from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


__lowercase									=   logging.get_logger(__name__)


class       lowerCamelCase_	(  snake_case_					):






    '''simple docstring'''






    a__      :		str         = ["""input_features""", """attention_mask"""]







    def __init__(      self					,					__lowercase=80					,					__lowercase=16_000					,					__lowercase=0.0					,					__lowercase=10					,					__lowercase=25					,					__lowercase="hamming_window"					,					__lowercase=32_768.0					,					__lowercase=0.97					,					__lowercase=1.0					,					__lowercase=True					,					__lowercase=True					,					__lowercase=False					,					**__lowercase					,					)			->      List[str]:
        super().__init__(feature_size=A_					,					sampling_rate=A_					,					padding_value=A_					,					**A_)

        __UpperCamelCase	:Tuple         =						feature_size
        __UpperCamelCase	:Tuple         =						sampling_rate
        __UpperCamelCase	:Dict         =						padding_value
        __UpperCamelCase	:int         =						hop_length
        __UpperCamelCase	:Union[str, Any]         =						win_length
        __UpperCamelCase	:List[str]         =						frame_signal_scale
        __UpperCamelCase	:Any         =						preemphasis_coeff
        __UpperCamelCase	:Any         =						mel_floor
        __UpperCamelCase	:Optional[int]         =						normalize_means
        __UpperCamelCase	:List[Any]         =						normalize_vars
        __UpperCamelCase	:Union[str, Any]         =						win_function
        __UpperCamelCase	:List[str]         =						return_attention_mask

        __UpperCamelCase	:Optional[int]         =						win_length * sampling_rate // 1_000
        __UpperCamelCase	:Dict         =						hop_length * sampling_rate // 1_000

        __UpperCamelCase	:str         =						optimal_fft_length(self.sample_size)
        __UpperCamelCase	:Tuple         =						(self.n_fft // 2) + 1







    def      UpperCamelCase__  (      self					,					__lowercase)			->      np.ndarray:






        if self.win_function == "hamming_window":
            __UpperCamelCase	:int         =						window_function(window_length=self.sample_size					,					name=self.win_function					,					periodic=A_)
        else:
            __UpperCamelCase	:Tuple         =						window_function(window_length=self.sample_size					,					name=self.win_function)

        __UpperCamelCase	:int         =						mel_filter_bank(
            num_frequency_bins=self.n_freqs					,					num_mel_filters=self.feature_size					,					min_frequency=0.0					,					max_frequency=self.sampling_rate / 2.0					,					sampling_rate=self.sampling_rate					,					)

        __UpperCamelCase	:str         =						spectrogram(
            one_waveform * self.frame_signal_scale					,					window=A_					,					frame_length=self.sample_size					,					hop_length=self.sample_stride					,					fft_length=self.n_fft					,					center=A_					,					preemphasis=self.preemphasis_coeff					,					mel_filters=A_					,					mel_floor=self.mel_floor					,					log_mel='''log'''					,					)
        return msfc_features.T







    def      UpperCamelCase__  (      self					,					__lowercase					,					__lowercase					,					__lowercase)			->      Dict:






        if self.normalize_means:
            __UpperCamelCase	:List[Any]         =						x[:input_length].mean(axis=0)
            __UpperCamelCase	:Optional[Any]         =						np.subtract(A_					,					A_)
        if self.normalize_vars:
            __UpperCamelCase	:Optional[Any]         =						x[:input_length].std(axis=0)
            __UpperCamelCase	:str         =						np.divide(A_					,					A_)

        if input_length < x.shape[0]:
            __UpperCamelCase	:Tuple         =						padding_value

        # make sure array is in float32
        __UpperCamelCase	:List[Any]         =						x.astype(np.floataa)

        return x







    def      UpperCamelCase__  (      self					,					__lowercase					,					__lowercase = None)			->      List[np.ndarray]:
        __UpperCamelCase	:List[str]         =						attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
        return [self._normalize_one(A_					,					A_					,					self.padding_value) for x, n in zip(A_					,					A_)]







    def __call__(      self					,					__lowercase					,					__lowercase = False					,					__lowercase = None					,					__lowercase = False					,					__lowercase = None					,					__lowercase = None					,					__lowercase = None					,					__lowercase = None					,					**__lowercase					,					)			->      BatchFeature:






        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	:List[Any]         =						isinstance(A_					,					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	:Optional[Any]         =						is_batched_numpy or (
            isinstance(A_					,					(list, tuple)) and (isinstance(raw_speech[0]					,					(np.ndarray, tuple, list)))
        )

        if is_batched:
            __UpperCamelCase	:List[Any]         =						[np.asarray(A_					,					dtype=np.floataa) for speech in raw_speech]
        elif not is_batched and not isinstance(A_					,					np.ndarray):
            __UpperCamelCase	:Optional[int]         =						np.asarray(A_					,					dtype=np.floataa)
        elif isinstance(A_					,					np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
            __UpperCamelCase	:Optional[int]         =						raw_speech.astype(np.floataa)

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

        # extract fbank features
        __UpperCamelCase	:Tuple         =						[self._extract_mfsc_features(A_) for one_waveform in raw_speech]

        # convert into correct format for padding
        __UpperCamelCase	:List[Any]         =						BatchFeature({'''input_features''': features})

        __UpperCamelCase	:Union[str, Any]         =						self.pad(
            A_					,					padding=A_					,					max_length=A_					,					truncation=A_					,					pad_to_multiple_of=A_					,					return_attention_mask=A_					,					**A_					,					)
        # make sure list is in array format
        __UpperCamelCase	:Tuple         =						padded_inputs.get('''input_features''')
        if isinstance(input_features[0]					,					A_):
            __UpperCamelCase	:List[str]         =						[np.asarray(A_					,					dtype=np.floataa) for feature in input_features]

        __UpperCamelCase	:Union[str, Any]         =						padded_inputs.get('''attention_mask''')
        if attention_mask is not None:
            __UpperCamelCase	:int         =						[np.asarray(A_					,					dtype=np.intaa) for array in attention_mask]

        if self.normalize_means or self.normalize_vars:
            __UpperCamelCase	:Dict         =						(
                np.array(A_					,					dtype=np.intaa)
                if self._get_padding_strategies(A_					,					max_length=A_) is not PaddingStrategy.DO_NOT_PAD
                and padding
                else None
            )
            __UpperCamelCase	:Optional[int]         =						self.normalize(
                padded_inputs['''input_features''']					,					attention_mask=A_)

        if return_tensors is not None:
            __UpperCamelCase	:List[str]         =						padded_inputs.convert_to_tensors(A_)

        return padded_inputs







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase					:    Union[str, Any]           = {
    'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
    'processing_git': ['GitProcessor'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    List[Any]           = [
						    'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'GitForCausalLM',
						    'GitModel',
						    'GitPreTrainedModel',
						    'GitVisionModel',
						]

if TYPE_CHECKING:
						from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
						from .processing_git import GitProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_git import (
												    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GitForCausalLM,
												    GitModel,
												    GitPreTrainedModel,
												    GitVisionModel,
												)

else:
						import sys

						lowerCAmelCase					:    Optional[Any]           = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




