





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_xlnet import XLNetTokenizer
else:
      SCREAMING_SNAKE_CASE__		:					Tuple						=					None


SCREAMING_SNAKE_CASE__		:					int						=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:					Tuple						=					{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__		:					List[Any]						=					{
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

SCREAMING_SNAKE_CASE__		:					int						=					{
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

SCREAMING_SNAKE_CASE__		:					Any						=					"▁"

# Segments (not really needed)
SCREAMING_SNAKE_CASE__		:					Any						=					0
SCREAMING_SNAKE_CASE__		:					Any						=					1
SCREAMING_SNAKE_CASE__		:					Union[str, Any]						=					2
SCREAMING_SNAKE_CASE__		:					List[str]						=					3
SCREAMING_SNAKE_CASE__		:					Tuple						=					4

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     VOCAB_FILES_NAMES
  lowercase_			=     PRETRAINED_VOCAB_FILES_MAP
  lowercase_			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase_			=     'left'
  lowercase_			=     XLNetTokenizer




  def __init__(      self     :	Union[str, Any]     , a_     :	int=None     , a_     :	Dict=None     , a_     :	Optional[Any]=False     , a_     :	Tuple=True     , a_     :	Dict=False     , a_     :	str="<s>"     , a_     :	str="</s>"     , a_     :	int="<unk>"     , a_     :	Tuple="<sep>"     , a_     :	Union[str, Any]="<pad>"     , a_     :	List[str]="<cls>"     , a_     :	List[Any]="<mask>"     , a_     :	List[str]=["<eop>", "<eod>"]     , **a_     :	int     , )->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		AddedToken(a_     , lstrip=a_     , rstrip=a_				) if isinstance(a_     , a_				) else mask_token

       super().__init__(
           vocab_file=a_     , tokenizer_file=a_     , do_lower_case=a_     , remove_space=a_     , keep_accents=a_     , bos_token=a_     , eos_token=a_     , unk_token=a_     , sep_token=a_     , pad_token=a_     , cls_token=a_     , mask_token=a_     , additional_special_tokens=a_     , **a_     , )

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		3
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		do_lower_case
       SCREAMING_SNAKE_CASE__				:				str			=		remove_space
       SCREAMING_SNAKE_CASE__				:				Tuple			=		keep_accents
       SCREAMING_SNAKE_CASE__				:				str			=		vocab_file
       SCREAMING_SNAKE_CASE__				:				Any			=		False if not self.vocab_file else True




  def 	__lowercase(      self     :	Any     , a_     :	List[int]     , a_     :	Optional[List[int]] = None				)->       List[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		[self.sep_token_id]
       SCREAMING_SNAKE_CASE__				:				Dict			=		[self.cls_token_id]
       if token_ids_a is None:
            return token_ids_a + sep + cls
       return token_ids_a + sep + token_ids_a + sep + cls




  def 	__lowercase(      self     :	str     , a_     :	List[int]     , a_     :	Optional[List[int]] = None				)->       List[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[self.sep_token_id]
       SCREAMING_SNAKE_CASE__				:				Dict			=		[2]

       if token_ids_a is None:
            return len(token_ids_a + sep				) * [0] + cls_segment_id
       return len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1] + cls_segment_id




  def 	__lowercase(      self     :	List[Any]     , a_     :	str     , a_     :	Optional[str] = None				)->       Tuple[str]:




       """simple docstring"""
       if not self.can_save_slow_tokenizer:
            raise ValueError(
                'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                'tokenizer.'				)

       if not os.path.isdir(a_				):
            logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
            return
       SCREAMING_SNAKE_CASE__				:				Tuple			=		os.path.join(
           a_     , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

       if os.path.abspath(self.vocab_file				) != os.path.abspath(a_				):
            copyfile(self.vocab_file     , a_				)

       return (out_vocab_file,)




from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class       snake_case					:
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     42




  def 	__lowercase(      self     :	List[Any]				)->       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     :	Dict				)->       Tuple:




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




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




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




  def 	__lowercase(      self     :	Tuple				)->       torch.Tensor:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		torch.arange(self.height * self.width				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.stack(
           [
               pixel_indices % self.width,
               torch.div(a_     , self.width     , rounding_mode='trunc'				),
           ]     , axis=1     , )
       return coords




  @property
  def 	__lowercase(      self     :	Any				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							*SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.shape
       SCREAMING_SNAKE_CASE__				:				Tuple			=		int(np.prod(a_				)				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.get_image_coords()
       SCREAMING_SNAKE_CASE__				:				Dict			=		torch.broadcast_to(coords.unsqueeze(0				)     , [batch_size * inner_batch_size, *coords.shape]				)
       SCREAMING_SNAKE_CASE__				:				Any			=		self.get_camera_rays(a_				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		rays.view(a_     , inner_batch_size * self.height * self.width     , 2     , 3				)

       return rays




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	torch.Tensor				)->       torch.Tensor:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							*SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Dict			=		coords.shape
       assert n_coords == 2
       assert batch_size == self.origin.shape[0]

       SCREAMING_SNAKE_CASE__				:				str			=		coords.view(a_     , -1     , 2				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.resolution()
       SCREAMING_SNAKE_CASE__				:				str			=		self.fov()

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

       SCREAMING_SNAKE_CASE__				:				List[str]			=		fracs.view(a_     , -1     , 2				)
       SCREAMING_SNAKE_CASE__				:				str			=		(
           self.z.view(a_     , 1     , 3				)
           + self.x.view(a_     , 1     , 3				) * fracs[:, :, :1]
           + self.y.view(a_     , 1     , 3				) * fracs[:, :, 1:]
       )
       SCREAMING_SNAKE_CASE__				:				Tuple			=		directions / directions.norm(dim=-1     , keepdim=a_				)
       SCREAMING_SNAKE_CASE__				:				Any			=		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 	__lowercase(      self     :	Optional[int]     , a_     :	int     , a_     :	int				)->       "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=a_     , height=a_     , x_fov=self.x_fov     , y_fov=self.y_fov     , )







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		[]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[]
     SCREAMING_SNAKE_CASE__				:				str			=		[]
     for theta in np.linspace(0     ,  2 * np.pi     ,  num=20 ):
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		np.array([np.sin(lowercase__ ), np.cos(lowercase__ ), -0.5] )
          z /= np.sqrt(np.sum(z**2 ) )
          SCREAMING_SNAKE_CASE__				:				Tuple			=		-z * 4
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		np.array([np.cos(lowercase__ ), -np.sin(lowercase__ ), 0.0] )
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		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__ ))     ,  )



