





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class     snake_case_		:






 '''simple docstring'''





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

 def  __UpperCAmelCase						(  self		)	->       Dict:
    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  __UpperCAmelCase						(  self		)	->       Tuple:
    return torch.from_numpy(np.array([self.width, self.height],		dtype=np.floataa		)		)

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    return torch.from_numpy(np.array([self.x_fov, self.y_fov],		dtype=np.floataa		)		)

 def  __UpperCAmelCase						(  self		)	->       torch.Tensor:
    UpperCAmelCase__						=torch.arange(self.height * self.width		)
    UpperCAmelCase__						=torch.stack(
        [
            pixel_indices % self.width,
            torch.div(A_,		self.width,		rounding_mode="trunc"		),
        ],		axis=1,		)
    return coords

 @property
 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__			,							*UpperCAmelCase__						=self.shape
    UpperCAmelCase__						=int(np.prod(A_		)		)

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

    UpperCAmelCase__						=rays.view(A_,		inner_batch_size * self.height * self.width,		2,		3		)

    return rays

 def  __UpperCAmelCase						(  self,		A_		)	->       torch.Tensor:
    UpperCAmelCase__			,							*UpperCAmelCase__			,							UpperCAmelCase__						=coords.shape
    assert n_coords == 2
    assert batch_size == self.origin.shape[0]

    UpperCAmelCase__						=coords.view(A_,		-1,		2		)

    UpperCAmelCase__						=self.resolution()
    UpperCAmelCase__						=self.fov()

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

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






 def  __UpperCAmelCase						(  self,		A_,		A_		)	->       "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=A_,		height=A_,		x_fov=self.x_fov,		y_fov=self.y_fov,		)






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

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










from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'EncodecFeatureExtractor'
 __UpperCamelCase       =      ('T5Tokenizer', 'T5TokenizerFast')

 def __init__(  self,		A_,		A_		)	->       Optional[int]:
    super().__init__(A_,		A_		)
    UpperCAmelCase__						=self.feature_extractor
    UpperCAmelCase__						=False

 def  __UpperCAmelCase						(  self,		A_=None,		A_=None,		A_=True		)	->       Union[str, Any]:
    return self.tokenizer.get_decoder_prompt_ids(task=A_,		language=A_,		no_timestamps=A_		)

 def __call__(  self,		*A_,		**A_		)	->       Any:
    # For backward compatibility
    if self._in_target_context_manager:
       return self.current_processor(*A_,		**A_		)

    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("sampling_rate",		A_		)
    UpperCAmelCase__						=kwargs.pop("text",		A_		)
    if len(A_		) > 0:
       UpperCAmelCase__						=args[0]
       UpperCAmelCase__						=args[1:]

    if audio is None and text is None:
       raise ValueError("You need to specify either an `audio` or `text` input to process."		)

    if text is not None:
       UpperCAmelCase__						=self.tokenizer(A_,		**A_		)

    if audio is not None:
       UpperCAmelCase__						=self.feature_extractor(A_,		*A_,		sampling_rate=A_,		**A_		)

    if audio is None:
       return inputs

    elif text is None:
       return audio_inputs

    else:
       UpperCAmelCase__						=audio_inputs["input_values"]
       if "padding_mask" in audio_inputs:
          UpperCAmelCase__						=audio_inputs["padding_mask"]
       return inputs

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       Dict:
    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("padding_mask",		A_		)

    if len(A_		) > 0:
       UpperCAmelCase__						=args[0]
       UpperCAmelCase__						=args[1:]

    if audio_values is not None:
       return self._decode_audio(A_,		padding_mask=A_		)
    else:
       return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)






 def  __UpperCAmelCase						(  self,		A_,		A_ = None		)	->       List[np.ndarray]:
    UpperCAmelCase__						=to_numpy(A_		)
    UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=audio_values.shape

    if padding_mask is None:
       return list(A_		)

    UpperCAmelCase__						=to_numpy(A_		)

    # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
    # token (so that the generated audio values are **not** treated as padded tokens)
    UpperCAmelCase__						=seq_len - padding_mask.shape[-1]
    UpperCAmelCase__						=1 - self.feature_extractor.padding_value
    UpperCAmelCase__						=np.pad(A_,		((0, 0), (0, difference)),		"constant",		constant_values=A_		)

    UpperCAmelCase__						=audio_values.tolist()
    for i in range(A_		):
       UpperCAmelCase__						=np.asarray(audio_values[i]		)[
           padding_mask[i][None, :] != self.feature_extractor.padding_value
       ]
       UpperCAmelCase__						=sliced_audio.reshape(A_,		-1		)

    return audio_values



