


'''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


_lowercase    :    Tuple			     =  logging.get_logger(__name__)






class  lowerCamelCase_(     lowerCAmelCase ):
				__magic_name__       :   str							= ["audio_values", "audio_mask"]




				def __init__(	self    :				Tuple   ,					lowerCAmelCase    :				str=2048   ,					lowerCAmelCase    :				str=1   ,					lowerCAmelCase    :				Any=[16, 16]   ,					lowerCAmelCase    :				List[str]=128   ,					lowerCAmelCase    :				Tuple=44100   ,					lowerCAmelCase    :				Dict=86   ,					lowerCAmelCase    :				List[str]=2048   ,					lowerCAmelCase    :				int=0.0   ,					**lowerCAmelCase    :				Optional[Any]   ,					)->  Dict:

										"""simple docstring"""




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

										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=lowerCAmelCase   ,					min_frequency=0.0   ,					max_frequency=22050.0   ,					sampling_rate=lowerCAmelCase   ,					norm='''slaney'''   ,					mel_scale='''slaney'''   ,					).T




				def   a__(	self    :				Optional[int]   ,					lowerCAmelCase    :				np.array   )->  np.ndarray:

										"""simple docstring"""




										UpperCAmelCase         =			spectrogram(
										    lowerCAmelCase   ,					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    :				Dict   ,					lowerCAmelCase    :				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,					lowerCAmelCase    :				Optional[Union[str, TensorType]] = None   ,					lowerCAmelCase    :				Optional[bool] = True   ,					lowerCAmelCase    :				Optional[int] = None   ,					lowerCAmelCase    :				bool = False   ,					lowerCAmelCase    :				bool = False   ,					**lowerCAmelCase    :				Optional[int]   ,					)->  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(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)   ))
										)
										if is_batched:
																UpperCAmelCase         =			[np.asarray([speech]   ,					dtype=np.floataa   ).T for speech in raw_speech]
										elif not is_batched and not isinstance(lowerCAmelCase   ,					np.ndarray   ):
																UpperCAmelCase         =			np.asarray(lowerCAmelCase   ,					dtype=np.floataa   )
										elif isinstance(lowerCAmelCase   ,					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]   ,					lowerCAmelCase   ):
																UpperCAmelCase         =			[np.asarray(lowerCAmelCase   ,					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(lowerCAmelCase   ).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(lowerCAmelCase   ), 1, max_time_len, self.feature_size]   ).astype(np.floataa   )
										UpperCAmelCase         =			padded_audio_features * self.padding_value
										for i in range(len(lowerCAmelCase   )   ):
																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=lowerCAmelCase   ,					tensor_type=lowerCAmelCase   )
										return encoded_inputs









'''simple docstring'''



# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowercase    :    Any			     =  {
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    int			     =  [
	    """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """CpmAntForCausalLM""",
	    """CpmAntModel""",
	    """CpmAntPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
	from .tokenization_cpmant import CpmAntTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_cpmant import (
		    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CpmAntForCausalLM,
		    CpmAntModel,
		    CpmAntPreTrainedModel,
		)


else:
	import sys

	_lowercase    :    Dict			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





