




'''simple docstring'''




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

import numpy as np
import torch

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


a_   :	str							   = logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					torch.FloatTensor
       lowercase			:					Optional[torch.FloatTensor]   		=None





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



       """simple docstring"""
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(__snake_case							:    Any     ):
                     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 (				lowerCamelCase__    ,      lowerCamelCase__       ):
       lowercase			:					Union[str, Any]   		=[e.name for e in KarrasDiffusionSchedulers]
       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 = None,	lowerCAmelCase = "epsilon",	lowerCAmelCase = "linear",	lowerCAmelCase = False,	lowerCAmelCase = "linspace",	lowerCAmelCase = 0,	):



              """simple docstring"""







              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						)

              lowerCamelCase_							=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
              lowerCamelCase_							=np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
              lowerCamelCase_							=torch.from_numpy(lowerCAmelCase						)

              # setable values
              lowerCamelCase_							=None
              lowerCamelCase_							=np.linspace(0,	num_train_timesteps - 1,	lowerCAmelCase,	dtype=lowerCAmelCase						)[::-1].copy()
              lowerCamelCase_							=torch.from_numpy(lowerCAmelCase						)
              lowerCamelCase_							=False
              lowerCamelCase_							=use_karras_sigmas






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              if self.config.timestep_spacing in ["linspace", "trailing"]:
                     return self.sigmas.max()

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






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              if isinstance(lowerCAmelCase,	torch.Tensor						):
                     lowerCamelCase_							=timestep.to(self.timesteps.device						)
              lowerCamelCase_							=(self.timesteps == timestep).nonzero().item()
              lowerCamelCase_							=self.sigmas[step_index]

              lowerCamelCase_							=sample / ((sigma**2 + 1) ** 0.5)

              lowerCamelCase_							=True
              return sample






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



              """simple docstring"""







              lowerCamelCase_							=num_inference_steps

              # "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,	self.config.num_train_timesteps - 1,	lowerCAmelCase,	dtype=lowerCAmelCase						)[
                         ::-1
                     ].copy()
              elif self.config.timestep_spacing == "leading":
                     lowerCamelCase_							=self.config.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_							=self.config.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(self.config.num_train_timesteps,	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_							=np.log(lowerCAmelCase						)

              if self.config.interpolation_type == "linear":
                     lowerCamelCase_							=np.interp(lowerCAmelCase,	np.arange(0,	len(lowerCAmelCase						)						),	lowerCAmelCase						)
              elif self.config.interpolation_type == "log_linear":
                     lowerCamelCase_							=torch.linspace(np.log(sigmas[-1]						),	np.log(sigmas[0]						),	num_inference_steps + 1						).exp()
              else:
                     raise ValueError(
                         f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
                         ''' \'linear\' or \'log_linear\''''						)

              if self.use_karras_sigmas:
                     lowerCamelCase_							=self._convert_to_karras(in_sigmas=lowerCAmelCase,	num_inference_steps=self.num_inference_steps						)
                     lowerCamelCase_							=np.array([self._sigma_to_t(lowerCAmelCase,	lowerCAmelCase						) for sigma in sigmas]						)

              lowerCamelCase_							=np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
              lowerCamelCase_							=torch.from_numpy(lowerCAmelCase						).to(device=lowerCAmelCase						)
              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(device=lowerCAmelCase						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=np.log(lowerCAmelCase						)

              # get distribution
              lowerCamelCase_							=log_sigma - log_sigmas[:, np.newaxis]

              # get sigmas range
              lowerCamelCase_							=np.cumsum((dists >= 0),	axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
              lowerCamelCase_							=low_idx + 1

              lowerCamelCase_							=log_sigmas[low_idx]
              lowerCamelCase_							=log_sigmas[high_idx]

              # interpolate sigmas
              lowerCamelCase_							=(low - log_sigma) / (low - high)
              lowerCamelCase_							=np.clip(lowerCAmelCase,	0,	1						)

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






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=in_sigmas[-1].item()
              lowerCamelCase_							=in_sigmas[0].item()

              lowerCamelCase_							=7.0  # 7.0 is the value used in the paper
              lowerCamelCase_							=np.linspace(0,	1,	lowerCAmelCase						)
              lowerCamelCase_							=sigma_min ** (1 / rho)
              lowerCamelCase_							=sigma_max ** (1 / rho)
              lowerCamelCase_							=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
              return sigmas






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase = 0.0,	lowerCAmelCase = 0.0,	lowerCAmelCase = float('''inf'''						),	lowerCAmelCase = 1.0,	lowerCAmelCase = None,	lowerCAmelCase = True,	):



              """simple docstring"""







              if (
                  isinstance(lowerCAmelCase,	lowerCAmelCase						)
                  or isinstance(lowerCAmelCase,	torch.IntTensor						)
                  or isinstance(lowerCAmelCase,	torch.LongTensor						)
              ):
                     raise ValueError(
                         (
                             '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                             ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
                             ''' one of the `scheduler.timesteps` as a timestep.'''
                         ),	)

              if not self.is_scale_input_called:
                     logger.warning(
                         '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                         '''See `StableDiffusionPipeline` for a usage example.'''						)

              if isinstance(lowerCAmelCase,	torch.Tensor						):
                     lowerCamelCase_							=timestep.to(self.timesteps.device						)

              lowerCamelCase_							=(self.timesteps == timestep).nonzero().item()
              lowerCamelCase_							=self.sigmas[step_index]

              lowerCamelCase_							=min(s_churn / (len(self.sigmas						) - 1),	2**0.5 - 1						) if s_tmin <= sigma <= s_tmax else 0.0

              lowerCamelCase_							=randn_tensor(
                  model_output.shape,	dtype=model_output.dtype,	device=model_output.device,	generator=lowerCAmelCase						)

              lowerCamelCase_							=noise * s_noise
              lowerCamelCase_							=sigma * (gamma + 1)

              if gamma > 0:
                     lowerCamelCase_							=sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

              # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
              # NOTE: "original_sample" should not be an expected prediction_type but is left in for
              # backwards compatibility
              if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
                     lowerCamelCase_							=model_output
              elif self.config.prediction_type == "epsilon":
                     lowerCamelCase_							=sample - sigma_hat * model_output
              elif self.config.prediction_type == "v_prediction":
                     # * c_out + input * c_skip
                     lowerCamelCase_							=model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
              else:
                     raise ValueError(
                         f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''						)

              # 2. Convert to an ODE derivative
              lowerCamelCase_							=(sample - pred_original_sample) / sigma_hat

              lowerCamelCase_							=self.sigmas[step_index + 1] - sigma_hat

              lowerCamelCase_							=sample + derivative * dt

              if not return_dict:
                     return (prev_sample,)

              return EulerDiscreteSchedulerOutput(prev_sample=lowerCAmelCase,	pred_original_sample=lowerCAmelCase						)






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



              """simple docstring"""







              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_							=[(schedule_timesteps == t).nonzero().item() 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						):



              """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,)




