




"""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:
       a         =      None

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

a         =      {
    '''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'''
        ),
    },
}

a         =      {
    '''google/bigbird-roberta-base''': 4_096,
    '''google/bigbird-roberta-large''': 4_096,
    '''google/bigbird-base-trivia-itc''': 4_096,
}


a         =      '''▁'''





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Union[str, Any]				  =     VOCAB_FILES_NAMES
 UpperCAmelCase   :					int				  =     PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase   :					int				  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase   :					Optional[Any]				  =     BigBirdTokenizer
 UpperCAmelCase   :					Optional[int]				  =     ['''input_ids''', '''attention_mask''']
 UpperCAmelCase   :					List[int]				  =     []

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

       # Mask token behave like a normal word, i.e. include the space before it
       _A        =       AddedToken(_UpperCAmelCase   ,							lstrip=_UpperCAmelCase   ,							rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		) else mask_token

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

       _A        =       vocab_file
       _A        =       False if not self.vocab_file else True

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	List[int]   ,							_UpperCAmelCase	:	Optional[List[int]] = None		):
       _A        =       [self.sep_token_id]
       _A        =       [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       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	List[int]   ,							_UpperCAmelCase	:	Optional[List[int]] = None   ,							_UpperCAmelCase	:	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(_UpperCAmelCase		)) + [1]
       return [1] + ([0] * len(_UpperCAmelCase		)) + [1] + ([0] * len(_UpperCAmelCase		)) + [1]

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	List[int]   ,							_UpperCAmelCase	:	Optional[List[int]] = None		):
       _A        =       [self.sep_token_id]
       _A        =       [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       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	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(_UpperCAmelCase		):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
             return
       _A        =       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		):
             copyfile(self.vocab_file   ,							_UpperCAmelCase		)

       return (out_vocab_file,)







"""simple docstring"""

from dataclasses import dataclass
from typing import Dict, 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 .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder





@dataclass
class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					"DiagonalGaussianDistribution"





class     lowercase_	(					__lowerCAmelCase      ,     __lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					List[Any]				  =     True

 @register_to_config
 def __init__(			self	:	List[str]   ,							_UpperCAmelCase	:	int = 3   ,							_UpperCAmelCase	:	int = 3   ,							_UpperCAmelCase	:	Tuple[str] = ("DownEncoderBlock2D",)   ,							_UpperCAmelCase	:	Tuple[str] = ("UpDecoderBlock2D",)   ,							_UpperCAmelCase	:	Tuple[int] = (64,)   ,							_UpperCAmelCase	:	int = 1   ,							_UpperCAmelCase	:	str = "silu"   ,							_UpperCAmelCase	:	int = 4   ,							_UpperCAmelCase	:	int = 32   ,							_UpperCAmelCase	:	int = 32   ,							_UpperCAmelCase	:	float = 0.1_8215   ,							):
       super().__init__()

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

       # pass init params to Decoder
       _A        =       Decoder(
           in_channels=_UpperCAmelCase   ,							out_channels=_UpperCAmelCase   ,							up_block_types=_UpperCAmelCase   ,							block_out_channels=_UpperCAmelCase   ,							layers_per_block=_UpperCAmelCase   ,							norm_num_groups=_UpperCAmelCase   ,							act_fn=_UpperCAmelCase   ,							)

       _A        =       nn.Convad(2 * latent_channels   ,							2 * latent_channels   ,							1		)
       _A        =       nn.Convad(_UpperCAmelCase   ,							_UpperCAmelCase   ,							1		)

       _A        =       False
       _A        =       False

       # only relevant if vae tiling is enabled
       _A        =       self.config.sample_size
       _A        =       (
           self.config.sample_size[0]
           if isinstance(self.config.sample_size   ,							(list, tuple)		)
           else self.config.sample_size
       )
       _A        =       int(sample_size / (2 ** (len(self.config.block_out_channels		) - 1))		)
       _A        =       0.25

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	Tuple=False		):
       if isinstance(_UpperCAmelCase   ,							(Encoder, Decoder)		):
             _A        =       value

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	bool = True		):
       _A        =       use_tiling

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       self.enable_tiling(_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       True

 def       lowerCAmelCase_							(			self	:	str		):
       _A        =       False

 @property
 # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
 def       lowerCAmelCase_							(			self	:	str		):
       _A        =       {}

       def fn_recursive_add_processors(_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	torch.nn.Module   ,							_UpperCAmelCase	:	Dict[str, AttentionProcessor]		):
             if hasattr(_UpperCAmelCase   ,							'set_processor'		):
                   _A        =       module.processor

             for sub_name, child in module.named_children():
                   fn_recursive_add_processors(F'''{name}.{sub_name}'''   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)

             return processors

       for name, module in self.named_children():
             fn_recursive_add_processors(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)

       return processors

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Union[AttentionProcessor, Dict[str, AttentionProcessor]]		):
       _A        =       len(self.attn_processors.keys()		)

       if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		) and len(_UpperCAmelCase		) != count:
             raise ValueError(
                 F'''A dict of processors was passed, but the number of processors {len(_UpperCAmelCase		)} does not match the'''
                 F''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''		)

       def fn_recursive_attn_processor(_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	torch.nn.Module   ,							_UpperCAmelCase	:	int		):
             if hasattr(_UpperCAmelCase   ,							'set_processor'		):
                   if not isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		):
                         module.set_processor(_UpperCAmelCase		)
                   else:
                         module.set_processor(processor.pop(F'''{name}.processor'''		)		)

             for sub_name, child in module.named_children():
                   fn_recursive_attn_processor(F'''{name}.{sub_name}'''   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)

       for name, module in self.named_children():
             fn_recursive_attn_processor(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[str]		):
       self.set_attn_processor(AttnProcessor()		)

 @apply_forward_hook
 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	bool = True		):
       if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
             return self.tiled_encode(_UpperCAmelCase   ,							return_dict=_UpperCAmelCase		)

       if self.use_slicing and x.shape[0] > 1:
             _A        =       [self.encoder(_UpperCAmelCase		) for x_slice in x.split(1		)]
             _A        =       torch.cat(_UpperCAmelCase		)
       else:
             _A        =       self.encoder(_UpperCAmelCase		)

       _A        =       self.quant_conv(_UpperCAmelCase		)
       _A        =       DiagonalGaussianDistribution(_UpperCAmelCase		)

       if not return_dict:
             return (posterior,)

       return AutoencoderKLOutput(latent_dist=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	bool = True		):
       if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
             return self.tiled_decode(_UpperCAmelCase   ,							return_dict=_UpperCAmelCase		)

       _A        =       self.post_quant_conv(_UpperCAmelCase		)
       _A        =       self.decoder(_UpperCAmelCase		)

       if not return_dict:
             return (dec,)

       return DecoderOutput(sample=_UpperCAmelCase		)

 @apply_forward_hook
 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	bool = True		):
       if self.use_slicing and z.shape[0] > 1:
             _A        =       [self._decode(_UpperCAmelCase		).sample for z_slice in z.split(1		)]
             _A        =       torch.cat(_UpperCAmelCase		)
       else:
             _A        =       self._decode(_UpperCAmelCase		).sample

       if not return_dict:
             return (decoded,)

       return DecoderOutput(sample=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	Any		):
       _A        =       min(a.shape[2]   ,							b.shape[2]   ,							_UpperCAmelCase		)
       for y in range(_UpperCAmelCase		):
             _A        =       a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
       return b

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	List[Any]   ,							_UpperCAmelCase	:	List[Any]		):
       _A        =       min(a.shape[3]   ,							b.shape[3]   ,							_UpperCAmelCase		)
       for x in range(_UpperCAmelCase		):
             _A        =       a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
       return b

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	bool = True		):
       _A        =       int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)		)
       _A        =       int(self.tile_latent_min_size * self.tile_overlap_factor		)
       _A        =       self.tile_latent_min_size - blend_extent

       # Split the image into 512x512 tiles and encode them separately.
       _A        =       []
       for i in range(0   ,							x.shape[2]   ,							_UpperCAmelCase		):
             _A        =       []
             for j in range(0   ,							x.shape[3]   ,							_UpperCAmelCase		):
                   _A        =       x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
                   _A        =       self.encoder(_UpperCAmelCase		)
                   _A        =       self.quant_conv(_UpperCAmelCase		)
                   row.append(_UpperCAmelCase		)
             rows.append(_UpperCAmelCase		)
       _A        =       []
       for i, row in enumerate(_UpperCAmelCase		):
             _A        =       []
             for j, tile in enumerate(_UpperCAmelCase		):
                   # blend the above tile and the left tile
                   # to the current tile and add the current tile to the result row
                   if i > 0:
                         _A        =       self.blend_v(rows[i - 1][j]   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
                   if j > 0:
                         _A        =       self.blend_h(row[j - 1]   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
                   result_row.append(tile[:, :, :row_limit, :row_limit]		)
             result_rows.append(torch.cat(_UpperCAmelCase   ,							dim=3		)		)

       _A        =       torch.cat(_UpperCAmelCase   ,							dim=2		)
       _A        =       DiagonalGaussianDistribution(_UpperCAmelCase		)

       if not return_dict:
             return (posterior,)

       return AutoencoderKLOutput(latent_dist=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Tuple   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	bool = True		):
       _A        =       int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)		)
       _A        =       int(self.tile_sample_min_size * self.tile_overlap_factor		)
       _A        =       self.tile_sample_min_size - blend_extent

       # Split z into overlapping 64x64 tiles and decode them separately.
       # The tiles have an overlap to avoid seams between tiles.
       _A        =       []
       for i in range(0   ,							z.shape[2]   ,							_UpperCAmelCase		):
             _A        =       []
             for j in range(0   ,							z.shape[3]   ,							_UpperCAmelCase		):
                   _A        =       z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                   _A        =       self.post_quant_conv(_UpperCAmelCase		)
                   _A        =       self.decoder(_UpperCAmelCase		)
                   row.append(_UpperCAmelCase		)
             rows.append(_UpperCAmelCase		)
       _A        =       []
       for i, row in enumerate(_UpperCAmelCase		):
             _A        =       []
             for j, tile in enumerate(_UpperCAmelCase		):
                   # blend the above tile and the left tile
                   # to the current tile and add the current tile to the result row
                   if i > 0:
                         _A        =       self.blend_v(rows[i - 1][j]   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
                   if j > 0:
                         _A        =       self.blend_h(row[j - 1]   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
                   result_row.append(tile[:, :, :row_limit, :row_limit]		)
             result_rows.append(torch.cat(_UpperCAmelCase   ,							dim=3		)		)

       _A        =       torch.cat(_UpperCAmelCase   ,							dim=2		)
       if not return_dict:
             return (dec,)

       return DecoderOutput(sample=_UpperCAmelCase		)



 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	bool = False   ,							_UpperCAmelCase	:	bool = True   ,							_UpperCAmelCase	:	Optional[torch.Generator] = None   ,							):
       _A        =       sample
       _A        =       self.encode(_UpperCAmelCase		).latent_dist
       if sample_posterior:
             _A        =       posterior.sample(generator=_UpperCAmelCase		)
       else:
             _A        =       posterior.mode()
       _A        =       self.decode(_UpperCAmelCase		).sample

       if not return_dict:
             return (dec,)

       return DecoderOutput(sample=_UpperCAmelCase		)

