'''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 __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__=0.9_9_9    ,  UpperCamelCase__="cosine"    ,  )     ->   Tuple:
    if alpha_transform_type == "cosine":

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

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

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




class    a__				(       UpperCAmelCase__							,       UpperCAmelCase__			):
     lowerCamelCase  :					Tuple							=[e.name for e in KarrasDiffusionSchedulers]
     lowerCamelCase  :					Tuple							=2






     @register_to_config
     def __init__(			self		:		Any  ,    a		:		int = 10_00  ,    a		:		float = 0.0_00_85  ,    a		:		float = 0.0_12  ,    a		:		str = "linear"  ,    a		:		Optional[Union[np.ndarray, List[float]]] = None  ,    a		:		str = "epsilon"  ,    a		:		Optional[bool] = False  ,    a		:		Optional[bool] = False  ,    a		:		float = 1.0  ,    a		:		str = "linspace"  ,    a		:		int = 0  ,    ):





         """simple docstring"""



         if trained_betas is not None:
             __lowerCamelCase          =  torch.tensor(a  ,    dtype=torch.floataa   )
         elif beta_schedule == "linear":
             __lowerCamelCase          =  torch.linspace(a  ,    a  ,    a  ,    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  ,    a  ,    dtype=torch.floataa   ) ** 2
             )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             __lowerCamelCase          =  betas_for_alpha_bar(a  ,    alpha_transform_type='''cosine'''   )
         elif beta_schedule == "exp":
             __lowerCamelCase          =  betas_for_alpha_bar(a  ,    alpha_transform_type='''exp'''   )
         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(a  ,    a  ,    a   )
         __lowerCamelCase          =  use_karras_sigmas






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]  ,    a		:		str  ,    a		:		Optional[int]=None   ):





         """simple docstring"""



         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(a   ) > 1 else 0
         else:
             __lowerCamelCase          =  timestep.cpu().item() if torch.is_tensor(a   ) else timestep
             __lowerCamelCase          =  self._index_counter[timestep_int]

         return indices[pos].item()






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """simple docstring"""



         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		:		List[str]  ,    a		:		torch.FloatTensor  ,    a		:		Union[float, torch.FloatTensor]  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  self.index_for_timestep(a   )

         __lowerCamelCase          =  self.sigmas[step_index]
         __lowerCamelCase          =  sample / ((sigma**2 + 1) ** 0.5)
         return sample






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    a		:		int  ,    a		:		Union[str, torch.device] = None  ,    a		:		Optional[int] = None  ,    ):





         """simple docstring"""



         __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  ,    a  ,    dtype=a   )[::-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  ,    a   ) * step_ratio).round()[::-1].copy().astype(a   )
             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(a  ,    0  ,    -step_ratio   )).round().copy().astype(a   )
             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(a   )
         __lowerCamelCase          =  np.interp(a  ,    np.arange(0  ,    len(a   )   )  ,    a   )

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

         __lowerCamelCase          =  np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
         __lowerCamelCase          =  torch.from_numpy(a   ).to(device=a   )
         __lowerCamelCase          =  torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2   ), sigmas[-1:]]   )

         __lowerCamelCase          =  torch.from_numpy(a   )
         __lowerCamelCase          =  torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2   )]   )

         if str(a   ).startswith('''mps'''   ):
             # mps does not support float64
             __lowerCamelCase          =  timesteps.to(a  ,    dtype=torch.floataa   )
         else:
             __lowerCamelCase          =  timesteps.to(device=a   )

         # empty dt and derivative
         __lowerCamelCase          =  None
         __lowerCamelCase          =  None

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]  ,    a		:		Tuple  ,    a		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  np.log(a   )

         # 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(a  ,    0  ,    1   )

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		str  ,    a		:		torch.FloatTensor  ,    a		:		str   ):





         """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  ,    a   )
         __lowerCamelCase          =  sigma_min ** (1 / rho)
         __lowerCamelCase          =  sigma_max ** (1 / rho)
         __lowerCamelCase          =  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         return self.dt is None






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any  ,    a		:		Union[torch.FloatTensor, np.ndarray]  ,    a		:		Union[float, torch.FloatTensor]  ,    a		:		Union[torch.FloatTensor, np.ndarray]  ,    a		:		bool = True  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  self.index_for_timestep(a   )

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

         if self.state_in_first_order:
             __lowerCamelCase          =  self.sigmas[step_index]
             __lowerCamelCase          =  self.sigmas[step_index + 1]
         else:
             # 2nd order / Heun's method
             __lowerCamelCase          =  self.sigmas[step_index - 1]
             __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_next
             __lowerCamelCase          =  sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
             __lowerCamelCase          =  sigma_hat if self.state_in_first_order else sigma_next
             __lowerCamelCase          =  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
         elif self.config.prediction_type == "sample":
             __lowerCamelCase          =  model_output
         else:
             raise ValueError(
                 f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""   )

         if self.config.clip_sample:
             __lowerCamelCase          =  pred_original_sample.clamp(
                 -self.config.clip_sample_range  ,    self.config.clip_sample_range   )

         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_next - sigma_hat

             # store for 2nd order step
             __lowerCamelCase          =  derivative
             __lowerCamelCase          =  dt
             __lowerCamelCase          =  sample
         else:
             # 2. 2nd order / Heun's method
             __lowerCamelCase          =  (sample - pred_original_sample) / sigma_next
             __lowerCamelCase          =  (self.prev_derivative + derivative) / 2

             # 3. take prev timestep & sample
             __lowerCamelCase          =  self.dt
             __lowerCamelCase          =  self.sample

             # free dt and derivative
             # Note, this puts the scheduler in "first order mode"
             __lowerCamelCase          =  None
             __lowerCamelCase          =  None
             __lowerCamelCase          =  None

         __lowerCamelCase          =  sample + derivative * dt

         if not return_dict:
             return (prev_sample,)

         return SchedulerOutput(prev_sample=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    a		:		torch.FloatTensor  ,    a		:		torch.FloatTensor  ,    a		:		torch.FloatTensor  ,    ):





         """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(a   ):
             # 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(a  ,    a   ) 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		:		int   ):





         """simple docstring"""



         return self.config.num_train_timesteps

'''simple docstring'''




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


__UpperCAmelCase												={
    "Acehnese Arabic": "ace_Arab",
    "Acehnese Latin": "ace_Latn",
    "Mesopotamian Arabic": "acm_Arab",
    "Ta'izzi-Adeni Arabic": "acq_Arab",
    "Tunisian Arabic": "aeb_Arab",
    "Afrikaans": "afr_Latn",
    "South Levantine Arabic": "ajp_Arab",
    "Akan": "aka_Latn",
    "Amharic": "amh_Ethi",
    "North Levantine Arabic": "apc_Arab",
    "Modern Standard Arabic": "arb_Arab",
    "Modern Standard Arabic Romanized": "arb_Latn",
    "Najdi Arabic": "ars_Arab",
    "Moroccan Arabic": "ary_Arab",
    "Egyptian Arabic": "arz_Arab",
    "Assamese": "asm_Beng",
    "Asturian": "ast_Latn",
    "Awadhi": "awa_Deva",
    "Central Aymara": "ayr_Latn",
    "South Azerbaijani": "azb_Arab",
    "North Azerbaijani": "azj_Latn",
    "Bashkir": "bak_Cyrl",
    "Bambara": "bam_Latn",
    "Balinese": "ban_Latn",
    "Belarusian": "bel_Cyrl",
    "Bemba": "bem_Latn",
    "Bengali": "ben_Beng",
    "Bhojpuri": "bho_Deva",
    "Banjar Arabic": "bjn_Arab",
    "Banjar Latin": "bjn_Latn",
    "Standard Tibetan": "bod_Tibt",
    "Bosnian": "bos_Latn",
    "Buginese": "bug_Latn",
    "Bulgarian": "bul_Cyrl",
    "Catalan": "cat_Latn",
    "Cebuano": "ceb_Latn",
    "Czech": "ces_Latn",
    "Chokwe": "cjk_Latn",
    "Central Kurdish": "ckb_Arab",
    "Crimean Tatar": "crh_Latn",
    "Welsh": "cym_Latn",
    "Danish": "dan_Latn",
    "German": "deu_Latn",
    "Southwestern Dinka": "dik_Latn",
    "Dyula": "dyu_Latn",
    "Dzongkha": "dzo_Tibt",
    "Greek": "ell_Grek",
    "English": "eng_Latn",
    "Esperanto": "epo_Latn",
    "Estonian": "est_Latn",
    "Basque": "eus_Latn",
    "Ewe": "ewe_Latn",
    "Faroese": "fao_Latn",
    "Fijian": "fij_Latn",
    "Finnish": "fin_Latn",
    "Fon": "fon_Latn",
    "French": "fra_Latn",
    "Friulian": "fur_Latn",
    "Nigerian Fulfulde": "fuv_Latn",
    "Scottish Gaelic": "gla_Latn",
    "Irish": "gle_Latn",
    "Galician": "glg_Latn",
    "Guarani": "grn_Latn",
    "Gujarati": "guj_Gujr",
    "Haitian Creole": "hat_Latn",
    "Hausa": "hau_Latn",
    "Hebrew": "heb_Hebr",
    "Hindi": "hin_Deva",
    "Chhattisgarhi": "hne_Deva",
    "Croatian": "hrv_Latn",
    "Hungarian": "hun_Latn",
    "Armenian": "hye_Armn",
    "Igbo": "ibo_Latn",
    "Ilocano": "ilo_Latn",
    "Indonesian": "ind_Latn",
    "Icelandic": "isl_Latn",
    "Italian": "ita_Latn",
    "Javanese": "jav_Latn",
    "Japanese": "jpn_Jpan",
    "Kabyle": "kab_Latn",
    "Jingpho": "kac_Latn",
    "Kamba": "kam_Latn",
    "Kannada": "kan_Knda",
    "Kashmiri Arabic": "kas_Arab",
    "Kashmiri Devanagari": "kas_Deva",
    "Georgian": "kat_Geor",
    "Central Kanuri Arabic": "knc_Arab",
    "Central Kanuri Latin": "knc_Latn",
    "Kazakh": "kaz_Cyrl",
    "Kabiyè": "kbp_Latn",
    "Kabuverdianu": "kea_Latn",
    "Khmer": "khm_Khmr",
    "Kikuyu": "kik_Latn",
    "Kinyarwanda": "kin_Latn",
    "Kyrgyz": "kir_Cyrl",
    "Kimbundu": "kmb_Latn",
    "Northern Kurdish": "kmr_Latn",
    "Kikongo": "kon_Latn",
    "Korean": "kor_Hang",
    "Lao": "lao_Laoo",
    "Ligurian": "lij_Latn",
    "Limburgish": "lim_Latn",
    "Lingala": "lin_Latn",
    "Lithuanian": "lit_Latn",
    "Lombard": "lmo_Latn",
    "Latgalian": "ltg_Latn",
    "Luxembourgish": "ltz_Latn",
    "Luba-Kasai": "lua_Latn",
    "Ganda": "lug_Latn",
    "Luo": "luo_Latn",
    "Mizo": "lus_Latn",
    "Standard Latvian": "lvs_Latn",
    "Magahi": "mag_Deva",
    "Maithili": "mai_Deva",
    "Malayalam": "mal_Mlym",
    "Marathi": "mar_Deva",
    "Minangkabau Arabic ": "min_Arab",
    "Minangkabau Latin": "min_Latn",
    "Macedonian": "mkd_Cyrl",
    "Plateau Malagasy": "plt_Latn",
    "Maltese": "mlt_Latn",
    "Meitei Bengali": "mni_Beng",
    "Halh Mongolian": "khk_Cyrl",
    "Mossi": "mos_Latn",
    "Maori": "mri_Latn",
    "Burmese": "mya_Mymr",
    "Dutch": "nld_Latn",
    "Norwegian Nynorsk": "nno_Latn",
    "Norwegian Bokmål": "nob_Latn",
    "Nepali": "npi_Deva",
    "Northern Sotho": "nso_Latn",
    "Nuer": "nus_Latn",
    "Nyanja": "nya_Latn",
    "Occitan": "oci_Latn",
    "West Central Oromo": "gaz_Latn",
    "Odia": "ory_Orya",
    "Pangasinan": "pag_Latn",
    "Eastern Panjabi": "pan_Guru",
    "Papiamento": "pap_Latn",
    "Western Persian": "pes_Arab",
    "Polish": "pol_Latn",
    "Portuguese": "por_Latn",
    "Dari": "prs_Arab",
    "Southern Pashto": "pbt_Arab",
    "Ayacucho Quechua": "quy_Latn",
    "Romanian": "ron_Latn",
    "Rundi": "run_Latn",
    "Russian": "rus_Cyrl",
    "Sango": "sag_Latn",
    "Sanskrit": "san_Deva",
    "Santali": "sat_Olck",
    "Sicilian": "scn_Latn",
    "Shan": "shn_Mymr",
    "Sinhala": "sin_Sinh",
    "Slovak": "slk_Latn",
    "Slovenian": "slv_Latn",
    "Samoan": "smo_Latn",
    "Shona": "sna_Latn",
    "Sindhi": "snd_Arab",
    "Somali": "som_Latn",
    "Southern Sotho": "sot_Latn",
    "Spanish": "spa_Latn",
    "Tosk Albanian": "als_Latn",
    "Sardinian": "srd_Latn",
    "Serbian": "srp_Cyrl",
    "Swati": "ssw_Latn",
    "Sundanese": "sun_Latn",
    "Swedish": "swe_Latn",
    "Swahili": "swh_Latn",
    "Silesian": "szl_Latn",
    "Tamil": "tam_Taml",
    "Tatar": "tat_Cyrl",
    "Telugu": "tel_Telu",
    "Tajik": "tgk_Cyrl",
    "Tagalog": "tgl_Latn",
    "Thai": "tha_Thai",
    "Tigrinya": "tir_Ethi",
    "Tamasheq Latin": "taq_Latn",
    "Tamasheq Tifinagh": "taq_Tfng",
    "Tok Pisin": "tpi_Latn",
    "Tswana": "tsn_Latn",
    "Tsonga": "tso_Latn",
    "Turkmen": "tuk_Latn",
    "Tumbuka": "tum_Latn",
    "Turkish": "tur_Latn",
    "Twi": "twi_Latn",
    "Central Atlas Tamazight": "tzm_Tfng",
    "Uyghur": "uig_Arab",
    "Ukrainian": "ukr_Cyrl",
    "Umbundu": "umb_Latn",
    "Urdu": "urd_Arab",
    "Northern Uzbek": "uzn_Latn",
    "Venetian": "vec_Latn",
    "Vietnamese": "vie_Latn",
    "Waray": "war_Latn",
    "Wolof": "wol_Latn",
    "Xhosa": "xho_Latn",
    "Eastern Yiddish": "ydd_Hebr",
    "Yoruba": "yor_Latn",
    "Yue Chinese": "yue_Hant",
    "Chinese Simplified": "zho_Hans",
    "Chinese Traditional": "zho_Hant",
    "Standard Malay": "zsm_Latn",
    "Zulu": "zul_Latn",
}




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Any							="facebook/nllb-200-distilled-600M"
     lowerCamelCase  :					Optional[Any]							=(
         "This is a tool that translates text from a language to another. It takes three inputs: `text`, which should "
         "be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, "
         "which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in "
         "plain English, such as 'Romanian', or 'Albanian'. It returns the text translated in `tgt_lang`."
     )
     lowerCamelCase  :					Tuple							="translator"
     lowerCamelCase  :					Any							=AutoTokenizer
     lowerCamelCase  :					Dict							=AutoModelForSeqaSeqLM
     lowerCamelCase  :					Union[str, Any]							=LANGUAGE_CODES

     lowerCamelCase  :					Any							=["text", "text", "text"]
     lowerCamelCase  :					Union[str, Any]							=["text"]






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    a		:		Any  ,    a		:		List[str]  ,    a		:		Any   ):





         """simple docstring"""



         if src_lang not in self.lang_to_code:
             raise ValueError(f"""{src_lang} is not a supported language."""   )
         if tgt_lang not in self.lang_to_code:
             raise ValueError(f"""{tgt_lang} is not a supported language."""   )
         __lowerCamelCase          =  self.lang_to_code[src_lang]
         __lowerCamelCase          =  self.lang_to_code[tgt_lang]
         return self.pre_processor._build_translation_inputs(
             a  ,    return_tensors='''pt'''  ,    src_lang=a  ,    tgt_lang=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str  ,    a		:		Optional[Any]   ):





         """simple docstring"""



         return self.model.generate(**a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    a		:		str   ):





         """simple docstring"""



         return self.post_processor.decode(outputs[0].tolist()  ,    skip_special_tokens=a   )
