




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_		      = logging.getLogger(__name__)
@dataclass
class 						_A					:
  _UpperCamelCase   :			Optional[int] =			field(
      default=1_2_8			,   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=lowercase__			,   metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}      )
  _UpperCamelCase   :			bool =			field(
      default=lowercase__			,   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.'''
          )
      }			,   )
  _UpperCamelCase   :			Optional[int] =			field(
      default=lowercase__			,   metadata={
          '''help''': (
              '''For debugging purposes or quicker training, truncate the number of training examples to this '''
              '''value if set.'''
          )
      }			,   )
  _UpperCamelCase   :			Optional[int] =			field(
      default=lowercase__			,   metadata={
          '''help''': (
              '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
              '''value if set.'''
          )
      }			,   )
  _UpperCamelCase   :			Optional[int] =			field(
      default=lowercase__			,   metadata={
          '''help''': (
              '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
              '''value if set.'''
          )
      }			,   )


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






def 					snake_case(				) ->							int:





     '''simple docstring'''


     lowercase					:   Any								= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
     lowercase					:   Tuple								= 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''' ,		__lowerCamelCase    )

     # 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					:   List[Any]								= training_args.get_process_log_level()
     logger.setLevel(__lowerCamelCase    )
     datasets.utils.logging.set_verbosity(__lowerCamelCase    )
     transformers.utils.logging.set_verbosity(__lowerCamelCase    )
     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					:   Dict								= 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					:   Tuple								= 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					:   List[Any]								= 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					:   str								= train_dataset.features["""label"""].names

     if training_args.do_eval:
          lowercase					:   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					:   str								= eval_dataset.features["""label"""].names

     if training_args.do_predict:
          lowercase					:   Optional[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					:   List[str]								= predict_dataset.features["""label"""].names

     # Labels
     lowercase					:   Tuple								= len(__lowerCamelCase    )

     # 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[Any]								= AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path ,		num_labels=__lowerCamelCase ,		idalabel={str(__lowerCamelCase    ): label for i, label in enumerate(__lowerCamelCase    )} ,		labelaid={label: i for i, label in enumerate(__lowerCamelCase    )} ,		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					:   Tuple								= 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=__lowerCamelCase ,		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					:   List[Any]								= """max_length"""
     else:
          # We will pad later, dynamically at batch creation, to the max sequence length in each batch
          lowercase					:   List[Any]								= False

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

     if training_args.do_train:
          if data_args.max_train_samples is not None:
               lowercase					:   int								= min(len(__lowerCamelCase    ) ,		data_args.max_train_samples    )
               lowercase					:   Dict								= train_dataset.select(range(__lowerCamelCase    )    )
          with training_args.main_process_first(desc='''train dataset map pre-processing'''    ):
               lowercase					:   Union[str, Any]								= train_dataset.map(
                   __lowerCamelCase ,		batched=__lowerCamelCase ,		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(__lowerCamelCase    )    ) ,		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					:   Tuple								= min(len(__lowerCamelCase    ) ,		data_args.max_eval_samples    )
               lowercase					:   List[str]								= eval_dataset.select(range(__lowerCamelCase    )    )
          with training_args.main_process_first(desc='''validation dataset map pre-processing'''    ):
               lowercase					:   Dict								= eval_dataset.map(
                   __lowerCamelCase ,		batched=__lowerCamelCase ,		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					:   Dict								= min(len(__lowerCamelCase    ) ,		data_args.max_predict_samples    )
               lowercase					:   Tuple								= predict_dataset.select(range(__lowerCamelCase    )    )
          with training_args.main_process_first(desc='''prediction dataset map pre-processing'''    ):
               lowercase					:   Any								= predict_dataset.map(
                   __lowerCamelCase ,		batched=__lowerCamelCase ,		load_from_cache_file=not data_args.overwrite_cache ,		desc='''Running tokenizer on prediction dataset''' ,		)

          # Get the metric function
     lowercase					:   Tuple								= 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(__magic_name__    ):
          lowercase					:   Optional[Any]								= p.predictions[0] if isinstance(p.predictions ,		__lowerCamelCase    ) else p.predictions
          lowercase					:   str								= np.argmax(__lowerCamelCase ,		axis=1    )
          return metric.compute(predictions=__lowerCamelCase ,		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					:   Any								= default_data_collator
     elif training_args.fpaa:
          lowercase					:   Tuple								= DataCollatorWithPadding(__lowerCamelCase ,		pad_to_multiple_of=8    )
     else:
          lowercase					:   int								= None

     # Initialize our Trainer
     lowercase					:   Union[str, Any]								= Trainer(
         model=__lowerCamelCase ,		args=__lowerCamelCase ,		train_dataset=train_dataset if training_args.do_train else None ,		eval_dataset=eval_dataset if training_args.do_eval else None ,		compute_metrics=__lowerCamelCase ,		tokenizer=__lowerCamelCase ,		data_collator=__lowerCamelCase ,		)

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

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

          trainer.log_metrics('''train''' ,		__lowerCamelCase    )
          trainer.save_metrics('''train''' ,		__lowerCamelCase    )
          trainer.save_state()

     # Evaluation
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''    )
          lowercase					:   Dict								= trainer.evaluate(eval_dataset=__lowerCamelCase    )

          lowercase					:   List[str]								= data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCamelCase    )
          lowercase					:   Tuple								= min(__lowerCamelCase ,		len(__lowerCamelCase    )    )

          trainer.log_metrics('''eval''' ,		__lowerCamelCase    )
          trainer.save_metrics('''eval''' ,		__lowerCamelCase    )

     # Prediction
     if training_args.do_predict:
          logger.info('''*** Predict ***'''    )
          lowercase					:   Optional[Any]								= trainer.predict(__lowerCamelCase ,		metric_key_prefix='''predict'''    )

          lowercase					:   int								= (
              data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCamelCase    )
          )
          lowercase					:   Optional[int]								= min(__lowerCamelCase ,		len(__lowerCamelCase    )    )

          trainer.log_metrics('''predict''' ,		__lowerCamelCase    )
          trainer.save_metrics('''predict''' ,		__lowerCamelCase    )

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


if __name__ == "__main__":
 main()







import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : List[str]							       =	'MCTCTFeatureExtractor'
      a  : str							       =	'AutoTokenizer'
      def __init__(      self					:			Tuple		,      __lowercase					:			int		,      __lowercase					:			Dict					)							->      Any:
        super().__init__(__lowercase		,      __lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  self.feature_extractor
        __UpperCAmelCase							:	Optional[int]           =  False
      def __call__(      self					:			int		,      *__lowercase					:			Tuple		,      **__lowercase					:			Optional[int]					)							->      Union[str, Any]:
        # For backward compatibility
        if self._in_target_context_manager:
          return self.current_processor(*__lowercase		,      **__lowercase					)

        if "raw_speech" in kwargs:
          warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""					)
          __UpperCAmelCase							:	Dict           =  kwargs.pop("""raw_speech"""					)
        else:
          __UpperCAmelCase							:	Dict           =  kwargs.pop("""audio"""		,      __lowercase					)
        __UpperCAmelCase							:	List[str]           =  kwargs.pop("""sampling_rate"""		,      __lowercase					)
        __UpperCAmelCase							:	Tuple           =  kwargs.pop("""text"""		,      __lowercase					)
        if len(__lowercase					) > 0:
          __UpperCAmelCase							:	Tuple           =  args[0]
          __UpperCAmelCase							:	str           =  args[1:]

        if audio is None and text is None:
          raise ValueError("""You need to specify either an `audio` or `text` input to process."""					)

        if audio is not None:
          __UpperCAmelCase							:	Tuple           =  self.feature_extractor(__lowercase		,      *__lowercase		,      sampling_rate=__lowercase		,      **__lowercase					)
        if text is not None:
          __UpperCAmelCase							:	str           =  self.tokenizer(__lowercase		,      **__lowercase					)

        if text is None:
          return inputs
        elif audio is None:
          return encodings
        else:
          __UpperCAmelCase							:	Dict           =  encodings["""input_ids"""]
          return inputs
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      *__lowercase					:			List[Any]		,      **__lowercase					:			int					)							->      List[Any]:
        return self.tokenizer.batch_decode(*__lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			Optional[int]		,      *__lowercase					:			Optional[Any]		,      **__lowercase					:			List[str]					)							->      int:
        # For backward compatibility
        if self._in_target_context_manager:
          return self.current_processor.pad(*__lowercase		,      **__lowercase					)

        __UpperCAmelCase							:	Optional[int]           =  kwargs.pop("""input_features"""		,      __lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  kwargs.pop("""labels"""		,      __lowercase					)
        if len(__lowercase					) > 0:
          __UpperCAmelCase							:	Union[str, Any]           =  args[0]
          __UpperCAmelCase							:	str           =  args[1:]

        if input_features is not None:
          __UpperCAmelCase							:	Any           =  self.feature_extractor.pad(__lowercase		,      *__lowercase		,      **__lowercase					)
        if labels is not None:
          __UpperCAmelCase							:	Union[str, Any]           =  self.tokenizer.pad(__lowercase		,      **__lowercase					)

        if labels is None:
          return input_features
        elif input_features is None:
          return labels
        else:
          __UpperCAmelCase							:	Any           =  labels["""input_ids"""]
          return input_features
      def 	UpperCAmelCase      (      self					:			Any		,      *__lowercase					:			Union[str, Any]		,      **__lowercase					:			Dict					)							->      List[Any]:
        return self.tokenizer.decode(*__lowercase		,      **__lowercase					)




      @contextmanager
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      Tuple:
        warnings.warn(
            """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
            """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
            """your audio inputs, or in a separate call."""					)
        __UpperCAmelCase							:	Any           =  True
        __UpperCAmelCase							:	Optional[int]           =  self.tokenizer
        yield
        __UpperCAmelCase							:	List[Any]           =  self.feature_extractor
        __UpperCAmelCase							:	int           =  False


