from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__lowerCamelCase					:	Dict       =  logging.get_logger(__name__)






class 		a__						(					lowercase__					):
					A	       =							['input_features', 'attention_mask']

					def __init__(    self      :       int,_A      :       int=80,_A      :       str=1_6000,_A      :       Optional[Any]=80,_A      :       List[Any]=0.0,_A      :       Union[str, Any]=True,_A      :       Tuple=True,_A      :       Union[str, Any]=True,**_A      :       List[Any],):







							"""simple docstring"""






							super().__init__(feature_size=__lowercase,sampling_rate=__lowercase,padding_value=__lowercase,**__lowercase    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    num_mel_bins
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    do_ceptral_normalize
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    normalize_means
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    normalize_vars
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    True

					def 				__UpperCamelCase   (    self      :       int,_A      :       np.ndarray,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    torch.from_numpy(__lowercase    ).unsqueeze(0    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    ta_kaldi.fbank(__lowercase,num_mel_bins=self.num_mel_bins,sample_frequency=self.sampling_rate    )
							return features.numpy()

					@staticmethod
					def 				__UpperCamelCase   (    _A      :       np.ndarray,_A      :       int,_A      :       Optional[bool] = True,_A      :       Optional[bool] = True,_A      :       float = 0.0,):







							"""simple docstring"""






							if normalize_means:
									SCREAMING_SNAKE_CASE_  :		str				      =    x[:input_length].mean(axis=0    )
									SCREAMING_SNAKE_CASE_  :		int				      =    np.subtract(__lowercase,__lowercase    )
							if normalize_vars:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    x[:input_length].std(axis=0    )
									SCREAMING_SNAKE_CASE_  :		int				      =    np.divide(__lowercase,__lowercase    )

							if input_length < x.shape[0]:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    padding_value

							# make sure array is in float32
							SCREAMING_SNAKE_CASE_  :		int				      =    x.astype(np.floataa    )

							return x

					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       List[np.ndarray],_A      :       Optional[np.ndarray] = None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    attention_mask.sum(-1    ) if attention_mask is not None else [x.shape[0] for x in input_features]
							return [
							    self.utterance_cmvn(__lowercase,__lowercase,self.normalize_means,self.normalize_vars,self.padding_value    )
							    for x, n in zip(__lowercase,__lowercase    )
							]






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







							"""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} was trained using a sampling rate of'
											    F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
											    F' {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_  :		List[str]				      =    isinstance(__lowercase,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_  :		Dict				      =    is_batched_numpy or (
							    isinstance(__lowercase,(list, tuple)    ) and (isinstance(raw_speech[0],(np.ndarray, tuple, list)    ))
							)

							if is_batched:
									SCREAMING_SNAKE_CASE_  :		Any				      =    [np.asarray(__lowercase,dtype=np.floataa    ) for speech in raw_speech]
							elif not is_batched and not isinstance(__lowercase,np.ndarray    ):
									SCREAMING_SNAKE_CASE_  :		int				      =    np.asarray(__lowercase,dtype=np.floataa    )
							elif isinstance(__lowercase,np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    raw_speech.astype(np.floataa    )

							# always return batch
							if not is_batched:
									SCREAMING_SNAKE_CASE_  :		int				      =    [raw_speech]

							# extract fbank features
							SCREAMING_SNAKE_CASE_  :		Dict				      =    [self._extract_fbank_features(__lowercase    ) for waveform in raw_speech]

							# convert into correct format for padding
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    BatchFeature({"input_features": features}    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.pad(
							    __lowercase,padding=__lowercase,max_length=__lowercase,truncation=__lowercase,pad_to_multiple_of=__lowercase,return_attention_mask=__lowercase,**__lowercase,)

							# make sure list is in array format
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    padded_inputs.get("input_features"    )
							if isinstance(input_features[0],__lowercase    ):
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    [np.asarray(__lowercase,dtype=np.floataa    ) for feature in input_features]

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    padded_inputs.get("attention_mask"    )
							if attention_mask is not None:
									SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [np.asarray(__lowercase,dtype=np.intaa    ) for array in attention_mask]

							# Utterance-level cepstral mean and variance normalization
							if self.do_ceptral_normalize:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    (
									    np.array(__lowercase,dtype=np.intaa    )
									    if self._get_padding_strategies(__lowercase,max_length=__lowercase    ) is not PaddingStrategy.DO_NOT_PAD
									    else None
									)
									SCREAMING_SNAKE_CASE_  :		str				      =    self.normalize(
									    padded_inputs["input_features"],attention_mask=__lowercase    )

							if return_tensors is not None:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    padded_inputs.convert_to_tensors(__lowercase    )

							return padded_inputs








from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		:       Optional[int]			     = logging.get_logger(__name__)

a		:       List[Any]			     = {
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : List[Any]							       =	'xglm'
      a  : str							       =	['past_key_values']

      a  : Any							       =	{
          'num_attention_heads': 'attention_heads',
          'hidden_size': 'd_model',
          'num_hidden_layers': 'num_layers',
      }
      def __init__(      self					:			Optional[int]		,      __lowercase					:			int=256008		,      __lowercase					:			Tuple=2048		,      __lowercase					:			List[Any]=1024		,      __lowercase					:			str=4096		,      __lowercase					:			Optional[Any]=24		,      __lowercase					:			Optional[int]=16		,      __lowercase					:			List[Any]="gelu"		,      __lowercase					:			str=0.1		,      __lowercase					:			Dict=0.1		,      __lowercase					:			Tuple=0.0		,      __lowercase					:			Optional[int]=0.0		,      __lowercase					:			Dict=0.02		,      __lowercase					:			Optional[int]=True		,      __lowercase					:			Any=True		,      __lowercase					:			Dict=2		,      __lowercase					:			Optional[Any]=1		,      __lowercase					:			List[Any]=0		,      __lowercase					:			Optional[Any]=2		,      **__lowercase					:			List[str]		,      )							->      Optional[int]:
        __UpperCAmelCase							:	List[str]           =  vocab_size
        __UpperCAmelCase							:	Optional[Any]           =  max_position_embeddings
        __UpperCAmelCase							:	Optional[Any]           =  d_model
        __UpperCAmelCase							:	str           =  ffn_dim
        __UpperCAmelCase							:	List[str]           =  num_layers
        __UpperCAmelCase							:	Dict           =  attention_heads
        __UpperCAmelCase							:	str           =  activation_function
        __UpperCAmelCase							:	Optional[Any]           =  dropout
        __UpperCAmelCase							:	Any           =  attention_dropout
        __UpperCAmelCase							:	int           =  activation_dropout
        __UpperCAmelCase							:	Tuple           =  layerdrop
        __UpperCAmelCase							:	Tuple           =  init_std
        __UpperCAmelCase							:	List[str]           =  scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCAmelCase							:	Union[str, Any]           =  use_cache

        super().__init__(
            pad_token_id=__lowercase		,      bos_token_id=__lowercase		,      eos_token_id=__lowercase		,      decoder_start_token_id=__lowercase		,      **__lowercase		,      )


