





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

import numpy as np

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



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :int						   =       ['input_features']





      def __init__(			self	,							A_=80	,							A_=1_6000	,							A_=160	,							A_=30	,							A_=400	,							A_=0.0	,							A_=False	,							**A_	,							):
       '''simple docstring'''




       super().__init__(
           feature_size=A_	,							sampling_rate=A_	,							padding_value=A_	,							return_attention_mask=A_	,							**A_	,							)
       UpperCamelCase     : Tuple	     =       n_fft
       UpperCamelCase     : Tuple	     =       hop_length
       UpperCamelCase     : str	     =       chunk_length
       UpperCamelCase     : List[str]	     =       chunk_length * sampling_rate
       UpperCamelCase     : int	     =       self.n_samples // hop_length
       UpperCamelCase     : Tuple	     =       sampling_rate
       UpperCamelCase     : List[Any]	     =       mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2	,							num_mel_filters=A_	,							min_frequency=0.0	,							max_frequency=80_00.0	,							sampling_rate=A_	,							norm="slaney"	,							mel_scale="slaney"	,							)





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : str	     =       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	,							log_mel="log10"	,							)
       UpperCamelCase     : Union[str, Any]	     =       log_spec[:, :-1]
       UpperCamelCase     : Tuple	     =       np.maximum(A_	,							log_spec.max() - 8.0       )
       UpperCamelCase     : int	     =       (log_spec + 4.0) / 4.0
       return log_spec





      @staticmethod
      # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
      def 	__UpperCamelCase(			A_	,							A_	,							A_ = 0.0       ):
       '''simple docstring'''




       if attention_mask is not None:
        UpperCamelCase     : Optional[Any]	     =       np.array(A_	,							np.intaa       )
        UpperCamelCase     : str	     =       []

        for vector, length in zip(A_	,							attention_mask.sum(-1       )       ):
         UpperCamelCase     : Tuple	     =       (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7       )
         if length < normed_slice.shape[0]:
          UpperCamelCase     : List[str]	     =       padding_value

         normed_input_values.append(A_       )
       else:
        UpperCamelCase     : str	     =       [(x - x.mean()) / np.sqrt(x.var() + 1e-7       ) for x in input_values]

       return normed_input_values





      def __call__(			self	,							A_	,							A_ = True	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = "max_length"	,							A_ = None	,							A_ = None	,							A_ = None	,							**A_	,							):
       '''simple docstring'''




       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."       )

       UpperCamelCase     : List[str]	     =       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}"""       )
       UpperCamelCase     : Optional[Any]	     =       is_batched_numpy or (
           isinstance(A_	,							(list, tuple)       ) and (isinstance(raw_speech[0]	,							(np.ndarray, tuple, list)       ))
       )

       if is_batched:
        UpperCamelCase     : Tuple	     =       [np.asarray([speech]	,							dtype=np.floataa       ).T for speech in raw_speech]
       elif not is_batched and not isinstance(A_	,							np.ndarray       ):
        UpperCamelCase     : Union[str, Any]	     =       np.asarray(A_	,							dtype=np.floataa       )
       elif isinstance(A_	,							np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
        UpperCamelCase     : str	     =       raw_speech.astype(np.floataa       )

       # always return batch
       if not is_batched:
        UpperCamelCase     : List[Any]	     =       [np.asarray([raw_speech]       ).T]

       UpperCamelCase     : Optional[Any]	     =       BatchFeature({"input_features": raw_speech}       )

       # convert into correct format for padding

       UpperCamelCase     : Tuple	     =       self.pad(
           A_	,							padding=A_	,							max_length=max_length if max_length else self.n_samples	,							truncation=A_	,							pad_to_multiple_of=A_	,							return_attention_mask=return_attention_mask or do_normalize	,							)

       # zero-mean and unit-variance normalization
       if do_normalize:
        UpperCamelCase     : List[Any]	     =       self.zero_mean_unit_var_norm(
            padded_inputs["input_features"]	,							attention_mask=padded_inputs["attention_mask"]	,							padding_value=self.padding_value	,							)
        UpperCamelCase     : Optional[int]	     =       np.stack(padded_inputs["input_features"]	,							axis=0       )

       # make sure list is in array format
       UpperCamelCase     : Optional[int]	     =       padded_inputs.get("input_features"       ).transpose(2	,							0	,							1       )

       UpperCamelCase     : Optional[Any]	     =       [self._np_extract_fbank_features(A_       ) for waveform in input_features[0]]

       if isinstance(input_features[0]	,							A_       ):
        UpperCamelCase     : List[Any]	     =       [np.asarray(A_	,							dtype=np.floataa       ) for feature in input_features]
       else:
        UpperCamelCase     : Optional[int]	     =       input_features

       if return_attention_mask:
        # rescale from sample (48000) to feature (3000)
        UpperCamelCase     : Union[str, Any]	     =       padded_inputs["attention_mask"][:, :: self.hop_length]

       if return_tensors is not None:
        UpperCamelCase     : Optional[int]	     =       padded_inputs.convert_to_tensors(A_       )

       return padded_inputs





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       copy.deepcopy(self.__dict__       )
       UpperCamelCase     : List[Any]	     =       self.__class__.__name__
       if "mel_filters" in output:
        del output["mel_filters"]
       return output







import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


__lowerCamelCase	:					List[str]			  =					logging.get_logger(__name__)


__lowerCamelCase	:					Any			  =					r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

"""



class  A__       (	__snake_case      ):





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       raise NotImplementedError("StoppingCriteria needs to be subclassed"       )



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_ = None       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       max_length
       UpperCamelCase     : Dict	     =       max_position_embeddings





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       input_ids.shape[-1]
       UpperCamelCase     : str	     =       cur_len >= self.max_length
       if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
        logger.warning_once(
            "This is a friendly reminder - the current text generation call will exceed the model's predefined "
            F"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
            "exceptions, performance degradation, or nothing at all."       )
       return is_done



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_       ):
       '''simple docstring'''




       warnings.warn(
           "The class `MaxNewTokensCriteria` is deprecated. "
           F"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
           "with `max_length = start_length + max_new_tokens` instead."	,							A_	,							)
       UpperCamelCase     : Union[str, Any]	     =       start_length
       UpperCamelCase     : List[str]	     =       max_new_tokens
       UpperCamelCase     : Tuple	     =       start_length + max_new_tokens





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       return input_ids.shape[-1] >= self.max_length



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_ = None       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       max_time
       UpperCamelCase     : Dict	     =       time.time() if initial_timestamp is None else initial_timestamp





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       return time.time() - self.initial_timestamp > self.max_time



class  A__       (	__snake_case      ):





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       return any(criteria(A_	,							A_       ) for criteria in self       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       for stopping_criterium in self:
        if isinstance(A_	,							A_       ):
         return stopping_criterium.max_length
        elif isinstance(A_	,							A_       ):
         return stopping_criterium.max_length
       return None



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			StoppingCriteriaList:
 UpperCamelCase     : Tuple	     =       stopping_criteria.max_length
 UpperCamelCase     : Union[str, Any]	     =       deepcopy(_lowerCAmelCase					)
 if stopping_max_length is not None and stopping_max_length != max_length:
  warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter"							,       _lowerCAmelCase					)
 elif stopping_max_length is None:
  new_stopping_criteria.append(MaxLengthCriteria(max_length=_lowerCAmelCase					)					)
 return new_stopping_criteria
