





"""simple docstring"""





from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_UpperCamelCase					      =   8



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Optional[Any]     ,    SCREAMING_SNAKE_CASE							:				Optional[int]=BITS ):


							'''simple docstring'''







							__lowerCamelCase   :   Dict       =x.device

							__lowerCamelCase   :   int       =(x * 255).int().clamp(0     ,    255 )

							__lowerCamelCase   :   List[Any]       =2 ** torch.arange(bits - 1     ,    -1     ,    -1     ,    device=SCREAMING_SNAKE_CASE )
							__lowerCamelCase   :   Optional[Any]       =rearrange(SCREAMING_SNAKE_CASE     ,    '''d -> d 1 1''' )
							__lowerCamelCase   :   str       =rearrange(SCREAMING_SNAKE_CASE     ,    '''b c h w -> b c 1 h w''' )

							__lowerCamelCase   :   Tuple       =((x & mask) != 0).float()
							__lowerCamelCase   :   Any       =rearrange(SCREAMING_SNAKE_CASE     ,    '''b c d h w -> b (c d) h w''' )
							__lowerCamelCase   :   List[Any]       =bits * 2 - 1
							return bits



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Union[str, Any]     ,    SCREAMING_SNAKE_CASE							:				Dict=BITS ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[int]       =x.device

							__lowerCamelCase   :   Union[str, Any]       =(x > 0).int()
							__lowerCamelCase   :   int       =2 ** torch.arange(bits - 1     ,    -1     ,    -1     ,    device=SCREAMING_SNAKE_CASE     ,    dtype=torch.intaa )

							__lowerCamelCase   :   Optional[int]       =rearrange(SCREAMING_SNAKE_CASE     ,    '''d -> d 1 1''' )
							__lowerCamelCase   :   List[Any]       =rearrange(SCREAMING_SNAKE_CASE     ,    '''b (c d) h w -> b c d h w'''     ,    d=8 )
							__lowerCamelCase   :   Tuple       =reduce(x * mask     ,    '''b c d h w -> b c h w'''     ,    '''sum''' )
							return (dec / 255).clamp(0.0     ,    1.0 )



def 					lowerCAmelCase_      (   self							:				Union[str, Any]     ,    SCREAMING_SNAKE_CASE							:				torch.FloatTensor     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				torch.FloatTensor     ,    SCREAMING_SNAKE_CASE							:				float = 0.0     ,    SCREAMING_SNAKE_CASE							:				bool = True     ,    SCREAMING_SNAKE_CASE							:				Tuple=None     ,    SCREAMING_SNAKE_CASE							:				bool = True     ,    ):


							'''simple docstring'''







							if self.num_inference_steps is None:
														raise ValueError(
														    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )

							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
							# Ideally, read DDIM paper in-detail understanding

							# Notation (<variable name> -> <name in paper>
							# - pred_noise_t -> e_theta(x_t, t)
							# - pred_original_sample -> f_theta(x_t, t) or x_0
							# - std_dev_t -> sigma_t
							# - eta -> η
							# - pred_sample_direction -> "direction pointing to x_t"
							# - pred_prev_sample -> "x_t-1"

							# 1. get previous step value (=t-1)
							__lowerCamelCase   :   Tuple       =timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 2. compute alphas, betas
							__lowerCamelCase   :   Dict       =self.alphas_cumprod[timestep]
							__lowerCamelCase   :   Optional[Any]       =self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

							__lowerCamelCase   :   str       =1 - alpha_prod_t

							# 3. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							__lowerCamelCase   :   List[Any]       =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

							# 4. Clip "predicted x_0"
							__lowerCamelCase   :   Tuple       =self.bit_scale
							if self.config.clip_sample:
														__lowerCamelCase   :   Any       =torch.clamp(SCREAMING_SNAKE_CASE     ,    -scale     ,    SCREAMING_SNAKE_CASE )

							# 5. compute variance: "sigma_t(η)" -> see formula (16)
							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
							__lowerCamelCase   :   int       =self._get_variance(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
							__lowerCamelCase   :   Tuple       =eta * variance ** 0.5

							if use_clipped_model_output:
														# the model_output is always re-derived from the clipped x_0 in Glide
														__lowerCamelCase   :   str       =(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

							# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							__lowerCamelCase   :   Optional[int]       =(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							__lowerCamelCase   :   Union[str, Any]       =alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if eta > 0:
														# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
														__lowerCamelCase   :   int       =model_output.device if torch.is_tensor(SCREAMING_SNAKE_CASE ) else '''cpu'''
														__lowerCamelCase   :   List[Any]       =torch.randn(model_output.shape     ,    dtype=model_output.dtype     ,    generator=SCREAMING_SNAKE_CASE ).to(SCREAMING_SNAKE_CASE )
														__lowerCamelCase   :   List[Any]       =self._get_variance(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ) ** 0.5 * eta * noise

														__lowerCamelCase   :   Dict       =prev_sample + variance

							if not return_dict:
														return (prev_sample,)

							return DDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE     ,    pred_original_sample=SCREAMING_SNAKE_CASE )



def 					lowerCAmelCase_      (   self							:				Optional[int]     ,    SCREAMING_SNAKE_CASE							:				torch.FloatTensor     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				torch.FloatTensor     ,    SCREAMING_SNAKE_CASE							:				Any="epsilon"     ,    SCREAMING_SNAKE_CASE							:				Optional[Any]=None     ,    SCREAMING_SNAKE_CASE							:				bool = True     ,    ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[int]       =timestep

							if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
														__lowerCamelCase     ,    __lowerCamelCase   :   Tuple       =torch.split(SCREAMING_SNAKE_CASE     ,    sample.shape[1]     ,    dim=1 )
							else:
														__lowerCamelCase   :   Optional[int]       =None

							# 1. compute alphas, betas
							__lowerCamelCase   :   List[Any]       =self.alphas_cumprod[t]
							__lowerCamelCase   :   Tuple       =self.alphas_cumprod[t - 1] if t > 0 else self.one
							__lowerCamelCase   :   Optional[int]       =1 - alpha_prod_t
							__lowerCamelCase   :   List[Any]       =1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if prediction_type == "epsilon":
														__lowerCamelCase   :   str       =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif prediction_type == "sample":
														__lowerCamelCase   :   List[str]       =model_output
							else:
														raise ValueError(F'Unsupported prediction_type {prediction_type}.' )

							# 3. Clip "predicted x_0"
							__lowerCamelCase   :   List[str]       =self.bit_scale
							if self.config.clip_sample:
														__lowerCamelCase   :   Union[str, Any]       =torch.clamp(SCREAMING_SNAKE_CASE     ,    -scale     ,    SCREAMING_SNAKE_CASE )

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							__lowerCamelCase   :   Any       =(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
							__lowerCamelCase   :   Tuple       =self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							__lowerCamelCase   :   int       =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							__lowerCamelCase   :   Optional[Any]       =0
							if t > 0:
														__lowerCamelCase   :   Tuple       =torch.randn(
														    model_output.size()     ,    dtype=model_output.dtype     ,    layout=model_output.layout     ,    generator=SCREAMING_SNAKE_CASE ).to(model_output.device )
														__lowerCamelCase   :   List[Any]       =(self._get_variance(SCREAMING_SNAKE_CASE     ,    predicted_variance=SCREAMING_SNAKE_CASE ) ** 0.5) * noise

							__lowerCamelCase   :   int       =pred_prev_sample + variance

							if not return_dict:
														return (pred_prev_sample,)

							return DDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE     ,    pred_original_sample=SCREAMING_SNAKE_CASE )
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




				def __init__(  self	:int    ,     __lowercase	:UNetaDConditionModel    ,     __lowercase	:Union[DDIMScheduler, DDPMScheduler]    ,     __lowercase	:Optional[float] = 1.0    ,     ):
											super().__init__()
											__lowerCamelCase   :   int       =bit_scale
											__lowerCamelCase   :   Dict       =(
											    ddim_bit_scheduler_step if isinstance(__lowercase    ,     __lowercase      ) else ddpm_bit_scheduler_step
											)

											self.register_modules(unet=__lowercase    ,     scheduler=__lowercase      )




				@torch.no_grad()
				def __call__(  self	:str    ,     __lowercase	:Optional[int] = 256    ,     __lowercase	:Optional[int] = 256    ,     __lowercase	:Optional[int] = 50    ,     __lowercase	:Optional[torch.Generator] = None    ,     __lowercase	:Optional[int] = 1    ,     __lowercase	:Optional[str] = "pil"    ,     __lowercase	:bool = True    ,     **__lowercase	:Optional[int]    ,     ):
											__lowerCamelCase   :   Optional[int]       =torch.randn(
											    (batch_size, self.unet.config.in_channels, height, width)    ,     generator=__lowercase    ,     )
											__lowerCamelCase   :   Any       =decimal_to_bits(__lowercase      ) * self.bit_scale
											__lowerCamelCase   :   List[str]       =latents.to(self.device      )

											self.scheduler.set_timesteps(__lowercase      )

											for t in self.progress_bar(self.scheduler.timesteps      ):
																		# predict the noise residual
																		__lowerCamelCase   :   List[str]       =self.unet(__lowercase    ,     __lowercase      ).sample

																		# compute the previous noisy sample x_t -> x_t-1
																		__lowerCamelCase   :   Dict       =self.scheduler.step(__lowercase    ,     __lowercase    ,     __lowercase      ).prev_sample

											__lowerCamelCase   :   str       =bits_to_decimal(__lowercase      )

											if output_type == "pil":
																		__lowerCamelCase   :   List[Any]       =self.numpy_to_pil(__lowercase      )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=__lowercase      )











"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_camembert import CamembertTokenizer
else:
							_UpperCamelCase					      =   None


_UpperCamelCase					      =   logging.get_logger(__name__)

_UpperCamelCase					      =   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase					      =   {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
    },
}

_UpperCamelCase					      =   {
    'camembert-base': 512,
}

_UpperCamelCase					      =   '▁'
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							List[str]									=		VOCAB_FILES_NAMES
				__snake_case :							Optional[int]									=		PRETRAINED_VOCAB_FILES_MAP
				__snake_case :							Dict									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__snake_case :							List[str]									=		["""input_ids""", """attention_mask"""]
				__snake_case :							List[Any]									=		CamembertTokenizer




				def __init__(  self	:List[Any]    ,     __lowercase	:Optional[int]=None    ,     __lowercase	:str=None    ,     __lowercase	:Optional[Any]="<s>"    ,     __lowercase	:List[str]="</s>"    ,     __lowercase	:Tuple="</s>"    ,     __lowercase	:int="<s>"    ,     __lowercase	:Union[str, Any]="<unk>"    ,     __lowercase	:Optional[int]="<pad>"    ,     __lowercase	:Union[str, Any]="<mask>"    ,     __lowercase	:Tuple=["<s>NOTUSED", "</s>NOTUSED"]    ,     **__lowercase	:List[str]    ,     ):
											# Mask token behave like a normal word, i.e. include the space before it
											__lowerCamelCase   :   Optional[int]       =AddedToken(__lowercase    ,     lstrip=__lowercase    ,     rstrip=__lowercase      ) if isinstance(__lowercase    ,     __lowercase      ) else mask_token

											super().__init__(
											    __lowercase    ,     tokenizer_file=__lowercase    ,     bos_token=__lowercase    ,     eos_token=__lowercase    ,     sep_token=__lowercase    ,     cls_token=__lowercase    ,     unk_token=__lowercase    ,     pad_token=__lowercase    ,     mask_token=__lowercase    ,     additional_special_tokens=__lowercase    ,     **__lowercase    ,     )

											__lowerCamelCase   :   Any       =vocab_file
											__lowerCamelCase   :   Union[str, Any]       =False if not self.vocab_file else True




				def        __lowercase	(  self	:List[str]    ,     __lowercase	:List[int]    ,     __lowercase	:Optional[List[int]] = None      ):

											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											__lowerCamelCase   :   Tuple       =[self.cls_token_id]
											__lowerCamelCase   :   str       =[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:List[int]    ,     __lowercase	:Optional[List[int]] = None      ):
											__lowerCamelCase   :   Any       =[self.sep_token_id]
											__lowerCamelCase   :   str       =[self.cls_token_id]

											if token_ids_a is None:
																		return len(cls + token_ids_a + sep      ) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:str    ,     __lowercase	:Optional[str] = None      ):
											if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																		    '''tokenizer.'''      )

											if not os.path.isdir(__lowercase      ):
																		logger.error(f'Vocabulary path ({save_directory}) should be a directory'      )
																		return
											__lowerCamelCase   :   List[str]       =os.path.join(
											    __lowercase    ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

											if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowercase      ):
																		copyfile(self.vocab_file    ,     __lowercase      )

											return (out_vocab_file,)




