





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	["""image_processor""", """tokenizer"""]
 UpperCAmelCase__									=	"""ChineseCLIPImageProcessor"""
 UpperCAmelCase__									=	("""BertTokenizer""", """BertTokenizerFast""")
 def __init__(   self						:   Optional[Any]    ,  UpperCAmelCase						:   Dict=None    ,  UpperCAmelCase						:   int=None    ,  **UpperCAmelCase						:   Any			)    ->			Dict:
     lowerCamelCase__					:   Optional[int]      				=	None
     if "feature_extractor" in kwargs:
         warnings.warn(
             'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
             ' instead.'    ,  UpperCAmelCase    ,  )
         lowerCamelCase__					:   Union[str, Any]      				=	kwargs.pop('feature_extractor'			)

     lowerCamelCase__					:   Dict      				=	image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError('You need to specify an `image_processor`.'			)
     if tokenizer is None:
         raise ValueError('You need to specify a `tokenizer`.'			)

     super().__init__(UpperCAmelCase    ,  UpperCAmelCase			)
     lowerCamelCase__					:   Dict      				=	self.image_processor
 def __call__(   self						:   Any    ,  UpperCAmelCase						:   Tuple=None    ,  UpperCAmelCase						:   int=None    ,  UpperCAmelCase						:   Dict=None    ,  **UpperCAmelCase						:   List[Any]			)    ->			Optional[Any]:

     if text is None and images is None:
         raise ValueError('You have to specify either text or images. Both cannot be none.'			)

     if text is not None:
         lowerCamelCase__					:   Optional[Any]      				=	self.tokenizer(UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  **UpperCAmelCase			)

     if images is not None:
         lowerCamelCase__					:   str      				=	self.image_processor(UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  **UpperCAmelCase			)

     if text is not None and images is not None:
         lowerCamelCase__					:   List[Any]      				=	image_features.pixel_values
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**UpperCAmelCase			)    ,  tensor_type=UpperCAmelCase			)
 def   A_			(   self						:   List[Any]    ,  *UpperCAmelCase						:   Tuple    ,  **UpperCAmelCase						:   List[str]			)    ->			Union[str, Any]:
     return self.tokenizer.batch_decode(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   List[str]    ,  *UpperCAmelCase						:   int    ,  **UpperCAmelCase						:   Dict			)    ->			Tuple:
     return self.tokenizer.decode(*UpperCAmelCase    ,  **UpperCAmelCase			)
 @property
 def   A_			(   self						:   List[str]			)    ->			List[Any]:
     lowerCamelCase__					:   List[str]      				=	self.tokenizer.model_input_names
     lowerCamelCase__					:   str      				=	self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)







 @property
 def   A_			(   self						:   Union[str, Any]			)    ->			int:
     warnings.warn(
         '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,  UpperCAmelCase    ,  )
     return self.image_processor_class












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

    for i, (row_id, row_dict) in enumerate(generate_fn()				):
        lowerCamelCase__				,							lowerCamelCase__					:   Tuple      				=	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  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Dict      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   Dict      				=	spark.range(10				).repartition(1				)
    lowerCamelCase__					:   List[str]      				=	SparkExamplesIterable(_UpperCAmelCase				)
    assert it.n_shards == 1
    for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
        assert row_id == F"""0_{i}"""
        assert row_dict == {"id": i}


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

        lowerCamelCase__					:   Tuple      				=	SparkExamplesIterable(_UpperCAmelCase				).shuffle_data_sources(_UpperCAmelCase				)
        assert shuffled_it.n_shards == 3
        for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
            lowerCamelCase__				,							lowerCamelCase__					:   Union[str, Any]      				=	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  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Optional[Any]      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   Tuple      				=	spark.range(20				).repartition(4				)

    # Partitions 0 and 2
    lowerCamelCase__					:   Optional[Any]      				=	SparkExamplesIterable(_UpperCAmelCase				).shard_data_sources(worker_id=0		,			num_workers=2				)
    assert shard_it_a.n_shards == 2
    lowerCamelCase__					:   List[str]      				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase		,			[0, 2]				)
    for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
        lowerCamelCase__				,							lowerCamelCase__					:   int      				=	expected_row_ids_and_row_dicts_a[i]
        assert row_id == expected_row_id
        assert row_dict == expected_row_dict

    # Partitions 1 and 3
    lowerCamelCase__					:   Dict      				=	SparkExamplesIterable(_UpperCAmelCase				).shard_data_sources(worker_id=1		,			num_workers=2				)
    assert shard_it_a.n_shards == 2
    lowerCamelCase__					:   Optional[int]      				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase		,			[1, 3]				)
    for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
        lowerCamelCase__				,							lowerCamelCase__					:   Any      				=	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  SCREAMING_SNAKE_CASE   ( )    ->  Dict:
    lowerCamelCase__					:   int      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   str      				=	spark.range(100				).repartition(1				)
    lowerCamelCase__					:   Dict      				=	Spark(_UpperCAmelCase				)
    # 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





