




import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


_snake_case										=       os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
  from run_translation import main  # noqa


set_seed(42)
_snake_case										=       "sshleifer/student_marian_en_ro_6_1"
_snake_case										=       "sshleifer/tiny-mbart"







@require_torch
class 						lowercase							(   __lowerCAmelCase		):

     def 					a__	(  self		,     _a=False		,     _a=None		,     _a=True		,     _a=True		,     _a=True		,     _a=True		,     )						->							int:
           _A							:     Any			=						self.run_trainer(
               eval_steps=1		,     max_len=12		,     model_name=lowerCAmelCase_		,     num_train_epochs=1		,     distributed=lowerCAmelCase_		,     extra_args_str=lowerCAmelCase_		,     predict_with_generate=lowerCAmelCase_		,     do_train=lowerCAmelCase_		,     do_eval=lowerCAmelCase_		,     do_predict=lowerCAmelCase_		,     )
           _A							:     List[Any]			=						TrainerState.load_from_json(os.path.join(lowerCAmelCase_		,     """trainer_state.json"""				)				).log_history

           if not do_eval:
                 return

           _A							:     Tuple			=						[log for log in logs if """eval_loss""" in log.keys()]

           _A							:     List[Any]			=						eval_metrics[0]
           if predict_with_generate:
                 assert "eval_bleu" in first_step_stats

                 _A							:     str			=						eval_metrics[-1]
                 assert isinstance(last_step_stats["""eval_bleu"""]		,     lowerCAmelCase_				)
                 assert not math.isnan(float(last_step_stats["""eval_loss"""]				)				), "eval_loss must not be `nan`"

     @require_torch_non_multi_gpu
     def 					a__	(  self				)						->							Optional[int]:
           self.run_seqaseq_quick()

     @require_torch_multi_gpu
     def 					a__	(  self				)						->							Dict:
           self.run_seqaseq_quick(distributed=lowerCAmelCase_				)

     @require_torch_multi_gpu
     def 					a__	(  self				)						->							Dict:
           self.run_seqaseq_quick(distributed=lowerCAmelCase_				)

     @unittest.skip("""Requires an update of the env running those tests"""				)
     @require_torch_multi_gpu
     @require_fairscale
     def 					a__	(  self				)						->							str:
           self.run_seqaseq_quick(distributed=lowerCAmelCase_		,     extra_args_str="""--sharded_ddp simple"""				)

     @unittest.skip("""Requires an update of the env running those tests"""				)
     @require_torch_multi_gpu
     @require_fairscale
     def 					a__	(  self				)						->							Dict:
           self.run_seqaseq_quick(distributed=lowerCAmelCase_		,     extra_args_str="""--sharded_ddp simple --fp16"""				)

     @unittest.skip("""Requires an update of the env running those tests"""				)
     @require_torch_multi_gpu
     @require_fairscale
     def 					a__	(  self				)						->							Optional[Any]:
           self.run_seqaseq_quick(distributed=lowerCAmelCase_		,     extra_args_str="""--sharded_ddp zero_dp_2"""		,     predict_with_generate=lowerCAmelCase_				)

     @unittest.skip("""Requires an update of the env running those tests"""				)
     @require_torch_multi_gpu
     @require_fairscale
     def 					a__	(  self				)						->							Tuple:
           self.run_seqaseq_quick(
               distributed=lowerCAmelCase_		,     extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""		,     predict_with_generate=lowerCAmelCase_				)

     @require_apex
     @require_torch_gpu
     def 					a__	(  self				)						->							int:
           # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
           # program and it breaks other tests that run from the same pytest worker, therefore until this is
           # sorted out it must be run only in an external program, that is distributed=True in this
           # test and only under one or more gpus - if we want cpu will need to make a special test
           #
           # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
           # 2nd main() call it botches the future eval.
           #
           self.run_seqaseq_quick(distributed=lowerCAmelCase_		,     extra_args_str="""--fp16 --fp16_backend=apex"""				)
           # test 2nd time - was getting eval_loss': nan'
           # to reproduce the problem set distributed=False
           self.run_seqaseq_quick(distributed=lowerCAmelCase_		,     extra_args_str="""--fp16 --fp16_backend=apex"""				)

     @parameterized.expand(["""base""", """low""", """high""", """mixed"""]				)
     @require_torch_multi_gpu
     def 					a__	(  self		,     _a				)						->							int:
           # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
           _A							:     int			=						{
               # test with the default log_level - should be info and thus log info once
               """base""": {"""extra_args_str""": """""", """n_matches""": 1},
               # test with low log_level and log_level_replica - should be noisy on all processes
               # now the info string should appear twice on 2 processes
               """low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
               # test with high log_level and low log_level_replica
               # now the info string should appear once only on the replica
               """high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
               # test with high log_level and log_level_replica - should be quiet on all processes
               """mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
           }

           _A							:     Any			=						experiments[experiment_id]
           _A							:     Tuple			=						{"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
           _A							:     Tuple			=						"""Running training"""
           with CaptureStderr() as cl:
                 self.run_seqaseq_quick(**lowerCAmelCase_		,     extra_args_str=data["""extra_args_str"""]				)
           _A							:     str			=						len(re.findall(lowerCAmelCase_		,     cl.err				)				)
           self.assertEqual(lowerCAmelCase_		,     data["""n_matches"""]				)

     @slow
     def 					a__	(  self				)						->							Dict:
           _A							:     List[str]			=						self.run_trainer(
               eval_steps=2		,     max_len=128		,     model_name=lowerCAmelCase_		,     learning_rate=3e-4		,     num_train_epochs=10		,     distributed=lowerCAmelCase_		,     )

           # Check metrics
           _A							:     Dict			=						TrainerState.load_from_json(os.path.join(lowerCAmelCase_		,     """trainer_state.json"""				)				).log_history
           _A							:     int			=						[log for log in logs if """eval_loss""" in log.keys()]
           _A							:     Optional[Any]			=						eval_metrics[0]
           _A							:     int			=						eval_metrics[-1]

           assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
           assert isinstance(last_step_stats["""eval_bleu"""]		,     lowerCAmelCase_				)

           # test if do_predict saves generations and metrics
           _A							:     Union[str, Any]			=						os.listdir(lowerCAmelCase_				)
           _A							:     Any			=						{os.path.basename(lowerCAmelCase_				) for p in contents}
           assert "generated_predictions.txt" in contents
           assert "predict_results.json" in contents

     @slow
     @require_bitsandbytes
     def 					a__	(  self				)						->							Optional[Any]:
           from transformers.training_args import OptimizerNames

           def train_and_return_metrics(_a				) -> Tuple[int, float]:
                 _A							:     int			=						"""--skip_memory_metrics 0"""

                 _A							:     List[str]			=						self.run_trainer(
                     max_len=128		,     model_name=lowerCAmelCase_		,     learning_rate=3e-4		,     num_train_epochs=1		,     optim=lowerCAmelCase_		,     distributed=lowerCAmelCase_		,     extra_args_str=lowerCAmelCase_		,     do_eval=lowerCAmelCase_		,     do_predict=lowerCAmelCase_		,     n_gpus_to_use=1		,     )

                 # Check metrics
                 _A							:     Optional[int]			=						TrainerState.load_from_json(Path(lowerCAmelCase_		,     """trainer_state.json"""				)				).log_history
                 _A							:     str			=						int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20				)
                 _A							:     List[str]			=						int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20				)

                 _A							:     Union[str, Any]			=						logs[0]["""train_loss"""]
                 return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

           _A			,   _A			,   _A							:     str			=						train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value				)
           _A			,   _A			,   _A							:     Optional[int]			=						train_and_return_metrics(OptimizerNames.ADAMW_BNB.value				)

           _A							:     str			=						gpu_alloc_mem_orig - gpu_alloc_mem_bnb

           _A							:     Any			=						gpu_peak_mem_orig + gpu_alloc_mem_orig
           _A							:     List[str]			=						gpu_peak_mem_bnb + gpu_alloc_mem_bnb
           _A							:     Optional[Any]			=						gpu_total_mem_orig - gpu_total_mem_bnb

           # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
           # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
           # in 2 bytes and the diff in optim memory usage is derived as so:
           #
           # - normal 25*8=~200MB (8 bytes per param)
           # - bnb    25*2= ~50MB (2 bytes per param)
           #
           # Thus we should expect ~150MB total memory saved.
           #
           # Peak memory should be the same - the total should be different by about that same margin
           #
           # After leaving a small margin to accommodate for differences between gpus let's check
           # that we have at least 120MB in savings
           _A							:     Any			=						120

           # uncomment the following if this test starts failing - requires py38 for a new print feature
           # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
           # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
           # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
           # print(f"{gpu_alloc_mem_diff=}MB")
           # print(f"{gpu_peak_mem_diff=}MB")
           # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
           # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

           self.assertGreater(
               lowerCAmelCase_		,     lowerCAmelCase_		,     """should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
               F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
               F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''		,     )

           self.assertGreater(
               lowerCAmelCase_		,     lowerCAmelCase_		,     """should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
               F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
               F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''		,     )

           self.assertEqual(
               lowerCAmelCase_		,     lowerCAmelCase_		,     F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''				)



     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a = 3e-3		,     _a = "adafactor"		,     _a = False		,     _a = None		,     _a = 0		,     _a = True		,     _a = True		,     _a = True		,     _a = True		,     _a = None		,     )						->							str:
           _A							:     int			=						self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
           _A							:     Dict			=						self.get_auto_remove_tmp_dir()
           _A							:     str			=						F'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(lowerCAmelCase_				)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(lowerCAmelCase_				)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

           _A							:     Dict			=						F'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(lowerCAmelCase_				)}
        '''.split()

           _A							:     Dict			=						"""
            --do_predict
        """.split()

           _A							:     str			=						[]
           if do_train:
                 args += args_train

           if do_eval:
                 args += args_eval

           if do_predict:
                 args += args_predict

           if predict_with_generate:
                 args += "--predict_with_generate".split()

           if do_train:
                 if optim == "adafactor":
                       args += "--adafactor".split()
                 else:
                       args += F'''--optim {optim}'''.split()

           if extra_args_str is not None:
                 args += extra_args_str.split()

           if distributed:
                 if n_gpus_to_use is None:
                       _A							:     List[Any]			=						get_gpu_count()
                 _A							:     List[Any]			=						get_torch_dist_unique_port()
                 _A							:     int			=						F'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
                 _A							:     Optional[int]			=						[sys.executable] + distributed_args + args
                 # keep for quick debug
                 # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
                 execute_subprocess_async(lowerCAmelCase_		,     env=self.get_env()				)
           else:
                 _A							:     str			=						["""run_translation.py"""] + args
                 with patch.object(lowerCAmelCase_		,     """argv"""		,     lowerCAmelCase_				):
                       main()

           return output_dir


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      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
        _A        =  dict(zip(lowerCAmelCase_		,				range(len(lowerCAmelCase_							)							)							)							)

        _A        =  {
            """unk_token""": """<unk>""",
            """bos_token""": """<s>""",
            """eos_token""": """</s>""",
        }
        _A        =  {
            """feature_size""": 1,
            """padding_value""": 0.0,
            """sampling_rate""": 1_60_00,
            """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		,				lowerCAmelCase_							)
        with open(self.vocab_file		,				"""w"""		,				encoding="""utf-8"""							) as fp:
               fp.write(json.dumps(lowerCAmelCase_							) + """\n"""							)

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

        # load decoder from hub
        _A        =  """hf-internal-testing/ngram-beam-search-decoder"""



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Tuple:
        _A        =  self.add_kwargs_tokens_map.copy()
        kwargs.update(lowerCAmelCase_							)
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Any:
        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Any:
        return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        shutil.rmtree(self.tmpdirname							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  self.get_tokenizer()
        _A        =  self.get_feature_extractor()
        _A        =  self.get_decoder()

        _A        =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_		,				decoder=lowerCAmelCase_							)

        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		,				lowerCAmelCase_							)

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

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



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _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      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _A        =  self.get_tokenizer()
        # add token to trigger raise
        tokenizer.add_tokens(["""xx"""]							)
        with self.assertRaisesRegex(lowerCAmelCase_		,				"""include"""							):
               WavaVecaProcessorWithLM(
                   tokenizer=lowerCAmelCase_		,				feature_extractor=self.get_feature_extractor()		,				decoder=self.get_decoder()							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  self.get_feature_extractor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_decoder()

        _A        =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_		,				decoder=lowerCAmelCase_							)

        _A        =  floats_list((3, 10_00)							)

        _A        =  feature_extractor(lowerCAmelCase_		,				return_tensors="""np"""							)
        _A        =  processor(lowerCAmelCase_		,				return_tensors="""np"""							)

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



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  self.get_feature_extractor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_decoder()

        _A        =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_		,				decoder=lowerCAmelCase_							)

        _A        =  """This is a test string"""

        _A        =  processor(text=lowerCAmelCase_							)

        _A        =  tokenizer(lowerCAmelCase_							)

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



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_=(2, 10, 16)		,				lowerCAmelCase_=77							)  ->					Tuple:
        np.random.seed(lowerCAmelCase_							)
        return np.random.rand(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  self.get_feature_extractor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_decoder()

        _A        =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_		,				decoder=lowerCAmelCase_							)

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

        _A        =  processor.decode(lowerCAmelCase_							)

        _A        =  decoder.decode_beams(lowerCAmelCase_							)[0]

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



 @parameterized.expand([[None], ["""fork"""], ["""spawn"""]]							)
 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					Optional[int]:
        _A        =  self.get_feature_extractor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_decoder()

        _A        =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_		,				decoder=lowerCAmelCase_							)

        _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(lowerCAmelCase_							)
        else:
               with get_context(lowerCAmelCase_							).Pool() as pool:
                      _A        =  processor.batch_decode(lowerCAmelCase_		,				lowerCAmelCase_							)

        _A        =  list(lowerCAmelCase_							)

        with get_context("""fork"""							).Pool() as p:
               _A        =  decoder.decode_beams_batch(lowerCAmelCase_		,				lowerCAmelCase_							)

        _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(lowerCAmelCase_		,				decoded_processor.text							)
        self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""]		,				decoded_processor.text							)
        self.assertListEqual(lowerCAmelCase_		,				decoded_processor.logit_score							)
        self.assertListEqual(lowerCAmelCase_		,				decoded_processor.lm_score							)



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  self.get_feature_extractor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_decoder()

        _A        =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_		,				decoder=lowerCAmelCase_							)

        _A        =  self._get_dummy_logits()

        _A        =  15
        _A        =  -20.0
        _A        =  -4.0

        _A        =  processor.batch_decode(
            lowerCAmelCase_		,				beam_width=lowerCAmelCase_		,				beam_prune_logp=lowerCAmelCase_		,				token_min_logp=lowerCAmelCase_		,				)
        _A        =  decoded_processor_out.text

        _A        =  list(lowerCAmelCase_							)

        with get_context("""fork"""							).Pool() as pool:
               _A        =  decoder.decode_beams_batch(
                   lowerCAmelCase_		,				lowerCAmelCase_		,				beam_width=lowerCAmelCase_		,				beam_prune_logp=lowerCAmelCase_		,				token_min_logp=lowerCAmelCase_		,				)

        _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(lowerCAmelCase_		,				lowerCAmelCase_							)
        self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""]		,				lowerCAmelCase_							)

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

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



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  self.get_feature_extractor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_decoder()

        _A        =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_		,				decoder=lowerCAmelCase_							)

        _A        =  self._get_dummy_logits()

        _A        =  2.0
        _A        =  5.0
        _A        =  -20.0
        _A        =  True

        _A        =  processor.batch_decode(
            lowerCAmelCase_		,				alpha=lowerCAmelCase_		,				beta=lowerCAmelCase_		,				unk_score_offset=lowerCAmelCase_		,				lm_score_boundary=lowerCAmelCase_		,				)
        _A        =  decoded_processor_out.text

        _A        =  list(lowerCAmelCase_							)
        decoder.reset_params(
            alpha=lowerCAmelCase_		,				beta=lowerCAmelCase_		,				unk_score_offset=lowerCAmelCase_		,				lm_score_boundary=lowerCAmelCase_		,				)

        with get_context("""fork"""							).Pool() as pool:
               _A        =  decoder.decode_beams_batch(
                   lowerCAmelCase_		,				lowerCAmelCase_		,				)

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

        self.assertListEqual(lowerCAmelCase_		,				lowerCAmelCase_							)
        self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""]		,				lowerCAmelCase_							)
        _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		,				lowerCAmelCase_							)



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



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  snapshot_download("""hf-internal-testing/processor_with_lm"""							)

        _A        =  WavaVecaProcessorWithLM.from_pretrained(lowerCAmelCase_							)

        _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(lowerCAmelCase_							)
        _A        =  os.listdir(lowerCAmelCase_							)

        local_decoder_files.sort()
        expected_decoder_files.sort()

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



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""							)
        _A        =  AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm"""							)

        _A        =  floats_list((3, 10_00)							)

        _A        =  processor_wavaveca(lowerCAmelCase_		,				return_tensors="""np"""							)
        _A        =  processor_auto(lowerCAmelCase_		,				return_tensors="""np"""							)

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

        _A        =  self._get_dummy_logits()

        _A        =  processor_wavaveca.batch_decode(lowerCAmelCase_							)
        _A        =  processor_auto.batch_decode(lowerCAmelCase_							)

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



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  self.get_feature_extractor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_decoder()

        _A        =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_		,				decoder=lowerCAmelCase_							)

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



 @staticmethod
 def      UpperCAmelCase							(					lowerCAmelCase_		,				lowerCAmelCase_							)  ->					int:
        _A        =  [d[key] for d in offsets]
        return retrieved_list



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""							)
        _A        =  self._get_dummy_logits()[0]

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

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



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""							)
        _A        =  self._get_dummy_logits()

        _A        =  processor.batch_decode(lowerCAmelCase_		,				output_word_offsets=lowerCAmelCase_							)

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

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



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

        _A        =  load_dataset("""common_voice"""		,				"""en"""		,				split="""train"""		,				streaming=lowerCAmelCase_							)
        _A        =  ds.cast_column("""audio"""		,				datasets.Audio(sampling_rate=1_60_00							)							)
        _A        =  iter(lowerCAmelCase_							)
        _A        =  next(lowerCAmelCase_							)

        _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(lowerCAmelCase_							).logits.cpu().numpy()

        _A        =  processor.decode(logits[0]		,				output_word_offsets=lowerCAmelCase_							)

        _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(lowerCAmelCase_		,				"""word"""							)							)		,				lowerCAmelCase_							)
        self.assertEqual(""" """.join(self.get_from_offsets(lowerCAmelCase_		,				"""word"""							)							)		,				output.text							)

        # output times
        _A        =  torch.tensor(self.get_from_offsets(lowerCAmelCase_		,				"""start_time"""							)							)
        _A        =  torch.tensor(self.get_from_offsets(lowerCAmelCase_		,				"""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(lowerCAmelCase_		,				lowerCAmelCase_		,				atol=0.01							)							)
        self.assertTrue(torch.allclose(lowerCAmelCase_		,				lowerCAmelCase_		,				atol=0.01							)							)




