"""simple docstring"""




import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase   :				Tuple     			=						logging.get_logger(__name__)
_lowerCamelCase   :				Optional[Any]     			=						{'''vocab_file''': '''spiece.model'''}

_lowerCamelCase   :				Optional[int]     			=						{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    }
}

_lowerCamelCase   :				int     			=						{
    '''albert-base-v1''': 5_12,
    '''albert-large-v1''': 5_12,
    '''albert-xlarge-v1''': 5_12,
    '''albert-xxlarge-v1''': 5_12,
    '''albert-base-v2''': 5_12,
    '''albert-large-v2''': 5_12,
    '''albert-xlarge-v2''': 5_12,
    '''albert-xxlarge-v2''': 5_12,
}

_lowerCamelCase   :				str     			=						'''▁'''


class   lowercase      (       a       ):
							lowercase__  :    int									=							VOCAB_FILES_NAMES
							lowercase__  :    Tuple									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Dict									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




							def __init__(  self				:   Optional[Any]		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   List[Any]=True		,							_UpperCamelCase				:   Union[str, Any]=True		,							_UpperCamelCase				:   Tuple=False		,							_UpperCamelCase				:   Optional[Any]="[CLS]"		,							_UpperCamelCase				:   Tuple="[SEP]"		,							_UpperCamelCase				:   Tuple="<unk>"		,							_UpperCamelCase				:   str="[SEP]"		,							_UpperCamelCase				:   Union[str, Any]="<pad>"		,							_UpperCamelCase				:   Any="[CLS]"		,							_UpperCamelCase				:   str="[MASK]"		,							_UpperCamelCase				:   Optional[Dict[str, Any]] = None		,							**_UpperCamelCase				:   List[str]		,							)   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    (
												    AddedToken(_UpperCamelCase		,							lstrip=_UpperCamelCase		,							rstrip=_UpperCamelCase		,							normalized=_UpperCamelCase )
												    if isinstance(_UpperCamelCase		,							_UpperCamelCase )
												    else mask_token
												)

												SCREAMING_SNAKE_CASE           =    {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    do_lower_case=_UpperCamelCase		,							remove_space=_UpperCamelCase		,							keep_accents=_UpperCamelCase		,							bos_token=_UpperCamelCase		,							eos_token=_UpperCamelCase		,							unk_token=_UpperCamelCase		,							sep_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							cls_token=_UpperCamelCase		,							mask_token=_UpperCamelCase		,							sp_model_kwargs=self.sp_model_kwargs		,							**_UpperCamelCase		,							)

												SCREAMING_SNAKE_CASE           =    do_lower_case
												SCREAMING_SNAKE_CASE           =    remove_space
												SCREAMING_SNAKE_CASE           =    keep_accents
												SCREAMING_SNAKE_CASE           =    vocab_file

												SCREAMING_SNAKE_CASE           =    spm.SentencePieceProcessor(**self.sp_model_kwargs )
												self.sp_model.Load(_UpperCamelCase )




							@property
							def 		__snake_case(  self				:   Dict )   ->			Dict:



												'''simple docstring'''

												return len(self.sp_model )




							def 		__snake_case(  self				:   int )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    {self.convert_ids_to_tokens(_UpperCamelCase ): i for i in range(self.vocab_size )}
												vocab.update(self.added_tokens_encoder )
												return vocab




							def __getstate__(  self				:   Dict )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.__dict__.copy()
												SCREAMING_SNAKE_CASE           =    None
												return state




							def __setstate__(  self				:   List[Any]		,							_UpperCamelCase				:   List[Any] )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    d

												# for backward compatibility
												if not hasattr(self		,							"sp_model_kwargs" ):
																	SCREAMING_SNAKE_CASE           =    {}

												SCREAMING_SNAKE_CASE           =    spm.SentencePieceProcessor(**self.sp_model_kwargs )
												self.sp_model.Load(self.vocab_file )




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



												'''simple docstring'''

												if self.remove_space:
																	SCREAMING_SNAKE_CASE           =    " ".join(inputs.strip().split() )
												else:
																	SCREAMING_SNAKE_CASE           =    inputs
												SCREAMING_SNAKE_CASE           =    outputs.replace("``"		,							"\"" ).replace("''"		,							"\"" )

												if not self.keep_accents:
																	SCREAMING_SNAKE_CASE           =    unicodedata.normalize("NFKD"		,							_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    "".join([c for c in outputs if not unicodedata.combining(_UpperCamelCase )] )
												if self.do_lower_case:
																	SCREAMING_SNAKE_CASE           =    outputs.lower()

												return outputs




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   str )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.preprocess_text(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    self.sp_model.encode(_UpperCamelCase		,							out_type=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    []
												for piece in pieces:
																	if len(_UpperCamelCase ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
																						SCREAMING_SNAKE_CASE           =    self.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCamelCase		,							"" ) )
																						if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																											if len(cur_pieces[0] ) == 1:
																																SCREAMING_SNAKE_CASE           =    cur_pieces[1:]
																											else:
																																SCREAMING_SNAKE_CASE           =    cur_pieces[0][1:]
																						cur_pieces.append(piece[-1] )
																						new_pieces.extend(_UpperCamelCase )
																	else:
																						new_pieces.append(_UpperCamelCase )

												return new_pieces




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   Dict )   ->			str:



												'''simple docstring'''

												return self.sp_model.PieceToId(_UpperCamelCase )




							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   Dict )   ->			Tuple:



												'''simple docstring'''

												return self.sp_model.IdToPiece(_UpperCamelCase )




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   Union[str, Any] )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    []
												SCREAMING_SNAKE_CASE           =    ""
												SCREAMING_SNAKE_CASE           =    False
												for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																						if not prev_is_special:
																											out_string += " "
																						out_string += self.sp_model.decode(_UpperCamelCase ) + token
																						SCREAMING_SNAKE_CASE           =    True
																						SCREAMING_SNAKE_CASE           =    []
																	else:
																						current_sub_tokens.append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    False
												out_string += self.sp_model.decode(_UpperCamelCase )
												return out_string.strip()




							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_SNAKE_CASE           =    [self.cls_token_id]
												if token_ids_a is None:
																	return cls + token_ids_a + sep
												return cls + token_ids_a + sep + token_ids_a + sep




							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None		,							_UpperCamelCase				:   bool = False )   ->			List[int]:



												'''simple docstring'''

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=_UpperCamelCase		,							token_ids_a=_UpperCamelCase		,							already_has_special_tokens=_UpperCamelCase )

												if token_ids_a is not None:
																	return [1] + ([0] * len(_UpperCamelCase )) + [1] + ([0] * len(_UpperCamelCase )) + [1]
												return [1] + ([0] * len(_UpperCamelCase )) + [1]




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_SNAKE_CASE           =    [self.cls_token_id]

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





							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

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

												if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCamelCase ) and os.path.isfile(self.vocab_file ):
																	copyfile(self.vocab_file		,							_UpperCamelCase )
												elif not os.path.isfile(self.vocab_file ):
																	with open(_UpperCamelCase		,							"wb" ) as fi:
																						SCREAMING_SNAKE_CASE           =    self.sp_model.serialized_model_proto()
																						fi.write(_UpperCamelCase )

												return (out_vocab_file,)





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch


@dataclass
class   lowercase      :
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    int
							lowercase__  :    int
							lowercase__  :    float
							lowercase__  :    float
							lowercase__  :    Tuple[int]




							def 		__snake_case(  self				:   str )   ->			Dict:



												'''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 		__snake_case(  self				:   int )   ->			str:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Tuple )   ->			List[str]:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Any )   ->			torch.Tensor:



												'''simple docstring'''

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




							@property
							def 		__snake_case(  self				:   Any )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				*SCREAMING_SNAKE_CASE           =    self.shape
												SCREAMING_SNAKE_CASE           =    int(np.prod(_UpperCamelCase ) )

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

												SCREAMING_SNAKE_CASE           =    rays.view(_UpperCamelCase		,							inner_batch_size * self.height * self.width		,							2		,							3 )

												return rays




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   torch.Tensor )   ->			torch.Tensor:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE           =    coords.view(_UpperCamelCase		,							-1		,							2 )

												SCREAMING_SNAKE_CASE           =    self.resolution()
												SCREAMING_SNAKE_CASE           =    self.fov()

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

												SCREAMING_SNAKE_CASE           =    fracs.view(_UpperCamelCase		,							-1		,							2 )
												SCREAMING_SNAKE_CASE           =    (
												    self.z.view(_UpperCamelCase		,							1		,							3 )
												    + self.x.view(_UpperCamelCase		,							1		,							3 ) * fracs[:, :, :1]
												    + self.y.view(_UpperCamelCase		,							1		,							3 ) * fracs[:, :, 1:]
												)
												SCREAMING_SNAKE_CASE           =    directions / directions.norm(dim=-1		,							keepdim=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    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 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   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=_UpperCamelCase		,							height=_UpperCamelCase		,							x_fov=self.x_fov		,							y_fov=self.y_fov		,							)





def 		__lowerCamelCase							(UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    []
					for theta in np.linspace(0 ,	2 * np.pi ,	num=2_0					):
										SCREAMING_SNAKE_CASE           =    np.array([np.sin(UpperCAmelCase__					), np.cos(UpperCAmelCase__					), -0.5]					)
										z /= np.sqrt(np.sum(z**2					)					)
										SCREAMING_SNAKE_CASE           =    -z * 4
										SCREAMING_SNAKE_CASE           =    np.array([np.cos(UpperCAmelCase__					), -np.sin(UpperCAmelCase__					), 0.0]					)
										SCREAMING_SNAKE_CASE           =    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__					)) ,	)




