




import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=5 )			->							int:


			'''simple docstring'''







			assert masked_input.count('<mask>' ) == 1
			SCREAMING_SNAKE_CASE_								=						torch.tensor(tokenizer.encode(SCREAMING_SNAKE_CASE       ,    add_special_tokens=SCREAMING_SNAKE_CASE ) ).unsqueeze(0 )  # Batch size 1
			SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE )[0]  # The last hidden-state is the first element of the output tuple
			SCREAMING_SNAKE_CASE_								=						(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
			SCREAMING_SNAKE_CASE_								=						logits[0, masked_index, :]
			SCREAMING_SNAKE_CASE_								=						logits.softmax(dim=0 )
			SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						prob.topk(k=SCREAMING_SNAKE_CASE       ,    dim=0 )
			SCREAMING_SNAKE_CASE_								=						' '.join(
			    [tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(SCREAMING_SNAKE_CASE ) )] )
			SCREAMING_SNAKE_CASE_								=						tokenizer.mask_token
			SCREAMING_SNAKE_CASE_								=						[]
			for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ' ) ):
						SCREAMING_SNAKE_CASE_								=						predicted_token_bpe.replace('\u2581'       ,    ' ' )
						if " {0}".format(SCREAMING_SNAKE_CASE ) in masked_input:
									topk_filled_outputs.append(
									    (
									        masked_input.replace(' {0}'.format(SCREAMING_SNAKE_CASE )       ,    SCREAMING_SNAKE_CASE ),
									        values[index].item(),
									        predicted_token,
									    ) )
						else:
									topk_filled_outputs.append(
									    (
									        masked_input.replace(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ),
									        values[index].item(),
									        predicted_token,
									    ) )
			return topk_filled_outputs


SCREAMING_SNAKE_CASE__     :     Any		    =       CamembertTokenizer.from_pretrained("camembert-base")
SCREAMING_SNAKE_CASE__     :     List[str]		    =       CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))






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__     :     Union[str, Any]		    =       logging.get_logger(__name__)



class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Union[str, Any]:




				"""simple docstring"""



				super().__init__()
				SCREAMING_SNAKE_CASE_								=						nn.ModuleList(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = False      ,     SCREAMING_SNAKE_CASE = True      ,     )  ->					Union[ControlNetOutput, Tuple]:




				"""simple docstring"""



				for i, (image, scale, controlnet) in enumerate(zip(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     self.nets      )      ):
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						controlnet(
							    SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     )

							# merge samples
							if i == 0:
										SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						down_samples, mid_sample
							else:
										SCREAMING_SNAKE_CASE_								=						[
										    samples_prev + samples_curr
										    for samples_prev, samples_curr in zip(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
										]
										mid_block_res_sample += mid_sample

				return down_block_res_samples, mid_block_res_sample





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = True      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = False      ,     SCREAMING_SNAKE_CASE = None      ,     )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				SCREAMING_SNAKE_CASE_								=						save_directory
				for controlnet in self.nets:
							controlnet.save_pretrained(
							    SCREAMING_SNAKE_CASE      ,     is_main_process=SCREAMING_SNAKE_CASE      ,     save_function=SCREAMING_SNAKE_CASE      ,     safe_serialization=SCREAMING_SNAKE_CASE      ,     variant=SCREAMING_SNAKE_CASE      ,     )

							idx += 1
							SCREAMING_SNAKE_CASE_								=						model_path_to_save + f'_{idx}'





	@classmethod
	def 						A_(							cls      ,     SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				SCREAMING_SNAKE_CASE_								=						[]

				# 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_								=						pretrained_model_path
				while os.path.isdir(SCREAMING_SNAKE_CASE      ):
							SCREAMING_SNAKE_CASE_								=						ControlNetModel.from_pretrained(SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )
							controlnets.append(SCREAMING_SNAKE_CASE      )

							idx += 1
							SCREAMING_SNAKE_CASE_								=						pretrained_model_path + f'_{idx}'

				logger.info(f'{len(SCREAMING_SNAKE_CASE      )} controlnets loaded from {pretrained_model_path}.'      )

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

				return cls(SCREAMING_SNAKE_CASE      )
