






from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class 							SCREAMING_SNAKE_CASE__		:





     """simple docstring"""






     a_			=	4_2  # [batch_size x 3]
     a_			=	4_2  # [batch_size x 3]
     a_			=	4_2  # [batch_size x 3]
     a_			=	4_2  # [batch_size x 3]
     a_			=	4_2
     a_			=	4_2
     a_			=	4_2
     a_			=	4_2
     a_			=	4_2



     def  _lowercase  (      self  :						List[Any] ):
      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  :						str ):
      return torch.from_numpy(np.array([self.width, self.height]							,	dtype=np.floataa ) )



     def  _lowercase  (      self  :						List[Any] ):
      return torch.from_numpy(np.array([self.x_fov, self.y_fov]							,	dtype=np.floataa ) )



     def  _lowercase  (      self  :						str ):
      snake_case__				:   List[str]     =						torch.arange(self.height * self.width )
      snake_case__				:   List[str]     =						torch.stack(
          [
              pixel_indices % self.width,
              torch.div(UpperCamelCase__							,	self.width							,	rounding_mode="trunc" ),
          ]							,	axis=1							,	)
      return coords



     @property
     def  _lowercase  (      self  :						List[Any] ):
      snake_case__				:   List[Any]     =						self.shape
      snake_case__				:   List[str]     =						int(np.prod(UpperCamelCase__ ) )

      snake_case__				:   Union[str, Any]     =						self.get_image_coords()
      snake_case__				:   List[str]     =						torch.broadcast_to(coords.unsqueeze(0 )							,	[batch_size * inner_batch_size, *coords.shape] )
      snake_case__				:   List[str]     =						self.get_camera_rays(UpperCamelCase__ )

      snake_case__				:   str     =						rays.view(UpperCamelCase__							,	inner_batch_size * self.height * self.width							,	2							,	3 )

      return rays



     def  _lowercase  (      self  :						Dict							,	__A  :						torch.Tensor ):
      snake_case__				:   Optional[Any]     =						coords.shape
      assert n_coords == 2
      assert batch_size == self.origin.shape[0]

      snake_case__				:   Union[str, Any]     =						coords.view(UpperCamelCase__							,	-1							,	2 )

      snake_case__				:   Any     =						self.resolution()
      snake_case__				:   str     =						self.fov()

      snake_case__				:   str     =						(flat.float() / (res - 1)) * 2 - 1
      snake_case__				:   List[str]     =						fracs * torch.tan(fov / 2 )

      snake_case__				:   int     =						fracs.view(UpperCamelCase__							,	-1							,	2 )
      snake_case__				:   Optional[Any]     =						(
          self.z.view(UpperCamelCase__							,	1							,	3 )
          + self.x.view(UpperCamelCase__							,	1							,	3 ) * fracs[:, :, :1]
          + self.y.view(UpperCamelCase__							,	1							,	3 ) * fracs[:, :, 1:]
      )
      snake_case__				:   Tuple     =						directions / directions.norm(dim=-1							,	keepdim=UpperCamelCase__ )
      snake_case__				:   Union[str, Any]     =						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  _lowercase  (      self  :						Union[str, Any]							,	__A  :						int							,	__A  :						int ):
      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   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[str]     ):
 snake_case__				:   Tuple     =						[]
 snake_case__				:   Dict     =						[]
 snake_case__				:   List[str]     =						[]
 snake_case__				:   Tuple     =						[]
 for theta in np.linspace(0  ,						2 * np.pi  ,						num=20     ):
  snake_case__				:   int     =						np.array([np.sin(_lowercase     ), np.cos(_lowercase     ), -0.5]     )
  z /= np.sqrt(np.sum(z**2     )     )
  snake_case__				:   Tuple     =						-z * 4
  snake_case__				:   Optional[int]     =						np.array([np.cos(_lowercase     ), -np.sin(_lowercase     ), 0.0]     )
  snake_case__				:   Tuple     =						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     ))  ,						)











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase      :    List[str]   		=   {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    str   		=   ["""XLNetTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Dict   		=   ["""XLNetTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    str   		=   [
	    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLNetForMultipleChoice""",
	    """XLNetForQuestionAnswering""",
	    """XLNetForQuestionAnsweringSimple""",
	    """XLNetForSequenceClassification""",
	    """XLNetForTokenClassification""",
	    """XLNetLMHeadModel""",
	    """XLNetModel""",
	    """XLNetPreTrainedModel""",
	    """load_tf_weights_in_xlnet""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Dict   		=   [
	    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLNetForMultipleChoice""",
	    """TFXLNetForQuestionAnsweringSimple""",
	    """TFXLNetForSequenceClassification""",
	    """TFXLNetForTokenClassification""",
	    """TFXLNetLMHeadModel""",
	    """TFXLNetMainLayer""",
	    """TFXLNetModel""",
	    """TFXLNetPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet import XLNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet_fast import XLNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlnet import (
		    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLNetForMultipleChoice,
		    XLNetForQuestionAnswering,
		    XLNetForQuestionAnsweringSimple,
		    XLNetForSequenceClassification,
		    XLNetForTokenClassification,
		    XLNetLMHeadModel,
		    XLNetModel,
		    XLNetPreTrainedModel,
		    load_tf_weights_in_xlnet,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlnet import (
		    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLNetForMultipleChoice,
		    TFXLNetForQuestionAnsweringSimple,
		    TFXLNetForSequenceClassification,
		    TFXLNetForTokenClassification,
		    TFXLNetLMHeadModel,
		    TFXLNetMainLayer,
		    TFXLNetModel,
		    TFXLNetPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase      :    int   		=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



