

import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
       import faiss



@require_faiss
class       __snake_case  (    lowerCAmelCase__       ):

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						tempfile.mkdtemp()
      SCREAMING_SNAKE_CASE_									=						8

      # DPR tok
      SCREAMING_SNAKE_CASE_									=						[
          '[UNK]',
          '[CLS]',
          '[SEP]',
          '[PAD]',
          '[MASK]',
          'want',
          '##want',
          '##ed',
          'wa',
          'un',
          'runn',
          '##ing',
          ',',
          'low',
          'lowest',
      ]
      SCREAMING_SNAKE_CASE_									=						os.path.join(self.tmpdirname						,		'dpr_tokenizer')
      os.makedirs(_A						,		exist_ok=_A)
      SCREAMING_SNAKE_CASE_									=						os.path.join(_A						,		DPR_VOCAB_FILES_NAMES['vocab_file'])
      with open(self.vocab_file						,		'w'						,		encoding='utf-8') as vocab_writer:
          vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))

      # BART tok
      SCREAMING_SNAKE_CASE_									=						[
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          '\u0120',
          '\u0120l',
          '\u0120n',
          '\u0120lo',
          '\u0120low',
          'er',
          '\u0120lowest',
          '\u0120newer',
          '\u0120wider',
          '<unk>',
      ]
      SCREAMING_SNAKE_CASE_									=						dict(zip(_A						,		range(len(_A))))
      SCREAMING_SNAKE_CASE_									=						['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
      SCREAMING_SNAKE_CASE_									=						{'unk_token': '<unk>'}

      SCREAMING_SNAKE_CASE_									=						os.path.join(self.tmpdirname						,		'bart_tokenizer')
      os.makedirs(_A						,		exist_ok=_A)
      SCREAMING_SNAKE_CASE_									=						os.path.join(_A						,		BART_VOCAB_FILES_NAMES['vocab_file'])
      SCREAMING_SNAKE_CASE_									=						os.path.join(_A						,		BART_VOCAB_FILES_NAMES['merges_file'])
      with open(self.vocab_file						,		'w'						,		encoding='utf-8') as fp:
          fp.write(json.dumps(_A) + '\n')
      with open(self.merges_file						,		'w'						,		encoding='utf-8') as fp:
          fp.write('\n'.join(_A))

  def 						lowerCAmelCase__     (					self):
      return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						,		'dpr_tokenizer'))

  def 						lowerCAmelCase__     (					self):
      return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						,		'dpr_tokenizer'))

  def 						lowerCAmelCase__     (					self):
      return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname						,		'bart_tokenizer'))

  def 						lowerCAmelCase__     (					self):
      shutil.rmtree(self.tmpdirname)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						Dataset.from_dict(
          {
              'id': ['0', '1'],
              'text': ['foo', 'bar'],
              'title': ['Foo', 'Bar'],
              'embeddings': [np.ones(self.retrieval_vector_size), 2 * np.ones(self.retrieval_vector_size)],
          })
      dataset.add_faiss_index('embeddings'						,		string_factory='Flat'						,		metric_type=faiss.METRIC_INNER_PRODUCT)
      return dataset

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_dataset()
      SCREAMING_SNAKE_CASE_									=						RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,		question_encoder=DPRConfig().to_dict()						,		generator=BartConfig().to_dict()						,		)
      with patch('transformers.models.rag.retrieval_rag.load_dataset') as mock_load_dataset:
          SCREAMING_SNAKE_CASE_									=						dataset
          SCREAMING_SNAKE_CASE_									=						RagRetriever(
              _A						,		question_encoder_tokenizer=self.get_dpr_tokenizer()						,		generator_tokenizer=self.get_bart_tokenizer()						,		)
      return retriever

  def 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_dataset()
      SCREAMING_SNAKE_CASE_									=						RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,		question_encoder=DPRConfig().to_dict()						,		generator=BartConfig().to_dict()						,		index_name='custom'						,		)
      if from_disk:
          SCREAMING_SNAKE_CASE_									=						os.path.join(self.tmpdirname						,		'dataset')
          SCREAMING_SNAKE_CASE_									=						os.path.join(self.tmpdirname						,		'index.faiss')
          dataset.get_index('embeddings').save(os.path.join(self.tmpdirname						,		'index.faiss'))
          dataset.drop_index('embeddings')
          dataset.save_to_disk(os.path.join(self.tmpdirname						,		'dataset'))
          del dataset
          SCREAMING_SNAKE_CASE_									=						RagRetriever(
              _A						,		question_encoder_tokenizer=self.get_dpr_tokenizer()						,		generator_tokenizer=self.get_bart_tokenizer()						,		)
      else:
          SCREAMING_SNAKE_CASE_									=						RagRetriever(
              _A						,		question_encoder_tokenizer=self.get_dpr_tokenizer()						,		generator_tokenizer=self.get_bart_tokenizer()						,		index=CustomHFIndex(config.retrieval_vector_size						,		_A)						,		)
      return retriever

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						Dataset.from_dict(
          {
              'id': ['0', '1'],
              'text': ['foo', 'bar'],
              'title': ['Foo', 'Bar'],
              'embeddings': [np.ones(self.retrieval_vector_size + 1), 2 * np.ones(self.retrieval_vector_size + 1)],
          })
      dataset.add_faiss_index('embeddings'						,		string_factory='Flat'						,		metric_type=faiss.METRIC_INNER_PRODUCT)

      SCREAMING_SNAKE_CASE_									=						os.path.join(self.tmpdirname						,		'hf_bert_base.hnswSQ8_correct_phi_128.c_index')
      dataset.save_faiss_index('embeddings'						,		index_file_name + '.index.dpr')
      pickle.dump(dataset['id']						,		open(index_file_name + '.index_meta.dpr'						,		'wb'))

      SCREAMING_SNAKE_CASE_									=						os.path.join(self.tmpdirname						,		'psgs_w100.tsv.pkl')
      SCREAMING_SNAKE_CASE_									=						{sample['id']: [sample['text'], sample['title']] for sample in dataset}
      pickle.dump(_A						,		open(_A						,		'wb'))

      SCREAMING_SNAKE_CASE_									=						RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,		question_encoder=DPRConfig().to_dict()						,		generator=BartConfig().to_dict()						,		index_name='legacy'						,		index_path=self.tmpdirname						,		)
      SCREAMING_SNAKE_CASE_									=						RagRetriever(
          _A						,		question_encoder_tokenizer=self.get_dpr_tokenizer()						,		generator_tokenizer=self.get_bart_tokenizer())
      return retriever

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						1
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_canonical_hf_index_retriever()
      SCREAMING_SNAKE_CASE_									=						np.array(
          [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]						,		dtype=np.floataa)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						retriever.retrieve(_A						,		n_docs=_A)
      self.assertEqual(retrieved_doc_embeds.shape						,		(2, n_docs, self.retrieval_vector_size))
      self.assertEqual(len(_A)						,		2)
      self.assertEqual(sorted(doc_dicts[0])						,		['embeddings', 'id', 'text', 'title'])
      self.assertEqual(len(doc_dicts[0]['id'])						,		_A)
      self.assertEqual(doc_dicts[0]['id'][0]						,		'1')  # max inner product is reached with second doc
      self.assertEqual(doc_dicts[1]['id'][0]						,		'0')  # max inner product is reached with first doc
      self.assertListEqual(doc_ids.tolist()						,		[[1], [0]])

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_canonical_hf_index_retriever()
      with tempfile.TemporaryDirectory() as tmp_dirname:
          with patch('transformers.models.rag.retrieval_rag.load_dataset') as mock_load_dataset:
              SCREAMING_SNAKE_CASE_									=						self.get_dummy_dataset()
              retriever.save_pretrained(_A)
              SCREAMING_SNAKE_CASE_									=						RagRetriever.from_pretrained(_A)
              self.assertIsInstance(_A						,		_A)
              SCREAMING_SNAKE_CASE_									=						np.array(
                  [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]						,		dtype=np.floataa)
              SCREAMING_SNAKE_CASE_									=						retriever.retrieve(_A						,		n_docs=1)
              self.assertTrue(out is not None)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						1
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_custom_hf_index_retriever(from_disk=_A)
      SCREAMING_SNAKE_CASE_									=						np.array(
          [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]						,		dtype=np.floataa)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						retriever.retrieve(_A						,		n_docs=_A)
      self.assertEqual(retrieved_doc_embeds.shape						,		(2, n_docs, self.retrieval_vector_size))
      self.assertEqual(len(_A)						,		2)
      self.assertEqual(sorted(doc_dicts[0])						,		['embeddings', 'id', 'text', 'title'])
      self.assertEqual(len(doc_dicts[0]['id'])						,		_A)
      self.assertEqual(doc_dicts[0]['id'][0]						,		'1')  # max inner product is reached with second doc
      self.assertEqual(doc_dicts[1]['id'][0]						,		'0')  # max inner product is reached with first doc
      self.assertListEqual(doc_ids.tolist()						,		[[1], [0]])

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_custom_hf_index_retriever(from_disk=_A)
      with tempfile.TemporaryDirectory() as tmp_dirname:
          retriever.save_pretrained(_A)
          SCREAMING_SNAKE_CASE_									=						RagRetriever.from_pretrained(_A)
          self.assertIsInstance(_A						,		_A)
          SCREAMING_SNAKE_CASE_									=						np.array(
              [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]						,		dtype=np.floataa)
          SCREAMING_SNAKE_CASE_									=						retriever.retrieve(_A						,		n_docs=1)
          self.assertTrue(out is not None)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						1
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_custom_hf_index_retriever(from_disk=_A)
      SCREAMING_SNAKE_CASE_									=						np.array(
          [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]						,		dtype=np.floataa)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						retriever.retrieve(_A						,		n_docs=_A)
      self.assertEqual(retrieved_doc_embeds.shape						,		(2, n_docs, self.retrieval_vector_size))
      self.assertEqual(len(_A)						,		2)
      self.assertEqual(sorted(doc_dicts[0])						,		['embeddings', 'id', 'text', 'title'])
      self.assertEqual(len(doc_dicts[0]['id'])						,		_A)
      self.assertEqual(doc_dicts[0]['id'][0]						,		'1')  # max inner product is reached with second doc
      self.assertEqual(doc_dicts[1]['id'][0]						,		'0')  # max inner product is reached with first doc
      self.assertListEqual(doc_ids.tolist()						,		[[1], [0]])

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_custom_hf_index_retriever(from_disk=_A)
      with tempfile.TemporaryDirectory() as tmp_dirname:
          retriever.save_pretrained(_A)
          SCREAMING_SNAKE_CASE_									=						RagRetriever.from_pretrained(_A)
          self.assertIsInstance(_A						,		_A)
          SCREAMING_SNAKE_CASE_									=						np.array(
              [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]						,		dtype=np.floataa)
          SCREAMING_SNAKE_CASE_									=						retriever.retrieve(_A						,		n_docs=1)
          self.assertTrue(out is not None)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						1
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_legacy_index_retriever()
      SCREAMING_SNAKE_CASE_									=						np.array(
          [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]						,		dtype=np.floataa)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						retriever.retrieve(_A						,		n_docs=_A)
      self.assertEqual(retrieved_doc_embeds.shape						,		(2, n_docs, self.retrieval_vector_size))
      self.assertEqual(len(_A)						,		2)
      self.assertEqual(sorted(doc_dicts[0])						,		['text', 'title'])
      self.assertEqual(len(doc_dicts[0]['text'])						,		_A)
      self.assertEqual(doc_dicts[0]['text'][0]						,		'bar')  # max inner product is reached with second doc
      self.assertEqual(doc_dicts[1]['text'][0]						,		'foo')  # max inner product is reached with first doc
      self.assertListEqual(doc_ids.tolist()						,		[[1], [0]])

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_legacy_index_retriever()
      with tempfile.TemporaryDirectory() as tmp_dirname:
          retriever.save_pretrained(_A)
          SCREAMING_SNAKE_CASE_									=						RagRetriever.from_pretrained(_A)
          self.assertIsInstance(_A						,		_A)
          SCREAMING_SNAKE_CASE_									=						np.array(
              [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]						,		dtype=np.floataa)
          SCREAMING_SNAKE_CASE_									=						retriever.retrieve(_A						,		n_docs=1)
          self.assertTrue(out is not None)

  @require_torch
  @require_tokenizers
  @require_sentencepiece
  def 						lowerCAmelCase__     (					self):
      import torch

      SCREAMING_SNAKE_CASE_									=						1
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_canonical_hf_index_retriever()
      SCREAMING_SNAKE_CASE_									=						[[5, 7], [10, 11]]
      SCREAMING_SNAKE_CASE_									=						np.array(
          [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]						,		dtype=np.floataa)
      SCREAMING_SNAKE_CASE_									=						retriever(_A						,		_A						,		prefix=retriever.config.generator.prefix						,		n_docs=_A)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						(
          out['context_input_ids'],
          out['context_attention_mask'],
          out['retrieved_doc_embeds'],
      )
      self.assertEqual(retrieved_doc_embeds.shape						,		(2, n_docs, self.retrieval_vector_size))
      self.assertIsInstance(_A						,		_A)
      self.assertIsInstance(_A						,		_A)
      self.assertIsInstance(_A						,		np.ndarray)

      SCREAMING_SNAKE_CASE_									=						retriever(
          _A						,		_A						,		prefix=retriever.config.generator.prefix						,		n_docs=_A						,		return_tensors='pt'						,		)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						(  # noqa: F841
          out['context_input_ids'],
          out['context_attention_mask'],
          out['retrieved_doc_embeds'],
          out['doc_ids'],
      )
      self.assertEqual(retrieved_doc_embeds.shape						,		(2, n_docs, self.retrieval_vector_size))
      self.assertIsInstance(_A						,		torch.Tensor)
      self.assertIsInstance(_A						,		torch.Tensor)
      self.assertIsInstance(_A						,		torch.Tensor)


  @require_torch
  @require_tokenizers
  @require_sentencepiece
  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_dpr_ctx_encoder_tokenizer()
      SCREAMING_SNAKE_CASE_									=						1
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_custom_hf_index_retriever(from_disk=_A)
      retriever.set_ctx_encoder_tokenizer(_A)

      SCREAMING_SNAKE_CASE_									=						[[5, 7], [10, 11]]
      SCREAMING_SNAKE_CASE_									=						np.array(
          [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]						,		dtype=np.floataa)
      SCREAMING_SNAKE_CASE_									=						retriever(_A						,		_A						,		prefix=retriever.config.generator.prefix						,		n_docs=_A)

      self.assertEqual(
          len(_A)						,		6)  # check whether the retriever output consist of 6 attributes including tokenized docs
      self.assertEqual(
          all(k in out for k in ('tokenized_doc_ids', 'tokenized_doc_attention_mask'))						,		_A)  # check for doc token related keys in dictionary.





import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


UpperCamelCase__  :      Union[str, Any]			=    logging.get_logger(__name__)


UpperCamelCase__  :      Optional[Any]			=    {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase__  :      List[str]			=    {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
}

UpperCamelCase__  :      str			=    {
    "facebook/bart-base": 1_024,
    "facebook/bart-large": 1_024,
    "facebook/bart-large-mnli": 1_024,
    "facebook/bart-large-cnn": 1_024,
    "facebook/bart-large-xsum": 1_024,
    "yjernite/bart_eli5": 1_024,
}


@lru_cache()
def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						(
        list(range(ord('!'       ) ,   ord('~'       ) + 1       )       ) + list(range(ord('¡'       ) ,   ord('¬'       ) + 1       )       ) + list(range(ord('®'       ) ,   ord('ÿ'       ) + 1       )       )
    )
    SCREAMING_SNAKE_CASE_									=						bs[:]
    SCREAMING_SNAKE_CASE_									=						0
    for b in range(2**8       ):
        if b not in bs:
            bs.append(_SCREAMING_SNAKE_CASE       )
            cs.append(2**8 + n       )
            n += 1
    SCREAMING_SNAKE_CASE_									=						[chr(_SCREAMING_SNAKE_CASE       ) for n in cs]
    return dict(zip(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[str]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						set()
    SCREAMING_SNAKE_CASE_									=						word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)       )
        SCREAMING_SNAKE_CASE_									=						char
    return pairs





class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	str						 =  VOCAB_FILES_NAMES
  __lowerCAmelCase     :	Any						 =  PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase     :	Dict						 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase     :	List[Any]						 =  ['input_ids', 'attention_mask']

  def __init__(					self						,		_A						,		_A						,		_A="replace"						,		_A="<s>"						,		_A="</s>"						,		_A="</s>"						,		_A="<s>"						,		_A="<unk>"						,		_A="<pad>"						,		_A="<mask>"						,		_A=False						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						AddedToken(_A						,		lstrip=_A						,		rstrip=_A) if isinstance(_A						,		_A) else bos_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(_A						,		lstrip=_A						,		rstrip=_A) if isinstance(_A						,		_A) else eos_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(_A						,		lstrip=_A						,		rstrip=_A) if isinstance(_A						,		_A) else sep_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(_A						,		lstrip=_A						,		rstrip=_A) if isinstance(_A						,		_A) else cls_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(_A						,		lstrip=_A						,		rstrip=_A) if isinstance(_A						,		_A) else unk_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(_A						,		lstrip=_A						,		rstrip=_A) if isinstance(_A						,		_A) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      SCREAMING_SNAKE_CASE_									=						AddedToken(_A						,		lstrip=_A						,		rstrip=_A) if isinstance(_A						,		_A) else mask_token

      super().__init__(
          errors=_A						,		bos_token=_A						,		eos_token=_A						,		unk_token=_A						,		sep_token=_A						,		cls_token=_A						,		pad_token=_A						,		mask_token=_A						,		add_prefix_space=_A						,		**_A						,		)

      with open(_A						,		encoding='utf-8') as vocab_handle:
          SCREAMING_SNAKE_CASE_									=						json.load(_A)
      SCREAMING_SNAKE_CASE_									=						{v: k for k, v in self.encoder.items()}
      SCREAMING_SNAKE_CASE_									=						errors  # how to handle errors in decoding
      SCREAMING_SNAKE_CASE_									=						bytes_to_unicode()
      SCREAMING_SNAKE_CASE_									=						{v: k for k, v in self.byte_encoder.items()}
      with open(_A						,		encoding='utf-8') as merges_handle:
          SCREAMING_SNAKE_CASE_									=						merges_handle.read().split('\n')[1:-1]
      SCREAMING_SNAKE_CASE_									=						[tuple(merge.split()) for merge in bpe_merges]
      SCREAMING_SNAKE_CASE_									=						dict(zip(_A						,		range(len(_A))))
      SCREAMING_SNAKE_CASE_									=						{}
      SCREAMING_SNAKE_CASE_									=						add_prefix_space

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      SCREAMING_SNAKE_CASE_									=						re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+')

  @property
  def 						lowerCAmelCase__     (					self):
      return len(self.encoder)

  def 						lowerCAmelCase__     (					self):
      return dict(self.encoder						,		**self.added_tokens_encoder)

  def 						lowerCAmelCase__     (					self						,		_A):
      if token in self.cache:
          return self.cache[token]
      SCREAMING_SNAKE_CASE_									=						tuple(_A)
      SCREAMING_SNAKE_CASE_									=						get_pairs(_A)

      if not pairs:
          return token

      while True:
          SCREAMING_SNAKE_CASE_									=						min(_A						,		key=lambda _A: self.bpe_ranks.get(_A						,		float('inf')))
          if bigram not in self.bpe_ranks:
              break
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						bigram
          SCREAMING_SNAKE_CASE_									=						[]
          SCREAMING_SNAKE_CASE_									=						0
          while i < len(_A):
              try:
                  SCREAMING_SNAKE_CASE_									=						word.index(_A						,		_A)
              except ValueError:
                  new_word.extend(word[i:])
                  break
              else:
                  new_word.extend(word[i:j])
                  SCREAMING_SNAKE_CASE_									=						j

              if word[i] == first and i < len(_A) - 1 and word[i + 1] == second:
                  new_word.append(first + second)
                  i += 2
              else:
                  new_word.append(word[i])
                  i += 1
          SCREAMING_SNAKE_CASE_									=						tuple(_A)
          SCREAMING_SNAKE_CASE_									=						new_word
          if len(_A) == 1:
              break
          else:
              SCREAMING_SNAKE_CASE_									=						get_pairs(_A)
      SCREAMING_SNAKE_CASE_									=						' '.join(_A)
      SCREAMING_SNAKE_CASE_									=						word
      return word

  def 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						[]
      for token in re.findall(self.pat						,		_A):
          SCREAMING_SNAKE_CASE_									=						''.join(
              self.byte_encoder[b] for b in token.encode('utf-8'))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
          bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_A).split(' '))
      return bpe_tokens

  def 						lowerCAmelCase__     (					self						,		_A):
      return self.encoder.get(_A						,		self.encoder.get(self.unk_token))

  def 						lowerCAmelCase__     (					self						,		_A):
      return self.decoder.get(_A)

  def 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						''.join(_A)
      SCREAMING_SNAKE_CASE_									=						bytearray([self.byte_decoder[c] for c in text]).decode('utf-8'						,		errors=self.errors)
      return text

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):
      if not os.path.isdir(_A):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
          return
      SCREAMING_SNAKE_CASE_									=						os.path.join(
          _A						,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
      SCREAMING_SNAKE_CASE_									=						os.path.join(
          _A						,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

      with open(_A						,		'w'						,		encoding='utf-8') as f:
          f.write(json.dumps(self.encoder						,		indent=2						,		sort_keys=_A						,		ensure_ascii=_A) + '\n')

      SCREAMING_SNAKE_CASE_									=						0
      with open(_A						,		'w'						,		encoding='utf-8') as writer:
          writer.write('#version: 0.2\n')
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						,		key=lambda _A: kv[1]):
              if index != token_index:
                  logger.warning(
                      f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                      ' Please check that the tokenizer is not corrupted!')
                  SCREAMING_SNAKE_CASE_									=						token_index
              writer.write(' '.join(_A) + '\n')
              index += 1

      return vocab_file, merge_file

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):
      if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      SCREAMING_SNAKE_CASE_									=						[self.cls_token_id]
      SCREAMING_SNAKE_CASE_									=						[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None						,		_A = False):
      if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=_A						,		token_ids_a=_A						,		already_has_special_tokens=_A)

      if token_ids_a is None:
          return [1] + ([0] * len(_A)) + [1]
      return [1] + ([0] * len(_A)) + [1, 1] + ([0] * len(_A)) + [1]

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):
      SCREAMING_SNAKE_CASE_									=						[self.sep_token_id]
      SCREAMING_SNAKE_CASE_									=						[self.cls_token_id]

      if token_ids_a is None:
          return len(cls + token_ids_a + sep) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


  def 						lowerCAmelCase__     (					self						,		_A						,		_A=False						,		**_A):
      SCREAMING_SNAKE_CASE_									=						kwargs.pop('add_prefix_space'						,		self.add_prefix_space)
      if (is_split_into_words or add_prefix_space) and (len(_A) > 0 and not text[0].isspace()):
          SCREAMING_SNAKE_CASE_									=						' ' + text
      return (text, kwargs)


