




"""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


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

transformers_logging.set_verbosity_info()



def 	lowercase_				(			__UpperCAmelCase				)						->    Any:
      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_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Optional[Any]:
      return max(metric_fn(__UpperCAmelCase    ,		__UpperCAmelCase				) for gt in ground_truths				)



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Optional[int]:
      lowerCAmelCase__      :			Optional[int]   =	[line.strip() for line in open(__UpperCAmelCase    ,		"""r"""				).readlines()]
      lowerCAmelCase__      :			Dict   =	[]

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

      lowerCAmelCase__      :			Optional[Any]   =	0
      for prediction, ground_truths in zip(__UpperCAmelCase    ,		__UpperCAmelCase				):
            total += 1
            em += metric_max_over_ground_truths(__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)
            fa += metric_max_over_ground_truths(__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)

      lowerCAmelCase__      :			List[str]   =	100.0 * em / total
      lowerCAmelCase__      :			List[str]   =	100.0 * fa / total

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



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Any:
      lowerCAmelCase__      :			Union[str, Any]   =	args.k
      lowerCAmelCase__      :			str   =	[line.strip() for line in open(__UpperCAmelCase    ,		"""r"""				).readlines()]
      lowerCAmelCase__      :			Optional[int]   =	[line.strip() for line in open(__UpperCAmelCase    ,		"""r"""				).readlines()]

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

      lowerCAmelCase__      :			Tuple   =	100.0 * em / total
      logger.info(f"""Precision@{k}: {em: .2f}"""				)



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Optional[int]:
      def strip_title(__UpperCAmelCase				):
            if title.startswith("""\""""				):
                  lowerCAmelCase__      :			List[Any]   =	title[1:]
            if title.endswith("""\""""				):
                  lowerCAmelCase__      :			Tuple   =	title[:-1]
            return title

      lowerCAmelCase__      :			Dict   =	rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          __UpperCAmelCase    ,		return_tensors="""pt"""    ,		padding=__UpperCAmelCase    ,		truncation=__UpperCAmelCase    ,		)["""input_ids"""].to(args.device				)

      lowerCAmelCase__      :			Any   =	rag_model.rag.question_encoder(__UpperCAmelCase				)
      lowerCAmelCase__      :			Dict   =	question_enc_outputs[0]

      lowerCAmelCase__      :			Dict   =	rag_model.retriever(
          __UpperCAmelCase    ,		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"""    ,		)
      lowerCAmelCase__      :			int   =	rag_model.retriever.index.get_doc_dicts(result.doc_ids				)
      lowerCAmelCase__      :			int   =	[]
      for docs in all_docs:
            lowerCAmelCase__      :			Union[str, Any]   =	[strip_title(__UpperCAmelCase				) for title in docs["""title"""]]
            provenance_strings.append("""\t""".join(__UpperCAmelCase				)				)
      return provenance_strings



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Tuple:
      with torch.no_grad():
            lowerCAmelCase__      :			int   =	rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                __UpperCAmelCase    ,		return_tensors="""pt"""    ,		padding=__UpperCAmelCase    ,		truncation=__UpperCAmelCase				)

            lowerCAmelCase__      :			Tuple   =	inputs_dict.input_ids.to(args.device				)
            lowerCAmelCase__      :			List[str]   =	inputs_dict.attention_mask.to(args.device				)
            lowerCAmelCase__      :			List[str]   =	rag_model.generate(  # rag_model overwrites generate
                __UpperCAmelCase    ,		attention_mask=__UpperCAmelCase    ,		num_beams=args.num_beams    ,		min_length=args.min_length    ,		max_length=args.max_length    ,		early_stopping=__UpperCAmelCase    ,		num_return_sequences=1    ,		bad_words_ids=[[0, 0]]    ,		)
            lowerCAmelCase__      :			Dict   =	rag_model.retriever.generator_tokenizer.batch_decode(__UpperCAmelCase    ,		skip_special_tokens=__UpperCAmelCase				)

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

            return answers



def 	lowercase_				(			)						->    Dict:
      lowerCAmelCase__      :			List[Any]   =	argparse.ArgumentParser()
      parser.add_argument(
          """--model_type"""    ,		choices=["""rag_sequence""", """rag_token""", """bart"""]    ,		type=__UpperCAmelCase    ,		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=__UpperCAmelCase    ,		choices=["""exact""", """compressed""", """legacy"""]    ,		type=__UpperCAmelCase    ,		help="""RAG model retriever type"""    ,		)
      parser.add_argument(
          """--index_path"""    ,		default=__UpperCAmelCase    ,		type=__UpperCAmelCase    ,		help="""Path to the retrieval index"""    ,		)
      parser.add_argument("""--n_docs"""    ,		default=5    ,		type=__UpperCAmelCase    ,		help="""Number of retrieved docs"""				)
      parser.add_argument(
          """--model_name_or_path"""    ,		default=__UpperCAmelCase    ,		type=__UpperCAmelCase    ,		required=__UpperCAmelCase    ,		help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""    ,		)
      parser.add_argument(
          """--eval_mode"""    ,		choices=["""e2e""", """retrieval"""]    ,		default="""e2e"""    ,		type=__UpperCAmelCase    ,		help=(
              """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
              """ precision@k."""
          )    ,		)
      parser.add_argument("""--k"""    ,		default=1    ,		type=__UpperCAmelCase    ,		help="""k for the precision@k calculation"""				)
      parser.add_argument(
          """--evaluation_set"""    ,		default=__UpperCAmelCase    ,		type=__UpperCAmelCase    ,		required=__UpperCAmelCase    ,		help="""Path to a file containing evaluation samples"""    ,		)
      parser.add_argument(
          """--gold_data_path"""    ,		default=__UpperCAmelCase    ,		type=__UpperCAmelCase    ,		required=__UpperCAmelCase    ,		help="""Path to a tab-separated file with gold samples"""    ,		)
      parser.add_argument(
          """--gold_data_mode"""    ,		default="""qa"""    ,		type=__UpperCAmelCase    ,		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=__UpperCAmelCase    ,		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=__UpperCAmelCase    ,		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=__UpperCAmelCase    ,		help="""Number of beams to be used when generating answers"""    ,		)
      parser.add_argument("""--min_length"""    ,		default=1    ,		type=__UpperCAmelCase    ,		help="""Min length of the generated answers"""				)
      parser.add_argument("""--max_length"""    ,		default=50    ,		type=__UpperCAmelCase    ,		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."""    ,		)
      lowerCAmelCase__      :			int   =	parser.parse_args()
      lowerCAmelCase__      :			List[Any]   =	torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""				)
      return args



def 	lowercase_				(			__UpperCAmelCase				)						->    str:
      lowerCAmelCase__      :			List[Any]   =	{}
      if args.model_type is None:
            lowerCAmelCase__      :			Tuple   =	infer_model_type(args.model_name_or_path				)
            assert args.model_type is not None
      if args.model_type.startswith("""rag"""				):
            lowerCAmelCase__      :			Any   =	RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
            lowerCAmelCase__      :			Dict   =	args.n_docs
            if args.index_name is not None:
                  lowerCAmelCase__      :			str   =	args.index_name
            if args.index_path is not None:
                  lowerCAmelCase__      :			List[str]   =	args.index_path
      else:
            lowerCAmelCase__      :			Optional[Any]   =	BartForConditionalGeneration

      lowerCAmelCase__      :			Union[str, Any]   =	(
          [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"""    ,		__UpperCAmelCase				)

      lowerCAmelCase__      :			Union[str, Any]   =	get_scores if args.eval_mode == """e2e""" else get_precision_at_k
      lowerCAmelCase__      :			Dict   =	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(__UpperCAmelCase    ,		args.predictions_path    ,		args.gold_data_path				)
                  continue

            logger.info("""***** Running evaluation for {} *****""".format(__UpperCAmelCase				)				)
            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"""				):
                  lowerCAmelCase__      :			Tuple   =	RagRetriever.from_pretrained(__UpperCAmelCase    ,		**__UpperCAmelCase				)
                  lowerCAmelCase__      :			Dict   =	model_class.from_pretrained(__UpperCAmelCase    ,		retriever=__UpperCAmelCase    ,		**__UpperCAmelCase				)
                  model.retriever.init_retrieval()
            else:
                  lowerCAmelCase__      :			Union[str, Any]   =	model_class.from_pretrained(__UpperCAmelCase    ,		**__UpperCAmelCase				)
            model.to(args.device				)

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

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


if __name__ == "__main__":
  _A													=      get_args()
  main(args)











"""simple docstring"""


import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_A													=      get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
  from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

_A													=      2_5_6_0_4_7
_A													=      2_5_6_1_4_5


@require_sentencepiece
@require_tokenizers
class _lowerCamelCase    (					a_       ,	unittest.TestCase						):
   _lowerCamelCase    :Any     				=     NllbTokenizer
   _lowerCamelCase    :Dict     				=     NllbTokenizerFast
   _lowerCamelCase    :str     				=     True
   _lowerCamelCase    :Optional[Any]     				=     True
   _lowerCamelCase    :Union[str, Any]     				=     {}






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						int:

         """simple docstring"""
         super().setUp()

         # We have a SentencePiece fixture for testing
         lowerCAmelCase__      :			Optional[int]   =	NllbTokenizer(UpperCamelCase   ,     keep_accents=UpperCamelCase   )
         tokenizer.save_pretrained(self.tmpdirname   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	NllbTokenizer(UpperCamelCase   ,     keep_accents=UpperCamelCase   )

         lowerCAmelCase__      :			List[str]   =	tokenizer.tokenize("""This is a test"""   )
         self.assertListEqual(UpperCamelCase   ,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]   )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(UpperCamelCase   )   ,     [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]   ,     )

         lowerCAmelCase__      :			Dict   =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""   )
         self.assertListEqual(
             UpperCamelCase   ,     [
                 SPIECE_UNDERLINE + """I""",
                 SPIECE_UNDERLINE + """was""",
                 SPIECE_UNDERLINE + """b""",
                 """or""",
                 """n""",
                 SPIECE_UNDERLINE + """in""",
                 SPIECE_UNDERLINE + """""",
                 """9""",
                 """2""",
                 """0""",
                 """0""",
                 """0""",
                 """,""",
                 SPIECE_UNDERLINE + """and""",
                 SPIECE_UNDERLINE + """this""",
                 SPIECE_UNDERLINE + """is""",
                 SPIECE_UNDERLINE + """f""",
                 """al""",
                 """s""",
                 """é""",
                 """.""",
             ]   ,     )
         lowerCAmelCase__      :			Optional[Any]   =	tokenizer.convert_tokens_to_ids(UpperCamelCase   )
         self.assertListEqual(
             UpperCamelCase   ,     [
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             ]   ,     )

         lowerCAmelCase__      :			List[str]   =	tokenizer.convert_ids_to_tokens(UpperCamelCase   )
         self.assertListEqual(
             UpperCamelCase   ,     [
                 SPIECE_UNDERLINE + """I""",
                 SPIECE_UNDERLINE + """was""",
                 SPIECE_UNDERLINE + """b""",
                 """or""",
                 """n""",
                 SPIECE_UNDERLINE + """in""",
                 SPIECE_UNDERLINE + """""",
                 """<unk>""",
                 """2""",
                 """0""",
                 """0""",
                 """0""",
                 """,""",
                 SPIECE_UNDERLINE + """and""",
                 SPIECE_UNDERLINE + """this""",
                 SPIECE_UNDERLINE + """is""",
                 SPIECE_UNDERLINE + """f""",
                 """al""",
                 """s""",
                 """<unk>""",
                 """.""",
             ]   ,     )






   def 	_lowerCAmelCase   (		self       :		Any   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	(self.rust_tokenizer_class, """hf-internal-testing/tiny-random-nllb""", {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
                     lowerCAmelCase__      :			Union[str, Any]   =	self.rust_tokenizer_class.from_pretrained(UpperCamelCase   ,     **UpperCamelCase   )
                     lowerCAmelCase__      :			str   =	self.tokenizer_class.from_pretrained(UpperCamelCase   ,     **UpperCamelCase   )

                     lowerCAmelCase__      :			int   =	tempfile.mkdtemp()

                     lowerCAmelCase__      :			Tuple   =	tokenizer_r.save_pretrained(UpperCamelCase   )
                     lowerCAmelCase__      :			Optional[int]   =	tokenizer_p.save_pretrained(UpperCamelCase   )

                     # Checks it save with the same files + the tokenizer.json file for the fast one
                     self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files   )   )
                     lowerCAmelCase__      :			Dict   =	tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f   )
                     self.assertSequenceEqual(UpperCamelCase   ,     UpperCamelCase   )

                     # Checks everything loads correctly in the same way
                     lowerCAmelCase__      :			int   =	tokenizer_r.from_pretrained(UpperCamelCase   )
                     lowerCAmelCase__      :			Union[str, Any]   =	tokenizer_p.from_pretrained(UpperCamelCase   )

                     # Check special tokens are set accordingly on Rust and Python
                     for key in tokenizer_pp.special_tokens_map:
                           self.assertTrue(hasattr(UpperCamelCase   ,     UpperCamelCase   )   )

                     shutil.rmtree(UpperCamelCase   )

                     # Save tokenizer rust, legacy_format=True
                     lowerCAmelCase__      :			List[str]   =	tempfile.mkdtemp()

                     lowerCAmelCase__      :			Optional[Any]   =	tokenizer_r.save_pretrained(UpperCamelCase   ,     legacy_format=UpperCamelCase   )
                     lowerCAmelCase__      :			List[str]   =	tokenizer_p.save_pretrained(UpperCamelCase   )

                     # Checks it save with the same files
                     self.assertSequenceEqual(UpperCamelCase   ,     UpperCamelCase   )

                     # Checks everything loads correctly in the same way
                     lowerCAmelCase__      :			List[str]   =	tokenizer_r.from_pretrained(UpperCamelCase   )
                     lowerCAmelCase__      :			Optional[Any]   =	tokenizer_p.from_pretrained(UpperCamelCase   )

                     # Check special tokens are set accordingly on Rust and Python
                     for key in tokenizer_pp.special_tokens_map:
                           self.assertTrue(hasattr(UpperCamelCase   ,     UpperCamelCase   )   )

                     shutil.rmtree(UpperCamelCase   )

                     # Save tokenizer rust, legacy_format=False
                     lowerCAmelCase__      :			List[Any]   =	tempfile.mkdtemp()

                     lowerCAmelCase__      :			int   =	tokenizer_r.save_pretrained(UpperCamelCase   ,     legacy_format=UpperCamelCase   )
                     lowerCAmelCase__      :			str   =	tokenizer_p.save_pretrained(UpperCamelCase   )

                     # Checks it saved the tokenizer.json file
                     self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files   )   )

                     # Checks everything loads correctly in the same way
                     lowerCAmelCase__      :			Dict   =	tokenizer_r.from_pretrained(UpperCamelCase   )
                     lowerCAmelCase__      :			Optional[int]   =	tokenizer_p.from_pretrained(UpperCamelCase   )

                     # Check special tokens are set accordingly on Rust and Python
                     for key in tokenizer_pp.special_tokens_map:
                           self.assertTrue(hasattr(UpperCamelCase   ,     UpperCamelCase   )   )

                     shutil.rmtree(UpperCamelCase   )






   @require_torch
   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						List[str]:

         """simple docstring"""
         if not self.test_seqaseq:
               return

         lowerCAmelCase__      :			int   =	self.get_tokenizers()
         for tokenizer in tokenizers:
               with self.subTest(f"""{tokenizer.__class__.__name__}"""   ):
                     # Longer text that will definitely require truncation.
                     lowerCAmelCase__      :			Any   =	[
                         """ UN Chief Says There Is No Military Solution in Syria""",
                         """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"""
                         """ Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"""
                         """ will only worsen the violence and misery for millions of people.""",
                     ]
                     lowerCAmelCase__      :			Optional[int]   =	[
                         """Şeful ONU declară că nu există o soluţie militară în Siria""",
                         """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"""
                         """ Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"""
                         """ că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
                     ]
                     try:
                           lowerCAmelCase__      :			Dict   =	tokenizer.prepare_seqaseq_batch(
                               src_texts=UpperCamelCase   ,     tgt_texts=UpperCamelCase   ,     max_length=3   ,     max_target_length=10   ,     return_tensors="""pt"""   ,     src_lang="""eng_Latn"""   ,     tgt_lang="""ron_Latn"""   ,     )
                     except NotImplementedError:
                           return
                     self.assertEqual(batch.input_ids.shape[1]   ,     3   )
                     self.assertEqual(batch.labels.shape[1]   ,     10   )
                     # max_target_length will default to max_length if not specified
                     lowerCAmelCase__      :			str   =	tokenizer.prepare_seqaseq_batch(
                         UpperCamelCase   ,     tgt_texts=UpperCamelCase   ,     max_length=3   ,     return_tensors="""pt"""   )
                     self.assertEqual(batch.input_ids.shape[1]   ,     3   )
                     self.assertEqual(batch.labels.shape[1]   ,     3   )

                     lowerCAmelCase__      :			int   =	tokenizer.prepare_seqaseq_batch(
                         src_texts=UpperCamelCase   ,     max_length=3   ,     max_target_length=10   ,     return_tensors="""pt"""   )
                     self.assertEqual(batch_encoder_only.input_ids.shape[1]   ,     3   )
                     self.assertEqual(batch_encoder_only.attention_mask.shape[1]   ,     3   )
                     self.assertNotIn("""decoder_input_ids"""   ,     UpperCamelCase   )






   @unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece."""   )
   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Dict:

         """simple docstring"""
         pass






   def 	_lowerCAmelCase   (		self       :		str   )			->						Union[str, Any]:

         """simple docstring"""
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
                     lowerCAmelCase__      :			str   =	[AddedToken("""<special>"""   ,     lstrip=UpperCamelCase   )]

                     lowerCAmelCase__      :			Optional[Any]   =	self.rust_tokenizer_class.from_pretrained(
                         UpperCamelCase   ,     additional_special_tokens=UpperCamelCase   ,     **UpperCamelCase   )
                     lowerCAmelCase__      :			Dict   =	tokenizer_r.encode("""Hey this is a <special> token"""   )

                     lowerCAmelCase__      :			Dict   =	tokenizer_r.encode("""<special>"""   ,     add_special_tokens=UpperCamelCase   )[0]

                     self.assertTrue(special_token_id in r_output   )

                     if self.test_slow_tokenizer:
                           lowerCAmelCase__      :			List[Any]   =	self.rust_tokenizer_class.from_pretrained(
                               UpperCamelCase   ,     additional_special_tokens=UpperCamelCase   ,     **UpperCamelCase   ,     )
                           lowerCAmelCase__      :			Dict   =	self.tokenizer_class.from_pretrained(
                               UpperCamelCase   ,     additional_special_tokens=UpperCamelCase   ,     **UpperCamelCase   )

                           lowerCAmelCase__      :			Optional[int]   =	tokenizer_p.encode("""Hey this is a <special> token"""   )

                           lowerCAmelCase__      :			Dict   =	tokenizer_cr.encode("""Hey this is a <special> token"""   )

                           self.assertEqual(UpperCamelCase   ,     UpperCamelCase   )
                           self.assertEqual(UpperCamelCase   ,     UpperCamelCase   )
                           self.assertTrue(special_token_id in p_output   )
                           self.assertTrue(special_token_id in cr_output   )






@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCamelCase    (					unittest.TestCase						):
   _lowerCamelCase    :int     				=     "facebook/nllb-200-distilled-600M"
   _lowerCamelCase    :List[str]     				=     [
       " UN Chief Says There Is No Military Solution in Syria",
       " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
   ]
   _lowerCamelCase    :Optional[Any]     				=     [
       "Şeful ONU declară că nu există o soluţie militară în Siria",
       "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
       " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
       " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
   ]
   _lowerCamelCase    :Tuple     				=     [
       256047,
       16297,
       134408,
       8165,
       248066,
       14734,
       950,
       1135,
       105721,
       3573,
       83,
       27352,
       108,
       49486,
       2,
   ]






   @classmethod
   def 	_lowerCAmelCase   (		cls       :		Optional[Any]   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			NllbTokenizer   =	NllbTokenizer.from_pretrained(
             cls.checkpoint_name   ,     src_lang="""eng_Latn"""   ,     tgt_lang="""ron_Latn"""   )
         lowerCAmelCase__      :			Optional[Any]   =	1
         return cls






   def 	_lowerCAmelCase   (		self       :		Any   )			->						int:

         """simple docstring"""
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""]   ,     25_60_01   )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""]   ,     25_60_02   )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""]   ,     25_60_57   )






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
         self.assertListEqual(self.expected_src_tokens   ,     UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Optional[int]:

         """simple docstring"""
         self.assertIn(UpperCamelCase   ,     self.tokenizer.all_special_ids   )
         # fmt: off
         lowerCAmelCase__      :			str   =	[RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
         # fmt: on

         lowerCAmelCase__      :			Any   =	self.tokenizer.decode(UpperCamelCase   ,     skip_special_tokens=UpperCamelCase   )
         lowerCAmelCase__      :			Tuple   =	self.tokenizer.decode(generated_ids[1:]   ,     skip_special_tokens=UpperCamelCase   )
         self.assertEqual(UpperCamelCase   ,     UpperCamelCase   )
         self.assertNotIn(self.tokenizer.eos_token   ,     UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						List[str]:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	["""this is gunna be a long sentence """ * 20]
         assert isinstance(src_text[0]   ,     UpperCamelCase   )
         lowerCAmelCase__      :			int   =	10
         lowerCAmelCase__      :			Any   =	self.tokenizer(UpperCamelCase   ,     max_length=UpperCamelCase   ,     truncation=UpperCamelCase   ).input_ids[0]
         self.assertEqual(ids[-1]   ,     2   )
         self.assertEqual(ids[0]   ,     UpperCamelCase   )
         self.assertEqual(len(UpperCamelCase   )   ,     UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Any   )			->						Any:

         """simple docstring"""
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]   )   ,     [25_62_03, 3]   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	tempfile.mkdtemp()
         lowerCAmelCase__      :			int   =	self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	NllbTokenizer.from_pretrained(UpperCamelCase   )
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids   ,     UpperCamelCase   )






   @require_torch
   def 	_lowerCAmelCase   (		self       :		int   )			->						List[str]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.tokenizer(
             self.src_text   ,     text_target=self.tgt_text   ,     padding=UpperCamelCase   ,     truncation=UpperCamelCase   ,     max_length=len(self.expected_src_tokens   )   ,     return_tensors="""pt"""   ,     )
         lowerCAmelCase__      :			int   =	shift_tokens_right(
             batch["""labels"""]   ,     self.tokenizer.pad_token_id   ,     self.tokenizer.lang_code_to_id["""ron_Latn"""]   )

         self.assertIsInstance(UpperCamelCase   ,     UpperCamelCase   )

         self.assertEqual((2, 15)   ,     batch.input_ids.shape   )
         self.assertEqual((2, 15)   ,     batch.attention_mask.shape   )
         lowerCAmelCase__      :			Dict   =	batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens   ,     UpperCamelCase   )
         self.assertEqual(UpperCamelCase   ,     batch.decoder_input_ids[0, 0]   )  # EOS
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens   ,     [EN_CODE]   )
         self.assertEqual(self.tokenizer.suffix_tokens   ,     [self.tokenizer.eos_token_id]   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	self.tokenizer(self.src_text   ,     padding=UpperCamelCase   ,     truncation=UpperCamelCase   ,     max_length=3   ,     return_tensors="""pt"""   )
         lowerCAmelCase__      :			Any   =	self.tokenizer(
             text_target=self.tgt_text   ,     padding=UpperCamelCase   ,     truncation=UpperCamelCase   ,     max_length=10   ,     return_tensors="""pt"""   )
         lowerCAmelCase__      :			str   =	targets["""input_ids"""]
         lowerCAmelCase__      :			Any   =	shift_tokens_right(
             UpperCamelCase   ,     self.tokenizer.pad_token_id   ,     decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]   ,     )

         self.assertEqual(batch.input_ids.shape[1]   ,     3   )
         self.assertEqual(batch.decoder_input_ids.shape[1]   ,     10   )






   @require_torch
   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	self.tokenizer._build_translation_inputs(
             """A test"""   ,     return_tensors="""pt"""   ,     src_lang="""eng_Latn"""   ,     tgt_lang="""fra_Latn"""   )

         self.assertEqual(
             nested_simplify(UpperCamelCase   )   ,     {
                 # A, test, EOS, en_XX
                 """input_ids""": [[25_60_47, 70, 73_56, 2]],
                 """attention_mask""": [[1, 1, 1, 1]],
                 # ar_AR
                 """forced_bos_token_id""": 25_60_57,
             }   ,     )






   @require_torch
   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	True
         lowerCAmelCase__      :			str   =	self.tokenizer(
             """UN Chief says there is no military solution in Syria"""   ,     src_lang="""eng_Latn"""   ,     tgt_lang="""fra_Latn"""   )
         self.assertEqual(
             inputs.input_ids   ,     [1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47]   )

         lowerCAmelCase__      :			List[str]   =	False
         lowerCAmelCase__      :			Union[str, Any]   =	self.tokenizer(
             """UN Chief says there is no military solution in Syria"""   ,     src_lang="""eng_Latn"""   ,     tgt_lang="""fra_Latn"""   )
         self.assertEqual(
             inputs.input_ids   ,     [25_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2]   )





