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 TensorType, logging


UpperCamelCase__       							=      logging.get_logger(__name__)





class   A							(       UpperCAmelCase_  ):
   __UpperCAmelCase				:				str          =    ['input_values', 'attention_mask']


   def __init__(self  :		List[str] ,						__UpperCAmelCase  :		Optional[Any]=1 ,						__UpperCAmelCase  :		Union[str, Any]=1_6_0_0_0 ,						__UpperCAmelCase  :		Optional[Any]=1_2_8 ,						__UpperCAmelCase  :		Optional[int]=1_0_2_4 ,						__UpperCAmelCase  :		Optional[int]=0.0 ,						__UpperCAmelCase  :		Tuple=True ,						__UpperCAmelCase  :		Optional[int]=-4.2677393 ,						__UpperCAmelCase  :		Union[str, Any]=4.5689974 ,						__UpperCAmelCase  :		List[Any]=False ,						**__UpperCAmelCase  :		Union[str, Any] ,						)		->       Union[str, Any]:
         """simple docstring"""



         super().__init__(feature_size=__UpperCAmelCase ,						sampling_rate=__UpperCAmelCase ,						padding_value=__UpperCAmelCase ,						**__UpperCAmelCase	)
         UpperCAmelCase__			=						num_mel_bins
         UpperCAmelCase__			=						max_length
         UpperCAmelCase__			=						do_normalize
         UpperCAmelCase__			=						mean
         UpperCAmelCase__			=						std
         UpperCAmelCase__			=						return_attention_mask


   def     lowercase_   (self  :		int ,						__UpperCAmelCase  :		np.ndarray ,						__UpperCAmelCase  :		int ,						)		->       np.ndarray:
         """simple docstring"""



         UpperCAmelCase__			=						torch.from_numpy(__UpperCAmelCase	).unsqueeze(0	)
         UpperCAmelCase__			=						ta_kaldi.fbank(
             __UpperCAmelCase ,						htk_compat=__UpperCAmelCase ,						sample_frequency=self.sampling_rate ,						use_energy=__UpperCAmelCase ,						window_type="hanning" ,						num_mel_bins=self.num_mel_bins ,						dither=0.0 ,						frame_shift=1_0 ,						)

         UpperCAmelCase__			=						fbank.shape[0]
         UpperCAmelCase__			=						max_length - n_frames

         # pad or truncate, depending on difference
         if difference > 0:
               UpperCAmelCase__			=						torch.nn.ZeroPadad((0, 0, 0, difference)	)
               UpperCAmelCase__			=						pad_module(__UpperCAmelCase	)
         elif difference < 0:
               UpperCAmelCase__			=						fbank[0:max_length, :]

         UpperCAmelCase__			=						fbank.numpy()

         return fbank


   def     lowercase_   (self  :		Optional[Any] ,						__UpperCAmelCase  :		np.ndarray	)		->       np.ndarray:
         """simple docstring"""



         return (input_values - (self.mean)) / (self.std * 2)





   def __call__(self  :		List[str] ,						__UpperCAmelCase  :		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,						__UpperCAmelCase  :		Optional[int] = None ,						__UpperCAmelCase  :		Optional[Union[str, TensorType]] = None ,						**__UpperCAmelCase  :		List[str] ,						)		->       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} 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."	)

         UpperCAmelCase__			=						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}"""	)
         UpperCAmelCase__			=						is_batched_numpy or (
             isinstance(__UpperCAmelCase ,						(list, tuple)	) and (isinstance(raw_speech[0] ,						(np.ndarray, tuple, list)	))
         )

         if is_batched:
               UpperCAmelCase__			=						[np.asarray(__UpperCAmelCase ,						dtype=np.floataa	) for speech in raw_speech]
         elif not is_batched and not isinstance(__UpperCAmelCase ,						np.ndarray	):
               UpperCAmelCase__			=						np.asarray(__UpperCAmelCase ,						dtype=np.floataa	)
         elif isinstance(__UpperCAmelCase ,						np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
               UpperCAmelCase__			=						raw_speech.astype(np.floataa	)

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

         # extract fbank features and pad/truncate to max_length
         UpperCAmelCase__			=						[self._extract_fbank_features(__UpperCAmelCase ,						max_length=self.max_length	) for waveform in raw_speech]

         # convert into BatchFeature
         UpperCAmelCase__			=						BatchFeature({"input_values": features}	)

         # make sure list is in array format
         UpperCAmelCase__			=						padded_inputs.get("input_values"	)
         if isinstance(input_values[0] ,						__UpperCAmelCase	):
               UpperCAmelCase__			=						[np.asarray(__UpperCAmelCase ,						dtype=np.floataa	) for feature in input_values]

         # normalization
         if self.do_normalize:
               UpperCAmelCase__			=						[self.normalize(__UpperCAmelCase	) for feature in input_values]

         if return_tensors is not None:
               UpperCAmelCase__			=						padded_inputs.convert_to_tensors(__UpperCAmelCase	)

         return padded_inputs




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


UpperCamelCase__       							=      logging.get_logger(__name__)

UpperCamelCase__       							=      {
    'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json'
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}





class   A							(       UpperCAmelCase_  ):
   __UpperCAmelCase				:				int          =    'cpmant'


   def __init__(self  :		Optional[Any] ,						__UpperCAmelCase  :		int = 3_0_7_2_0 ,						__UpperCAmelCase  :		int = 4_0_9_6 ,						__UpperCAmelCase  :		int = 3_2 ,						__UpperCAmelCase  :		int = 1_2_8 ,						__UpperCAmelCase  :		int = 1_0_2_4_0 ,						__UpperCAmelCase  :		int = 4_8 ,						__UpperCAmelCase  :		int = 0.0 ,						__UpperCAmelCase  :		int = 5_1_2 ,						__UpperCAmelCase  :		int = 2_0_4_8 ,						__UpperCAmelCase  :		int = 1E-6 ,						__UpperCAmelCase  :		float = 1.0 ,						__UpperCAmelCase  :		int = 3_2 ,						__UpperCAmelCase  :		int = 3_2 ,						__UpperCAmelCase  :		int = 3_2 ,						__UpperCAmelCase  :		bool = True ,						**__UpperCAmelCase  :		Dict ,						)		->       int:
         """simple docstring"""



         super().__init__(**__UpperCAmelCase	)
         UpperCAmelCase__			=						prompt_types
         UpperCAmelCase__			=						prompt_length
         UpperCAmelCase__			=						segment_types
         UpperCAmelCase__			=						hidden_size
         UpperCAmelCase__			=						num_attention_heads
         UpperCAmelCase__			=						dim_head
         UpperCAmelCase__			=						dim_ff
         UpperCAmelCase__			=						num_hidden_layers
         UpperCAmelCase__			=						position_bias_num_buckets
         UpperCAmelCase__			=						position_bias_max_distance
         UpperCAmelCase__			=						dropout_p
         UpperCAmelCase__			=						eps
         UpperCAmelCase__			=						use_cache
         UpperCAmelCase__			=						vocab_size
         UpperCAmelCase__			=						init_std



