




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__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							str       =      []
     for part_id in partition_order:
          UpperCAmelCase_      :							List[Any]       =      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 lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							int       =      pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
     UpperCAmelCase_      :							Dict       =      spark.range(100				).repartition(1				)
     UpperCAmelCase_      :							Optional[Any]       =      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 lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							List[Any]       =      pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
     UpperCAmelCase_      :							Dict       =      spark.range(10				).repartition(2				)
     UpperCAmelCase_      :							str       =      [1, 0]
     UpperCAmelCase_      :							List[str]       =      _generate_iterable_examples(_lowercase			,						_lowercase				)  # Reverse the partitions.
     UpperCAmelCase_      :							Optional[Any]       =      _get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase			,						_lowercase				)

     for i, (row_id, row_dict) in enumerate(generate_fn()				):
          UpperCAmelCase_      :							List[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 lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							Dict       =      pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
     UpperCAmelCase_      :							Union[str, Any]       =      spark.range(10				).repartition(1				)
     UpperCAmelCase_      :							Optional[int]       =      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 lowerCamelCase__       (      ):

     '''simple docstring'''


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

          UpperCAmelCase_      :							List[Any]       =      SparkExamplesIterable(_lowercase				).shuffle_data_sources(_lowercase				)
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(_lowercase				):
               UpperCAmelCase_      :							int       =      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__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
     UpperCAmelCase_      :							Optional[int]       =      spark.range(20				).repartition(4				)

     # Partitions 0 and 2
     UpperCAmelCase_      :							List[str]       =      SparkExamplesIterable(_lowercase				).shard_data_sources(worker_id=0			,						num_workers=2				)
     assert shard_it_a.n_shards == 2
     UpperCAmelCase_      :							Optional[Any]       =      _get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase			,						[0, 2]				)
     for i, (row_id, row_dict) in enumerate(_lowercase				):
          UpperCAmelCase_      :							List[Any]       =      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_      :							int       =      SparkExamplesIterable(_lowercase				).shard_data_sources(worker_id=1			,						num_workers=2				)
     assert shard_it_a.n_shards == 2
     UpperCAmelCase_      :							str       =      _get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase			,						[1, 3]				)
     for i, (row_id, row_dict) in enumerate(_lowercase				):
          UpperCAmelCase_      :							int       =      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__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							List[Any]       =      pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
     UpperCAmelCase_      :							str       =      spark.range(100				).repartition(1				)
     UpperCAmelCase_      :							List[str]       =      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() == 100





from ...processing_utils import ProcessorMixin

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''SpeechT5FeatureExtractor'''
    lowerCAmelCase        =				'''SpeechT5Tokenizer'''





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		int:
         super().__init__(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def __call__(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		int:
         UpperCAmelCase_      :							List[str]       =      kwargs.pop('''audio'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      kwargs.pop('''text'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      kwargs.pop('''text_target'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      kwargs.pop('''audio_target'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Tuple       =      kwargs.pop('''sampling_rate'''       ,_SCREAMING_SNAKE_CASE  )

         if audio is not None and text is not None:
              raise ValueError(
                  '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'''  )
         if audio_target is not None and text_target is not None:
              raise ValueError(
                  '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'''  )
         if audio is None and audio_target is None and text is None and text_target is None:
              raise ValueError(
                  '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'''  )

         if audio is not None:
              UpperCAmelCase_      :							Optional[Any]       =      self.feature_extractor(_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,sampling_rate=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         elif text is not None:
              UpperCAmelCase_      :							Dict       =      self.tokenizer(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							List[str]       =      None

         if audio_target is not None:
              UpperCAmelCase_      :							List[Any]       =      self.feature_extractor(audio_target=_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,sampling_rate=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							int       =      targets['''input_values''']
         elif text_target is not None:
              UpperCAmelCase_      :							Optional[int]       =      self.tokenizer(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Optional[Any]       =      targets['''input_ids''']
         else:
              UpperCAmelCase_      :							Tuple       =      None

         if inputs is None:
              return targets

         if targets is not None:
              UpperCAmelCase_      :							Dict       =      labels

              UpperCAmelCase_      :							Optional[int]       =      targets.get('''attention_mask'''  )
              if decoder_attention_mask is not None:
                   UpperCAmelCase_      :							List[Any]       =      decoder_attention_mask

         return inputs





    def 							a__				(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Tuple:
         UpperCAmelCase_      :							Dict       =      kwargs.pop('''input_values'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      kwargs.pop('''input_ids'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      kwargs.pop('''labels'''       ,_SCREAMING_SNAKE_CASE  )

         if input_values is not None and input_ids is not None:
              raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.'''  )
         if input_values is None and input_ids is None and labels is None:
              raise ValueError(
                  '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'''  )

         if input_values is not None:
              UpperCAmelCase_      :							Tuple       =      self.feature_extractor.pad(_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         elif input_ids is not None:
              UpperCAmelCase_      :							Tuple       =      self.tokenizer.pad(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							List[str]       =      None

         if labels is not None:
              if "input_ids" in labels or (isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) and "input_ids" in labels[0]):
                   UpperCAmelCase_      :							Tuple       =      self.tokenizer.pad(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							Any       =      targets['''input_ids''']
              else:
                   UpperCAmelCase_      :							int       =      self.feature_extractor.feature_size
                   UpperCAmelCase_      :							List[str]       =      self.feature_extractor.num_mel_bins
                   UpperCAmelCase_      :							Optional[Any]       =      self.feature_extractor.pad(_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							List[Any]       =      feature_size_hack
                   UpperCAmelCase_      :							List[Any]       =      targets['''input_values''']
         else:
              UpperCAmelCase_      :							Optional[int]       =      None

         if inputs is None:
              return targets

         if targets is not None:
              UpperCAmelCase_      :							Optional[int]       =      labels

              UpperCAmelCase_      :							Union[str, Any]       =      targets.get('''attention_mask'''  )
              if decoder_attention_mask is not None:
                   UpperCAmelCase_      :							List[Any]       =      decoder_attention_mask

         return inputs





    def 							a__				(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Optional[Any]:
         return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )