





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 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Any      ,				snake_case__	:Any      ,				snake_case__	:str      ,				snake_case__	:Dict  )   ->    List[str]:
  if isinstance(UpperCamelCase_      ,				UpperCamelCase_  ):
    _lowercase						    =			np.full((len(UpperCamelCase_  ), sequence_length, 2)      ,				UpperCamelCase_  )
  else:
    _lowercase						    =			np.full((len(UpperCamelCase_  ), sequence_length)      ,				UpperCamelCase_  )

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

  return out_tensor.tolist()

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Dict  )   ->    Union[str, Any]:
  _lowercase						    =			ord(UpperCamelCase_  )
  if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
    return True
  _lowercase						    =			unicodedata.category(UpperCamelCase_  )
  if cat.startswith('P'  ):
    return True
  return False







@dataclass
class 					A_					(   A__						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			Optional[Any]  			=	4_2
       SCREAMING_SNAKE_CASE_   :			Tuple  			=	True
       SCREAMING_SNAKE_CASE_   :			Optional[Any]  			=	None
       SCREAMING_SNAKE_CASE_   :			Dict  			=	None
       SCREAMING_SNAKE_CASE_   :			Optional[int]  			=	-1_0_0
       SCREAMING_SNAKE_CASE_   :			Tuple  			=	'''pt'''







       def  __UpperCAmelCase			(     self						:						Dict    ,__A						:						Optional[Any]	)    ->       Union[str, Any]:
         import torch

         _lowercase						    =			'label' if 'label' in features[0].keys() else 'labels'
         _lowercase						    =			[feature[label_name] for feature in features] if label_name in features[0].keys() else None
         _lowercase						    =			self.tokenizer.pad(
             a__    ,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

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

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

         return batch




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


_lowercase    	=		logging.get_logger(__name__)



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''timm_backbone'''




     def __init__(					self			,    a__=None			,    a__=3			,    a__=True			,    a__=True			,    a__=None			,    **a__			,    ):
         super().__init__(**a__)
         A__					=       backbone
         A__					=       num_channels
         A__					=       features_only
         A__					=       use_pretrained_backbone
         A__					=       True
         A__					=       out_indices if out_indices is not None else (-1,)
