


'''simple docstring'''

import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


a_  :				Dict						= logging.get_logger(__name__)





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  ["input_features"]






  def __init__(    self			,  UpperCamelCase=80			,  UpperCamelCase=1_6000			,  UpperCamelCase=160			,  UpperCamelCase=30			,  UpperCamelCase=400			,  UpperCamelCase=0.0			,  UpperCamelCase=False			,  **UpperCamelCase			,  ):







   """simple docstring"""
   super().__init__(
       feature_size=UpperCamelCase			,  sampling_rate=UpperCamelCase			,  padding_value=UpperCamelCase			,  return_attention_mask=UpperCamelCase			,  **UpperCamelCase			,  )
   lowerCamelCase_      =  n_fft
   lowerCamelCase_      =  hop_length
   lowerCamelCase_      =  chunk_length
   lowerCamelCase_      =  chunk_length * sampling_rate
   lowerCamelCase_      =  self.n_samples // hop_length
   lowerCamelCase_      =  sampling_rate
   lowerCamelCase_      =  mel_filter_bank(
       num_frequency_bins=1 + n_fft // 2			,  num_mel_filters=UpperCamelCase			,  min_frequency=0.0			,  max_frequency=8_000.0			,  sampling_rate=UpperCamelCase			,  norm="slaney"			,  mel_scale="slaney"			,  )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  spectrogram(
       UpperCamelCase			,  window_function(self.n_fft			,  "hann"     )			,  frame_length=self.n_fft			,  hop_length=self.hop_length			,  power=2.0			,  mel_filters=self.mel_filters			,  log_mel="log10"			,  )
   lowerCamelCase_      =  log_spec[:, :-1]
   lowerCamelCase_      =  np.maximum(UpperCamelCase			,  log_spec.max() - 8.0     )
   lowerCamelCase_      =  (log_spec + 4.0) / 4.0
   return log_spec






  @staticmethod
  # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
  def   snake_case		(    UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase = 0.0     ):







   """simple docstring"""
   if attention_mask is not None:
    lowerCamelCase_      =  np.array(UpperCamelCase			,  np.intaa     )
    lowerCamelCase_      =  []

    for vector, length in zip(UpperCamelCase			,  attention_mask.sum(-1     )     ):
     lowerCamelCase_      =  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7     )
     if length < normed_slice.shape[0]:
      lowerCamelCase_      =  padding_value

     normed_input_values.append(UpperCamelCase     )
   else:
    lowerCamelCase_      =  [(x - x.mean()) / np.sqrt(x.var() + 1e-7     ) for x in input_values]

   return normed_input_values






  def __call__(    self			,  UpperCamelCase			,  UpperCamelCase = True			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = "max_length"			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""

   if sampling_rate is not None:
    if sampling_rate != self.sampling_rate:
     raise ValueError(
         f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
         f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
         f''' was sampled 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."     )

   lowerCamelCase_      =  isinstance(UpperCamelCase			,  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}'''     )
   lowerCamelCase_      =  is_batched_numpy or (
       isinstance(UpperCamelCase			,  (list, tuple)     ) and (isinstance(raw_speech[0]			,  (np.ndarray, tuple, list)     ))
   )

   if is_batched:
    lowerCamelCase_      =  [np.asarray([speech]			,  dtype=np.floataa     ).T for speech in raw_speech]
   elif not is_batched and not isinstance(UpperCamelCase			,  np.ndarray     ):
    lowerCamelCase_      =  np.asarray(UpperCamelCase			,  dtype=np.floataa     )
   elif isinstance(UpperCamelCase			,  np.ndarray     ) and raw_speech.dtype is np.dtype(np.floataa     ):
    lowerCamelCase_      =  raw_speech.astype(np.floataa     )

   # always return batch
   if not is_batched:
    lowerCamelCase_      =  [np.asarray([raw_speech]     ).T]

   lowerCamelCase_      =  BatchFeature({"input_features": raw_speech}     )

   # convert into correct format for padding

   lowerCamelCase_      =  self.pad(
       UpperCamelCase			,  padding=UpperCamelCase			,  max_length=max_length if max_length else self.n_samples			,  truncation=UpperCamelCase			,  pad_to_multiple_of=UpperCamelCase			,  return_attention_mask=return_attention_mask or do_normalize			,  )

   # zero-mean and unit-variance normalization
   if do_normalize:
    lowerCamelCase_      =  self.zero_mean_unit_var_norm(
        padded_inputs["input_features"]			,  attention_mask=padded_inputs["attention_mask"]			,  padding_value=self.padding_value			,  )
    lowerCamelCase_      =  np.stack(padded_inputs["input_features"]			,  axis=0     )

   # make sure list is in array format
   lowerCamelCase_      =  padded_inputs.get("input_features"     ).transpose(2			,  0			,  1     )

   lowerCamelCase_      =  [self._np_extract_fbank_features(UpperCamelCase     ) for waveform in input_features[0]]

   if isinstance(input_features[0]			,  UpperCamelCase     ):
    lowerCamelCase_      =  [np.asarray(UpperCamelCase			,  dtype=np.floataa     ) for feature in input_features]
   else:
    lowerCamelCase_      =  input_features

   if return_attention_mask:
    # rescale from sample (48000) to feature (3000)
    lowerCamelCase_      =  padded_inputs["attention_mask"][:, :: self.hop_length]

   if return_tensors is not None:
    lowerCamelCase_      =  padded_inputs.convert_to_tensors(UpperCamelCase     )

   return padded_inputs







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  copy.deepcopy(self.__dict__     )
   lowerCamelCase_      =  self.__class__.__name__
   if "mel_filters" in output:
    del output["mel_filters"]
   return output









import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
						import torch

						from transformers import AutoModelForImageClassification

if is_vision_available():
						from transformers import AutoImageProcessor


@require_torch
@require_vision
class  A		(  unittest.TestCase     ):



		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''   )
								lowerCAmelCase_      			=  AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''   )
								model.to(UpperCamelCase__   )

								from datasets import load_dataset

								lowerCAmelCase_      			=  load_dataset('''nielsr/rvlcdip-demo'''   )

								lowerCAmelCase_      			=  dataset['''train'''][0]['''image'''].convert('''RGB'''   )

								lowerCAmelCase_      			=  image_processor(UpperCamelCase__,	return_tensors='''pt'''   ).to(UpperCamelCase__   )

								# forward pass
								with torch.no_grad():
														lowerCAmelCase_      			=  model(**UpperCamelCase__   )
														lowerCAmelCase_      			=  outputs.logits

								lowerCAmelCase_      			=  torch.Size((1, 16)   )
								self.assertEqual(logits.shape,	UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.tensor(
								    [-0.4_158, -0.4_092, -0.4_347],	device=UpperCamelCase__,	dtype=torch.float,	)
								self.assertTrue(torch.allclose(logits[0, :3],	UpperCamelCase__,	atol=1E-4   )   )




