




"""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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		int:
    A__	=    []
    for part_id in partition_order:
        A__	=    df.where(f"""SPARK_PARTITION_ID() = {part_id}"""						).collect()
        for row_idx, row in enumerate(lowercase_						):
            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       (							)						->		Tuple:
    A__	=    pyspark.sql.SparkSession.builder.master("local[*]"						).appName("pyspark"						).getOrCreate()
    A__	=    spark.range(1_00						).repartition(1						)
    A__	=    Spark(lowercase_						)
    # 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       (							)						->		int:
    A__	=    pyspark.sql.SparkSession.builder.master("local[*]"						).appName("pyspark"						).getOrCreate()
    A__	=    spark.range(10						).repartition(2						)
    A__	=    [1, 0]
    A__	=    _generate_iterable_examples(lowercase_      ,			lowercase_						)  # Reverse the partitions.
    A__	=    _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase_      ,			lowercase_						)

    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   _SCREAMING_SNAKE_CASE       (							)						->		Optional[int]:
    A__	=    pyspark.sql.SparkSession.builder.master("local[*]"						).appName("pyspark"						).getOrCreate()
    A__	=    spark.range(10						).repartition(1						)
    A__	=    SparkExamplesIterable(lowercase_						)
    assert it.n_shards == 1
    for i, (row_id, row_dict) in enumerate(lowercase_						):
        assert row_id == f"""0_{i}"""
        assert row_dict == {"id": i}







@require_not_windows
@require_dill_gt_0_3_2
def   _SCREAMING_SNAKE_CASE       (							)						->		List[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 lowercase_						: x.reverse()
        A__	=    _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase_      ,			[2, 1, 0]						)

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

    # Partitions 0 and 2
    A__	=    SparkExamplesIterable(lowercase_						).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(lowercase_      ,			[0, 2]						)
    for i, (row_id, row_dict) in enumerate(lowercase_						):
        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(lowercase_						).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(lowercase_      ,			[1, 3]						)
    for i, (row_id, row_dict) in enumerate(lowercase_						):
        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   _SCREAMING_SNAKE_CASE       (							)						->		Dict:
    A__	=    pyspark.sql.SparkSession.builder.master("local[*]"						).appName("pyspark"						).getOrCreate()
    A__	=    spark.range(1_00						).repartition(1						)
    A__	=    Spark(lowercase_						)
    # 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 warnings

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




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				['''image_processor''', '''tokenizer''']
    lowercase__						 =				'''ViTImageProcessor'''
    lowercase__						 =				('''CLIPTokenizer''', '''CLIPTokenizerFast''')




    def __init__(   self						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	Dict=None     ,			**snake_case_						:	Optional[Any]				)				->							List[str]:




     '''simple docstring'''

     A__	=    None
     if "feature_extractor" in kwargs:
      warnings.warn(
          "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
          " instead."     ,			snake_case_     ,			)
      A__	=    kwargs.pop("feature_extractor"				)

     A__	=    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__(snake_case_     ,			snake_case_				)




    def __call__(   self						:	int     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	int=None     ,			snake_case_						:	Dict=None     ,			snake_case_						:	int=None     ,			**snake_case_						:	List[str]				)				->							Union[str, Any]:




     '''simple docstring'''

     if text is None and visual_prompt is None and images is None:
      raise ValueError("You have to specify either text, visual prompt or images."				)

     if text is not None and visual_prompt is not None:
      raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."				)

     if text is not None:
      A__	=    self.tokenizer(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if visual_prompt is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if images is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if visual_prompt is not None and images is not None:
      A__	=    {
          "pixel_values": image_features.pixel_values,
          "conditional_pixel_values": prompt_features.pixel_values,
      }
      return encoding
     elif text is not None and images is not None:
      A__	=    image_features.pixel_values
      return encoding
     elif text is not None:
      return encoding
     elif visual_prompt is not None:
      A__	=    {
          "conditional_pixel_values": prompt_features.pixel_values,
      }
      return encoding
     else:
      return BatchEncoding(data=dict(**snake_case_				)     ,			tensor_type=snake_case_				)




    def 							__magic_name__							(   self						:	Tuple     ,			*snake_case_						:	List[str]     ,			**snake_case_						:	Optional[int]				)				->							str:




     '''simple docstring'''

     return self.tokenizer.batch_decode(*snake_case_     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]     ,			*snake_case_						:	Any     ,			**snake_case_						:	List[Any]				)				->							List[str]:




     '''simple docstring'''

     return self.tokenizer.decode(*snake_case_     ,			**snake_case_				)




    @property
    def 							__magic_name__							(   self						:	int				)				->							Optional[int]:




     '''simple docstring'''

     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,			snake_case_     ,			)
     return self.image_processor_class




    @property
    def 							__magic_name__							(   self						:	int				)				->							int:




     '''simple docstring'''

     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,			snake_case_     ,			)
     return self.image_processor





