





from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


_UpperCAmelCase        =				logging.get_logger(__name__)



class 							_UpperCamelCase					(	__lowerCAmelCase     ):
       _UpperCamelCase				:					Dict         =   ['''audio_values''', '''audio_mask''']

       def __init__(		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: str=2048					,   _SCREAMING_SNAKE_CASE: Dict=1					,   _SCREAMING_SNAKE_CASE: List[Any]=[16, 16]					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=128					,   _SCREAMING_SNAKE_CASE: Tuple=44100					,   _SCREAMING_SNAKE_CASE: Tuple=86					,   _SCREAMING_SNAKE_CASE: Any=2048					,   _SCREAMING_SNAKE_CASE: Dict=0.0					,   **_SCREAMING_SNAKE_CASE: int					,   )       -> Tuple:




              """simple docstring"""


              super().__init__(
                  feature_size=lowerCAmelCase_					,   sampling_rate=lowerCAmelCase_					,   padding_value=lowerCAmelCase_					,   **lowerCAmelCase_					,   )

              UpperCamelCase_          =       spectrogram_length
              UpperCamelCase_          =       num_channels
              UpperCamelCase_          =       patch_size
              UpperCamelCase_          =       feature_size // self.patch_size[1]
              UpperCamelCase_          =       n_fft
              UpperCamelCase_          =       sampling_rate // hop_length_to_sampling_rate
              UpperCamelCase_          =       sampling_rate
              UpperCamelCase_          =       padding_value
              UpperCamelCase_          =       mel_filter_bank(
                  num_frequency_bins=1 + n_fft // 2					,   num_mel_filters=lowerCAmelCase_					,   min_frequency=0.0					,   max_frequency=22050.0					,   sampling_rate=lowerCAmelCase_					,   norm="slaney"					,   mel_scale="slaney"					,   ).T

       def lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: Dict		)       -> np.ndarray:




              """simple docstring"""


              UpperCamelCase_          =       spectrogram(
                  lowerCAmelCase_					,   window_function(self.n_fft					,   "hann"		)					,   frame_length=self.n_fft					,   hop_length=self.hop_length					,   power=2.0					,   mel_filters=self.mel_filters.T					,   log_mel="dB"					,   db_range=80.0					,   )
              UpperCamelCase_          =       log_spec[:, :-1]
              UpperCamelCase_          =       log_spec - 20.0
              UpperCamelCase_          =       np.clip(log_spec / 40.0					,   -2.0					,   0.0		) + 1.0
              return log_spec



       def __call__(		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Tuple = None					,   _SCREAMING_SNAKE_CASE: Optional[Any] = True					,   _SCREAMING_SNAKE_CASE: List[str] = None					,   _SCREAMING_SNAKE_CASE: str = False					,   _SCREAMING_SNAKE_CASE: Optional[Any] = False					,   **_SCREAMING_SNAKE_CASE: Tuple					,   )       -> BatchFeature:




              """simple docstring"""


              if sampling_rate is not None:
                     if sampling_rate != self.sampling_rate:
                            raise ValueError(
                                "This feature extractor is set to support sampling rate"
                                f''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
                                f''' with {self.sampling_rate} and not {sampling_rate}.'''		)
              else:
                     logger.warning(
                         "It is strongly recommended to pass the `sampling_rate` argument to this function. "
                         "Failing to do so can result in silent errors that might be hard to debug."		)

              UpperCamelCase_          =       isinstance(lowerCAmelCase_					,   np.ndarray		) and len(raw_speech.shape		) > 1
              if is_batched_numpy and len(raw_speech.shape		) > 2:
                     raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''		)
              UpperCamelCase_          =       is_batched_numpy or (
                  isinstance(lowerCAmelCase_					,   (list, tuple)		) and (isinstance(raw_speech[0]					,   (np.ndarray, tuple, list)		))
              )
              if is_batched:
                     UpperCamelCase_          =       [np.asarray([speech]					,   dtype=np.floataa		).T for speech in raw_speech]
              elif not is_batched and not isinstance(lowerCAmelCase_					,   np.ndarray		):
                     UpperCamelCase_          =       np.asarray(lowerCAmelCase_					,   dtype=np.floataa		)
              elif isinstance(lowerCAmelCase_					,   np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
                     UpperCamelCase_          =       raw_speech.astype(np.floataa		)
              # always return batch
              if not is_batched:
                     UpperCamelCase_          =       [np.asarray([raw_speech]		).T]

              # Convert audio signals to log mel spectrograms, truncate by time axis
              UpperCamelCase_          =       [
                  self._np_extract_fbank_features(waveform.squeeze()		).T[: self.spectrogram_length] for waveform in raw_speech
              ]
              if isinstance(audio_features[0]					,   lowerCAmelCase_		):
                     UpperCamelCase_          =       [np.asarray(lowerCAmelCase_					,   dtype=np.floataa		) for feature in audio_features]

              # Create audio attention mask
              UpperCamelCase_          =       max(
                  [ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len for feature in audio_features]		)  # The maximum number of audio patches in a batch
              if return_attention_mask:
                     UpperCamelCase_          =       [
                         (ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len) * [1]
                         + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len) * [0]
                         for feature in audio_features
                     ]
                     UpperCamelCase_          =       np.array(lowerCAmelCase_		).astype(np.floataa		)

              # convert into correct format for padding
              UpperCamelCase_          =       max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
              UpperCamelCase_          =       np.ones([len(lowerCAmelCase_		), 1, max_time_len, self.feature_size]		).astype(np.floataa		)
              UpperCamelCase_          =       padded_audio_features * self.padding_value
              for i in range(len(lowerCAmelCase_		)		):
                     UpperCamelCase_          =       audio_features[i]
                     UpperCamelCase_          =       feature

              # return as BatchFeature
              if return_attention_mask:
                     UpperCamelCase_          =       {"audio_values": padded_audio_features, "audio_mask": audio_mask}
              else:
                     UpperCamelCase_          =       {"audio_values": padded_audio_features}

              UpperCamelCase_          =       BatchFeature(data=lowerCAmelCase_					,   tensor_type=lowerCAmelCase_		)
              return encoded_inputs








import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 							_UpperCamelCase					(	lowerCAmelCase_  ,						unittest.TestCase     ):
       _UpperCamelCase				:					Union[str, Any]         =   XLMTokenizer
       _UpperCamelCase				:					Optional[int]         =   False

       def lowercase (		self: Dict		)       -> Union[str, Any]:




              """simple docstring"""


              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              UpperCamelCase_          =  [
                  "l",
                  "o",
                  "w",
                  "e",
                  "r",
                  "s",
                  "t",
                  "i",
                  "d",
                  "n",
                  "w</w>",
                  "r</w>",
                  "t</w>",
                  "lo",
                  "low",
                  "er</w>",
                  "low</w>",
                  "lowest</w>",
                  "newer</w>",
                  "wider</w>",
                  "<unk>",
              ]
              UpperCamelCase_          =  dict(zip(_SCREAMING_SNAKE_CASE					,   range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
              UpperCamelCase_          =  ["l o 123", "lo w 1456", "e r</w> 1789", ""]

              UpperCamelCase_          =  os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES["vocab_file"]		)
              UpperCamelCase_          =  os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES["merges_file"]		)
              with open(self.vocab_file					,   "w"		) as fp:
                     fp.write(json.dumps(_SCREAMING_SNAKE_CASE		)		)
              with open(self.merges_file					,   "w"		) as fp:
                     fp.write("\n".join(_SCREAMING_SNAKE_CASE		)		)

       def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: List[str]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  "lower newer"
              UpperCamelCase_          =  "lower newer"
              return input_text, output_text

       def lowercase (		self: int		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  XLMTokenizer(self.vocab_file					,   self.merges_file		)

              UpperCamelCase_          =  "lower"
              UpperCamelCase_          =  ["low", "er</w>"]
              UpperCamelCase_          =  tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  tokens + ["<unk>"]
              UpperCamelCase_          =  [14, 15, 20]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)



       @slow
       def lowercase (		self: Optional[int]		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  XLMTokenizer.from_pretrained("xlm-mlm-en-2048"		)

              UpperCamelCase_          =  tokenizer.encode("sequence builders"					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tokenizer.encode("multi-sequence build"					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              assert encoded_sentence == [0] + text + [1]
              assert encoded_pair == [0] + text + [1] + text_a + [1]

