



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


__snake_case						:str      		=     logging.get_logger(__name__)

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    List[Any]											=						['''input_features''', '''is_longer''']



       def __init__(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=64     ,		__SCREAMING_SNAKE_CASE							:     str=48_000     ,		__SCREAMING_SNAKE_CASE							:     int=480     ,		__SCREAMING_SNAKE_CASE							:     Tuple=10     ,		__SCREAMING_SNAKE_CASE							:     Tuple=1_024     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0.0     ,		__SCREAMING_SNAKE_CASE							:     Tuple=False     ,		__SCREAMING_SNAKE_CASE							:     float = 0     ,		__SCREAMING_SNAKE_CASE							:     float = 14_000     ,		__SCREAMING_SNAKE_CASE							:     int = None     ,		__SCREAMING_SNAKE_CASE							:     str = "fusion"     ,		__SCREAMING_SNAKE_CASE							:     str = "repeatpad"     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]     ,		):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				copy.deepcopy(self.__dict__)
           __a						       =				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 		_lowerCamelCase			(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     np.array     ,		__SCREAMING_SNAKE_CASE							:     Optional[np.array] = None):


           '''simple docstring'''

           __a						       =				spectrogram(
               __SCREAMING_SNAKE_CASE     ,		window_function(self.fft_window_size     ,		'''hann''')     ,		frame_length=self.fft_window_size     ,		hop_length=self.hop_length     ,		power=2.0     ,		mel_filters=__SCREAMING_SNAKE_CASE     ,		log_mel='''dB'''     ,		)
           return log_mel_spectrogram.T



       def 		_lowerCamelCase			(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

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

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

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



       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     np.array     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

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

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

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

           return input_mel, longer





       def __call__(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,		__SCREAMING_SNAKE_CASE							:     str = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[str] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[str, TensorType]] = None     ,		**__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		):


           '''simple docstring'''

           __a						       =				truncation if truncation is not None else self.truncation
           __a						       =				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.''')

           __a						       =				isinstance(__SCREAMING_SNAKE_CASE     ,		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(__SCREAMING_SNAKE_CASE     ,		(list, tuple)) and (isinstance(raw_speech[0]     ,		(np.ndarray, tuple, list)))
           )

           if is_batched:
               __a						       =				[np.asarray(__SCREAMING_SNAKE_CASE     ,		dtype=np.floataa) for speech in raw_speech]
           elif not is_batched and not isinstance(__SCREAMING_SNAKE_CASE     ,		np.ndarray):
               __a						       =				np.asarray(__SCREAMING_SNAKE_CASE     ,		dtype=np.floataa)
           elif isinstance(__SCREAMING_SNAKE_CASE     ,		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(__SCREAMING_SNAKE_CASE)]

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

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

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

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

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

           __a						       =				{'''input_features''': input_mel, '''is_longer''': is_longer}
           __a						       =				BatchFeature(__SCREAMING_SNAKE_CASE)

           if return_tensors is not None:
               __a						       =				input_features.convert_to_tensors(__SCREAMING_SNAKE_CASE)

           return input_features










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

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
        __a						       =				np.full((len(_UpperCAmelCase				), sequence_length, 2)   , _UpperCAmelCase				)
    else:
        __a						       =				np.full((len(_UpperCAmelCase				), sequence_length)   , _UpperCAmelCase				)

    for i, tensor in enumerate(_UpperCAmelCase				):
        if padding_side == "right":
            if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
                __a						       =				tensor[:sequence_length]
            else:
                __a						       =				tensor[:sequence_length]
        else:
            if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
                __a						       =				tensor[:sequence_length]
            else:
                __a						       =				tensor[:sequence_length]

    return out_tensor.tolist()





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				ord(_UpperCAmelCase				)
    if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
        return True
    __a						       =				unicodedata.category(_UpperCAmelCase				)
    if cat.startswith('''P'''				):
        return True
    return False

@dataclass
class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    PreTrainedTokenizerBase
       UpperCamelCase__  :    Union[bool, str, PaddingStrategy]											=						True
       UpperCamelCase__  :    Optional[int]											=						None
       UpperCamelCase__  :    Optional[int]											=						None
       UpperCamelCase__  :    int											=						-100
       UpperCamelCase__  :    str											=						"pt"



       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]):


           '''simple docstring'''

           import torch

           __a						       =				'''label''' if '''label''' in features[0].keys() else '''labels'''
           __a						       =				[feature[label_name] for feature in features] if label_name in features[0].keys() else None
           __a						       =				self.tokenizer.pad(
               __SCREAMING_SNAKE_CASE     ,		padding=self.padding     ,		max_length=self.max_length     ,		pad_to_multiple_of=self.pad_to_multiple_of     ,		return_tensors='''pt''' if labels is None else None     ,		)

           if labels is None:
               return batch

           __a						       =				torch.tensor(batch['''entity_ids''']).shape[1]
           __a						       =				self.tokenizer.padding_side
           if padding_side == "right":
               __a						       =				[
                   list(__SCREAMING_SNAKE_CASE) + [self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE)) for label in labels
               ]
           else:
               __a						       =				[
                   [self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE)) + list(__SCREAMING_SNAKE_CASE) for label in labels
               ]

           __a						       =				[feature['''ner_tags'''] for feature in features]
           __a						       =				padding_tensor(__SCREAMING_SNAKE_CASE     ,		-1     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				[feature['''original_entity_spans'''] for feature in features]
           __a						       =				padding_tensor(__SCREAMING_SNAKE_CASE     ,		(-1, -1)     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				{k: torch.tensor(__SCREAMING_SNAKE_CASE     ,		dtype=torch.intaa) for k, v in batch.items()}

           return batch





