






"""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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
     a__          =  []
     for part_id in partition_order:
          a__          =  df.where(f'SPARK_PARTITION_ID() = {part_id}').collect()
          for row_idx, row in enumerate(lowerCamelCase_):
               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        SCREAMING_SNAKE_CASE       (      ):
     a__          =  pyspark.sql.SparkSession.builder.master('''local[*]''').appName('''pyspark''').getOrCreate()
     a__          =  spark.range(100).repartition(1)
     a__          =  Spark(lowerCamelCase_)
     # 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        SCREAMING_SNAKE_CASE       (      ):
     a__          =  pyspark.sql.SparkSession.builder.master('''local[*]''').appName('''pyspark''').getOrCreate()
     a__          =  spark.range(10).repartition(2)
     a__          =  [1, 0]
     a__          =  _generate_iterable_examples(lowerCamelCase_ ,     lowerCamelCase_)  # Reverse the partitions.
     a__          =  _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCamelCase_ ,     lowerCamelCase_)

     for i, (row_id, row_dict) in enumerate(generate_fn()):
          a__       ,a__          =  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        SCREAMING_SNAKE_CASE       (      ):
     a__          =  pyspark.sql.SparkSession.builder.master('''local[*]''').appName('''pyspark''').getOrCreate()
     a__          =  spark.range(10).repartition(1)
     a__          =  SparkExamplesIterable(lowerCamelCase_)
     assert it.n_shards == 1
     for i, (row_id, row_dict) in enumerate(lowerCamelCase_):
          assert row_id == f'0_{i}'
          assert row_dict == {"id": i}





@require_not_windows
@require_dill_gt_0_3_2
def        SCREAMING_SNAKE_CASE       (      ):
     a__          =  pyspark.sql.SparkSession.builder.master('''local[*]''').appName('''pyspark''').getOrCreate()
     a__          =  spark.range(30).repartition(3)
     # Mock the generator so that shuffle reverses the partition indices.
     with patch('''numpy.random.Generator''') as generator_mock:
          a__          =  lambda lowerCamelCase_: x.reverse()
          a__          =  _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCamelCase_ ,     [2, 1, 0])

          a__          =  SparkExamplesIterable(lowerCamelCase_).shuffle_data_sources(lowerCamelCase_)
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(lowerCamelCase_):
               a__       ,a__          =  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        SCREAMING_SNAKE_CASE       (      ):
     a__          =  pyspark.sql.SparkSession.builder.master('''local[*]''').appName('''pyspark''').getOrCreate()
     a__          =  spark.range(20).repartition(4)

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

     # Partitions 1 and 3
     a__          =  SparkExamplesIterable(lowerCamelCase_).shard_data_sources(worker_id=1 ,     num_workers=2)
     assert shard_it_a.n_shards == 2
     a__          =  _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCamelCase_ ,     [1, 3])
     for i, (row_id, row_dict) in enumerate(lowerCamelCase_):
          a__       ,a__          =  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        SCREAMING_SNAKE_CASE       (      ):
     a__          =  pyspark.sql.SparkSession.builder.master('''local[*]''').appName('''pyspark''').getOrCreate()
     a__          =  spark.range(100).repartition(1)
     a__          =  Spark(lowerCamelCase_)
     # 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"""







import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  OmegaConf.load(lowerCamelCase_)
					a__          =  torch.load(lowerCamelCase_ ,     map_location='''cpu''')['''model''']
					a__          =  list(state_dict.keys())

					# extract state_dict for VQVAE
					a__          =  {}
					a__          =  '''first_stage_model.'''
					for key in keys:
										if key.startswith(lowerCamelCase_):
															a__          =  state_dict[key]

    # extract state_dict for UNetLDM
					a__          =  {}
					a__          =  '''model.diffusion_model.'''
					for key in keys:
										if key.startswith(lowerCamelCase_):
															a__          =  state_dict[key]

					a__          =  config.model.params.first_stage_config.params
					a__          =  config.model.params.unet_config.params

					a__          =  VQModel(**lowerCamelCase_).eval()
					vqvae.load_state_dict(lowerCamelCase_)

					a__          =  UNetLDMModel(**lowerCamelCase_).eval()
					unet.load_state_dict(lowerCamelCase_)

					a__          =  DDIMScheduler(
					    timesteps=config.model.params.timesteps ,     beta_schedule='''scaled_linear''' ,     beta_start=config.model.params.linear_start ,     beta_end=config.model.params.linear_end ,     clip_sample=lowerCamelCase_ ,     )

					a__          =  LDMPipeline(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					pipeline.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
	__a       :	Tuple             = argparse.ArgumentParser()
	parser.add_argument('--checkpoint_path', type=str, required=True)
	parser.add_argument('--config_path', type=str, required=True)
	parser.add_argument('--output_path', type=str, required=True)
	__a       :	Optional[int]             = parser.parse_args()

	convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


