

import math
import sys



def   __UpperCAmelCase					(      __a :     str      )     ->	str:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						''''''
					try:
										with open(__a						,'''rb'''      ) as binary_file:
															_a      :       List[str]   =						binary_file.read()
										for dat in data:
															_a      :       str   =						F"""{dat:08b}"""
															result += curr_byte
										return result
					except OSError:
										print('''File not accessible'''      )
										sys.exit()



def   __UpperCAmelCase					(      __a :     str      )     ->	str:

					"""simple docstring"""

					_a      :       Any   =						{'''0''': '''0''', '''1''': '''1'''}
					_a ,   _a      :       Union[str, Any]   =						'''''', ''''''
					_a      :       Union[str, Any]   =						len(__a      )

					for i in range(len(__a      )      ):
										curr_string += data_bits[i]
										if curr_string not in lexicon:
															continue

										_a      :       Any   =						lexicon[curr_string]
										result += last_match_id
										_a      :       Optional[Any]   =						last_match_id + '''0'''

										if math.loga(__a      ).is_integer():
															_a      :       str   =						{}
															for curr_key in list(__a      ):
																				_a      :       Optional[int]   =						lexicon.pop(__a      )
															_a      :       Union[str, Any]   =						new_lex

										_a      :       Tuple   =						last_match_id + '''1'''
										index += 1
										_a      :       Dict   =						''''''
					return result



def   __UpperCAmelCase					(      __a :     str						,__a :     str      )     ->	None:

					"""simple docstring"""

					_a      :       List[str]   =						8
					try:
										with open(__a						,'''wb'''      ) as opened_file:
															_a      :       Optional[int]   =						[
															    to_write[i : i + byte_length]
															    for i in range(0						,len(__a      )						,__a      )
															]

															if len(result_byte_array[-1]      ) % byte_length == 0:
																				result_byte_array.append('''10000000'''      )
															else:
																				result_byte_array[-1] += "1" + "0" * (
																				    byte_length - len(result_byte_array[-1]      ) - 1
																				)

															for elem in result_byte_array[:-1]:
																				opened_file.write(int(__a						,2      ).to_bytes(1						,byteorder='''big'''      )      )
					except OSError:
										print('''File not accessible'''      )
										sys.exit()



def   __UpperCAmelCase					(      __a :     str      )     ->	str:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						0
					for letter in data_bits:
										if letter == "1":
															break
										counter += 1

					_a      :       Union[str, Any]   =						data_bits[counter:]
					_a      :       Optional[Any]   =						data_bits[counter + 1 :]
					return data_bits



def   __UpperCAmelCase					(      __a :     str						,__a :     str      )     ->	None:

					"""simple docstring"""

					_a      :       List[Any]   =						read_file_binary(__a      )
					_a      :       Any   =						remove_prefix(__a      )
					_a      :       Tuple   =						decompress_data(__a      )
					write_file_binary(__a						,__a      )


if __name__ == "__main__":
			compress(sys.argv[1], sys.argv[2])






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
			a__												=						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=512,
			    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   __UpperCAmelCase					(      __a :     Any      )     ->	List[Any]:

								"""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__												=						parser.parse_args()

			a__												=						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)



