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







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






def  lowerCAmelCase__			(  )				->		Dict:
			# 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.

			lowerCAmelCase__      :      Any         =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
			lowerCAmelCase__      :      int         =		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()

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

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

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

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

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

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

						lowerCAmelCase__      :      Dict         =		data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase_     )
						lowerCAmelCase__      :      int         =		min(lowerCamelCase_       ,							len(lowerCamelCase_     )     )

						trainer.log_metrics('eval'       ,							lowerCamelCase_     )
						trainer.save_metrics('eval'       ,							lowerCamelCase_     )

			# Prediction
			if training_args.do_predict:
						logger.info('*** Predict ***'     )
						lowerCAmelCase__      :      List[Any]         =		trainer.predict(lowerCamelCase_       ,							metric_key_prefix='predict'     )

						lowerCAmelCase__      :      Tuple         =		(
						    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowerCamelCase_     )
						)
						lowerCAmelCase__      :      Union[str, Any]         =		min(lowerCamelCase_       ,							len(lowerCamelCase_     )     )

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

						lowerCAmelCase__      :      List[str]         =		np.argmax(lowerCamelCase_       ,							axis=1     )
						lowerCAmelCase__      :      Optional[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_     ):
															lowerCAmelCase__      :      Union[str, Any]         =		label_list[item]
															writer.write(F'''{index}\t{item}\n'''     )


if __name__ == "__main__":
					main()



import random
from typing import Any



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						list[Any]:
    for _ in range(len(lowerCamelCase_ ) ):
        _lowercase       :							Optional[int]      		=		random.randint(0							, len(lowerCamelCase_ ) - 1 )
        _lowercase       :							str      		=		random.randint(0							, len(lowerCamelCase_ ) - 1 )
        _lowercase		,				_lowercase       :							Optional[int]      		=		data[b], data[a]
    return data


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE       :      str												= [0, 1, 2, 3, 4, 5, 6, 7]
 SCREAMING_SNAKE_CASE       :      int												= ["python", "says", "hello", "!"]
 print("Fisher-Yates Shuffle:")
 print("List", integers, strings)
 print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))


