
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   UpperCAmelCase		:
   A__					:		str						     =     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."
           )
       }       ,)
   A__					:		Union[str, Any]						     =     field(
       default=_UpperCAmelCase       ,metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
   A__					:		Union[str, Any]						     =     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."
           )
       }       ,)
   A__					:		Any						     =     field(
       default=_UpperCAmelCase       ,metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of training examples to this "
               "value if set."
           )
       }       ,)
   A__					:		Any						     =     field(
       default=_UpperCAmelCase       ,metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
               "value if set."
           )
       }       ,)
   A__					:		Any						     =     field(
       default=_UpperCAmelCase       ,metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of prediction examples to this "
               "value if set."
           )
       }       ,)






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



def 			UpperCamelCase					(    ):
       snake_case       :   Any							=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
       snake_case				,					snake_case				,					snake_case       :   List[Any]							=      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()

       snake_case       :   int							=      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.
       snake_case       :   List[str]							=      None
       if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
              snake_case       :   Optional[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:
                     snake_case       :   List[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:
                     snake_case       :   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				,					)
              snake_case       :   List[Any]							=      train_dataset.features["label"].names

       if training_args.do_eval:
              snake_case       :   Tuple							=      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				,					)
              snake_case       :   Tuple							=      eval_dataset.features["label"].names

       if training_args.do_predict:
              snake_case       :   str							=      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				,					)
              snake_case       :   int							=      predict_dataset.features["label"].names

       # Labels
       snake_case       :   str							=      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.
       snake_case       :   Tuple							=      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				,					)
       snake_case       :   Union[str, Any]							=      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				,					)
       snake_case       :   Dict							=      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:
              snake_case       :   Optional[int]							=      "max_length"
       else:
              # We will pad later, dynamically at batch creation, to the max sequence length in each batch
              snake_case       :   Optional[int]							=      False

       def preprocess_function(__lowerCamelCase     : Dict    ):
              # 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:
                     snake_case       :   Tuple							=      min(len(__lowerCamelCase    )				,					data_args.max_train_samples    )
                     snake_case       :   List[Any]							=      train_dataset.select(range(__lowerCamelCase    )    )
              with training_args.main_process_first(desc="train dataset map pre-processing"    ):
                     snake_case       :   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:
                     snake_case       :   Dict							=      min(len(__lowerCamelCase    )				,					data_args.max_eval_samples    )
                     snake_case       :   List[str]							=      eval_dataset.select(range(__lowerCamelCase    )    )
              with training_args.main_process_first(desc="validation dataset map pre-processing"    ):
                     snake_case       :   Optional[int]							=      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:
                     snake_case       :   str							=      min(len(__lowerCamelCase    )				,					data_args.max_predict_samples    )
                     snake_case       :   Optional[int]							=      predict_dataset.select(range(__lowerCamelCase    )    )
              with training_args.main_process_first(desc="prediction dataset map pre-processing"    ):
                     snake_case       :   str							=      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
       snake_case       :   Optional[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(__lowerCamelCase     : EvalPrediction    ):
              snake_case       :   Union[str, Any]							=      p.predictions[0] if isinstance(p.predictions				,					__lowerCamelCase    ) else p.predictions
              snake_case       :   Any							=      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:
              snake_case       :   Optional[int]							=      default_data_collator
       elif training_args.fpaa:
              snake_case       :   Optional[Any]							=      DataCollatorWithPadding(__lowerCamelCase				,					pad_to_multiple_of=8    )
       else:
              snake_case       :   Any							=      None

       # Initialize our Trainer
       snake_case       :   Optional[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:
              snake_case       :   Optional[Any]							=      None
              if training_args.resume_from_checkpoint is not None:
                     snake_case       :   List[Any]							=      training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     snake_case       :   Any							=      last_checkpoint
              snake_case       :   List[Any]							=      trainer.train(resume_from_checkpoint=__lowerCamelCase    )
              snake_case       :   Optional[Any]							=      train_result.metrics
              snake_case       :   List[Any]							=      (
                  data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCamelCase    )
              )
              snake_case       :   List[str]							=      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 ***"    )
              snake_case       :   List[str]							=      trainer.evaluate(eval_dataset=__lowerCamelCase    )

              snake_case       :   int							=      data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCamelCase    )
              snake_case       :   Tuple							=      min(__lowerCamelCase				,					len(__lowerCamelCase    )    )

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

       # Prediction
       if training_args.do_predict:
              logger.info("*** Predict ***"    )
              snake_case				,					snake_case				,					snake_case       :   Union[str, Any]							=      trainer.predict(__lowerCamelCase				,					metric_key_prefix="predict"    )

              snake_case       :   str							=      (
                  data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCamelCase    )
              )
              snake_case       :   Dict							=      min(__lowerCamelCase				,					len(__lowerCamelCase    )    )

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

              snake_case       :   Tuple							=      np.argmax(__lowerCamelCase				,					axis=1    )
              snake_case       :   Union[str, Any]							=      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    ):
                                   snake_case       :   Dict							=      label_list[item]
                                   writer.write(f"""{index}\t{item}\n"""    )


if __name__ == "__main__":
   main()












"""simple docstring"""





from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate

@dataclass(frozen=_UpperCAmelCase					)
class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      field(default='language-modeling'     ,  metadata={'include_in_asdict_even_if_is_default': True}					)

      _SCREAMING_SNAKE_CASE					=      Features({'text': Value('string'					)}					)
      _SCREAMING_SNAKE_CASE					=      Features({}					)
      _SCREAMING_SNAKE_CASE					=      "text"





      @property
      def     _snake_case			(       self )		->     Dict[str, str]:
            return {self.text_column: "text"}






