




'''simple docstring'''


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
def 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a    ,	a    ,	a = None    ,	):
						__snake_case						 =   {}

						if train_file is not None:
												__snake_case						 =   [train_file]
						if eval_file is not None:
												__snake_case						 =   [eval_file]
						if test_file is not None:
												__snake_case						 =   [test_file]

						__snake_case						 =   datasets.load_dataset('csv'    ,	data_files=a							)
						__snake_case						 =   list(ds[list(files.keys()							)[0]].features.keys()							)
						__snake_case						 =   features_name.pop(a							)
						__snake_case						 =   list(set(ds[list(files.keys()							)[0]][label_name]							)							)
						__snake_case						 =   {label: i for i, label in enumerate(a							)}
						__snake_case						 =   tokenizer.model_input_names
						__snake_case						 =   {}

						if len(a							) == 1:
												for k in files.keys():
																		__snake_case						 =   ds[k].map(
																		    lambda a							: tokenizer.batch_encode_plus(
																		        example[features_name[0]]    ,	truncation=a    ,	max_length=a    ,	padding='max_length'							)    ,	batched=a    ,	)
						elif len(a							) == 2:
												for k in files.keys():
																		__snake_case						 =   ds[k].map(
																		    lambda a							: tokenizer.batch_encode_plus(
																		        (example[features_name[0]], example[features_name[1]])    ,	truncation=a    ,	max_length=a    ,	padding='max_length'    ,	)    ,	batched=a    ,	)

						def gen_train():
												for ex in transformed_ds[datasets.Split.TRAIN]:
																		__snake_case						 =   {k: v for k, v in ex.items() if k in input_names}
																		__snake_case						 =   labelaid[ex[label_name]]
																		yield (d, label)

						def gen_val():
												for ex in transformed_ds[datasets.Split.VALIDATION]:
																		__snake_case						 =   {k: v for k, v in ex.items() if k in input_names}
																		__snake_case						 =   labelaid[ex[label_name]]
																		yield (d, label)

						def gen_test():
												for ex in transformed_ds[datasets.Split.TEST]:
																		__snake_case						 =   {k: v for k, v in ex.items() if k in input_names}
																		__snake_case						 =   labelaid[ex[label_name]]
																		yield (d, label)

						__snake_case						 =   (
						    tf.data.Dataset.from_generator(
						        a    ,	({k: tf.intaa for k in input_names}, tf.intaa)    ,	({k: tf.TensorShape([None]							) for k in input_names}, tf.TensorShape([]							))    ,	)
						    if datasets.Split.TRAIN in transformed_ds
						    else None
						)

						if train_ds is not None:
												__snake_case						 =   train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]							)							)							)

						__snake_case						 =   (
						    tf.data.Dataset.from_generator(
						        a    ,	({k: tf.intaa for k in input_names}, tf.intaa)    ,	({k: tf.TensorShape([None]							) for k in input_names}, tf.TensorShape([]							))    ,	)
						    if datasets.Split.VALIDATION in transformed_ds
						    else None
						)

						if val_ds is not None:
												__snake_case						 =   val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]							)							)							)

						__snake_case						 =   (
						    tf.data.Dataset.from_generator(
						        a    ,	({k: tf.intaa for k in input_names}, tf.intaa)    ,	({k: tf.TensorShape([None]							) for k in input_names}, tf.TensorShape([]							))    ,	)
						    if datasets.Split.TEST in transformed_ds
						    else None
						)

						if test_ds is not None:
												__snake_case						 =   test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]							)							)							)

						return train_ds, val_ds, test_ds, labelaid


_lowercase  =			logging.getLogger(__name__)
@dataclass
class 						a_				:
	lowercase_       :     int			  =      field(metadata={'''help''': '''Which column contains the label'''}						)
	lowercase_       :     str			  =      field(default=UpperCAmelCase__     ,						metadata={'''help''': '''The path of the training file'''}						)
	lowercase_       :     Optional[str]			  =      field(default=UpperCAmelCase__     ,						metadata={'''help''': '''The path of the development file'''}						)
	lowercase_       :     Optional[str]			  =      field(default=UpperCAmelCase__     ,						metadata={'''help''': '''The path of the test file'''}						)
	lowercase_       :     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.'''
	        )
	    }     ,						)
	lowercase_       :     bool			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}						)





@dataclass
class 						a_				:
	lowercase_       :     str			  =      field(
	    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}						)
	lowercase_       :     Optional[str]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
	lowercase_       :     Optional[str]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}						)
	lowercase_       :     bool			  =      field(default=UpperCAmelCase__     ,						metadata={'''help''': '''Set this flag to use fast tokenization.'''}						)
	# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
	# or just modify its tokenizer_config.json.
	lowercase_       :     Optional[str]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,						)





def 						lowerCamelCase__   (				):
						# 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.
						__snake_case						 =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)							)
						__snake_case ,       __snake_case ,       __snake_case						 =   parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir							)
						    and os.listdir(training_args.output_dir							)
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
												    ' --overwrite_output_dir to overcome.'							)

						# Setup logging
						logging.basicConfig(
						    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'    ,	datefmt='%m/%d/%Y %H:%M:%S'    ,	level=logging.INFO    ,	)
						logger.info(
						    f'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1							)}, '
						    f'16-bits training: {training_args.fpaa}'							)
						logger.info(f'Training/evaluation parameters {training_args}'							)

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						__snake_case						 =   AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,	cache_dir=model_args.cache_dir    ,	)

						__snake_case ,       __snake_case ,       __snake_case ,       __snake_case						 =   get_tfds(
						    train_file=data_args.train_file    ,	eval_file=data_args.dev_file    ,	test_file=data_args.test_file    ,	tokenizer=a    ,	label_column_id=data_args.label_column_id    ,	max_seq_length=data_args.max_seq_length    ,	)

						__snake_case						 =   AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,	num_labels=len(a							)    ,	labelaid=a    ,	idalabel={id: label for label, id in labelaid.items()}    ,	finetuning_task='text-classification'    ,	cache_dir=model_args.cache_dir    ,	)

						with training_args.strategy.scope():
												__snake_case						 =   TFAutoModelForSequenceClassification.from_pretrained(
												    model_args.model_name_or_path    ,	from_pt=bool('.bin' in model_args.model_name_or_path							)    ,	config=a    ,	cache_dir=model_args.cache_dir    ,	)

						def compute_metrics(a							) -> Dict:
												__snake_case						 =   np.argmax(p.predictions    ,	axis=1							)

												return {"acc": (preds == p.label_ids).mean()}

						# Initialize our Trainer
						__snake_case						 =   TFTrainer(
						    model=a    ,	args=a    ,	train_dataset=a    ,	eval_dataset=a    ,	compute_metrics=a    ,	)

						# Training
						if training_args.do_train:
												trainer.train()
												trainer.save_model()
												tokenizer.save_pretrained(training_args.output_dir							)

						# Evaluation
						__snake_case						 =   {}
						if training_args.do_eval:
												logger.info('*** Evaluate ***'							)
												__snake_case						 =   trainer.evaluate()
												__snake_case						 =   os.path.join(training_args.output_dir    ,	'eval_results.txt'							)

												with open(a    ,	'w'							) as writer:
																		logger.info('***** Eval results *****'							)

																		for key, value in result.items():
																								logger.info(f'  {key} = {value}'							)
																								writer.write(f'{key} = {value}\n'							)

																		results.update(a							)

						return results


if __name__ == "__main__":
	main()







'''simple docstring'''


import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


_lowercase  =			pytest.mark.integration
@require_faiss
class 						a_				(			UpperCAmelCase__						):




	def   lowercase__   (       self     :  List[Any]      ):
							__snake_case						 =   Dataset.from_dict({'filename': ['my_name-train' + '_' + str(__lowerCAmelCase      ) for x in np.arange(3_0      ).tolist()]}      )
							return dset




	def   lowercase__   (       self     :  List[str]      ):
							import faiss

							__snake_case						 =   self._create_dummy_dataset()
							__snake_case						 =   dset.map(
							    lambda __lowerCAmelCase       ,		__lowerCAmelCase      : {"vecs": i * np.ones(5       ,		dtype=np.floataa      )}       ,		with_indices=__lowerCAmelCase       ,		keep_in_memory=__lowerCAmelCase      )
							__snake_case						 =   dset.add_faiss_index('vecs'       ,		batch_size=1_0_0       ,		metric_type=faiss.METRIC_INNER_PRODUCT      )
							__snake_case ,       __snake_case						 =   dset.get_nearest_examples('vecs'       ,		np.ones(5       ,		dtype=np.floataa      )      )
							self.assertEqual(examples['filename'][0]       ,		'my_name-train_29'      )
							dset.drop_index('vecs'      )




	def   lowercase__   (       self     :  Optional[int]      ):
							import faiss

							__snake_case						 =   self._create_dummy_dataset()
							dset.add_faiss_index_from_external_arrays(
							    external_arrays=np.ones((3_0, 5)      ) * np.arange(3_0      ).reshape(-1       ,		1      )       ,		index_name='vecs'       ,		batch_size=1_0_0       ,		metric_type=faiss.METRIC_INNER_PRODUCT       ,		)
							__snake_case ,       __snake_case						 =   dset.get_nearest_examples('vecs'       ,		np.ones(5       ,		dtype=np.floataa      )      )
							self.assertEqual(examples['filename'][0]       ,		'my_name-train_29'      )




	def   lowercase__   (       self     :  Dict      ):
							import faiss

							__snake_case						 =   self._create_dummy_dataset()
							dset.add_faiss_index_from_external_arrays(
							    external_arrays=np.ones((3_0, 5)      ) * np.arange(3_0      ).reshape(-1       ,		1      )       ,		index_name='vecs'       ,		metric_type=faiss.METRIC_INNER_PRODUCT       ,		)

							# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
							# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
							# see https://bugs.python.org/issue14243 and
							# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
							with tempfile.NamedTemporaryFile(delete=__lowerCAmelCase      ) as tmp_file:
													dset.save_faiss_index('vecs'       ,		tmp_file.name      )
													dset.load_faiss_index('vecs2'       ,		tmp_file.name      )
							os.unlink(tmp_file.name      )

							__snake_case ,       __snake_case						 =   dset.get_nearest_examples('vecs2'       ,		np.ones(5       ,		dtype=np.floataa      )      )
							self.assertEqual(examples['filename'][0]       ,		'my_name-train_29'      )




	def   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   self._create_dummy_dataset()
							dset.add_faiss_index_from_external_arrays(
							    external_arrays=np.ones((3_0, 5)      ) * np.arange(3_0      ).reshape(-1       ,		1      )       ,		index_name='vecs'      )
							dset.drop_index('vecs'      )
							self.assertRaises(__lowerCAmelCase       ,		partial(dset.get_nearest_examples       ,		'vecs2'       ,		np.ones(5       ,		dtype=np.floataa      )      )      )




	def   lowercase__   (       self     :  List[str]      ):
							from elasticsearch import Elasticsearch

							__snake_case						 =   self._create_dummy_dataset()
							with patch('elasticsearch.Elasticsearch.search'      ) as mocked_search, patch(
							    'elasticsearch.client.IndicesClient.create'      ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk'      ) as mocked_bulk:
													__snake_case						 =   {'acknowledged': True}
													mocked_bulk.return_value([(True, None)] * 3_0      )
													__snake_case						 =   {'hits': {'hits': [{'_score': 1, '_id': 2_9}]}}
													__snake_case						 =   Elasticsearch()

													dset.add_elasticsearch_index('filename'       ,		es_client=__lowerCAmelCase      )
													__snake_case ,       __snake_case						 =   dset.get_nearest_examples('filename'       ,		'my_name-train_29'      )
													self.assertEqual(examples['filename'][0]       ,		'my_name-train_29'      )





@require_faiss
class 						a_				(			UpperCAmelCase__						):




	def   lowercase__   (       self     :  Dict      ):
							import faiss

							__snake_case						 =   FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT      )

							# add vectors
							index.add_vectors(np.eye(5       ,		dtype=np.floataa      )      )
							self.assertIsNotNone(index.faiss_index      )
							self.assertEqual(index.faiss_index.ntotal       ,		5      )
							index.add_vectors(np.zeros((5, 5)       ,		dtype=np.floataa      )      )
							self.assertEqual(index.faiss_index.ntotal       ,		1_0      )

							# single query
							__snake_case						 =   np.zeros(5       ,		dtype=np.floataa      )
							__snake_case						 =   1
							__snake_case ,       __snake_case						 =   index.search(__lowerCAmelCase      )
							self.assertRaises(__lowerCAmelCase       ,		index.search       ,		query.reshape(-1       ,		1      )      )
							self.assertGreater(scores[0]       ,		0      )
							self.assertEqual(indices[0]       ,		1      )

							# batched queries
							__snake_case						 =   np.eye(5       ,		dtype=np.floataa      )[::-1]
							__snake_case ,       __snake_case						 =   index.search_batch(__lowerCAmelCase      )
							self.assertRaises(__lowerCAmelCase       ,		index.search_batch       ,		queries[0]      )
							__snake_case						 =   [scores[0] for scores in total_scores]
							__snake_case						 =   [indices[0] for indices in total_indices]
							self.assertGreater(np.min(__lowerCAmelCase      )       ,		0      )
							self.assertListEqual([4, 3, 2, 1, 0]       ,		__lowerCAmelCase      )




	def   lowercase__   (       self     :  Optional[int]      ):
							import faiss

							__snake_case						 =   FaissIndex(string_factory='Flat'      )
							index.add_vectors(np.eye(5       ,		dtype=np.floataa      )      )
							self.assertIsInstance(index.faiss_index       ,		faiss.IndexFlat      )
							__snake_case						 =   FaissIndex(string_factory='LSH'      )
							index.add_vectors(np.eye(5       ,		dtype=np.floataa      )      )
							self.assertIsInstance(index.faiss_index       ,		faiss.IndexLSH      )
							with self.assertRaises(__lowerCAmelCase      ):
													__snake_case						 =   FaissIndex(string_factory='Flat'       ,		custom_index=faiss.IndexFlat(5      )      )




	def   lowercase__   (       self     :  int      ):
							import faiss

							__snake_case						 =   faiss.IndexFlat(5      )
							__snake_case						 =   FaissIndex(custom_index=__lowerCAmelCase      )
							index.add_vectors(np.eye(5       ,		dtype=np.floataa      )      )
							self.assertIsInstance(index.faiss_index       ,		faiss.IndexFlat      )




	def   lowercase__   (       self     :  Tuple      ):
							import faiss

							__snake_case						 =   FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT      )
							index.add_vectors(np.eye(5       ,		dtype=np.floataa      )      )

							# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
							# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
							# see https://bugs.python.org/issue14243 and
							# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
							with tempfile.NamedTemporaryFile(delete=__lowerCAmelCase      ) as tmp_file:
													index.save(tmp_file.name      )
													__snake_case						 =   FaissIndex.load(tmp_file.name      )
							os.unlink(tmp_file.name      )

							__snake_case						 =   np.zeros(5       ,		dtype=np.floataa      )
							__snake_case						 =   1
							__snake_case ,       __snake_case						 =   index.search(__lowerCAmelCase      )
							self.assertGreater(scores[0]       ,		0      )
							self.assertEqual(indices[0]       ,		1      )





@require_faiss
def 						lowerCamelCase__   (				a							):
						import faiss

						__snake_case						 =   FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT							)
						index.add_vectors(np.eye(5    ,	dtype=np.floataa							)							)

						__snake_case						 =   'index.faiss'
						__snake_case						 =   f'mock://{index_name}'
						index.save(a    ,	storage_options=mockfs.storage_options							)
						__snake_case						 =   FaissIndex.load(a    ,	storage_options=mockfs.storage_options							)

						__snake_case						 =   np.zeros(5    ,	dtype=np.floataa							)
						__snake_case						 =   1
						__snake_case ,       __snake_case						 =   index.search(a							)
						assert scores[0] > 0
						assert indices[0] == 1






@require_elasticsearch
class 						a_				(			UpperCAmelCase__						):




	def   lowercase__   (       self     :  int      ):
							from elasticsearch import Elasticsearch

							with patch('elasticsearch.Elasticsearch.search'      ) as mocked_search, patch(
							    'elasticsearch.client.IndicesClient.create'      ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk'      ) as mocked_bulk:
													__snake_case						 =   Elasticsearch()
													__snake_case						 =   {'acknowledged': True}
													__snake_case						 =   ElasticSearchIndex(es_client=__lowerCAmelCase      )
													mocked_bulk.return_value([(True, None)] * 3      )
													index.add_documents(['foo', 'bar', 'foobar']      )

													# single query
													__snake_case						 =   'foo'
													__snake_case						 =   {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
													__snake_case ,       __snake_case						 =   index.search(__lowerCAmelCase      )
													self.assertEqual(scores[0]       ,		1      )
													self.assertEqual(indices[0]       ,		0      )

													# single query with timeout
													__snake_case						 =   'foo'
													__snake_case						 =   {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
													__snake_case ,       __snake_case						 =   index.search(__lowerCAmelCase       ,		request_timeout=3_0      )
													self.assertEqual(scores[0]       ,		1      )
													self.assertEqual(indices[0]       ,		0      )

													# batched queries
													__snake_case						 =   ['foo', 'bar', 'foobar']
													__snake_case						 =   {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
													__snake_case ,       __snake_case						 =   index.search_batch(__lowerCAmelCase      )
													__snake_case						 =   [scores[0] for scores in total_scores]
													__snake_case						 =   [indices[0] for indices in total_indices]
													self.assertGreater(np.min(__lowerCAmelCase      )       ,		0      )
													self.assertListEqual([1, 1, 1]       ,		__lowerCAmelCase      )

													# batched queries with timeout
													__snake_case						 =   ['foo', 'bar', 'foobar']
													__snake_case						 =   {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
													__snake_case ,       __snake_case						 =   index.search_batch(__lowerCAmelCase       ,		request_timeout=3_0      )
													__snake_case						 =   [scores[0] for scores in total_scores]
													__snake_case						 =   [indices[0] for indices in total_indices]
													self.assertGreater(np.min(__lowerCAmelCase      )       ,		0      )
													self.assertListEqual([1, 1, 1]       ,		__lowerCAmelCase      )

