


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


_lowercase       :      List[Any]	=	logging.get_logger(__name__)






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				def __init__(						self				,   a__   )	->				Optional[Any]:
					super().__init__()
					A								=				nn.ModuleList(a__   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__ = None				,   a__ = None				,   a__ = None				,   a__ = None				,   a__ = False				,   a__ = True				,   )	->				Union[ControlNetOutput, Tuple]:
					for i, (image, scale, controlnet) in enumerate(zip(a__				,   a__				,   self.nets   )   ):
						A					,			A								=				controlnet(
						    a__				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__				,   )

						# merge samples
						if i == 0:
							A					,			A								=				down_samples, mid_sample
						else:
							A								=				[
							    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				,   )	->				Optional[int]:
					A								=				0
					A								=				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
						A								=				model_path_to_save + f'_{idx}'

				@classmethod
				def      _UpperCAmelCase					(						cls				,   a__				,   **a__   )	->				Dict:
					A								=				0
					A								=				[]

					# 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`, ...
					A								=				pretrained_model_path
					while os.path.isdir(a__   ):
						A								=				ControlNetModel.from_pretrained(a__				,   **a__   )
						controlnets.append(a__   )

						idx += 1
						A								=				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__   )







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any=5						) ->   Optional[Any]:





	"""simple docstring"""







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


_lowercase       :      Optional[int]	=	CamembertTokenizer.from_pretrained("camembert-base")
_lowercase       :      int	=	CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

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



