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_     (		lowercase:							Dict  ,					lowercase:							Union[str, Any]  ,					lowercase:							Union[str, Any]  ,					lowercase:							str		)    ->				List[Any]:







 '''simple docstring'''
 if isinstance(lowercase  ,					lowercase		):
  _UpperCamelCase:     Optional[int]							= np.full((len(lowercase		), sequence_length, 2)  ,					lowercase		)
 else:
  _UpperCamelCase:     Optional[int]							= np.full((len(lowercase		), sequence_length)  ,					lowercase		)

 for i, tensor in enumerate(lowercase		):
  if padding_side == "right":
   if isinstance(lowercase  ,					lowercase		):
    _UpperCamelCase:     Optional[Any]							= tensor[:sequence_length]
   else:
    _UpperCamelCase:     List[str]							= tensor[:sequence_length]
  else:
   if isinstance(lowercase  ,					lowercase		):
    _UpperCamelCase:     str							= tensor[:sequence_length]
   else:
    _UpperCamelCase:     Tuple							= tensor[:sequence_length]

 return out_tensor.tolist()




def       lowerCAmelCase_     (		lowercase:							List[str]		)    ->				str:







 '''simple docstring'''
 _UpperCamelCase:     List[Any]							= ord(lowercase		)
 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
 _UpperCamelCase:     int							= unicodedata.category(lowercase		)
 if cat.startswith('''P'''		):
  return True
 return False





@dataclass
class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    PreTrainedTokenizerBase
   lowerCAmelCase      :    Union[bool, str, PaddingStrategy]  			=   True
   lowerCAmelCase      :    Optional[int]  			=   None
   lowerCAmelCase      :    Optional[int]  			=   None
   lowerCAmelCase      :    int  			=   -1_0_0
   lowerCAmelCase      :    str  			=   "pt"



   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  str							):



    """simple docstring"""



    import torch

    _UpperCamelCase:     int							= '''label''' if '''label''' in features[0].keys() else '''labels'''
    _UpperCamelCase:     int							= [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    _UpperCamelCase:     Optional[Any]							= self.tokenizer.pad(
        _lowercase		,  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:     List[Any]							= torch.tensor(batch['''entity_ids''']							).shape[1]
    _UpperCamelCase:     Optional[int]							= self.tokenizer.padding_side
    if padding_side == "right":
     _UpperCamelCase:     int							= [
         list(_lowercase							) + [self.label_pad_token_id] * (sequence_length - len(_lowercase							)) for label in labels
     ]
    else:
     _UpperCamelCase:     Dict							= [
         [self.label_pad_token_id] * (sequence_length - len(_lowercase							)) + list(_lowercase							) for label in labels
     ]

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

    return batch
import warnings

from ..trainer import Trainer
from ..utils import logging


UpperCAmelCase_			    =							logging.get_logger(__name__)




class  __magic_name__  (		__a    ):





   """simple docstring"""




   def __init__(	self					:  List[Any]		,  _lowercase					:  int=None		,  **_lowercase					:  Optional[Any]							):



    """simple docstring"""



    warnings.warn(
        '''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
        '''instead.'''		,  _lowercase		,  )
    super().__init__(args=_lowercase		,  **_lowercase							)