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_      =      logging.getLogger(__name__)






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






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






def        __UpperCAmelCase    (       )->       Union[str, Any]:



			"""simple docstring"""







			lowercase							=		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
			lowercase							=		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()

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

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

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

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

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

			# Initialize our Trainer
			lowercase							=		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:
						lowercase							=		None
						if training_args.resume_from_checkpoint is not None:
									lowercase							=		training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									lowercase							=		last_checkpoint
						lowercase							=		trainer.train(resume_from_checkpoint=__lowerCAmelCase       )
						lowercase							=		train_result.metrics
						lowercase							=		(
						    data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase       )
						)
						lowercase							=		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 ***'''       )
						lowercase							=		trainer.evaluate(eval_dataset=__lowerCAmelCase       )

						lowercase							=		data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase       )
						lowercase							=		min(__lowerCAmelCase,		len(__lowerCAmelCase       )       )

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

			# Prediction
			if training_args.do_predict:
						logger.info('''*** Predict ***'''       )
						lowercase							=		trainer.predict(__lowerCAmelCase,		metric_key_prefix='''predict'''       )

						lowercase							=		(
						    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase       )
						)
						lowercase							=		min(__lowerCAmelCase,		len(__lowerCAmelCase       )       )

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

						lowercase							=		np.argmax(__lowerCAmelCase,		axis=1       )
						lowercase							=		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       ):
															lowercase							=		label_list[item]
															writer.write(f'{index}\t{item}\n'       )


if __name__ == "__main__":
						main()






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       int:



			"""simple docstring"""







			while a != 0:
						lowercase		,lowercase							=		b % a, a
			return b






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       int:



			"""simple docstring"""







			if gcd(UpperCAmelCase,		UpperCAmelCase       ) != 1:
						lowercase							=		f'mod inverse of {a!r} and {m!r} does not exist'
						raise ValueError(UpperCAmelCase       )
			lowercase		,lowercase		,lowercase							=		1, 0, a
			lowercase		,lowercase		,lowercase							=		0, 1, m
			while va != 0:
						lowercase							=		ua // va
						lowercase		,lowercase		,lowercase		,lowercase		,lowercase		,lowercase							=		(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
			return ua % m





