


'''simple docstring'''
import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


lowerCAmelCase__  =     logging.get_logger(__name__)







@dataclass
class 					_A						:



   '''simple docstring'''






   _lowercase    						= 42
   _lowercase    						= 42
   _lowercase    						= None
   _lowercase    						= None




   def   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	List[Any]:
          return json.dumps(dataclasses.asdict(self						)					, indent=2						) + "\n"







@dataclass(frozen=__lowerCamelCase							)
class 					_A						:



   '''simple docstring'''






   _lowercase    						= 42
   _lowercase    						= None
   _lowercase    						= None
   _lowercase    						= None




   def   __lowerCAmelCase						(	self	:					str						)->	str:
          return json.dumps(dataclasses.asdict(self						)						) + "\n"







class 					_A						:



   '''simple docstring'''






   def   __lowerCAmelCase						(	self	:					List[Any]					, lowerCamelCase	:					List[Any]						)->	Union[str, Any]:
          raise NotImplementedError()




   def   __lowerCAmelCase						(	self	:					Optional[int]					, lowerCamelCase	:					Union[str, Any]						)->	Dict:
          raise NotImplementedError()




   def   __lowerCAmelCase						(	self	:					Dict					, lowerCamelCase	:					str						)->	List[Any]:
          raise NotImplementedError()




   def   __lowerCAmelCase						(	self	:					Dict					, lowerCamelCase	:					List[str]						)->	Union[str, Any]:
          raise NotImplementedError()




   def   __lowerCAmelCase						(	self	:					Optional[int]						)->	Optional[Any]:
          raise NotImplementedError()




   def   __lowerCAmelCase						(	self	:					Optional[Any]					, lowerCamelCase	:					Optional[int]						)->	Union[str, Any]:
          if len(self.get_labels()						) > 1:
                 snake_case__					:	Dict          =					self.get_labels()[int(example.label						)]
          return example




   @classmethod
   def   __lowerCAmelCase						(	cls	:					Dict					, lowerCamelCase	:					int					, lowerCamelCase	:					str=None						)->	Tuple:
          with open(lowerCamelCase					, """r"""					, encoding="""utf-8-sig"""						) as f:
                 return list(csv.reader(lowerCamelCase					, delimiter="""\t"""					, quotechar=lowerCamelCase						)						)







class 					_A						(						__lowerCamelCase							):



   '''simple docstring'''






   def __init__(	self	:					Tuple					, lowerCamelCase	:					Dict=None					, lowerCamelCase	:					int=None					, lowerCamelCase	:					List[str]="classification"					, lowerCamelCase	:					Tuple=False						)->	int:
          snake_case__					:	List[str]          =					[] if labels is None else labels
          snake_case__					:	int          =					[] if examples is None else examples
          snake_case__					:	Optional[int]          =					mode
          snake_case__					:	List[str]          =					verbose




   def __len__(	self	:					int						)->	Optional[Any]:
          return len(self.examples						)




   def __getitem__(	self	:					Union[str, Any]					, lowerCamelCase	:					Union[str, Any]						)->	Tuple:
          if isinstance(lowerCamelCase					, lowerCamelCase						):
                 return SingleSentenceClassificationProcessor(labels=self.labels					, examples=self.examples[idx]						)
          return self.examples[idx]




   @classmethod
   def   __lowerCAmelCase						(	cls	:					Tuple					, lowerCamelCase	:					Optional[Any]					, lowerCamelCase	:					str=""					, lowerCamelCase	:					Optional[Any]=0					, lowerCamelCase	:					str=1					, lowerCamelCase	:					List[Any]=None					, lowerCamelCase	:					Optional[Any]=False					, **lowerCamelCase	:					Any						)->	List[Any]:
          snake_case__					:	int          =					cls(**lowerCamelCase						)
          processor.add_examples_from_csv(
              lowerCamelCase					, split_name=lowerCamelCase					, column_label=lowerCamelCase					, column_text=lowerCamelCase					, column_id=lowerCamelCase					, skip_first_row=lowerCamelCase					, overwrite_labels=lowerCamelCase					, overwrite_examples=lowerCamelCase					, )
          return processor




   @classmethod
   def   __lowerCAmelCase						(	cls	:					Optional[Any]					, lowerCamelCase	:					int					, lowerCamelCase	:					str=None					, **lowerCamelCase	:					str						)->	Any:
          snake_case__					:	List[Any]          =					cls(**lowerCamelCase						)
          processor.add_examples(lowerCamelCase					, labels=lowerCamelCase						)
          return processor




   def   __lowerCAmelCase						(	self	:					List[Any]					, lowerCamelCase	:					Optional[Any]					, lowerCamelCase	:					str=""					, lowerCamelCase	:					Dict=0					, lowerCamelCase	:					Optional[Any]=1					, lowerCamelCase	:					Tuple=None					, lowerCamelCase	:					Any=False					, lowerCamelCase	:					str=False					, lowerCamelCase	:					Tuple=False					, )->	Union[str, Any]:
          snake_case__					:	List[Any]          =					self._read_tsv(lowerCamelCase						)
          if skip_first_row:
                 snake_case__					:	Union[str, Any]          =					lines[1:]
          snake_case__					:	Optional[int]          =					[]
          snake_case__					:	Union[str, Any]          =					[]
          snake_case__					:	Tuple          =					[]
          for i, line in enumerate(lowerCamelCase						):
                 texts.append(line[column_text]						)
                 labels.append(line[column_label]						)
                 if column_id is not None:
                        ids.append(line[column_id]						)
                 else:
                        snake_case__					:	Dict          =					F"""{split_name}-{i}""" if split_name else str(lowerCamelCase						)
                        ids.append(lowerCamelCase						)

          return self.add_examples(
              lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, overwrite_labels=lowerCamelCase					, overwrite_examples=lowerCamelCase						)




   def   __lowerCAmelCase						(	self	:					Any					, lowerCamelCase	:					List[str]					, lowerCamelCase	:					Optional[Any]=None					, lowerCamelCase	:					Tuple=None					, lowerCamelCase	:					List[str]=False					, lowerCamelCase	:					List[str]=False						)->	int:
          if labels is not None and len(lowerCamelCase						) != len(lowerCamelCase						):
                 raise ValueError(
                     F"""Text and labels have mismatched lengths {len(lowerCamelCase						)} and {len(lowerCamelCase						)}"""						)
          if ids is not None and len(lowerCamelCase						) != len(lowerCamelCase						):
                 raise ValueError(F"""Text and ids have mismatched lengths {len(lowerCamelCase						)} and {len(lowerCamelCase						)}"""						)
          if ids is None:
                 snake_case__					:	List[str]          =					[None] * len(lowerCamelCase						)
          if labels is None:
                 snake_case__					:	Optional[int]          =					[None] * len(lowerCamelCase						)
          snake_case__					:	Optional[int]          =					[]
          snake_case__					:	List[str]          =					set()
          for text_or_text_and_label, label, guid in zip(lowerCamelCase					, lowerCamelCase					, lowerCamelCase						):
                 if isinstance(lowerCamelCase					, (tuple, list)						) and label is None:
                        snake_case__					:	Union[str, Any]          =					text_or_text_and_label
                 else:
                        snake_case__					:	Dict          =					text_or_text_and_label
                 added_labels.add(lowerCamelCase						)
                 examples.append(InputExample(guid=lowerCamelCase					, text_a=lowerCamelCase					, text_b=lowerCamelCase					, label=lowerCamelCase						)						)

          # Update examples
          if overwrite_examples:
                 snake_case__					:	List[Any]          =					examples
          else:
                 self.examples.extend(lowerCamelCase						)

          # Update labels
          if overwrite_labels:
                 snake_case__					:	List[Any]          =					list(lowerCamelCase						)
          else:
                 snake_case__					:	Optional[Any]          =					list(set(self.labels						).union(lowerCamelCase						)						)

          return self.examples




   def   __lowerCAmelCase						(	self	:					str					, lowerCamelCase	:					Optional[Any]					, lowerCamelCase	:					List[Any]=None					, lowerCamelCase	:					Optional[int]=False					, lowerCamelCase	:					Union[str, Any]=0					, lowerCamelCase	:					Optional[int]=True					, lowerCamelCase	:					int=None					, )->	int:
          if max_length is None:
                 snake_case__					:	List[Any]          =					tokenizer.max_len

          snake_case__					:	int          =					{label: i for i, label in enumerate(self.labels						)}

          snake_case__					:	str          =					[]
          for ex_index, example in enumerate(self.examples						):
                 if ex_index % 10_000 == 0:
                        logger.info(F"""Tokenizing example {ex_index}"""						)

                 snake_case__					:	int          =					tokenizer.encode(
                     example.text_a					, add_special_tokens=lowerCamelCase					, max_length=min(lowerCamelCase					, tokenizer.max_len						)					, )
                 all_input_ids.append(lowerCamelCase						)

          snake_case__					:	List[Any]          =					max(len(lowerCamelCase						) for input_ids in all_input_ids						)

          snake_case__					:	List[str]          =					[]
          for ex_index, (input_ids, example) in enumerate(zip(lowerCamelCase					, self.examples						)						):
                 if ex_index % 10_000 == 0:
                        logger.info(F"""Writing example {ex_index}/{len(self.examples						)}"""						)
                 # The mask has 1 for real tokens and 0 for padding tokens. Only real
                 # tokens are attended to.
                 snake_case__					:	Dict          =					[1 if mask_padding_with_zero else 0] * len(lowerCamelCase						)

                 # Zero-pad up to the sequence length.
                 snake_case__					:	Dict          =					batch_length - len(lowerCamelCase						)
                 if pad_on_left:
                        snake_case__					:	Optional[int]          =					([pad_token] * padding_length) + input_ids
                        snake_case__					:	List[Any]          =					([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                 else:
                        snake_case__					:	List[Any]          =					input_ids + ([pad_token] * padding_length)
                        snake_case__					:	List[str]          =					attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

                 if len(lowerCamelCase						) != batch_length:
                        raise ValueError(F"""Error with input length {len(lowerCamelCase						)} vs {batch_length}"""						)
                 if len(lowerCamelCase						) != batch_length:
                        raise ValueError(F"""Error with input length {len(lowerCamelCase						)} vs {batch_length}"""						)

                 if self.mode == "classification":
                        snake_case__					:	int          =					label_map[example.label]
                 elif self.mode == "regression":
                        snake_case__					:	Any          =					float(example.label						)
                 else:
                        raise ValueError(self.mode						)

                 if ex_index < 5 and self.verbose:
                        logger.info("""*** Example ***"""						)
                        logger.info(F"""guid: {example.guid}"""						)
                        logger.info(F"""input_ids: {" ".join([str(lowerCamelCase						) for x in input_ids]						)}"""						)
                        logger.info(F"""attention_mask: {" ".join([str(lowerCamelCase						) for x in attention_mask]						)}"""						)
                        logger.info(F"""label: {example.label} (id = {label})"""						)

                 features.append(InputFeatures(input_ids=lowerCamelCase					, attention_mask=lowerCamelCase					, label=lowerCamelCase						)						)

          if return_tensors is None:
                 return features
          elif return_tensors == "tf":
                 if not is_tf_available():
                        raise RuntimeError("""return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"""						)
                 import tensorflow as tf

                 def gen():
                        for ex in features:
                               yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

                 snake_case__					:	Dict          =					tf.data.Dataset.from_generator(
                     lowerCamelCase					, ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)					, ({"""input_ids""": tf.TensorShape([None]						), """attention_mask""": tf.TensorShape([None]						)}, tf.TensorShape([]						))					, )
                 return dataset
          elif return_tensors == "pt":
                 if not is_torch_available():
                        raise RuntimeError("""return_tensors set to 'pt' but PyTorch can't be imported"""						)
                 import torch
                 from torch.utils.data import TensorDataset

                 snake_case__					:	Optional[Any]          =					torch.tensor([f.input_ids for f in features]					, dtype=torch.long						)
                 snake_case__					:	Dict          =					torch.tensor([f.attention_mask for f in features]					, dtype=torch.long						)
                 if self.mode == "classification":
                        snake_case__					:	Dict          =					torch.tensor([f.label for f in features]					, dtype=torch.long						)
                 elif self.mode == "regression":
                        snake_case__					:	Union[str, Any]          =					torch.tensor([f.label for f in features]					, dtype=torch.float						)

                 snake_case__					:	List[str]          =					TensorDataset(lowerCamelCase					, lowerCamelCase					, lowerCamelCase						)
                 return dataset
          else:
                 raise ValueError("""return_tensors should be one of 'tf' or 'pt'"""						)











from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase			:  List[str]										=				logging.get_logger(__name__)

__UpperCAmelCase			:  int										=				{
    # pcqm4mv1 now deprecated
    "graphormer-base": "https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}







class        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	"""graphormer"""
       lowerCAmelCase__ 						=	["""past_key_values"""]




       def __init__(    self					:       List[Any]				,							A					:       int = 1				,							A					:       int = 512 * 9				,							A					:       int = 512 * 3				,							A					:       int = 512				,							A					:       int = 512				,							A					:       int = 512				,							A					:       int = 128				,							A					:       int = 5				,							A					:       int = 1_024				,							A					:       str = "multi_hop"				,							A					:       int = 512				,							A					:       bool = False				,							A					:       int = 12				,							A					:       int = 768				,							A					:       int = 768				,							A					:       int = 32				,							A					:       float = 0.1				,							A					:       float = 0.1				,							A					:       float = 0.0				,							A					:       bool = False				,							A					:       bool = False				,							A					:       bool = False				,							A					:       str = "gelu"				,							A					:       float = None				,							A					:       bool = False				,							A					:       int = 0				,							A					:       bool = False				,							A					:       float = 0.0				,							A					:       int = 8				,							A					:       int = None				,							A					:       int = None				,							A					:       bool = True				,							A					:       bool = True				,							A					:       Any=0				,							A					:       Optional[int]=1				,							A					:       Any=2				,							**A					:       Optional[Any]				,							):
         __snake_case:		List[str]					    =		num_classes
         __snake_case:		int					    =		num_atoms
         __snake_case:		List[str]					    =		num_in_degree
         __snake_case:		str					    =		num_out_degree
         __snake_case:		Tuple					    =		num_edges
         __snake_case:		Optional[int]					    =		num_spatial
         __snake_case:		int					    =		num_edge_dis
         __snake_case:		Tuple					    =		edge_type
         __snake_case:		str					    =		multi_hop_max_dist
         __snake_case:		List[str]					    =		spatial_pos_max
         __snake_case:		Optional[Any]					    =		max_nodes
         __snake_case:		Optional[int]					    =		num_hidden_layers
         __snake_case:		Union[str, Any]					    =		embedding_dim
         __snake_case:		Union[str, Any]					    =		embedding_dim
         __snake_case:		Optional[int]					    =		ffn_embedding_dim
         __snake_case:		List[str]					    =		num_attention_heads
         __snake_case:		Any					    =		dropout
         __snake_case:		Dict					    =		attention_dropout
         __snake_case:		Any					    =		layerdrop
         __snake_case:		Dict					    =		encoder_normalize_before
         __snake_case:		Any					    =		pre_layernorm
         __snake_case:		Optional[int]					    =		apply_graphormer_init
         __snake_case:		List[str]					    =		activation_fn
         __snake_case:		Optional[int]					    =		embed_scale
         __snake_case:		Dict					    =		freeze_embeddings
         __snake_case:		Any					    =		num_trans_layers_to_freeze
         __snake_case:		int					    =		share_input_output_embed
         __snake_case:		Dict					    =		traceable
         __snake_case:		str					    =		q_noise
         __snake_case:		Union[str, Any]					    =		qn_block_size

         # These parameters are here for future extensions
         # atm, the model only supports self attention
         __snake_case:		str					    =		kdim
         __snake_case:		Dict					    =		vdim
         __snake_case:		List[Any]					    =		self_attention
         __snake_case:		Tuple					    =		bias

         super().__init__(
             pad_token_id=A				,							bos_token_id=A				,							eos_token_id=A				,							**A				,							)






