



'''simple docstring'''





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 __A    (						unittest.TestCase		):



 '''simple docstring'''







 def       a__						(self )					->  Tuple:


      """simple docstring"""



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

      _a											=						{
          '''unk_token''': '''<unk>''',
          '''bos_token''': '''<s>''',
          '''eos_token''': '''</s>''',
      }
      _a											=						{
          '''feature_size''': 1,
          '''padding_value''': 0.0,
          '''sampling_rate''': 16_000,
          '''return_attention_mask''': False,
          '''do_normalize''': True,
      }

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

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

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



 def       a__						(self				,     **A )					->  Optional[Any]:


      """simple docstring"""



      _a											=						self.add_kwargs_tokens_map.copy()
      kwargs.update(A )
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname				,     **A )



 def       a__						(self				,     **A )					->  Dict:


      """simple docstring"""



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



 def       a__						(self				,     **A )					->  Dict:


      """simple docstring"""



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



 def       a__						(self )					->  Any:


      """simple docstring"""



      shutil.rmtree(self.tmpdirname )



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


      """simple docstring"""



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

      _a											=						WavaVecaProcessorWithLM(tokenizer=A				,     feature_extractor=A				,     decoder=A )

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

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

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

      # 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				,     A )



 def       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						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
      _a											=						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       a__						(self )					->  List[str]:


      """simple docstring"""



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



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



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

      _a											=						WavaVecaProcessorWithLM(tokenizer=A				,     feature_extractor=A				,     decoder=A )

      _a											=						floats_list((3, 1_000) )

      _a											=						feature_extractor(A				,     return_tensors='''np''' )
      _a											=						processor(A				,     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       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



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

      _a											=						WavaVecaProcessorWithLM(tokenizer=A				,     feature_extractor=A				,     decoder=A )

      _a											=						'''This is a test string'''

      _a											=						processor(text=A )

      _a											=						tokenizer(A )

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



 def       a__						(self				,     A=(2, 10, 16)				,     A=77 )					->  Any:


      """simple docstring"""



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



 def       a__						(self )					->  Any:


      """simple docstring"""



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

      _a											=						WavaVecaProcessorWithLM(tokenizer=A				,     feature_extractor=A				,     decoder=A )

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

      _a											=						processor.decode(A )

      _a											=						decoder.decode_beams(A )[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       a__						(self				,     A )					->  Union[str, Any]:


      """simple docstring"""



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

      _a											=						WavaVecaProcessorWithLM(tokenizer=A				,     feature_extractor=A				,     decoder=A )

      _a											=						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:
           _a											=						processor.batch_decode(A )
      else:
           with get_context(A ).Pool() as pool:
                _a											=						processor.batch_decode(A				,     A )

      _a											=						list(A )

      with get_context('''fork''' ).Pool() as p:
           _a											=						decoder.decode_beams_batch(A				,     A )

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



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



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

      _a											=						WavaVecaProcessorWithLM(tokenizer=A				,     feature_extractor=A				,     decoder=A )

      _a											=						self._get_dummy_logits()

      _a											=						15
      _a											=						-20.0
      _a											=						-4.0

      _a											=						processor.batch_decode(
          A				,     beam_width=A				,     beam_prune_logp=A				,     token_min_logp=A				,     )
      _a											=						decoded_processor_out.text

      _a											=						list(A )

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

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

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

      self.assertTrue(np.array_equal(A				,     decoded_processor_out.logit_score ) )
      self.assertTrue(np.allclose([-20.054, -18.447]				,     A				,     atol=1E-3 ) )

      self.assertTrue(np.array_equal(A				,     decoded_processor_out.lm_score ) )
      self.assertTrue(np.allclose([-15.554, -13.9474]				,     A				,     atol=1E-3 ) )



 def       a__						(self )					->  Any:


      """simple docstring"""



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

      _a											=						WavaVecaProcessorWithLM(tokenizer=A				,     feature_extractor=A				,     decoder=A )

      _a											=						self._get_dummy_logits()

      _a											=						2.0
      _a											=						5.0
      _a											=						-20.0
      _a											=						True

      _a											=						processor.batch_decode(
          A				,     alpha=A				,     beta=A				,     unk_score_offset=A				,     lm_score_boundary=A				,     )
      _a											=						decoded_processor_out.text

      _a											=						list(A )
      decoder.reset_params(
          alpha=A				,     beta=A				,     unk_score_offset=A				,     lm_score_boundary=A				,     )

      with get_context('''fork''' ).Pool() as pool:
           _a											=						decoder.decode_beams_batch(
               A				,     A				,     )

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

      self.assertListEqual(A				,     A )
      self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']				,     A )
      _a											=						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				,     A )



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


      """simple docstring"""



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

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

      _a											=						os.listdir(A )
      _a											=						['''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(A				,     A )



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


      """simple docstring"""



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

      _a											=						WavaVecaProcessorWithLM.from_pretrained(A )

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

      _a											=						os.listdir(A )
      _a											=						os.listdir(A )

      local_decoder_files.sort()
      expected_decoder_files.sort()

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



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


      """simple docstring"""



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

      _a											=						floats_list((3, 1_000) )

      _a											=						processor_wavaveca(A				,     return_tensors='''np''' )
      _a											=						processor_auto(A				,     return_tensors='''np''' )

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

      _a											=						self._get_dummy_logits()

      _a											=						processor_wavaveca.batch_decode(A )
      _a											=						processor_auto.batch_decode(A )

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



 def       a__						(self )					->  List[str]:


      """simple docstring"""



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

      _a											=						WavaVecaProcessorWithLM(tokenizer=A				,     feature_extractor=A				,     decoder=A )

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



 @staticmethod
 def       a__						(A				,     A )					->  List[str]:


      """simple docstring"""



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



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


      """simple docstring"""



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

      _a											=						processor.decode(A				,     output_word_offsets=A )
      # 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(A				,     A ) )

      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       a__						(self )					->  Dict:


      """simple docstring"""



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

      _a											=						processor.batch_decode(A				,     output_word_offsets=A )

      # 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(A				,     A ) )

      self.assertListEqual(
          [''' '''.join(self.get_from_offsets(A				,     '''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       a__						(self )					->  Any:


      """simple docstring"""



      import torch

      _a											=						load_dataset('''common_voice'''				,     '''en'''				,     split='''train'''				,     streaming=A )
      _a											=						ds.cast_column('''audio'''				,     datasets.Audio(sampling_rate=16_000 ) )
      _a											=						iter(A )
      _a											=						next(A )

      _a											=						AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
      _a											=						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
      _a											=						processor(sample['''audio''']['''array''']				,     return_tensors='''pt''' ).input_values

      with torch.no_grad():
           _a											=						model(A ).logits.cpu().numpy()

      _a											=						processor.decode(logits[0]				,     output_word_offsets=A )

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

      _a											=						'''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(A				,     '''word''' ) )				,     A )
      self.assertEqual(''' '''.join(self.get_from_offsets(A				,     '''word''' ) )				,     output.text )

      # output times
      _a											=						torch.tensor(self.get_from_offsets(A				,     '''start_time''' ) )
      _a											=						torch.tensor(self.get_from_offsets(A				,     '''end_time''' ) )

      # fmt: off
      _a											=						torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599] )
      _a											=						torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94] )
      # fmt: on

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








'''simple docstring'''





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


lowercase_	 =						{
    "Acehnese Arabic": "ace_Arab",
    "Acehnese Latin": "ace_Latn",
    "Mesopotamian Arabic": "acm_Arab",
    "Ta'izzi-Adeni Arabic": "acq_Arab",
    "Tunisian Arabic": "aeb_Arab",
    "Afrikaans": "afr_Latn",
    "South Levantine Arabic": "ajp_Arab",
    "Akan": "aka_Latn",
    "Amharic": "amh_Ethi",
    "North Levantine Arabic": "apc_Arab",
    "Modern Standard Arabic": "arb_Arab",
    "Modern Standard Arabic Romanized": "arb_Latn",
    "Najdi Arabic": "ars_Arab",
    "Moroccan Arabic": "ary_Arab",
    "Egyptian Arabic": "arz_Arab",
    "Assamese": "asm_Beng",
    "Asturian": "ast_Latn",
    "Awadhi": "awa_Deva",
    "Central Aymara": "ayr_Latn",
    "South Azerbaijani": "azb_Arab",
    "North Azerbaijani": "azj_Latn",
    "Bashkir": "bak_Cyrl",
    "Bambara": "bam_Latn",
    "Balinese": "ban_Latn",
    "Belarusian": "bel_Cyrl",
    "Bemba": "bem_Latn",
    "Bengali": "ben_Beng",
    "Bhojpuri": "bho_Deva",
    "Banjar Arabic": "bjn_Arab",
    "Banjar Latin": "bjn_Latn",
    "Standard Tibetan": "bod_Tibt",
    "Bosnian": "bos_Latn",
    "Buginese": "bug_Latn",
    "Bulgarian": "bul_Cyrl",
    "Catalan": "cat_Latn",
    "Cebuano": "ceb_Latn",
    "Czech": "ces_Latn",
    "Chokwe": "cjk_Latn",
    "Central Kurdish": "ckb_Arab",
    "Crimean Tatar": "crh_Latn",
    "Welsh": "cym_Latn",
    "Danish": "dan_Latn",
    "German": "deu_Latn",
    "Southwestern Dinka": "dik_Latn",
    "Dyula": "dyu_Latn",
    "Dzongkha": "dzo_Tibt",
    "Greek": "ell_Grek",
    "English": "eng_Latn",
    "Esperanto": "epo_Latn",
    "Estonian": "est_Latn",
    "Basque": "eus_Latn",
    "Ewe": "ewe_Latn",
    "Faroese": "fao_Latn",
    "Fijian": "fij_Latn",
    "Finnish": "fin_Latn",
    "Fon": "fon_Latn",
    "French": "fra_Latn",
    "Friulian": "fur_Latn",
    "Nigerian Fulfulde": "fuv_Latn",
    "Scottish Gaelic": "gla_Latn",
    "Irish": "gle_Latn",
    "Galician": "glg_Latn",
    "Guarani": "grn_Latn",
    "Gujarati": "guj_Gujr",
    "Haitian Creole": "hat_Latn",
    "Hausa": "hau_Latn",
    "Hebrew": "heb_Hebr",
    "Hindi": "hin_Deva",
    "Chhattisgarhi": "hne_Deva",
    "Croatian": "hrv_Latn",
    "Hungarian": "hun_Latn",
    "Armenian": "hye_Armn",
    "Igbo": "ibo_Latn",
    "Ilocano": "ilo_Latn",
    "Indonesian": "ind_Latn",
    "Icelandic": "isl_Latn",
    "Italian": "ita_Latn",
    "Javanese": "jav_Latn",
    "Japanese": "jpn_Jpan",
    "Kabyle": "kab_Latn",
    "Jingpho": "kac_Latn",
    "Kamba": "kam_Latn",
    "Kannada": "kan_Knda",
    "Kashmiri Arabic": "kas_Arab",
    "Kashmiri Devanagari": "kas_Deva",
    "Georgian": "kat_Geor",
    "Central Kanuri Arabic": "knc_Arab",
    "Central Kanuri Latin": "knc_Latn",
    "Kazakh": "kaz_Cyrl",
    "Kabiyè": "kbp_Latn",
    "Kabuverdianu": "kea_Latn",
    "Khmer": "khm_Khmr",
    "Kikuyu": "kik_Latn",
    "Kinyarwanda": "kin_Latn",
    "Kyrgyz": "kir_Cyrl",
    "Kimbundu": "kmb_Latn",
    "Northern Kurdish": "kmr_Latn",
    "Kikongo": "kon_Latn",
    "Korean": "kor_Hang",
    "Lao": "lao_Laoo",
    "Ligurian": "lij_Latn",
    "Limburgish": "lim_Latn",
    "Lingala": "lin_Latn",
    "Lithuanian": "lit_Latn",
    "Lombard": "lmo_Latn",
    "Latgalian": "ltg_Latn",
    "Luxembourgish": "ltz_Latn",
    "Luba-Kasai": "lua_Latn",
    "Ganda": "lug_Latn",
    "Luo": "luo_Latn",
    "Mizo": "lus_Latn",
    "Standard Latvian": "lvs_Latn",
    "Magahi": "mag_Deva",
    "Maithili": "mai_Deva",
    "Malayalam": "mal_Mlym",
    "Marathi": "mar_Deva",
    "Minangkabau Arabic ": "min_Arab",
    "Minangkabau Latin": "min_Latn",
    "Macedonian": "mkd_Cyrl",
    "Plateau Malagasy": "plt_Latn",
    "Maltese": "mlt_Latn",
    "Meitei Bengali": "mni_Beng",
    "Halh Mongolian": "khk_Cyrl",
    "Mossi": "mos_Latn",
    "Maori": "mri_Latn",
    "Burmese": "mya_Mymr",
    "Dutch": "nld_Latn",
    "Norwegian Nynorsk": "nno_Latn",
    "Norwegian Bokmål": "nob_Latn",
    "Nepali": "npi_Deva",
    "Northern Sotho": "nso_Latn",
    "Nuer": "nus_Latn",
    "Nyanja": "nya_Latn",
    "Occitan": "oci_Latn",
    "West Central Oromo": "gaz_Latn",
    "Odia": "ory_Orya",
    "Pangasinan": "pag_Latn",
    "Eastern Panjabi": "pan_Guru",
    "Papiamento": "pap_Latn",
    "Western Persian": "pes_Arab",
    "Polish": "pol_Latn",
    "Portuguese": "por_Latn",
    "Dari": "prs_Arab",
    "Southern Pashto": "pbt_Arab",
    "Ayacucho Quechua": "quy_Latn",
    "Romanian": "ron_Latn",
    "Rundi": "run_Latn",
    "Russian": "rus_Cyrl",
    "Sango": "sag_Latn",
    "Sanskrit": "san_Deva",
    "Santali": "sat_Olck",
    "Sicilian": "scn_Latn",
    "Shan": "shn_Mymr",
    "Sinhala": "sin_Sinh",
    "Slovak": "slk_Latn",
    "Slovenian": "slv_Latn",
    "Samoan": "smo_Latn",
    "Shona": "sna_Latn",
    "Sindhi": "snd_Arab",
    "Somali": "som_Latn",
    "Southern Sotho": "sot_Latn",
    "Spanish": "spa_Latn",
    "Tosk Albanian": "als_Latn",
    "Sardinian": "srd_Latn",
    "Serbian": "srp_Cyrl",
    "Swati": "ssw_Latn",
    "Sundanese": "sun_Latn",
    "Swedish": "swe_Latn",
    "Swahili": "swh_Latn",
    "Silesian": "szl_Latn",
    "Tamil": "tam_Taml",
    "Tatar": "tat_Cyrl",
    "Telugu": "tel_Telu",
    "Tajik": "tgk_Cyrl",
    "Tagalog": "tgl_Latn",
    "Thai": "tha_Thai",
    "Tigrinya": "tir_Ethi",
    "Tamasheq Latin": "taq_Latn",
    "Tamasheq Tifinagh": "taq_Tfng",
    "Tok Pisin": "tpi_Latn",
    "Tswana": "tsn_Latn",
    "Tsonga": "tso_Latn",
    "Turkmen": "tuk_Latn",
    "Tumbuka": "tum_Latn",
    "Turkish": "tur_Latn",
    "Twi": "twi_Latn",
    "Central Atlas Tamazight": "tzm_Tfng",
    "Uyghur": "uig_Arab",
    "Ukrainian": "ukr_Cyrl",
    "Umbundu": "umb_Latn",
    "Urdu": "urd_Arab",
    "Northern Uzbek": "uzn_Latn",
    "Venetian": "vec_Latn",
    "Vietnamese": "vie_Latn",
    "Waray": "war_Latn",
    "Wolof": "wol_Latn",
    "Xhosa": "xho_Latn",
    "Eastern Yiddish": "ydd_Hebr",
    "Yoruba": "yor_Latn",
    "Yue Chinese": "yue_Hant",
    "Chinese Simplified": "zho_Hans",
    "Chinese Traditional": "zho_Hant",
    "Standard Malay": "zsm_Latn",
    "Zulu": "zul_Latn",
}






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Dict    = 'facebook/nllb-200-distilled-600M'
 __lowerCamelCase					: Optional[Any]    = (
     'This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '
     'be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '
     'which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '
     'plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'
 )
 __lowerCamelCase					: Optional[int]    = 'translator'
 __lowerCamelCase					: int    = AutoTokenizer
 __lowerCamelCase					: List[Any]    = AutoModelForSeqaSeqLM
 __lowerCamelCase					: int    = LANGUAGE_CODES

 __lowerCamelCase					: Tuple    = ['text', 'text', 'text']
 __lowerCamelCase					: Optional[Any]    = ['text']



 def       a__						(self				,     A				,     A				,     A )					->  List[str]:


      """simple docstring"""



      if src_lang not in self.lang_to_code:
           raise ValueError(f'''{src_lang} is not a supported language.''' )
      if tgt_lang not in self.lang_to_code:
           raise ValueError(f'''{tgt_lang} is not a supported language.''' )
      _a											=						self.lang_to_code[src_lang]
      _a											=						self.lang_to_code[tgt_lang]
      return self.pre_processor._build_translation_inputs(
          A				,     return_tensors='''pt'''				,     src_lang=A				,     tgt_lang=A )



 def       a__						(self				,     A )					->  Optional[Any]:


      """simple docstring"""



      return self.model.generate(**A )



 def       a__						(self				,     A )					->  List[str]:


      """simple docstring"""



      return self.post_processor.decode(outputs[0].tolist()				,     skip_special_tokens=A )



