


from typing import List, Optional, Union

import numpy as np

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


A   :					Optional[Any]	=      logging.get_logger(__name__)







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    ["input_values", "padding_mask"]





       def __init__(     self							:    Any      ,   __lowerCamelCase							:    int = 1      ,   __lowerCamelCase							:    int = 24000      ,   __lowerCamelCase							:    float = 0.0      ,   __lowerCamelCase							:    float = None      ,   __lowerCamelCase							:    float = None      ,   **__lowerCamelCase							:    List[Any]      ,   ):


             '''simple docstring'''



             super().__init__(feature_size=__lowerCamelCase      ,   sampling_rate=__lowerCamelCase      ,   padding_value=__lowerCamelCase      ,   **__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						chunk_length_s
             lowerCamelCase__	:       List[str]					=						overlap





       @property
       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             if self.chunk_length_s is None:
                   return None
             else:
                   return int(self.chunk_length_s * self.sampling_rate )





       @property
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             if self.chunk_length_s is None or self.overlap is None:
                   return None
             else:
                   return max(1      ,   int((1.0 - self.overlap) * self.chunk_length ) )





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


             '''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 audio 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." )

             if padding and truncation:
                   raise ValueError("Both padding and truncation were set. Make sure you only set one." )
             elif padding is None:
                   # by default let's pad the inputs
                   lowerCamelCase__	:       int					=						True

             lowerCamelCase__	:       str					=						bool(
                 isinstance(__lowerCamelCase      ,   (list, tuple) ) and (isinstance(raw_audio[0]      ,   (np.ndarray, tuple, list) )) )

             if is_batched:
                   lowerCamelCase__	:       List[str]					=						[np.asarray(__lowerCamelCase      ,   dtype=np.floataa ).T for audio in raw_audio]
             elif not is_batched and not isinstance(__lowerCamelCase      ,   np.ndarray ):
                   lowerCamelCase__	:       Dict					=						np.asarray(__lowerCamelCase      ,   dtype=np.floataa )
             elif isinstance(__lowerCamelCase      ,   np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
                   lowerCamelCase__	:       Any					=						raw_audio.astype(np.floataa )

             # always return batch
             if not is_batched:
                   lowerCamelCase__	:       Any					=						[np.asarray(__lowerCamelCase ).T]

             # verify inputs are valid
             for idx, example in enumerate(__lowerCamelCase ):
                   if example.ndim > 2:
                         raise ValueError(f"Expected input shape (channels, length) but got shape {example.shape}" )
                   if self.feature_size == 1 and example.ndim != 1:
                         raise ValueError(f"Expected mono audio but example has {example.shape[-1]} channels" )
                   if self.feature_size == 2 and example.shape[-1] != 2:
                         raise ValueError(f"Expected stereo audio but example has {example.shape[-1]} channels" )

             lowerCamelCase__	:       List[Any]					=						None
             lowerCamelCase__	:       Union[str, Any]					=						BatchFeature({"input_values": raw_audio} )
             if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
                   if truncation:
                         lowerCamelCase__	:       Optional[int]					=						min(array.shape[0] for array in raw_audio )
                         lowerCamelCase__	:       Any					=						int(np.floor(max_length / self.chunk_stride ) )
                         lowerCamelCase__	:       Any					=						(nb_step - 1) * self.chunk_stride + self.chunk_length
                   elif padding:
                         lowerCamelCase__	:       Optional[int]					=						max(array.shape[0] for array in raw_audio )
                         lowerCamelCase__	:       Any					=						int(np.ceil(max_length / self.chunk_stride ) )
                         lowerCamelCase__	:       Tuple					=						(nb_step - 1) * self.chunk_stride + self.chunk_length
                         lowerCamelCase__	:       Optional[Any]					=						"max_length"
                   else:
                         lowerCamelCase__	:       Any					=						input_values

        # normal padding on batch
             if padded_inputs is None:
                   lowerCamelCase__	:       List[str]					=						self.pad(
                       __lowerCamelCase      ,   max_length=__lowerCamelCase      ,   truncation=__lowerCamelCase      ,   padding=__lowerCamelCase      ,   return_attention_mask=__lowerCamelCase      ,   )
                   if padding:
                         lowerCamelCase__	:       Dict					=						padded_inputs.pop("attention_mask" )

             lowerCamelCase__	:       Any					=						[]
             for example in padded_inputs.pop("input_values" ):
                   if self.feature_size == 1:
                         lowerCamelCase__	:       str					=						example[..., None]
                   input_values.append(example.T )

             lowerCamelCase__	:       str					=						input_values
             if return_tensors is not None:
                   lowerCamelCase__	:       Dict					=						padded_inputs.convert_to_tensors(__lowerCamelCase )

             return padded_inputs






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A   :					Union[str, Any]	=      logging.get_logger(__name__)

A   :					Dict	=      {
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "ibert"





       def __init__(     self							:    int      ,   __lowerCamelCase							:    List[str]=30522      ,   __lowerCamelCase							:    Optional[int]=768      ,   __lowerCamelCase							:    List[Any]=12      ,   __lowerCamelCase							:    str=12      ,   __lowerCamelCase							:    List[str]=3072      ,   __lowerCamelCase							:    Dict="gelu"      ,   __lowerCamelCase							:    Dict=0.1      ,   __lowerCamelCase							:    Optional[int]=0.1      ,   __lowerCamelCase							:    Any=512      ,   __lowerCamelCase							:    List[str]=2      ,   __lowerCamelCase							:    Union[str, Any]=0.0_2      ,   __lowerCamelCase							:    Any=1E-1_2      ,   __lowerCamelCase							:    int=1      ,   __lowerCamelCase							:    Optional[Any]=0      ,   __lowerCamelCase							:    int=2      ,   __lowerCamelCase							:    int="absolute"      ,   __lowerCamelCase							:    Tuple=False      ,   __lowerCamelCase							:    Dict="none"      ,   **__lowerCamelCase							:    Tuple      ,   ):


             '''simple docstring'''



             super().__init__(pad_token_id=__lowerCamelCase      ,   bos_token_id=__lowerCamelCase      ,   eos_token_id=__lowerCamelCase      ,   **__lowerCamelCase )

             lowerCamelCase__	:       Any					=						vocab_size
             lowerCamelCase__	:       Optional[Any]					=						hidden_size
             lowerCamelCase__	:       Optional[int]					=						num_hidden_layers
             lowerCamelCase__	:       int					=						num_attention_heads
             lowerCamelCase__	:       List[str]					=						hidden_act
             lowerCamelCase__	:       List[str]					=						intermediate_size
             lowerCamelCase__	:       Optional[int]					=						hidden_dropout_prob
             lowerCamelCase__	:       Any					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Tuple					=						max_position_embeddings
             lowerCamelCase__	:       Any					=						type_vocab_size
             lowerCamelCase__	:       Optional[int]					=						initializer_range
             lowerCamelCase__	:       Tuple					=						layer_norm_eps
             lowerCamelCase__	:       int					=						position_embedding_type
             lowerCamelCase__	:       List[str]					=						quant_mode
             lowerCamelCase__	:       int					=						force_dequant







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             if self.task == "multiple-choice":
                   lowerCamelCase__	:       Any					=						{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   lowerCamelCase__	:       Any					=						{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ] )


