
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


lowercase_ 				=			logging.get_logger(__name__)





class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			['''input_features''']



     def __init__(				self							:							Optional[Any]     ,						_A							:							Optional[int]=80     ,						_A							:							Dict=1_6000     ,						_A							:							Any=160     ,						_A							:							Optional[int]=30     ,						_A							:							Optional[int]=400     ,						_A							:							List[str]=0.0     ,						_A							:							Optional[int]=False     ,						**_A							:							Optional[Any]     ,						):







       """simple docstring"""




       super().__init__(
           feature_size=_lowerCAmelCase     ,						sampling_rate=_lowerCAmelCase     ,						padding_value=_lowerCAmelCase     ,						return_attention_mask=_lowerCAmelCase     ,						**_lowerCAmelCase     ,						)
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		n_fft
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		hop_length
       __SCREAMING_SNAKE_CASE      :	str					=		chunk_length
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		chunk_length * sampling_rate
       __SCREAMING_SNAKE_CASE      :	Dict					=		self.n_samples // hop_length
       __SCREAMING_SNAKE_CASE      :	int					=		sampling_rate
       __SCREAMING_SNAKE_CASE      :	Tuple					=		mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2     ,						num_mel_filters=_lowerCAmelCase     ,						min_frequency=0.0     ,						max_frequency=8000.0     ,						sampling_rate=_lowerCAmelCase     ,						norm='''slaney'''     ,						mel_scale='''slaney'''     ,						)



     def  UpperCAmelCase__							(				self							:							Dict     ,						_A							:							np.array ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Tuple					=		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     ,						log_mel='''log10'''     ,						)
       __SCREAMING_SNAKE_CASE      :	Any					=		log_spec[:, :-1]
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		np.maximum(_lowerCAmelCase     ,						log_spec.max() - 8.0 )
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		(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  UpperCAmelCase__							(				_A							:							List[np.ndarray]     ,						_A							:							List[np.ndarray]     ,						_A							:							float = 0.0 ):







       """simple docstring"""




       if attention_mask is not None:
         __SCREAMING_SNAKE_CASE      :	Tuple					=		np.array(_lowerCAmelCase     ,						np.intaa )
         __SCREAMING_SNAKE_CASE      :	int					=		[]

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

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

       return normed_input_values



     def __call__(				self							:							List[str]     ,						_A							:							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,						_A							:							bool = True     ,						_A							:							Optional[int] = None     ,						_A							:							Optional[Union[str, TensorType]] = None     ,						_A							:							Optional[bool] = None     ,						_A							:							Optional[str] = "max_length"     ,						_A							:							Optional[int] = None     ,						_A							:							Optional[int] = None     ,						_A							:							Optional[bool] = None     ,						**_A							:							int     ,						):







       """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.''' )

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		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}''' )
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		is_batched_numpy or (
           isinstance(_lowerCAmelCase     ,						(list, tuple) ) and (isinstance(raw_speech[0]     ,						(np.ndarray, tuple, list) ))
       )

       if is_batched:
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		[np.asarray([speech]     ,						dtype=np.floataa ).T for speech in raw_speech]
       elif not is_batched and not isinstance(_lowerCAmelCase     ,						np.ndarray ):
         __SCREAMING_SNAKE_CASE      :	Any					=		np.asarray(_lowerCAmelCase     ,						dtype=np.floataa )
       elif isinstance(_lowerCAmelCase     ,						np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
         __SCREAMING_SNAKE_CASE      :	Any					=		raw_speech.astype(np.floataa )

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

       __SCREAMING_SNAKE_CASE      :	int					=		BatchFeature({'''input_features''': raw_speech} )

       # convert into correct format for padding

       __SCREAMING_SNAKE_CASE      :	int					=		self.pad(
           _lowerCAmelCase     ,						padding=_lowerCAmelCase     ,						max_length=max_length if max_length else self.n_samples     ,						truncation=_lowerCAmelCase     ,						pad_to_multiple_of=_lowerCAmelCase     ,						return_attention_mask=return_attention_mask or do_normalize     ,						)

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

       # make sure list is in array format
       __SCREAMING_SNAKE_CASE      :	Dict					=		padded_inputs.get('''input_features''' ).transpose(2     ,						0     ,						1 )

       __SCREAMING_SNAKE_CASE      :	str					=		[self._np_extract_fbank_features(_lowerCAmelCase ) for waveform in input_features[0]]

       if isinstance(input_features[0]     ,						_lowerCAmelCase ):
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		[np.asarray(_lowerCAmelCase     ,						dtype=np.floataa ) for feature in input_features]
       else:
         __SCREAMING_SNAKE_CASE      :	str					=		input_features

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

       if return_tensors is not None:
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		padded_inputs.convert_to_tensors(_lowerCAmelCase )

       return padded_inputs




     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Tuple					=		copy.deepcopy(self.__dict__ )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.__class__.__name__
       if "mel_filters" in output:
         del output["mel_filters"]
       return output




import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ImageGPTImageProcessor


class      __lowerCAmelCase  (			unittest.TestCase       ):






  """simple docstring"""





  def __init__(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							List[Any]=7     ,					_lowerCAmelCase							:							Any=3     ,					_lowerCAmelCase							:							Dict=1_8     ,					_lowerCAmelCase							:							Tuple=3_0     ,					_lowerCAmelCase							:							Optional[int]=4_0_0     ,					_lowerCAmelCase							:							Optional[int]=True     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							List[Any]=True     ,					) ->			Dict:




     """simple docstring"""
     snake_case_            =       size if size is not None else {"height": 1_8, "width": 1_8}
     snake_case_            =       parent
     snake_case_            =       batch_size
     snake_case_            =       num_channels
     snake_case_            =       image_size
     snake_case_            =       min_resolution
     snake_case_            =       max_resolution
     snake_case_            =       do_resize
     snake_case_            =       size
     snake_case_            =       do_normalize



  def        lowerCAmelCase__	(  self							:							str	) ->			Optional[int]:




     """simple docstring"""
     return {
         # here we create 2 clusters for the sake of simplicity
         "clusters": np.asarray(
             [
                 [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
                 [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
             ]	),
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
     }






@require_torch
@require_vision
class      __lowerCAmelCase  (			a			,	unittest.TestCase       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							ImageGPTImageProcessor if is_vision_available() else None



  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       ImageGPTImageProcessingTester(self	)



  @property
  def        lowerCAmelCase__	(  self							:							str	) ->			Union[str, Any]:




     """simple docstring"""
     return self.image_processor_tester.prepare_image_processor_dict()



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"clusters"	)	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"do_resize"	)	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"size"	)	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"do_normalize"	)	)



  def        lowerCAmelCase__	(  self							:							int	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.image_processing_class.from_dict(self.image_processor_dict	)
     self.assertEqual(image_processor.size     ,					{"height": 1_8, "width": 1_8}	)

     snake_case_            =       self.image_processing_class.from_dict(self.image_processor_dict     ,					size=4_2	)
     self.assertEqual(image_processor.size     ,					{"height": 4_2, "width": 4_2}	)



  def        lowerCAmelCase__	(  self							:							Tuple	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)
     snake_case_            =       json.loads(image_processor.to_json_string()	)
     for key, value in self.image_processor_dict.items():
        if key == "clusters":
           self.assertTrue(np.array_equal(_lowerCAmelCase     ,					obj[key]	)	)
        else:
           self.assertEqual(obj[key]     ,					_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							int	) ->			Optional[Any]:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)

     with tempfile.TemporaryDirectory() as tmpdirname:
        snake_case_            =       os.path.join(_lowerCAmelCase     ,					"image_processor.json"	)
        image_processor_first.to_json_file(_lowerCAmelCase	)
        snake_case_            =       self.image_processing_class.from_json_file(_lowerCAmelCase	).to_dict()

     snake_case_            =       image_processor_first.to_dict()
     for key, value in image_processor_first.items():
        if key == "clusters":
           self.assertTrue(np.array_equal(_lowerCAmelCase     ,					image_processor_second[key]	)	)
        else:
           self.assertEqual(image_processor_first[key]     ,					_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)

     with tempfile.TemporaryDirectory() as tmpdirname:
        image_processor_first.save_pretrained(_lowerCAmelCase	)
        snake_case_            =       self.image_processing_class.from_pretrained(_lowerCAmelCase	).to_dict()

     snake_case_            =       image_processor_first.to_dict()
     for key, value in image_processor_first.items():
        if key == "clusters":
           self.assertTrue(np.array_equal(_lowerCAmelCase     ,					image_processor_second[key]	)	)
        else:
           self.assertEqual(image_processor_first[key]     ,					_lowerCAmelCase	)



  @unittest.skip("ImageGPT requires clusters at initialization"	)
  def        lowerCAmelCase__	(  self							:							List[str]	) ->			int:




     """simple docstring"""
     pass






def       _lowerCAmelCase    (				)->Optional[Any]:
   '''simple docstring'''






   snake_case_            =       load_dataset("hf-internal-testing/fixtures_image_utils"		,    split="test"				)

   snake_case_            =       Image.open(dataset[4]["file"]				)
   snake_case_            =       Image.open(dataset[5]["file"]				)

   snake_case_            =       [imagea, imagea]

   return images



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






  """simple docstring"""





  @slow
  def        lowerCAmelCase__	(  self							:							List[str]	) ->			int:




     """simple docstring"""
     snake_case_            =       ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"	)

     snake_case_            =       prepare_images()

     # test non-batched
     snake_case_            =       image_processing(images[0]     ,					return_tensors="pt"	)

     self.assertIsInstance(encoding.input_ids     ,					torch.LongTensor	)
     self.assertEqual(encoding.input_ids.shape     ,					(1, 1_0_2_4)	)

     snake_case_            =       [3_0_6, 1_9_1, 1_9_1]
     self.assertEqual(encoding.input_ids[0, :3].tolist()     ,					_lowerCAmelCase	)

     # test batched
     snake_case_            =       image_processing(_lowerCAmelCase     ,					return_tensors="pt"	)

     self.assertIsInstance(encoding.input_ids     ,					torch.LongTensor	)
     self.assertEqual(encoding.input_ids.shape     ,					(2, 1_0_2_4)	)

     snake_case_            =       [3_0_3, 1_3, 1_3]
     self.assertEqual(encoding.input_ids[1, -3:].tolist()     ,					_lowerCAmelCase	)




