
from typing import TYPE_CHECKING

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


A__			:    str         =				{
    'configuration_chinese_clip': [
        'CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ChineseCLIPConfig',
        'ChineseCLIPOnnxConfig',
        'ChineseCLIPTextConfig',
        'ChineseCLIPVisionConfig',
    ],
    'processing_chinese_clip': ['ChineseCLIPProcessor'],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__			:    Tuple         =				['ChineseCLIPFeatureExtractor']
							A__			:    Dict         =				['ChineseCLIPImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__			:    Any         =				[
							    'CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ChineseCLIPModel',
							    'ChineseCLIPPreTrainedModel',
							    'ChineseCLIPTextModel',
							    'ChineseCLIPVisionModel',
							]

if TYPE_CHECKING:
							from .configuration_chinese_clip import (
							    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ChineseCLIPConfig,
							    ChineseCLIPOnnxConfig,
							    ChineseCLIPTextConfig,
							    ChineseCLIPVisionConfig,
							)
							from .processing_chinese_clip import ChineseCLIPProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_chinese_clip import (
														    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ChineseCLIPModel,
														    ChineseCLIPPreTrainedModel,
														    ChineseCLIPTextModel,
														    ChineseCLIPVisionModel,
														)

else:
							import sys

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












import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


A__			:    Tuple         =				logging.get_logger(__name__)
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= ["""input_features""", """is_longer"""]




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					int=64,						lowerCamelCase		:					Union[str, Any]=48_000,						lowerCamelCase		:					str=480,						lowerCamelCase		:					Tuple=10,						lowerCamelCase		:					List[Any]=1_024,						lowerCamelCase		:					Optional[int]=0.0,						lowerCamelCase		:					Optional[Any]=False,						lowerCamelCase		:					float = 0,						lowerCamelCase		:					float = 14_000,						lowerCamelCase		:					int = None,						lowerCamelCase		:					str = "fusion",						lowerCamelCase		:					str = "repeatpad",						**lowerCamelCase		:					Dict,						):

								'''simple docstring'''
								super().__init__(
								    feature_size=lowerCamelCase,						sampling_rate=lowerCamelCase,						padding_value=lowerCamelCase,						return_attention_mask=lowerCamelCase,						**lowerCamelCase,						)
								lowercase__     				=    top_db
								lowercase__     				=    truncation
								lowercase__     				=    padding
								lowercase__     				=    fft_window_size
								lowercase__     				=    (fft_window_size >> 1) + 1
								lowercase__     				=    hop_length
								lowercase__     				=    max_length_s
								lowercase__     				=    max_length_s * sampling_rate
								lowercase__     				=    sampling_rate
								lowercase__     				=    frequency_min
								lowercase__     				=    frequency_max
								lowercase__     				=    mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins,						num_mel_filters=lowerCamelCase,						min_frequency=lowerCamelCase,						max_frequency=lowerCamelCase,						sampling_rate=lowerCamelCase,						norm=lowerCamelCase,						mel_scale='''htk''',						)
								lowercase__     				=    mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins,						num_mel_filters=lowerCamelCase,						min_frequency=lowerCamelCase,						max_frequency=lowerCamelCase,						sampling_rate=lowerCamelCase,						norm='''slaney''',						mel_scale='''slaney''',						)




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    copy.deepcopy(self.__dict__						)
								lowercase__     				=    self.__class__.__name__
								if "mel_filters" in output:
												del output["mel_filters"]
								if "mel_filters_slaney" in output:
												del output["mel_filters_slaney"]
								return output




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					np.array,						lowerCamelCase		:					Optional[np.array] = None						):

								'''simple docstring'''
								lowercase__     				=    spectrogram(
								    lowerCamelCase,						window_function(self.fft_window_size,						'''hann'''						),						frame_length=self.fft_window_size,						hop_length=self.hop_length,						power=2.0,						mel_filters=lowerCamelCase,						log_mel='''dB''',						)
								return log_mel_spectrogram.T




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					str,						lowerCamelCase		:					List[str],						lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    np.array_split(list(range(0,						total_frames - chunk_frames + 1						)						),						3						)
								if len(ranges[1]						) == 0:
												# if the audio is too short, we just use the first chunk
												lowercase__     				=    [0]
								if len(ranges[2]						) == 0:
												# if the audio is too short, we just use the first chunk
												lowercase__     				=    [0]
								# randomly choose index for each part
								lowercase__     				=    np.random.choice(ranges[0]						)
								lowercase__     				=    np.random.choice(ranges[1]						)
								lowercase__     				=    np.random.choice(ranges[2]						)

								lowercase__     				=    mel[idx_front : idx_front + chunk_frames, :]
								lowercase__     				=    mel[idx_middle : idx_middle + chunk_frames, :]
								lowercase__     				=    mel[idx_back : idx_back + chunk_frames, :]

								lowercase__     				=    torch.tensor(mel[None, None, :]						)
								lowercase__     				=    torch.nn.functional.interpolate(
								    lowerCamelCase,						size=[chunk_frames, 64],						mode='''bilinear''',						align_corners=lowerCamelCase						)
								lowercase__     				=    mel_shrink[0][0].numpy()
								lowercase__     				=    np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back],						axis=0						)
								return mel_fusion




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					np.array,						lowerCamelCase		:					int,						lowerCamelCase		:					Dict,						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								if waveform.shape[0] > max_length:
												if truncation == "rand_trunc":
																lowercase__     				=    True
																# random crop to max_length (for compatibility) -> this should be handled by self.pad
																lowercase__     				=    len(lowerCamelCase						) - max_length
																lowercase__     				=    np.random.randint(0,						overflow + 1						)
																lowercase__     				=    waveform[idx : idx + max_length]
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters_slaney						)[None, :]
												elif truncation == "fusion":
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters						)
																lowercase__     				=    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																lowercase__     				=    mel.shape[0]
																if chunk_frames == total_frames:
																				# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																				# In this case, we just use the whole audio.
																				lowercase__     				=    np.stack([mel, mel, mel, mel],						axis=0						)
																				lowercase__     				=    False
																else:
																				lowercase__     				=    self._random_mel_fusion(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)
																				lowercase__     				=    True
												else:
																raise NotImplementedError(F"""data_truncating {truncation} not implemented"""						)

								else:
												lowercase__     				=    False
												# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
												if waveform.shape[0] < max_length:
																if padding == "repeat":
																				lowercase__     				=    int(max_length / len(lowerCamelCase						)						)
																				lowercase__     				=    np.stack(np.tile(lowerCamelCase,						n_repeat + 1						)						)[:max_length]
																if padding == "repeatpad":
																				lowercase__     				=    int(max_length / len(lowerCamelCase						)						)
																				lowercase__     				=    np.stack(np.tile(lowerCamelCase,						lowerCamelCase						)						)
																lowercase__     				=    np.pad(lowerCamelCase,						(0, max_length - waveform.shape[0]),						mode='''constant''',						constant_values=0						)

												if truncation == "fusion":
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters						)
																lowercase__     				=    np.stack([input_mel, input_mel, input_mel, input_mel],						axis=0						)
												else:
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters_slaney						)[None, :]

								return input_mel, longer




				def __call__(  self		:					Union[str, Any],						lowerCamelCase		:					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],						lowerCamelCase		:					str = None,						lowerCamelCase		:					Optional[str] = None,						lowerCamelCase		:					Optional[int] = None,						lowerCamelCase		:					Optional[int] = None,						lowerCamelCase		:					Optional[Union[str, TensorType]] = None,						**lowerCamelCase		:					List[str],						):

								'''simple docstring'''
								lowercase__     				=    truncation if truncation is not None else self.truncation
								lowercase__     				=    padding if padding else self.padding

								if sampling_rate is not None:
												if sampling_rate != self.sampling_rate:
																raise ValueError(
																    F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
																    F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
																    F""" was sampled 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.'''						)

								lowercase__     				=    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}"""						)
								lowercase__     				=    is_batched_numpy or (
								    isinstance(lowerCamelCase,						(list, tuple)						) and (isinstance(raw_speech[0],						(np.ndarray, tuple, list)						))
								)

								if is_batched:
												lowercase__     				=    [np.asarray(lowerCamelCase,						dtype=np.floataa						) for speech in raw_speech]
								elif not is_batched and not isinstance(lowerCamelCase,						np.ndarray						):
												lowercase__     				=    np.asarray(lowerCamelCase,						dtype=np.floataa						)
								elif isinstance(lowerCamelCase,						np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
												lowercase__     				=    raw_speech.astype(np.floataa						)

								# always return batch
								if not is_batched:
												lowercase__     				=    [np.asarray(lowerCamelCase						)]

								# convert to mel spectrogram, truncate and pad if needed.
								lowercase__     				=    [
								    self._get_input_mel(lowerCamelCase,						max_length if max_length else self.nb_max_samples,						lowerCamelCase,						lowerCamelCase						)
								    for waveform in raw_speech
								]

								lowercase__     				=    []
								lowercase__     				=    []
								for mel, longer in padded_inputs:
												input_mel.append(lowerCamelCase						)
												is_longer.append(lowerCamelCase						)

								if truncation == "fusion" and sum(lowerCamelCase						) == 0:
												# if no audio is longer than 10s, then randomly select one audio to be longer
												lowercase__     				=    np.random.randint(0,						len(lowerCamelCase						)						)
												lowercase__     				=    True

								if isinstance(input_mel[0],						lowerCamelCase						):
												lowercase__     				=    [np.asarray(lowerCamelCase,						dtype=np.floataa						) for feature in input_mel]

								# is_longer is a list of bool
								lowercase__     				=    [[longer] for longer in is_longer]

								lowercase__     				=    {'''input_features''': input_mel, '''is_longer''': is_longer}
								lowercase__     				=    BatchFeature(lowerCamelCase						)

								if return_tensors is not None:
												lowercase__     				=    input_features.convert_to_tensors(lowerCamelCase						)

								return input_features




