





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   : Union[str, Any]			=				logging.getLogger(__name__)


@dataclass
class   A      :
			'''simple docstring'''




			__lowerCamelCase     :	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.'''
			        )
			    }       ,					)
			__lowerCamelCase     :	bool          =   field(
			    default=SCREAMING_SNAKE_CASE       ,					metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}   )
			__lowerCamelCase     :	bool          =   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     :	Optional[int]          =   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     :	Optional[int]          =   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     :	Optional[int]          =   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   A      :
			'''simple docstring'''




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







def        __lowerCamelCase (							)		->    Optional[Any]:






		"""simple docstring"""

		A__  				=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
		A__				,      A__				,      A__  				=       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"""   ,					__a   )

		# 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()

		A__  				=       training_args.get_process_log_level()
		logger.setLevel(__a   )
		datasets.utils.logging.set_verbosity(__a   )
		transformers.utils.logging.set_verbosity(__a   )
		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.
		A__  				=       None
		if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
				A__  				=       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:
						A__  				=       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:
						A__  				=       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   ,					)
				A__  				=       train_dataset.features["""label"""].names

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

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

		# Labels
		A__  				=       len(__a   )

		# Load pretrained model and tokenizer
		# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		A__  				=       AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,					num_labels=__a   ,					idalabel={str(__a   ): label for i, label in enumerate(__a   )}   ,					labelaid={label: i for i, label in enumerate(__a   )}   ,					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   ,					)
		A__  				=       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   ,					)
		A__  				=       AutoModelForSequenceClassification.from_pretrained(
		    model_args.model_name_or_path   ,					from_tf=bool(""".ckpt""" in model_args.model_name_or_path   )   ,					config=__a   ,					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:
				A__  				=       """max_length"""
		else:
				# We will pad later, dynamically at batch creation, to the max sequence length in each batch
				A__  				=       False

		def preprocess_function(__a       :Optional[Any]   ):
				# Tokenize the texts
				return tokenizer(
				    examples["""premise"""]   ,					examples["""hypothesis"""]   ,					padding=__a   ,					max_length=data_args.max_seq_length   ,					truncation=__a   ,					)

		if training_args.do_train:
				if data_args.max_train_samples is not None:
						A__  				=       min(len(__a   )   ,					data_args.max_train_samples   )
						A__  				=       train_dataset.select(range(__a   )   )
				with training_args.main_process_first(desc="""train dataset map pre-processing"""   ):
						A__  				=       train_dataset.map(
						    __a   ,					batched=__a   ,					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(__a   )   )   ,					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:
						A__  				=       min(len(__a   )   ,					data_args.max_eval_samples   )
						A__  				=       eval_dataset.select(range(__a   )   )
				with training_args.main_process_first(desc="""validation dataset map pre-processing"""   ):
						A__  				=       eval_dataset.map(
						    __a   ,					batched=__a   ,					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:
						A__  				=       min(len(__a   )   ,					data_args.max_predict_samples   )
						A__  				=       predict_dataset.select(range(__a   )   )
				with training_args.main_process_first(desc="""prediction dataset map pre-processing"""   ):
						A__  				=       predict_dataset.map(
						    __a   ,					batched=__a   ,					load_from_cache_file=not data_args.overwrite_cache   ,					desc="""Running tokenizer on prediction dataset"""   ,					)

    # Get the metric function
		A__  				=       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(__a       :EvalPrediction   ):
				A__  				=       p.predictions[0] if isinstance(p.predictions   ,					__a   ) else p.predictions
				A__  				=       np.argmax(__a   ,					axis=1   )
				return metric.compute(predictions=__a   ,					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:
				A__  				=       default_data_collator
		elif training_args.fpaa:
				A__  				=       DataCollatorWithPadding(__a   ,					pad_to_multiple_of=8   )
		else:
				A__  				=       None

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

		# Training
		if training_args.do_train:
				A__  				=       None
				if training_args.resume_from_checkpoint is not None:
						A__  				=       training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						A__  				=       last_checkpoint
				A__  				=       trainer.train(resume_from_checkpoint=__a   )
				A__  				=       train_result.metrics
				A__  				=       (
				    data_args.max_train_samples if data_args.max_train_samples is not None else len(__a   )
				)
				A__  				=       min(__a   ,					len(__a   )   )

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

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

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

				A__  				=       data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__a   )
				A__  				=       min(__a   ,					len(__a   )   )

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

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

				A__  				=       (
				    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__a   )
				)
				A__  				=       min(__a   ,					len(__a   )   )

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

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


if __name__ == "__main__":
					main()










import unittest

import numpy as np







def        __lowerCamelCase (							__a       :np.ndarray   ,					__a       :np.ndarray   ,					__a       :np.ndarray   ,					__a       :np.ndarray | None = None   ,					)		->    np.ndarray:






		"""simple docstring"""

		A__  				=       np.shape(__a   )
		A__  				=       np.shape(__a   )
		A__  				=       np.shape(__a   )

		if shape_a[0] != shape_b[0]:
				A__  				=       (
				    """Expected the same number of rows for A and B. """
				    F'Instead found A of size {shape_a} and B of size {shape_b}'
				)
				raise ValueError(__a   )

		if shape_b[1] != shape_c[1]:
				A__  				=       (
				    """Expected the same number of columns for B and C. """
				    F'Instead found B of size {shape_b} and C of size {shape_c}'
				)
				raise ValueError(__a   )

		A__  				=       pseudo_inv
		if a_inv is None:
				try:
						A__  				=       np.linalg.inv(__a   )
				except np.linalg.LinAlgError:
						raise ValueError(
						    """Input matrix A is not invertible. Cannot compute Schur complement."""   )

		return mat_c - mat_b.T @ a_inv @ mat_b


class   A      (unittest.TestCase   ):
			'''simple docstring'''




			def        a_	(      self       :   Union[str, Any]       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       np.array([[2, 1], [6, 3]]       )

					A__  				=       schur_complement(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )

					A__  				=       np.block([[a, b], [b.T, c]]       )

					A__  				=       np.linalg.det(__lowerCAmelCase       )
					A__  				=       np.linalg.det(__lowerCAmelCase       )
					A__  				=       np.linalg.det(__lowerCAmelCase       )

					self.assertAlmostEqual(__lowerCAmelCase							,     det_a * det_s       )






			def        a_	(      self       :   str       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       np.array([[2, 1], [6, 3]]       )

					with self.assertRaises(__lowerCAmelCase       ):
							schur_complement(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )







			def        a_	(      self       :   List[str]       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       np.array([[2, 1, 3], [6, 3, 5]]       )

					with self.assertRaises(__lowerCAmelCase       ):
							schur_complement(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					unittest.main()



