






import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


UpperCAmelCase__						:int          =			{
    "n_samples": 64,
    "horizon": 32,
    "num_inference_steps": 20,
    "n_guide_steps": 2,  # can set to 0 for faster sampling, does not use value network
    "scale_grad_by_std": True,
    "scale": 0.1,
    "eta": 0.0,
    "t_grad_cutoff": 2,
    "device": "cpu",
}


if __name__ == "__main__":
   UpperCAmelCase__						:str          =			"hopper-medium-v2"
   UpperCAmelCase__						:Optional[int]          =			gym.make(env_name)

   UpperCAmelCase__						:List[Any]          =			ValueGuidedRLPipeline.from_pretrained(
       """bglick13/hopper-medium-v2-value-function-hor32""",
       env=env,
   )

   env.seed(0)
   UpperCAmelCase__						:List[str]          =			env.reset()
   UpperCAmelCase__						:List[Any]          =			0
   UpperCAmelCase__						:int          =			0
   UpperCAmelCase__						:List[str]          =			1_000
   UpperCAmelCase__						:str          =			[obs.copy()]
   try:
      for t in tqdm.tqdm(range(T)):
         # call the policy
         UpperCAmelCase__						:List[str]          =			pipeline(obs, planning_horizon=32)

         # execute action in environment
         UpperCAmelCase__						:Dict          =			env.step(denorm_actions)
         UpperCAmelCase__						:List[Any]          =			env.get_normalized_score(total_reward)

         # update return
         total_reward += reward
         total_score += score
         print(
             f'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
             f''' {total_score}'''
         )

         # save observations for rendering
         rollout.append(next_observation.copy())

         UpperCAmelCase__						:Union[str, Any]          =			next_observation
   except KeyboardInterrupt:
      pass

   print(f'''Total reward: {total_reward}''')











'''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     __lowercase  (_lowercase,						_lowercase     )   ->     List[str]:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				[]
     for part_id in partition_order:
          __lowerCamelCase						:       Union[str, 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     __lowercase  ()   ->     Optional[int]:


     """simple docstring"""
     __lowerCamelCase						:       int				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       List[str]				   =				spark.range(100     ).repartition(1     )
     __lowerCamelCase						:       str				   =				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     __lowercase  ()   ->     Optional[Any]:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       Dict				   =				spark.range(10     ).repartition(2     )
     __lowerCamelCase						:       List[Any]				   =				[1, 0]
     __lowerCamelCase						:       Any				   =				_generate_iterable_examples(_lowercase,						_lowercase     )  # Reverse the partitions.
     __lowerCamelCase						:       int				   =				_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase,						_lowercase     )

     for i, (row_id, row_dict) in enumerate(generate_fn()     ):
          __lowerCamelCase   ,      __lowerCamelCase						:       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     __lowercase  ()   ->     int:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       Union[str, Any]				   =				spark.range(10     ).repartition(1     )
     __lowerCamelCase						:       Dict				   =				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     __lowercase  ()   ->     Tuple:


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

          __lowerCamelCase						:       Tuple				   =				SparkExamplesIterable(_lowercase     ).shuffle_data_sources(_lowercase     )
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(_lowercase     ):
               __lowerCamelCase   ,      __lowerCamelCase						:       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     __lowercase  ()   ->     str:


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

     # Partitions 0 and 2
     __lowerCamelCase						:       Union[str, Any]				   =				SparkExamplesIterable(_lowercase     ).shard_data_sources(worker_id=0,						num_workers=2     )
     assert shard_it_a.n_shards == 2
     __lowerCamelCase						:       Optional[int]				   =				_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase,						[0, 2]     )
     for i, (row_id, row_dict) in enumerate(_lowercase     ):
          __lowerCamelCase   ,      __lowerCamelCase						:       int				   =				expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict

     # Partitions 1 and 3
     __lowerCamelCase						:       List[str]				   =				SparkExamplesIterable(_lowercase     ).shard_data_sources(worker_id=1,						num_workers=2     )
     assert shard_it_a.n_shards == 2
     __lowerCamelCase						:       int				   =				_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase,						[1, 3]     )
     for i, (row_id, row_dict) in enumerate(_lowercase     ):
          __lowerCamelCase   ,      __lowerCamelCase						:       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     __lowercase  ()   ->     int:


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



