"""simple docstring"""


import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


__UpperCamelCase									=			logging.getLogger(__name__)
torch.set_grad_enabled(False)
__UpperCamelCase									=			'''cuda''' if torch.cuda.is_available() else '''cpu'''







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase=100						,					UpperCAmelCase=" "					)				->      List[str]:
     snake_case_					       =					text.split(UpperCAmelCase					)
     return [character.join(text[i : i + n]					).strip() for i in range(0						,					len(UpperCAmelCase					)						,					UpperCAmelCase					)]







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      dict:
     snake_case_       ,	snake_case_					       =					[], []
     for title, text in zip(documents['title']						,					documents['text']					):
          if text is not None:
               for passage in split_text(UpperCAmelCase					):
                    titles.append(title if title is not None else ''					)
                    texts.append(UpperCAmelCase					)
     return {"title": titles, "text": texts}







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)				->      dict:
     snake_case_					       =					ctx_tokenizer(
         documents['title']						,					documents['text']						,					truncation=UpperCAmelCase						,					padding='longest'						,					return_tensors='pt'					)['input_ids']
     snake_case_					       =					ctx_encoder(input_ids.to(device=UpperCAmelCase					)						,					return_dict=UpperCAmelCase					).pooler_output
     return {"embeddings": embeddings.detach().cpu().numpy()}







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					)				->      int:
     ######################################
     logger.info('Step 1 - Create the dataset'					)
     ######################################

     # The dataset needed for RAG must have three columns:
     # - title (string): title of the document
     # - text (string): text of a passage of the document
     # - embeddings (array of dimension d): DPR representation of the passage
     # Let's say you have documents in tab-separated csv files with columns "title" and "text"
     assert os.path.isfile(rag_example_args.csv_path					), "Please provide a valid path to a csv file"

     # You can load a Dataset object this way
     snake_case_					       =					load_dataset(
         'csv'						,					data_files=[rag_example_args.csv_path]						,					split='train'						,					delimiter='\t'						,					column_names=['title', 'text']					)

     # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

     # Then split the documents into passages of 100 words
     snake_case_					       =					dataset.map(UpperCAmelCase						,					batched=UpperCAmelCase						,					num_proc=processing_args.num_proc					)

     # And compute the embeddings
     snake_case_					       =					DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=UpperCAmelCase					)
     snake_case_					       =					DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
     snake_case_					       =					Features(
         {'text': Value('string'					), 'title': Value('string'					), 'embeddings': Sequence(Value('float32'					)					)}					)  # optional, save as float32 instead of float64 to save space
     snake_case_					       =					dataset.map(
         partial(UpperCAmelCase						,					ctx_encoder=UpperCAmelCase						,					ctx_tokenizer=UpperCAmelCase					)						,					batched=UpperCAmelCase						,					batch_size=processing_args.batch_size						,					features=UpperCAmelCase						,					)

     # And finally save your dataset
     snake_case_					       =					os.path.join(rag_example_args.output_dir						,					'my_knowledge_dataset'					)
     dataset.save_to_disk(UpperCAmelCase					)
     # from datasets import load_from_disk
     # dataset = load_from_disk(passages_path)  # to reload the dataset

     ######################################
     logger.info('Step 2 - Index the dataset'					)
     ######################################

     # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
     snake_case_					       =					faiss.IndexHNSWFlat(index_hnsw_args.d						,					index_hnsw_args.m						,					faiss.METRIC_INNER_PRODUCT					)
     dataset.add_faiss_index('embeddings'						,					custom_index=UpperCAmelCase					)

     # And save the index
     snake_case_					       =					os.path.join(rag_example_args.output_dir						,					'my_knowledge_dataset_hnsw_index.faiss'					)
     dataset.get_index('embeddings'					).save(UpperCAmelCase					)
     # dataset.load_faiss_index("embeddings", index_path)  # to reload the index




@dataclass
class  UpperCamelCase					:
       SCREAMING_SNAKE_CASE_			=      field(
           default=str(Path(lowerCAmelCase__   ).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv"   )    ,		metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default="facebook/rag-sequence-nq"    ,		metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"}    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default="facebook/dpr-ctx_encoder-multiset-base"    ,		metadata={
               "help": (
                   "The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"
                   " 'facebook/dpr-ctx_encoder-multiset-base'"
               )
           }    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default=str(Path(lowerCAmelCase__   ).parent / "test_run" / "dummy-kb"   )    ,		metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}    ,		)




@dataclass
class  UpperCamelCase					:
       SCREAMING_SNAKE_CASE_			=      field(
           default=lowerCAmelCase__    ,		metadata={
               "help": "The number of processes to use to split the documents into passages. Default is single process."
           }    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default=1_6    ,		metadata={
               "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
           }    ,		)




@dataclass
class  UpperCamelCase					:
       SCREAMING_SNAKE_CASE_			=      field(
           default=7_6_8    ,		metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}    ,		)
       SCREAMING_SNAKE_CASE_			=      field(
           default=1_2_8    ,		metadata={
               "help": (
                   "The number of bi-directional links created for every new element during the HNSW index construction."
               )
           }    ,		)


if __name__ == "__main__":
    logging.basicConfig(level=logging.WARNING)
    logger.setLevel(logging.INFO)

    __UpperCamelCase									=			HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
    __UpperCamelCase		,    __UpperCamelCase		,    __UpperCamelCase									=			parser.parse_args_into_dataclasses()
    with TemporaryDirectory() as tmp_dir:
        __UpperCamelCase									=			rag_example_args.output_dir or tmp_dir
        main(rag_example_args, processing_args, index_hnsw_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


UpperCAmelCase :       Optional[int]			   =     get_tests_dir('fixtures/test_sentencepiece.model')


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

UpperCAmelCase :       Optional[int]			   =     2_5_6_0_4_7
UpperCAmelCase :       Union[str, Any]			   =     2_5_6_1_4_5







@require_sentencepiece
@require_tokenizers
class 						lowerCAmelCase__   (      a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      NllbTokenizer
 lowerCAmelCase__  					=      NllbTokenizerFast
 lowerCAmelCase__  					=      True
 lowerCAmelCase__  					=      True
 lowerCAmelCase__  					=      {}
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> int:


      """simple docstring"""




      super().setUp()

      # We have a SentencePiece fixture for testing
      __SCREAMING_SNAKE_CASE     =      NllbTokenizer(__SCREAMING_SNAKE_CASE					,							keep_accents=__SCREAMING_SNAKE_CASE			)
      tokenizer.save_pretrained(self.tmpdirname			)
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      NllbTokenizer(__SCREAMING_SNAKE_CASE					,							keep_accents=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      tokenizer.tokenize("""This is a test"""			)
      self.assertListEqual(__SCREAMING_SNAKE_CASE					,							["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]			)

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE			)					,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]					,							)

      __SCREAMING_SNAKE_CASE     =      tokenizer.tokenize("""I was born in 92000, and this is falsé."""			)
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE					,							[
              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""",
              """.""",
          ]					,							)
      __SCREAMING_SNAKE_CASE     =      tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE			)
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE					,							[
              value + tokenizer.fairseq_offset
              for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
          ]					,							)

      __SCREAMING_SNAKE_CASE     =      tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE			)
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE					,							[
              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       UpperCAmelCase__		(		self					:       Dict			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      (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})'			):
                __SCREAMING_SNAKE_CASE     =      self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)

                __SCREAMING_SNAKE_CASE     =      tempfile.mkdtemp()

                __SCREAMING_SNAKE_CASE     =      tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE			)

                # 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			)			)
                __SCREAMING_SNAKE_CASE     =      tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f			)
                self.assertSequenceEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)

                # Checks everything loads correctly in the same way
                __SCREAMING_SNAKE_CASE     =      tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE			)

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

                shutil.rmtree(__SCREAMING_SNAKE_CASE			)

                # Save tokenizer rust, legacy_format=True
                __SCREAMING_SNAKE_CASE     =      tempfile.mkdtemp()

                __SCREAMING_SNAKE_CASE     =      tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE					,							legacy_format=__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE			)

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

                # Checks everything loads correctly in the same way
                __SCREAMING_SNAKE_CASE     =      tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE			)

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

                shutil.rmtree(__SCREAMING_SNAKE_CASE			)

                # Save tokenizer rust, legacy_format=False
                __SCREAMING_SNAKE_CASE     =      tempfile.mkdtemp()

                __SCREAMING_SNAKE_CASE     =      tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE					,							legacy_format=__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE			)

                # 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
                __SCREAMING_SNAKE_CASE     =      tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE			)

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

                shutil.rmtree(__SCREAMING_SNAKE_CASE			)
 @require_torch
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Any:


      """simple docstring"""




      if not self.test_seqaseq:
           return

      __SCREAMING_SNAKE_CASE     =      self.get_tokenizers()
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'			):
                # Longer text that will definitely require truncation.
                __SCREAMING_SNAKE_CASE     =      [
                    """ 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.""",
                ]
                __SCREAMING_SNAKE_CASE     =      [
                    """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:
                     __SCREAMING_SNAKE_CASE     =      tokenizer.prepare_seqaseq_batch(
                         src_texts=__SCREAMING_SNAKE_CASE					,							tgt_texts=__SCREAMING_SNAKE_CASE					,							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
                __SCREAMING_SNAKE_CASE     =      tokenizer.prepare_seqaseq_batch(
                    __SCREAMING_SNAKE_CASE					,							tgt_texts=__SCREAMING_SNAKE_CASE					,							max_length=3					,							return_tensors="""pt"""			)
                self.assertEqual(batch.input_ids.shape[1]					,							3			)
                self.assertEqual(batch.labels.shape[1]					,							3			)

                __SCREAMING_SNAKE_CASE     =      tokenizer.prepare_seqaseq_batch(
                    src_texts=__SCREAMING_SNAKE_CASE					,							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"""					,							__SCREAMING_SNAKE_CASE			)
 @unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece."""			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Optional[int]:


      """simple docstring"""




      pass


 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Dict:


      """simple docstring"""




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

                __SCREAMING_SNAKE_CASE     =      self.rust_tokenizer_class.from_pretrained(
                    __SCREAMING_SNAKE_CASE					,							additional_special_tokens=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_r.encode("""Hey this is a <special> token"""			)

                __SCREAMING_SNAKE_CASE     =      tokenizer_r.encode("""<special>"""					,							add_special_tokens=__SCREAMING_SNAKE_CASE			)[0]

                self.assertTrue(special_token_id in r_output			)

                if self.test_slow_tokenizer:
                     __SCREAMING_SNAKE_CASE     =      self.rust_tokenizer_class.from_pretrained(
                         __SCREAMING_SNAKE_CASE					,							additional_special_tokens=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE					,							)
                     __SCREAMING_SNAKE_CASE     =      self.tokenizer_class.from_pretrained(
                         __SCREAMING_SNAKE_CASE					,							additional_special_tokens=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)

                     __SCREAMING_SNAKE_CASE     =      tokenizer_p.encode("""Hey this is a <special> token"""			)

                     __SCREAMING_SNAKE_CASE     =      tokenizer_cr.encode("""Hey this is a <special> token"""			)

                     self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
                     self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
                     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					):



 """simple docstring"""



 lowerCAmelCase__  					=      "facebook/nllb-200-distilled-600M"
 lowerCAmelCase__  					=      [
     " 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__  					=      [
     "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__  					=      [
     256047,
     16297,
     134408,
     8165,
     248066,
     14734,
     950,
     1135,
     105721,
     3573,
     83,
     27352,
     108,
     49486,
     2,
 ]
 @classmethod
 def       UpperCAmelCase__		(		cls					:       List[Any]			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      NllbTokenizer.from_pretrained(
          cls.checkpoint_name					,							src_lang="""eng_Latn"""					,							tgt_lang="""ron_Latn"""			)
      __SCREAMING_SNAKE_CASE     =      1
      return cls
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> List[str]:


      """simple docstring"""




      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""]					,							256_001			)
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""]					,							256_002			)
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""]					,							256_057			)
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
      self.assertListEqual(self.expected_src_tokens					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Any			)  -> int:


      """simple docstring"""




      self.assertIn(__SCREAMING_SNAKE_CASE					,							self.tokenizer.all_special_ids			)
      # fmt: off
      __SCREAMING_SNAKE_CASE     =      [RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
      # fmt: on

      __SCREAMING_SNAKE_CASE     =      self.tokenizer.decode(__SCREAMING_SNAKE_CASE					,							skip_special_tokens=__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      self.tokenizer.decode(generated_ids[1:]					,							skip_special_tokens=__SCREAMING_SNAKE_CASE			)
      self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
      self.assertNotIn(self.tokenizer.eos_token					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Any:


      """simple docstring"""




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


      """simple docstring"""




      self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]			)					,							[256_203, 3]			)
 def       UpperCAmelCase__		(		self					:       str			)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      tempfile.mkdtemp()
      __SCREAMING_SNAKE_CASE     =      self.tokenizer.fairseq_tokens_to_ids
      self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      NllbTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)
      self.assertDictEqual(new_tok.fairseq_tokens_to_ids					,							__SCREAMING_SNAKE_CASE			)
 @require_torch
 def       UpperCAmelCase__		(		self					:       str			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.tokenizer(
          self.src_text					,							text_target=self.tgt_text					,							padding=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							max_length=len(self.expected_src_tokens			)					,							return_tensors="""pt"""					,							)
      __SCREAMING_SNAKE_CASE     =      shift_tokens_right(
          batch["""labels"""]					,							self.tokenizer.pad_token_id					,							self.tokenizer.lang_code_to_id["""ron_Latn"""]			)

      self.assertIsInstance(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)

      self.assertEqual((2, 15)					,							batch.input_ids.shape			)
      self.assertEqual((2, 15)					,							batch.attention_mask.shape			)
      __SCREAMING_SNAKE_CASE     =      batch.input_ids.tolist()[0]
      self.assertListEqual(self.expected_src_tokens					,							__SCREAMING_SNAKE_CASE			)
      self.assertEqual(__SCREAMING_SNAKE_CASE					,							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       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.tokenizer(self.src_text					,							padding=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							max_length=3					,							return_tensors="""pt"""			)
      __SCREAMING_SNAKE_CASE     =      self.tokenizer(
          text_target=self.tgt_text					,							padding=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							max_length=10					,							return_tensors="""pt"""			)
      __SCREAMING_SNAKE_CASE     =      targets["""input_ids"""]
      __SCREAMING_SNAKE_CASE     =      shift_tokens_right(
          __SCREAMING_SNAKE_CASE					,							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       UpperCAmelCase__		(		self					:       int			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.tokenizer._build_translation_inputs(
          """A test"""					,							return_tensors="""pt"""					,							src_lang="""eng_Latn"""					,							tgt_lang="""fra_Latn"""			)

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE			)					,							{
              # A, test, EOS, en_XX
              """input_ids""": [[256_047, 70, 7_356, 2]],
              """attention_mask""": [[1, 1, 1, 1]],
              # ar_AR
              """forced_bos_token_id""": 256_057,
          }					,							)


 @require_torch
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      True
      __SCREAMING_SNAKE_CASE     =      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					,							[16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047]			)

      __SCREAMING_SNAKE_CASE     =      False
      __SCREAMING_SNAKE_CASE     =      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					,							[256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2]			)






