




import os
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


SCREAMING_SNAKE_CASE:     Tuple									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     List[str]									= {'''vocab_file''': '''sentencepiece.bpe.model'''}

SCREAMING_SNAKE_CASE:     Tuple									= {
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
}

SCREAMING_SNAKE_CASE:     Any									= {
    '''moussaKam/mbarthez''': 1_0_2_4,
    '''moussaKam/barthez''': 1_0_2_4,
    '''moussaKam/barthez-orangesum-title''': 1_0_2_4,
}

SCREAMING_SNAKE_CASE:     Dict									= '''▁'''



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =VOCAB_FILES_NAMES
						lowerCAmelCase__			      =PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase__			      =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase__			      =["input_ids", "attention_mask"]



						def __init__(			self	:   Union[str, Any]					,							snake_case__	:   List[str]					,							snake_case__	:   Dict="<s>"					,							snake_case__	:   Union[str, Any]="</s>"					,							snake_case__	:   int="</s>"					,							snake_case__	:   str="<s>"					,							snake_case__	:   Optional[Any]="<unk>"					,							snake_case__	:   Any="<pad>"					,							snake_case__	:   Tuple="<mask>"					,							snake_case__	:   Optional[Dict[str, Any]] = None					,							**snake_case__	:   Optional[Any]					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							AddedToken(snake_case__					,							lstrip=snake_case__					,							rstrip=snake_case__  ) if isinstance(snake_case__					,							snake_case__  ) else mask_token

								SCREAMING_SNAKE_CASE_					      =							{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=snake_case__					,							eos_token=snake_case__					,							unk_token=snake_case__					,							sep_token=snake_case__					,							cls_token=snake_case__					,							pad_token=snake_case__					,							mask_token=snake_case__					,							sp_model_kwargs=self.sp_model_kwargs					,							**snake_case__					,							)

								SCREAMING_SNAKE_CASE_					      =							vocab_file
								SCREAMING_SNAKE_CASE_					      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(str(snake_case__  )  )

								SCREAMING_SNAKE_CASE_					      =							{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

								SCREAMING_SNAKE_CASE_					      =							len(self.sp_model  ) - 1
								SCREAMING_SNAKE_CASE_					      =							{v: k for k, v in self.fairseq_tokens_to_ids.items()}



						def 	__a					(			self	:   Optional[int]					,							snake_case__	:   List[int]					,							snake_case__	:   Optional[List[int]] = None  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Any					,							snake_case__	:   List[int]					,							snake_case__	:   Optional[List[int]] = None					,							snake_case__	:   bool = False  ):

								"""simple docstring"""

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

								if token_ids_a is None:
										return [1] + ([0] * len(snake_case__  )) + [1]
								return [1] + ([0] * len(snake_case__  )) + [1, 1] + ([0] * len(snake_case__  )) + [1]



						def 	__a					(			self	:   List[str]					,							snake_case__	:   List[int]					,							snake_case__	:   Optional[List[int]] = None  ):

								"""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 + sep + token_ids_a + sep  ) * [0]



						@property
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								return len(self.sp_model  )



						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   List[str]					,							snake_case__	:   str  ):

								"""simple docstring"""

								return self.sp_model.encode(snake_case__					,							out_type=snake_case__  )



						def 	__a					(			self	:   int					,							snake_case__	:   Union[str, Any]  ):

								"""simple docstring"""

								if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
								SCREAMING_SNAKE_CASE_					      =							self.sp_model.PieceToId(snake_case__  )

								return spm_id if spm_id else self.unk_token_id



						def 	__a					(			self	:   List[str]					,							snake_case__	:   int  ):

								"""simple docstring"""

								if index in self.fairseq_ids_to_tokens:
										return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(snake_case__  )



						def 	__a					(			self	:   Any					,							snake_case__	:   Tuple  ):

								"""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(snake_case__  ) + token
												SCREAMING_SNAKE_CASE_					      =							True
												SCREAMING_SNAKE_CASE_					      =							[]
										else:
												current_sub_tokens.append(snake_case__  )
												SCREAMING_SNAKE_CASE_					      =							False
								out_string += self.sp_model.decode(snake_case__  )
								return out_string.strip()



						def __getstate__(			self	:   int  ):

								"""simple docstring"""

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



						def __setstate__(			self	:   int					,							snake_case__	:   Optional[int]  ):

								"""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 	__a					(			self	:   Tuple					,							snake_case__	:   str					,							snake_case__	:   Optional[str] = None  ):

								"""simple docstring"""

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

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

								return (out_vocab_file,)





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



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



						def 	__a					(			self	:   Optional[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 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							torch.arange(self.height * self.width  )
								SCREAMING_SNAKE_CASE_					      =							torch.stack(
								    [
								        pixel_indices % self.width,
								        torch.div(snake_case__					,							self.width					,							rounding_mode='trunc'  ),
								    ]					,							axis=1					,							)
								return coords



						@property
						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				*SCREAMING_SNAKE_CASE_					      =							self.shape
								SCREAMING_SNAKE_CASE_					      =							int(np.prod(snake_case__  )  )

								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(snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							rays.view(snake_case__					,							inner_batch_size * self.height * self.width					,							2					,							3  )

								return rays



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






						def 	__a					(			self	:   Optional[int]					,							snake_case__	:   int					,							snake_case__	:   int  ):

								"""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=snake_case__					,							height=snake_case__					,							x_fov=self.x_fov					,							y_fov=self.y_fov					,							)


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