






'''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__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Tuple:
      UpperCamelCase       						=      []
      for part_id in partition_order:
            UpperCamelCase       						=      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 					lowercase__				(   )->				str:
      UpperCamelCase       						=      pyspark.sql.SparkSession.builder.master("""local[*]"""					).appName("""pyspark"""					).getOrCreate()
      UpperCamelCase       						=      spark.range(100					).repartition(1					)
      UpperCamelCase       						=      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 					lowercase__				(   )->				Any:
      UpperCamelCase       						=      pyspark.sql.SparkSession.builder.master("""local[*]"""					).appName("""pyspark"""					).getOrCreate()
      UpperCamelCase       						=      spark.range(10					).repartition(2					)
      UpperCamelCase       						=      [1, 0]
      UpperCamelCase       						=      _generate_iterable_examples(__UpperCamelCase	,		__UpperCamelCase					)  # Reverse the partitions.
      UpperCamelCase       						=      _get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCamelCase	,		__UpperCamelCase					)

      for i, (row_id, row_dict) in enumerate(generate_fn()					):
            UpperCamelCase						,UpperCamelCase       						=      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__				(   )->				Tuple:
      UpperCamelCase       						=      pyspark.sql.SparkSession.builder.master("""local[*]"""					).appName("""pyspark"""					).getOrCreate()
      UpperCamelCase       						=      spark.range(10					).repartition(1					)
      UpperCamelCase       						=      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 					lowercase__				(   )->				Union[str, Any]:
      UpperCamelCase       						=      pyspark.sql.SparkSession.builder.master("""local[*]"""					).appName("""pyspark"""					).getOrCreate()
      UpperCamelCase       						=      spark.range(30					).repartition(3					)
      # Mock the generator so that shuffle reverses the partition indices.
      with patch("""numpy.random.Generator"""					) as generator_mock:
            UpperCamelCase       						=      lambda __UpperCamelCase					: x.reverse()
            UpperCamelCase       						=      _get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCamelCase	,		[2, 1, 0]					)

            UpperCamelCase       						=      SparkExamplesIterable(__UpperCamelCase					).shuffle_data_sources(__UpperCamelCase					)
            assert shuffled_it.n_shards == 3
            for i, (row_id, row_dict) in enumerate(__UpperCamelCase					):
                  UpperCamelCase						,UpperCamelCase       						=      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__				(   )->				List[Any]:
      UpperCamelCase       						=      pyspark.sql.SparkSession.builder.master("""local[*]"""					).appName("""pyspark"""					).getOrCreate()
      UpperCamelCase       						=      spark.range(20					).repartition(4					)

      # Partitions 0 and 2
      UpperCamelCase       						=      SparkExamplesIterable(__UpperCamelCase					).shard_data_sources(worker_id=0	,		num_workers=2					)
      assert shard_it_a.n_shards == 2
      UpperCamelCase       						=      _get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCamelCase	,		[0, 2]					)
      for i, (row_id, row_dict) in enumerate(__UpperCamelCase					):
            UpperCamelCase						,UpperCamelCase       						=      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       						=      SparkExamplesIterable(__UpperCamelCase					).shard_data_sources(worker_id=1	,		num_workers=2					)
      assert shard_it_a.n_shards == 2
      UpperCamelCase       						=      _get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCamelCase	,		[1, 3]					)
      for i, (row_id, row_dict) in enumerate(__UpperCamelCase					):
            UpperCamelCase						,UpperCamelCase       						=      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__				(   )->				Tuple:
      UpperCamelCase       						=      pyspark.sql.SparkSession.builder.master("""local[*]"""					).appName("""pyspark"""					).getOrCreate()
      UpperCamelCase       						=      spark.range(100					).repartition(1					)
      UpperCamelCase       						=      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'''

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()

     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

     parser.add_argument(
         '--txt2img_unclip',
         default='kakaobrain/karlo-v1-alpha',
         type=str,
         required=False,
         help='The pretrained txt2img unclip.',
     )

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

     SCREAMING_SNAKE_CASE__											=     UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

     SCREAMING_SNAKE_CASE__											=     CLIPImageProcessor()
     SCREAMING_SNAKE_CASE__											=     CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

     SCREAMING_SNAKE_CASE__											=     UnCLIPImageVariationPipeline(
         decoder=txtaimg.decoder,
         text_encoder=txtaimg.text_encoder,
         tokenizer=txtaimg.tokenizer,
         text_proj=txtaimg.text_proj,
         feature_extractor=feature_extractor,
         image_encoder=image_encoder,
         super_res_first=txtaimg.super_res_first,
         super_res_last=txtaimg.super_res_last,
         decoder_scheduler=txtaimg.decoder_scheduler,
         super_res_scheduler=txtaimg.super_res_scheduler,
     )

     imgaimg.save_pretrained(args.dump_path)
