




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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''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(__snake_case   ):
									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 					lowerCamelCase_    (    ):
			'''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(__snake_case   )
			# 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 					lowerCamelCase_    (    ):
			'''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(__snake_case					,	__snake_case   )  # Reverse the partitions.
			SCREAMING_SNAKE_CASE           =					_get_expected_row_ids_and_row_dicts_for_partition_order(__snake_case					,	__snake_case   )

			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 					lowerCamelCase_    (    ):
			'''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(__snake_case   )
			assert it.n_shards == 1
			for i, (row_id, row_dict) in enumerate(__snake_case   ):
						assert row_id == f"""0_{i}"""
						assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def 					lowerCamelCase_    (    ):
			'''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 SCREAMING_SNAKE_CASE   : x.reverse()
						SCREAMING_SNAKE_CASE           =					_get_expected_row_ids_and_row_dicts_for_partition_order(__snake_case					,	[2, 1, 0]   )

						SCREAMING_SNAKE_CASE           =					SparkExamplesIterable(__snake_case   ).shuffle_data_sources(__snake_case   )
						assert shuffled_it.n_shards == 3
						for i, (row_id, row_dict) in enumerate(__snake_case   ):
									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 					lowerCamelCase_    (    ):
			'''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(__snake_case   ).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(__snake_case					,	[0, 2]   )
			for i, (row_id, row_dict) in enumerate(__snake_case   ):
						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(__snake_case   ).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(__snake_case					,	[1, 3]   )
			for i, (row_id, row_dict) in enumerate(__snake_case   ):
						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 					lowerCamelCase_    (    ):
			'''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(__snake_case   )
			# 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






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
		__A						:							Tuple							=  argparse.ArgumentParser()

		parser.add_argument(
		    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
		)
		parser.add_argument(
		    """--original_config_file""",
		    type=str,
		    required=True,
		    help="""The YAML config file corresponding to the original architecture.""",
		)
		parser.add_argument(
		    """--num_in_channels""",
		    default=None,
		    type=int,
		    help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
		)
		parser.add_argument(
		    """--image_size""",
		    default=5_1_2,
		    type=int,
		    help=(
		        """The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
		        """ Base. Use 768 for Stable Diffusion v2."""
		    ),
		)
		parser.add_argument(
		    """--extract_ema""",
		    action="""store_true""",
		    help=(
		        """Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
		        """ or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
		        """ higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
		    ),
		)
		parser.add_argument(
		    """--upcast_attention""",
		    action="""store_true""",
		    help=(
		        """Whether the attention computation should always be upcasted. This is necessary when running stable"""
		        """ diffusion 2.1."""
		    ),
		)
		parser.add_argument(
		    """--from_safetensors""",
		    action="""store_true""",
		    help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
		)
		parser.add_argument(
		    """--to_safetensors""",
		    action="""store_true""",
		    help="""Whether to store pipeline in safetensors format or not.""",
		)
		parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
		parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
		def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
					'''simple docstring'''
					if string == "True":
								return True
					elif string == "False":
								return False
					else:
								raise ValueError(f"""could not parse string as bool {string}"""   )

		parser.add_argument(
		    """--use_linear_projection""", help="""Override for use linear projection""", required=False, type=parse_bool
		)

		parser.add_argument("""--cross_attention_dim""", help="""Override for cross attention_dim""", required=False, type=int)

		__A						:							Union[str, Any]							=  parser.parse_args()

		__A						:							Any							=  download_controlnet_from_original_ckpt(
		    checkpoint_path=args.checkpoint_path,
		    original_config_file=args.original_config_file,
		    image_size=args.image_size,
		    extract_ema=args.extract_ema,
		    num_in_channels=args.num_in_channels,
		    upcast_attention=args.upcast_attention,
		    from_safetensors=args.from_safetensors,
		    device=args.device,
		    use_linear_projection=args.use_linear_projection,
		    cross_attention_dim=args.cross_attention_dim,
		)




		controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)






