from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)


@dataclass
class 						lowercase    (     a     ):
							lowercase__						:       Union[List[PIL.Image.Image], np.ndarray]
							lowercase__						:       Optional[List[bool]]
							lowercase__						:       Optional[List[bool]]


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
							from .pipeline_if import IFPipeline
							from .pipeline_if_imgaimg import IFImgaImgPipeline
							from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
							from .pipeline_if_inpainting import IFInpaintingPipeline
							from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
							from .pipeline_if_superresolution import IFSuperResolutionPipeline
							from .safety_checker import IFSafetyChecker
							from .watermark import IFWatermarker





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


_lowerCamelCase					:   Union[str, Any]							=		logging.get_logger(__name__)


class 						lowercase    (     a     ):
							lowercase__						:       Optional[Any]								=							["""input_features""", """is_longer"""]




							def __init__(						self   :					str	,							_UpperCamelCase   :					Optional[int]=64	,							_UpperCamelCase   :					Any=48_000	,							_UpperCamelCase   :					Optional[Any]=480	,							_UpperCamelCase   :					List[Any]=10	,							_UpperCamelCase   :					Any=1_024	,							_UpperCamelCase   :					List[Any]=0.0	,							_UpperCamelCase   :					Any=False	,							_UpperCamelCase   :					float = 0	,							_UpperCamelCase   :					float = 14_000	,							_UpperCamelCase   :					int = None	,							_UpperCamelCase   :					str = "fusion"	,							_UpperCamelCase   :					str = "repeatpad"	,							**_UpperCamelCase   :					Optional[Any]	,							)					->				Dict:



												'''simple docstring'''

												super().__init__(
												    feature_size=_UpperCamelCase	,							sampling_rate=_UpperCamelCase	,							padding_value=_UpperCamelCase	,							return_attention_mask=_UpperCamelCase	,							**_UpperCamelCase	,							)
												SCREAMING_SNAKE_CASE    			=       top_db
												SCREAMING_SNAKE_CASE    			=       truncation
												SCREAMING_SNAKE_CASE    			=       padding
												SCREAMING_SNAKE_CASE    			=       fft_window_size
												SCREAMING_SNAKE_CASE    			=       (fft_window_size >> 1) + 1
												SCREAMING_SNAKE_CASE    			=       hop_length
												SCREAMING_SNAKE_CASE    			=       max_length_s
												SCREAMING_SNAKE_CASE    			=       max_length_s * sampling_rate
												SCREAMING_SNAKE_CASE    			=       sampling_rate
												SCREAMING_SNAKE_CASE    			=       frequency_min
												SCREAMING_SNAKE_CASE    			=       frequency_max
												SCREAMING_SNAKE_CASE    			=       mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins	,							num_mel_filters=_UpperCamelCase	,							min_frequency=_UpperCamelCase	,							max_frequency=_UpperCamelCase	,							sampling_rate=_UpperCamelCase	,							norm=_UpperCamelCase	,							mel_scale="htk"	,							)
												SCREAMING_SNAKE_CASE    			=       mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins	,							num_mel_filters=_UpperCamelCase	,							min_frequency=_UpperCamelCase	,							max_frequency=_UpperCamelCase	,							sampling_rate=_UpperCamelCase	,							norm="slaney"	,							mel_scale="slaney"	,							)




							def 	__snake_case(						self   :					str      )					->				Dict[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       copy.deepcopy(self.__dict__      )
												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					np.array	,							_UpperCamelCase   :					Optional[np.array] = None      )					->				np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       spectrogram(
												    _UpperCamelCase	,							window_function(self.fft_window_size	,							"hann"      )	,							frame_length=self.fft_window_size	,							hop_length=self.hop_length	,							power=2.0	,							mel_filters=_UpperCamelCase	,							log_mel="dB"	,							)
												return log_mel_spectrogram.T




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					str      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       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    			=       [0]
												if len(ranges[2]      ) == 0:
																	# if the audio is too short, we just use the first chunk
																	SCREAMING_SNAKE_CASE    			=       [0]
												# randomly choose index for each part
												SCREAMING_SNAKE_CASE    			=       np.random.choice(ranges[0]      )
												SCREAMING_SNAKE_CASE    			=       np.random.choice(ranges[1]      )
												SCREAMING_SNAKE_CASE    			=       np.random.choice(ranges[2]      )

												SCREAMING_SNAKE_CASE    			=       mel[idx_front : idx_front + chunk_frames, :]
												SCREAMING_SNAKE_CASE    			=       mel[idx_middle : idx_middle + chunk_frames, :]
												SCREAMING_SNAKE_CASE    			=       mel[idx_back : idx_back + chunk_frames, :]

												SCREAMING_SNAKE_CASE    			=       torch.tensor(mel[None, None, :]      )
												SCREAMING_SNAKE_CASE    			=       torch.nn.functional.interpolate(
												    _UpperCamelCase	,							size=[chunk_frames, 64]	,							mode="bilinear"	,							align_corners=_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       mel_shrink[0][0].numpy()
												SCREAMING_SNAKE_CASE    			=       np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]	,							axis=0      )
												return mel_fusion




							def 	__snake_case(						self   :					Optional[int]	,							_UpperCamelCase   :					np.array	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					List[str]      )					->				np.array:



												'''simple docstring'''

												if waveform.shape[0] > max_length:
																	if truncation == "rand_trunc":
																						SCREAMING_SNAKE_CASE    			=       True
																						# random crop to max_length (for compatibility) -> this should be handled by self.pad
																						SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      ) - max_length
																						SCREAMING_SNAKE_CASE    			=       np.random.randint(0	,							overflow + 1      )
																						SCREAMING_SNAKE_CASE    			=       waveform[idx : idx + max_length]
																						SCREAMING_SNAKE_CASE    			=       self._np_extract_fbank_features(_UpperCamelCase	,							self.mel_filters_slaney      )[None, :]
																	elif truncation == "fusion":
																						SCREAMING_SNAKE_CASE    			=       self._np_extract_fbank_features(_UpperCamelCase	,							self.mel_filters      )
																						SCREAMING_SNAKE_CASE    			=       max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																						SCREAMING_SNAKE_CASE    			=       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    			=       np.stack([mel, mel, mel, mel]	,							axis=0      )
																											SCREAMING_SNAKE_CASE    			=       False
																						else:
																											SCREAMING_SNAKE_CASE    			=       self._random_mel_fusion(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																											SCREAMING_SNAKE_CASE    			=       True
																	else:
																						raise NotImplementedError(F"data_truncating {truncation} not implemented"      )

												else:
																	SCREAMING_SNAKE_CASE    			=       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    			=       int(max_length / len(_UpperCamelCase      )      )
																											SCREAMING_SNAKE_CASE    			=       np.stack(np.tile(_UpperCamelCase	,							n_repeat + 1      )      )[:max_length]
																						if padding == "repeatpad":
																											SCREAMING_SNAKE_CASE    			=       int(max_length / len(_UpperCamelCase      )      )
																											SCREAMING_SNAKE_CASE    			=       np.stack(np.tile(_UpperCamelCase	,							_UpperCamelCase      )      )
																						SCREAMING_SNAKE_CASE    			=       np.pad(_UpperCamelCase	,							(0, max_length - waveform.shape[0])	,							mode="constant"	,							constant_values=0      )

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

												return input_mel, longer





							def __call__(						self   :					Dict	,							_UpperCamelCase   :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,							_UpperCamelCase   :					str = None	,							_UpperCamelCase   :					Optional[str] = None	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							**_UpperCamelCase   :					Tuple	,							)					->				BatchFeature:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       truncation if truncation is not None else self.truncation
												SCREAMING_SNAKE_CASE    			=       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    			=       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}"      )
												SCREAMING_SNAKE_CASE    			=       is_batched_numpy or (
												    isinstance(_UpperCamelCase	,							(list, tuple)      ) and (isinstance(raw_speech[0]	,							(np.ndarray, tuple, list)      ))
												)

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

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

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

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

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

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

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

												SCREAMING_SNAKE_CASE    			=       {"input_features": input_mel, "is_longer": is_longer}
												SCREAMING_SNAKE_CASE    			=       BatchFeature(_UpperCamelCase      )

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

												return input_features




