




'''simple docstring'''



from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any] ,						_lowerCamelCase				:  List[Any]						)-> Optional[Any]:




     '''simple docstring'''





     __snake_case            =	[]
     for part_id in partition_order:
          __snake_case            =	df.where(f'''SPARK_PARTITION_ID() = {part_id}'''						).collect()
          for row_idx, row in enumerate(_lowerCamelCase						):
               expected_row_ids_and_row_dicts.append((f'''{part_id}_{row_idx}''', row.asDict())						)
     return expected_row_ids_and_row_dicts







@require_not_windows
@require_dill_gt_0_3_2
def       _UpperCamelCase			()-> Any:




     '''simple docstring'''





     __snake_case            =	pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
     __snake_case            =	spark.range(1_00						).repartition(1						)
     __snake_case            =	Spark(_lowerCamelCase						)
     # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
     # that each partition can hold 2 rows.
     spark_builder._repartition_df_if_needed(max_shard_size=16						)
     # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
     assert spark_builder.df.rdd.getNumPartitions() == 50







@require_not_windows
@require_dill_gt_0_3_2
def       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





     __snake_case            =	pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
     __snake_case            =	spark.range(10						).repartition(2						)
     __snake_case            =	[1, 0]
     __snake_case            =	_generate_iterable_examples(_lowerCamelCase ,						_lowerCamelCase						)  # Reverse the partitions.
     __snake_case            =	_get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCamelCase ,						_lowerCamelCase						)

     for i, (row_id, row_dict) in enumerate(generate_fn()						):
          __snake_case       ,     __snake_case            =	expected_row_ids_and_row_dicts[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def       _UpperCamelCase			()-> int:




     '''simple docstring'''





     __snake_case            =	pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
     __snake_case            =	spark.range(10						).repartition(1						)
     __snake_case            =	SparkExamplesIterable(_lowerCamelCase						)
     assert it.n_shards == 1
     for i, (row_id, row_dict) in enumerate(_lowerCamelCase						):
          assert row_id == f'''0_{i}'''
          assert row_dict == {"id": i}







@require_not_windows
@require_dill_gt_0_3_2
def       _UpperCamelCase			()-> Union[str, Any]:




     '''simple docstring'''





     __snake_case            =	pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
     __snake_case            =	spark.range(30						).repartition(3						)
     # Mock the generator so that shuffle reverses the partition indices.
     with patch('''numpy.random.Generator'''						) as generator_mock:
          __snake_case            =	lambda _lowerCamelCase						: x.reverse()
          __snake_case            =	_get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCamelCase ,						[2, 1, 0]						)

          __snake_case            =	SparkExamplesIterable(_lowerCamelCase						).shuffle_data_sources(_lowerCamelCase						)
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(_lowerCamelCase						):
               __snake_case       ,     __snake_case            =	expected_row_ids_and_row_dicts[i]
               assert row_id == expected_row_id
               assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





     __snake_case            =	pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
     __snake_case            =	spark.range(20						).repartition(4						)

     # Partitions 0 and 2
     __snake_case            =	SparkExamplesIterable(_lowerCamelCase						).shard_data_sources(worker_id=0 ,						num_workers=2						)
     assert shard_it_a.n_shards == 2
     __snake_case            =	_get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCamelCase ,						[0, 2]						)
     for i, (row_id, row_dict) in enumerate(_lowerCamelCase						):
          __snake_case       ,     __snake_case            =	expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict

     # Partitions 1 and 3
     __snake_case            =	SparkExamplesIterable(_lowerCamelCase						).shard_data_sources(worker_id=1 ,						num_workers=2						)
     assert shard_it_a.n_shards == 2
     __snake_case            =	_get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCamelCase ,						[1, 3]						)
     for i, (row_id, row_dict) in enumerate(_lowerCamelCase						):
          __snake_case       ,     __snake_case            =	expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def       _UpperCamelCase			()-> Optional[int]:




     '''simple docstring'''





     __snake_case            =	pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
     __snake_case            =	spark.range(1_00						).repartition(1						)
     __snake_case            =	Spark(_lowerCamelCase						)
     # Choose a small max_shard_size for maximum partitioning.
     spark_builder._repartition_df_if_needed(max_shard_size=1						)
     # The new number of partitions should not be greater than the number of rows.
     assert spark_builder.df.rdd.getNumPartitions() == 1_00












'''simple docstring'''



import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   int										=      BarthezTokenizer
       __lowercase	:   Any										=      BarthezTokenizerFast
       __lowercase	:   Dict										=      True
       __lowercase	:   Optional[int]										=      True


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            super().setUp()

            __snake_case            =	BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''	)
            tokenizer.save_pretrained(self.tmpdirname	)
            tokenizer.save_pretrained(self.tmpdirname     ,	legacy_format=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	tokenizer


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	'''<pad>'''
            __snake_case            =	1

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


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	list(self.get_tokenizer().get_vocab().keys()	)

            self.assertEqual(vocab_keys[0]     ,	'''<s>'''	)
            self.assertEqual(vocab_keys[1]     ,	'''<pad>'''	)
            self.assertEqual(vocab_keys[-1]     ,	'''<mask>'''	)
            self.assertEqual(len(__SCREAMING_SNAKE_CASE	)     ,	10_1122	)


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size     ,	10_1122	)


       @require_torch
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
            __snake_case            =	[0, 57, 3018, 7_0307, 91, 2]

            __snake_case            =	self.tokenizer(
                __SCREAMING_SNAKE_CASE     ,	max_length=len(__SCREAMING_SNAKE_CASE	)     ,	padding=__SCREAMING_SNAKE_CASE     ,	truncation=__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	)
            self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            self.assertEqual((2, 6)     ,	batch.input_ids.shape	)
            self.assertEqual((2, 6)     ,	batch.attention_mask.shape	)
            __snake_case            =	batch.input_ids.tolist()[0]
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            if not self.test_rust_tokenizer:
                 return

            __snake_case            =	self.get_tokenizer()
            __snake_case            =	self.get_rust_tokenizer()

            __snake_case            =	'''I was born in 92000, and this is falsé.'''

            __snake_case            =	tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	tokenizer.encode(__SCREAMING_SNAKE_CASE     ,	add_special_tokens=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE     ,	add_special_tokens=__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.get_rust_tokenizer()
            __snake_case            =	tokenizer.encode(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)




       @slow
       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	{'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], '''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, 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], [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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
            # fmt: on

            # moussaKam/mbarthez is a french model. So we also use french texts.
            __snake_case            =	[
                '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
                '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
                '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
                '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
                '''telles que la traduction et la synthèse de texte.''',
            ]

            self.tokenizer_integration_test_util(
                expected_encoding=__SCREAMING_SNAKE_CASE     ,	model_name='''moussaKam/mbarthez'''     ,	revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''     ,	sequences=__SCREAMING_SNAKE_CASE     ,	)






