



import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
       from huggingface_hub import snapshot_download
       from pyctcdecode import BeamSearchDecoderCTC

       from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
       from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
       from transformers import WavaVecaForCTC



@require_pyctcdecode
class       UpperCAmelCase__(							unittest.TestCase	):

   '''simple docstring'''




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




         """simple docstring"""

         lowercase__     =  '| <pad> <unk> <s> </s> a b c d e f g h i j k'.split()
         lowercase__     =  dict(zip(lowerCAmelCase			,				range(len(lowerCAmelCase))))

         lowercase__     =  {
             'unk_token': '<unk>',
             'bos_token': '<s>',
             'eos_token': '</s>',
         }
         lowercase__     =  {
             'feature_size': 1,
             'padding_value': 0.0,
             'sampling_rate': 1_60_00,
             'return_attention_mask': False,
             'do_normalize': True,
         }

         lowercase__     =  tempfile.mkdtemp()
         lowercase__     =  os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['vocab_file'])
         lowercase__     =  os.path.join(self.tmpdirname			,				lowerCAmelCase)
         with open(self.vocab_file			,				'w'			,				encoding='utf-8') as fp:
               fp.write(json.dumps(lowerCAmelCase) + '\n')

         with open(self.feature_extraction_file			,				'w'			,				encoding='utf-8') as fp:
               fp.write(json.dumps(lowerCAmelCase) + '\n')

         # load decoder from hub
         lowercase__     =  'hf-internal-testing/ngram-beam-search-decoder'


   def 			UpperCAmelCase		(						self  :			Tuple			,				**lowerCAmelCase  :			Optional[int])	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  self.add_kwargs_tokens_map.copy()
         kwargs.update(lowerCAmelCase)
         return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname			,				**lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				**lowerCAmelCase  :			Union[str, Any])	->				Optional[int]:




         """simple docstring"""

         return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname			,				**lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			str			,				**lowerCAmelCase  :			int)	->				Optional[int]:




         """simple docstring"""

         return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name			,				**lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Tuple:




         """simple docstring"""

         shutil.rmtree(self.tmpdirname)


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




         """simple docstring"""

         lowercase__     =  self.get_tokenizer()
         lowercase__     =  self.get_feature_extractor()
         lowercase__     =  self.get_decoder()

         lowercase__     =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase			,				feature_extractor=lowerCAmelCase			,				decoder=lowerCAmelCase)

         processor.save_pretrained(self.tmpdirname)
         lowercase__     =  WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname)

         # tokenizer
         self.assertEqual(processor.tokenizer.get_vocab()			,				tokenizer.get_vocab())
         self.assertIsInstance(processor.tokenizer			,				lowerCAmelCase)

         # feature extractor
         self.assertEqual(processor.feature_extractor.to_json_string()			,				feature_extractor.to_json_string())
         self.assertIsInstance(processor.feature_extractor			,				lowerCAmelCase)

         # decoder
         self.assertEqual(processor.decoder._alphabet.labels			,				decoder._alphabet.labels)
         self.assertEqual(
             processor.decoder.model_container[decoder._model_key]._unigram_set			,				decoder.model_container[decoder._model_key]._unigram_set			,				)
         self.assertIsInstance(processor.decoder			,				lowerCAmelCase)


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




         """simple docstring"""

         lowercase__     =  WavaVecaProcessorWithLM(
             tokenizer=self.get_tokenizer()			,				feature_extractor=self.get_feature_extractor()			,				decoder=self.get_decoder())
         processor.save_pretrained(self.tmpdirname)

         # make sure that error is thrown when decoder alphabet doesn't match
         lowercase__     =  WavaVecaProcessorWithLM.from_pretrained(
             self.tmpdirname			,				alpha=5.0			,				beta=3.0			,				score_boundary=-7.0			,				unk_score_offset=3)

         # decoder
         self.assertEqual(processor.language_model.alpha			,				5.0)
         self.assertEqual(processor.language_model.beta			,				3.0)
         self.assertEqual(processor.language_model.score_boundary			,				-7.0)
         self.assertEqual(processor.language_model.unk_score_offset			,				3)


   def 			UpperCAmelCase		(						self  :			int)	->				str:




         """simple docstring"""

         lowercase__     =  self.get_tokenizer()
         # add token to trigger raise
         tokenizer.add_tokens(['xx'])
         with self.assertRaisesRegex(lowerCAmelCase			,				'include'):
               WavaVecaProcessorWithLM(
                   tokenizer=lowerCAmelCase			,				feature_extractor=self.get_feature_extractor()			,				decoder=self.get_decoder())


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




         """simple docstring"""

         lowercase__     =  self.get_feature_extractor()
         lowercase__     =  self.get_tokenizer()
         lowercase__     =  self.get_decoder()

         lowercase__     =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase			,				feature_extractor=lowerCAmelCase			,				decoder=lowerCAmelCase)

         lowercase__     =  floats_list((3, 10_00))

         lowercase__     =  feature_extractor(lowerCAmelCase			,				return_tensors='np')
         lowercase__     =  processor(lowerCAmelCase			,				return_tensors='np')

         for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()			,				input_processor[key].sum()			,				delta=1E-2)


   def 			UpperCAmelCase		(						self  :			Tuple)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  self.get_feature_extractor()
         lowercase__     =  self.get_tokenizer()
         lowercase__     =  self.get_decoder()

         lowercase__     =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase			,				feature_extractor=lowerCAmelCase			,				decoder=lowerCAmelCase)

         lowercase__     =  'This is a test string'

         lowercase__     =  processor(text=lowerCAmelCase)

         lowercase__     =  tokenizer(lowerCAmelCase)

         for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]			,				encoded_processor[key])


   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			Tuple=(2, 10, 16)			,				lowerCAmelCase  :			int=77)	->				Optional[Any]:




         """simple docstring"""

         np.random.seed(lowerCAmelCase)
         return np.random.rand(*lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Dict)	->				int:




         """simple docstring"""

         lowercase__     =  self.get_feature_extractor()
         lowercase__     =  self.get_tokenizer()
         lowercase__     =  self.get_decoder()

         lowercase__     =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase			,				feature_extractor=lowerCAmelCase			,				decoder=lowerCAmelCase)

         lowercase__     =  self._get_dummy_logits(shape=(10, 16)			,				seed=13)

         lowercase__     =  processor.decode(lowerCAmelCase)

         lowercase__     =  decoder.decode_beams(lowerCAmelCase)[0]

         self.assertEqual(decoded_decoder[0]			,				decoded_processor.text)
         self.assertEqual('</s> <s> </s>'			,				decoded_processor.text)
         self.assertEqual(decoded_decoder[-2]			,				decoded_processor.logit_score)
         self.assertEqual(decoded_decoder[-1]			,				decoded_processor.lm_score)


   @parameterized.expand([[None], ['fork'], ['spawn']])
   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			Optional[int])	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  self.get_feature_extractor()
         lowercase__     =  self.get_tokenizer()
         lowercase__     =  self.get_decoder()

         lowercase__     =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase			,				feature_extractor=lowerCAmelCase			,				decoder=lowerCAmelCase)

         lowercase__     =  self._get_dummy_logits()

         # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
         #       otherwise, the LM won't be available to the pool's sub-processes.
         # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
         if pool_context is None:
               lowercase__     =  processor.batch_decode(lowerCAmelCase)
         else:
               with get_context(lowerCAmelCase).Pool() as pool:
                     lowercase__     =  processor.batch_decode(lowerCAmelCase			,				lowerCAmelCase)

         lowercase__     =  list(lowerCAmelCase)

         with get_context('fork').Pool() as p:
               lowercase__     =  decoder.decode_beams_batch(lowerCAmelCase			,				lowerCAmelCase)

         lowercase__,	lowercase__,	lowercase__     =  [], [], []
         for beams in decoded_beams:
               texts_decoder.append(beams[0][0])
               logit_scores_decoder.append(beams[0][-2])
               lm_scores_decoder.append(beams[0][-1])

         self.assertListEqual(lowerCAmelCase			,				decoded_processor.text)
         self.assertListEqual(['<s> <s> </s>', '<s> <s> <s>']			,				decoded_processor.text)
         self.assertListEqual(lowerCAmelCase			,				decoded_processor.logit_score)
         self.assertListEqual(lowerCAmelCase			,				decoded_processor.lm_score)


   def 			UpperCAmelCase		(						self  :			List[Any])	->				Dict:




         """simple docstring"""

         lowercase__     =  self.get_feature_extractor()
         lowercase__     =  self.get_tokenizer()
         lowercase__     =  self.get_decoder()

         lowercase__     =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase			,				feature_extractor=lowerCAmelCase			,				decoder=lowerCAmelCase)

         lowercase__     =  self._get_dummy_logits()

         lowercase__     =  15
         lowercase__     =  -20.0
         lowercase__     =  -4.0

         lowercase__     =  processor.batch_decode(
             lowerCAmelCase			,				beam_width=lowerCAmelCase			,				beam_prune_logp=lowerCAmelCase			,				token_min_logp=lowerCAmelCase			,				)
         lowercase__     =  decoded_processor_out.text

         lowercase__     =  list(lowerCAmelCase)

         with get_context('fork').Pool() as pool:
               lowercase__     =  decoder.decode_beams_batch(
                   lowerCAmelCase			,				lowerCAmelCase			,				beam_width=lowerCAmelCase			,				beam_prune_logp=lowerCAmelCase			,				token_min_logp=lowerCAmelCase			,				)

         lowercase__     =  [d[0][0] for d in decoded_decoder_out]
         lowercase__     =  [d[0][2] for d in decoded_decoder_out]
         lowercase__     =  [d[0][3] for d in decoded_decoder_out]

         self.assertListEqual(lowerCAmelCase			,				lowerCAmelCase)
         self.assertListEqual(['</s> <s> <s>', '<s> <s> <s>']			,				lowerCAmelCase)

         self.assertTrue(np.array_equal(lowerCAmelCase			,				decoded_processor_out.logit_score))
         self.assertTrue(np.allclose([-20.0_54, -18.4_47]			,				lowerCAmelCase			,				atol=1E-3))

         self.assertTrue(np.array_equal(lowerCAmelCase			,				decoded_processor_out.lm_score))
         self.assertTrue(np.allclose([-15.5_54, -13.94_74]			,				lowerCAmelCase			,				atol=1E-3))


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




         """simple docstring"""

         lowercase__     =  self.get_feature_extractor()
         lowercase__     =  self.get_tokenizer()
         lowercase__     =  self.get_decoder()

         lowercase__     =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase			,				feature_extractor=lowerCAmelCase			,				decoder=lowerCAmelCase)

         lowercase__     =  self._get_dummy_logits()

         lowercase__     =  2.0
         lowercase__     =  5.0
         lowercase__     =  -20.0
         lowercase__     =  True

         lowercase__     =  processor.batch_decode(
             lowerCAmelCase			,				alpha=lowerCAmelCase			,				beta=lowerCAmelCase			,				unk_score_offset=lowerCAmelCase			,				lm_score_boundary=lowerCAmelCase			,				)
         lowercase__     =  decoded_processor_out.text

         lowercase__     =  list(lowerCAmelCase)
         decoder.reset_params(
             alpha=lowerCAmelCase			,				beta=lowerCAmelCase			,				unk_score_offset=lowerCAmelCase			,				lm_score_boundary=lowerCAmelCase			,				)

         with get_context('fork').Pool() as pool:
               lowercase__     =  decoder.decode_beams_batch(
                   lowerCAmelCase			,				lowerCAmelCase			,				)

         lowercase__     =  [d[0][0] for d in decoded_decoder_out]

         self.assertListEqual(lowerCAmelCase			,				lowerCAmelCase)
         self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>']			,				lowerCAmelCase)
         lowercase__     =  processor.decoder.model_container[processor.decoder._model_key]
         self.assertEqual(lm_model.alpha			,				2.0)
         self.assertEqual(lm_model.beta			,				5.0)
         self.assertEqual(lm_model.unk_score_offset			,				-20.0)
         self.assertEqual(lm_model.score_boundary			,				lowerCAmelCase)


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




         """simple docstring"""

         lowercase__     =  WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')

         lowercase__     =  processor.decoder.model_container[processor.decoder._model_key]
         lowercase__     =  Path(language_model._kenlm_model.path.decode('utf-8')).parent.parent.absolute()

         lowercase__     =  os.listdir(lowerCAmelCase)
         lowercase__     =  ['alphabet.json', 'language_model']

         downloaded_decoder_files.sort()
         expected_decoder_files.sort()

         # test that only decoder relevant files from
         # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
         # are downloaded and none of the rest (e.g. README.md, ...)
         self.assertListEqual(lowerCAmelCase			,				lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[str])	->				Tuple:




         """simple docstring"""

         lowercase__     =  snapshot_download('hf-internal-testing/processor_with_lm')

         lowercase__     =  WavaVecaProcessorWithLM.from_pretrained(lowerCAmelCase)

         lowercase__     =  processor.decoder.model_container[processor.decoder._model_key]
         lowercase__     =  Path(language_model._kenlm_model.path.decode('utf-8')).parent.parent.absolute()

         lowercase__     =  os.listdir(lowerCAmelCase)
         lowercase__     =  os.listdir(lowerCAmelCase)

         local_decoder_files.sort()
         expected_decoder_files.sort()

         # test that both decoder form hub and local files in cache are the same
         self.assertListEqual(lowerCAmelCase			,				lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Dict)	->				Tuple:




         """simple docstring"""

         lowercase__     =  WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')
         lowercase__     =  AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm')

         lowercase__     =  floats_list((3, 10_00))

         lowercase__     =  processor_wavaveca(lowerCAmelCase			,				return_tensors='np')
         lowercase__     =  processor_auto(lowerCAmelCase			,				return_tensors='np')

         for key in input_wavaveca.keys():
               self.assertAlmostEqual(input_wavaveca[key].sum()			,				input_auto[key].sum()			,				delta=1E-2)

         lowercase__     =  self._get_dummy_logits()

         lowercase__     =  processor_wavaveca.batch_decode(lowerCAmelCase)
         lowercase__     =  processor_auto.batch_decode(lowerCAmelCase)

         self.assertListEqual(decoded_wavaveca.text			,				decoded_auto.text)


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




         """simple docstring"""

         lowercase__     =  self.get_feature_extractor()
         lowercase__     =  self.get_tokenizer()
         lowercase__     =  self.get_decoder()

         lowercase__     =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase			,				feature_extractor=lowerCAmelCase			,				decoder=lowerCAmelCase)

         self.assertListEqual(
             processor.model_input_names			,				feature_extractor.model_input_names			,				msg='`processor` and `feature_extractor` model input names do not match'			,				)


   @staticmethod
   def 			UpperCAmelCase		(						lowerCAmelCase  :			int			,				lowerCAmelCase  :			str)	->				int:




         """simple docstring"""

         lowercase__     =  [d[key] for d in offsets]
         return retrieved_list


   def 			UpperCAmelCase		(						self  :			Dict)	->				int:




         """simple docstring"""

         lowercase__     =  WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')
         lowercase__     =  self._get_dummy_logits()[0]

         lowercase__     =  processor.decode(lowerCAmelCase			,				output_word_offsets=lowerCAmelCase)
         # check Wav2Vec2CTCTokenizerOutput keys for word
         self.assertEqual(len(outputs.keys())			,				4)
         self.assertTrue('text' in outputs)
         self.assertTrue('word_offsets' in outputs)
         self.assertTrue(isinstance(lowerCAmelCase			,				lowerCAmelCase))

         self.assertEqual(' '.join(self.get_from_offsets(outputs['word_offsets']			,				'word'))			,				outputs.text)
         self.assertListEqual(self.get_from_offsets(outputs['word_offsets']			,				'word')			,				['<s>', '<s>', '</s>'])
         self.assertListEqual(self.get_from_offsets(outputs['word_offsets']			,				'start_offset')			,				[0, 2, 4])
         self.assertListEqual(self.get_from_offsets(outputs['word_offsets']			,				'end_offset')			,				[1, 3, 5])


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




         """simple docstring"""

         lowercase__     =  WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')
         lowercase__     =  self._get_dummy_logits()

         lowercase__     =  processor.batch_decode(lowerCAmelCase			,				output_word_offsets=lowerCAmelCase)

         # check Wav2Vec2CTCTokenizerOutput keys for word
         self.assertEqual(len(outputs.keys())			,				4)
         self.assertTrue('text' in outputs)
         self.assertTrue('word_offsets' in outputs)
         self.assertTrue(isinstance(lowerCAmelCase			,				lowerCAmelCase))

         self.assertListEqual(
             [' '.join(self.get_from_offsets(lowerCAmelCase			,				'word')) for o in outputs['word_offsets']]			,				outputs.text)
         self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]			,				'word')			,				['<s>', '<s>', '</s>'])
         self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]			,				'start_offset')			,				[0, 2, 4])
         self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]			,				'end_offset')			,				[1, 3, 5])



   @slow
   @require_torch
   @require_torchaudio
   def 			UpperCAmelCase		(						self  :			Tuple)	->				Any:




         """simple docstring"""

         import torch

         lowercase__     =  load_dataset('common_voice'			,				'en'			,				split='train'			,				streaming=lowerCAmelCase)
         lowercase__     =  ds.cast_column('audio'			,				datasets.Audio(sampling_rate=1_60_00))
         lowercase__     =  iter(lowerCAmelCase)
         lowercase__     =  next(lowerCAmelCase)

         lowercase__     =  AutoProcessor.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm')
         lowercase__     =  WavaVecaForCTC.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm')

         # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
         lowercase__     =  processor(sample['audio']['array']			,				return_tensors='pt').input_values

         with torch.no_grad():
               lowercase__     =  model(lowerCAmelCase).logits.cpu().numpy()

         lowercase__     =  processor.decode(logits[0]			,				output_word_offsets=lowerCAmelCase)

         lowercase__     =  model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
         lowercase__     =  [
             {
                 'start_time': d['start_offset'] * time_offset,
                 'end_time': d['end_offset'] * time_offset,
                 'word': d['word'],
             }
             for d in output['word_offsets']
         ]

         lowercase__     =  'WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'

         # output words
         self.assertEqual(' '.join(self.get_from_offsets(lowerCAmelCase			,				'word'))			,				lowerCAmelCase)
         self.assertEqual(' '.join(self.get_from_offsets(lowerCAmelCase			,				'word'))			,				output.text)

         # output times
         lowercase__     =  torch.tensor(self.get_from_offsets(lowerCAmelCase			,				'start_time'))
         lowercase__     =  torch.tensor(self.get_from_offsets(lowerCAmelCase			,				'end_time'))

         # fmt: off
         lowercase__     =  torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99])
         lowercase__     =  torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94])
         # fmt: on

         self.assertTrue(torch.allclose(lowerCAmelCase			,				lowerCAmelCase			,				atol=0.01))
         self.assertTrue(torch.allclose(lowerCAmelCase			,				lowerCAmelCase			,				atol=0.01))





import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a__      :				Optional[int]   	=			logging.get_logger(__name__)


a__      :				Dict   	=			{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

a__      :				str   	=			{
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

a__      :				Any   	=			{"facebook/blenderbot_small-90M": 5_12}

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  set()
      lowercase__     =  word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)	)
            lowercase__     =  char

      lowercase__     =  set(A__	)
      return pairs



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  List[str]       				=    VOCAB_FILES_NAMES
   A    :  str       				=    PRETRAINED_VOCAB_FILES_MAP
   A    :  int       				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A    :  Tuple       				=    ["input_ids", "attention_mask"]


   def __init__(						self  :			Optional[int]			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			int="__start__"			,				lowerCAmelCase  :			Dict="__end__"			,				lowerCAmelCase  :			Any="__unk__"			,				lowerCAmelCase  :			str="__null__"			,				**lowerCAmelCase  :			Optional[Any]			,				)	->				List[str]:




         """simple docstring"""

         super().__init__(unk_token=lowerCAmelCase			,				bos_token=lowerCAmelCase			,				eos_token=lowerCAmelCase			,				pad_token=lowerCAmelCase			,				**lowerCAmelCase)

         with open(lowerCAmelCase			,				encoding='utf-8') as vocab_handle:
               lowercase__     =  json.load(lowerCAmelCase)
         lowercase__     =  {v: k for k, v in self.encoder.items()}
         with open(lowerCAmelCase			,				encoding='utf-8') as merges_handle:
               lowercase__     =  merges_handle.read().split('\n')[1:-1]
         lowercase__     =  [tuple(merge.split()) for merge in merges]
         lowercase__     =  dict(zip(lowerCAmelCase			,				range(len(lowerCAmelCase))))
         lowercase__     =  {}


   @property
   def 			UpperCAmelCase		(						self  :			int)	->				int:




         """simple docstring"""

         return len(self.encoder)


   def 			UpperCAmelCase		(						self  :			str)	->				Dict:




         """simple docstring"""

         return dict(self.encoder			,				**self.added_tokens_encoder)


   def 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			str)	->				str:




         """simple docstring"""

         if token in self.cache:
               return self.cache[token]
         lowercase__     =  re.sub('([.,!?()])'			,				R' \1'			,				lowerCAmelCase)
         lowercase__     =  re.sub('(\')'			,				R' \1 '			,				lowerCAmelCase)
         lowercase__     =  re.sub(R'\s{2,}'			,				' '			,				lowerCAmelCase)
         if "\n" in token:
               lowercase__     =  token.replace('\n'			,				' __newln__')

         lowercase__     =  token.split(' ')
         lowercase__     =  []
         for token in tokens:
               if not len(lowerCAmelCase):
                     continue

               lowercase__     =  token.lower()
               lowercase__     =  tuple(lowerCAmelCase)
               lowercase__     =  tuple(list(word[:-1]) + [word[-1] + '</w>'])
               lowercase__     =  get_pairs(lowerCAmelCase)

               if not pairs:
                     words.append(lowerCAmelCase)
                     continue

               while True:
                     lowercase__     =  min(lowerCAmelCase			,				key=lambda lowerCAmelCase: self.bpe_ranks.get(lowerCAmelCase			,				float('inf')))
                     if bigram not in self.bpe_ranks:
                           break
                     lowercase__,	lowercase__     =  bigram
                     lowercase__     =  []
                     lowercase__     =  0

                     while i < len(lowerCAmelCase):
                           try:
                                 lowercase__     =  word.index(lowerCAmelCase			,				lowerCAmelCase)
                                 new_word.extend(word[i:j])
                                 lowercase__     =  j
                           except ValueError:
                                 new_word.extend(word[i:])
                                 break

                           if word[i] == first and i < len(lowerCAmelCase) - 1 and word[i + 1] == second:
                                 new_word.append(first + second)
                                 i += 2
                           else:
                                 new_word.append(word[i])
                                 i += 1
                     lowercase__     =  tuple(lowerCAmelCase)
                     lowercase__     =  new_word
                     if len(lowerCAmelCase) == 1:
                           break
                     else:
                           lowercase__     =  get_pairs(lowerCAmelCase)
               lowercase__     =  '@@ '.join(lowerCAmelCase)
               lowercase__     =  word[:-4]

               lowercase__     =  word
               words.append(lowerCAmelCase)
         return " ".join(lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[str]			,				lowerCAmelCase  :			str)	->				List[str]:




         """simple docstring"""

         lowercase__     =  []

         lowercase__     =  re.findall(R'\S+\n?'			,				lowerCAmelCase)

         for token in words:
               split_tokens.extend(list(self.bpe(lowerCAmelCase).split(' ')))
         return split_tokens


   def 			UpperCAmelCase		(						self  :			int			,				lowerCAmelCase  :			str)	->				int:




         """simple docstring"""

         lowercase__     =  token.lower()
         return self.encoder.get(lowerCAmelCase			,				self.encoder.get(self.unk_token))


   def 			UpperCAmelCase		(						self  :			Optional[int]			,				lowerCAmelCase  :			int)	->				str:




         """simple docstring"""

         return self.decoder.get(lowerCAmelCase			,				self.unk_token)


   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			List[str])	->				str:




         """simple docstring"""

         lowercase__     =  ' '.join(lowerCAmelCase).replace('@@ '			,				'').strip()
         return out_string



   def 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			Optional[str] = None)	->				Tuple[str]:




         """simple docstring"""

         if not os.path.isdir(lowerCAmelCase):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
               return
         lowercase__     =  os.path.join(
             lowerCAmelCase			,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
         lowercase__     =  os.path.join(
             lowerCAmelCase			,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

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

         lowercase__     =  0
         with open(lowerCAmelCase			,				'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 lowerCAmelCase: 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!')
                           lowercase__     =  token_index
                     writer.write(' '.join(lowerCAmelCase) + '\n')
                     index += 1

         return vocab_file, merge_file
