






from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch


@dataclass
class      lowercase__     :
	a_         =42  # [batch_size x 3]
	a_         =42  # [batch_size x 3]
	a_         =42  # [batch_size x 3]
	a_         =42  # [batch_size x 3]
	a_         =42
	a_         =42
	a_         =42
	a_         =42
	a_         =42

	def 		UpperCAmelCase  (							self       )->					str:







							'''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 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self       )->					torch.Tensor:







							'''simple docstring'''





							lowerCAmelCase__       =						torch.arange(self.height * self.width       )
							lowerCAmelCase__       =						torch.stack(
							    [
							        pixel_indices % self.width,
							        torch.div(__UpperCAmelCase		,    self.width		,    rounding_mode="trunc"       ),
							    ]		,    axis=1		,    )
							return coords

	@property
	def 		UpperCAmelCase  (							self       )->					str:







							'''simple docstring'''





							lowerCAmelCase__      ,							*lowerCAmelCase__       =						self.shape
							lowerCAmelCase__       =						int(np.prod(__UpperCAmelCase       )       )

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

							lowerCAmelCase__       =						rays.view(__UpperCAmelCase		,    inner_batch_size * self.height * self.width		,    2		,    3       )

							return rays

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					torch.Tensor:







							'''simple docstring'''





							lowerCAmelCase__      ,							*lowerCAmelCase__      ,							lowerCAmelCase__       =						coords.shape
							assert n_coords == 2
							assert batch_size == self.origin.shape[0]

							lowerCAmelCase__       =						coords.view(__UpperCAmelCase		,    -1		,    2       )

							lowerCAmelCase__       =						self.resolution()
							lowerCAmelCase__       =						self.fov()

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

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


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





def        _a  (     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_   ))    ,   )














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_							=    {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SEWForCTC''',
	    '''SEWForSequenceClassification''',
	    '''SEWModel''',
	    '''SEWPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_sew import (
		    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SEWForCTC,
		    SEWForSequenceClassification,
		    SEWModel,
		    SEWPreTrainedModel,
		)


else:
	import sys

	a_							=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






