
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""")

_lowercase											=    logging.getLogger(__name__)


@dataclass
class 			lowercase_		:
       __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=A  ,      metadata={"help": "Overwrite the cached preprocessed datasets or not."}       )
       __lowerCamelCase				       =						field(
           default=A  ,      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=A  ,      metadata={
               "help": (
                   "For debugging purposes or quicker training, truncate the number of training examples to this "
                   "value if set."
               )
           }  ,      )
       __lowerCamelCase				       =						field(
           default=A  ,      metadata={
               "help": (
                   "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                   "value if set."
               )
           }  ,      )
       __lowerCamelCase				       =						field(
           default=A  ,      metadata={
               "help": (
                   "For debugging purposes or quicker training, truncate the number of prediction examples to this "
                   "value if set."
               )
           }  ,      )






@dataclass
class 			lowercase_		:
       __lowerCamelCase				       =						field(
           default=A  ,      metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}       )
       __lowerCamelCase				       =						field(
           default=A  ,      metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."}       )
       __lowerCamelCase				       =						field(
           default=A  ,      metadata={"help": "Train language if it is different from the evaluation language."}       )
       __lowerCamelCase				       =						field(
           default=A  ,      metadata={"help": "Pretrained config name or path if not the same as model_name"}       )
       __lowerCamelCase				       =						field(
           default=A  ,      metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}       )
       __lowerCamelCase				       =						field(
           default=A  ,      metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}  ,      )
       __lowerCamelCase				       =						field(
           default=A  ,      metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"}  ,      )
       __lowerCamelCase				       =						field(
           default=A  ,      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=A  ,      metadata={
               "help": (
                   "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                   "with private models)."
               )
           }  ,      )
       __lowerCamelCase				       =						field(
           default=A  ,      metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}  ,      )






def 							SCREAMING_SNAKE_CASE_	(					)			->						Any:
       # 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_							:				str		=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
       SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[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'''			,							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_							:				Tuple		=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_							:				Any		=None
       if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
              SCREAMING_SNAKE_CASE_							:				List[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:
                     SCREAMING_SNAKE_CASE_							:				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:
                     SCREAMING_SNAKE_CASE_							:				Union[str, 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			,							)
              SCREAMING_SNAKE_CASE_							:				str		=train_dataset.features['''label'''].names

       if training_args.do_eval:
              SCREAMING_SNAKE_CASE_							:				List[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			,							)
              SCREAMING_SNAKE_CASE_							:				Any		=eval_dataset.features['''label'''].names

       if training_args.do_predict:
              SCREAMING_SNAKE_CASE_							:				List[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			,							)
              SCREAMING_SNAKE_CASE_							:				Tuple		=predict_dataset.features['''label'''].names

       # Labels
       SCREAMING_SNAKE_CASE_							:				List[str]		=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_							:				int		=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_							:				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			,							)
       SCREAMING_SNAKE_CASE_							:				Tuple		=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_							:				str		='''max_length'''
       else:
              # We will pad later, dynamically at batch creation, to the max sequence length in each batch
              SCREAMING_SNAKE_CASE_							:				List[str]		=False

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

       # Initialize our Trainer
       SCREAMING_SNAKE_CASE_							:				Any		=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_							:				Dict		=None
              if training_args.resume_from_checkpoint is not None:
                     SCREAMING_SNAKE_CASE_							:				Dict		=training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     SCREAMING_SNAKE_CASE_							:				Dict		=last_checkpoint
              SCREAMING_SNAKE_CASE_							:				List[Any]		=trainer.train(resume_from_checkpoint=UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Any		=train_result.metrics
              SCREAMING_SNAKE_CASE_							:				int		=(
                  data_args.max_train_samples if data_args.max_train_samples is not None else len(UpperCAmelCase_				)
              )
              SCREAMING_SNAKE_CASE_							:				List[Any]		=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_							:				Dict		=trainer.evaluate(eval_dataset=UpperCAmelCase_				)

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=data_args.max_eval_samples if data_args.max_eval_samples is not None else len(UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				int		=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_							:				Tuple		=trainer.predict(UpperCAmelCase_			,							metric_key_prefix='''predict'''				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=(
                  data_args.max_predict_samples if data_args.max_predict_samples is not None else len(UpperCAmelCase_				)
              )
              SCREAMING_SNAKE_CASE_							:				Dict		=min(UpperCAmelCase_			,							len(UpperCAmelCase_				)				)

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

              SCREAMING_SNAKE_CASE_							:				List[Any]		=np.argmax(UpperCAmelCase_			,							axis=1				)
              SCREAMING_SNAKE_CASE_							:				Dict		=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_							:				int		=label_list[item]
                                   writer.write(f'{index}\t{item}\n'				)


if __name__ == "__main__":
 main()


def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int | float | str				)			->						tuple[int, int]:
       try:
              SCREAMING_SNAKE_CASE_							:				int		=float(UpperCAmelCase_				)
       except ValueError:
              raise ValueError('''Please enter a valid number'''				)
       SCREAMING_SNAKE_CASE_							:				Any		=decimal - int(UpperCAmelCase_				)
       if fractional_part == 0:
              return int(UpperCAmelCase_				), 1
       else:
              SCREAMING_SNAKE_CASE_							:				Any		=len(str(UpperCAmelCase_				).split('''.'''				)[1]				)
              SCREAMING_SNAKE_CASE_							:				str		=int(decimal * (1_0**number_of_frac_digits)				)
              SCREAMING_SNAKE_CASE_							:				Any		=1_0**number_of_frac_digits
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Tuple		=denominator, numerator
              while True:
                     SCREAMING_SNAKE_CASE_							:				Any		=dividend % divisor
                     if remainder == 0:
                            break
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=divisor, remainder
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=numerator / divisor, denominator / divisor
              return int(UpperCAmelCase_				), int(UpperCAmelCase_				)


if __name__ == "__main__":
 print(F"{decimal_to_fraction(2) = }")
 print(F"{decimal_to_fraction(89.0) = }")
 print(F"{decimal_to_fraction('67') = }")
 print(F"{decimal_to_fraction('45.0') = }")
 print(F"{decimal_to_fraction(1.5) = }")
 print(F"{decimal_to_fraction('6.25') = }")
 print(F"{decimal_to_fraction('78td') = }")
