



"""simple docstring"""






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__        =				logging.get_logger(__name__)

lowerCAmelCase__        =				{'''vocab_file''': '''sentencepiece.bpe.model'''}

lowerCAmelCase__        =				{
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
}

lowerCAmelCase__        =				{
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

lowerCAmelCase__        =				'''▁'''






class        __snake_case       (	_lowercase):
  snake_case__				:   int  			=						VOCAB_FILES_NAMES
  snake_case__				:   Any  			=						PRETRAINED_VOCAB_FILES_MAP
  snake_case__				:   Optional[Any]  			=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case__				:   Union[str, Any]  			=						["input_ids", "attention_mask"]




  def __init__( self     :						Union[str, Any]						,					__lowerCAmelCase     :						Optional[int]						,					__lowerCAmelCase     :						Optional[Any]="<s>"						,					__lowerCAmelCase     :						Dict="</s>"						,					__lowerCAmelCase     :						Optional[int]="</s>"						,					__lowerCAmelCase     :						Optional[int]="<s>"						,					__lowerCAmelCase     :						Optional[int]="<unk>"						,					__lowerCAmelCase     :						str="<pad>"						,					__lowerCAmelCase     :						Optional[Any]="<mask>"						,					__lowerCAmelCase     :						Optional[Dict[str, Any]] = None						,					**__lowerCAmelCase     :						Optional[int]						,					):






         """simple docstring"""






         _lowerCamelCase						:				Dict    			= AddedToken(__lowerCAmelCase						,					lstrip=__lowerCAmelCase						,					rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,					__lowerCAmelCase		) else mask_token

         _lowerCamelCase						:				List[Any]    			= {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=__lowerCAmelCase						,					eos_token=__lowerCAmelCase						,					unk_token=__lowerCAmelCase						,					sep_token=__lowerCAmelCase						,					cls_token=__lowerCAmelCase						,					pad_token=__lowerCAmelCase						,					mask_token=__lowerCAmelCase						,					sp_model_kwargs=self.sp_model_kwargs						,					**__lowerCAmelCase						,					)

         _lowerCamelCase						:				Dict    			= vocab_file
         _lowerCamelCase						:				int    			= spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(str(__lowerCAmelCase		)		)

         _lowerCamelCase						:				Union[str, Any]    			= {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

         _lowerCamelCase						:				int    			= len(self.sp_model		) - 1
         _lowerCamelCase						:				List[str]    			= {v: k for k, v in self.fairseq_tokens_to_ids.items()}




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict						,					__lowerCAmelCase     :						List[int]						,					__lowerCAmelCase     :						Optional[List[int]] = None		):






         """simple docstring"""






         if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         _lowerCamelCase						:				List[str]    			= [self.cls_token_id]
         _lowerCamelCase						:				int    			= [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict						,					__lowerCAmelCase     :						List[int]						,					__lowerCAmelCase     :						Optional[List[int]] = None						,					__lowerCAmelCase     :						bool = False		):






         """simple docstring"""






         if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=__lowerCAmelCase						,					token_ids_a=__lowerCAmelCase						,					already_has_special_tokens=__lowerCAmelCase		)

         if token_ids_a is None:
                return [1] + ([0] * len(__lowerCAmelCase		)) + [1]
         return [1] + ([0] * len(__lowerCAmelCase		)) + [1, 1] + ([0] * len(__lowerCAmelCase		)) + [1]




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]						,					__lowerCAmelCase     :						List[int]						,					__lowerCAmelCase     :						Optional[List[int]] = None		):






         """simple docstring"""






         _lowerCamelCase						:				List[Any]    			= [self.sep_token_id]
         _lowerCamelCase						:				Optional[int]    			= [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]




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]		):






         """simple docstring"""






         return len(self.sp_model		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         _lowerCamelCase						:				int    			= {self.convert_ids_to_tokens(__lowerCAmelCase		): i for i in range(self.vocab_size		)}
         vocab.update(self.added_tokens_encoder		)
         return vocab




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]						,					__lowerCAmelCase     :						str		):






         """simple docstring"""






         return self.sp_model.encode(__lowerCAmelCase						,					out_type=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]						,					__lowerCAmelCase     :						List[str]		):






         """simple docstring"""






         if token in self.fairseq_tokens_to_ids:
                return self.fairseq_tokens_to_ids[token]
         _lowerCamelCase						:				List[Any]    			= self.sp_model.PieceToId(__lowerCAmelCase		)

         return spm_id if spm_id else self.unk_token_id




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]						,					__lowerCAmelCase     :						str		):






         """simple docstring"""






         if index in self.fairseq_ids_to_tokens:
                return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						str						,					__lowerCAmelCase     :						Dict		):






         """simple docstring"""






         _lowerCamelCase						:				Tuple    			= []
         _lowerCamelCase						:				Tuple    			= ''''''
         _lowerCamelCase						:				Dict    			= False
         for token in tokens:
                # make sure that special tokens are not decoded using sentencepiece model
                if token in self.all_special_tokens:
                       if not prev_is_special:
                              out_string += " "
                       out_string += self.sp_model.decode(__lowerCAmelCase		) + token
                       _lowerCamelCase						:				Union[str, Any]    			= True
                       _lowerCamelCase						:				List[Any]    			= []
                else:
                       current_sub_tokens.append(__lowerCAmelCase		)
                       _lowerCamelCase						:				List[str]    			= False
         out_string += self.sp_model.decode(__lowerCAmelCase		)
         return out_string.strip()




  def __getstate__( self     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				Dict    			= self.__dict__.copy()
         _lowerCamelCase						:				int    			= None
         return state




  def __setstate__( self     :						int						,					__lowerCAmelCase     :						Any		):






         """simple docstring"""






         _lowerCamelCase						:				Union[str, Any]    			= d

         # for backward compatibility
         if not hasattr(self						,					'''sp_model_kwargs'''		):
                _lowerCamelCase						:				Dict    			= {}

         _lowerCamelCase						:				Optional[Any]    			= spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(self.vocab_file		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple						,					__lowerCAmelCase     :						str						,					__lowerCAmelCase     :						Optional[str] = None		):






         """simple docstring"""






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

         if os.path.abspath(self.vocab_file		) != os.path.abspath(__lowerCAmelCase		) and os.path.isfile(self.vocab_file		):
                copyfile(self.vocab_file						,					__lowerCAmelCase		)
         elif not os.path.isfile(self.vocab_file		):
                with open(__lowerCAmelCase						,					'''wb'''		) as fi:
                       _lowerCamelCase						:				Tuple    			= self.sp_model.serialized_model_proto()
                       fi.write(__lowerCAmelCase		)

         return (out_vocab_file,)








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


snake_case_ : List[str]    		=       8


def    A						(__A   :    Union[str, Any]   ,				__A   :    List[Any]=BITS       )   ->      Tuple:







     """simple docstring"""


     UpperCAmelCase_          =							x.device

     UpperCAmelCase_          =							(x * 255).int().clamp(0   ,				255       )

     UpperCAmelCase_          =							2 ** torch.arange(bits - 1   ,				-1   ,				-1   ,				device=__A       )
     UpperCAmelCase_          =							rearrange(__A   ,				'''d -> d 1 1'''       )
     UpperCAmelCase_          =							rearrange(__A   ,				'''b c h w -> b c 1 h w'''       )

     UpperCAmelCase_          =							((x & mask) != 0).float()
     UpperCAmelCase_          =							rearrange(__A   ,				'''b c d h w -> b (c d) h w'''       )
     UpperCAmelCase_          =							bits * 2 - 1
     return bits


def    A						(__A   :    Dict   ,				__A   :    Tuple=BITS       )   ->      List[str]:







     """simple docstring"""


     UpperCAmelCase_          =							x.device

     UpperCAmelCase_          =							(x > 0).int()
     UpperCAmelCase_          =							2 ** torch.arange(bits - 1   ,				-1   ,				-1   ,				device=__A   ,				dtype=torch.intaa       )

     UpperCAmelCase_          =							rearrange(__A   ,				'''d -> d 1 1'''       )
     UpperCAmelCase_          =							rearrange(__A   ,				'''b (c d) h w -> b c d h w'''   ,				d=8       )
     UpperCAmelCase_          =							reduce(x * mask   ,				'''b c d h w -> b c h w'''   ,				'''sum'''       )
     return (dec / 255).clamp(0.0   ,				1.0       )


def    A						(self   :    List[Any]   ,				__A   :    torch.FloatTensor   ,				__A   :    int   ,				__A   :    torch.FloatTensor   ,				__A   :    float = 0.0   ,				__A   :    bool = True   ,				__A   :    Tuple=None   ,				__A   :    bool = True   ,				)   ->      Union[DDIMSchedulerOutput, Tuple]:







     """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)
     UpperCAmelCase_          =							timestep - self.config.num_train_timesteps // self.num_inference_steps

     # 2. compute alphas, betas
     UpperCAmelCase_          =							self.alphas_cumprod[timestep]
     UpperCAmelCase_          =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

     UpperCAmelCase_          =							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
     UpperCAmelCase_          =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

     # 4. Clip "predicted x_0"
     UpperCAmelCase_          =							self.bit_scale
     if self.config.clip_sample:
          UpperCAmelCase_          =							torch.clamp(__A   ,				-scale   ,				__A       )

     # 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)
     UpperCAmelCase_          =							self._get_variance(__A   ,				__A       )
     UpperCAmelCase_          =							eta * variance ** 0.5

     if use_clipped_model_output:
          # the model_output is always re-derived from the clipped x_0 in Glide
          UpperCAmelCase_          =							(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
     UpperCAmelCase_          =							(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
     UpperCAmelCase_          =							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
          UpperCAmelCase_          =							model_output.device if torch.is_tensor(__A       ) else '''cpu'''
          UpperCAmelCase_          =							torch.randn(model_output.shape   ,				dtype=model_output.dtype   ,				generator=__A       ).to(__A       )
          UpperCAmelCase_          =							self._get_variance(__A   ,				__A       ) ** 0.5 * eta * noise

          UpperCAmelCase_          =							prev_sample + variance

     if not return_dict:
          return (prev_sample,)

     return DDIMSchedulerOutput(prev_sample=__A   ,				pred_original_sample=__A       )


def    A						(self   :    Optional[int]   ,				__A   :    torch.FloatTensor   ,				__A   :    int   ,				__A   :    torch.FloatTensor   ,				__A   :    int="epsilon"   ,				__A   :    Optional[Any]=None   ,				__A   :    bool = True   ,				)   ->      Union[DDPMSchedulerOutput, Tuple]:







     """simple docstring"""


     UpperCAmelCase_          =							timestep

     if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
          UpperCAmelCase_     ,       UpperCAmelCase_          =							torch.split(__A   ,				sample.shape[1]   ,				dim=1       )
     else:
          UpperCAmelCase_          =							None

     # 1. compute alphas, betas
     UpperCAmelCase_          =							self.alphas_cumprod[t]
     UpperCAmelCase_          =							self.alphas_cumprod[t - 1] if t > 0 else self.one
     UpperCAmelCase_          =							1 - alpha_prod_t
     UpperCAmelCase_          =							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":
          UpperCAmelCase_          =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif prediction_type == "sample":
          UpperCAmelCase_          =							model_output
     else:
          raise ValueError(F"""Unsupported prediction_type {prediction_type}."""       )

     # 3. Clip "predicted x_0"
     UpperCAmelCase_          =							self.bit_scale
     if self.config.clip_sample:
          UpperCAmelCase_          =							torch.clamp(__A   ,				-scale   ,				__A       )

     # 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
     UpperCAmelCase_          =							(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
     UpperCAmelCase_          =							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
     UpperCAmelCase_          =							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     UpperCAmelCase_          =							0
     if t > 0:
          UpperCAmelCase_          =							torch.randn(
              model_output.size()   ,				dtype=model_output.dtype   ,				layout=model_output.layout   ,				generator=__A       ).to(model_output.device       )
          UpperCAmelCase_          =							(self._get_variance(__A   ,				predicted_variance=__A       ) ** 0.5) * noise

     UpperCAmelCase_          =							pred_prev_sample + variance

     if not return_dict:
          return (pred_prev_sample,)

     return DDPMSchedulerOutput(prev_sample=__A   ,				pred_original_sample=__A       )







class __snake_case			(			a		):







   def __init__(      self		:						Union[str, Any]					,						_snake_case		:						UNetaDConditionModel					,						_snake_case		:						Union[DDIMScheduler, DDPMScheduler]					,						_snake_case		:						Optional[float] = 1.0					,						):

        """simple docstring"""


        super().__init__()
        UpperCAmelCase_          =							bit_scale
        UpperCAmelCase_          =							(
            ddim_bit_scheduler_step if isinstance(_snake_case					,						_snake_case) else ddpm_bit_scheduler_step
        )

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







   @torch.no_grad()
   def __call__(      self		:						Union[str, Any]					,						_snake_case		:						Optional[int] = 256					,						_snake_case		:						Optional[int] = 256					,						_snake_case		:						Optional[int] = 50					,						_snake_case		:						Optional[torch.Generator] = None					,						_snake_case		:						Optional[int] = 1					,						_snake_case		:						Optional[str] = "pil"					,						_snake_case		:						bool = True					,						**_snake_case		:						Optional[Any]					,						):

        """simple docstring"""


        UpperCAmelCase_          =							torch.randn(
            (batch_size, self.unet.config.in_channels, height, width)					,						generator=_snake_case					,						)
        UpperCAmelCase_          =							decimal_to_bits(_snake_case) * self.bit_scale
        UpperCAmelCase_          =							latents.to(self.device)

        self.scheduler.set_timesteps(_snake_case)

        for t in self.progress_bar(self.scheduler.timesteps):
             # predict the noise residual
             UpperCAmelCase_          =							self.unet(_snake_case					,						_snake_case).sample

             # compute the previous noisy sample x_t -> x_t-1
             UpperCAmelCase_          =							self.scheduler.step(_snake_case					,						_snake_case					,						_snake_case).prev_sample

        UpperCAmelCase_          =							bits_to_decimal(_snake_case)

        if output_type == "pil":
             UpperCAmelCase_          =							self.numpy_to_pil(_snake_case)

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=_snake_case)






