
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


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





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					["audio_values", "audio_mask"]






	def __init__(	self :			List[Any]							,A :			Tuple=20_48							,A :			Optional[Any]=1							,A :			str=[16, 16]							,A :			List[str]=1_28							,A :			Any=4_41_00							,A :			int=86							,A :			Optional[Any]=20_48							,A :			Optional[Any]=0.0							,**A :			int							,):
								super().__init__(
								    feature_size=A							,sampling_rate=A							,padding_value=A							,**A							,)

								__A             =			spectrogram_length
								__A             =			num_channels
								__A             =			patch_size
								__A             =			feature_size // self.patch_size[1]
								__A             =			n_fft
								__A             =			sampling_rate // hop_length_to_sampling_rate
								__A             =			sampling_rate
								__A             =			padding_value
								__A             =			mel_filter_bank(
								    num_frequency_bins=1 + n_fft // 2							,num_mel_filters=A							,min_frequency=0.0							,max_frequency=2_20_50.0							,sampling_rate=A							,norm="slaney"							,mel_scale="slaney"							,).T






	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			np.array			):
								__A             =			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							,)
								__A             =			log_spec[:, :-1]
								__A             =			log_spec - 20.0
								__A             =			np.clip(log_spec / 40.0							,-2.0							,0.0			) + 1.0
								return log_spec







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

								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."			)

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

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

								# Create audio attention mask
								__A             =			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:
															__A             =			[
															    (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
															]
															__A             =			np.array(A			).astype(np.floataa			)

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

								# return as BatchFeature
								if return_attention_mask:
															__A             =			{"audio_values": padded_audio_features, "audio_mask": audio_mask}
								else:
															__A             =			{"audio_values": padded_audio_features}

								__A             =			BatchFeature(data=A							,tensor_type=A			)
								return encoded_inputs


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE							:Any										=    {
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[Any]										=    [
					    'RagModel',
					    'RagPreTrainedModel',
					    'RagSequenceForGeneration',
					    'RagTokenForGeneration',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any										=    [
					    'TFRagModel',
					    'TFRagPreTrainedModel',
					    'TFRagSequenceForGeneration',
					    'TFRagTokenForGeneration',
					]


if TYPE_CHECKING:
					from .configuration_rag import RagConfig
					from .retrieval_rag import RagRetriever
					from .tokenization_rag import RagTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_rag import (
										    TFRagModel,
										    TFRagPreTrainedModel,
										    TFRagSequenceForGeneration,
										    TFRagTokenForGeneration,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE							:int										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
