



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	:   str    =   logging.getLogger(__name__)


@dataclass
class 	__snake_case   :
		SCREAMING_SNAKE_CASE__								=	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.'
		        )
		    }							, )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=SCREAMING_SNAKE_CASE							, metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
		SCREAMING_SNAKE_CASE__								=	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.'
		        )
		    }							, )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=SCREAMING_SNAKE_CASE							, metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    }							, )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=SCREAMING_SNAKE_CASE							, metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
		            'value if set.'
		        )
		    }							, )
		SCREAMING_SNAKE_CASE__								=	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 	__snake_case   :
		SCREAMING_SNAKE_CASE__								=	field(
		    default=SCREAMING_SNAKE_CASE							, metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=SCREAMING_SNAKE_CASE							, metadata={'help': 'Evaluation language. Also train language if `train_language` is set to None.'} )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=SCREAMING_SNAKE_CASE							, metadata={'help': 'Train language if it is different from the evaluation language.'} )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=SCREAMING_SNAKE_CASE							, metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=SCREAMING_SNAKE_CASE							, metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=SCREAMING_SNAKE_CASE							, metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}							, )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=SCREAMING_SNAKE_CASE							, metadata={'help': 'arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'}							, )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=SCREAMING_SNAKE_CASE							, metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}							, )
		SCREAMING_SNAKE_CASE__								=	field(
		    default='main'							, metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}							, )
		SCREAMING_SNAKE_CASE__								=	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).'
		        )
		    }							, )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=SCREAMING_SNAKE_CASE							, metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}							, )







def     UpperCAmelCase_		(					)	->     List[str]:



				"""simple docstring"""





				lowerCAmelCase__		  =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
				lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  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'    ,			snake_case__					)

				# 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__		  =  training_args.get_process_log_level()
				logger.setLevel(snake_case__					)
				datasets.utils.logging.set_verbosity(snake_case__					)
				transformers.utils.logging.set_verbosity(snake_case__					)
				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__		  =  None
				if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
								lowerCAmelCase__		  =  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__		  =  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__		  =  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__		  =  train_dataset.features['label'].names

				if training_args.do_eval:
								lowerCAmelCase__		  =  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__		  =  eval_dataset.features['label'].names

				if training_args.do_predict:
								lowerCAmelCase__		  =  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__		  =  predict_dataset.features['label'].names

				# Labels
				lowerCAmelCase__		  =  len(snake_case__					)

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

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

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

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

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

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

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

								trainer.log_metrics('train'    ,			snake_case__					)
								trainer.save_metrics('train'    ,			snake_case__					)
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								logger.info('*** Evaluate ***'					)
								lowerCAmelCase__		  =  trainer.evaluate(eval_dataset=snake_case__					)

								lowerCAmelCase__		  =  data_args.max_eval_samples if data_args.max_eval_samples is not None else len(snake_case__					)
								lowerCAmelCase__		  =  min(snake_case__    ,			len(snake_case__					)					)

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

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

								lowerCAmelCase__		  =  (
								    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(snake_case__					)
								)
								lowerCAmelCase__		  =  min(snake_case__    ,			len(snake_case__					)					)

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

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


if __name__ == "__main__":
	main()





from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_lowerCAmelCase	:   int    =   datasets.load_iris()

_lowerCAmelCase	:   Tuple    =   np.array(data["data"])
_lowerCAmelCase	:   Any    =   np.array(data["target"])
_lowerCAmelCase	:   Union[str, Any]    =   data["target_names"]

_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase	:   Optional[Any]    =   train_test_split(X, y)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Tuple:



				"""simple docstring"""





				return np.linalg.norm(np.array(snake_case__					) - np.array(snake_case__					)					)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__=5					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  zip(snake_case__    ,			snake_case__					)
				# List of distances of all points from the point to be classified
				lowerCAmelCase__		  =  []
				for data_point in data:
								lowerCAmelCase__		  =  euclidean_distance(data_point[0]    ,			snake_case__					)
								distances.append((distance, data_point[1])					)
				# Choosing 'k' points with the least distances.
				lowerCAmelCase__		  =  [i[1] for i in sorted(snake_case__					)[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				lowerCAmelCase__		  =  Counter(snake_case__					).most_common(1					)[0][0]
				return classes[result]


if __name__ == "__main__":
	print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
