


'''simple docstring'''






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def  _snake_case       (					A			,   A=0.999			,   A="cosine"			,   )						->   int:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(A	):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2	) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(A	):
																					return math.exp(t * -12.0	)

							else:
														raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""	)

							lowerCAmelCase__	       =     []
							for i in range(A	):
														lowerCAmelCase__	       =     i / num_diffusion_timesteps
														lowerCAmelCase__	       =     (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(A	) / alpha_bar_fn(A	)			,   A	)	)
							return torch.tensor(A			,   dtype=torch.floataa	)






class 		a__    (							a__    ,    a__  ):





							'''simple docstring'''






							lowercase__      :       Tuple     						=				[e.name for e in KarrasDiffusionSchedulers]
							lowercase__      :       List[Any]     						=				2







							@register_to_config
							def __init__(		self ,   lowerCamelCase_ = 10_00 ,   lowerCamelCase_ = 0.00_085 ,   lowerCamelCase_ = 0.012 ,   lowerCamelCase_ = "linear" ,   lowerCamelCase_ = None ,   lowerCamelCase_ = "epsilon" ,   lowerCamelCase_ = "linspace" ,   lowerCamelCase_ = 0 ,   )	->							Tuple:
														if trained_betas is not None:
																					lowerCAmelCase__	       =     torch.tensor(lowerCamelCase_ ,   dtype=torch.floataa       )
														elif beta_schedule == "linear":
																					lowerCAmelCase__	       =     torch.linspace(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   dtype=torch.floataa       )
														elif beta_schedule == "scaled_linear":
																					# this schedule is very specific to the latent diffusion model.
																					lowerCAmelCase__	       =     (
																					    torch.linspace(beta_start**0.5 ,   beta_end**0.5 ,   lowerCamelCase_ ,   dtype=torch.floataa       ) ** 2
																					)
														elif beta_schedule == "squaredcos_cap_v2":
																					# Glide cosine schedule
																					lowerCAmelCase__	       =     betas_for_alpha_bar(lowerCamelCase_       )
														else:
																					raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""       )

														lowerCAmelCase__	       =     1.0 - self.betas
														lowerCAmelCase__	       =     torch.cumprod(self.alphas ,   dim=0       )

														#  set all values
														self.set_timesteps(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_=None       )	->							Any:
														if schedule_timesteps is None:
																					lowerCAmelCase__	       =     self.timesteps

														lowerCAmelCase__	       =     (schedule_timesteps == timestep).nonzero()

														# The sigma index that is taken for the **very** first `step`
														# is always the second index (or the last index if there is only 1)
														# This way we can ensure we don't accidentally skip a sigma in
														# case we start in the middle of the denoising schedule (e.g. for image-to-image)
														if len(self._index_counter       ) == 0:
																					lowerCAmelCase__	       =     1 if len(lowerCamelCase_       ) > 1 else 0
														else:
																					lowerCAmelCase__	       =     timestep.cpu().item() if torch.is_tensor(lowerCamelCase_       ) else timestep
																					lowerCAmelCase__	       =     self._index_counter[timestep_int]

														return indices[pos].item()







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														# standard deviation of the initial noise distribution
														if self.config.timestep_spacing in ["linspace", "trailing"]:
																					return self.sigmas.max()

														return (self.sigmas.max() ** 2 + 1) ** 0.5







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   )	->							torch.FloatTensor:
														lowerCAmelCase__	       =     self.index_for_timestep(lowerCamelCase_       )

														if self.state_in_first_order:
																					lowerCAmelCase__	       =     self.sigmas[step_index]
														else:
																					lowerCAmelCase__	       =     self.sigmas_interpol[step_index]

														lowerCAmelCase__	       =     sample / ((sigma**2 + 1) ** 0.5)
														return sample







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   )	->							Optional[Any]:
														lowerCAmelCase__	       =     num_inference_steps

														lowerCAmelCase__	       =     num_train_timesteps or self.config.num_train_timesteps

														# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
														if self.config.timestep_spacing == "linspace":
																					lowerCAmelCase__	       =     np.linspace(0 ,   num_train_timesteps - 1 ,   lowerCamelCase_ ,   dtype=lowerCamelCase_       )[::-1].copy()
														elif self.config.timestep_spacing == "leading":
																					lowerCAmelCase__	       =     num_train_timesteps // self.num_inference_steps
																					# creates integer timesteps by multiplying by ratio
																					# casting to int to avoid issues when num_inference_step is power of 3
																					lowerCAmelCase__	       =     (np.arange(0 ,   lowerCamelCase_       ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase_       )
																					timesteps += self.config.steps_offset
														elif self.config.timestep_spacing == "trailing":
																					lowerCAmelCase__	       =     num_train_timesteps / self.num_inference_steps
																					# creates integer timesteps by multiplying by ratio
																					# casting to int to avoid issues when num_inference_step is power of 3
																					lowerCAmelCase__	       =     (np.arange(lowerCamelCase_ ,   0 ,   -step_ratio       )).round().copy().astype(lowerCamelCase_       )
																					timesteps -= 1
														else:
																					raise ValueError(
																					    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""       )

														lowerCAmelCase__	       =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
														lowerCAmelCase__	       =     torch.from_numpy(np.log(lowerCamelCase_       )       ).to(lowerCamelCase_       )

														lowerCAmelCase__	       =     np.interp(lowerCamelCase_ ,   np.arange(0 ,   len(lowerCamelCase_       )       ) ,   lowerCamelCase_       )
														lowerCAmelCase__	       =     np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
														lowerCAmelCase__	       =     torch.from_numpy(lowerCamelCase_       ).to(device=lowerCamelCase_       )

														# interpolate sigmas
														lowerCAmelCase__	       =     sigmas.log().lerp(sigmas.roll(1       ).log() ,   0.5       ).exp()

														lowerCAmelCase__	       =     torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2       ), sigmas[-1:]]       )
														lowerCAmelCase__	       =     torch.cat(
														    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2       ), sigmas_interpol[-1:]]       )

														if str(lowerCamelCase_       ).startswith('''mps'''       ):
																					# mps does not support float64
																					lowerCAmelCase__	       =     torch.from_numpy(lowerCamelCase_       ).to(lowerCamelCase_ ,   dtype=torch.floataa       )
														else:
																					lowerCAmelCase__	       =     torch.from_numpy(lowerCamelCase_       ).to(lowerCamelCase_       )

														# interpolate timesteps
														lowerCAmelCase__	       =     self.sigma_to_t(lowerCamelCase_       ).to(lowerCamelCase_ ,   dtype=timesteps.dtype       )
														lowerCAmelCase__	       =     torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,   dim=-1       ).flatten()

														lowerCAmelCase__	       =     torch.cat([timesteps[:1], interleaved_timesteps]       )

														lowerCAmelCase__	       =     None

														# for exp beta schedules, such as the one for `pipeline_shap_e.py`
														# we need an index counter
														lowerCAmelCase__	       =     defaultdict(lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							List[Any]:
														# get log sigma
														lowerCAmelCase__	       =     sigma.log()

														# get distribution
														lowerCAmelCase__	       =     log_sigma - self.log_sigmas[:, None]

														# get sigmas range
														lowerCAmelCase__	       =     dists.ge(0       ).cumsum(dim=0       ).argmax(dim=0       ).clamp(max=self.log_sigmas.shape[0] - 2       )
														lowerCAmelCase__	       =     low_idx + 1

														lowerCAmelCase__	       =     self.log_sigmas[low_idx]
														lowerCAmelCase__	       =     self.log_sigmas[high_idx]

														# interpolate sigmas
														lowerCAmelCase__	       =     (low - log_sigma) / (low - high)
														lowerCAmelCase__	       =     w.clamp(0 ,   1       )

														# transform interpolation to time range
														lowerCAmelCase__	       =     (1 - w) * low_idx + w * high_idx
														lowerCAmelCase__	       =     t.view(sigma.shape       )
														return t







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														return self.sample is None







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = True ,   )	->							Union[SchedulerOutput, Tuple]:
														lowerCAmelCase__	       =     self.index_for_timestep(lowerCamelCase_       )

														# advance index counter by 1
														lowerCAmelCase__	       =     timestep.cpu().item() if torch.is_tensor(lowerCamelCase_       ) else timestep
														self._index_counter[timestep_int] += 1

														if self.state_in_first_order:
																					lowerCAmelCase__	       =     self.sigmas[step_index]
																					lowerCAmelCase__	       =     self.sigmas_interpol[step_index + 1]
																					lowerCAmelCase__	       =     self.sigmas[step_index + 1]
														else:
																					# 2nd order / KDPM2's method
																					lowerCAmelCase__	       =     self.sigmas[step_index - 1]
																					lowerCAmelCase__	       =     self.sigmas_interpol[step_index]
																					lowerCAmelCase__	       =     self.sigmas[step_index]

														# currently only gamma=0 is supported. This usually works best anyways.
														# We can support gamma in the future but then need to scale the timestep before
														# passing it to the model which requires a change in API
														lowerCAmelCase__	       =     0
														lowerCAmelCase__	       =     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

														# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
														if self.config.prediction_type == "epsilon":
																					lowerCAmelCase__	       =     sigma_hat if self.state_in_first_order else sigma_interpol
																					lowerCAmelCase__	       =     sample - sigma_input * model_output
														elif self.config.prediction_type == "v_prediction":
																					lowerCAmelCase__	       =     sigma_hat if self.state_in_first_order else sigma_interpol
																					lowerCAmelCase__	       =     model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																					    sample / (sigma_input**2 + 1)
																					)
														elif self.config.prediction_type == "sample":
																					raise NotImplementedError('''prediction_type not implemented yet: sample'''       )
														else:
																					raise ValueError(
																					    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""       )

														if self.state_in_first_order:
																					# 2. Convert to an ODE derivative for 1st order
																					lowerCAmelCase__	       =     (sample - pred_original_sample) / sigma_hat
																					# 3. delta timestep
																					lowerCAmelCase__	       =     sigma_interpol - sigma_hat

																					# store for 2nd order step
																					lowerCAmelCase__	       =     sample
														else:
																					# DPM-Solver-2
																					# 2. Convert to an ODE derivative for 2nd order
																					lowerCAmelCase__	       =     (sample - pred_original_sample) / sigma_interpol

																					# 3. delta timestep
																					lowerCAmelCase__	       =     sigma_next - sigma_hat

																					lowerCAmelCase__	       =     self.sample
																					lowerCAmelCase__	       =     None

														lowerCAmelCase__	       =     sample + derivative * dt

														if not return_dict:
																					return (prev_sample,)

														return SchedulerOutput(prev_sample=lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   )	->							torch.FloatTensor:
														# Make sure sigmas and timesteps have the same device and dtype as original_samples
														lowerCAmelCase__	       =     self.sigmas.to(device=original_samples.device ,   dtype=original_samples.dtype       )
														if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase_       ):
																					# mps does not support float64
																					lowerCAmelCase__	       =     self.timesteps.to(original_samples.device ,   dtype=torch.floataa       )
																					lowerCAmelCase__	       =     timesteps.to(original_samples.device ,   dtype=torch.floataa       )
														else:
																					lowerCAmelCase__	       =     self.timesteps.to(original_samples.device       )
																					lowerCAmelCase__	       =     timesteps.to(original_samples.device       )

														lowerCAmelCase__	       =     [self.index_for_timestep(lowerCamelCase_ ,   lowerCamelCase_       ) for t in timesteps]

														lowerCAmelCase__	       =     sigmas[step_indices].flatten()
														while len(sigma.shape       ) < len(original_samples.shape       ):
																					lowerCAmelCase__	       =     sigma.unsqueeze(-1       )

														lowerCAmelCase__	       =     original_samples + noise * sigma
														return noisy_samples







							def __len__(		self       )	->							str:
														return self.config.num_train_timesteps



'''simple docstring'''






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def  _snake_case       (					A			,   A			,   A			,   A=5	)						->   List[str]:
							# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
							assert masked_input.count('''<mask>'''	) == 1
							lowerCAmelCase__	       =     torch.tensor(tokenizer.encode(A			,   add_special_tokens=A	)	).unsqueeze(0	)  # Batch size 1
							lowerCAmelCase__	       =     model(A	)[0]  # The last hidden-state is the first element of the output tuple
							lowerCAmelCase__	       =     (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
							lowerCAmelCase__	       =     logits[0, masked_index, :]
							lowerCAmelCase__	       =     logits.softmax(dim=0	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     prob.topk(k=A			,   dim=0	)
							lowerCAmelCase__	       =     ''' '''.join(
							    [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(A	)	)]	)
							lowerCAmelCase__	       =     tokenizer.mask_token
							lowerCAmelCase__	       =     []
							for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''	)	):
														lowerCAmelCase__	       =     predicted_token_bpe.replace('''\u2581'''			,   ''' '''	)
														if " {0}".format(A	) in masked_input:
																					topk_filled_outputs.append(
																					    (
																					        masked_input.replace(''' {0}'''.format(A	)			,   A	),
																					        values[index].item(),
																					        predicted_token,
																					    )	)
														else:
																					topk_filled_outputs.append(
																					    (
																					        masked_input.replace(A			,   A	),
																					        values[index].item(),
																					        predicted_token,
																					    )	)
							return topk_filled_outputs


__UpperCAmelCase       =							CamembertTokenizer.from_pretrained('''camembert-base''')
__UpperCAmelCase       =							CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

__UpperCAmelCase       =							'''Le camembert est <mask> :)'''






print(fill_mask(masked_input, model, tokenizer, topk=3))