'''simple docstring'''




from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
     import pyximport

     pyximport.install(setup_args={'include_dirs': np.get_include()})
     from . import algos_graphormer  # noqa E402



def 						__snake_case (       lowerCAmelCase       :     List[Any]      ,  lowerCAmelCase       :     int = 512			):
 __UpperCAmelCase         =					x.shape[1] if len(x.shape			) > 1 else 1
 __UpperCAmelCase         =					1 + np.arange(0      ,  feature_num * offset      ,  lowerCAmelCase      ,  dtype=np.intaa			)
 __UpperCAmelCase         =					x + feature_offset
 return x



def 						__snake_case (       lowerCAmelCase       :     List[str]      ,  lowerCAmelCase       :     Optional[int]=True			):
 requires_backends(lowerCAmelCase      ,  ['cython']			)

 if keep_features and "edge_attr" in item.keys():  # edge_attr
  __UpperCAmelCase         =					np.asarray(item['edge_attr']      ,  dtype=np.intaa			)
 else:
  __UpperCAmelCase         =					np.ones((len(item['edge_index'][0]			), 1)      ,  dtype=np.intaa			)  # same embedding for all

 if keep_features and "node_feat" in item.keys():  # input_nodes
  __UpperCAmelCase         =					np.asarray(item['node_feat']      ,  dtype=np.intaa			)
 else:
  __UpperCAmelCase         =					np.ones((item['num_nodes'], 1)      ,  dtype=np.intaa			)  # same embedding for all

 __UpperCAmelCase         =					np.asarray(item['edge_index']      ,  dtype=np.intaa			)

 __UpperCAmelCase         =					convert_to_single_emb(lowerCAmelCase			) + 1
 __UpperCAmelCase         =					item['num_nodes']

 if len(edge_attr.shape			) == 1:
  __UpperCAmelCase         =					edge_attr[:, None]
 __UpperCAmelCase         =					np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]      ,  dtype=np.intaa			)
 __UpperCAmelCase         =					convert_to_single_emb(lowerCAmelCase			) + 1

 # node adj matrix [num_nodes, num_nodes] bool
 __UpperCAmelCase         =					np.zeros([num_nodes, num_nodes]      ,  dtype=lowerCAmelCase			)
 __UpperCAmelCase         =					True

 __UpperCAmelCase  ,			__UpperCAmelCase         =					algos_graphormer.floyd_warshall(lowerCAmelCase			)
 __UpperCAmelCase         =					np.amax(lowerCAmelCase			)

 __UpperCAmelCase         =					algos_graphormer.gen_edge_input(lowerCAmelCase      ,  lowerCAmelCase      ,  lowerCAmelCase			)
 __UpperCAmelCase         =					np.zeros([num_nodes + 1, num_nodes + 1]      ,  dtype=np.single			)  # with graph token

 # combine
 __UpperCAmelCase         =					input_nodes + 1  # we shift all indices by one for padding
 __UpperCAmelCase         =					attn_bias
 __UpperCAmelCase         =					attn_edge_type
 __UpperCAmelCase         =					shortest_path_result.astype(np.intaa			) + 1  # we shift all indices by one for padding
 __UpperCAmelCase         =					np.sum(lowerCAmelCase      ,  axis=1			).reshape(-1			) + 1  # we shift all indices by one for padding
 __UpperCAmelCase         =					item['in_degree']  # for undirected graph
 __UpperCAmelCase         =					input_edges + 1  # we shift all indices by one for padding
 if "labels" not in item:
  __UpperCAmelCase         =					item['y']

 return item

class   _lowercase:



   """simple docstring"""





   def __init__(							self:    Optional[int]			,a:    Optional[Any]=20			,a:    str=False	):
    if not is_cython_available():
     raise ImportError('Graphormer preprocessing needs Cython (pyximport)'	)

    __UpperCAmelCase         =					spatial_pos_max
    __UpperCAmelCase         =					on_the_fly_processing





   def __call__(							self:    List[str]			,a:    List[dict]	):
    if self.on_the_fly_processing:
     __UpperCAmelCase         =					[preprocess_item(a	) for i in features]

    if not isinstance(features[0]			,a	):
     __UpperCAmelCase         =					[vars(a	) for f in features]
    __UpperCAmelCase         =					{}

    __UpperCAmelCase         =					max(len(i['input_nodes']	) for i in features	)
    __UpperCAmelCase         =					len(features[0]['input_nodes'][0]	)
    __UpperCAmelCase         =					len(features[0]['attn_edge_type'][0][0]	)
    __UpperCAmelCase         =					max(len(i['input_edges'][0][0]	) for i in features	)
    __UpperCAmelCase         =					len(features[0]['input_edges'][0][0][0]	)
    __UpperCAmelCase         =					len(a	)

    __UpperCAmelCase         =					torch.zeros(a			,max_node_num + 1			,max_node_num + 1			,dtype=torch.float	)
    __UpperCAmelCase         =					torch.zeros(a			,a			,a			,a			,dtype=torch.long	)
    __UpperCAmelCase         =					torch.zeros(a			,a			,a			,dtype=torch.long	)
    __UpperCAmelCase         =					torch.zeros(a			,a			,dtype=torch.long	)
    __UpperCAmelCase         =					torch.zeros(a			,a			,a			,dtype=torch.long	)
    __UpperCAmelCase         =					torch.zeros(
        a			,a			,a			,a			,a			,dtype=torch.long	)

    for ix, f in enumerate(a	):
     for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
      __UpperCAmelCase         =					torch.tensor(f[k]	)

     if len(f['attn_bias'][1:, 1:][f['spatial_pos'] >= self.spatial_pos_max]	) > 0:
      __UpperCAmelCase         =					float('-inf'	)

     __UpperCAmelCase         =					f['attn_bias']
     __UpperCAmelCase         =					f[
         'attn_edge_type'
     ]
     __UpperCAmelCase         =					f['spatial_pos']
     __UpperCAmelCase         =					f['in_degree']
     __UpperCAmelCase         =					f['input_nodes']
     __UpperCAmelCase         =					f['input_edges']

    __UpperCAmelCase         =					batch['in_degree']

    __UpperCAmelCase         =					features[0]['labels']
    if len(a	) == 1:  # one task
     if isinstance(sample[0]			,a	):  # regression
      __UpperCAmelCase         =					torch.from_numpy(np.concatenate([i['labels'] for i in features]	)	)
     else:  # binary classification
      __UpperCAmelCase         =					torch.from_numpy(np.concatenate([i['labels'] for i in features]	)	)
    else:  # multi task classification, left to float to keep the NaNs
     __UpperCAmelCase         =					torch.from_numpy(np.stack([i['labels'] for i in features]			,axis=0	)	)

    return batch




'''simple docstring'''




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


_UpperCamelCase				:		int       			=		logging.get_logger(__name__)

_UpperCamelCase				:		Tuple       			=		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase				:		int       			=		{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

_UpperCamelCase				:		List[str]       			=		{
    'allenai/longformer-base-4096': 40_96,
    'allenai/longformer-large-4096': 40_96,
    'allenai/longformer-large-4096-finetuned-triviaqa': 40_96,
    'allenai/longformer-base-4096-extra.pos.embd.only': 40_96,
    'allenai/longformer-large-4096-extra.pos.embd.only': 40_96,
}

class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  VOCAB_FILES_NAMES
   __lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
   __lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCamelCase    =  ['''input_ids''', '''attention_mask''']
   __lowerCamelCase    =  LongformerTokenizer





   def __init__(							self:    Any			,a:    Optional[int]=None			,a:    Optional[int]=None			,a:    int=None			,a:    Any="replace"			,a:    Optional[int]="<s>"			,a:    Any="</s>"			,a:    Optional[int]="</s>"			,a:    Dict="<s>"			,a:    Dict="<unk>"			,a:    int="<pad>"			,a:    List[str]="<mask>"			,a:    Union[str, Any]=False			,a:    Any=True			,**a:    str			,):
    super().__init__(
        a			,a			,tokenizer_file=a			,errors=a			,bos_token=a			,eos_token=a			,sep_token=a			,cls_token=a			,unk_token=a			,pad_token=a			,mask_token=a			,add_prefix_space=a			,trim_offsets=a			,**a			,)

    __UpperCAmelCase         =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
    if pre_tok_state.get('add_prefix_space'			,a	) != add_prefix_space:
     __UpperCAmelCase         =					getattr(a			,pre_tok_state.pop('type'	)	)
     __UpperCAmelCase         =					add_prefix_space
     __UpperCAmelCase         =					pre_tok_class(**a	)

    __UpperCAmelCase         =					add_prefix_space

    __UpperCAmelCase         =					'post_processor'
    __UpperCAmelCase         =					getattr(self.backend_tokenizer			,a			,a	)
    if tokenizer_component_instance:
     __UpperCAmelCase         =					json.loads(tokenizer_component_instance.__getstate__()	)

     # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
     if "sep" in state:
      __UpperCAmelCase         =					tuple(state['sep']	)
     if "cls" in state:
      __UpperCAmelCase         =					tuple(state['cls']	)

     __UpperCAmelCase         =					False

     if state.get('add_prefix_space'			,a	) != add_prefix_space:
      __UpperCAmelCase         =					add_prefix_space
      __UpperCAmelCase         =					True

     if state.get('trim_offsets'			,a	) != trim_offsets:
      __UpperCAmelCase         =					trim_offsets
      __UpperCAmelCase         =					True

     if changes_to_apply:
      __UpperCAmelCase         =					getattr(a			,state.pop('type'	)	)
      __UpperCAmelCase         =					component_class(**a	)
      setattr(self.backend_tokenizer			,a			,a	)





   @property
   def 			snake_case	(							self:    List[Any]	):
    if self._mask_token is None:
     if self.verbose:
      logger.error('Using mask_token, but it is not set yet.'	)
     return None
    return str(self._mask_token	)





   @mask_token.setter
   def 			snake_case	(							self:    int			,a:    Optional[int]	):
    __UpperCAmelCase         =					AddedToken(a			,lstrip=a			,rstrip=a	) if isinstance(a			,a	) else value
    __UpperCAmelCase         =					value





   def 			snake_case	(							self:    Optional[int]			,*a:    Dict			,**a:    Dict	):
    __UpperCAmelCase         =					kwargs.get('is_split_into_words'			,a	)
    assert self.add_prefix_space or not is_split_into_words, (
        f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
        "to use it with pretokenized inputs."
    )

    return super()._batch_encode_plus(*a			,**a	)





   def 			snake_case	(							self:    str			,*a:    Dict			,**a:    str	):
    __UpperCAmelCase         =					kwargs.get('is_split_into_words'			,a	)

    assert self.add_prefix_space or not is_split_into_words, (
        f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
        "to use it with pretokenized inputs."
    )

    return super()._encode_plus(*a			,**a	)





   def 			snake_case	(							self:    List[str]			,a:    str			,a:    Optional[str] = None	):
    __UpperCAmelCase         =					self._tokenizer.model.save(a			,name=a	)
    return tuple(a	)





   def 			snake_case	(							self:    Tuple			,a:    Dict			,a:    Tuple=None	):
    __UpperCAmelCase         =					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
    if token_ids_a is None:
     return output

    return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





   def 			snake_case	(							self:    int			,a:    List[int]			,a:    Optional[List[int]] = None	):
    __UpperCAmelCase         =					[self.sep_token_id]
    __UpperCAmelCase         =					[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]



