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


A   :     Optional[int]       =				logging.get_logger(__name__)


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  ['''audio_values''', '''audio_mask''']







  def __init__(							self       :				Dict     ,   __magic_name__       :				Any=2_048     ,   __magic_name__       :				Optional[int]=1     ,   __magic_name__       :				Any=[16, 16]     ,   __magic_name__       :				Tuple=128     ,   __magic_name__       :				Optional[int]=44_100     ,   __magic_name__       :				Union[str, Any]=86     ,   __magic_name__       :				int=2_048     ,   __magic_name__       :				Optional[int]=0.0     ,   **__magic_name__       :				List[str]     ,   )     ->		List[str]:
        super().__init__(
            feature_size=__magic_name__     ,   sampling_rate=__magic_name__     ,   padding_value=__magic_name__     ,   **__magic_name__     ,   )

        SCREAMING_SNAKE_CASE_      =     spectrogram_length
        SCREAMING_SNAKE_CASE_      =     num_channels
        SCREAMING_SNAKE_CASE_      =     patch_size
        SCREAMING_SNAKE_CASE_      =     feature_size // self.patch_size[1]
        SCREAMING_SNAKE_CASE_      =     n_fft
        SCREAMING_SNAKE_CASE_      =     sampling_rate // hop_length_to_sampling_rate
        SCREAMING_SNAKE_CASE_      =     sampling_rate
        SCREAMING_SNAKE_CASE_      =     padding_value
        SCREAMING_SNAKE_CASE_      =     mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2     ,   num_mel_filters=__magic_name__     ,   min_frequency=0.0     ,   max_frequency=2_2050.0     ,   sampling_rate=__magic_name__     ,   norm="slaney"     ,   mel_scale="slaney"     ,   ).T







  def       __A   (							self       :				Any     ,   __magic_name__       :				np.array      )     ->		np.ndarray:
        SCREAMING_SNAKE_CASE_      =     spectrogram(
            __magic_name__     ,   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     ,   )
        SCREAMING_SNAKE_CASE_      =     log_spec[:, :-1]
        SCREAMING_SNAKE_CASE_      =     log_spec - 20.0
        SCREAMING_SNAKE_CASE_      =     np.clip(log_spec / 40.0     ,   -2.0     ,   0.0      ) + 1.0
        return log_spec







  def __call__(							self       :				Any     ,   __magic_name__       :				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,   __magic_name__       :				Optional[Union[str, TensorType]] = None     ,   __magic_name__       :				Optional[bool] = True     ,   __magic_name__       :				Optional[int] = None     ,   __magic_name__       :				bool = False     ,   __magic_name__       :				bool = False     ,   **__magic_name__       :				Union[str, Any]     ,   )     ->		BatchFeature:

        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."      )

        SCREAMING_SNAKE_CASE_      =     isinstance(__magic_name__     ,   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}'''      )
        SCREAMING_SNAKE_CASE_      =     is_batched_numpy or (
            isinstance(__magic_name__     ,   (list, tuple)      ) and (isinstance(raw_speech[0]     ,   (np.ndarray, tuple, list)      ))
        )
        if is_batched:
              SCREAMING_SNAKE_CASE_      =     [np.asarray([speech]     ,   dtype=np.floataa      ).T for speech in raw_speech]
        elif not is_batched and not isinstance(__magic_name__     ,   np.ndarray      ):
              SCREAMING_SNAKE_CASE_      =     np.asarray(__magic_name__     ,   dtype=np.floataa      )
        elif isinstance(__magic_name__     ,   np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
              SCREAMING_SNAKE_CASE_      =     raw_speech.astype(np.floataa      )
        # always return batch
        if not is_batched:
              SCREAMING_SNAKE_CASE_      =     [np.asarray([raw_speech]      ).T]

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

        # Create audio attention mask
        SCREAMING_SNAKE_CASE_      =     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:
              SCREAMING_SNAKE_CASE_      =     [
                  (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
              ]
              SCREAMING_SNAKE_CASE_      =     np.array(__magic_name__      ).astype(np.floataa      )

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

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

        SCREAMING_SNAKE_CASE_      =     BatchFeature(data=__magic_name__     ,   tensor_type=__magic_name__      )
        return encoded_inputs



import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin


@require_jieba
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  CpmAntTokenizer
  lowerCamelCase__   =  False







  def       __A   (							self       :				List[str]      )     ->		str:
        super().setUp()

        SCREAMING_SNAKE_CASE_      =     [
            "<d>",
            "</d>",
            "<s>",
            "</s>",
            "</_>",
            "<unk>",
            "<pad>",
            "</n>",
            "我",
            "是",
            "C",
            "P",
            "M",
            "A",
            "n",
            "t",
        ]
        SCREAMING_SNAKE_CASE_      =     os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]      )
        with open(self.vocab_file     ,   "w"     ,   encoding="utf-8"      ) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )







  @tooslow
  def       __A   (							self       :				int      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"      )
        SCREAMING_SNAKE_CASE_      =     "今天天气真好！"
        SCREAMING_SNAKE_CASE_      =     ["今天", "天气", "真", "好", "！"]
        SCREAMING_SNAKE_CASE_      =     tokenizer.tokenize(__magic_name__      )
        self.assertListEqual(__magic_name__     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     "今天天气真好！"
        SCREAMING_SNAKE_CASE_      =     [tokenizer.bos_token] + tokens

        SCREAMING_SNAKE_CASE_      =     [6, 9_802, 14_962, 2_082, 831, 244]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__      )     ,   __magic_name__      )

        SCREAMING_SNAKE_CASE_      =     tokenizer.decode(__magic_name__      )
        self.assertEqual(__magic_name__     ,   __magic_name__      )


