





"""simple docstring"""





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_big_bird import BigBirdTokenizer
else:
							_UpperCamelCase					      =   None

_UpperCamelCase					      =   logging.get_logger(__name__)
_UpperCamelCase					      =   {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase					      =   {
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    },
    'tokenizer_file': {
        'google/bigbird-roberta-base': (
            'https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'
        ),
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'
        ),
    },
}

_UpperCamelCase					      =   {
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}


_UpperCamelCase					      =   '▁'
class 		SCREAMING_SNAKE_CASE_				(     UpperCamelCase__ ):
				"""simple docstring"""
				__snake_case :							str									=		VOCAB_FILES_NAMES
				__snake_case :							int									=		PRETRAINED_VOCAB_FILES_MAP
				__snake_case :							Tuple									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__snake_case :							Dict									=		BigBirdTokenizer
				__snake_case :							int									=		["""input_ids""", """attention_mask"""]
				__snake_case :							int									=		[]




				def __init__(  self	:str    ,     __lowercase	:Tuple=None    ,     __lowercase	:Optional[Any]=None    ,     __lowercase	:Any="<unk>"    ,     __lowercase	:Any="<s>"    ,     __lowercase	:Union[str, Any]="</s>"    ,     __lowercase	:List[str]="<pad>"    ,     __lowercase	:Optional[Any]="[SEP]"    ,     __lowercase	:str="[MASK]"    ,     __lowercase	:Optional[Any]="[CLS]"    ,     **__lowercase	:Union[str, Any]    ,     ):
											__lowerCamelCase   :   Optional[Any]       =AddedToken(__A    ,     lstrip=__A    ,     rstrip=__A      ) if isinstance(__A    ,     __A      ) else bos_token
											__lowerCamelCase   :   Any       =AddedToken(__A    ,     lstrip=__A    ,     rstrip=__A      ) if isinstance(__A    ,     __A      ) else eos_token
											__lowerCamelCase   :   Any       =AddedToken(__A    ,     lstrip=__A    ,     rstrip=__A      ) if isinstance(__A    ,     __A      ) else unk_token
											__lowerCamelCase   :   str       =AddedToken(__A    ,     lstrip=__A    ,     rstrip=__A      ) if isinstance(__A    ,     __A      ) else pad_token
											__lowerCamelCase   :   int       =AddedToken(__A    ,     lstrip=__A    ,     rstrip=__A      ) if isinstance(__A    ,     __A      ) else cls_token
											__lowerCamelCase   :   Optional[int]       =AddedToken(__A    ,     lstrip=__A    ,     rstrip=__A      ) if isinstance(__A    ,     __A      ) else sep_token

											# Mask token behave like a normal word, i.e. include the space before it
											__lowerCamelCase   :   Optional[int]       =AddedToken(__A    ,     lstrip=__A    ,     rstrip=__A      ) if isinstance(__A    ,     __A      ) else mask_token

											super().__init__(
											    __A    ,     tokenizer_file=__A    ,     bos_token=__A    ,     eos_token=__A    ,     unk_token=__A    ,     sep_token=__A    ,     pad_token=__A    ,     cls_token=__A    ,     mask_token=__A    ,     **__A    ,     )

											__lowerCamelCase   :   Optional[int]       =vocab_file
											__lowerCamelCase   :   Optional[Any]       =False if not self.vocab_file else True




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:List[int]    ,     __lowercase	:Optional[List[int]] = None      ):
											__lowerCamelCase   :   int       =[self.sep_token_id]
											__lowerCamelCase   :   Optional[int]       =[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        __lowercase	(  self	:Optional[int]    ,     __lowercase	:List[int]    ,     __lowercase	:Optional[List[int]] = None    ,     __lowercase	:bool = False      ):



											if already_has_special_tokens:
																		if token_ids_a is not None:
																									raise ValueError(
																									    '''You should not supply a second sequence if the provided sequence of '''
																									    '''ids is already formatted with special tokens for the model.'''      )
																		return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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




				def        __lowercase	(  self	:Optional[Any]    ,     __lowercase	:List[int]    ,     __lowercase	:Optional[List[int]] = None      ):
											__lowerCamelCase   :   Dict       =[self.sep_token_id]
											__lowerCamelCase   :   Tuple       =[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        __lowercase	(  self	:int    ,     __lowercase	:str    ,     __lowercase	:Optional[str] = None      ):



											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
											__lowerCamelCase   :   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 Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer




@dataclass
class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       42




class 	UpperCamelCase_							(  UpperCamelCase__					,	UpperCamelCase__     ):


							@register_to_config
							def __init__(				self     :Union[str, Any]				,					__A     :int = 3				,					__A     :int = 3				,					__A     :Tuple[str] = ("DownEncoderBlock2D",)				,					__A     :Tuple[str] = ("UpDecoderBlock2D",)				,					__A     :Tuple[int] = (64,)				,					__A     :int = 1				,					__A     :str = "silu"				,					__A     :int = 3				,					__A     :int = 32				,					__A     :int = 256				,					__A     :int = 32				,					__A     :Optional[int] = None				,					__A     :float = 0.1_8_2_1_5				,					__A     :str = "group"				,					) -> Any:



									"""simple docstring"""



									super().__init__()

									# pass init params to Encoder
									SCREAMING_SNAKE_CASE__								=   Encoder(
									    in_channels=__A				,					out_channels=__A				,					down_block_types=__A				,					block_out_channels=__A				,					layers_per_block=__A				,					act_fn=__A				,					norm_num_groups=__A				,					double_z=__A				,					)

									SCREAMING_SNAKE_CASE__								=   vq_embed_dim if vq_embed_dim is not None else latent_channels

									SCREAMING_SNAKE_CASE__								=   nn.Convad(__A				,					__A				,					1		)
									SCREAMING_SNAKE_CASE__								=   VectorQuantizer(__A				,					__A				,					beta=0.2_5				,					remap=__A				,					sane_index_shape=__A		)
									SCREAMING_SNAKE_CASE__								=   nn.Convad(__A				,					__A				,					1		)

									# pass init params to Decoder
									SCREAMING_SNAKE_CASE__								=   Decoder(
									    in_channels=__A				,					out_channels=__A				,					up_block_types=__A				,					block_out_channels=__A				,					layers_per_block=__A				,					act_fn=__A				,					norm_num_groups=__A				,					norm_type=__A				,					)


							@apply_forward_hook
							def   _snake_case	(				self     :Union[str, Any]				,					__A     :torch.FloatTensor				,					__A     :bool = True		) -> VQEncoderOutput:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.encoder(__A		)
									SCREAMING_SNAKE_CASE__								=   self.quant_conv(__A		)

									if not return_dict:
											return (h,)

									return VQEncoderOutput(latents=__A		)


							@apply_forward_hook
							def   _snake_case	(				self     :Tuple				,					__A     :torch.FloatTensor				,					__A     :bool = False				,					__A     :bool = True		) -> Union[DecoderOutput, torch.FloatTensor]:



									"""simple docstring"""



									if not force_not_quantize:
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.quantize(__A		)
									else:
											SCREAMING_SNAKE_CASE__								=   h
									SCREAMING_SNAKE_CASE__								=   self.post_quant_conv(__A		)
									SCREAMING_SNAKE_CASE__								=   self.decoder(__A				,					quant if self.config.norm_type == """spatial""" else None		)

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=__A		)


							def   _snake_case	(				self     :int				,					__A     :torch.FloatTensor				,					__A     :bool = True		) -> Union[DecoderOutput, torch.FloatTensor]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   sample
									SCREAMING_SNAKE_CASE__								=   self.encode(__A		).latents
									SCREAMING_SNAKE_CASE__								=   self.decode(__A		).sample

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=__A		)