





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


__UpperCAmelCase	:       List[Any]    	=							logging.get_logger(__name__)







class    __snake_case	(							_a      ):





       '''simple docstring'''







       lowerCAmelCase__						=					["""audio_values""", """audio_mask"""]




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

         __snake_case:						str			  =						spectrogram_length
         __snake_case:						Optional[int]			  =						num_channels
         __snake_case:						Optional[Any]			  =						patch_size
         __snake_case:						Any			  =						feature_size // self.patch_size[1]
         __snake_case:						Optional[int]			  =						n_fft
         __snake_case:						Optional[int]			  =						sampling_rate // hop_length_to_sampling_rate
         __snake_case:						Optional[Any]			  =						sampling_rate
         __snake_case:						Optional[int]			  =						padding_value
         __snake_case:						Tuple			  =						mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2							,    num_mel_filters=A							,    min_frequency=0.0							,    max_frequency=22_050.0							,    sampling_rate=A							,    norm="""slaney"""							,    mel_scale="""slaney"""							,    ).T




       def 							UpperCAmelCase__			(  self			:   List[str]							,    A			:   Tuple		):
         __snake_case:						Tuple			  =						spectrogram(
             A							,    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							,    )
         __snake_case:						List[Any]			  =						log_spec[:, :-1]
         __snake_case:						Union[str, Any]			  =						log_spec - 20.0
         __snake_case:						Any			  =						np.clip(log_spec / 40.0							,    -2.0							,    0.0		) + 1.0
         return log_spec






       def __call__(  self			:   int							,    A			:   Tuple							,    A			:   Tuple = None							,    A			:   Dict = True							,    A			:   int = None							,    A			:   int = False							,    A			:   List[Any] = False							,    **A			:   List[str]							,    ):

         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."""		)

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

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

         # Create audio attention mask
         __snake_case:						Dict			  =						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:
           __snake_case:						str			  =						[
               (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
           ]
           __snake_case:						Any			  =						np.array(A		).astype(np.floataa		)

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

         # return as BatchFeature
         if return_attention_mask:
           __snake_case:						Union[str, Any]			  =						{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
         else:
           __snake_case:						Dict			  =						{'audio_values': padded_audio_features}

         __snake_case:						Tuple			  =						BatchFeature(data=A							,    tensor_type=A		)
         return encoded_inputs













import unittest

from diffusers.models.unet_ad_blocks import *  # noqa F403
from diffusers.utils import torch_device

from .test_unet_blocks_common import UNetBlockTesterMixin







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					DownBlockaD  # noqa F405
       lowerCAmelCase__						=					"""down"""




       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						str			  =						[-0.0232, -0.9869, 0.8054, -0.0637, -0.1688, -1.4264, 0.4470, -1.3394, 0.0904]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					ResnetDownsampleBlockaD  # noqa F405
       lowerCAmelCase__						=					"""down"""




       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         __snake_case:						Union[str, Any]			  =						[0.0710, 0.2410, -0.7320, -1.0757, -1.1343, 0.3540, -0.0133, -0.2576, 0.0948]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					AttnDownBlockaD  # noqa F405
       lowerCAmelCase__						=					"""down"""




       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						Union[str, Any]			  =						[0.0636, 0.8964, -0.6234, -1.0131, 0.0844, 0.4935, 0.3437, 0.0911, -0.2957]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					CrossAttnDownBlockaD  # noqa F405
       lowerCAmelCase__						=					"""down"""




       def 							UpperCAmelCase__			(  self			:   List[str]		):
         __snake_case  ,       __snake_case:						List[str]			  =						super().prepare_init_args_and_inputs_for_common()
         __snake_case:						List[Any]			  =						32
         return init_dict, inputs_dict






       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						[0.2238, -0.7396, -0.2255, -0.3829, 0.1925, 1.1665, 0.0603, -0.7295, 0.1983]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					SimpleCrossAttnDownBlockaD  # noqa F405
       lowerCAmelCase__						=					"""down"""




       @property
       def 							UpperCAmelCase__			(  self			:   Tuple		):
         return super().get_dummy_input(include_encoder_hidden_states=A		)




       def 							UpperCAmelCase__			(  self			:   int		):
         __snake_case  ,       __snake_case:						Union[str, Any]			  =						super().prepare_init_args_and_inputs_for_common()
         __snake_case:						Optional[Any]			  =						32
         return init_dict, inputs_dict






       @unittest.skipIf(torch_device == """mps"""							,    """MPS result is not consistent"""		)
       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         __snake_case:						Optional[Any]			  =						[0.7921, -0.0992, -0.1962, -0.7695, -0.4242, 0.7804, 0.4737, 0.2765, 0.3338]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					SkipDownBlockaD  # noqa F405
       lowerCAmelCase__						=					"""down"""




       @property
       def 							UpperCAmelCase__			(  self			:   Any		):
         return super().get_dummy_input(include_skip_sample=A		)






       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						Optional[Any]			  =						[-0.0845, -0.2087, -0.2465, 0.0971, 0.1900, -0.0484, 0.2664, 0.4179, 0.5069]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					AttnSkipDownBlockaD  # noqa F405
       lowerCAmelCase__						=					"""down"""




       @property
       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         return super().get_dummy_input(include_skip_sample=A		)






       def 							UpperCAmelCase__			(  self			:   int		):
         __snake_case:						str			  =						[0.5539, 0.1609, 0.4924, 0.0537, -0.1995, 0.4050, 0.0979, -0.2721, -0.0642]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					DownEncoderBlockaD  # noqa F405
       lowerCAmelCase__						=					"""down"""




       @property
       def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
         return super().get_dummy_input(include_temb=A		)




       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         __snake_case:						str			  =						{
             """in_channels""": 32,
             """out_channels""": 32,
         }
         __snake_case:						Dict			  =						self.dummy_input
         return init_dict, inputs_dict






       def 							UpperCAmelCase__			(  self			:   str		):
         __snake_case:						Optional[int]			  =						[1.1102, 0.5302, 0.4872, -0.0023, -0.8042, 0.0483, -0.3489, -0.5632, 0.7626]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					AttnDownEncoderBlockaD  # noqa F405
       lowerCAmelCase__						=					"""down"""




       @property
       def 							UpperCAmelCase__			(  self			:   List[str]		):
         return super().get_dummy_input(include_temb=A		)




       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         __snake_case:						Optional[Any]			  =						{
             """in_channels""": 32,
             """out_channels""": 32,
         }
         __snake_case:						Tuple			  =						self.dummy_input
         return init_dict, inputs_dict






       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         __snake_case:						Dict			  =						[0.8966, -0.1486, 0.8568, 0.8141, -0.9046, -0.1342, -0.0972, -0.7417, 0.1538]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					UNetMidBlockaD  # noqa F405
       lowerCAmelCase__						=					"""mid"""




       def 							UpperCAmelCase__			(  self			:   str		):
         __snake_case:						Optional[int]			  =						{
             """in_channels""": 32,
             """temb_channels""": 128,
         }
         __snake_case:						List[str]			  =						self.dummy_input
         return init_dict, inputs_dict






       def 							UpperCAmelCase__			(  self			:   str		):
         __snake_case:						Tuple			  =						[-0.1062, 1.7248, 0.3494, 1.4569, -0.0910, -1.2421, -0.9984, 0.6736, 1.0028]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					UNetMidBlockaDCrossAttn  # noqa F405
       lowerCAmelCase__						=					"""mid"""




       def 							UpperCAmelCase__			(  self			:   str		):
         __snake_case  ,       __snake_case:						int			  =						super().prepare_init_args_and_inputs_for_common()
         __snake_case:						int			  =						32
         return init_dict, inputs_dict






       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Optional[Any]			  =						[0.0187, 2.4220, 0.4484, 1.1203, -0.6121, -1.5122, -0.8270, 0.7851, 1.8335]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					UNetMidBlockaDSimpleCrossAttn  # noqa F405
       lowerCAmelCase__						=					"""mid"""




       @property
       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         return super().get_dummy_input(include_encoder_hidden_states=A		)




       def 							UpperCAmelCase__			(  self			:   str		):
         __snake_case  ,       __snake_case:						Any			  =						super().prepare_init_args_and_inputs_for_common()
         __snake_case:						str			  =						32
         return init_dict, inputs_dict






       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Optional[Any]			  =						[0.7143, 1.9974, 0.5448, 1.3977, 0.1282, -1.1237, -1.4238, 0.5530, 0.8880]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					UpBlockaD  # noqa F405
       lowerCAmelCase__						=					"""up"""




       @property
       def 							UpperCAmelCase__			(  self			:   Tuple		):
         return super().get_dummy_input(include_res_hidden_states_tuple=A		)






       def 							UpperCAmelCase__			(  self			:   Tuple		):
         __snake_case:						Tuple			  =						[-0.2041, -0.4165, -0.3022, 0.0041, -0.6628, -0.7053, 0.1928, -0.0325, 0.0523]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					ResnetUpsampleBlockaD  # noqa F405
       lowerCAmelCase__						=					"""up"""




       @property
       def 							UpperCAmelCase__			(  self			:   Tuple		):
         return super().get_dummy_input(include_res_hidden_states_tuple=A		)






       def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
         __snake_case:						int			  =						[0.2287, 0.3549, -0.1346, 0.4797, -0.1715, -0.9649, 0.7305, -0.5864, -0.6244]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					CrossAttnUpBlockaD  # noqa F405
       lowerCAmelCase__						=					"""up"""




       @property
       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         return super().get_dummy_input(include_res_hidden_states_tuple=A		)




       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case  ,       __snake_case:						Any			  =						super().prepare_init_args_and_inputs_for_common()
         __snake_case:						Optional[int]			  =						32
         return init_dict, inputs_dict






       def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
         __snake_case:						List[Any]			  =						[-0.1403, -0.3515, -0.0420, -0.1425, 0.3167, 0.5094, -0.2181, 0.5931, 0.5582]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					SimpleCrossAttnUpBlockaD  # noqa F405
       lowerCAmelCase__						=					"""up"""




       @property
       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         return super().get_dummy_input(include_res_hidden_states_tuple=A							,    include_encoder_hidden_states=A		)




       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case  ,       __snake_case:						Optional[Any]			  =						super().prepare_init_args_and_inputs_for_common()
         __snake_case:						str			  =						32
         return init_dict, inputs_dict






       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         __snake_case:						Union[str, Any]			  =						[0.2645, 0.1480, 0.0909, 0.8044, -0.9758, -0.9083, 0.0994, -1.1453, -0.7402]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					AttnUpBlockaD  # noqa F405
       lowerCAmelCase__						=					"""up"""




       @property
       def 							UpperCAmelCase__			(  self			:   int		):
         return super().get_dummy_input(include_res_hidden_states_tuple=A		)






       @unittest.skipIf(torch_device == """mps"""							,    """MPS result is not consistent"""		)
       def 							UpperCAmelCase__			(  self			:   List[str]		):
         __snake_case:						Optional[Any]			  =						[0.0979, 0.1326, 0.0021, 0.0659, 0.2249, 0.0059, 0.1132, 0.5952, 0.1033]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					SkipUpBlockaD  # noqa F405
       lowerCAmelCase__						=					"""up"""




       @property
       def 							UpperCAmelCase__			(  self			:   str		):
         return super().get_dummy_input(include_res_hidden_states_tuple=A		)






       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Optional[int]			  =						[-0.0893, -0.1234, -0.1506, -0.0332, 0.0123, -0.0211, 0.0566, 0.0143, 0.0362]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					AttnSkipUpBlockaD  # noqa F405
       lowerCAmelCase__						=					"""up"""




       @property
       def 							UpperCAmelCase__			(  self			:   str		):
         return super().get_dummy_input(include_res_hidden_states_tuple=A		)






       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						[0.0361, 0.0617, 0.2787, -0.0350, 0.0342, 0.3421, -0.0843, 0.0913, 0.3015]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					UpDecoderBlockaD  # noqa F405
       lowerCAmelCase__						=					"""up"""




       @property
       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         return super().get_dummy_input(include_temb=A		)




       def 							UpperCAmelCase__			(  self			:   str		):
         __snake_case:						Union[str, Any]			  =						{"""in_channels""": 32, """out_channels""": 32}

         __snake_case:						Dict			  =						self.dummy_input
         return init_dict, inputs_dict






       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						Dict			  =						[0.4404, 0.1998, -0.9886, -0.3320, -0.3128, -0.7034, -0.6955, -0.2338, -0.3137]
         super().test_output(A		)







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					AttnUpDecoderBlockaD  # noqa F405
       lowerCAmelCase__						=					"""up"""




       @property
       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         return super().get_dummy_input(include_temb=A		)




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						{"""in_channels""": 32, """out_channels""": 32}

         __snake_case:						Any			  =						self.dummy_input
         return init_dict, inputs_dict






       def 							UpperCAmelCase__			(  self			:   int		):
         __snake_case:						Any			  =						[0.6738, 0.4491, 0.1055, 1.0710, 0.7316, 0.3339, 0.3352, 0.1023, 0.3568]
         super().test_output(A		)






