from argparse import ArgumentParser

from . import BaseTransformersCLICommand





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[Any]  ):
					return DownloadCommand(args.model      , args.cache_dir      , args.force      , args.trust_remote_code  )


class 						lowercase    (     a     ):




							@staticmethod
							def 	__snake_case(						_UpperCamelCase   :					ArgumentParser      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       parser.add_parser("download"      )
												download_parser.add_argument(
												    "--cache-dir"	,							type=_UpperCamelCase	,							default=_UpperCamelCase	,							help="Path to location to store the models"      )
												download_parser.add_argument(
												    "--force"	,							action="store_true"	,							help="Force the model to be download even if already in cache-dir"      )
												download_parser.add_argument(
												    "--trust-remote-code"	,							action="store_true"	,							help="Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"	,							)
												download_parser.add_argument("model"	,							type=_UpperCamelCase	,							help="Name of the model to download"      )
												download_parser.set_defaults(func=_UpperCamelCase      )




							def __init__(						self   :					Union[str, Any]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					bool	,							_UpperCamelCase   :					bool      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       model
												SCREAMING_SNAKE_CASE    			=       cache
												SCREAMING_SNAKE_CASE    			=       force
												SCREAMING_SNAKE_CASE    			=       trust_remote_code





							def 	__snake_case(						self   :					Any      )					->				int:



												'''simple docstring'''

												from ..models.auto import AutoModel, AutoTokenizer

												AutoModel.from_pretrained(
												    self._model	,							cache_dir=self._cache	,							force_download=self._force	,							trust_remote_code=self._trust_remote_code      )
												AutoTokenizer.from_pretrained(
												    self._model	,							cache_dir=self._cache	,							force_download=self._force	,							trust_remote_code=self._trust_remote_code      )





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




