

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_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   []
     for part_id in partition_order:
          SCREAMING_SNAKE_CASE__   =   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     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   pyspark.sql.SparkSession.builder.master('''local[*]'''    ).appName('''pyspark'''    ).getOrCreate()
     SCREAMING_SNAKE_CASE__   =   spark.range(1_00    ).repartition(1    )
     SCREAMING_SNAKE_CASE__   =   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     UpperCAmelCase_       (						):





     '''simple docstring'''




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

     for i, (row_id, row_dict) in enumerate(generate_fn()    ):
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   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'''




     SCREAMING_SNAKE_CASE__   =   pyspark.sql.SparkSession.builder.master('''local[*]'''    ).appName('''pyspark'''    ).getOrCreate()
     SCREAMING_SNAKE_CASE__   =   spark.range(10    ).repartition(1    )
     SCREAMING_SNAKE_CASE__   =   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     UpperCAmelCase_       (						):





     '''simple docstring'''




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

          SCREAMING_SNAKE_CASE__   =   SparkExamplesIterable(_A    ).shuffle_data_sources(_A    )
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(_A    ):
               SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   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'''




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

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

     # Partitions 1 and 3
     SCREAMING_SNAKE_CASE__   =   SparkExamplesIterable(_A    ).shard_data_sources(worker_id=1				,		num_workers=2    )
     assert shard_it_a.n_shards == 2
     SCREAMING_SNAKE_CASE__   =   _get_expected_row_ids_and_row_dicts_for_partition_order(_A				,		[1, 3]    )
     for i, (row_id, row_dict) in enumerate(_A    ):
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   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'''




     SCREAMING_SNAKE_CASE__   =   pyspark.sql.SparkSession.builder.master('''local[*]'''    ).appName('''pyspark'''    ).getOrCreate()
     SCREAMING_SNAKE_CASE__   =   spark.range(1_00    ).repartition(1    )
     SCREAMING_SNAKE_CASE__   =   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() == 1_00





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE					:  Any             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  int             =      {
    '''edbeeching/decision-transformer-gym-hopper-medium''': (
        '''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "decision_transformer"
  a									= ["past_key_values"]
  a									= {
      "max_position_embeddings": "n_positions",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }






  def __init__(							self						:		Tuple     ,       __lowerCamelCase						:		Any=17     ,       __lowerCamelCase						:		Any=4     ,       __lowerCamelCase						:		List[Any]=128     ,       __lowerCamelCase						:		Union[str, Any]=4096     ,       __lowerCamelCase						:		Optional[Any]=True     ,       __lowerCamelCase						:		Any=1     ,       __lowerCamelCase						:		List[Any]=1024     ,       __lowerCamelCase						:		Any=3     ,       __lowerCamelCase						:		Dict=1     ,       __lowerCamelCase						:		List[Any]=None     ,       __lowerCamelCase						:		str="relu"     ,       __lowerCamelCase						:		Dict=0.1     ,       __lowerCamelCase						:		int=0.1     ,       __lowerCamelCase						:		List[str]=0.1     ,       __lowerCamelCase						:		str=1e-5     ,       __lowerCamelCase						:		Optional[Any]=0.02     ,       __lowerCamelCase						:		int=True     ,       __lowerCamelCase						:		Tuple=True     ,       __lowerCamelCase						:		Optional[int]=5_0256     ,       __lowerCamelCase						:		Tuple=5_0256     ,       __lowerCamelCase						:		int=False     ,       __lowerCamelCase						:		Optional[int]=False     ,       **__lowerCamelCase						:		Tuple     ,       )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   state_dim
       SCREAMING_SNAKE_CASE__   =   act_dim
       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   max_ep_len
       SCREAMING_SNAKE_CASE__   =   action_tanh
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   n_positions
       SCREAMING_SNAKE_CASE__   =   n_layer
       SCREAMING_SNAKE_CASE__   =   n_head
       SCREAMING_SNAKE_CASE__   =   n_inner
       SCREAMING_SNAKE_CASE__   =   activation_function
       SCREAMING_SNAKE_CASE__   =   resid_pdrop
       SCREAMING_SNAKE_CASE__   =   embd_pdrop
       SCREAMING_SNAKE_CASE__   =   attn_pdrop
       SCREAMING_SNAKE_CASE__   =   layer_norm_epsilon
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   scale_attn_weights
       SCREAMING_SNAKE_CASE__   =   use_cache
       SCREAMING_SNAKE_CASE__   =   scale_attn_by_inverse_layer_idx
       SCREAMING_SNAKE_CASE__   =   reorder_and_upcast_attn

       SCREAMING_SNAKE_CASE__   =   bos_token_id
       SCREAMING_SNAKE_CASE__   =   eos_token_id

       super().__init__(bos_token_id=__lowerCamelCase     ,       eos_token_id=__lowerCamelCase     ,       **__lowerCamelCase )


