



"""simple docstring"""
import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


UpperCAmelCase    :		Dict      =			logging.get_logger(__name__)


class  SCREAMING_SNAKE_CASE__			(				__UpperCAmelCase ):
    lowercase__  			= ["input_values", "attention_mask"]

    def __init__(					self      :					Any			,	lowerCAmelCase_      :					int = 1			,	lowerCAmelCase_      :					int = 1_6_0_0_0			,	lowerCAmelCase_      :					float = 0.0			,	lowerCAmelCase_      :					bool = False			,	lowerCAmelCase_      :					int = 8_0			,	lowerCAmelCase_      :					int = 1_6			,	lowerCAmelCase_      :					int = 6_4			,	lowerCAmelCase_      :					str = "hann_window"			,	lowerCAmelCase_      :					float = 1.0			,	lowerCAmelCase_      :					float = 8_0			,	lowerCAmelCase_      :					float = 7_6_0_0			,	lowerCAmelCase_      :					float = 1E-10			,	lowerCAmelCase_      :					int = 2			,	lowerCAmelCase_      :					bool = True			,	**lowerCAmelCase_      :					List[Any]			,	):







      """simple docstring"""







      super().__init__(feature_size=snake_case__			,	sampling_rate=snake_case__			,	padding_value=snake_case__			,	**snake_case__)
      lowercase_             =			do_normalize
      lowercase_             =			return_attention_mask

      lowercase_             =			num_mel_bins
      lowercase_             =			hop_length
      lowercase_             =			win_length
      lowercase_             =			win_function
      lowercase_             =			frame_signal_scale
      lowercase_             =			fmin
      lowercase_             =			fmax
      lowercase_             =			mel_floor
      lowercase_             =			reduction_factor

      lowercase_             =			win_length * sampling_rate // 1_0_0_0
      lowercase_             =			hop_length * sampling_rate // 1_0_0_0
      lowercase_             =			optimal_fft_length(self.sample_size)
      lowercase_             =			(self.n_fft // 2) + 1

      lowercase_             =			window_function(window_length=self.sample_size			,	name=self.win_function			,	periodic=snake_case__)

      lowercase_             =			mel_filter_bank(
          num_frequency_bins=self.n_freqs			,	num_mel_filters=self.num_mel_bins			,	min_frequency=self.fmin			,	max_frequency=self.fmax			,	sampling_rate=self.sampling_rate			,	norm="""slaney"""			,	mel_scale="""slaney"""			,	)

      if frame_signal_scale != 1.0:
        warnings.warn(
            """The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"""			,	snake_case__			,	)
      if reduction_factor != 2.0:
        warnings.warn(
            """The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"""			,	snake_case__			,	)

    @staticmethod
    # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
    def        _UpperCAmelCase						(					lowerCAmelCase_      :					List[np.ndarray]			,	lowerCAmelCase_      :					List[np.ndarray]			,	lowerCAmelCase_      :					float = 0.0):







      """simple docstring"""







      if attention_mask is not None:
        lowercase_             =			np.array(snake_case__			,	np.intaa)
        lowercase_             =			[]

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

          normed_input_values.append(snake_case__)
      else:
        lowercase_             =			[(x - x.mean()) / np.sqrt(x.var() + 1E-7) for x in input_values]

      return normed_input_values

    def        _UpperCAmelCase						(					self      :					Tuple			,	lowerCAmelCase_      :					np.ndarray			,	):







      """simple docstring"""







      lowercase_             =			spectrogram(
          snake_case__			,	window=self.window			,	frame_length=self.sample_size			,	hop_length=self.sample_stride			,	fft_length=self.n_fft			,	mel_filters=self.mel_filters			,	mel_floor=self.mel_floor			,	log_mel="""log10"""			,	)
      return log_mel_spec.T

    def __call__(					self      :					List[Any]			,	lowerCAmelCase_      :					Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,	lowerCAmelCase_      :					Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,	lowerCAmelCase_      :					Union[bool, str, PaddingStrategy] = False			,	lowerCAmelCase_      :					Optional[int] = None			,	lowerCAmelCase_      :					bool = False			,	lowerCAmelCase_      :					Optional[int] = None			,	lowerCAmelCase_      :					Optional[bool] = None			,	lowerCAmelCase_      :					Optional[Union[str, TensorType]] = None			,	lowerCAmelCase_      :					Optional[int] = None			,	**lowerCAmelCase_      :					List[Any]			,	):







      """simple docstring"""







      if audio is None and audio_target is None:
        raise ValueError("""You must provide either `audio` or `audio_target` values.""")

      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 audio 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.""")

      if audio is not None:
        lowercase_             =			self._process_audio(
            snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	**snake_case__			,	)
      else:
        lowercase_             =			None

      if audio_target is not None:
        lowercase_             =			self._process_audio(
            snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	**snake_case__			,	)

        if inputs is None:
          return inputs_target
        else:
          lowercase_             =			inputs_target["input_values"]
          lowercase_             =			inputs_target.get("""attention_mask""")
          if decoder_attention_mask is not None:
            lowercase_             =			decoder_attention_mask

      return inputs

    def        _UpperCAmelCase						(					self      :					List[Any]			,	lowerCAmelCase_      :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,	lowerCAmelCase_      :					bool = False			,	lowerCAmelCase_      :					Union[bool, str, PaddingStrategy] = False			,	lowerCAmelCase_      :					Optional[int] = None			,	lowerCAmelCase_      :					bool = False			,	lowerCAmelCase_      :					Optional[int] = None			,	lowerCAmelCase_      :					Optional[bool] = None			,	lowerCAmelCase_      :					Optional[Union[str, TensorType]] = None			,	**lowerCAmelCase_      :					int			,	):







      """simple docstring"""







      lowercase_             =			isinstance(snake_case__			,	np.ndarray) and len(speech.shape) > 1
      if is_batched_numpy and len(speech.shape) > 2:
        raise ValueError(F'''Only mono-channel audio is supported for input to {self}''')
      lowercase_             =			is_batched_numpy or (
          isinstance(snake_case__			,	(list, tuple)) and (isinstance(speech[0]			,	(np.ndarray, tuple, list)))
      )

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

      # always return batch
      if not is_batched:
        lowercase_             =			[speech]

      # needed to make pad() work on spectrogram inputs
      lowercase_             =			self.feature_size

      # convert into correct format for padding
      if is_target:
        lowercase_             =			[self._extract_mel_features(snake_case__) for waveform in speech]
        lowercase_             =			BatchFeature({"""input_values""": features})
        lowercase_             =			self.num_mel_bins
      else:
        lowercase_             =			BatchFeature({"""input_values""": speech})

      lowercase_             =			self.pad(
          snake_case__			,	padding=snake_case__			,	max_length=snake_case__			,	truncation=snake_case__			,	pad_to_multiple_of=snake_case__			,	return_attention_mask=snake_case__			,	**snake_case__			,	)

      lowercase_             =			feature_size_hack

      # convert input values to correct format
      lowercase_             =			padded_inputs["input_values"]
      if not isinstance(input_values[0]			,	np.ndarray):
        lowercase_             =			[np.asarray(snake_case__			,	dtype=np.floataa) for array in input_values]
      elif (
          not isinstance(snake_case__			,	np.ndarray)
          and isinstance(input_values[0]			,	np.ndarray)
          and input_values[0].dtype is np.dtype(np.floataa)
      ):
        lowercase_             =			[array.astype(np.floataa) for array in input_values]
      elif isinstance(snake_case__			,	np.ndarray) and input_values.dtype is np.dtype(np.floataa):
        lowercase_             =			input_values.astype(np.floataa)

      # convert attention_mask to correct format
      lowercase_             =			padded_inputs.get("""attention_mask""")
      if attention_mask is not None:
        lowercase_             =			[np.asarray(snake_case__			,	dtype=np.intaa) for array in attention_mask]

      # zero-mean and unit-variance normalization
      if not is_target and self.do_normalize:
        lowercase_             =			(
            attention_mask
            if self._get_padding_strategies(snake_case__			,	max_length=snake_case__) is not PaddingStrategy.DO_NOT_PAD
            else None
        )
        lowercase_             =			self.zero_mean_unit_var_norm(
            padded_inputs["""input_values"""]			,	attention_mask=snake_case__			,	padding_value=self.padding_value)

      if return_tensors is not None:
        lowercase_             =			padded_inputs.convert_to_tensors(snake_case__)

      return padded_inputs




    def        _UpperCAmelCase						(					self      :					List[Any]):







      """simple docstring"""







      lowercase_             =			super().to_dict()

      # Don't serialize these as they are derived from the other properties.
      lowercase_             =			["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
      for name in names:
        if name in output:
          del output[name]

      return output



'''simple docstring'''





import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()






class 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Tuple				=							PriorTransformer
 lowercase_					:    Dict				=							"hidden_states"







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							4
   UpperCAmelCase__      :					List[Any]				       =							8
   UpperCAmelCase__      :					Union[str, Any]				       =							7

   UpperCAmelCase__      :					Tuple				       =							floats_tensor((batch_size, embedding_dim) ).to(snake_case__ )

   UpperCAmelCase__      :					List[Any]				       =							floats_tensor((batch_size, embedding_dim) ).to(snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }







 def 							UpperCamelCase  (					self		:  int			,				snake_case__		:  List[str]=0 ):



   '''simple docstring'''




   torch.manual_seed(snake_case__ )
   UpperCAmelCase__      :					str				       =							4
   UpperCAmelCase__      :					int				       =							8
   UpperCAmelCase__      :					Optional[int]				       =							7

   UpperCAmelCase__      :					Any				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )

   UpperCAmelCase__      :					Union[str, Any]				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }







 @property
 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   return (4, 8)







 @property
 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   return (4, 8)







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							{
       "num_attention_heads": 2,
       "attention_head_dim": 4,
       "num_layers": 2,
       "embedding_dim": 8,
       "num_embeddings": 7,
       "additional_embeddings": 4,
   }
   UpperCAmelCase__      :					Optional[Any]				       =							self.dummy_input
   return init_dict, inputs_dict







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					List[str]				       =							PriorTransformer.from_pretrained(
       "hf-internal-testing/prior-dummy"			,				output_loading_info=snake_case__ )
   self.assertIsNotNone(snake_case__ )
   self.assertEqual(len(loading_info["missing_keys"] )			,				0 )

   model.to(snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							model(**self.dummy_input )[0]

   assert hidden_states is not None, "Make sure output is not None"







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					int				       =							self.prepare_init_args_and_inputs_for_common()

   UpperCAmelCase__      :					Tuple				       =							self.model_class(**snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							inspect.signature(model.forward )
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   UpperCAmelCase__      :					int				       =							[*signature.parameters.keys()]

   UpperCAmelCase__      :					Optional[Any]				       =							["hidden_states", "timestep"]
   self.assertListEqual(arg_names[:2]			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy" )
   UpperCAmelCase__      :					List[Any]				       =							model.to(snake_case__ )

   if hasattr(snake_case__			,				"set_default_attn_processor" ):
     model.set_default_attn_processor()

   UpperCAmelCase__      :					int				       =							self.get_dummy_seed_input()

   with torch.no_grad():
     UpperCAmelCase__      :					Tuple				       =							model(**snake_case__ )[0]

   UpperCAmelCase__      :					Any				       =							output[0, :5].flatten().cpu()
   print(snake_case__ )

   # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
   # the expected output slices are not the same for CPU and GPU.
   UpperCAmelCase__      :					Optional[int]				       =							torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239] )
   self.assertTrue(torch_all_close(snake_case__			,				snake_case__			,				rtol=1e-2 ) )






@slow
class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  Optional[int]=1			,				snake_case__		:  Any=7_68			,				snake_case__		:  List[Any]=77			,				snake_case__		:  Tuple=0 ):



   '''simple docstring'''




   torch.manual_seed(snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							batch_size
   UpperCAmelCase__      :					Any				       =							embedding_dim
   UpperCAmelCase__      :					Any				       =							num_embeddings

   UpperCAmelCase__      :					str				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )

   UpperCAmelCase__      :					Optional[Any]				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )
   UpperCAmelCase__      :					Any				       =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 @parameterized.expand(
     [
         # fmt: off
         [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
         [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
         # fmt: on
     ] )
 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  List[str]			,				snake_case__		:  Optional[int] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"			,				subfolder="prior" )
   model.to(snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							self.get_dummy_seed_input(seed=snake_case__ )

   with torch.no_grad():
     UpperCAmelCase__      :					Any				       =							model(**snake_case__ )[0]

   assert list(sample.shape ) == [1, 7_68]

   UpperCAmelCase__      :					List[str]				       =							sample[0, :8].flatten().cpu()
   print(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							torch.tensor(snake_case__ )

   assert torch_all_close(snake_case__			,				snake_case__			,				atol=1e-3 )



