



from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

a__			=		logging.get_logger(__name__)







@add_end_docstrings(SCREAMING_SNAKE_CASE_     )
class 						snake_case  (		SCREAMING_SNAKE_CASE_     ):



   '''simple docstring'''






   def __init__(    self							:	str ,						*lowerCAmelCase							:	str ,						**lowerCAmelCase							:	List[Any]) -> Any:

    """simple docstring"""







    super().__init__(*lowerCamelCase__ ,						**lowerCamelCase__)
    self.check_model_type(lowerCamelCase__)

   def    UpperCamelCase_     (    self							:	Any ,						lowerCAmelCase							:	Dict=None ,						lowerCAmelCase							:	int=None ,						lowerCAmelCase							:	Union[str, Any]=None ,						**lowerCAmelCase							:	List[Any]) -> List[str]:

    """simple docstring"""







    _snake_case		,			_snake_case			:     str 		=    {}, {}
    if padding is not None:
     _snake_case			:     List[str] 		=    padding
    if truncation is not None:
     _snake_case			:     Optional[int] 		=    truncation
    if top_k is not None:
     _snake_case			:     List[Any] 		=    top_k
    return preprocess_params, {}, postprocess_params

   def __call__(    self							:	str ,						lowerCAmelCase							:	Union[str, Any] ,						lowerCAmelCase							:	List[str] = None ,						**lowerCAmelCase							:	Union[str, Any]) -> List[Any]:

    """simple docstring"""







    if isinstance(lowerCamelCase__ ,						(Image.Image, str)) and isinstance(lowerCamelCase__ ,						lowerCamelCase__):
     _snake_case			:     Dict 		=    {"""image""": image, """question""": question}
    else:
     _snake_case			:     str 		=    image
    _snake_case			:     Union[str, Any] 		=    super().__call__(lowerCamelCase__ ,						**lowerCamelCase__)
    return results

   def    UpperCamelCase_     (    self							:	Dict ,						lowerCAmelCase							:	Tuple ,						lowerCAmelCase							:	Optional[Any]=False ,						lowerCAmelCase							:	Optional[int]=False) -> Union[str, Any]:

    """simple docstring"""







    _snake_case			:     str 		=    load_image(inputs["""image"""])
    _snake_case			:     Optional[Any] 		=    self.tokenizer(
        inputs["""question"""] ,						return_tensors=self.framework ,						padding=lowerCamelCase__ ,						truncation=lowerCamelCase__)
    _snake_case			:     Dict 		=    self.image_processor(images=lowerCamelCase__ ,						return_tensors=self.framework)
    model_inputs.update(lowerCamelCase__)
    return model_inputs

   def    UpperCamelCase_     (    self							:	Optional[Any] ,						lowerCAmelCase							:	Any) -> Union[str, Any]:

    """simple docstring"""







    _snake_case			:     str 		=    self.model(**lowerCamelCase__)
    return model_outputs


   def    UpperCamelCase_     (    self							:	Tuple ,						lowerCAmelCase							:	Optional[Any] ,						lowerCAmelCase							:	Optional[Any]=5) -> Optional[int]:

    """simple docstring"""







    if top_k > self.model.config.num_labels:
     _snake_case			:     Optional[Any] 		=    self.model.config.num_labels

    if self.framework == "pt":
     _snake_case			:     int 		=    model_outputs.logits.sigmoid()[0]
     _snake_case		,			_snake_case			:     Union[str, Any] 		=    probs.topk(lowerCamelCase__)
    else:
     raise ValueError(F'''Unsupported framework: {self.framework}''')

    _snake_case			:     Optional[Any] 		=    scores.tolist()
    _snake_case			:     int 		=    ids.tolist()
    return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase__ ,						lowerCamelCase__)]






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        =    logging.get_logger(__name__)







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       ['''input_features''', '''is_longer''']


			def __init__(      self			,						lowerCamelCase__=64			,						lowerCamelCase__=48_000			,						lowerCamelCase__=480			,						lowerCamelCase__=10			,						lowerCamelCase__=1_024			,						lowerCamelCase__=0.0			,						lowerCamelCase__=False			,						lowerCamelCase__ = 0			,						lowerCamelCase__ = 14_000			,						lowerCamelCase__ = None			,						lowerCamelCase__ = "fusion"			,						lowerCamelCase__ = "repeatpad"			,						**lowerCamelCase__			,						)    ->      Tuple:



							'''simple docstring'''
							super().__init__(
							    feature_size=lowerCamelCase__			,						sampling_rate=lowerCamelCase__			,						padding_value=lowerCamelCase__			,						return_attention_mask=lowerCamelCase__			,						**lowerCamelCase__			,						)
							__lowerCamelCase		       =    top_db
							__lowerCamelCase		       =    truncation
							__lowerCamelCase		       =    padding
							__lowerCamelCase		       =    fft_window_size
							__lowerCamelCase		       =    (fft_window_size >> 1) + 1
							__lowerCamelCase		       =    hop_length
							__lowerCamelCase		       =    max_length_s
							__lowerCamelCase		       =    max_length_s * sampling_rate
							__lowerCamelCase		       =    sampling_rate
							__lowerCamelCase		       =    frequency_min
							__lowerCamelCase		       =    frequency_max
							__lowerCamelCase		       =    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'			,						)
							__lowerCamelCase		       =    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							)    ->      Dict[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    copy.deepcopy(self.__dict__							)
							__lowerCamelCase		       =    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			,						lowerCamelCase__			,						lowerCamelCase__ = None							)    ->      np.ndarray:



							'''simple docstring'''
							__lowerCamelCase		       =    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			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    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
											__lowerCamelCase		       =    [0]
							if len(ranges[2]							) == 0:
											# if the audio is too short, we just use the first chunk
											__lowerCamelCase		       =    [0]
							# randomly choose index for each part
							__lowerCamelCase		       =    np.random.choice(ranges[0]							)
							__lowerCamelCase		       =    np.random.choice(ranges[1]							)
							__lowerCamelCase		       =    np.random.choice(ranges[2]							)

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

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


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      np.array:



							'''simple docstring'''
							if waveform.shape[0] > max_length:
											if truncation == "rand_trunc":
															__lowerCamelCase		       =    True
															# random crop to max_length (for compatibility) -> this should be handled by self.pad
															__lowerCamelCase		       =    len(lowerCamelCase__							) - max_length
															__lowerCamelCase		       =    np.random.randint(0			,						overflow + 1							)
															__lowerCamelCase		       =    waveform[idx : idx + max_length]
															__lowerCamelCase		       =    self._np_extract_fbank_features(lowerCamelCase__			,						self.mel_filters_slaney							)[None, :]
											elif truncation == "fusion":
															__lowerCamelCase		       =    self._np_extract_fbank_features(lowerCamelCase__			,						self.mel_filters							)
															__lowerCamelCase		       =    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
															__lowerCamelCase		       =    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.
																			__lowerCamelCase		       =    np.stack([mel, mel, mel, mel]			,						axis=0							)
																			__lowerCamelCase		       =    False
															else:
																			__lowerCamelCase		       =    self._random_mel_fusion(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)
																			__lowerCamelCase		       =    True
											else:
															raise NotImplementedError(f"""data_truncating {truncation} not implemented"""							)

							else:
											__lowerCamelCase		       =    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":
																			__lowerCamelCase		       =    int(max_length / len(lowerCamelCase__							)							)
																			__lowerCamelCase		       =    np.stack(np.tile(lowerCamelCase__			,						n_repeat + 1							)							)[:max_length]
															if padding == "repeatpad":
																			__lowerCamelCase		       =    int(max_length / len(lowerCamelCase__							)							)
																			__lowerCamelCase		       =    np.stack(np.tile(lowerCamelCase__			,						lowerCamelCase__							)							)
															__lowerCamelCase		       =    np.pad(lowerCamelCase__			,						(0, max_length - waveform.shape[0])			,						mode='constant'			,						constant_values=0							)

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

							return input_mel, longer






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



							'''simple docstring'''
							__lowerCamelCase		       =    truncation if truncation is not None else self.truncation
							__lowerCamelCase		       =    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.'							)

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

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

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

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

							__lowerCamelCase		       =    []
							__lowerCamelCase		       =    []
							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
											__lowerCamelCase		       =    np.random.randint(0			,						len(lowerCamelCase__							)							)
											__lowerCamelCase		       =    True

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

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

							__lowerCamelCase		       =    {'input_features': input_mel, 'is_longer': is_longer}
							__lowerCamelCase		       =    BatchFeature(lowerCamelCase__							)

							if return_tensors is not None:
											__lowerCamelCase		       =    input_features.convert_to_tensors(lowerCamelCase__							)

							return input_features


