






'''simple docstring'''




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


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







class  A_						(A_				):
							"""simple docstring"""




							a__ 		=						["input_values", "padding_mask"]

							def __init__(		self      :Dict							,				lowerCAmelCase__      :int = 1							,				lowerCAmelCase__      :int = 24_000							,				lowerCAmelCase__      :float = 0.0							,				lowerCAmelCase__      :float = None							,				lowerCAmelCase__      :float = None							,				**lowerCAmelCase__      :Dict							,				)					->	int:


										'''simple docstring'''




										super().__init__(feature_size=snake_case__							,				sampling_rate=snake_case__							,				padding_value=snake_case__							,				**snake_case__					)
										snake_case_					:       List[Any] =							chunk_length_s
										snake_case_					:       Optional[Any] =							overlap

							@property
							def 				_A						(		self      :Any					)					->	Optional[int]:


										'''simple docstring'''




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

							@property
							def 				_A						(		self      :Optional[int]					)					->	Optional[int]:


										'''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							,				)					->	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 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
													snake_case_					:       int =							True

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

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

										# always return batch
										if not is_batched:
													snake_case_					:       Optional[Any] =							[np.asarray(snake_case__					).T]

										# verify inputs are valid
										for idx, example in enumerate(snake_case__					):
													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'''					)

										snake_case_					:       Optional[int] =							None
										snake_case_					:       str =							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:
																snake_case_					:       List[str] =							min(array.shape[0] for array in raw_audio					)
																snake_case_					:       Any =							int(np.floor(max_length / self.chunk_stride					)					)
																snake_case_					:       List[str] =							(nb_step - 1) * self.chunk_stride + self.chunk_length
													elif padding:
																snake_case_					:       Tuple =							max(array.shape[0] for array in raw_audio					)
																snake_case_					:       Optional[Any] =							int(np.ceil(max_length / self.chunk_stride					)					)
																snake_case_					:       Tuple =							(nb_step - 1) * self.chunk_stride + self.chunk_length
																snake_case_					:       List[Any] =							"max_length"
													else:
																snake_case_					:       Any =							input_values

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

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

										snake_case_					:       str =							input_values
										if return_tensors is not None:
													snake_case_					:       List[Any] =							padded_inputs.convert_to_tensors(snake_case__					)

										return padded_inputs






from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase				=			logging.get_logger(__name__)

__lowerCamelCase				=			{
    """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     UpperCAmelCase						(		A_					):
   A__   :						Optional[int]			=	"ibert"



   def __init__(self						:   List[Any]       ,		snake_case__						:   int=3_05_22       ,		snake_case__						:   int=7_68       ,		snake_case__						:   Any=12       ,		snake_case__						:   str=12       ,		snake_case__						:   Optional[int]=30_72       ,		snake_case__						:   Union[str, Any]="gelu"       ,		snake_case__						:   Any=0.1       ,		snake_case__						:   List[Any]=0.1       ,		snake_case__						:   str=5_12       ,		snake_case__						:   Optional[int]=2       ,		snake_case__						:   Any=0.02       ,		snake_case__						:   List[Any]=1e-12       ,		snake_case__						:   Optional[Any]=1       ,		snake_case__						:   List[Any]=0       ,		snake_case__						:   str=2       ,		snake_case__						:   Tuple="absolute"       ,		snake_case__						:   List[str]=False       ,		snake_case__						:   Tuple="none"       ,		**snake_case__						:   Tuple       ,		)						->      Union[str, Any]:


          '''simple docstring'''


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

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






class     UpperCAmelCase						(		A_					):



   @property
   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Mapping[str, Mapping[int, str]]:


          '''simple docstring'''


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





