
from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


lowerCamelCase_						     =					logging.get_logger(__name__)




class      __A(    __lowerCamelCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      ["""input_features""", """attention_mask"""]







  def __init__(self	,      SCREAMING_SNAKE_CASE_=80	,      SCREAMING_SNAKE_CASE_=1_60_00	,      SCREAMING_SNAKE_CASE_=0.0	,      SCREAMING_SNAKE_CASE_=10	,      SCREAMING_SNAKE_CASE_=25	,      SCREAMING_SNAKE_CASE_="hamming_window"	,      SCREAMING_SNAKE_CASE_=3_2768.0	,      SCREAMING_SNAKE_CASE_=0.97	,      SCREAMING_SNAKE_CASE_=1.0	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=False	,      **SCREAMING_SNAKE_CASE_	,      ):
       super().__init__(feature_size=SCREAMING_SNAKE_CASE_	,      sampling_rate=SCREAMING_SNAKE_CASE_	,      padding_value=SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    feature_size
       UpperCamelCase__				=    sampling_rate
       UpperCamelCase__				=    padding_value
       UpperCamelCase__				=    hop_length
       UpperCamelCase__				=    win_length
       UpperCamelCase__				=    frame_signal_scale
       UpperCamelCase__				=    preemphasis_coeff
       UpperCamelCase__				=    mel_floor
       UpperCamelCase__				=    normalize_means
       UpperCamelCase__				=    normalize_vars
       UpperCamelCase__				=    win_function
       UpperCamelCase__				=    return_attention_mask

       UpperCamelCase__				=    win_length * sampling_rate // 10_00
       UpperCamelCase__				=    hop_length * sampling_rate // 10_00

       UpperCamelCase__				=    optimal_fft_length(self.sample_size						)
       UpperCamelCase__				=    (self.n_fft // 2) + 1







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       if self.win_function == "hamming_window":
            UpperCamelCase__				=    window_function(window_length=self.sample_size	,      name=self.win_function	,      periodic=SCREAMING_SNAKE_CASE_						)
       else:
            UpperCamelCase__				=    window_function(window_length=self.sample_size	,      name=self.win_function						)

       UpperCamelCase__				=    mel_filter_bank(
           num_frequency_bins=self.n_freqs	,      num_mel_filters=self.feature_size	,      min_frequency=0.0	,      max_frequency=self.sampling_rate / 2.0	,      sampling_rate=self.sampling_rate	,      )

       UpperCamelCase__				=    spectrogram(
           one_waveform * self.frame_signal_scale	,      window=SCREAMING_SNAKE_CASE_	,      frame_length=self.sample_size	,      hop_length=self.sample_stride	,      fft_length=self.n_fft	,      center=SCREAMING_SNAKE_CASE_	,      preemphasis=self.preemphasis_coeff	,      mel_filters=SCREAMING_SNAKE_CASE_	,      mel_floor=self.mel_floor	,      log_mel="""log"""	,      )
       return msfc_features.T







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       # make sure we normalize float32 arrays
       if self.normalize_means:
            UpperCamelCase__				=    x[:input_length].mean(axis=0						)
            UpperCamelCase__				=    np.subtract(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
       if self.normalize_vars:
            UpperCamelCase__				=    x[:input_length].std(axis=0						)
            UpperCamelCase__				=    np.divide(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

       if input_length < x.shape[0]:
            UpperCamelCase__				=    padding_value

       # make sure array is in float32
       UpperCamelCase__				=    x.astype(np.floataa						)

       return x







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = None						):
       UpperCamelCase__				=    attention_mask.sum(-1						) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [self._normalize_one(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      self.padding_value						) for x, n in zip(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)]







  def __call__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      **SCREAMING_SNAKE_CASE_	,      ):

       if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     F"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
                     F" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
                     F" {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(SCREAMING_SNAKE_CASE_	,      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(SCREAMING_SNAKE_CASE_	,      (list, tuple)						) and (isinstance(raw_speech[0]	,      (np.ndarray, tuple, list)						))
       )

       if is_batched:
            UpperCamelCase__				=    [np.asarray(SCREAMING_SNAKE_CASE_	,      dtype=np.floataa						) for speech in raw_speech]
       elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_	,      np.ndarray						):
            UpperCamelCase__				=    np.asarray(SCREAMING_SNAKE_CASE_	,      dtype=np.floataa						)
       elif isinstance(SCREAMING_SNAKE_CASE_	,      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__				=    [raw_speech]

       # extract fbank features
       UpperCamelCase__				=    [self._extract_mfsc_features(SCREAMING_SNAKE_CASE_						) for one_waveform in raw_speech]

       # convert into correct format for padding
       UpperCamelCase__				=    BatchFeature({"""input_features""": features}						)

       UpperCamelCase__				=    self.pad(
           SCREAMING_SNAKE_CASE_	,      padding=SCREAMING_SNAKE_CASE_	,      max_length=SCREAMING_SNAKE_CASE_	,      truncation=SCREAMING_SNAKE_CASE_	,      pad_to_multiple_of=SCREAMING_SNAKE_CASE_	,      return_attention_mask=SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_	,      )
       # make sure list is in array format
       UpperCamelCase__				=    padded_inputs.get("""input_features"""						)
       if isinstance(input_features[0]	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    [np.asarray(SCREAMING_SNAKE_CASE_	,      dtype=np.floataa						) for feature in input_features]

       UpperCamelCase__				=    padded_inputs.get("""attention_mask"""						)
       if attention_mask is not None:
            UpperCamelCase__				=    [np.asarray(SCREAMING_SNAKE_CASE_	,      dtype=np.intaa						) for array in attention_mask]

       if self.normalize_means or self.normalize_vars:
            UpperCamelCase__				=    (
                np.array(SCREAMING_SNAKE_CASE_	,      dtype=np.intaa						)
                if self._get_padding_strategies(SCREAMING_SNAKE_CASE_	,      max_length=SCREAMING_SNAKE_CASE_						) is not PaddingStrategy.DO_NOT_PAD
                and padding
                else None
            )
            UpperCamelCase__				=    self.normalize(
                padded_inputs["""input_features"""]	,      attention_mask=SCREAMING_SNAKE_CASE_						)

       if return_tensors is not None:
            UpperCamelCase__				=    padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_						)

       return padded_inputs







import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
    import torch

    from transformers import AutoModelForCausalLM




@require_torch
class      __A(    unittest.TestCase			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.decode(greedy_ids[0]						)

       with CaptureStdout() as cs:
            UpperCamelCase__				=    TextStreamer(SCREAMING_SNAKE_CASE_						)
            model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_	,      streamer=SCREAMING_SNAKE_CASE_						)
       # The greedy text should be printed to stdout, except for the final "\n" in the streamer
       UpperCamelCase__				=    cs.out[:-1]

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.decode(greedy_ids[0]						)

       UpperCamelCase__				=    TextIteratorStreamer(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
       UpperCamelCase__				=    Thread(target=model.generate	,      kwargs=SCREAMING_SNAKE_CASE_						)
       thread.start()
       UpperCamelCase__				=    """"""
       for new_text in streamer:
            streamer_text += new_text

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    greedy_ids[:, input_ids.shape[1] :]
       UpperCamelCase__				=    tokenizer.decode(new_greedy_ids[0]						)

       with CaptureStdout() as cs:
            UpperCamelCase__				=    TextStreamer(SCREAMING_SNAKE_CASE_	,      skip_prompt=SCREAMING_SNAKE_CASE_						)
            model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_	,      streamer=SCREAMING_SNAKE_CASE_						)
       # The greedy text should be printed to stdout, except for the final "\n" in the streamer
       UpperCamelCase__				=    cs.out[:-1]

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
       # with actual models -- the dummy models' tokenizers are not aligned with their models, and
       # `skip_special_tokens=True` has no effect on them
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""distilgpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""distilgpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    torch.ones((1, 5)	,      device=SCREAMING_SNAKE_CASE_						).long() * model.config.bos_token_id
       with CaptureStdout() as cs:
            UpperCamelCase__				=    TextStreamer(SCREAMING_SNAKE_CASE_	,      skip_special_tokens=SCREAMING_SNAKE_CASE_						)
            model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=1	,      do_sample=SCREAMING_SNAKE_CASE_	,      streamer=SCREAMING_SNAKE_CASE_						)

       # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
       # re-tokenized, must only contain one token
       UpperCamelCase__				=    cs.out[:-1]  # Remove the final "\n"
       UpperCamelCase__				=    tokenizer(SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""						)
       self.assertEqual(streamer_text_tokenized.input_ids.shape	,      (1, 1)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    TextIteratorStreamer(SCREAMING_SNAKE_CASE_	,      timeout=0.001						)
       UpperCamelCase__				=    {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
       UpperCamelCase__				=    Thread(target=model.generate	,      kwargs=SCREAMING_SNAKE_CASE_						)
       thread.start()

       # The streamer will timeout after 0.001 seconds, so an exception will be raised
       with self.assertRaises(SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    """"""
            for new_text in streamer:
                 streamer_text += new_text





