
'''simple docstring'''







from typing import TYPE_CHECKING

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


lowercase    :							Any      =					{
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Tuple      =					["BloomTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Dict      =					[
					    "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "BloomForCausalLM",
					    "BloomModel",
					    "BloomPreTrainedModel",
					    "BloomForSequenceClassification",
					    "BloomForTokenClassification",
					    "BloomForQuestionAnswering",
					]

if TYPE_CHECKING:
					from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bloom_fast import BloomTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bloom import (
										    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BloomForCausalLM,
										    BloomForQuestionAnswering,
										    BloomForSequenceClassification,
										    BloomForTokenClassification,
										    BloomModel,
										    BloomPreTrainedModel,
										)

else:
					import sys

					lowercase    :							Optional[int]      =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









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		:       int			     = logging.getLogger(__name__)
@dataclass
class     a    :



      """simple docstring"""


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



      """simple docstring"""


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






def 				lowerCamelCase__      (    ):
  # 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.

  __UpperCAmelCase							:	Any           =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
  __UpperCAmelCase      , __UpperCAmelCase      , __UpperCAmelCase							:	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()

  __UpperCAmelCase							:	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.
  __UpperCAmelCase							:	Dict           =  None
  if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
    __UpperCAmelCase							:	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:
      __UpperCAmelCase							:	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:
      __UpperCAmelCase							:	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  ,		)
    __UpperCAmelCase							:	str           =  train_dataset.features["""label"""].names

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

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

  # Labels
  __UpperCAmelCase							:	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.
  __UpperCAmelCase							:	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  ,		)
  __UpperCAmelCase							:	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  ,		)
  __UpperCAmelCase							:	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:
    __UpperCAmelCase							:	List[Any]           =  """max_length"""
  else:
    # We will pad later, dynamically at batch creation, to the max sequence length in each batch
    __UpperCAmelCase							:	List[Any]           =  False

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

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

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

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

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

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

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

    __UpperCAmelCase							:	Optional[int]           =  np.argmax(__lowerCamelCase  ,		axis=1		)
    __UpperCAmelCase							:	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		):
          __UpperCAmelCase							:	Tuple           =  label_list[item]
          writer.write(f"""{index}\t{item}\n"""		)


if __name__ == "__main__":
       main()


