




'''simple docstring'''




import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class 						__UpperCamelCase (				lowercase_       ):
       lowercase			:					torch.FloatTensor
       lowercase			:					torch.FloatTensor





def   a_							(   __snake_case							:    Dict     , __snake_case							:    Union[str, Any]=0.9_9_9     , __snake_case							:    Union[str, Any]="cosine"     , )							->		Dict:



       """simple docstring"""







       if alpha_transform_type == "cosine":

              def alpha_bar_fn(__snake_case							:    int     ):
                     return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2     ) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(__snake_case							:    int     ):
                     return math.exp(t * -1_2.0     )

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

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


class 						__UpperCamelCase (				lowercase_    ,      lowercase_       ):
       lowercase			:					List[str]   		=1






       @register_to_config
       def __init__(							self,	lowerCAmelCase = 1_000,	lowerCAmelCase = 0.0_0_0_1,	lowerCAmelCase = 0.0_2,	lowerCAmelCase = "linear",	lowerCAmelCase = 0.0,	lowerCAmelCase = None,	lowerCAmelCase = True,	):



              """simple docstring"""







              if trained_betas is not None:
                     lowerCamelCase_							=torch.from_numpy(lowerCAmelCase						)
              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						)
              elif beta_schedule == "sigmoid":
                     # GeoDiff sigmoid schedule
                     lowerCamelCase_							=torch.linspace(-6,	6,	lowerCAmelCase						)
                     lowerCamelCase_							=torch.sigmoid(lowerCAmelCase						) * (beta_end - beta_start) + beta_start
              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						)
              lowerCamelCase_							=torch.tensor(1.0						)

              lowerCamelCase_							=torch.tensor(1.0						)

              # standard deviation of the initial noise distribution
              lowerCamelCase_							=1.0

              # setable values
              lowerCamelCase_							=None
              lowerCamelCase_							=torch.from_numpy(np.arange(0,	lowerCAmelCase						)[::-1].copy()						)

              lowerCamelCase_							=eta






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              return sample






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = 10,	lowerCAmelCase = 10,	lowerCAmelCase = None,	):



              """simple docstring"""







              lowerCamelCase_							=min(self.config.num_train_timesteps,	lowerCAmelCase						)
              lowerCamelCase_							=num_inference_steps

              lowerCamelCase_							=[]

              lowerCamelCase_							={}
              for j in range(0,	num_inference_steps - jump_length,	lowerCAmelCase						):
                     lowerCamelCase_							=jump_n_sample - 1

              lowerCamelCase_							=num_inference_steps
              while t >= 1:
                     lowerCamelCase_							=t - 1
                     timesteps.append(lowerCAmelCase						)

                     if jumps.get(lowerCAmelCase,	0						) > 0:
                            lowerCamelCase_							=jumps[t] - 1
                            for _ in range(lowerCAmelCase						):
                                   lowerCamelCase_							=t + 1
                                   timesteps.append(lowerCAmelCase						)

              lowerCamelCase_							=np.array(lowerCAmelCase						) * (self.config.num_train_timesteps // self.num_inference_steps)
              lowerCamelCase_							=torch.from_numpy(lowerCAmelCase						).to(lowerCAmelCase						)






       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=t - self.config.num_train_timesteps // self.num_inference_steps

              lowerCamelCase_							=self.alphas_cumprod[t]
              lowerCamelCase_							=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
              lowerCamelCase_							=1 - alpha_prod_t
              lowerCamelCase_							=1 - alpha_prod_t_prev

              # For t > 0, compute predicted variance βt (see formula (6) and (7) from
              # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
              # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
              # variance to pred_sample
              # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
              # without eta.
              # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
              lowerCamelCase_							=(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

              return variance






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase = None,	lowerCAmelCase = True,	):



              """simple docstring"""







              lowerCamelCase_							=timestep
              lowerCamelCase_							=timestep - self.config.num_train_timesteps // self.num_inference_steps

              # 1. compute alphas, betas
              lowerCamelCase_							=self.alphas_cumprod[t]
              lowerCamelCase_							=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
              lowerCamelCase_							=1 - alpha_prod_t

              # 2. compute predicted original sample from predicted noise also called
              # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
              lowerCamelCase_							=(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

              # 3. Clip "predicted x_0"
              if self.config.clip_sample:
                     lowerCamelCase_							=torch.clamp(lowerCAmelCase,	-1,	1						)

              # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
              # substitute formula (7) in the algorithm coming from DDPM paper
              # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
              # DDIM schedule gives the same results as DDPM with eta = 1.0
              # Noise is being reused in 7. and 8., but no impact on quality has
              # been observed.

              # 5. Add noise
              lowerCamelCase_							=model_output.device
              lowerCamelCase_							=randn_tensor(model_output.shape,	generator=lowerCAmelCase,	device=lowerCAmelCase,	dtype=model_output.dtype						)
              lowerCamelCase_							=self.eta * self._get_variance(lowerCAmelCase						) ** 0.5

              lowerCamelCase_							=0
              if t > 0 and self.eta > 0:
                     lowerCamelCase_							=std_dev_t * noise

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

              # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
              lowerCamelCase_							=alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

              # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
              lowerCamelCase_							=(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

              # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
              lowerCamelCase_							=mask * prev_known_part + (1.0 - mask) * prev_unknown_part

              if not return_dict:
                     return (
                         pred_prev_sample,
                         pred_original_sample,
                     )

              return RePaintSchedulerOutput(prev_sample=lowerCAmelCase,	pred_original_sample=lowerCAmelCase						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase=None						):



              """simple docstring"""







              lowerCamelCase_							=self.config.num_train_timesteps // self.num_inference_steps

              for i in range(lowerCAmelCase						):
                     lowerCamelCase_							=self.betas[timestep + i]
                     if sample.device.type == "mps":
                            # randn does not work reproducibly on mps
                            lowerCamelCase_							=randn_tensor(sample.shape,	dtype=sample.dtype,	generator=lowerCAmelCase						)
                            lowerCamelCase_							=noise.to(sample.device						)
                     else:
                            lowerCamelCase_							=randn_tensor(sample.shape,	generator=lowerCAmelCase,	device=sample.device,	dtype=sample.dtype						)

                     # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
                     lowerCamelCase_							=(1 - beta) ** 0.5 * sample + beta**0.5 * noise

              return sample






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	):



              """simple docstring"""







              raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''						)






       def __len__(							self						):



              """simple docstring"""







              return self.config.num_train_timesteps










'''simple docstring'''




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

from tokenizers import processors

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


if is_sentencepiece_available():
     from .tokenization_mbartaa import MBartaaTokenizer
else:
     a_   :	Tuple							   = None


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

a_   :	Any							   = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

a_   :	Any							   = {
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"""
        ),
    },
}

a_   :	Dict							   = {
    """facebook/mbart-large-50-one-to-many-mmt""": 10_24,
}

# fmt: off
a_   :	List[str]							   = ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]


class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					Any   		=VOCAB_FILES_NAMES
       lowercase			:					str   		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase			:					Dict   		=PRETRAINED_VOCAB_FILES_MAP
       lowercase			:					str   		=['input_ids', 'attention_mask']
       lowercase			:					List[Any]   		=MBartaaTokenizer

       lowercase			:					List[int]   		=[]
       lowercase			:					List[int]   		=[]






       def __init__(							self,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase="</s>",	lowerCAmelCase="</s>",	lowerCAmelCase="<s>",	lowerCAmelCase="<unk>",	lowerCAmelCase="<pad>",	lowerCAmelCase="<mask>",	**lowerCAmelCase,	):



              """simple docstring"""







              lowerCamelCase_							=AddedToken(lowerCAmelCase,	lstrip=lowerCAmelCase,	rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase,	lowerCAmelCase						) else mask_token

              lowerCamelCase_							=kwargs.get('''additional_special_tokens''',	[]						)
              kwargs["additional_special_tokens"] += [
                  code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
              ]

              super().__init__(
                  lowerCAmelCase,	src_lang=lowerCAmelCase,	tgt_lang=lowerCAmelCase,	tokenizer_file=lowerCAmelCase,	eos_token=lowerCAmelCase,	sep_token=lowerCAmelCase,	cls_token=lowerCAmelCase,	unk_token=lowerCAmelCase,	pad_token=lowerCAmelCase,	mask_token=lowerCAmelCase,	**lowerCAmelCase,	)

              lowerCamelCase_							=vocab_file
              lowerCamelCase_							=False if not self.vocab_file else True

              lowerCamelCase_							={
                  lang_code: self.convert_tokens_to_ids(lowerCAmelCase						) for lang_code in FAIRSEQ_LANGUAGE_CODES
              }

              lowerCamelCase_							=src_lang if src_lang is not None else '''en_XX'''
              lowerCamelCase_							=tgt_lang
              lowerCamelCase_							=self.lang_code_to_id[self._src_lang]
              self.set_src_lang_special_tokens(self._src_lang						)






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return self._src_lang






       @src_lang.setter
       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=new_src_lang
              self.set_src_lang_special_tokens(self._src_lang						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              if token_ids_a is None:
                     return self.prefix_tokens + token_ids_a + self.suffix_tokens
              # We don't expect to process pairs, but leave the pair logic for API consistency
              return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = "en_XX",	lowerCAmelCase = None,	lowerCAmelCase = "ro_RO",	**lowerCAmelCase,	):



              """simple docstring"""







              lowerCamelCase_							=src_lang
              lowerCamelCase_							=tgt_lang
              return super().prepare_seqaseq_batch(lowerCAmelCase,	lowerCAmelCase,	**lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              return self.set_src_lang_special_tokens(self.src_lang						)






       def   lowercase__  (							self						):



              """simple docstring"""







              return self.set_tgt_lang_special_tokens(self.tgt_lang						)






       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=self.convert_tokens_to_ids(lowerCAmelCase						)
              lowerCamelCase_							=[self.cur_lang_code_id]
              lowerCamelCase_							=[self.eos_token_id]

              lowerCamelCase_							=self.convert_ids_to_tokens(self.prefix_tokens						)
              lowerCamelCase_							=self.convert_ids_to_tokens(self.suffix_tokens						)

              lowerCamelCase_							=processors.TemplateProcessing(
                  single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str,	pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,	self.prefix_tokens + self.suffix_tokens						)						),	)






       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=self.convert_tokens_to_ids(lowerCAmelCase						)
              lowerCamelCase_							=[self.cur_lang_code_id]
              lowerCamelCase_							=[self.eos_token_id]

              lowerCamelCase_							=self.convert_ids_to_tokens(self.prefix_tokens						)
              lowerCamelCase_							=self.convert_ids_to_tokens(self.suffix_tokens						)

              lowerCamelCase_							=processors.TemplateProcessing(
                  single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str,	pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,	self.prefix_tokens + self.suffix_tokens						)						),	)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	**lowerCAmelCase						):



              """simple docstring"""







              if src_lang is None or tgt_lang is None:
                     raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''						)
              lowerCamelCase_							=src_lang
              lowerCamelCase_							=self(lowerCAmelCase,	add_special_tokens=lowerCAmelCase,	return_tensors=lowerCAmelCase,	**lowerCAmelCase						)
              lowerCamelCase_							=self.convert_tokens_to_ids(lowerCAmelCase						)
              lowerCamelCase_							=tgt_lang_id
              return inputs






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              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(lowerCAmelCase						):
                     logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
                     return
              lowerCamelCase_							=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						):
                     copyfile(self.vocab_file,	lowerCAmelCase						)

              return (out_vocab_file,)




