

'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
    import torch

if is_datasets_available():
    from datasets import load_dataset

_snake_case         =  random.Random()







def  _A		(     snake_case		,							snake_case=1.0		,							snake_case=None		,							snake_case=None					)      ->     Optional[Any]:
   if rng is None:
      _lowercase      :				Optional[Any]      					=       global_rng

   _lowercase      :				Optional[int]      					=       []
   for batch_idx in range(shape[0]					):
      values.append([]					)
      for _ in range(shape[1]					):
         values[-1].append(rng.random() * scale					)

   return values


class        a__  (							unittest.TestCase  ):


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase=7      ,    _UpperCamelCase=400      ,    _UpperCamelCase=2000      ,    _UpperCamelCase=2048      ,    _UpperCamelCase=128      ,    _UpperCamelCase=1      ,    _UpperCamelCase=512      ,    _UpperCamelCase=30      ,    _UpperCamelCase=44100      ,    ):





      """simple docstring"""





      _lowercase      :				str      					=       parent
      _lowercase      :				Any      					=       batch_size
      _lowercase      :				List[Any]      					=       min_seq_length
      _lowercase      :				Tuple      					=       max_seq_length
      _lowercase      :				Tuple      					=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      _lowercase      :				Optional[int]      					=       spectrogram_length
      _lowercase      :				Optional[Any]      					=       feature_size
      _lowercase      :				int      					=       num_audio_channels
      _lowercase      :				Optional[Any]      					=       hop_length
      _lowercase      :				List[Any]      					=       chunk_length
      _lowercase      :				List[str]      					=       sampling_rate


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return {
          "spectrogram_length": self.spectrogram_length,
          "feature_size": self.feature_size,
          "num_audio_channels": self.num_audio_channels,
          "hop_length": self.hop_length,
          "chunk_length": self.chunk_length,
          "sampling_rate": self.sampling_rate,
      }






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase=False      ,    _UpperCamelCase=False  ):





      """simple docstring"""





      def _flatten(_UpperCamelCase  ):
         return list(itertools.chain(*_UpperCamelCase  )  )

      if equal_length:
         _lowercase      :				List[str]      					=       [floats_list((self.max_seq_length, self.feature_size)  ) for _ in range(self.batch_size  )]
      else:
         # make sure that inputs increase in size
         _lowercase      :				Optional[Any]      					=       [
             floats_list((x, self.feature_size)  )
             for x in range(self.min_seq_length      ,    self.max_seq_length      ,    self.seq_length_diff  )
         ]
      if numpify:
         _lowercase      :				Union[str, Any]      					=       [np.asarray(_UpperCamelCase  ) for x in speech_inputs]
      return speech_inputs




@require_torch
@require_torchaudio
class        a__  (							lowerCamelCase_  ,				unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							TvltFeatureExtractor


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       TvltFeatureExtractionTester(self  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       self.feature_extraction_class(**self.feat_extract_dict  )
      self.assertTrue(hasattr(_UpperCamelCase      ,    "spectrogram_length"  )  )
      self.assertTrue(hasattr(_UpperCamelCase      ,    "feature_size"  )  )
      self.assertTrue(hasattr(_UpperCamelCase      ,    "num_audio_channels"  )  )
      self.assertTrue(hasattr(_UpperCamelCase      ,    "hop_length"  )  )
      self.assertTrue(hasattr(_UpperCamelCase      ,    "chunk_length"  )  )
      self.assertTrue(hasattr(_UpperCamelCase      ,    "sampling_rate"  )  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       self.feature_extraction_class(**self.feat_extract_dict  )

      with tempfile.TemporaryDirectory() as tmpdirname:
         _lowercase      :				int      					=       feat_extract_first.save_pretrained(_UpperCamelCase  )[0]
         check_json_file_has_correct_format(_UpperCamelCase  )
         _lowercase      :				Tuple      					=       self.feature_extraction_class.from_pretrained(_UpperCamelCase  )

      _lowercase      :				Optional[int]      					=       feat_extract_first.to_dict()
      _lowercase      :				str      					=       feat_extract_second.to_dict()
      _lowercase      :				Tuple      					=       dict_first.pop("mel_filters"  )
      _lowercase      :				int      					=       dict_second.pop("mel_filters"  )
      self.assertTrue(np.allclose(_UpperCamelCase      ,    _UpperCamelCase  )  )
      self.assertEqual(_UpperCamelCase      ,    _UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       self.feature_extraction_class(**self.feat_extract_dict  )

      with tempfile.TemporaryDirectory() as tmpdirname:
         _lowercase      :				str      					=       os.path.join(_UpperCamelCase      ,    "feat_extract.json"  )
         feat_extract_first.to_json_file(_UpperCamelCase  )
         _lowercase      :				Tuple      					=       self.feature_extraction_class.from_json_file(_UpperCamelCase  )

      _lowercase      :				Tuple      					=       feat_extract_first.to_dict()
      _lowercase      :				int      					=       feat_extract_second.to_dict()
      _lowercase      :				List[str]      					=       dict_first.pop("mel_filters"  )
      _lowercase      :				Optional[Any]      					=       dict_second.pop("mel_filters"  )
      self.assertTrue(np.allclose(_UpperCamelCase      ,    _UpperCamelCase  )  )
      self.assertEqual(_UpperCamelCase      ,    _UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       self.feature_extraction_class(**self.feat_extract_dict  )

      # create three inputs of length 800, 1000, and 1200
      _lowercase      :				List[str]      					=       [floats_list((1, x)  )[0] for x in range(800      ,    1400      ,    200  )]
      _lowercase      :				str      					=       [np.asarray(_UpperCamelCase  ) for speech_input in speech_inputs]

      # Test not batched input
      _lowercase      :				Union[str, Any]      					=       feature_extractor(np_speech_inputs[0]      ,    return_tensors="np"      ,    sampling_rate=44100  ).audio_values

      self.assertTrue(encoded_audios.ndim == 4  )
      self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size  )
      self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length  )
      self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels  )

      # Test batched
      _lowercase      :				List[Any]      					=       feature_extractor(_UpperCamelCase      ,    return_tensors="np"      ,    sampling_rate=44100  ).audio_values

      self.assertTrue(encoded_audios.ndim == 4  )
      self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size  )
      self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length  )
      self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels  )

      # Test audio masking
      _lowercase      :				Union[str, Any]      					=       feature_extractor(
          _UpperCamelCase      ,    return_tensors="np"      ,    sampling_rate=44100      ,    mask_audio=_UpperCamelCase  ).audio_values

      self.assertTrue(encoded_audios.ndim == 4  )
      self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size  )
      self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length  )
      self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels  )

      # Test 2-D numpy arrays are batched.
      _lowercase      :				Any      					=       [floats_list((1, x)  )[0] for x in (800, 800, 800)]
      _lowercase      :				List[str]      					=       np.asarray(_UpperCamelCase  )
      _lowercase      :				str      					=       feature_extractor(_UpperCamelCase      ,    return_tensors="np"      ,    sampling_rate=44100  ).audio_values
      self.assertTrue(encoded_audios.ndim == 4  )
      self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size  )
      self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length  )
      self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				str      					=       load_dataset("hf-internal-testing/librispeech_asr_dummy"      ,    "clean"      ,    split="validation"  )
      # automatic decoding with librispeech
      _lowercase      :				Tuple      					=       ds.sort("id"  ).select(range(_UpperCamelCase  )  )[:num_samples]["audio"]

      return [x["array"] for x in speech_samples]






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       self._load_datasamples(1  )
      _lowercase      :				List[Any]      					=       TvltFeatureExtractor()
      _lowercase      :				Optional[int]      					=       feature_extractor(_UpperCamelCase      ,    return_tensors="pt"  ).audio_values

      self.assertEquals(audio_values.shape      ,    (1, 1, 192, 128)  )

      _lowercase      :				Optional[int]      					=       torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]  )
      self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]      ,    _UpperCamelCase      ,    atol=1E-4  )  )









'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case         =  logging.get_logger(__name__)

_snake_case         =  {
    'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Any     	=							'roc_bert'


   def __init__(			self      ,    _UpperCamelCase=30522      ,    _UpperCamelCase=768      ,    _UpperCamelCase=12      ,    _UpperCamelCase=12      ,    _UpperCamelCase=3072      ,    _UpperCamelCase="gelu"      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=512      ,    _UpperCamelCase=2      ,    _UpperCamelCase=0.0_2      ,    _UpperCamelCase=1E-1_2      ,    _UpperCamelCase=True      ,    _UpperCamelCase=0      ,    _UpperCamelCase="absolute"      ,    _UpperCamelCase=None      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=768      ,    _UpperCamelCase=910      ,    _UpperCamelCase=512      ,    _UpperCamelCase=24858      ,    _UpperCamelCase=True      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				str      					=       vocab_size
      _lowercase      :				List[str]      					=       max_position_embeddings
      _lowercase      :				List[Any]      					=       hidden_size
      _lowercase      :				Dict      					=       num_hidden_layers
      _lowercase      :				str      					=       num_attention_heads
      _lowercase      :				int      					=       intermediate_size
      _lowercase      :				Optional[Any]      					=       hidden_act
      _lowercase      :				Union[str, Any]      					=       hidden_dropout_prob
      _lowercase      :				Dict      					=       attention_probs_dropout_prob
      _lowercase      :				Dict      					=       initializer_range
      _lowercase      :				List[Any]      					=       type_vocab_size
      _lowercase      :				Tuple      					=       layer_norm_eps
      _lowercase      :				Optional[int]      					=       use_cache
      _lowercase      :				Tuple      					=       enable_pronunciation
      _lowercase      :				Optional[int]      					=       enable_shape
      _lowercase      :				int      					=       pronunciation_embed_dim
      _lowercase      :				List[str]      					=       pronunciation_vocab_size
      _lowercase      :				int      					=       shape_embed_dim
      _lowercase      :				str      					=       shape_vocab_size
      _lowercase      :				str      					=       concat_input
      _lowercase      :				Dict      					=       position_embedding_type
      _lowercase      :				Optional[Any]      					=       classifier_dropout
      super().__init__(pad_token_id=_UpperCamelCase      ,    **_UpperCamelCase  )






