'''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 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str    )    ->    Any:


       """simple docstring"""







       _UpperCAmelCase     :  List[Any]							      =    []
       for part_id in partition_order:
              _UpperCAmelCase     :  Any							      =    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 	UpperCamelCase_							(					)    ->    int:


       """simple docstring"""







       _UpperCAmelCase     :  str							      =    pyspark.sql.SparkSession.builder.master("local[*]"    ).appName("pyspark"    ).getOrCreate()
       _UpperCAmelCase     :  Optional[Any]							      =    spark.range(100    ).repartition(1    )
       _UpperCAmelCase     :  Optional[int]							      =    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 	UpperCamelCase_							(					)    ->    Dict:


       """simple docstring"""







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

       for i, (row_id, row_dict) in enumerate(generate_fn()    ):
              _UpperCAmelCase      ,  _UpperCAmelCase     :  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 	UpperCamelCase_							(					)    ->    Optional[int]:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[int]							      =    pyspark.sql.SparkSession.builder.master("local[*]"    ).appName("pyspark"    ).getOrCreate()
       _UpperCAmelCase     :  List[str]							      =    spark.range(10    ).repartition(1    )
       _UpperCAmelCase     :  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 	UpperCamelCase_							(					)    ->    str:


       """simple docstring"""







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

              _UpperCAmelCase     :  Optional[Any]							      =    SparkExamplesIterable(_UpperCAmelCase    ).shuffle_data_sources(_UpperCAmelCase    )
              assert shuffled_it.n_shards == 3
              for i, (row_id, row_dict) in enumerate(_UpperCAmelCase    ):
                     _UpperCAmelCase      ,  _UpperCAmelCase     :  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_							(					)    ->    Optional[int]:


       """simple docstring"""







       _UpperCAmelCase     :  List[str]							      =    pyspark.sql.SparkSession.builder.master("local[*]"    ).appName("pyspark"    ).getOrCreate()
       _UpperCAmelCase     :  Optional[int]							      =    spark.range(20    ).repartition(4    )

       # Partitions 0 and 2
       _UpperCAmelCase     :  Dict							      =    SparkExamplesIterable(_UpperCAmelCase    ).shard_data_sources(worker_id=0			,	num_workers=2    )
       assert shard_it_a.n_shards == 2
       _UpperCAmelCase     :  int							      =    _get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase			,	[0, 2]    )
       for i, (row_id, row_dict) in enumerate(_UpperCAmelCase    ):
              _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[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     :  Any							      =    SparkExamplesIterable(_UpperCAmelCase    ).shard_data_sources(worker_id=1			,	num_workers=2    )
       assert shard_it_a.n_shards == 2
       _UpperCAmelCase     :  int							      =    _get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase			,	[1, 3]    )
       for i, (row_id, row_dict) in enumerate(_UpperCAmelCase    ):
              _UpperCAmelCase      ,  _UpperCAmelCase     :  Union[str, 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 	UpperCamelCase_							(					)    ->    Union[str, Any]:


       """simple docstring"""







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


'''simple docstring'''




from typing import Any

def 	UpperCamelCase_							(					_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      dict			,	_UpperCAmelCase  :      dict			,	_UpperCAmelCase  :      dict			,	)    ->    list:


       """simple docstring"""







       _validation(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	)
       # Creates data structures and fill initial step
       _UpperCAmelCase     :  dict							      =    {}
       _UpperCAmelCase     :  dict							      =    {}
       for state in states_space:
              _UpperCAmelCase     :  Union[str, Any]							      =    observations_space[0]
              _UpperCAmelCase     :  Tuple							      =    (
                  initial_probabilities[state] * emission_probabilities[state][observation]
              )
              _UpperCAmelCase     :  List[str]							      =    None

       # Fills the data structure with the probabilities of
       # different transitions and pointers to previous states
       for o in range(1			,	len(_UpperCAmelCase    )    ):
              _UpperCAmelCase     :  Optional[Any]							      =    observations_space[o]
              _UpperCAmelCase     :  int							      =    observations_space[o - 1]
              for state in states_space:
                     # Calculates the argmax for probability function
                     _UpperCAmelCase     :  str							      =    ""
                     _UpperCAmelCase     :  Tuple							      =    -1
                     for k_state in states_space:
                            _UpperCAmelCase     :  Any							      =    (
                                probabilities[(k_state, prior_observation)]
                                * transition_probabilities[k_state][state]
                                * emission_probabilities[state][observation]
                            )
                            if probability > max_probability:
                                   _UpperCAmelCase     :  Union[str, Any]							      =    probability
                                   _UpperCAmelCase     :  str							      =    k_state

            # Update probabilities and pointers dicts
                     _UpperCAmelCase     :  Optional[int]							      =    (
                         probabilities[(arg_max, prior_observation)]
                         * transition_probabilities[arg_max][state]
                         * emission_probabilities[state][observation]
                     )

                     _UpperCAmelCase     :  Tuple							      =    arg_max

    # The final observation
       _UpperCAmelCase     :  Optional[Any]							      =    observations_space[len(_UpperCAmelCase    ) - 1]

       # argmax for given final observation
       _UpperCAmelCase     :  List[str]							      =    ""
       _UpperCAmelCase     :  Any							      =    -1
       for k_state in states_space:
              _UpperCAmelCase     :  Optional[int]							      =    probabilities[(k_state, final_observation)]
              if probability > max_probability:
                     _UpperCAmelCase     :  int							      =    probability
                     _UpperCAmelCase     :  Dict							      =    k_state
       _UpperCAmelCase     :  Dict							      =    arg_max

       # Process pointers backwards
       _UpperCAmelCase     :  List[Any]							      =    last_state
       _UpperCAmelCase     :  str							      =    []
       for o in range(len(_UpperCAmelCase    ) - 1			,	-1			,	-1    ):
              result.append(_UpperCAmelCase    )
              _UpperCAmelCase     :  List[Any]							      =    pointers[previous, observations_space[o]]
       result.reverse()

       return result

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       _validate_not_empty(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	)
       _validate_lists(_UpperCAmelCase			,	_UpperCAmelCase    )
       _validate_dicts(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       if not all(
           [
               observations_space,
               states_space,
               initial_probabilities,
               transition_probabilities,
               emission_probabilities,
           ]    ):
              raise ValueError("There's an empty parameter"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any    )    ->    None:


       """simple docstring"""







       _validate_list(_UpperCAmelCase			,	"observations_space"    )
       _validate_list(_UpperCAmelCase			,	"states_space"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       if not isinstance(_object			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  Optional[int]							      =    F"""{var_name} must be a list"""
              raise ValueError(_UpperCAmelCase    )
       else:
              for x in _object:
                     if not isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ):
                            _UpperCAmelCase     :  Optional[int]							      =    F"""{var_name} must be a list of strings"""
                            raise ValueError(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       _validate_dict(_UpperCAmelCase			,	"initial_probabilities"			,	_UpperCAmelCase    )
       _validate_nested_dict(_UpperCAmelCase			,	"transition_probabilities"    )
       _validate_nested_dict(_UpperCAmelCase			,	"emission_probabilities"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       _validate_dict(_object			,	_UpperCAmelCase			,	_UpperCAmelCase    )
       for x in _object.values():
              _validate_dict(_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      type			,	_UpperCAmelCase  :      bool = False    )    ->    None:


       """simple docstring"""







       if not isinstance(_object			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  Any							      =    F"""{var_name} must be a dict"""
              raise ValueError(_UpperCAmelCase    )
       if not all(isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ) for x in _object    ):
              _UpperCAmelCase     :  Tuple							      =    F"""{var_name} all keys must be strings"""
              raise ValueError(_UpperCAmelCase    )
       if not all(isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ) for x in _object.values()    ):
              _UpperCAmelCase     :  List[str]							      =    "nested dictionary " if nested else ""
              _UpperCAmelCase     :  List[str]							      =    F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
              raise ValueError(_UpperCAmelCase    )


if __name__ == "__main__":
  from doctest import testmod

  testmod()

