

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


UpperCamelCase__		   =    logging.get_logger(__name__)

class 						__snake_case						(							snake_case__     ):
							"""simple docstring"""







							__SCREAMING_SNAKE_CASE													=       ["input_values", "attention_mask"]


							def __init__(		self			,   _UpperCamelCase=1			,   _UpperCamelCase=1_60_00			,   _UpperCamelCase=0.0			,   _UpperCamelCase=False			,   _UpperCamelCase=True			,   **_UpperCamelCase			,   )			->       List[Any]:






														"""simple docstring"""


														super().__init__(feature_size=_UpperCamelCase			,   sampling_rate=_UpperCamelCase			,   padding_value=_UpperCamelCase			,   **_UpperCamelCase      )
														__snake_case					  =       return_attention_mask
														__snake_case					  =       do_normalize


							@staticmethod
							def  a		(		_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase = 0.0      )			->       List[np.ndarray]:






														"""simple docstring"""


														if attention_mask is not None:
																					__snake_case					  =       np.array(_UpperCamelCase			,   np.intaa      )
																					__snake_case					  =       []

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

																												normed_input_values.append(_UpperCamelCase      )
														else:
																					__snake_case					  =       [(x - x.mean()) / np.sqrt(x.var() + 1E-7      ) for x in input_values]

														return normed_input_values






							def __call__(		self			,   _UpperCamelCase			,   _UpperCamelCase = False			,   _UpperCamelCase = None			,   _UpperCamelCase = False			,   _UpperCamelCase = None			,   _UpperCamelCase = None			,   _UpperCamelCase = None			,   _UpperCamelCase = None			,   **_UpperCamelCase			,   )			->       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."""      )

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

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

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

														__snake_case					  =       self.pad(
														    _UpperCamelCase			,   padding=_UpperCamelCase			,   max_length=_UpperCamelCase			,   truncation=_UpperCamelCase			,   pad_to_multiple_of=_UpperCamelCase			,   return_attention_mask=_UpperCamelCase			,   )

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

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

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

														if return_tensors is not None:
																					__snake_case					  =       padded_inputs.convert_to_tensors(_UpperCamelCase      )

														return padded_inputs




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__		   =    {
    '''configuration_splinter''': ['''SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SplinterConfig'''],
    '''tokenization_splinter''': ['''SplinterTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		   =    ['''SplinterTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		   =    [
        '''SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''SplinterForQuestionAnswering''',
        '''SplinterForPreTraining''',
        '''SplinterLayer''',
        '''SplinterModel''',
        '''SplinterPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
    from .tokenization_splinter import SplinterTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_splinter_fast import SplinterTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_splinter import (
            SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
            SplinterForPreTraining,
            SplinterForQuestionAnswering,
            SplinterLayer,
            SplinterModel,
            SplinterPreTrainedModel,
        )


else:
    import sys

    UpperCamelCase__		   =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

