


'''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


__UpperCAmelCase      :int			    =       logging.get_logger(__name__)



class      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			str							       =       ["input_features"]

     def __init__(    self					:		List[str]							,			snake_case					:		Union[str, Any]=80							,			snake_case					:		Optional[int]=1_6000							,			snake_case					:		Dict=160							,			snake_case					:		Optional[Any]=30							,			snake_case					:		List[str]=400							,			snake_case					:		Optional[int]=0.0							,			snake_case					:		Any=False							,			**snake_case					:		List[Any]							,			)						->							Optional[Any]:
          super().__init__(
              feature_size=snake_case							,			sampling_rate=snake_case							,			padding_value=snake_case							,			return_attention_mask=snake_case							,			**snake_case							,			)
          __UpperCAmelCase     :			Optional[Any]  							=      n_fft
          __UpperCAmelCase     :			Union[str, Any]  							=      hop_length
          __UpperCAmelCase     :			Union[str, Any]  							=      chunk_length
          __UpperCAmelCase     :			Tuple  							=      chunk_length * sampling_rate
          __UpperCAmelCase     :			Any  							=      self.n_samples // hop_length
          __UpperCAmelCase     :			Any  							=      sampling_rate
          __UpperCAmelCase     :			Optional[Any]  							=      mel_filter_bank(
              num_frequency_bins=1 + n_fft // 2							,			num_mel_filters=snake_case							,			min_frequency=0.0							,			max_frequency=8_000.0							,			sampling_rate=snake_case							,			norm='''slaney'''							,			mel_scale='''slaney'''							,			)

     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		np.array      )						->							np.ndarray:
          __UpperCAmelCase     :			Dict  							=      spectrogram(
              snake_case							,			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'''							,			)
          __UpperCAmelCase     :			Tuple  							=      log_spec[:, :-1]
          __UpperCAmelCase     :			str  							=      np.maximum(snake_case							,			log_spec.max() - 8.0      )
          __UpperCAmelCase     :			Any  							=      (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 			lowerCamelCase__			(    snake_case					:		List[np.ndarray]							,			snake_case					:		List[np.ndarray]							,			snake_case					:		float = 0.0      )						->							List[np.ndarray]:
          if attention_mask is not None:
               __UpperCAmelCase     :			List[str]  							=      np.array(snake_case							,			np.intaa      )
               __UpperCAmelCase     :			Optional[Any]  							=      []

               for vector, length in zip(snake_case							,			attention_mask.sum(-1      )      ):
                    __UpperCAmelCase     :			List[str]  							=      (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7      )
                    if length < normed_slice.shape[0]:
                         __UpperCAmelCase     :			Optional[Any]  							=      padding_value

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

          return normed_input_values

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

          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.'''      )

          __UpperCAmelCase     :			List[str]  							=      isinstance(snake_case							,			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     :			int  							=      is_batched_numpy or (
              isinstance(snake_case							,			(list, tuple)      ) and (isinstance(raw_speech[0]							,			(np.ndarray, tuple, list)      ))
          )

          if is_batched:
               __UpperCAmelCase     :			Any  							=      [np.asarray([speech]							,			dtype=np.floataa      ).T for speech in raw_speech]
          elif not is_batched and not isinstance(snake_case							,			np.ndarray      ):
               __UpperCAmelCase     :			Dict  							=      np.asarray(snake_case							,			dtype=np.floataa      )
          elif isinstance(snake_case							,			np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
               __UpperCAmelCase     :			str  							=      raw_speech.astype(np.floataa      )

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

          __UpperCAmelCase     :			Tuple  							=      BatchFeature({'''input_features''': raw_speech}      )

          # convert into correct format for padding

          __UpperCAmelCase     :			Optional[int]  							=      self.pad(
              snake_case							,			padding=snake_case							,			max_length=max_length if max_length else self.n_samples							,			truncation=snake_case							,			pad_to_multiple_of=snake_case							,			return_attention_mask=return_attention_mask or do_normalize							,			)

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

          # make sure list is in array format
          __UpperCAmelCase     :			List[Any]  							=      padded_inputs.get('''input_features'''      ).transpose(2							,			0							,			1      )

          __UpperCAmelCase     :			Optional[int]  							=      [self._np_extract_fbank_features(snake_case      ) for waveform in input_features[0]]

          if isinstance(input_features[0]							,			snake_case      ):
               __UpperCAmelCase     :			str  							=      [np.asarray(snake_case							,			dtype=np.floataa      ) for feature in input_features]
          else:
               __UpperCAmelCase     :			int  							=      input_features

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

          if return_tensors is not None:
               __UpperCAmelCase     :			Tuple  							=      padded_inputs.convert_to_tensors(snake_case      )

          return padded_inputs






     def 			lowerCamelCase__			(    self					:		Any      )						->							Dict[str, Any]:
          __UpperCAmelCase     :			int  							=      copy.deepcopy(self.__dict__      )
          __UpperCAmelCase     :			Any  							=      self.__class__.__name__
          if "mel_filters" in output:
               del output["mel_filters"]
          return output



'''simple docstring'''



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class      a    :


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			torch.Tensor  # [batch_size x 3]
     SCREAMING_SNAKE_CASE	:			torch.Tensor  # [batch_size x 3]
     SCREAMING_SNAKE_CASE	:			torch.Tensor  # [batch_size x 3]
     SCREAMING_SNAKE_CASE	:			torch.Tensor  # [batch_size x 3]
     SCREAMING_SNAKE_CASE	:			int
     SCREAMING_SNAKE_CASE	:			int
     SCREAMING_SNAKE_CASE	:			float
     SCREAMING_SNAKE_CASE	:			float
     SCREAMING_SNAKE_CASE	:			Tuple[int]

     def 			lowerCamelCase__			(    self					:		Any      )						->							int:
          assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
          assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
          assert len(self.x.shape      ) == len(self.y.shape      ) == len(self.z.shape      ) == len(self.origin.shape      ) == 2

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							str:
          return torch.from_numpy(np.array([self.width, self.height]							,			dtype=np.floataa      )      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							Optional[Any]:
          return torch.from_numpy(np.array([self.x_fov, self.y_fov]							,			dtype=np.floataa      )      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							torch.Tensor:
          __UpperCAmelCase     :			Dict  							=      torch.arange(self.height * self.width      )
          __UpperCAmelCase     :			Dict  							=      torch.stack(
              [
                  pixel_indices % self.width,
                  torch.div(snake_case							,			self.width							,			rounding_mode='''trunc'''      ),
              ]							,			axis=1							,			)
          return coords

     @property
     def 			lowerCamelCase__			(    self					:		Any      )						->							int:
          __UpperCAmelCase							,       *__UpperCAmelCase     :			str  							=      self.shape
          __UpperCAmelCase     :			Dict  							=      int(np.prod(snake_case      )      )

          __UpperCAmelCase     :			Tuple  							=      self.get_image_coords()
          __UpperCAmelCase     :			List[Any]  							=      torch.broadcast_to(coords.unsqueeze(0      )							,			[batch_size * inner_batch_size, *coords.shape]      )
          __UpperCAmelCase     :			Any  							=      self.get_camera_rays(snake_case      )

          __UpperCAmelCase     :			List[str]  							=      rays.view(snake_case							,			inner_batch_size * self.height * self.width							,			2							,			3      )

          return rays

     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		torch.Tensor      )						->							torch.Tensor:
          __UpperCAmelCase							,       *__UpperCAmelCase							,       __UpperCAmelCase     :			List[str]  							=      coords.shape
          assert n_coords == 2
          assert batch_size == self.origin.shape[0]

          __UpperCAmelCase     :			List[str]  							=      coords.view(snake_case							,			-1							,			2      )

          __UpperCAmelCase     :			Optional[Any]  							=      self.resolution()
          __UpperCAmelCase     :			Tuple  							=      self.fov()

          __UpperCAmelCase     :			Optional[int]  							=      (flat.float() / (res - 1)) * 2 - 1
          __UpperCAmelCase     :			Union[str, Any]  							=      fracs * torch.tan(fov / 2      )

          __UpperCAmelCase     :			str  							=      fracs.view(snake_case							,			-1							,			2      )
          __UpperCAmelCase     :			Any  							=      (
              self.z.view(snake_case							,			1							,			3      )
              + self.x.view(snake_case							,			1							,			3      ) * fracs[:, :, :1]
              + self.y.view(snake_case							,			1							,			3      ) * fracs[:, :, 1:]
          )
          __UpperCAmelCase     :			Union[str, Any]  							=      directions / directions.norm(dim=-1							,			keepdim=snake_case      )
          __UpperCAmelCase     :			Union[str, Any]  							=      torch.stack(
              [
                  torch.broadcast_to(self.origin.view(snake_case							,			1							,			3      )							,			[batch_size, directions.shape[1], 3]      ),
                  directions,
              ]							,			dim=2							,			)
          return rays.view(snake_case							,			*snake_case							,			2							,			3      )






     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		int							,			snake_case					:		int      )						->							"DifferentiableProjectiveCamera":
          assert width * self.height == height * self.width, "The aspect ratio should not change."
          return DifferentiableProjectiveCamera(
              origin=self.origin							,			x=self.x							,			y=self.y							,			z=self.z							,			width=snake_case							,			height=snake_case							,			x_fov=self.x_fov							,			y_fov=self.y_fov							,			)




def      _a     (						_lowercase :     int  ):





     '''simple docstring'''






     __UpperCAmelCase     :			str  							=      []
     __UpperCAmelCase     :			Optional[int]  							=      []
     __UpperCAmelCase     :			Union[str, Any]  							=      []
     __UpperCAmelCase     :			List[Any]  							=      []
     for theta in np.linspace(0      , 2 * np.pi      , num=20  ):
          __UpperCAmelCase     :			Dict  							=      np.array([np.sin(_lowercase  ), np.cos(_lowercase  ), -0.5]  )
          z /= np.sqrt(np.sum(z**2  )  )
          __UpperCAmelCase     :			Any  							=      -z * 4
          __UpperCAmelCase     :			Dict  							=      np.array([np.cos(_lowercase  ), -np.sin(_lowercase  ), 0.0]  )
          __UpperCAmelCase     :			List[str]  							=      np.cross(_lowercase      , _lowercase  )
          origins.append(_lowercase  )
          xs.append(_lowercase  )
          ys.append(_lowercase  )
          zs.append(_lowercase  )
     return DifferentiableProjectiveCamera(
         origin=torch.from_numpy(np.stack(_lowercase      , axis=0  )  ).float()      , x=torch.from_numpy(np.stack(_lowercase      , axis=0  )  ).float()      , y=torch.from_numpy(np.stack(_lowercase      , axis=0  )  ).float()      , z=torch.from_numpy(np.stack(_lowercase      , axis=0  )  ).float()      , width=_lowercase      , height=_lowercase      , x_fov=0.7      , y_fov=0.7      , shape=(1, len(_lowercase  ))      , )