'''simple docstring'''




import math
import sys

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str    )    ->    str:


       """simple docstring"""







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

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str    )    ->    str:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[Any]							      =    {"0": "0", "1": "1"}
       _UpperCAmelCase      ,  _UpperCAmelCase     :  str							      =    "", ""
       _UpperCAmelCase     :  Any							      =    len(_UpperCAmelCase    )

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

              _UpperCAmelCase     :  List[Any]							      =    lexicon[curr_string]
              result += last_match_id
              _UpperCAmelCase     :  Tuple							      =    last_match_id + "0"

              if math.loga(_UpperCAmelCase    ).is_integer():
                     _UpperCAmelCase     :  List[Any]							      =    {}
                     for curr_key in list(_UpperCAmelCase    ):
                            _UpperCAmelCase     :  Any							      =    lexicon.pop(_UpperCAmelCase    )
                     _UpperCAmelCase     :  int							      =    new_lex

              _UpperCAmelCase     :  Dict							      =    last_match_id + "1"
              index += 1
              _UpperCAmelCase     :  Tuple							      =    ""
       return result

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       _UpperCAmelCase     :  Tuple							      =    8
       try:
              with open(_UpperCAmelCase			,	"wb"    ) as opened_file:
                     _UpperCAmelCase     :  str							      =    [
                         to_write[i : i + byte_length]
                         for i in range(0			,	len(_UpperCAmelCase    )			,	_UpperCAmelCase    )
                     ]

                     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(_UpperCAmelCase			,	2    ).to_bytes(1			,	byteorder="big"    )    )
       except OSError:
              print("File not accessible"    )
              sys.exit()

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str    )    ->    str:


       """simple docstring"""







       _UpperCAmelCase     :  Tuple							      =    0
       for letter in data_bits:
              if letter == "1":
                     break
              counter += 1

       _UpperCAmelCase     :  List[Any]							      =    data_bits[counter:]
       _UpperCAmelCase     :  Union[str, Any]							      =    data_bits[counter + 1 :]
       return data_bits

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[int]							      =    read_file_binary(_UpperCAmelCase    )
       _UpperCAmelCase     :  List[Any]							      =    remove_prefix(_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    decompress_data(_UpperCAmelCase    )
       write_file_binary(_UpperCAmelCase			,	_UpperCAmelCase    )


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


'''simple docstring'''




import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


__SCREAMING_SNAKE_CASE       :     str     =  logging.get_logger(__name__)

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[int]						,					A						:				Union[List[ControlNetModel], Tuple[ControlNetModel]]       ):
             super().__init__()
             _UpperCAmelCase     :  Optional[int]							      =    nn.ModuleList(A       )




      def      _A	(       self						:				Dict						,					A						:				torch.FloatTensor						,					A						:				Union[torch.Tensor, float, int]						,					A						:				torch.Tensor						,					A						:				List[torch.tensor]						,					A						:				List[float]						,					A						:				Optional[torch.Tensor] = None						,					A						:				Optional[torch.Tensor] = None						,					A						:				Optional[torch.Tensor] = None						,					A						:				Optional[Dict[str, Any]] = None						,					A						:				bool = False						,					A						:				bool = True						,					):
             for i, (image, scale, controlnet) in enumerate(zip(A						,					A						,					self.nets       )       ):
                    _UpperCAmelCase      ,  _UpperCAmelCase     :  str							      =    controlnet(
                        A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					)

                    # merge samples
                    if i == 0:
                           _UpperCAmelCase      ,  _UpperCAmelCase     :  List[Any]							      =    down_samples, mid_sample
                    else:
                           _UpperCAmelCase     :  Optional[int]							      =    [
                               samples_prev + samples_curr
                               for samples_prev, samples_curr in zip(A						,					A       )
                           ]
                           mid_block_res_sample += mid_sample

             return down_block_res_samples, mid_block_res_sample




      def      _A	(       self						:				List[str]						,					A						:				Union[str, os.PathLike]						,					A						:				bool = True						,					A						:				Callable = None						,					A						:				bool = False						,					A						:				Optional[str] = None						,					):
             _UpperCAmelCase     :  str							      =    0
             _UpperCAmelCase     :  str							      =    save_directory
             for controlnet in self.nets:
                    controlnet.save_pretrained(
                        A						,					is_main_process=A						,					save_function=A						,					safe_serialization=A						,					variant=A						,					)

                    idx += 1
                    _UpperCAmelCase     :  Tuple							      =    model_path_to_save + F"""_{idx}"""





      @classmethod
      def      _A	(       cls						:				int						,					A						:				Optional[Union[str, os.PathLike]]						,					**A						:				Tuple       ):
             _UpperCAmelCase     :  str							      =    0
             _UpperCAmelCase     :  int							      =    []

             # load controlnet and append to list until no controlnet directory exists anymore
             # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
             # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
             _UpperCAmelCase     :  int							      =    pretrained_model_path
             while os.path.isdir(A       ):
                    _UpperCAmelCase     :  List[str]							      =    ControlNetModel.from_pretrained(A						,					**A       )
                    controlnets.append(A       )

                    idx += 1
                    _UpperCAmelCase     :  Dict							      =    pretrained_model_path + F"""_{idx}"""

             logger.info(F"""{len(A       )} controlnets loaded from {pretrained_model_path}."""       )

             if len(A       ) == 0:
                    raise ValueError(
                        F"""No ControlNets found under {os.path.dirname(A       )}. Expected at least {pretrained_model_path + '_0'}."""       )

             return cls(A       )

