


"""simple docstring"""


from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
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():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

a_       							=    logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase_)
class snake_case      (  UpperCAmelCase_):







		def __init__(					self      :       Optional[Any]    ,   *a__      :       int    ,   **a__      :       int		)       ->						List[Any]:





						'''simple docstring'''







						super().__init__(*_lowercase    ,   **_lowercase		)
						requires_backends(self    ,   "vision"		)
						self.check_model_type(_lowercase		)







		def __call__(					self      :       Optional[int]    ,   a__      :       List[Any]    ,   **a__      :       Any		)       ->						int:





						'''simple docstring'''







						return super().__call__(_lowercase    ,   **_lowercase		)







		def  a_ (					self      :       int    ,   **a__      :       Optional[Any]		)       ->						Optional[int]:





						'''simple docstring'''







						return {}, {}, {}







		def  a_ (					self      :       Optional[Any]    ,   a__      :       Optional[int]		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			load_image(_lowercase		)
						_A					  =			image.size
						_A					  =			self.image_processor(images=_lowercase    ,   return_tensors=self.framework		)
						return model_inputs







		def  a_ (					self      :       Tuple    ,   a__      :       int		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			self.model(**_lowercase		)
						return model_outputs







		def  a_ (					self      :       Dict    ,   a__      :       List[str]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			model_outputs.predicted_depth
						_A					  =			torch.nn.functional.interpolate(
						    predicted_depth.unsqueeze(1		)    ,   size=self.image_size[::-1]    ,   mode="bicubic"    ,   align_corners=_lowercase		)
						_A					  =			prediction.squeeze().cpu().numpy()
						_A					  =			(output * 2_55 / np.max(_lowercase		)).astype("uint8"		)
						_A					  =			Image.fromarray(_lowercase		)
						_A					  =			{}
						_A					  =			predicted_depth
						_A					  =			depth
						return output_dict



"""simple docstring"""


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

import numpy as np

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 snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			['input_features']







		def __init__(					self      :       int    ,   a__      :       Optional[Any]=80    ,   a__      :       Optional[int]=1_60_00    ,   a__      :       int=1_60    ,   a__      :       Union[str, Any]=30    ,   a__      :       Tuple=4_00    ,   a__      :       List[Any]=0.0    ,   a__      :       Optional[Any]=False    ,   **a__      :       List[Any]    ,   )       ->						str:





						'''simple docstring'''







						super().__init__(
						    feature_size=a__    ,   sampling_rate=a__    ,   padding_value=a__    ,   return_attention_mask=a__    ,   **a__    ,   )
						_A					  =			n_fft
						_A					  =			hop_length
						_A					  =			chunk_length
						_A					  =			chunk_length * sampling_rate
						_A					  =			self.n_samples // hop_length
						_A					  =			sampling_rate
						_A					  =			mel_filter_bank(
						    num_frequency_bins=1 + n_fft // 2    ,   num_mel_filters=a__    ,   min_frequency=0.0    ,   max_frequency=8_0_0_0.0    ,   sampling_rate=a__    ,   norm="slaney"    ,   mel_scale="slaney"    ,   )







		def  a_ (					self      :       int    ,   a__      :       np.array		)       ->						np.ndarray:





						'''simple docstring'''







						_A					  =			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    ,   log_mel="log10"    ,   )
						_A					  =			log_spec[:, :-1]
						_A					  =			np.maximum(a__    ,   log_spec.max() - 8.0		)
						_A					  =			(log_spec + 4.0) / 4.0
						return log_spec







		@staticmethod
		# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
		def  a_ (					a__      :       List[np.ndarray]    ,   a__      :       List[np.ndarray]    ,   a__      :       float = 0.0		)       ->						List[np.ndarray]:





						'''simple docstring'''







						if attention_mask is not None:
										_A					  =			np.array(a__    ,   np.intaa		)
										_A					  =			[]

										for vector, length in zip(a__    ,   attention_mask.sum(-1		)		):
														_A					  =			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7		)
														if length < normed_slice.shape[0]:
																		_A					  =			padding_value

														normed_input_values.append(a__		)
						else:
										_A					  =			[(x - x.mean()) / np.sqrt(x.var() + 1E-7		) for x in input_values]

						return normed_input_values







		def __call__(					self      :       Optional[int]    ,   a__      :       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,   a__      :       bool = True    ,   a__      :       Optional[int] = None    ,   a__      :       Optional[Union[str, TensorType]] = None    ,   a__      :       Optional[bool] = None    ,   a__      :       Optional[str] = "max_length"    ,   a__      :       Optional[int] = None    ,   a__      :       Optional[int] = None    ,   a__      :       Optional[bool] = None    ,   **a__      :       Dict    ,   )       ->						BatchFeature:





						'''simple docstring'''







						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."		)

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

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

						# always return batch
						if not is_batched:
										_A					  =			[np.asarray([raw_speech]		).T]

						_A					  =			BatchFeature({"input_features": raw_speech}		)

						# convert into correct format for padding

						_A					  =			self.pad(
						    a__    ,   padding=a__    ,   max_length=max_length if max_length else self.n_samples    ,   truncation=a__    ,   pad_to_multiple_of=a__    ,   return_attention_mask=return_attention_mask or do_normalize    ,   )

						# zero-mean and unit-variance normalization
						if do_normalize:
										_A					  =			self.zero_mean_unit_var_norm(
										    padded_inputs["input_features"]    ,   attention_mask=padded_inputs["attention_mask"]    ,   padding_value=self.padding_value    ,   )
										_A					  =			np.stack(padded_inputs["input_features"]    ,   axis=0		)

						# make sure list is in array format
						_A					  =			padded_inputs.get("input_features"		).transpose(2    ,   0    ,   1		)

						_A					  =			[self._np_extract_fbank_features(a__		) for waveform in input_features[0]]

						if isinstance(input_features[0]    ,   a__		):
										_A					  =			[np.asarray(a__    ,   dtype=np.floataa		) for feature in input_features]
						else:
										_A					  =			input_features

						if return_attention_mask:
										# rescale from sample (48000) to feature (3000)
										_A					  =			padded_inputs["attention_mask"][:, :: self.hop_length]

						if return_tensors is not None:
										_A					  =			padded_inputs.convert_to_tensors(a__		)

						return padded_inputs







		def  a_ (					self      :       Dict		)       ->						Dict[str, Any]:





						'''simple docstring'''







						_A					  =			copy.deepcopy(self.__dict__		)
						_A					  =			self.__class__.__name__
						if "mel_filters" in output:
										del output["mel_filters"]
						return output