





import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
       import numpy as np
       import torch

       from . import (
           AlbertForPreTraining,
           BartForConditionalGeneration,
           BertForPreTraining,
           BertForQuestionAnswering,
           BertForSequenceClassification,
           CamembertForMaskedLM,
           CTRLLMHeadModel,
           DistilBertForMaskedLM,
           DistilBertForQuestionAnswering,
           DPRContextEncoder,
           DPRQuestionEncoder,
           DPRReader,
           ElectraForPreTraining,
           FlaubertWithLMHeadModel,
           GPTaLMHeadModel,
           LayoutLMForMaskedLM,
           LxmertForPreTraining,
           LxmertVisualFeatureEncoder,
           OpenAIGPTLMHeadModel,
           RobertaForMaskedLM,
           RobertaForSequenceClassification,
           TaForConditionalGeneration,
           TransfoXLLMHeadModel,
           XLMRobertaForMaskedLM,
           XLMWithLMHeadModel,
           XLNetLMHeadModel,
       )


logging.set_verbosity_info()

lowercase    : Tuple    	=  {
    """bart""": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """bert""": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-base-cased-finetuned-mrpc""": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """dpr""": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """gpt2""": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlnet""": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm""": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm-roberta""": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """transfo-xl""": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """openai-gpt""": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """roberta""": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """layoutlm""": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """roberta-large-mnli""": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """camembert""": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """flaubert""": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert""": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert-base-distilled-squad""": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert""": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert-visual-feature-encoder""": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """ctrl""": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """albert""": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """t5""": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """electra""": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """wav2vec2""": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase=False    ,       lowerCAmelCase=True					):
       if model_type not in MODEL_CLASSES:
              raise ValueError(f'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()					)}.'''					)

       _UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase              =						MODEL_CLASSES[model_type]

       # Initialise TF model
       if config_file in aws_config_map:
              _UpperCamelCase              =						cached_file(lowerCAmelCase    ,       lowerCAmelCase    ,       force_download=not use_cached_models					)
       _UpperCamelCase              =						config_class.from_json_file(lowerCAmelCase					)
       _UpperCamelCase              =						True
       _UpperCamelCase              =						True
       print(f'''Building TensorFlow model from configuration: {config}'''					)
       _UpperCamelCase              =						model_class(lowerCAmelCase					)

       # Load weights from tf checkpoint
       if pytorch_checkpoint_path in aws_config_map.keys():
              _UpperCamelCase              =						cached_file(
                  lowerCAmelCase    ,       lowerCAmelCase    ,       force_download=not use_cached_models					)
       # Load PyTorch checkpoint in tf2 model:
       _UpperCamelCase              =						load_pytorch_checkpoint_in_tfa_model(lowerCAmelCase    ,       lowerCAmelCase					)

       if compare_with_pt_model:
              _UpperCamelCase              =						tf_model(tf_model.dummy_inputs    ,       training=lowerCAmelCase					)  # build the network

              _UpperCamelCase              =						torch.load(lowerCAmelCase    ,       map_location='''cpu'''					)
              _UpperCamelCase              =						pt_model_class.from_pretrained(
                  pretrained_model_name_or_path=lowerCAmelCase    ,       config=lowerCAmelCase    ,       state_dict=lowerCAmelCase					)

              with torch.no_grad():
                     _UpperCamelCase              =						pt_model(**pt_model.dummy_inputs					)

              _UpperCamelCase              =						pto[0].numpy()
              _UpperCamelCase              =						tfo[0].numpy()
              _UpperCamelCase              =						np.amax(np.abs(np_pt - np_tf					)					)
              print(f'''Max absolute difference between models outputs {diff}'''					)
              assert diff <= 2E-2, f'''Error, model absolute difference is >2e-2: {diff}'''

       # Save pytorch-model
       print(f'''Save TensorFlow model to {tf_dump_path}'''					)
       tf_model.save_weights(lowerCAmelCase    ,       save_format='''h5'''					)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase=None    ,       lowerCAmelCase=None    ,       lowerCAmelCase=False    ,       lowerCAmelCase=False    ,       lowerCAmelCase=False    ,       lowerCAmelCase=False    ,       ):
       if args_model_type is None:
              _UpperCamelCase              =						list(MODEL_CLASSES.keys()					)
       else:
              _UpperCamelCase              =						[args_model_type]

       for j, model_type in enumerate(lowerCAmelCase    ,       start=1					):
              print('''=''' * 100					)
              print(f''' Converting model type {j}/{len(lowerCAmelCase					)}: {model_type}'''					)
              print('''=''' * 100					)
              if model_type not in MODEL_CLASSES:
                     raise ValueError(f'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()					)}.'''					)

              _UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase              =						MODEL_CLASSES[model_type]

              if model_shortcut_names_or_path is None:
                     _UpperCamelCase              =						list(aws_model_maps.keys()					)
              if config_shortcut_names_or_path is None:
                     _UpperCamelCase              =						model_shortcut_names_or_path

              for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                  zip(lowerCAmelCase    ,       lowerCAmelCase					)    ,       start=1					):
                     print('''-''' * 100					)
                     if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                            if not only_convert_finetuned_models:
                                   print(f'''    Skipping finetuned checkpoint {model_shortcut_name}'''					)
                                   continue
                            _UpperCamelCase              =						model_shortcut_name
                     elif only_convert_finetuned_models:
                            print(f'''    Skipping not finetuned checkpoint {model_shortcut_name}'''					)
                            continue
                     print(
                         f'''    Converting checkpoint {i}/{len(lowerCAmelCase					)}: {model_shortcut_name} - model_type {model_type}'''					)
                     print('''-''' * 100					)

                     if config_shortcut_name in aws_config_map:
                            _UpperCamelCase              =						cached_file(lowerCAmelCase    ,       lowerCAmelCase    ,       force_download=not use_cached_models					)
                     else:
                            _UpperCamelCase              =						config_shortcut_name

                     if model_shortcut_name in aws_model_maps:
                            _UpperCamelCase              =						cached_file(lowerCAmelCase    ,       lowerCAmelCase    ,       force_download=not use_cached_models					)
                     else:
                            _UpperCamelCase              =						model_shortcut_name

                     if os.path.isfile(lowerCAmelCase					):
                            _UpperCamelCase              =						'''converted_model'''

                     convert_pt_checkpoint_to_tf(
                         model_type=lowerCAmelCase    ,       pytorch_checkpoint_path=lowerCAmelCase    ,       config_file=lowerCAmelCase    ,       tf_dump_path=os.path.join(lowerCAmelCase    ,       model_shortcut_name + '''-tf_model.h5'''					)    ,       compare_with_pt_model=lowerCAmelCase    ,       )
                     if remove_cached_files:
                            os.remove(lowerCAmelCase					)
                            os.remove(lowerCAmelCase					)


if __name__ == "__main__":
       lowercase    : List[Any]    	=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--tf_dump_path""", default=None, type=str, required=True, help="""Path to the output Tensorflow dump file."""
       )
       parser.add_argument(
           """--model_type""",
           default=None,
           type=str,
           help=(
               F'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
               """convert all the models from AWS."""
           ),
       )
       parser.add_argument(
           """--pytorch_checkpoint_path""",
           default=None,
           type=str,
           help=(
               """Path to the PyTorch checkpoint path or shortcut name to download from AWS. """
               """If not given, will download and convert all the checkpoints from AWS."""
           ),
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           help=(
               """The config json file corresponding to the pre-trained model. \n"""
               """This specifies the model architecture. If not given and """
               """--pytorch_checkpoint_path is not given or is a shortcut name """
               """use the configuration associated to the shortcut name on the AWS"""
           ),
       )
       parser.add_argument(
           """--compare_with_pt_model""", action="""store_true""", help="""Compare Tensorflow and PyTorch model predictions."""
       )
       parser.add_argument(
           """--use_cached_models""",
           action="""store_true""",
           help="""Use cached models if possible instead of updating to latest checkpoint versions.""",
       )
       parser.add_argument(
           """--remove_cached_files""",
           action="""store_true""",
           help="""Remove pytorch models after conversion (save memory when converting in batches).""",
       )
       parser.add_argument("""--only_convert_finetuned_models""", action="""store_true""", help="""Only convert finetuned models.""")
       lowercase    : List[str]    	=  parser.parse_args()

       # if args.pytorch_checkpoint_path is not None:
       #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
       #                                 args.pytorch_checkpoint_path,
       #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
       #                                 args.tf_dump_path,
       #                                 compare_with_pt_model=args.compare_with_pt_model,
       #                                 use_cached_models=args.use_cached_models)
       # else:
       convert_all_pt_checkpoints_to_tf(
           args.model_type.lower() if args.model_type is not None else None,
           args.tf_dump_path,
           model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
           if args.pytorch_checkpoint_path is not None
           else None,
           config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
           compare_with_pt_model=args.compare_with_pt_model,
           use_cached_models=args.use_cached_models,
           remove_cached_files=args.remove_cached_files,
           only_convert_finetuned_models=args.only_convert_finetuned_models,
       )













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      )      )






