

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

import numpy as np
import torch

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_						     =    logging.get_logger(__name__)






class _UpperCamelCase							(							lowerCamelCase__   ):
			'''simple docstring'''







			lowerCamelCase__           =['input_features', 'is_longer']
			def __init__(			self							: Dict      ,      a							: Optional[int]=64      ,      a							: Dict=4_8000      ,      a							: List[Any]=480      ,      a							: Optional[Any]=10      ,      a							: Union[str, Any]=1024      ,      a							: List[Any]=0.0      ,      a							: str=False      ,      a							: Optional[int] = 0      ,      a							: Optional[int] = 1_4000      ,      a							: Any = None      ,      a							: List[Any] = "fusion"      ,      a							: Any = "repeatpad"      ,      **a							: Tuple      ,      )							->       Dict:

				"""simple docstring"""

				super().__init__(
				    feature_size=__lowerCamelCase      ,      sampling_rate=__lowerCamelCase      ,      padding_value=__lowerCamelCase      ,      return_attention_mask=__lowerCamelCase      ,      **__lowerCamelCase      ,      )
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       top_db
				SCREAMING_SNAKE_CASE   :      Tuple				     =       truncation
				SCREAMING_SNAKE_CASE   :      str				     =       padding
				SCREAMING_SNAKE_CASE   :      Dict				     =       fft_window_size
				SCREAMING_SNAKE_CASE   :      str				     =       (fft_window_size >> 1) + 1
				SCREAMING_SNAKE_CASE   :      Tuple				     =       hop_length
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       max_length_s
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       max_length_s * sampling_rate
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       sampling_rate
				SCREAMING_SNAKE_CASE   :      Dict				     =       frequency_min
				SCREAMING_SNAKE_CASE   :      List[str]				     =       frequency_max
				SCREAMING_SNAKE_CASE   :      List[str]				     =       mel_filter_bank(
				    num_frequency_bins=self.nb_frequency_bins      ,      num_mel_filters=__lowerCamelCase      ,      min_frequency=__lowerCamelCase      ,      max_frequency=__lowerCamelCase      ,      sampling_rate=__lowerCamelCase      ,      norm=__lowerCamelCase      ,      mel_scale="htk"      ,      )
				SCREAMING_SNAKE_CASE   :      Any				     =       mel_filter_bank(
				    num_frequency_bins=self.nb_frequency_bins      ,      num_mel_filters=__lowerCamelCase      ,      min_frequency=__lowerCamelCase      ,      max_frequency=__lowerCamelCase      ,      sampling_rate=__lowerCamelCase      ,      norm="slaney"      ,      mel_scale="slaney"      ,      )
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Dict[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       copy.deepcopy(self.__dict__	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.__class__.__name__
				if "mel_filters" in output:
					del output["mel_filters"]
				if "mel_filters_slaney" in output:
					del output["mel_filters_slaney"]
				return output
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Optional[int]      ,      a							: Any = None	)							->       np.ndarray:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       spectrogram(
				    __lowerCamelCase      ,      window_function(self.fft_window_size      ,      "hann"	)      ,      frame_length=self.fft_window_size      ,      hop_length=self.hop_length      ,      power=2.0      ,      mel_filters=__lowerCamelCase      ,      log_mel="dB"      ,      )
				return log_mel_spectrogram.T
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: Optional[Any]      ,      a							: Tuple      ,      a							: Tuple	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       np.array_split(list(range(0      ,      total_frames - chunk_frames + 1	)	)      ,      3	)
				if len(ranges[1]	) == 0:
					# if the audio is too short, we just use the first chunk
					SCREAMING_SNAKE_CASE   :      Dict				     =       [0]
				if len(ranges[2]	) == 0:
					# if the audio is too short, we just use the first chunk
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [0]
				# randomly choose index for each part
				SCREAMING_SNAKE_CASE   :      int				     =       np.random.choice(ranges[0]	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       np.random.choice(ranges[1]	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       np.random.choice(ranges[2]	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       mel[idx_front : idx_front + chunk_frames, :]
				SCREAMING_SNAKE_CASE   :      Dict				     =       mel[idx_middle : idx_middle + chunk_frames, :]
				SCREAMING_SNAKE_CASE   :      Any				     =       mel[idx_back : idx_back + chunk_frames, :]

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       torch.tensor(mel[None, None, :]	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.nn.functional.interpolate(
				    __lowerCamelCase      ,      size=[chunk_frames, 64]      ,      mode="bilinear"      ,      align_corners=__lowerCamelCase	)
				SCREAMING_SNAKE_CASE   :      str				     =       mel_shrink[0][0].numpy()
				SCREAMING_SNAKE_CASE   :      List[str]				     =       np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]      ,      axis=0	)
				return mel_fusion
			def 							__UpperCamelCase (			self							: Dict      ,      a							: Optional[int]      ,      a							: Optional[int]      ,      a							: List[str]      ,      a							: str	)							->       np.array:

				"""simple docstring"""

				if waveform.shape[0] > max_length:
					if truncation == "rand_trunc":
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       True
						# random crop to max_length (for compatibility) -> this should be handled by self.pad
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       len(__lowerCamelCase	) - max_length
						SCREAMING_SNAKE_CASE   :      List[str]				     =       np.random.randint(0      ,      overflow + 1	)
						SCREAMING_SNAKE_CASE   :      Any				     =       waveform[idx : idx + max_length]
						SCREAMING_SNAKE_CASE   :      List[str]				     =       self._np_extract_fbank_features(__lowerCamelCase      ,      self.mel_filters_slaney	)[None, :]
					elif truncation == "fusion":
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self._np_extract_fbank_features(__lowerCamelCase      ,      self.mel_filters	)
						SCREAMING_SNAKE_CASE   :      Dict				     =       max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
						SCREAMING_SNAKE_CASE   :      Dict				     =       mel.shape[0]
						if chunk_frames == total_frames:
							# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
							# In this case, we just use the whole audio.
							SCREAMING_SNAKE_CASE   :      str				     =       np.stack([mel, mel, mel, mel]      ,      axis=0	)
							SCREAMING_SNAKE_CASE   :      int				     =       False
						else:
							SCREAMING_SNAKE_CASE   :      List[str]				     =       self._random_mel_fusion(__lowerCamelCase      ,      __lowerCamelCase      ,      __lowerCamelCase	)
							SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       True
					else:
						raise NotImplementedError(F"data_truncating {truncation} not implemented"	)

				else:
					SCREAMING_SNAKE_CASE   :      str				     =       False
					# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
					if waveform.shape[0] < max_length:
						if padding == "repeat":
							SCREAMING_SNAKE_CASE   :      str				     =       int(max_length / len(__lowerCamelCase	)	)
							SCREAMING_SNAKE_CASE   :      int				     =       np.stack(np.tile(__lowerCamelCase      ,      n_repeat + 1	)	)[:max_length]
						if padding == "repeatpad":
							SCREAMING_SNAKE_CASE   :      Any				     =       int(max_length / len(__lowerCamelCase	)	)
							SCREAMING_SNAKE_CASE   :      Any				     =       np.stack(np.tile(__lowerCamelCase      ,      __lowerCamelCase	)	)
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       np.pad(__lowerCamelCase      ,      (0, max_length - waveform.shape[0])      ,      mode="constant"      ,      constant_values=0	)

					if truncation == "fusion":
						SCREAMING_SNAKE_CASE   :      Tuple				     =       self._np_extract_fbank_features(__lowerCamelCase      ,      self.mel_filters	)
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       np.stack([input_mel, input_mel, input_mel, input_mel]      ,      axis=0	)
					else:
						SCREAMING_SNAKE_CASE   :      Any				     =       self._np_extract_fbank_features(__lowerCamelCase      ,      self.mel_filters_slaney	)[None, :]

				return input_mel, longer






			def __call__(			self							: Optional[Any]      ,      a							: Any      ,      a							: Optional[int] = None      ,      a							: Tuple = None      ,      a							: Tuple = None      ,      a							: Tuple = None      ,      a							: Optional[int] = None      ,      **a							: Optional[int]      ,      )							->       BatchFeature:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       truncation if truncation is not None else self.truncation
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       padding if padding else self.padding

				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   :      int				     =       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[int]				     =       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(__lowerCamelCase      ,      dtype=np.floataa	) for speech in raw_speech]
				elif not is_batched and not isinstance(__lowerCamelCase      ,      np.ndarray	):
					SCREAMING_SNAKE_CASE   :      int				     =       np.asarray(__lowerCamelCase      ,      dtype=np.floataa	)
				elif isinstance(__lowerCamelCase      ,      np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       raw_speech.astype(np.floataa	)

				# always return batch
				if not is_batched:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       [np.asarray(__lowerCamelCase	)]

				# convert to mel spectrogram, truncate and pad if needed.
				SCREAMING_SNAKE_CASE   :      str				     =       [
				    self._get_input_mel(__lowerCamelCase      ,      max_length if max_length else self.nb_max_samples      ,      __lowerCamelCase      ,      __lowerCamelCase	)
				    for waveform in raw_speech
				]

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
				for mel, longer in padded_inputs:
					input_mel.append(__lowerCamelCase	)
					is_longer.append(__lowerCamelCase	)

				if truncation == "fusion" and sum(__lowerCamelCase	) == 0:
					# if no audio is longer than 10s, then randomly select one audio to be longer
					SCREAMING_SNAKE_CASE   :      Any				     =       np.random.randint(0      ,      len(__lowerCamelCase	)	)
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       True

				if isinstance(input_mel[0]      ,      __lowerCamelCase	):
					SCREAMING_SNAKE_CASE   :      Any				     =       [np.asarray(__lowerCamelCase      ,      dtype=np.floataa	) for feature in input_mel]

				# is_longer is a list of bool
				SCREAMING_SNAKE_CASE   :      Any				     =       [[longer] for longer in is_longer]

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {'''input_features''': input_mel, '''is_longer''': is_longer}
				SCREAMING_SNAKE_CASE   :      Dict				     =       BatchFeature(__lowerCamelCase	)

				if return_tensors is not None:
					SCREAMING_SNAKE_CASE   :      Any				     =       input_features.convert_to_tensors(__lowerCamelCase	)

				return input_features


from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =42






class _UpperCamelCase							(							__A							,	__A   ):
			'''simple docstring'''







			@register_to_config
			def __init__(			self							: str      ,      a							: int = 16      ,      a							: int = 88      ,      a							: Optional[int] = None      ,      a							: Optional[int] = None      ,      a							: int = 1      ,      a							: float = 0.0      ,      a							: int = 32      ,      a							: Optional[int] = None      ,      a							: bool = False      ,      a							: Optional[int] = None      ,      a							: str = "geglu"      ,      a							: bool = True      ,      a							: bool = True      ,      )							->       str:

				"""simple docstring"""

				super().__init__()
				SCREAMING_SNAKE_CASE   :      str				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      Tuple				     =       attention_head_dim
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       num_attention_heads * attention_head_dim

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       in_channels

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.nn.GroupNorm(num_groups=a      ,      num_channels=a      ,      eps=1e-6      ,      affine=a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       nn.Linear(a      ,      a	)

				# 3. Define transformers blocks
				SCREAMING_SNAKE_CASE   :      List[str]				     =       nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            a      ,      a      ,      a      ,      dropout=a      ,      cross_attention_dim=a      ,      activation_fn=a      ,      attention_bias=a      ,      double_self_attention=a      ,      norm_elementwise_affine=a      ,      )
				        for d in range(a	)
				    ]	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       nn.Linear(a      ,      a	)






			def 							__UpperCamelCase (			self							: Any      ,      a							: int      ,      a							: Optional[int]=None      ,      a							: List[str]=None      ,      a							: List[str]=None      ,      a							: Union[str, Any]=1      ,      a							: Any=None      ,      a							: bool = True      ,      )							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Any				     =       hidden_states.shape
				SCREAMING_SNAKE_CASE   :      Any				     =       batch_frames // num_frames

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       hidden_states

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       hidden_states[None, :].reshape(a      ,      a      ,      a      ,      a      ,      a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       hidden_states.permute(0      ,      2      ,      1      ,      3      ,      4	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.norm(a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       hidden_states.permute(0      ,      3      ,      4      ,      2      ,      1	).reshape(batch_size * height * width      ,      a      ,      a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.proj_in(a	)

				# 2. Blocks
				for block in self.transformer_blocks:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       block(
					    a      ,      encoder_hidden_states=a      ,      timestep=a      ,      cross_attention_kwargs=a      ,      class_labels=a      ,      )

				# 3. Output
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.proj_out(a	)
				SCREAMING_SNAKE_CASE   :      int				     =       (
				    hidden_states[None, None, :]
				    .reshape(a      ,      a      ,      a      ,      a      ,      a	)
				    .permute(0      ,      3      ,      4      ,      1      ,      2	)
				    .contiguous()
				)
				SCREAMING_SNAKE_CASE   :      Dict				     =       hidden_states.reshape(a      ,      a      ,      a      ,      a	)

				SCREAMING_SNAKE_CASE   :      int				     =       hidden_states + residual

				if not return_dict:
					return (output,)

				return TransformerTemporalModelOutput(sample=a	)