
'''simple docstring'''
from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


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


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							["""audio_values""", """audio_mask"""]







				def __init__(       self						,				A_=2048						,				A_=1						,				A_=[16, 16]						,				A_=128						,				A_=44100						,				A_=86						,				A_=2048						,				A_=0.0						,				**A_						,				)->		Dict:







											'''simple docstring'''






											super().__init__(
											    feature_size=A_						,				sampling_rate=A_						,				padding_value=A_						,				**A_						,				)

											UpperCamelCase   			=     spectrogram_length
											UpperCamelCase   			=     num_channels
											UpperCamelCase   			=     patch_size
											UpperCamelCase   			=     feature_size // self.patch_size[1]
											UpperCamelCase   			=     n_fft
											UpperCamelCase   			=     sampling_rate // hop_length_to_sampling_rate
											UpperCamelCase   			=     sampling_rate
											UpperCamelCase   			=     padding_value
											UpperCamelCase   			=     mel_filter_bank(
											    num_frequency_bins=1 + n_fft // 2						,				num_mel_filters=A_						,				min_frequency=0.0						,				max_frequency=22_050.0						,				sampling_rate=A_						,				norm='slaney'						,				mel_scale='slaney'						,				).T







				def 						UpperCAmelCase_    (       self						,				A_     )->		np.ndarray:







											'''simple docstring'''






											UpperCamelCase   			=     spectrogram(
											    A_						,				window_function(self.n_fft						,				'hann'     )						,				frame_length=self.n_fft						,				hop_length=self.hop_length						,				power=2.0						,				mel_filters=self.mel_filters.T						,				log_mel='dB'						,				db_range=80.0						,				)
											UpperCamelCase   			=     log_spec[:, :-1]
											UpperCamelCase   			=     log_spec - 20.0
											UpperCamelCase   			=     np.clip(log_spec / 40.0						,				-2.0						,				0.0     ) + 1.0
											return log_spec







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







											'''simple docstring'''






											if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																									raise ValueError(
																									    'This feature extractor is set to support sampling rate'
																									    F''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
																									    F''' with {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(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   			=     is_batched_numpy or (
											    isinstance(A_						,				(list, tuple)     ) and (isinstance(raw_speech[0]						,				(np.ndarray, tuple, list)     ))
											)
											if is_batched:
																		UpperCamelCase   			=     [np.asarray([speech]						,				dtype=np.floataa     ).T for speech in raw_speech]
											elif not is_batched and not isinstance(A_						,				np.ndarray     ):
																		UpperCamelCase   			=     np.asarray(A_						,				dtype=np.floataa     )
											elif isinstance(A_						,				np.ndarray     ) and raw_speech.dtype is np.dtype(np.floataa     ):
																		UpperCamelCase   			=     raw_speech.astype(np.floataa     )
											# always return batch
											if not is_batched:
																		UpperCamelCase   			=     [np.asarray([raw_speech]     ).T]

											# Convert audio signals to log mel spectrograms, truncate by time axis
											UpperCamelCase   			=     [
											    self._np_extract_fbank_features(waveform.squeeze()     ).T[: self.spectrogram_length] for waveform in raw_speech
											]
											if isinstance(audio_features[0]						,				A_     ):
																		UpperCamelCase   			=     [np.asarray(A_						,				dtype=np.floataa     ) for feature in audio_features]

											# Create audio attention mask
											UpperCamelCase   			=     max(
											    [ceil(feature.shape[0] / self.patch_size[0]     ) * self.freq_len for feature in audio_features]     )  # The maximum number of audio patches in a batch
											if return_attention_mask:
																		UpperCamelCase   			=     [
																		    (ceil(feature.shape[0] / self.patch_size[0]     ) * self.freq_len) * [1]
																		    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]     ) * self.freq_len) * [0]
																		    for feature in audio_features
																		]
																		UpperCamelCase   			=     np.array(A_     ).astype(np.floataa     )

											# convert into correct format for padding
											UpperCamelCase   			=     max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
											UpperCamelCase   			=     np.ones([len(A_     ), 1, max_time_len, self.feature_size]     ).astype(np.floataa     )
											UpperCamelCase   			=     padded_audio_features * self.padding_value
											for i in range(len(A_     )     ):
																		UpperCamelCase   			=     audio_features[i]
																		UpperCamelCase   			=     feature

											# return as BatchFeature
											if return_attention_mask:
																		UpperCamelCase   			=     {'audio_values': padded_audio_features, 'audio_mask': audio_mask}
											else:
																		UpperCamelCase   			=     {'audio_values': padded_audio_features}

											UpperCamelCase   			=     BatchFeature(data=A_						,				tensor_type=A_     )
											return encoded_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__)




