




import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


__A   :  Union[str, Any]					=      logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()

def 						snake_case__       (					_lowerCamelCase				)			->Optional[Any]:






		"""simple docstring"""


		if "token" in model_name_or_path:
				return "rag_token"
		if "sequence" in model_name_or_path:
				return "rag_sequence"
		if "bart" in model_name_or_path:
				return "bart"
		return None

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->Optional[int]:






		"""simple docstring"""


		return max(metric_fn(_lowerCamelCase,  _lowerCamelCase				) for gt in ground_truths				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->Any:






		"""simple docstring"""


		__lowercase				:		Tuple												=				[line.strip() for line in open(_lowerCamelCase,  "r"				).readlines()]
		__lowercase				:		Dict												=				[]

		if args.gold_data_mode == "qa":
				__lowercase				:		List[Any]												=				pd.read_csv(_lowerCamelCase,  sep="\t",  header=_lowerCamelCase				)
				for answer_list in data[1]:
						__lowercase				:		Any												=				ast.literal_eval(_lowerCamelCase				)
						answers.append(_lowerCamelCase				)
		else:
				__lowercase				:		Optional[Any]												=				[line.strip() for line in open(_lowerCamelCase,  "r"				).readlines()]
				__lowercase				:		str												=				[[reference] for reference in references]

		__lowercase				:		Tuple												=				0
		for prediction, ground_truths in zip(_lowerCamelCase,  _lowerCamelCase				):
				total += 1
				em += metric_max_over_ground_truths(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)
				fa += metric_max_over_ground_truths(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)

		__lowercase				:		Any												=				1_00.0 * em / total
		__lowercase				:		Optional[int]												=				1_00.0 * fa / total

		logger.info(F'F1: {fa:.2f}'				)
		logger.info(F'EM: {em:.2f}'				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->Tuple:






		"""simple docstring"""


		__lowercase				:		Tuple												=				args.k
		__lowercase				:		Optional[int]												=				[line.strip() for line in open(_lowerCamelCase,  "r"				).readlines()]
		__lowercase				:		List[str]												=				[line.strip() for line in open(_lowerCamelCase,  "r"				).readlines()]

		__lowercase				:		str												=				0
		for hypo, reference in zip(_lowerCamelCase,  _lowerCamelCase				):
				__lowercase				:		Tuple												=				set(hypo.split("\t"				)[:k]				)
				__lowercase				:		Union[str, Any]												=				set(reference.split("\t"				)				)
				total += 1
				em += len(hypo_provenance & ref_provenance				) / k

		__lowercase				:		List[Any]												=				1_00.0 * em / total
		logger.info(F'Precision@{k}: {em: .2f}'				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->List[Any]:






		"""simple docstring"""


		def strip_title(_lowerCamelCase				):
				if title.startswith("\""				):
						__lowercase				:		List[Any]												=				title[1:]
				if title.endswith("\""				):
						__lowercase				:		Union[str, Any]												=				title[:-1]
				return title

		__lowercase				:		int												=				rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    _lowerCamelCase,  return_tensors="pt",  padding=_lowerCamelCase,  truncation=_lowerCamelCase,  )["input_ids"].to(args.device				)

		__lowercase				:		Optional[Any]												=				rag_model.rag.question_encoder(_lowerCamelCase				)
		__lowercase				:		Dict												=				question_enc_outputs[0]

		__lowercase				:		Any												=				rag_model.retriever(
		    _lowerCamelCase,  question_enc_pool_output.cpu().detach().to(torch.floataa				).numpy(),  prefix=rag_model.rag.generator.config.prefix,  n_docs=rag_model.config.n_docs,  return_tensors="pt",  )
		__lowercase				:		Optional[Any]												=				rag_model.retriever.index.get_doc_dicts(result.doc_ids				)
		__lowercase				:		Tuple												=				[]
		for docs in all_docs:
				__lowercase				:		List[str]												=				[strip_title(_lowerCamelCase				) for title in docs["title"]]
				provenance_strings.append("\t".join(_lowerCamelCase				)				)
		return provenance_strings

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->int:






		"""simple docstring"""


		with torch.no_grad():
				__lowercase				:		Optional[int]												=				rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    _lowerCamelCase,  return_tensors="pt",  padding=_lowerCamelCase,  truncation=_lowerCamelCase				)

				__lowercase				:		str												=				inputs_dict.input_ids.to(args.device				)
				__lowercase				:		Union[str, Any]												=				inputs_dict.attention_mask.to(args.device				)
				__lowercase				:		Union[str, Any]												=				rag_model.generate(  # rag_model overwrites generate
				    _lowerCamelCase,  attention_mask=_lowerCamelCase,  num_beams=args.num_beams,  min_length=args.min_length,  max_length=args.max_length,  early_stopping=_lowerCamelCase,  num_return_sequences=1,  bad_words_ids=[[0, 0]],  )
				__lowercase				:		Union[str, Any]												=				rag_model.retriever.generator_tokenizer.batch_decode(_lowerCamelCase,  skip_special_tokens=_lowerCamelCase				)

				if args.print_predictions:
						for q, a in zip(_lowerCamelCase,  _lowerCamelCase				):
								logger.info("Q: {} - A: {}".format(_lowerCamelCase,  _lowerCamelCase				)				)

				return answers

def 						snake_case__       (					)			->List[Any]:






		"""simple docstring"""


		__lowercase				:		int												=				argparse.ArgumentParser()
		parser.add_argument(
		    "--model_type",  choices=["rag_sequence", "rag_token", "bart"],  type=_lowerCamelCase,  help=(
		        "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
		        " model_name_or_path"
		    ),  )
		parser.add_argument(
		    "--index_name",  default=_lowerCamelCase,  choices=["exact", "compressed", "legacy"],  type=_lowerCamelCase,  help="RAG model retriever type",  )
		parser.add_argument(
		    "--index_path",  default=_lowerCamelCase,  type=_lowerCamelCase,  help="Path to the retrieval index",  )
		parser.add_argument("--n_docs",  default=5,  type=_lowerCamelCase,  help="Number of retrieved docs"				)
		parser.add_argument(
		    "--model_name_or_path",  default=_lowerCamelCase,  type=_lowerCamelCase,  required=_lowerCamelCase,  help="Path to pretrained checkpoints or model identifier from huggingface.co/models",  )
		parser.add_argument(
		    "--eval_mode",  choices=["e2e", "retrieval"],  default="e2e",  type=_lowerCamelCase,  help=(
		        "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
		        " precision@k."
		    ),  )
		parser.add_argument("--k",  default=1,  type=_lowerCamelCase,  help="k for the precision@k calculation"				)
		parser.add_argument(
		    "--evaluation_set",  default=_lowerCamelCase,  type=_lowerCamelCase,  required=_lowerCamelCase,  help="Path to a file containing evaluation samples",  )
		parser.add_argument(
		    "--gold_data_path",  default=_lowerCamelCase,  type=_lowerCamelCase,  required=_lowerCamelCase,  help="Path to a tab-separated file with gold samples",  )
		parser.add_argument(
		    "--gold_data_mode",  default="qa",  type=_lowerCamelCase,  choices=["qa", "ans"],  help=(
		        "Format of the gold data file"
		        "qa - a single line in the following format: question [tab] answer_list"
		        "ans - a single line of the gold file contains the expected answer string"
		    ),  )
		parser.add_argument(
		    "--predictions_path",  type=_lowerCamelCase,  default="predictions.txt",  help="Name of the predictions file, to be stored in the checkpoints directory",  )
		parser.add_argument(
		    "--eval_all_checkpoints",  action="store_true",  help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",  )
		parser.add_argument(
		    "--eval_batch_size",  default=8,  type=_lowerCamelCase,  help="Batch size per GPU/CPU for evaluation.",  )
		parser.add_argument(
		    "--recalculate",  help="Recalculate predictions even if the prediction file exists",  action="store_true",  )
		parser.add_argument(
		    "--num_beams",  default=4,  type=_lowerCamelCase,  help="Number of beams to be used when generating answers",  )
		parser.add_argument("--min_length",  default=1,  type=_lowerCamelCase,  help="Min length of the generated answers"				)
		parser.add_argument("--max_length",  default=50,  type=_lowerCamelCase,  help="Max length of the generated answers"				)

		parser.add_argument(
		    "--print_predictions",  action="store_true",  help="If True, prints predictions while evaluating.",  )
		parser.add_argument(
		    "--print_docs",  action="store_true",  help="If True, prints docs retried while generating.",  )
		__lowercase				:		List[Any]												=				parser.parse_args()
		__lowercase				:		Tuple												=				torch.device("cuda" if torch.cuda.is_available() else "cpu"				)
		return args

def 						snake_case__       (					_lowerCamelCase				)			->Optional[int]:






		"""simple docstring"""


		__lowercase				:		Dict												=				{}
		if args.model_type is None:
				__lowercase				:		Dict												=				infer_model_type(args.model_name_or_path				)
				assert args.model_type is not None
		if args.model_type.startswith("rag"				):
				__lowercase				:		Any												=				RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
				__lowercase				:		int												=				args.n_docs
				if args.index_name is not None:
						__lowercase				:		List[Any]												=				args.index_name
				if args.index_path is not None:
						__lowercase				:		Optional[int]												=				args.index_path
		else:
				__lowercase				:		Optional[int]												=				BartForConditionalGeneration

		__lowercase				:		List[str]												=				(
		    [f.path for f in os.scandir(args.model_name_or_path				) if f.is_dir()]
		    if args.eval_all_checkpoints
		    else [args.model_name_or_path]
		)

		logger.info("Evaluate the following checkpoints: %s",  _lowerCamelCase				)

		__lowercase				:		Optional[int]												=				get_scores if args.eval_mode == "e2e" else get_precision_at_k
		__lowercase				:		Any												=				evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

		for checkpoint in checkpoints:
				if os.path.exists(args.predictions_path				) and (not args.recalculate):
						logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path				)				)
						score_fn(_lowerCamelCase,  args.predictions_path,  args.gold_data_path				)
						continue

				logger.info("***** Running evaluation for {} *****".format(_lowerCamelCase				)				)
				logger.info("  Batch size = %d",  args.eval_batch_size				)
				logger.info("  Predictions will be stored under {}".format(args.predictions_path				)				)

				if args.model_type.startswith("rag"				):
						__lowercase				:		Dict												=				RagRetriever.from_pretrained(_lowerCamelCase,  **_lowerCamelCase				)
						__lowercase				:		Dict												=				model_class.from_pretrained(_lowerCamelCase,  retriever=_lowerCamelCase,  **_lowerCamelCase				)
						model.retriever.init_retrieval()
				else:
						__lowercase				:		Union[str, Any]												=				model_class.from_pretrained(_lowerCamelCase,  **_lowerCamelCase				)
				model.to(args.device				)

				with open(args.evaluation_set,  "r"				) as eval_file, open(args.predictions_path,  "w"				) as preds_file:
						__lowercase				:		Tuple												=				[]
						for line in tqdm(_lowerCamelCase				):
								questions.append(line.strip()				)
								if len(_lowerCamelCase				) == args.eval_batch_size:
										__lowercase				:		Any												=				evaluate_batch_fn(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)
										preds_file.write("\n".join(_lowerCamelCase				) + "\n"				)
										preds_file.flush()
										__lowercase				:		Dict												=				[]
						if len(_lowerCamelCase				) > 0:
								__lowercase				:		Tuple												=				evaluate_batch_fn(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)
								preds_file.write("\n".join(_lowerCamelCase				)				)
								preds_file.flush()

						score_fn(_lowerCamelCase,  args.predictions_path,  args.gold_data_path				)


if __name__ == "__main__":
						__A   :  List[Any]					=      get_args()
						main(args)






"""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


__A   :  Optional[int]					=      {
    '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       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     str       =      "facebook/nllb-200-distilled-600M"
				__UpperCAmelCase :     Tuple       =      (
				    "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`."
				)
				__UpperCAmelCase :     str       =      "translator"
				__UpperCAmelCase :     Optional[int]       =      AutoTokenizer
				__UpperCAmelCase :     Dict       =      AutoModelForSeqaSeqLM
				__UpperCAmelCase :     Tuple       =      LANGUAGE_CODES

				__UpperCAmelCase :     int       =      ["text", "text", "text"]
				__UpperCAmelCase :     int       =      ["text"]



				def       snake_case    (     self					:	Dict    ,   lowercase__					:	List[Any]    ,   lowercase__					:	Optional[Any]    ,   lowercase__					:	Any						):
						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.'						)
						__lowercase				:		List[str]												=				self.lang_to_code[src_lang]
						__lowercase				:		List[str]												=				self.lang_to_code[tgt_lang]
						return self.pre_processor._build_translation_inputs(
						    lowercase__    ,   return_tensors="pt"    ,   src_lang=lowercase__    ,   tgt_lang=lowercase__						)



				def       snake_case    (     self					:	str    ,   lowercase__					:	Tuple						):
						return self.model.generate(**lowercase__						)



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	List[Any]						):
						return self.post_processor.decode(outputs[0].tolist()    ,   skip_special_tokens=lowercase__						)
