"""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__  :  List[str]											=     logging.get_logger(__name__)
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			["input_values", "attention_mask"]







					def __init__(    self       :     Optional[int]   ,lowerCamelCase__       :     Union[str, Any]=1   ,lowerCamelCase__       :     Optional[Any]=16_000   ,lowerCamelCase__       :     Optional[int]=0.0   ,lowerCamelCase__       :     Any=False   ,lowerCamelCase__       :     List[Any]=True   ,**lowerCamelCase__       :     str   ,):
									super().__init__(feature_size=lowerCamelCase__   ,sampling_rate=lowerCamelCase__   ,padding_value=lowerCamelCase__   ,**lowerCamelCase__      )
									UpperCAmelCase__					=							return_attention_mask
									UpperCAmelCase__					=							do_normalize







					@staticmethod
					def 		__lowerCAmelCase						(    lowerCamelCase__       :     List[np.ndarray]   ,lowerCamelCase__       :     List[np.ndarray]   ,lowerCamelCase__       :     float = 0.0      ):
									if attention_mask is not None:
													UpperCAmelCase__					=							np.array(lowerCamelCase__   ,np.intaa      )
													UpperCAmelCase__					=							[]

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

																	normed_input_values.append(lowerCamelCase__      )
									else:
													UpperCAmelCase__					=							[(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__       :     Union[bool, str, PaddingStrategy] = False   ,lowerCamelCase__       :     Optional[int] = None   ,lowerCamelCase__       :     bool = False   ,lowerCamelCase__       :     Optional[int] = None   ,lowerCamelCase__       :     Optional[bool] = None   ,lowerCamelCase__       :     Optional[Union[str, TensorType]] = None   ,lowerCamelCase__       :     Optional[int] = None   ,**lowerCamelCase__       :     int   ,):

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

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

									# convert into correct format for padding
									UpperCAmelCase__					=							BatchFeature({'input_values': raw_speech}      )

									UpperCAmelCase__					=							self.pad(
									    lowerCamelCase__   ,padding=lowerCamelCase__   ,max_length=lowerCamelCase__   ,truncation=lowerCamelCase__   ,pad_to_multiple_of=lowerCamelCase__   ,return_attention_mask=lowerCamelCase__   ,)

									# convert input values to correct format
									UpperCAmelCase__					=							padded_inputs['input_values']
									if not isinstance(input_values[0]   ,np.ndarray      ):
													UpperCAmelCase__					=							[np.asarray(lowerCamelCase__   ,dtype=np.floataa      ) for array in input_values]
									elif (
									    not isinstance(lowerCamelCase__   ,np.ndarray      )
									    and isinstance(input_values[0]   ,np.ndarray      )
									    and input_values[0].dtype is np.dtype(np.floataa      )
									):
													UpperCAmelCase__					=							[array.astype(np.floataa      ) for array in input_values]
									elif isinstance(lowerCamelCase__   ,np.ndarray      ) and input_values.dtype is np.dtype(np.floataa      ):
													UpperCAmelCase__					=							input_values.astype(np.floataa      )

									# convert attention_mask to correct format
									UpperCAmelCase__					=							padded_inputs.get('attention_mask'      )
									if attention_mask is not None:
													UpperCAmelCase__					=							[np.asarray(lowerCamelCase__   ,dtype=np.intaa      ) for array in attention_mask]

									# zero-mean and unit-variance normalization
									if self.do_normalize:
													UpperCAmelCase__					=							(
													    attention_mask
													    if self._get_padding_strategies(lowerCamelCase__   ,max_length=lowerCamelCase__      ) is not PaddingStrategy.DO_NOT_PAD
													    else None
													)
													UpperCAmelCase__					=							self.zero_mean_unit_var_norm(
													    padded_inputs['input_values']   ,attention_mask=lowerCamelCase__   ,padding_value=self.padding_value      )

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

									return padded_inputs




"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__  :  str											=     logging.get_logger(__name__)

lowerCAmelCase__  :  Union[str, Any]											=     {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"plbart"
					snake_case__       =			["past_key_values"]
					snake_case__       =			{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







					def __init__(    self       :     str   ,lowerCamelCase__       :     List[Any]=50_005   ,lowerCamelCase__       :     Optional[Any]=1_024   ,lowerCamelCase__       :     List[str]=6   ,lowerCamelCase__       :     Tuple=3_072   ,lowerCamelCase__       :     Optional[int]=12   ,lowerCamelCase__       :     Optional[int]=6   ,lowerCamelCase__       :     Optional[int]=3_072   ,lowerCamelCase__       :     Any=12   ,lowerCamelCase__       :     str=0.0   ,lowerCamelCase__       :     str=0.0   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     str=True   ,lowerCamelCase__       :     Optional[int]="gelu"   ,lowerCamelCase__       :     Optional[Any]=768   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Union[str, Any]=0.0   ,lowerCamelCase__       :     List[str]=0.0_2   ,lowerCamelCase__       :     Optional[int]=0.0   ,lowerCamelCase__       :     Dict=True   ,lowerCamelCase__       :     List[Any]=1   ,lowerCamelCase__       :     List[str]=0   ,lowerCamelCase__       :     Any=2   ,lowerCamelCase__       :     Any=2   ,**lowerCamelCase__       :     Dict   ,):
									UpperCAmelCase__					=							vocab_size
									UpperCAmelCase__					=							max_position_embeddings
									UpperCAmelCase__					=							d_model
									UpperCAmelCase__					=							encoder_ffn_dim
									UpperCAmelCase__					=							encoder_layers
									UpperCAmelCase__					=							encoder_attention_heads
									UpperCAmelCase__					=							decoder_ffn_dim
									UpperCAmelCase__					=							decoder_layers
									UpperCAmelCase__					=							decoder_attention_heads
									UpperCAmelCase__					=							dropout
									UpperCAmelCase__					=							attention_dropout
									UpperCAmelCase__					=							activation_dropout
									UpperCAmelCase__					=							activation_function
									UpperCAmelCase__					=							init_std
									UpperCAmelCase__					=							encoder_layerdrop
									UpperCAmelCase__					=							decoder_layerdrop
									UpperCAmelCase__					=							classifier_dropout
									UpperCAmelCase__					=							use_cache
									UpperCAmelCase__					=							encoder_layers
									UpperCAmelCase__					=							scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=lowerCamelCase__   ,bos_token_id=lowerCamelCase__   ,eos_token_id=lowerCamelCase__   ,is_encoder_decoder=lowerCamelCase__   ,forced_eos_token_id=lowerCamelCase__   ,**lowerCamelCase__   ,)






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					@property
					def 		__lowerCAmelCase						(    self       :     int      ):
									return OrderedDict(
									    [
									        ('input_ids', {0: 'batch', 1: 'sequence'}),
									        ('attention_mask', {0: 'batch', 1: 'sequence'}),
									    ]      )







					@property
					def 		__lowerCAmelCase						(    self       :     Any      ):
									if self.use_past:
													return OrderedDict(
													    [
													        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
													        ('past_keys', {0: 'batch', 2: 'sequence'}),
													        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
													    ]      )
									else:
													return OrderedDict(
													    [
													        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
													        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
													    ]      )



