


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

import datasets
import datasets.config

from .utils import require_beam



class  lowerCAmelCase				(				datasets.BeamBasedBuilder							):



	def 				lowercase     (					self    ):
		return datasets.DatasetInfo(
		    features=datasets.Features({'content': datasets.Value('string'    )}    ) ,					supervised_keys=_SCREAMING_SNAKE_CASE ,					)



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__    ):
		return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,					gen_kwargs={'examples': get_test_dummy_examples()}    )]



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__    ):
		import apache_beam as beam

		return pipeline | "Load Examples" >> beam.Create(_SCREAMING_SNAKE_CASE    )



class  lowerCAmelCase				(				datasets.BeamBasedBuilder							):



	def 				lowercase     (					self    ):
		return datasets.DatasetInfo(
		    features=datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'    )}    )}    ) ,					supervised_keys=_SCREAMING_SNAKE_CASE ,					)



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__    ):
		return [
		    datasets.SplitGenerator(name=datasets.Split.TRAIN ,					gen_kwargs={'examples': get_test_nested_examples()}    )
		]



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__    ):
		import apache_beam as beam

		return pipeline | "Load Examples" >> beam.Create(_SCREAMING_SNAKE_CASE    )


def 			__UpperCamelCase    (    )    ->      Dict:




	"""simple docstring"""





	return [(i, {"content": content}) for i, content in enumerate(['foo', 'bar', 'foobar']				)]

def 			__UpperCamelCase    (    )    ->      Dict:




	"""simple docstring"""





	return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['foo', 'bar', 'foobar']				)]






class  lowerCAmelCase				(				lowerCAmelCase__							):



	@require_beam
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    int      							= len(get_test_dummy_examples()    )
		with tempfile.TemporaryDirectory() as tmp_cache_dir:
			lowerCAmelCase					:    Optional[Any]      							= DummyBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE ,					beam_runner='DirectRunner'    )
			builder.download_and_prepare()
			self.assertTrue(
			    os.path.exists(
			        os.path.join(_SCREAMING_SNAKE_CASE ,					builder.name ,					'default' ,					'0.0.0' ,					f"{builder.name}-train.arrow"    )    )    )
			self.assertDictEqual(builder.info.features ,					datasets.Features({'content': datasets.Value('string'    )}    )    )
			lowerCAmelCase					:    str      							= builder.as_dataset()
			self.assertEqual(dset['train'].num_rows ,					_SCREAMING_SNAKE_CASE    )
			self.assertEqual(dset['train'].info.splits['train'].num_examples ,					_SCREAMING_SNAKE_CASE    )
			self.assertDictEqual(dset['train'][0] ,					get_test_dummy_examples()[0][1]    )
			self.assertDictEqual(
			    dset['train'][expected_num_examples - 1] ,					get_test_dummy_examples()[expected_num_examples - 1][1]    )
			self.assertTrue(
			    os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE ,					builder.name ,					'default' ,					'0.0.0' ,					'dataset_info.json'    )    )    )
			del dset



	@require_beam
	def 				lowercase     (					self    ):
		import apache_beam as beam

		lowerCAmelCase					:    Any      							= beam.io.parquetio.WriteToParquet

		lowerCAmelCase					:    int      							= len(get_test_dummy_examples()    )
		with tempfile.TemporaryDirectory() as tmp_cache_dir:
			lowerCAmelCase					:    Any      							= DummyBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE ,					beam_runner='DirectRunner'    )
			with patch('apache_beam.io.parquetio.WriteToParquet'    ) as write_parquet_mock:
				lowerCAmelCase					:    int      							= partial(_SCREAMING_SNAKE_CASE ,					num_shards=2    )
				builder.download_and_prepare()
			self.assertTrue(
			    os.path.exists(
			        os.path.join(
			            _SCREAMING_SNAKE_CASE ,					builder.name ,					'default' ,					'0.0.0' ,					f"{builder.name}-train-00000-of-00002.arrow"    )    )    )
			self.assertTrue(
			    os.path.exists(
			        os.path.join(
			            _SCREAMING_SNAKE_CASE ,					builder.name ,					'default' ,					'0.0.0' ,					f"{builder.name}-train-00000-of-00002.arrow"    )    )    )
			self.assertDictEqual(builder.info.features ,					datasets.Features({'content': datasets.Value('string'    )}    )    )
			lowerCAmelCase					:    Tuple      							= builder.as_dataset()
			self.assertEqual(dset['train'].num_rows ,					_SCREAMING_SNAKE_CASE    )
			self.assertEqual(dset['train'].info.splits['train'].num_examples ,					_SCREAMING_SNAKE_CASE    )
			# Order is not preserved when sharding, so we just check that all the elements are there
			self.assertListEqual(sorted(dset['train']['content']    ) ,					sorted(['foo', 'bar', 'foobar']    )    )
			self.assertTrue(
			    os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE ,					builder.name ,					'default' ,					'0.0.0' ,					'dataset_info.json'    )    )    )
			del dset



	@require_beam
	def 				lowercase     (					self    ):
		with tempfile.TemporaryDirectory() as tmp_cache_dir:
			lowerCAmelCase					:    str      							= DummyBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE    )
			self.assertRaises(datasets.builder.MissingBeamOptions ,					builder.download_and_prepare    )



	@require_beam
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= len(get_test_nested_examples()    )
		with tempfile.TemporaryDirectory() as tmp_cache_dir:
			lowerCAmelCase					:    Optional[int]      							= NestedBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE ,					beam_runner='DirectRunner'    )
			builder.download_and_prepare()
			self.assertTrue(
			    os.path.exists(
			        os.path.join(_SCREAMING_SNAKE_CASE ,					builder.name ,					'default' ,					'0.0.0' ,					f"{builder.name}-train.arrow"    )    )    )
			self.assertDictEqual(
			    builder.info.features ,					datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'    )}    )}    )    )
			lowerCAmelCase					:    List[str]      							= builder.as_dataset()
			self.assertEqual(dset['train'].num_rows ,					_SCREAMING_SNAKE_CASE    )
			self.assertEqual(dset['train'].info.splits['train'].num_examples ,					_SCREAMING_SNAKE_CASE    )
			self.assertDictEqual(dset['train'][0] ,					get_test_nested_examples()[0][1]    )
			self.assertDictEqual(
			    dset['train'][expected_num_examples - 1] ,					get_test_nested_examples()[expected_num_examples - 1][1]    )
			self.assertTrue(
			    os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE ,					builder.name ,					'default' ,					'0.0.0' ,					'dataset_info.json'    )    )    )
			del dset




'''simple docstring'''




import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase  :		Union[str, Any]		     =	get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')



@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					Tuple			      = GPTSwaTokenizer
	_lowerCamelCase   :					str			      = False
	_lowerCamelCase   :					Dict			      = True
	_lowerCamelCase   :					Optional[Any]			      = False



	def 				lowercase     (					self    ):
		super().setUp()

		# We have a SentencePiece fixture for testing
		lowerCAmelCase					:    Tuple      							= GPTSwaTokenizer(snake_case__ ,					eos_token='<unk>' ,					bos_token='<unk>' ,					pad_token='<unk>'    )

		tokenizer.save_pretrained(self.tmpdirname    )



	def 				lowercase     (					self ,					snake_case__    ):
		lowerCAmelCase					:    List[Any]      							= 'This is a test'
		lowerCAmelCase					:    List[Any]      							= 'This is a test'
		return input_text, output_text



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= '<s>'
		lowerCAmelCase					:    Optional[int]      							= 1

		self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__    ) ,					snake_case__    )
		self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__    ) ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= list(self.get_tokenizer().get_vocab().keys()    )

		self.assertEqual(vocab_keys[0] ,					'<unk>'    )
		self.assertEqual(vocab_keys[1] ,					'<s>'    )
		self.assertEqual(vocab_keys[-1] ,					'j'    )
		self.assertEqual(len(snake_case__    ) ,					2000    )



	def 				lowercase     (					self    ):
		self.assertEqual(self.get_tokenizer().vocab_size ,					2000    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= GPTSwaTokenizer(snake_case__    )

		lowerCAmelCase					:    Optional[Any]      							= tokenizer.tokenize('This is a test'    )
		self.assertListEqual(snake_case__ ,					['▁This', '▁is', '▁a', '▁t', 'est']    )

		self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__    ) ,					[465, 287, 265, 631, 842]    )

		lowerCAmelCase					:    Tuple      							= tokenizer.tokenize('I was born in 92000, and this is falsé.'    )
		# fmt: off
		self.assertListEqual(
		    snake_case__ ,					['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.'] ,					)
		# fmt: on

		lowerCAmelCase					:    Optional[Any]      							= tokenizer.convert_tokens_to_ids(snake_case__    )
		self.assertListEqual(
		    snake_case__ ,					[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260] ,					)

		lowerCAmelCase					:    int      							= tokenizer.convert_ids_to_tokens(snake_case__    )
		# fmt: off
		self.assertListEqual(
		    snake_case__ ,					['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']    )
		# fmt: on



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= GPTSwaTokenizer(snake_case__    )
		lowerCAmelCase					:    Optional[int]      							= ['This is a test', 'I was born in 92000, and this is falsé.']
		lowerCAmelCase					:    Tuple      							= [
		    [465, 287, 265, 631, 842],
		    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
		]

		# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
		for text, expected_ids in zip(snake_case__ ,					snake_case__    ):
			self.assertListEqual(tokenizer.encode_fast(snake_case__    ) ,					snake_case__    )

		# Test that decode_fast returns the input text
		for text, token_ids in zip(snake_case__ ,					snake_case__    ):
			self.assertEqual(tokenizer.decode_fast(snake_case__    ) ,					snake_case__    )



	@slow
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= [
		    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
		    'Hey there, how are you doing this fine day?',
		    'This is a text with a trailing spaces followed by a dot     .',
		    'Häj sväjs lillebrör! =)',
		    'Det är inget fel på Mr. Cool',
		]

		# fmt: off
		lowerCAmelCase					:    Tuple      							= {'input_ids': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
		# fmt: on
		self.tokenizer_integration_test_util(
		    expected_encoding=snake_case__ ,					model_name='AI-Sweden/gpt-sw3-126m' ,					sequences=snake_case__ ,					)
