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   lowerCAmelCase_					(	__A,    __A				)	->							Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=    []
      for part_id in partition_order:
            UpperCAmelCase__								=    df.where(f"""SPARK_PARTITION_ID() = {part_id}"""				).collect()
            for row_idx, row in enumerate(a__				):
                  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   lowerCAmelCase_					(	)	->							Dict:







      '''simple docstring'''


      UpperCAmelCase__								=    pyspark.sql.SparkSession.builder.master("local[*]"				).appName("pyspark"				).getOrCreate()
      UpperCAmelCase__								=    spark.range(100				).repartition(1				)
      UpperCAmelCase__								=    Spark(a__				)
      # 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   lowerCAmelCase_					(	)	->							List[Any]:







      '''simple docstring'''


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

      for i, (row_id, row_dict) in enumerate(generate_fn()				):
            UpperCAmelCase__						, UpperCAmelCase__								=    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   lowerCAmelCase_					(	)	->							List[Any]:







      '''simple docstring'''


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




@require_not_windows
@require_dill_gt_0_3_2
def   lowerCAmelCase_					(	)	->							Optional[Any]:







      '''simple docstring'''


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

            UpperCAmelCase__								=    SparkExamplesIterable(a__				).shuffle_data_sources(a__				)
            assert shuffled_it.n_shards == 3
            for i, (row_id, row_dict) in enumerate(a__				):
                  UpperCAmelCase__						, UpperCAmelCase__								=    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   lowerCAmelCase_					(	)	->							Union[str, Any]:







      '''simple docstring'''


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

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

      # Partitions 1 and 3
      UpperCAmelCase__								=    SparkExamplesIterable(a__				).shard_data_sources(worker_id=1,    num_workers=2				)
      assert shard_it_a.n_shards == 2
      UpperCAmelCase__								=    _get_expected_row_ids_and_row_dicts_for_partition_order(a__,    [1, 3]				)
      for i, (row_id, row_dict) in enumerate(a__				):
            UpperCAmelCase__						, UpperCAmelCase__								=    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   lowerCAmelCase_					(	)	->							Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=    pyspark.sql.SparkSession.builder.master("local[*]"				).appName("pyspark"				).getOrCreate()
      UpperCAmelCase__								=    spark.range(100				).repartition(1				)
      UpperCAmelCase__								=    Spark(a__				)
      # 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






'''simple docstring'''



import warnings

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







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "CLIPImageProcessor"
 lowerCAmelCase__       = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
 def __init__(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			str=None   ,				**__SCREAMING_SNAKE_CASE   :			Tuple     )    ->					Dict:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE      = 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__(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=None   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					int:


      """simple docstring"""




      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:
           __SCREAMING_SNAKE_CASE      = self.tokenizer(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

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

      if text is not None and images is not None:
           __SCREAMING_SNAKE_CASE      = image_features.pixel_values
           return encoding
      elif text is not None:
           return encoding
      else:
           return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE     )   ,				tensor_type=__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[str]   ,				*__SCREAMING_SNAKE_CASE   :			Tuple   ,				**__SCREAMING_SNAKE_CASE   :			List[Any]     )    ->					Optional[int]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[Any]   ,				*__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					Optional[Any]:


      """simple docstring"""




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


 @property
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.tokenizer.model_input_names
      __SCREAMING_SNAKE_CASE      = self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )






