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")

A     :  Optional[int]					     =       logging.getLogger(__name__)


@dataclass
class   lowerCamelCase					:
  """simple docstring"""







  lowerCamelCase__     =						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.'''
          )
      }  ,      )
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      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__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={
          '''help''': (
              '''For debugging purposes or quicker training, truncate the number of training examples to this '''
              '''value if set.'''
          )
      }  ,      )
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={
          '''help''': (
              '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
              '''value if set.'''
          )
      }  ,      )
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={
          '''help''': (
              '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
              '''value if set.'''
          )
      }  ,      )



@dataclass
class   lowerCamelCase					:
  """simple docstring"""







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



def      a__ (		):
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.

      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"     ,      __UpperCamelCase				)

      # 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(__UpperCamelCase				)
      datasets.utils.logging.set_verbosity(__UpperCamelCase				)
      transformers.utils.logging.set_verbosity(__UpperCamelCase				)
      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(__UpperCamelCase				)

      # 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=__UpperCamelCase     ,      idalabel={str(__UpperCamelCase				): label for i, label in enumerate(__UpperCamelCase				)}     ,      labelaid={label: i for i, label in enumerate(__UpperCamelCase				)}     ,      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=__UpperCamelCase     ,      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(__UpperCamelCase				):
            # Tokenize the texts
            return tokenizer(
                examples["premise"]     ,      examples["hypothesis"]     ,      padding=__UpperCamelCase     ,      max_length=data_args.max_seq_length     ,      truncation=__UpperCamelCase     ,      )

      if training_args.do_train:
            if data_args.max_train_samples is not None:
                  SCREAMING_SNAKE_CASE_          =  min(len(__UpperCamelCase				)     ,      data_args.max_train_samples				)
                  SCREAMING_SNAKE_CASE_          =  train_dataset.select(range(__UpperCamelCase				)				)
            with training_args.main_process_first(desc="train dataset map pre-processing"				):
                  SCREAMING_SNAKE_CASE_          =  train_dataset.map(
                      __UpperCamelCase     ,      batched=__UpperCamelCase     ,      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(__UpperCamelCase				)				)     ,      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(__UpperCamelCase				)     ,      data_args.max_eval_samples				)
                  SCREAMING_SNAKE_CASE_          =  eval_dataset.select(range(__UpperCamelCase				)				)
            with training_args.main_process_first(desc="validation dataset map pre-processing"				):
                  SCREAMING_SNAKE_CASE_          =  eval_dataset.map(
                      __UpperCamelCase     ,      batched=__UpperCamelCase     ,      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(__UpperCamelCase				)     ,      data_args.max_predict_samples				)
                  SCREAMING_SNAKE_CASE_          =  predict_dataset.select(range(__UpperCamelCase				)				)
            with training_args.main_process_first(desc="prediction dataset map pre-processing"				):
                  SCREAMING_SNAKE_CASE_          =  predict_dataset.map(
                      __UpperCamelCase     ,      batched=__UpperCamelCase     ,      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(__UpperCamelCase				):
            SCREAMING_SNAKE_CASE_          =  p.predictions[0] if isinstance(p.predictions     ,      __UpperCamelCase				) else p.predictions
            SCREAMING_SNAKE_CASE_          =  np.argmax(__UpperCamelCase     ,      axis=1				)
            return metric.compute(predictions=__UpperCamelCase     ,      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(__UpperCamelCase     ,      pad_to_multiple_of=8				)
      else:
            SCREAMING_SNAKE_CASE_          =  None

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

      # 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=__UpperCamelCase				)
            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(__UpperCamelCase				)
            )
            SCREAMING_SNAKE_CASE_          =  min(__UpperCamelCase     ,      len(__UpperCamelCase				)				)

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

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

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

            SCREAMING_SNAKE_CASE_          =  data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__UpperCamelCase				)
            SCREAMING_SNAKE_CASE_          =  min(__UpperCamelCase     ,      len(__UpperCamelCase				)				)

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

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

            SCREAMING_SNAKE_CASE_          =  (
                data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__UpperCamelCase				)
            )
            SCREAMING_SNAKE_CASE_          =  min(__UpperCamelCase     ,      len(__UpperCamelCase				)				)

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

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


if __name__ == "__main__":
      main()



from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml


class   lowerCamelCase					(yaml.SafeLoader					):
  """simple docstring"""







  def    __A     (							self :       str  ,     __magic_name__ :       str   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  [self.constructed_objects[key_node] for key_node, _ in node.value]
        SCREAMING_SNAKE_CASE_          =  [tuple(__magic_name__   ) if isinstance(__magic_name__  ,     __magic_name__   ) else key for key in keys]
        SCREAMING_SNAKE_CASE_          =  Counter(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  [key for key in counter if counter[key] > 1]
        if duplicate_keys:
              raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}'''   )







  def    __A     (							self :       int  ,     __magic_name__ :       int  ,     __magic_name__ :       List[str]=False   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  super().construct_mapping(__magic_name__  ,     deep=__magic_name__   )
        self._check_no_duplicates_on_constructed_node(__magic_name__   )
        return mapping



def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  list(readme_content.splitlines()				)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            SCREAMING_SNAKE_CASE_          =  full_content[1:].index("---"				) + 1
            SCREAMING_SNAKE_CASE_          =  "\n".join(full_content[1:sep_idx]				)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]				)

      return None, "\n".join(__UpperCamelCase				)


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						{'''train_eval_index'''}  # train-eval-index in the YAML metadata







  @classmethod
  def    __A     (							cls :       Dict  ,     __magic_name__ :       Path   )  ->	"DatasetMetadata":
        with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(readme_file.read()   )
        if yaml_string is not None:
              return cls.from_yaml_string(__magic_name__   )
        else:
              return cls()







  def    __A     (							self :       str  ,     __magic_name__ :       Path   )  ->	List[str]:
        if path.exists():
              with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
                    SCREAMING_SNAKE_CASE_          =  readme_file.read()
        else:
              SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  self._to_readme(__magic_name__   )
        with open(__magic_name__  ,     "w"  ,     encoding="utf-8"   ) as readme_file:
              readme_file.write(__magic_name__   )







  def    __A     (							self :       Any  ,     __magic_name__ :       Optional[str] = None   )  ->	str:
        if readme_content is not None:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n" + content
        else:
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n"
        return full_content







  @classmethod
  def    __A     (							cls :       List[Any]  ,     __magic_name__ :       str   )  ->	"DatasetMetadata":
        SCREAMING_SNAKE_CASE_          =  yaml.load(__magic_name__  ,     Loader=_NoDuplicateSafeLoader   ) or {}

        # Convert the YAML keys to DatasetMetadata fields
        SCREAMING_SNAKE_CASE_          =  {
            (key.replace("-"  ,     "_"   ) if key.replace("-"  ,     "_"   ) in cls._FIELDS_WITH_DASHES else key): value
            for key, value in metadata_dict.items()
        }
        return cls(**__magic_name__   )







  def    __A     (							self :       Optional[Any]   )  ->	str:
        return yaml.safe_dump(
            {
                (key.replace("_"  ,     "-"   ) if key in self._FIELDS_WITH_DASHES else key): value
                for key, value in self.items()
            }  ,     sort_keys=__magic_name__  ,     allow_unicode=__magic_name__  ,     encoding="utf-8"  ,     ).decode("utf-8"   )



A     :  List[Any]					     =       {
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
      from argparse import ArgumentParser

      A     :  Optional[Any]					     =       ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
      ap.add_argument("readme_filepath")
      A     :  Union[str, Any]					     =       ap.parse_args()

      A     :  Union[str, Any]					     =       Path(args.readme_filepath)
      A     :  List[Any]					     =       DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)


