





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""")

SCREAMING_SNAKE_CASE_						       =	logging.getLogger(__name__)

@dataclass
class 		UpperCamelCase__			:



     '''simple docstring'''

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

@dataclass
class 		UpperCamelCase__			:



     '''simple docstring'''

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



def        __lowercase       (				)			->       str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
      SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						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"""			,						_SCREAMING_SNAKE_CASE     )

      # 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()

      SCREAMING_SNAKE_CASE		=						training_args.get_process_log_level()
      logger.setLevel(_SCREAMING_SNAKE_CASE     )
      datasets.utils.logging.set_verbosity(_SCREAMING_SNAKE_CASE     )
      transformers.utils.logging.set_verbosity(_SCREAMING_SNAKE_CASE     )
      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.
      SCREAMING_SNAKE_CASE		=						None
      if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
            SCREAMING_SNAKE_CASE		=						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:
                  SCREAMING_SNAKE_CASE		=						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:
                  SCREAMING_SNAKE_CASE		=						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			,						)
            SCREAMING_SNAKE_CASE		=						train_dataset.features["""label"""].names

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

      if training_args.do_predict:
            SCREAMING_SNAKE_CASE		=						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			,						)
            SCREAMING_SNAKE_CASE		=						predict_dataset.features["""label"""].names

      # Labels
      SCREAMING_SNAKE_CASE		=						len(_SCREAMING_SNAKE_CASE     )

      # Load pretrained model and tokenizer
      # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      SCREAMING_SNAKE_CASE		=						AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path			,						num_labels=_SCREAMING_SNAKE_CASE			,						idalabel={str(_SCREAMING_SNAKE_CASE     ): label for i, label in enumerate(_SCREAMING_SNAKE_CASE     )}			,						labelaid={label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE     )}			,						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			,						)
      SCREAMING_SNAKE_CASE		=						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			,						)
      SCREAMING_SNAKE_CASE		=						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			,						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:
            SCREAMING_SNAKE_CASE		=						"""max_length"""
      else:
            # We will pad later, dynamically at batch creation, to the max sequence length in each batch
            SCREAMING_SNAKE_CASE		=						False

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

      if training_args.do_train:
            if data_args.max_train_samples is not None:
                  SCREAMING_SNAKE_CASE		=						min(len(_SCREAMING_SNAKE_CASE     )			,						data_args.max_train_samples     )
                  SCREAMING_SNAKE_CASE		=						train_dataset.select(range(_SCREAMING_SNAKE_CASE     )     )
            with training_args.main_process_first(desc="""train dataset map pre-processing"""     ):
                  SCREAMING_SNAKE_CASE		=						train_dataset.map(
                      _SCREAMING_SNAKE_CASE			,						batched=_SCREAMING_SNAKE_CASE			,						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(_SCREAMING_SNAKE_CASE     )     )			,						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:
                  SCREAMING_SNAKE_CASE		=						min(len(_SCREAMING_SNAKE_CASE     )			,						data_args.max_eval_samples     )
                  SCREAMING_SNAKE_CASE		=						eval_dataset.select(range(_SCREAMING_SNAKE_CASE     )     )
            with training_args.main_process_first(desc="""validation dataset map pre-processing"""     ):
                  SCREAMING_SNAKE_CASE		=						eval_dataset.map(
                      _SCREAMING_SNAKE_CASE			,						batched=_SCREAMING_SNAKE_CASE			,						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:
                  SCREAMING_SNAKE_CASE		=						min(len(_SCREAMING_SNAKE_CASE     )			,						data_args.max_predict_samples     )
                  SCREAMING_SNAKE_CASE		=						predict_dataset.select(range(_SCREAMING_SNAKE_CASE     )     )
            with training_args.main_process_first(desc="""prediction dataset map pre-processing"""     ):
                  SCREAMING_SNAKE_CASE		=						predict_dataset.map(
                      _SCREAMING_SNAKE_CASE			,						batched=_SCREAMING_SNAKE_CASE			,						load_from_cache_file=not data_args.overwrite_cache			,						desc="""Running tokenizer on prediction dataset"""			,						)

    # Get the metric function
      SCREAMING_SNAKE_CASE		=						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(_SCREAMING_SNAKE_CASE     ):
            SCREAMING_SNAKE_CASE		=						p.predictions[0] if isinstance(p.predictions			,						_SCREAMING_SNAKE_CASE     ) else p.predictions
            SCREAMING_SNAKE_CASE		=						np.argmax(_SCREAMING_SNAKE_CASE			,						axis=1     )
            return metric.compute(predictions=_SCREAMING_SNAKE_CASE			,						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:
            SCREAMING_SNAKE_CASE		=						default_data_collator
      elif training_args.fpaa:
            SCREAMING_SNAKE_CASE		=						DataCollatorWithPadding(_SCREAMING_SNAKE_CASE			,						pad_to_multiple_of=8     )
      else:
            SCREAMING_SNAKE_CASE		=						None

      # Initialize our Trainer
      SCREAMING_SNAKE_CASE		=						Trainer(
          model=_SCREAMING_SNAKE_CASE			,						args=_SCREAMING_SNAKE_CASE			,						train_dataset=train_dataset if training_args.do_train else None			,						eval_dataset=eval_dataset if training_args.do_eval else None			,						compute_metrics=_SCREAMING_SNAKE_CASE			,						tokenizer=_SCREAMING_SNAKE_CASE			,						data_collator=_SCREAMING_SNAKE_CASE			,						)

      # Training
      if training_args.do_train:
            SCREAMING_SNAKE_CASE		=						None
            if training_args.resume_from_checkpoint is not None:
                  SCREAMING_SNAKE_CASE		=						training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  SCREAMING_SNAKE_CASE		=						last_checkpoint
            SCREAMING_SNAKE_CASE		=						trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE     )
            SCREAMING_SNAKE_CASE		=						train_result.metrics
            SCREAMING_SNAKE_CASE		=						(
                data_args.max_train_samples if data_args.max_train_samples is not None else len(_SCREAMING_SNAKE_CASE     )
            )
            SCREAMING_SNAKE_CASE		=						min(_SCREAMING_SNAKE_CASE			,						len(_SCREAMING_SNAKE_CASE     )     )

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

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

      # Evaluation
      if training_args.do_eval:
            logger.info("""*** Evaluate ***"""     )
            SCREAMING_SNAKE_CASE		=						trainer.evaluate(eval_dataset=_SCREAMING_SNAKE_CASE     )

            SCREAMING_SNAKE_CASE		=						data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_SCREAMING_SNAKE_CASE     )
            SCREAMING_SNAKE_CASE		=						min(_SCREAMING_SNAKE_CASE			,						len(_SCREAMING_SNAKE_CASE     )     )

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

      # Prediction
      if training_args.do_predict:
            logger.info("""*** Predict ***"""     )
            SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						trainer.predict(_SCREAMING_SNAKE_CASE			,						metric_key_prefix="""predict"""     )

            SCREAMING_SNAKE_CASE		=						(
                data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_SCREAMING_SNAKE_CASE     )
            )
            SCREAMING_SNAKE_CASE		=						min(_SCREAMING_SNAKE_CASE			,						len(_SCREAMING_SNAKE_CASE     )     )

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

            SCREAMING_SNAKE_CASE		=						np.argmax(_SCREAMING_SNAKE_CASE			,						axis=1     )
            SCREAMING_SNAKE_CASE		=						os.path.join(training_args.output_dir			,						"""predictions.txt"""     )
            if trainer.is_world_process_zero():
                  with open(_SCREAMING_SNAKE_CASE			,						"""w"""     ) as writer:
                        writer.write("""index\tprediction\n"""     )
                        for index, item in enumerate(_SCREAMING_SNAKE_CASE     ):
                              SCREAMING_SNAKE_CASE		=						label_list[item]
                              writer.write(F"""{index}\t{item}\n"""     )


if __name__ == "__main__":
      main()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE_						       =	{
    """configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
    """tokenization_mvp""": ["""MvpTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""MvpTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MvpForCausalLM""",
          """MvpForConditionalGeneration""",
          """MvpForQuestionAnswering""",
          """MvpForSequenceClassification""",
          """MvpModel""",
          """MvpPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
      from .tokenization_mvp import MvpTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mvp_fast import MvpTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mvp import (
                MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                MvpForCausalLM,
                MvpForConditionalGeneration,
                MvpForQuestionAnswering,
                MvpForSequenceClassification,
                MvpModel,
                MvpPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE_						       =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


