




from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__A        =    logging.get_logger(__name__)







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       ['''input_values''', '''padding_mask''']


			def __init__(      self			,						lowerCamelCase__ = 1			,						lowerCamelCase__ = 24_000			,						lowerCamelCase__ = 0.0			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						**lowerCamelCase__			,						)    ->      Union[str, Any]:



							'''simple docstring'''
							super().__init__(feature_size=lowerCamelCase__			,						sampling_rate=lowerCamelCase__			,						padding_value=lowerCamelCase__			,						**lowerCamelCase__							)
							__lowerCamelCase		       =    chunk_length_s
							__lowerCamelCase		       =    overlap


			@property
			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							if self.chunk_length_s is None:
											return None
							else:
											return int(self.chunk_length_s * self.sampling_rate							)


			@property
			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							if self.chunk_length_s is None or self.overlap is None:
											return None
							else:
											return max(1			,						int((1.0 - self.overlap) * self.chunk_length							)							)






			def __call__(      self			,						lowerCamelCase__			,						lowerCamelCase__ = None			,						lowerCamelCase__ = False			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						)    ->      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} was trained using a sampling rate of"""
															    f""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
															    f""" {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.'							)

							if padding and truncation:
											raise ValueError('Both padding and truncation were set. Make sure you only set one.'							)
							elif padding is None:
											# by default let's pad the inputs
											__lowerCamelCase		       =    True

							__lowerCamelCase		       =    bool(
							    isinstance(lowerCamelCase__			,						(list, tuple)							) and (isinstance(raw_audio[0]			,						(np.ndarray, tuple, list)							))							)

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

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

							# verify inputs are valid
							for idx, example in enumerate(lowerCamelCase__							):
											if example.ndim > 2:
															raise ValueError(f"""Expected input shape (channels, length) but got shape {example.shape}"""							)
											if self.feature_size == 1 and example.ndim != 1:
															raise ValueError(f"""Expected mono audio but example has {example.shape[-1]} channels"""							)
											if self.feature_size == 2 and example.shape[-1] != 2:
															raise ValueError(f"""Expected stereo audio but example has {example.shape[-1]} channels"""							)

							__lowerCamelCase		       =    None
							__lowerCamelCase		       =    BatchFeature({'input_values': raw_audio}							)
							if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
											if truncation:
															__lowerCamelCase		       =    min(array.shape[0] for array in raw_audio							)
															__lowerCamelCase		       =    int(np.floor(max_length / self.chunk_stride							)							)
															__lowerCamelCase		       =    (nb_step - 1) * self.chunk_stride + self.chunk_length
											elif padding:
															__lowerCamelCase		       =    max(array.shape[0] for array in raw_audio							)
															__lowerCamelCase		       =    int(np.ceil(max_length / self.chunk_stride							)							)
															__lowerCamelCase		       =    (nb_step - 1) * self.chunk_stride + self.chunk_length
															__lowerCamelCase		       =    'max_length'
											else:
															__lowerCamelCase		       =    input_values

        # normal padding on batch
							if padded_inputs is None:
											__lowerCamelCase		       =    self.pad(
											    lowerCamelCase__			,						max_length=lowerCamelCase__			,						truncation=lowerCamelCase__			,						padding=lowerCamelCase__			,						return_attention_mask=lowerCamelCase__			,						)
											if padding:
															__lowerCamelCase		       =    padded_inputs.pop('attention_mask'							)

							__lowerCamelCase		       =    []
							for example in padded_inputs.pop('input_values'							):
											if self.feature_size == 1:
															__lowerCamelCase		       =    example[..., None]
											input_values.append(example.T							)

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

							return padded_inputs








from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class     __lowerCAmelCase :

			"""simple docstring"""







			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42


			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							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       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							return torch.from_numpy(np.array([self.width, self.height]			,						dtype=np.floataa							)							)


			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''simple docstring'''
							return torch.from_numpy(np.array([self.x_fov, self.y_fov]			,						dtype=np.floataa							)							)


			def       lowercase_    (      self							)    ->      torch.Tensor:



							'''simple docstring'''
							__lowerCamelCase		       =    torch.arange(self.height * self.width							)
							__lowerCamelCase		       =    torch.stack(
							    [
							        pixel_indices % self.width,
							        torch.div(lowerCamelCase__			,						self.width			,						rounding_mode='trunc'							),
							    ]			,						axis=1			,						)
							return coords


			@property
			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase       ,						*__lowerCamelCase		       =    self.shape
							__lowerCamelCase		       =    int(np.prod(lowerCamelCase__							)							)

							__lowerCamelCase		       =    self.get_image_coords()
							__lowerCamelCase		       =    torch.broadcast_to(coords.unsqueeze(0							)			,						[batch_size * inner_batch_size, *coords.shape]							)
							__lowerCamelCase		       =    self.get_camera_rays(lowerCamelCase__							)

							__lowerCamelCase		       =    rays.view(lowerCamelCase__			,						inner_batch_size * self.height * self.width			,						2			,						3							)

							return rays


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      torch.Tensor:



							'''simple docstring'''
							__lowerCamelCase       ,						*__lowerCamelCase       ,						__lowerCamelCase		       =    coords.shape
							assert n_coords == 2
							assert batch_size == self.origin.shape[0]

							__lowerCamelCase		       =    coords.view(lowerCamelCase__			,						-1			,						2							)

							__lowerCamelCase		       =    self.resolution()
							__lowerCamelCase		       =    self.fov()

							__lowerCamelCase		       =    (flat.float() / (res - 1)) * 2 - 1
							__lowerCamelCase		       =    fracs * torch.tan(fov / 2							)

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






			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      "DifferentiableProjectiveCamera":



							'''simple docstring'''
							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=lowerCamelCase__			,						height=lowerCamelCase__			,						x_fov=self.x_fov			,						y_fov=self.y_fov			,						)







def 			lowerCamelCase_		(       UpperCamelCase__				:      int      )     ->       DifferentiableProjectiveCamera:





				"""simple docstring"""


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


