
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 __snake_case		(      unittest.TestCase     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
           lowercase							:     Tuple       	=      dict(zip(snake_case		,range(len(snake_case      )      )      )      )

           lowercase							:     int       	=      {
               """unk_token""": """<unk>""",
               """bos_token""": """<s>""",
               """eos_token""": """</s>""",
           }
           lowercase							:     str       	=      {
               """feature_size""": 1,
               """padding_value""": 0.0,
               """sampling_rate""": 16000,
               """return_attention_mask""": False,
               """do_normalize""": True,
           }

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

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

           # load decoder from hub
           lowercase							:     Tuple       	=      """hf-internal-testing/ngram-beam-search-decoder"""
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      self.add_kwargs_tokens_map.copy()
           kwargs.update(snake_case      )
           return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







           return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







           return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           shutil.rmtree(self.tmpdirname      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      self.get_tokenizer()
           lowercase							:     Tuple       	=      self.get_feature_extractor()
           lowercase							:     Optional[Any]       	=      self.get_decoder()

           lowercase							:     Any       	=      WavaVecaProcessorWithLM(tokenizer=snake_case		,feature_extractor=snake_case		,decoder=snake_case      )

           processor.save_pretrained(self.tmpdirname      )
           lowercase							:     List[str]       	=      WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname      )

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

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

           # 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		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      WavaVecaProcessorWithLM(
               tokenizer=self.get_tokenizer()		,feature_extractor=self.get_feature_extractor()		,decoder=self.get_decoder()      )
           processor.save_pretrained(self.tmpdirname      )

           # make sure that error is thrown when decoder alphabet doesn't match
           lowercase							:     Any       	=      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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.get_tokenizer()
           # add token to trigger raise
           tokenizer.add_tokens(["""xx"""]      )
           with self.assertRaisesRegex(snake_case		,"""include"""      ):
                  WavaVecaProcessorWithLM(
                      tokenizer=snake_case		,feature_extractor=self.get_feature_extractor()		,decoder=self.get_decoder()      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.get_feature_extractor()
           lowercase							:     Union[str, Any]       	=      self.get_tokenizer()
           lowercase							:     Optional[int]       	=      self.get_decoder()

           lowercase							:     str       	=      WavaVecaProcessorWithLM(tokenizer=snake_case		,feature_extractor=snake_case		,decoder=snake_case      )

           lowercase							:     Union[str, Any]       	=      floats_list((3, 1000)      )

           lowercase							:     List[str]       	=      feature_extractor(snake_case		,return_tensors="""np"""      )
           lowercase							:     Optional[int]       	=      processor(snake_case		,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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.get_feature_extractor()
           lowercase							:     List[str]       	=      self.get_tokenizer()
           lowercase							:     str       	=      self.get_decoder()

           lowercase							:     Union[str, Any]       	=      WavaVecaProcessorWithLM(tokenizer=snake_case		,feature_extractor=snake_case		,decoder=snake_case      )

           lowercase							:     str       	=      """This is a test string"""

           lowercase							:     Tuple       	=      processor(text=snake_case      )

           lowercase							:     Union[str, Any]       	=      tokenizer(snake_case      )

           for key in encoded_tok.keys():
                  self.assertListEqual(encoded_tok[key]		,encoded_processor[key]      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case=(2, 10, 16)		,snake_case=77      ):



           '''simple docstring'''







           np.random.seed(snake_case      )
           return np.random.rand(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.get_feature_extractor()
           lowercase							:     Any       	=      self.get_tokenizer()
           lowercase							:     Union[str, Any]       	=      self.get_decoder()

           lowercase							:     Optional[int]       	=      WavaVecaProcessorWithLM(tokenizer=snake_case		,feature_extractor=snake_case		,decoder=snake_case      )

           lowercase							:     int       	=      self._get_dummy_logits(shape=(10, 16)		,seed=13      )

           lowercase							:     Optional[int]       	=      processor.decode(snake_case      )

           lowercase							:     Tuple       	=      decoder.decode_beams(snake_case      )[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       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     str       	=      self.get_feature_extractor()
           lowercase							:     Tuple       	=      self.get_tokenizer()
           lowercase							:     Dict       	=      self.get_decoder()

           lowercase							:     Tuple       	=      WavaVecaProcessorWithLM(tokenizer=snake_case		,feature_extractor=snake_case		,decoder=snake_case      )

           lowercase							:     Optional[int]       	=      self._get_dummy_logits()

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

           lowercase							:     Dict       	=      list(snake_case      )

           with get_context("""fork"""      ).Pool() as p:
                  lowercase							:     List[str]       	=      decoder.decode_beams_batch(snake_case		,snake_case      )

           lowercase	,			lowercase	,			lowercase							:     List[str]       	=      [], [], []
           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(snake_case		,decoded_processor.text      )
           self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""]		,decoded_processor.text      )
           self.assertListEqual(snake_case		,decoded_processor.logit_score      )
           self.assertListEqual(snake_case		,decoded_processor.lm_score      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      self.get_feature_extractor()
           lowercase							:     int       	=      self.get_tokenizer()
           lowercase							:     Dict       	=      self.get_decoder()

           lowercase							:     int       	=      WavaVecaProcessorWithLM(tokenizer=snake_case		,feature_extractor=snake_case		,decoder=snake_case      )

           lowercase							:     int       	=      self._get_dummy_logits()

           lowercase							:     Any       	=      15
           lowercase							:     Union[str, Any]       	=      -20.0
           lowercase							:     Any       	=      -4.0

           lowercase							:     Dict       	=      processor.batch_decode(
               snake_case		,beam_width=snake_case		,beam_prune_logp=snake_case		,token_min_logp=snake_case		,)
           lowercase							:     Optional[int]       	=      decoded_processor_out.text

           lowercase							:     Tuple       	=      list(snake_case      )

           with get_context("""fork"""      ).Pool() as pool:
                  lowercase							:     int       	=      decoder.decode_beams_batch(
                      snake_case		,snake_case		,beam_width=snake_case		,beam_prune_logp=snake_case		,token_min_logp=snake_case		,)

           lowercase							:     Any       	=      [d[0][0] for d in decoded_decoder_out]
           lowercase							:     Tuple       	=      [d[0][2] for d in decoded_decoder_out]
           lowercase							:     List[Any]       	=      [d[0][3] for d in decoded_decoder_out]

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

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

           self.assertTrue(np.array_equal(snake_case		,decoded_processor_out.lm_score      )      )
           self.assertTrue(np.allclose([-15.554, -13.9_474]		,snake_case		,atol=1e-3      )      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      self.get_feature_extractor()
           lowercase							:     Union[str, Any]       	=      self.get_tokenizer()
           lowercase							:     Union[str, Any]       	=      self.get_decoder()

           lowercase							:     Optional[int]       	=      WavaVecaProcessorWithLM(tokenizer=snake_case		,feature_extractor=snake_case		,decoder=snake_case      )

           lowercase							:     Optional[int]       	=      self._get_dummy_logits()

           lowercase							:     List[str]       	=      2.0
           lowercase							:     Any       	=      5.0
           lowercase							:     Tuple       	=      -20.0
           lowercase							:     List[Any]       	=      True

           lowercase							:     Union[str, Any]       	=      processor.batch_decode(
               snake_case		,alpha=snake_case		,beta=snake_case		,unk_score_offset=snake_case		,lm_score_boundary=snake_case		,)
           lowercase							:     Union[str, Any]       	=      decoded_processor_out.text

           lowercase							:     List[Any]       	=      list(snake_case      )
           decoder.reset_params(
               alpha=snake_case		,beta=snake_case		,unk_score_offset=snake_case		,lm_score_boundary=snake_case		,)

           with get_context("""fork"""      ).Pool() as pool:
                  lowercase							:     str       	=      decoder.decode_beams_batch(
                      snake_case		,snake_case		,)

           lowercase							:     Any       	=      [d[0][0] for d in decoded_decoder_out]

           self.assertListEqual(snake_case		,snake_case      )
           self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""]		,snake_case      )
           lowercase							:     str       	=      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		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""      )

           lowercase							:     Optional[int]       	=      processor.decoder.model_container[processor.decoder._model_key]
           lowercase							:     Dict       	=      Path(language_model._kenlm_model.path.decode("""utf-8"""      )      ).parent.parent.absolute()

           lowercase							:     List[str]       	=      os.listdir(snake_case      )
           lowercase							:     Dict       	=      ["""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(snake_case		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      snapshot_download("""hf-internal-testing/processor_with_lm"""      )

           lowercase							:     Optional[int]       	=      WavaVecaProcessorWithLM.from_pretrained(snake_case      )

           lowercase							:     List[str]       	=      processor.decoder.model_container[processor.decoder._model_key]
           lowercase							:     Dict       	=      Path(language_model._kenlm_model.path.decode("""utf-8"""      )      ).parent.parent.absolute()

           lowercase							:     List[str]       	=      os.listdir(snake_case      )
           lowercase							:     Dict       	=      os.listdir(snake_case      )

           local_decoder_files.sort()
           expected_decoder_files.sort()

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



           '''simple docstring'''







           lowercase							:     str       	=      WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""      )
           lowercase							:     int       	=      AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm"""      )

           lowercase							:     Union[str, Any]       	=      floats_list((3, 1000)      )

           lowercase							:     Optional[int]       	=      processor_wavaveca(snake_case		,return_tensors="""np"""      )
           lowercase							:     Dict       	=      processor_auto(snake_case		,return_tensors="""np"""      )

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

           lowercase							:     str       	=      self._get_dummy_logits()

           lowercase							:     Optional[int]       	=      processor_wavaveca.batch_decode(snake_case      )
           lowercase							:     Any       	=      processor_auto.batch_decode(snake_case      )

           self.assertListEqual(decoded_wavaveca.text		,decoded_auto.text      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      self.get_feature_extractor()
           lowercase							:     str       	=      self.get_tokenizer()
           lowercase							:     int       	=      self.get_decoder()

           lowercase							:     List[str]       	=      WavaVecaProcessorWithLM(tokenizer=snake_case		,feature_extractor=snake_case		,decoder=snake_case      )

           self.assertListEqual(
               processor.model_input_names		,feature_extractor.model_input_names		,msg="""`processor` and `feature_extractor` model input names do not match"""		,)
    @staticmethod
    def       _SCREAMING_SNAKE_CASE       (  snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      [d[key] for d in offsets]
           return retrieved_list
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""      )
           lowercase							:     int       	=      self._get_dummy_logits()[0]

           lowercase							:     Optional[int]       	=      processor.decode(snake_case		,output_word_offsets=snake_case      )
           # 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(snake_case		,snake_case      )      )

           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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""      )
           lowercase							:     int       	=      self._get_dummy_logits()

           lowercase							:     Tuple       	=      processor.batch_decode(snake_case		,output_word_offsets=snake_case      )

           # 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(snake_case		,snake_case      )      )

           self.assertListEqual(
               [""" """.join(self.get_from_offsets(snake_case		,"""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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           import torch

           lowercase							:     Any       	=      load_dataset("""common_voice"""		,"""en"""		,split="""train"""		,streaming=snake_case      )
           lowercase							:     List[Any]       	=      ds.cast_column("""audio"""		,datasets.Audio(sampling_rate=16000      )      )
           lowercase							:     List[Any]       	=      iter(snake_case      )
           lowercase							:     Optional[int]       	=      next(snake_case      )

           lowercase							:     List[str]       	=      AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""      )
           lowercase							:     str       	=      WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""      )

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

           with torch.no_grad():
                  lowercase							:     Dict       	=      model(snake_case      ).logits.cpu().numpy()

           lowercase							:     Dict       	=      processor.decode(logits[0]		,output_word_offsets=snake_case      )

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

           lowercase							:     Tuple       	=      """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(snake_case		,"""word"""      )      )		,snake_case      )
           self.assertEqual(""" """.join(self.get_from_offsets(snake_case		,"""word"""      )      )		,output.text      )

           # output times
           lowercase							:     Tuple       	=      torch.tensor(self.get_from_offsets(snake_case		,"""start_time"""      )      )
           lowercase							:     Union[str, Any]       	=      torch.tensor(self.get_from_offsets(snake_case		,"""end_time"""      )      )

           # fmt: off
           lowercase							:     int       	=      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]      )
           lowercase							:     List[str]       	=      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(snake_case		,snake_case		,atol=0.01      )      )
           self.assertTrue(torch.allclose(snake_case		,snake_case		,atol=0.01      )      )


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

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
  from datasets import load_dataset

class __snake_case		(      lowerCAmelCase     ):
    _a		:						Union[str, Any]=				"microsoft/speecht5_tts"
    _a		:						Tuple=				(
        "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
        "text to read (in English) and returns a waveform object containing the sound."
    )
    _a		:						Dict=				"text_reader"
    _a		:						Optional[Any]=				SpeechTaProcessor
    _a		:						Tuple=				SpeechTaForTextToSpeech
    _a		:						Optional[int]=				SpeechTaHifiGan

    _a		:						Union[str, Any]=				["text"]
    _a		:						Optional[int]=				["audio"]
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.post_processor is None:
                  lowercase							:     Any       	=      """microsoft/speecht5_hifigan"""
           super().setup()
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.pre_processor(text=snake_case		,return_tensors="""pt"""		,truncation=snake_case      )

           if speaker_embeddings is None:
                  if not is_datasets_available():
                         raise ImportError("""Datasets needs to be installed if not passing speaker embeddings."""      )

                  lowercase							:     Tuple       	=      load_dataset("""Matthijs/cmu-arctic-xvectors"""		,split="""validation"""      )
                  lowercase							:     List[str]       	=      torch.tensor(embeddings_dataset[7305]["""xvector"""]      ).unsqueeze(0      )

           return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           with torch.no_grad():
                  return self.model.generate_speech(**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           with torch.no_grad():
                  return self.post_processor(snake_case      ).cpu().detach()
