from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE										=						{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_SCREAMING_SNAKE_CASE										=						{
    'vocab_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
    },
    'merges_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
    },
}

_SCREAMING_SNAKE_CASE										=						{'allegro/herbert-base-cased': 514}
_SCREAMING_SNAKE_CASE										=						{}



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Union[str, Any]    			=		VOCAB_FILES_NAMES
 lowerCamelCase	:Tuple    			=		PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase	:Dict    			=		PRETRAINED_INIT_CONFIGURATION
 lowerCamelCase	:List[Any]    			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase	:List[Any]    			=		HerbertTokenizer



 def __init__(					self		,				lowerCAmelCase_=None		,				lowerCAmelCase_=None		,				lowerCAmelCase_=None		,				lowerCAmelCase_="<s>"		,				lowerCAmelCase_="<unk>"		,				lowerCAmelCase_="<pad>"		,				lowerCAmelCase_="<mask>"		,				lowerCAmelCase_="</s>"		,				**lowerCAmelCase_		,				)  ->					Optional[int]:
        super().__init__(
            lowerCAmelCase_		,				lowerCAmelCase_		,				tokenizer_file=lowerCAmelCase_		,				cls_token=lowerCAmelCase_		,				unk_token=lowerCAmelCase_		,				pad_token=lowerCAmelCase_		,				mask_token=lowerCAmelCase_		,				sep_token=lowerCAmelCase_		,				**lowerCAmelCase_		,				)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = None							)  ->					List[int]:
        _A        =  [self.cls_token_id]
        _A        =  [self.sep_token_id]
        if token_ids_a is None:
               return cls + token_ids_a + sep

        return cls + token_ids_a + sep + token_ids_a + sep



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = None		,				lowerCAmelCase_ = False							)  ->					List[int]:
        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCAmelCase_		,				token_ids_a=lowerCAmelCase_		,				already_has_special_tokens=lowerCAmelCase_							)

        if token_ids_a is None:
               return [1] + ([0] * len(lowerCAmelCase_							)) + [1]
        return [1] + ([0] * len(lowerCAmelCase_							)) + [1] + ([0] * len(lowerCAmelCase_							)) + [1]



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = None							)  ->					List[int]:
        _A        =  [self.sep_token_id]
        _A        =  [self.cls_token_id]

        if token_ids_a is None:
               return len(cls + token_ids_a + sep							) * [0]
        return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = None							)  ->					Tuple[str]:
        _A        =  self._tokenizer.model.save(lowerCAmelCase_		,				name=lowerCAmelCase_							)
        return tuple(lowerCAmelCase_							)





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  snake_case  (       snake_case__			:Union[str, Any]   ,	snake_case__			:Dict)						->   Any:
       _A        =  []
       for part_id in partition_order:
              _A        =  df.where(F'''SPARK_PARTITION_ID() = {part_id}''').collect()
              for row_idx, row in enumerate(snake_case__):
                     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  snake_case  (       )						->   Optional[Any]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(100).repartition(1)
       _A        =  Spark(snake_case__)
       # 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  snake_case  (       )						->   Union[str, Any]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(10).repartition(2)
       _A        =  [1, 0]
       _A        =  _generate_iterable_examples(snake_case__   ,	snake_case__)  # Reverse the partitions.
       _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	snake_case__)

       for i, (row_id, row_dict) in enumerate(generate_fn()):
              _A   ,  _A        =  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  snake_case  (       )						->   int:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(10).repartition(1)
       _A        =  SparkExamplesIterable(snake_case__)
       assert it.n_shards == 1
       for i, (row_id, row_dict) in enumerate(snake_case__):
              assert row_id == F'''0_{i}'''
              assert row_dict == {"id": i}






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   Union[str, Any]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(30).repartition(3)
       # Mock the generator so that shuffle reverses the partition indices.
       with patch("""numpy.random.Generator""") as generator_mock:
              _A        =  lambda snake_case__: x.reverse()
              _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	[2, 1, 0])

              _A        =  SparkExamplesIterable(snake_case__).shuffle_data_sources(snake_case__)
              assert shuffled_it.n_shards == 3
              for i, (row_id, row_dict) in enumerate(snake_case__):
                     _A   ,  _A        =  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  snake_case  (       )						->   List[str]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(20).repartition(4)

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

       # Partitions 1 and 3
       _A        =  SparkExamplesIterable(snake_case__).shard_data_sources(worker_id=1   ,	num_workers=2)
       assert shard_it_a.n_shards == 2
       _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	[1, 3])
       for i, (row_id, row_dict) in enumerate(snake_case__):
              _A   ,  _A        =  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  snake_case  (       )						->   Tuple:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(100).repartition(1)
       _A        =  Spark(snake_case__)
       # 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() == 100




