




'''simple docstring'''



import copy
import random

from transformers import CLIPTokenizer






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							*A,							**A  ):







             '''simple docstring'''







             super().__init__(*A,							**A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						{}







       def        UpperCamelCase_							(      self,							A,							*A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						super().add_tokens(A,							*A,							**A  )
             if num_added_tokens == 0:
                   raise ValueError(
                       F"The tokenizer already contains the token {placeholder_token}. Please pass a different"
                       ' `placeholder_token` that is not already in the tokenizer.'  )







       def        UpperCamelCase_							(      self,							A,							*A,							A=1,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						[]
             if num_vec_per_token == 1:
                   self.try_adding_tokens(A,							*A,							**A  )
                   output.append(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						[]
                   for i in range(A  ):
                         SCREAMING_SNAKE_CASE   :      str    					=						placeholder_token + F"_{i}"
                         self.try_adding_tokens(A,							*A,							**A  )
                         output.append(A  )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
             for token in self.token_map:
                   if token in placeholder_token:
                         raise ValueError(
                             F"The tokenizer already has placeholder token {token} that can get confused with"
                             F" {placeholder_token}keep placeholder tokens independent"  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						output







       def        UpperCamelCase_							(      self,							A,							A=False,							A=1.0  ):







             '''simple docstring'''







             if isinstance(A,							A  ):
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						[]
                   for i in range(len(A  )  ):
                         output.append(self.replace_placeholder_tokens_in_text(text[i],							vector_shuffle=A  )  )
                   return output
             for placeholder_token in self.token_map:
                   if placeholder_token in text:
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						self.token_map[placeholder_token]
                         SCREAMING_SNAKE_CASE   :      int    					=						tokens[: 1 + int(len(A  ) * prop_tokens_to_load  )]
                         if vector_shuffle:
                               SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						copy.copy(A  )
                               random.shuffle(A  )
                         SCREAMING_SNAKE_CASE   :      Dict    					=						text.replace(A,							' '.join(A  )  )
             return text







       def __call__(      self,							A,							*A,							A=False,							A=1.0,							**A  ):







             '''simple docstring'''







             return super().__call__(
                 self.replace_placeholder_tokens_in_text(
                     A,							vector_shuffle=A,							prop_tokens_to_load=A  ),							*A,							**A,							)







       def        UpperCamelCase_							(      self,							A,							*A,							A=False,							A=1.0,							**A  ):







             '''simple docstring'''







             return super().encode(
                 self.replace_placeholder_tokens_in_text(
                     A,							vector_shuffle=A,							prop_tokens_to_load=A  ),							*A,							**A,							)








'''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  )


