


"""simple docstring"""







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


__lowercase    					=      logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def      lowercase			(				A_       )->      List[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      lowercase			(				A_  ,   A_  ,   A_       )->      str:
  '''simple docstring'''







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


def      lowercase			(				A_  ,   A_  ,   A_       )->      Dict:
  '''simple docstring'''







  a						:		int      							=			[line.strip() for line in open(__lowerCamelCase  ,   "r"       ).readlines()]
  a						:		Optional[int]      							=			[]

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

  a						:		Dict      							=			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       )

  a						:		str      							=			1_0_0.0 * em / total
  a						:		List[str]      							=			1_0_0.0 * fa / total

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


def      lowercase			(				A_  ,   A_  ,   A_       )->      Union[str, Any]:
  '''simple docstring'''







  a						:		Tuple      							=			args.k
  a						:		Optional[Any]      							=			[line.strip() for line in open(__lowerCamelCase  ,   "r"       ).readlines()]
  a						:		Optional[int]      							=			[line.strip() for line in open(__lowerCamelCase  ,   "r"       ).readlines()]

  a						:		Dict      							=			0
  for hypo, reference in zip(__lowerCamelCase  ,   __lowerCamelCase       ):
    a						:		List[Any]      							=			set(hypo.split("\t"       )[:k]       )
    a						:		int      							=			set(reference.split("\t"       )       )
    total += 1
    em += len(hypo_provenance & ref_provenance       ) / k

  a						:		Tuple      							=			1_0_0.0 * em / total
  logger.info(F'''Precision@{k}: {em: .2f}'''       )


def      lowercase			(				A_  ,   A_  ,   A_       )->      Tuple:
  '''simple docstring'''







  def strip_title(A_       ):
    if title.startswith("\""       ):
      a						:		List[str]      							=			title[1:]
    if title.endswith("\""       ):
      a						:		Optional[Any]      							=			title[:-1]
    return title

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

  a						:		Dict      							=			rag_model.rag.question_encoder(__lowerCamelCase       )
  a						:		Optional[Any]      							=			question_enc_outputs[0]

  a						:		List[str]      							=			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"  ,   )
  a						:		str      							=			rag_model.retriever.index.get_doc_dicts(result.doc_ids       )
  a						:		List[str]      							=			[]
  for docs in all_docs:
    a						:		Any      							=			[strip_title(__lowerCamelCase       ) for title in docs["title"]]
    provenance_strings.append("\t".join(__lowerCamelCase       )       )
  return provenance_strings


def      lowercase			(				A_  ,   A_  ,   A_       )->      str:
  '''simple docstring'''







  with torch.no_grad():
    a						:		List[Any]      							=			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        __lowerCamelCase  ,   return_tensors="pt"  ,   padding=__lowerCamelCase  ,   truncation=__lowerCamelCase       )

    a						:		str      							=			inputs_dict.input_ids.to(args.device       )
    a						:		Optional[Any]      							=			inputs_dict.attention_mask.to(args.device       )
    a						:		int      							=			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]]  ,   )
    a						:		int      							=			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      lowercase			(				)->      Optional[int]:
  '''simple docstring'''







  a						:		Dict      							=			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."  ,   )
  a						:		Optional[Any]      							=			parser.parse_args()
  a						:		int      							=			torch.device("cuda" if torch.cuda.is_available() else "cpu"       )
  return args


def      lowercase			(				A_       )->      Optional[Any]:
  '''simple docstring'''







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

  a						:		int      							=			(
      [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       )

  a						:		Dict      							=			get_scores if args.eval_mode == "e2e" else get_precision_at_k
  a						:		Tuple      							=			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"       ):
      a						:		List[Any]      							=			RagRetriever.from_pretrained(__lowerCamelCase  ,   **__lowerCamelCase       )
      a						:		Tuple      							=			model_class.from_pretrained(__lowerCamelCase  ,   retriever=__lowerCamelCase  ,   **__lowerCamelCase       )
      model.retriever.init_retrieval()
    else:
      a						:		Tuple      							=			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:
      a						:		Tuple      							=			[]
      for line in tqdm(__lowerCamelCase       ):
        questions.append(line.strip()       )
        if len(__lowerCamelCase       ) == args.eval_batch_size:
          a						:		Any      							=			evaluate_batch_fn(__lowerCamelCase  ,   __lowerCamelCase  ,   __lowerCamelCase       )
          preds_file.write("\n".join(__lowerCamelCase       ) + "\n"       )
          preds_file.flush()
          a						:		List[Any]      							=			[]
      if len(__lowerCamelCase       ) > 0:
        a						:		int      							=			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__":
 __lowercase    					=      get_args()
 main(args)













'''simple docstring'''


import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


lowerCAmelCase					:     int												=logging.get_logger(__name__)

lowerCAmelCase					:     List[str]												='''▁'''

lowerCAmelCase					:     List[str]												={
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCAmelCase					:     Optional[Any]												={
    '''vocab_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
    },
    '''spm_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_config_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
    },
}

lowerCAmelCase					:     int												={
    '''facebook/m2m100_418M''': 1_024,
}

# fmt: off
lowerCAmelCase					:     str												={
    '''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
    '''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}
class       a_   (				_lowerCAmelCase							):
     __A      =			VOCAB_FILES_NAMES
     __A      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A      =			PRETRAINED_VOCAB_FILES_MAP
     __A      =			["input_ids", "attention_mask"]

     __A      =			[]
     __A      =			[]





     def __init__( self		:					Any	,      lowercase		:					Any	,      lowercase		:					List[Any]	,      lowercase		:					int=None	,      lowercase		:					Optional[Any]=None	,      lowercase		:					Union[str, Any]="<s>"	,      lowercase		:					Any="</s>"	,      lowercase		:					Optional[int]="</s>"	,      lowercase		:					List[Any]="<pad>"	,      lowercase		:					Optional[int]="<unk>"	,      lowercase		:					Optional[int]="m2m100"	,      lowercase		:					Optional[Dict[str, Any]] = None	,      lowercase		:					Any=8	,      **lowercase		:					int	,      ):





        """simple docstring"""




        lowercase_			:Optional[int]    			= {} if sp_model_kwargs is None else sp_model_kwargs

        lowercase_			:Optional[Any]    			= language_codes
        lowercase_			:Tuple    			= FAIRSEQ_LANGUAGE_CODES[language_codes]
        lowercase_			:List[Any]    			= {lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

        lowercase_			:Union[str, Any]    			= kwargs.get("additional_special_tokens"	,      []						)
        kwargs["additional_special_tokens"] += [
            self.get_lang_token(lowercase						)
            for lang_code in fairseq_language_code
            if self.get_lang_token(lowercase						) not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            src_lang=lowercase	,      tgt_lang=lowercase	,      bos_token=lowercase	,      eos_token=lowercase	,      sep_token=lowercase	,      unk_token=lowercase	,      pad_token=lowercase	,      language_codes=lowercase	,      sp_model_kwargs=self.sp_model_kwargs	,      num_madeup_words=lowercase	,      **lowercase	,      )

        lowercase_			:Optional[int]    			= vocab_file
        lowercase_			:Any    			= load_json(lowercase						)
        lowercase_			:Optional[Any]    			= {v: k for k, v in self.encoder.items()}
        lowercase_			:List[str]    			= spm_file
        lowercase_			:List[str]    			= load_spm(lowercase	,      self.sp_model_kwargs						)

        lowercase_			:Optional[int]    			= len(self.encoder						)

        lowercase_			:int    			= {
            self.get_lang_token(lowercase						): self.encoder_size + i for i, lang_code in enumerate(lowercase						)
        }
        lowercase_			:List[Any]    			= {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowercase						)}
        lowercase_			:List[Any]    			= {v: k for k, v in self.lang_token_to_id.items()}

        lowercase_			:int    			= src_lang if src_lang is not None else "en"
        lowercase_			:Union[str, Any]    			= tgt_lang
        lowercase_			:List[Any]    			= self.get_lang_id(self._src_lang						)
        self.set_src_lang_special_tokens(self._src_lang						)

        lowercase_			:int    			= num_madeup_words





     @property
     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        return len(self.encoder						) + len(self.lang_token_to_id						)





     @property
     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        return self._src_lang





     @src_lang.setter
     def 							lowercase__      ( self		:					Optional[int]	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:str    			= new_src_lang
        self.set_src_lang_special_tokens(self._src_lang						)





     def 							lowercase__      ( self		:					Dict	,      lowercase		:					str						):





        """simple docstring"""




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





     def 							lowercase__      ( self		:					Tuple	,      lowercase		:					Dict						):





        """simple docstring"""




        if token in self.lang_token_to_id:
           return self.lang_token_to_id[token]
        return self.encoder.get(lowercase	,      self.encoder[self.unk_token]						)





     def 							lowercase__      ( self		:					Any	,      lowercase		:					int						):





        """simple docstring"""




        if index in self.id_to_lang_token:
           return self.id_to_lang_token[index]
        return self.decoder.get(lowercase	,      self.unk_token						)





     def 							lowercase__      ( self		:					int	,      lowercase		:					int						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= []
        lowercase_			:Any    			= ""
        for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
              out_string += self.sp_model.decode(lowercase						) + token
              lowercase_			:str    			= []
           else:
              current_sub_tokens.append(lowercase						)
        out_string += self.sp_model.decode(lowercase						)
        return out_string.strip()





     def 							lowercase__      ( self		:					Any	,      lowercase		:					List[int]	,      lowercase		:					Optional[List[int]] = None	,      lowercase		:					bool = False						):





        """simple docstring"""




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

        lowercase_			:List[Any]    			= [1] * len(self.prefix_tokens						)
        lowercase_			:List[Any]    			= [1] * len(self.suffix_tokens						)
        if token_ids_a is None:
           return prefix_ones + ([0] * len(lowercase						)) + suffix_ones
        return prefix_ones + ([0] * len(lowercase						)) + ([0] * len(lowercase						)) + suffix_ones





     def 							lowercase__      ( self		:					Union[str, Any]	,      lowercase		:					List[int]	,      lowercase		:					Optional[List[int]] = 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		:					Union[str, Any]						):





        """simple docstring"""




        lowercase_			:str    			= {self.convert_ids_to_tokens(lowercase						): i for i in range(self.vocab_size						)}
        vocab.update(self.added_tokens_encoder						)
        return vocab





     def __getstate__( self		:					str						):





        """simple docstring"""




        lowercase_			:Any    			= self.__dict__.copy()
        lowercase_			:str    			= None
        return state





     def __setstate__( self		:					Tuple	,      lowercase		:					Dict						):





        """simple docstring"""




        lowercase_			:int    			= d

        # for backward compatibility
        if not hasattr(self	,      "sp_model_kwargs"						):
           lowercase_			:List[str]    			= {}

        lowercase_			:List[Any]    			= load_spm(self.spm_file	,      self.sp_model_kwargs						)





     def 							lowercase__      ( self		:					str	,      lowercase		:					str	,      lowercase		:					Optional[str] = None						):





        """simple docstring"""




        lowercase_			:Dict    			= Path(lowercase						)
        if not save_dir.is_dir():
           raise OSError(F'{save_directory} should be a directory'						)
        lowercase_			:Dict    			= save_dir / (
            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
        )
        lowercase_			:Dict    			= save_dir / (
            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
        )

        save_json(self.encoder	,      lowercase						)

        if os.path.abspath(self.spm_file						) != os.path.abspath(lowercase						) and os.path.isfile(self.spm_file						):
           copyfile(self.spm_file	,      lowercase						)
        elif not os.path.isfile(self.spm_file						):
           with open(lowercase	,      "wb"						) as fi:
              lowercase_			:List[str]    			= self.sp_model.serialized_model_proto()
              fi.write(lowercase						)

        return (str(lowercase						), str(lowercase						))





     def 							lowercase__      ( self		:					List[str]	,      lowercase		:					List[str]	,      lowercase		:					str = "en"	,      lowercase		:					Optional[List[str]] = None	,      lowercase		:					str = "ro"	,      **lowercase		:					Optional[int]	,      ):





        """simple docstring"""




        lowercase_			:int    			= src_lang
        lowercase_			:Optional[int]    			= tgt_lang
        self.set_src_lang_special_tokens(self.src_lang						)
        return super().prepare_seqaseq_batch(lowercase	,      lowercase	,      **lowercase						)





     def 							lowercase__      ( self		:					List[Any]	,      lowercase		:					Any	,      lowercase		:					Optional[str]	,      lowercase		:					Optional[str]	,      **lowercase		:					Union[str, Any]						):





        """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"						)
        lowercase_			:List[str]    			= src_lang
        lowercase_			:Union[str, Any]    			= self(lowercase	,      add_special_tokens=lowercase	,      **lowercase						)
        lowercase_			:str    			= self.get_lang_id(lowercase						)
        lowercase_			:Union[str, Any]    			= tgt_lang_id
        return inputs





     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        self.set_src_lang_special_tokens(self.src_lang						)





     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        self.set_tgt_lang_special_tokens(self.tgt_lang						)





     def 							lowercase__      ( self		:					str	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:List[str]    			= self.get_lang_token(lowercase						)
        lowercase_			:List[str]    			= self.lang_token_to_id[lang_token]
        lowercase_			:List[Any]    			= [self.cur_lang_id]
        lowercase_			:str    			= [self.eos_token_id]





     def 							lowercase__      ( self		:					str	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= self.get_lang_token(lowercase						)
        lowercase_			:Tuple    			= self.lang_token_to_id[lang_token]
        lowercase_			:Dict    			= [self.cur_lang_id]
        lowercase_			:List[Any]    			= [self.eos_token_id]





     def 							lowercase__      ( self		:					Union[str, Any]	,      lowercase		:					str						):





        """simple docstring"""




        return self.lang_code_to_token[lang]







     def 							lowercase__      ( self		:					Dict	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= self.get_lang_token(lowercase						)
        return self.lang_token_to_id[lang_token]







def 							UpperCAmelCase_  (						__lowerCamelCase					:					str	,__lowerCamelCase					:					Dict[str, Any] ):
   lowercase_			:List[str]    			= sentencepiece.SentencePieceProcessor(**__lowerCamelCase )
   spm.Load(str(__lowerCamelCase ) )
   return spm
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):
   with open(__lowerCamelCase	,"r" ) as f:
      return json.load(__lowerCamelCase )


def 							UpperCAmelCase_  (						__lowerCamelCase					:					int	,__lowerCamelCase					:					str ):
   with open(__lowerCamelCase	,"w" ) as f:
      json.dump(__lowerCamelCase	,__lowerCamelCase	,indent=2 )


