






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__       		=  {
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__       		=  ["""RoFormerTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__       		=  [
         """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """RoFormerForCausalLM""",
         """RoFormerForMaskedLM""",
         """RoFormerForMultipleChoice""",
         """RoFormerForQuestionAnswering""",
         """RoFormerForSequenceClassification""",
         """RoFormerForTokenClassification""",
         """RoFormerLayer""",
         """RoFormerModel""",
         """RoFormerPreTrainedModel""",
         """load_tf_weights_in_roformer""",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__       		=  [
         """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFRoFormerForCausalLM""",
         """TFRoFormerForMaskedLM""",
         """TFRoFormerForMultipleChoice""",
         """TFRoFormerForQuestionAnswering""",
         """TFRoFormerForSequenceClassification""",
         """TFRoFormerForTokenClassification""",
         """TFRoFormerLayer""",
         """TFRoFormerModel""",
         """TFRoFormerPreTrainedModel""",
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__       		=  [
         """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """FlaxRoFormerForMaskedLM""",
         """FlaxRoFormerForMultipleChoice""",
         """FlaxRoFormerForQuestionAnswering""",
         """FlaxRoFormerForSequenceClassification""",
         """FlaxRoFormerForTokenClassification""",
         """FlaxRoFormerModel""",
         """FlaxRoFormerPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
     from .tokenization_roformer import RoFormerTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_roformer_fast import RoFormerTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roformer import (
              ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              RoFormerForCausalLM,
              RoFormerForMaskedLM,
              RoFormerForMultipleChoice,
              RoFormerForQuestionAnswering,
              RoFormerForSequenceClassification,
              RoFormerForTokenClassification,
              RoFormerLayer,
              RoFormerModel,
              RoFormerPreTrainedModel,
              load_tf_weights_in_roformer,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_roformer import (
              TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRoFormerForCausalLM,
              TFRoFormerForMaskedLM,
              TFRoFormerForMultipleChoice,
              TFRoFormerForQuestionAnswering,
              TFRoFormerForSequenceClassification,
              TFRoFormerForTokenClassification,
              TFRoFormerLayer,
              TFRoFormerModel,
              TFRoFormerPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_roformer import (
              FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              FlaxRoFormerForMaskedLM,
              FlaxRoFormerForMultipleChoice,
              FlaxRoFormerForQuestionAnswering,
              FlaxRoFormerForSequenceClassification,
              FlaxRoFormerForTokenClassification,
              FlaxRoFormerModel,
              FlaxRoFormerPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase__       		=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










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 	UpperCamelCase	(							snake_case__  : List[Any]						,snake_case__  : List[str]	):




     '''simple docstring'''






     __snake_case      :int 							=    []
     for part_id in partition_order:
          __snake_case      :int 							=    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 	UpperCamelCase	(							):




     '''simple docstring'''






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




     '''simple docstring'''






     __snake_case      :Tuple 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :int 							=    spark.range(10	).repartition(2	)
     __snake_case      :str 							=    [1, 0]
     __snake_case      :List[Any] 							=    _generate_iterable_examples(snake_case__						,snake_case__	)  # Reverse the partitions.
     __snake_case      :Union[str, Any] 							=    _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__						,snake_case__	)

     for i, (row_id, row_dict) in enumerate(generate_fn()	):
          __snake_case							,			__snake_case      :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 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :Union[str, Any] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Tuple 							=    spark.range(10	).repartition(1	)
     __snake_case      :Dict 							=    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 	UpperCamelCase	(							):




     '''simple docstring'''






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

          __snake_case      :Dict 							=    SparkExamplesIterable(snake_case__	).shuffle_data_sources(snake_case__	)
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(snake_case__	):
               __snake_case							,			__snake_case      :List[str] 							=    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 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :Union[str, Any] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Tuple 							=    spark.range(20	).repartition(4	)

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

     # Partitions 1 and 3
     __snake_case      :str 							=    SparkExamplesIterable(snake_case__	).shard_data_sources(worker_id=1						,num_workers=2	)
     assert shard_it_a.n_shards == 2
     __snake_case      :Optional[int] 							=    _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__						,[1, 3]	)
     for i, (row_id, row_dict) in enumerate(snake_case__	):
          __snake_case							,			__snake_case      :Dict 							=    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 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :Union[str, Any] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Tuple 							=    spark.range(100	).repartition(1	)
     __snake_case      :Dict 							=    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


