





import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


lowercase    : Dict    	=  logging.get_logger(__name__)

lowercase    : Tuple    	=  list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
lowercase    : str    	=  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class  __A:
     __A	  =       field(
         default=__UpperCAmelCase						,			metadata={"help": "Model type selected in the list: " + ", ".join(__UpperCAmelCase )} )
     __A	  =       field(
         default=__UpperCAmelCase						,			metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."} )
     __A	  =       field(
         default=1_28						,			metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }						,			)
     __A	  =       field(
         default=1_28						,			metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."}						,			)
     __A	  =       field(
         default=64						,			metadata={
             "help": (
                 "The maximum number of tokens for the question. Questions longer than this will "
                 "be truncated to this length."
             )
         }						,			)
     __A	  =       field(
         default=30						,			metadata={
             "help": (
                 "The maximum length of an answer that can be generated. This is needed because the start "
                 "and end predictions are not conditioned on one another."
             )
         }						,			)
     __A	  =       field(
         default=__UpperCAmelCase						,			metadata={"help": "Overwrite the cached training and evaluation sets"} )
     __A	  =       field(
         default=__UpperCAmelCase						,			metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."} )
     __A	  =       field(
         default=0.0						,			metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."} )
     __A	  =       field(
         default=20						,			metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."} )
     __A	  =       field(
         default=0						,			metadata={
             "help": (
                 "language id of input for language-specific xlm models (see"
                 " tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
             )
         }						,			)
     __A	  =       field(default=1						,			metadata={"help": "multiple threads for converting example to features"} )






class  __A(						__UpperCAmelCase ):
     __A	  =       "train"
     __A	  =       "dev"






class  __A(						__UpperCAmelCase ):
     __A	  =       42
     __A	  =       42
     __A	  =       42
     __A	  =       42

     def __init__(      self,       A,       A,       A = None,       A = Split.train,       A = False,       A = None,       A = "pt",       ):





            """simple docstring"""

            _UpperCamelCase              =						args
            _UpperCamelCase              =						is_language_sensitive
            _UpperCamelCase              =						SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
            if isinstance(A,       A      ):
                   try:
                          _UpperCamelCase              =						Split[mode]
                   except KeyError:
                          raise KeyError('''mode is not a valid split name'''      )
            _UpperCamelCase              =						mode
            # Load data features from cache or dataset file
            _UpperCamelCase              =						'''v2''' if args.version_2_with_negative else '''v1'''
            _UpperCamelCase              =						os.path.join(
                cache_dir if cache_dir is not None else args.data_dir,       F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}''',       )

            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.
            _UpperCamelCase              =						cached_features_file + '''.lock'''
            with FileLock(A      ):
                   if os.path.exists(A      ) and not args.overwrite_cache:
                          _UpperCamelCase              =						time.time()
                          _UpperCamelCase              =						torch.load(A      )

                          # Legacy cache files have only features, while new cache files
                          # will have dataset and examples also.
                          _UpperCamelCase              =						self.old_features['''features''']
                          _UpperCamelCase              =						self.old_features.get('''dataset''',       A      )
                          _UpperCamelCase              =						self.old_features.get('''examples''',       A      )
                          logger.info(
                              F'''Loading features from cached file {cached_features_file} [took %.3f s]''',       time.time() - start      )

                          if self.dataset is None or self.examples is None:
                                 logger.warning(
                                     F'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
                                     ''' future run'''      )
                   else:
                          if mode == Split.dev:
                                 _UpperCamelCase              =						self.processor.get_dev_examples(args.data_dir      )
                          else:
                                 _UpperCamelCase              =						self.processor.get_train_examples(args.data_dir      )

                          _UpperCamelCase       ,		_UpperCamelCase              =						squad_convert_examples_to_features(
                              examples=self.examples,       tokenizer=A,       max_seq_length=args.max_seq_length,       doc_stride=args.doc_stride,       max_query_length=args.max_query_length,       is_training=mode == Split.train,       threads=args.threads,       return_dataset=A,       )

                          _UpperCamelCase              =						time.time()
                          torch.save(
                              {'''features''': self.features, '''dataset''': self.dataset, '''examples''': self.examples},       A,       )
                          # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                          logger.info(
                              F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''      )

     def __len__(      self      ):





            """simple docstring"""

            return len(self.features      )



     def __getitem__(      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						self.features[i]

            _UpperCamelCase              =						torch.tensor(feature.input_ids,       dtype=torch.long      )
            _UpperCamelCase              =						torch.tensor(feature.attention_mask,       dtype=torch.long      )
            _UpperCamelCase              =						torch.tensor(feature.token_type_ids,       dtype=torch.long      )
            _UpperCamelCase              =						torch.tensor(feature.cls_index,       dtype=torch.long      )
            _UpperCamelCase              =						torch.tensor(feature.p_mask,       dtype=torch.float      )
            _UpperCamelCase              =						torch.tensor(feature.is_impossible,       dtype=torch.float      )

            _UpperCamelCase              =						{
                '''input_ids''': input_ids,
                '''attention_mask''': attention_mask,
                '''token_type_ids''': token_type_ids,
            }

            if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
                   del inputs["token_type_ids"]

            if self.args.model_type in ["xlnet", "xlm"]:
                   inputs.update({'''cls_index''': cls_index, '''p_mask''': p_mask}      )
                   if self.args.version_2_with_negative:
                          inputs.update({'''is_impossible''': is_impossible}      )
                   if self.is_language_sensitive:
                          inputs.update({'''langs''': (torch.ones(input_ids.shape,       dtype=torch.intaa      ) * self.args.lang_id)}      )

            if self.mode == Split.train:
                   _UpperCamelCase              =						torch.tensor(feature.start_position,       dtype=torch.long      )
                   _UpperCamelCase              =						torch.tensor(feature.end_position,       dtype=torch.long      )
                   inputs.update({'''start_positions''': start_positions, '''end_positions''': end_positions}      )

            return inputs













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 ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

            _UpperCamelCase              =						{
                '''unk_token''': '''<unk>''',
                '''bos_token''': '''<s>''',
                '''eos_token''': '''</s>''',
            }
            _UpperCamelCase              =						{
                '''feature_size''': 1,
                '''padding_value''': 0.0,
                '''sampling_rate''': 1_6000,
                '''return_attention_mask''': False,
                '''do_normalize''': True,
            }

            _UpperCamelCase              =						tempfile.mkdtemp()
            _UpperCamelCase              =						os.path.join(self.tmpdirname,       VOCAB_FILES_NAMES['''vocab_file''']      )
            _UpperCamelCase              =						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
            _UpperCamelCase              =						'''hf-internal-testing/ngram-beam-search-decoder'''

     def 	_UpperCamelCase     (      self,       **A      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self,       **A      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self,       **A      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            shutil.rmtree(self.tmpdirname      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

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

            processor.save_pretrained(self.tmpdirname      )
            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						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
            _UpperCamelCase              =						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      ):





            """simple docstring"""

            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

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

            _UpperCamelCase              =						floats_list((3, 1000)      )

            _UpperCamelCase              =						feature_extractor(A,       return_tensors='''np'''      )
            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

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

            _UpperCamelCase              =						'''This is a test string'''

            _UpperCamelCase              =						processor(text=A      )

            _UpperCamelCase              =						tokenizer(A      )

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

     def 	_UpperCamelCase     (      self,       A=(2, 10, 16),       A=77      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

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

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

            _UpperCamelCase              =						processor.decode(A      )

            _UpperCamelCase              =						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 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

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

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

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

            _UpperCamelCase              =						list(A      )

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

            _UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase              =						[], [], []
            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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

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

            _UpperCamelCase              =						self._get_dummy_logits()

            _UpperCamelCase              =						15
            _UpperCamelCase              =						-20.0
            _UpperCamelCase              =						-4.0

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

            _UpperCamelCase              =						list(A      )

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

            _UpperCamelCase              =						[d[0][0] for d in decoded_decoder_out]
            _UpperCamelCase              =						[d[0][2] for d in decoded_decoder_out]
            _UpperCamelCase              =						[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, -13.9_474],       A,       atol=1E-3      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

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

            _UpperCamelCase              =						self._get_dummy_logits()

            _UpperCamelCase              =						2.0
            _UpperCamelCase              =						5.0
            _UpperCamelCase              =						-20.0
            _UpperCamelCase              =						True

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

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

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

            _UpperCamelCase              =						[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      )
            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

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

            _UpperCamelCase              =						os.listdir(A      )
            _UpperCamelCase              =						['''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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

            _UpperCamelCase              =						WavaVecaProcessorWithLM.from_pretrained(A      )

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

            _UpperCamelCase              =						os.listdir(A      )
            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

            _UpperCamelCase              =						floats_list((3, 1000)      )

            _UpperCamelCase              =						processor_wavaveca(A,       return_tensors='''np'''      )
            _UpperCamelCase              =						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      )

            _UpperCamelCase              =						self._get_dummy_logits()

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

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

            _UpperCamelCase              =						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 	_UpperCamelCase     (      A,       A      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            import torch

            _UpperCamelCase              =						load_dataset('''common_voice''',       '''en''',       split='''train''',       streaming=A      )
            _UpperCamelCase              =						ds.cast_column('''audio''',       datasets.Audio(sampling_rate=1_6000      )      )
            _UpperCamelCase              =						iter(A      )
            _UpperCamelCase              =						next(A      )

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

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

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

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

            _UpperCamelCase              =						'''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
            _UpperCamelCase              =						torch.tensor(self.get_from_offsets(A,       '''start_time'''      )      )
            _UpperCamelCase              =						torch.tensor(self.get_from_offsets(A,       '''end_time'''      )      )

            # fmt: off
            _UpperCamelCase              =						torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599]      )
            _UpperCamelCase              =						torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94]      )
            # fmt: on

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






