




import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__A						:							Any							=  5_0_0_0_0
__A						:							Optional[int]							=  5_0_0_0

__A	,     __A						:							List[str]							=  os.path.split(__file__)
__A						:							List[str]							=  os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
@get_duration
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			for i in range(SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE           =					dataset[i]
@get_duration
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			for i in range(0					,	len(SCREAMING_SNAKE_CASE   )					,	SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE           =					dataset[i : i + batch_size]
@get_duration
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			with dataset.formatted_as(type=SCREAMING_SNAKE_CASE   ):
						for i in range(SCREAMING_SNAKE_CASE   ):
									SCREAMING_SNAKE_CASE           =					dataset[i]
@get_duration
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			with dataset.formatted_as(type=SCREAMING_SNAKE_CASE   ):
						for i in range(0					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
									SCREAMING_SNAKE_CASE           =					dataset[i : i + batch_size]
def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					{"""num examples""": SPEED_TEST_N_EXAMPLES}
			SCREAMING_SNAKE_CASE           =					[
			    (read, {"""length""": SMALL_TEST}),
			    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
			    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
			    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
			    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
			    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
			    (read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
			    (read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
			    (read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
			    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
			    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
			]

			SCREAMING_SNAKE_CASE           =					[
			    (read, {"""length""": SMALL_TEST}),
			    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
			    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
			    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
			    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
			    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
			    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
			    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
			]
			with tempfile.TemporaryDirectory() as tmp_dir:
						print("""generating dataset"""   )
						SCREAMING_SNAKE_CASE           =					datasets.Features(
						    {"""list""": datasets.Sequence(datasets.Value("""float32"""   )   ), """numbers""": datasets.Value("""float32"""   )}   )
						SCREAMING_SNAKE_CASE           =					generate_example_dataset(
						    os.path.join(SCREAMING_SNAKE_CASE					,	"""dataset.arrow"""   )					,	SCREAMING_SNAKE_CASE					,	num_examples=SCREAMING_SNAKE_CASE					,	seq_shapes={"""list""": (1_00,)}					,	)
						print("""first set of iterations"""   )
						for func, kwargs in functions:
									print(func.__name__					,	str(SCREAMING_SNAKE_CASE   )   )
									SCREAMING_SNAKE_CASE           =					func(SCREAMING_SNAKE_CASE					,	**SCREAMING_SNAKE_CASE   )

						print("""shuffling dataset"""   )
						SCREAMING_SNAKE_CASE           =					dataset.shuffle()
						print("""Second set of iterations (after shuffling"""   )
						for func, kwargs in functions_shuffled:
									print("""shuffled """					,	func.__name__					,	str(SCREAMING_SNAKE_CASE   )   )
									SCREAMING_SNAKE_CASE           =					func(
									    SCREAMING_SNAKE_CASE					,	**SCREAMING_SNAKE_CASE   )

			with open(SCREAMING_SNAKE_CASE					,	"""wb"""   ) as f:
						f.write(json.dumps(SCREAMING_SNAKE_CASE   ).encode("""utf-8"""   )   )


if __name__ == "__main__":  # useful to run the profiler
		benchmark_iterating()












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)






