





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


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	List[str]			=			["input_features"]






     def __init__(							self						:    str						,lowerCamelCase__						:    Union[str, Any]=80						,lowerCamelCase__						:    str=16000						,lowerCamelCase__						:    List[Any]=160						,lowerCamelCase__						:    Optional[int]=30						,lowerCamelCase__						:    Optional[int]=400						,lowerCamelCase__						:    Any=0.0						,lowerCamelCase__						:    Tuple=False						,**lowerCamelCase__						:    str						,)     ->		int:




           '''simple docstring'''


           super().__init__(
               feature_size=lowerCamelCase__						,sampling_rate=lowerCamelCase__						,padding_value=lowerCamelCase__						,return_attention_mask=lowerCamelCase__						,**lowerCamelCase__						,)
           SCREAMING_SNAKE_CASE		=						n_fft
           SCREAMING_SNAKE_CASE		=						hop_length
           SCREAMING_SNAKE_CASE		=						chunk_length
           SCREAMING_SNAKE_CASE		=						chunk_length * sampling_rate
           SCREAMING_SNAKE_CASE		=						self.n_samples // hop_length
           SCREAMING_SNAKE_CASE		=						sampling_rate
           SCREAMING_SNAKE_CASE		=						mel_filter_bank(
               num_frequency_bins=1 + n_fft // 2						,num_mel_filters=lowerCamelCase__						,min_frequency=0.0						,max_frequency=8000.0						,sampling_rate=lowerCamelCase__						,norm="""slaney"""						,mel_scale="""slaney"""						,)






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]						,lowerCamelCase__						:    np.array       )     ->		np.ndarray:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						spectrogram(
               lowerCamelCase__						,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"""						,)
           SCREAMING_SNAKE_CASE		=						log_spec[:, :-1]
           SCREAMING_SNAKE_CASE		=						np.maximum(lowerCamelCase__						,log_spec.max() - 8.0       )
           SCREAMING_SNAKE_CASE		=						(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 				SCREAMING_SNAKE_CASE__  (							lowerCamelCase__						:    List[np.ndarray]						,lowerCamelCase__						:    List[np.ndarray]						,lowerCamelCase__						:    float = 0.0       )     ->		List[np.ndarray]:




           '''simple docstring'''


           if attention_mask is not None:
                 SCREAMING_SNAKE_CASE		=						np.array(lowerCamelCase__						,np.intaa       )
                 SCREAMING_SNAKE_CASE		=						[]

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

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

           return normed_input_values






     def __call__(							self						:    Dict						,lowerCamelCase__						:    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]						,lowerCamelCase__						:    bool = True						,lowerCamelCase__						:    Optional[int] = None						,lowerCamelCase__						:    Optional[Union[str, TensorType]] = None						,lowerCamelCase__						:    Optional[bool] = None						,lowerCamelCase__						:    Optional[str] = "max_length"						,lowerCamelCase__						:    Optional[int] = None						,lowerCamelCase__						:    Optional[int] = None						,lowerCamelCase__						:    Optional[bool] = None						,**lowerCamelCase__						:    int						,)     ->		BatchFeature:




           '''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."""       )

           SCREAMING_SNAKE_CASE		=						isinstance(lowerCamelCase__						,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(lowerCamelCase__						,(list, tuple)       ) and (isinstance(raw_speech[0]						,(np.ndarray, tuple, list)       ))
           )

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

           SCREAMING_SNAKE_CASE		=						BatchFeature({"""input_features""": raw_speech}       )

           # convert into correct format for padding

           SCREAMING_SNAKE_CASE		=						self.pad(
               lowerCamelCase__						,padding=lowerCamelCase__						,max_length=max_length if max_length else self.n_samples						,truncation=lowerCamelCase__						,pad_to_multiple_of=lowerCamelCase__						,return_attention_mask=return_attention_mask or do_normalize						,)

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

           # make sure list is in array format
           SCREAMING_SNAKE_CASE		=						padded_inputs.get("""input_features"""       ).transpose(2						,0						,1       )

           SCREAMING_SNAKE_CASE		=						[self._np_extract_fbank_features(lowerCamelCase__       ) for waveform in input_features[0]]

           if isinstance(input_features[0]						,lowerCamelCase__       ):
                 SCREAMING_SNAKE_CASE		=						[np.asarray(lowerCamelCase__						,dtype=np.floataa       ) for feature in input_features]
           else:
                 SCREAMING_SNAKE_CASE		=						input_features

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

           if return_tensors is not None:
                 SCREAMING_SNAKE_CASE		=						padded_inputs.convert_to_tensors(lowerCamelCase__       )

           return padded_inputs






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		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"]
           return output









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_						   =       {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       ["""LlamaTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       ["""LlamaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       [
          """LlamaForCausalLM""",
          """LlamaModel""",
          """LlamaPreTrainedModel""",
          """LlamaForSequenceClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_llama import LlamaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_llama_fast import LlamaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
      import sys

      SCREAMING_SNAKE_CASE_						   =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


