
"""simple docstring"""


import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)->							Dict:







       """simple docstring"""
       if isinstance(UpperCAmelCase_		,      UpperCAmelCase_				):
              UpperCamelCase					 =		np.full((len(UpperCAmelCase_				), sequence_length, 2)		,      UpperCAmelCase_				)
       else:
              UpperCamelCase					 =		np.full((len(UpperCAmelCase_				), sequence_length)		,      UpperCAmelCase_				)

       for i, tensor in enumerate(UpperCAmelCase_				):
              if padding_side == "right":
                     if isinstance(UpperCAmelCase_		,      UpperCAmelCase_				):
                            UpperCamelCase					 =		tensor[:sequence_length]
                     else:
                            UpperCamelCase					 =		tensor[:sequence_length]
              else:
                     if isinstance(UpperCAmelCase_		,      UpperCAmelCase_				):
                            UpperCamelCase					 =		tensor[:sequence_length]
                     else:
                            UpperCamelCase					 =		tensor[:sequence_length]

       return out_tensor.tolist()



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							Optional[Any]:







       """simple docstring"""
       UpperCamelCase					 =		ord(UpperCAmelCase_				)
       if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
              return True
       UpperCamelCase					 =		unicodedata.category(UpperCAmelCase_				)
       if cat.startswith("P"				):
              return True
       return False



@dataclass
class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					PreTrainedTokenizerBase
       UpperCamelCase_		:					Union[bool, str, PaddingStrategy]   =			True
       UpperCamelCase_		:					Optional[int]   =			None
       UpperCamelCase_		:					Optional[int]   =			None
       UpperCamelCase_		:					int   =			-100
       UpperCamelCase_		:					str   =			"pt"






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							Tuple			)->  Union[str, Any]:







              """simple docstring"""
              import torch

              UpperCamelCase					 =		"label" if "label" in features[0].keys() else "labels"
              UpperCamelCase					 =		[feature[label_name] for feature in features] if label_name in features[0].keys() else None
              UpperCamelCase					 =		self.tokenizer.pad(
                  UpperCAmelCase_       ,      padding=self.padding       ,      max_length=self.max_length       ,      pad_to_multiple_of=self.pad_to_multiple_of       ,      return_tensors="pt" if labels is None else None       ,      )

              if labels is None:
                     return batch

              UpperCamelCase					 =		torch.tensor(batch["entity_ids"]			).shape[1]
              UpperCamelCase					 =		self.tokenizer.padding_side
              if padding_side == "right":
                     UpperCamelCase					 =		[
                         list(UpperCAmelCase_			) + [self.label_pad_token_id] * (sequence_length - len(UpperCAmelCase_			)) for label in labels
                     ]
              else:
                     UpperCamelCase					 =		[
                         [self.label_pad_token_id] * (sequence_length - len(UpperCAmelCase_			)) + list(UpperCAmelCase_			) for label in labels
                     ]

              UpperCamelCase					 =		[feature["ner_tags"] for feature in features]
              UpperCamelCase					 =		padding_tensor(UpperCAmelCase_       ,      -1       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)
              UpperCamelCase					 =		[feature["original_entity_spans"] for feature in features]
              UpperCamelCase					 =		padding_tensor(UpperCAmelCase_       ,      (-1, -1)       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)
              UpperCamelCase					 =		{k: torch.tensor(UpperCAmelCase_       ,      dtype=torch.intaa			) for k, v in batch.items()}

              return batch







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE      =     {
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GraphormerForGraphClassification""",
          """GraphormerModel""",
          """GraphormerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_graphormer import (
                GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                GraphormerForGraphClassification,
                GraphormerModel,
                GraphormerPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE      =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





