



from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
							from ..models.auto import AutoTokenizer


class   __lowerCAmelCase  :
					"""simple docstring"""






					def      _a				(  self       :							Dict					,       _snake_case       :							List[str]       ):






								"""simple docstring"""


								raise NotImplementedError()



					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								raise NotImplementedError()







class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					def __init__(  self       :							List[str]					,       _snake_case       :							"AutoTokenizer"					,       _snake_case       :							bool = False					,       **_snake_case       :							Tuple       ):






								"""simple docstring"""


								A__	  =     tokenizer
								A__	  =     skip_prompt
								A__	  =     decode_kwargs

								# variables used in the streaming process
								A__	  =     []
								A__	  =     0
								A__	  =     True



					def      _a				(  self       :							Optional[int]					,       _snake_case       :							str       ):






								"""simple docstring"""


								if len(value.shape       ) > 1 and value.shape[0] > 1:
											raise ValueError('TextStreamer only supports batch size 1'       )
								elif len(value.shape       ) > 1:
											A__	  =     value[0]

								if self.skip_prompt and self.next_tokens_are_prompt:
											A__	  =     False
											return

								# Add the new token to the cache and decodes the entire thing.
								self.token_cache.extend(value.tolist()       )
								A__	  =     self.tokenizer.decode(self.token_cache					,       **self.decode_kwargs       )

								# After the symbol for a new line, we flush the cache.
								if text.endswith('\n'       ):
											A__	  =     text[self.print_len :]
											A__	  =     []
											A__	  =     0
								# If the last token is a CJK character, we print the characters.
								elif len(_snake_case       ) > 0 and self._is_chinese_char(ord(text[-1]       )       ):
											A__	  =     text[self.print_len :]
											self.print_len += len(_snake_case       )
								# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
								# which may change with the subsequent token -- there are probably smarter ways to do this!)
								else:
											A__	  =     text[self.print_len : text.rfind(' '       ) + 1]
											self.print_len += len(_snake_case       )

								self.on_finalized_text(_snake_case       )



					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								if len(self.token_cache       ) > 0:
											A__	  =     self.tokenizer.decode(self.token_cache					,       **self.decode_kwargs       )
											A__	  =     text[self.print_len :]
											A__	  =     []
											A__	  =     0
								else:
											A__	  =     ''

								A__	  =     True
								self.on_finalized_text(_snake_case					,       stream_end=_snake_case       )



					def      _a				(  self       :							Optional[int]					,       _snake_case       :							str					,       _snake_case       :							bool = False       ):






								"""simple docstring"""


								print(_snake_case					,       flush=_snake_case					,       end='' if not stream_end else None       )



					def      _a				(  self       :							Any					,       _snake_case       :							str       ):






								"""simple docstring"""


								if (
								    (cp >= 0x4E00 and cp <= 0x9FFF)
								    or (cp >= 0x3400 and cp <= 0x4DBF)  #
								    or (cp >= 0x20000 and cp <= 0x2A6DF)  #
								    or (cp >= 0x2A700 and cp <= 0x2B73F)  #
								    or (cp >= 0x2B740 and cp <= 0x2B81F)  #
								    or (cp >= 0x2B820 and cp <= 0x2CEAF)  #
								    or (cp >= 0xF900 and cp <= 0xFAFF)
								    or (cp >= 0x2F800 and cp <= 0x2FA1F)  #
								):  #
											return True

								return False







class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					def __init__(  self       :							Dict					,       _snake_case       :							"AutoTokenizer"					,       _snake_case       :							bool = False					,       _snake_case       :							Optional[float] = None					,       **_snake_case       :							str       ):






								"""simple docstring"""


								super().__init__(_snake_case					,       _snake_case					,       **_snake_case       )
								A__	  =     Queue()
								A__	  =     None
								A__	  =     timeout



					def      _a				(  self       :							Optional[int]					,       _snake_case       :							str					,       _snake_case       :							bool = False       ):






								"""simple docstring"""


								self.text_queue.put(_snake_case					,       timeout=self.timeout       )
								if stream_end:
											self.text_queue.put(self.stop_signal					,       timeout=self.timeout       )



					def __iter__(  self       :							Optional[int]       ):






								"""simple docstring"""


								return self



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								A__	  =     self.text_queue.get(timeout=self.timeout       )
								if value == self.stop_signal:
											raise StopIteration()
								else:
											return value










'''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


UpperCamelCase_    	=				logging.get_logger(__name__)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             super().__init__()
             SCREAMING_SNAKE_CASE   :      Dict    					=						nn.ModuleList(A  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A = None,							A = None,							A = None,							A = None,							A = False,							A = True,							):







             '''simple docstring'''







             for i, (image, scale, controlnet) in enumerate(zip(A,							A,							self.nets  )  ):
                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						controlnet(
                       A,							A,							A,							A,							A,							A,							A,							A,							A,							A,							A,							)

                   # merge samples
                   if i == 0:
                         SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						down_samples, mid_sample
                   else:
                         SCREAMING_SNAKE_CASE   :      str    					=						[
                             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        UpperCamelCase_							(      self,							A,							A = True,							A = None,							A = False,							A = None,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						0
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						model_path_to_save + F"_{idx}"







       @classmethod
       def        UpperCamelCase_							(      cls,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						0
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						[]

             # 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`, ...
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						pretrained_model_path
             while os.path.isdir(A  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						ControlNetModel.from_pretrained(A,							**A  )
                   controlnets.append(A  )

                   idx += 1
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						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  )


