initialize our Trainer
  lowercase__	: List[Any]     		=   Trainer(
      model=lowercase_			,			args=lowercase_			,			train_dataset=train_dataset if training_args.do_train else None			,			eval_dataset=eval_dataset if training_args.do_eval else None			,			compute_metrics=lowercase_			,			tokenizer=lowercase_			,			data_collator=lowercase_			,			)

  # Training
  if training_args.do_train:
    lowercase__	: str     		=   None
    if training_args.resume_from_checkpoint is not None:
      lowercase__	: Tuple     		=   training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      lowercase__	: Optional[int]     		=   last_checkpoint
    lowercase__	: str     		=   trainer.train(resume_from_checkpoint=lowercase_ )
    lowercase__	: Union[str, Any]     		=   train_result.metrics
    lowercase__	: Optional[Any]     		=   (
        data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase_ )
    )
    lowercase__	: str     		=   min(lowercase_			,			len(lowercase_ ) )

    trainer.save_model()  # Saves the tokenizer too for easy upload

    trainer.log_metrics("""train"""			,			lowercase_ )
    trainer.save_metrics("""train"""			,			lowercase_ )
    trainer.save_state()

  # Evaluation
  if training_args.do_eval:
    logger.info("""*** Evaluate ***""" )
    lowercase__	: Union[str, Any]     		=   trainer.evaluate(eval_dataset=lowercase_ )

    lowercase__	: Dict     		=   data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase_ )
    lowercase__	: int     		=   min(lowercase_			,			len(lowercase_ ) )

    trainer.log_metrics("""eval"""			,			lowercase_ )
    trainer.save_metrics("""eval"""			,			lowercase_ )

  # Prediction
  if training_args.do_predict:
    logger.info("""*** Predict ***""" )
    lowercase__		,     lowercase__		,     lowercase__	: Optional[int]     		=   trainer.predict(lowercase_			,			metric_key_prefix="""predict""" )

    lowercase__	: str     		=   (
        data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowercase_ )
    )
    lowercase__	: Tuple     		=   min(lowercase_			,			len(lowercase_ ) )

    trainer.log_metrics("""predict"""			,			lowercase_ )
    trainer.save_metrics("""predict"""			,			lowercase_ )

    lowercase__	: str     		=   np.argmax(lowercase_			,			axis=1 )
    lowercase__	: Any     		=   os.path.join(training_args.output_dir			,			"""predictions.txt""" )
    if trainer.is_world_process_zero():
      with open(lowercase_			,			"""w""" ) as writer:
        writer.write("""index\tprediction\n""" )
        for index, item in enumerate(lowercase_ ):
          lowercase__	: Any     		=   label_list[item]
          writer.write(F'{index}\t{item}\n' )


if __name__ == "__main__":
     main()








from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Any	   =      ['image_processor', 'tokenizer']
 __lowerCAmelCase	:  Union[str, Any]	   =      'AutoImageProcessor'
 __lowerCAmelCase	:  int	   =      'AutoTokenizer'


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Union[str, Any]     		=   self.image_processor


 def __call__(       self       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if text is None and images is None:
     raise ValueError("""You have to specify either text or images. Both cannot be none.""")

   if text is not None:
     lowercase__	: List[str]     		=   self.tokenizer(SCREAMING_SNAKE_CASE_       ,	return_tensors=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)

   if images is not None:
     lowercase__	: Optional[int]     		=   self.image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)

   if text is not None and images is not None:
     lowercase__	: Union[str, Any]     		=   image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_)       ,	tensor_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)



 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return ["input_ids", "attention_mask", "pixel_values"]






