



import os
from collections.abc import Iterator





def 		a__			(		_UpperCamelCase			: str = "."							):
   for dir_path, dir_names, filenames in os.walk(_UpperCamelCase							):
      __lowerCamelCase													=    [d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
      for filename in filenames:
         if filename == "__init__.py":
            continue
         if os.path.splitext(_UpperCamelCase							)[1] in (".py", ".ipynb"):
            yield os.path.join(_UpperCamelCase ,_UpperCamelCase							).lstrip('''./'''							)





def 		a__			(		_UpperCamelCase			: Optional[int]							):
   return F"""{i * "  "}*""" if i else "\n##"





def 		a__			(		_UpperCamelCase			: str ,_UpperCamelCase			: str							):
   __lowerCamelCase													=    old_path.split(os.sep							)
   for i, new_part in enumerate(new_path.split(os.sep							)							):
      if (i + 1 > len(_UpperCamelCase							) or old_parts[i] != new_part) and new_part:
         print(F"""{md_prefix(_UpperCamelCase							)} {new_part.replace("_" ," "							).title()}"""							)
   return new_path





def 		a__			(		_UpperCamelCase			: str = "."							):
   __lowerCamelCase													=    ''''''
   for filepath in sorted(good_file_paths(_UpperCamelCase							)							):
      __lowerCamelCase							,__lowerCamelCase													=    os.path.split(_UpperCamelCase							)
      if filepath != old_path:
         __lowerCamelCase													=    print_path(_UpperCamelCase ,_UpperCamelCase							)
      __lowerCamelCase													=    (filepath.count(os.sep							) + 1) if filepath else 0
      __lowerCamelCase													=    F"""{filepath}/{filename}""".replace(''' ''' ,'''%20'''							)
      __lowerCamelCase													=    os.path.splitext(filename.replace('''_''' ,''' '''							).title()							)[0]
      print(F"""{md_prefix(_UpperCamelCase							)} [{filename}]({url})"""							)


if __name__ == "__main__":
  print_directory_md(""".""")











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 		a__			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: Any ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Any							):
   if isinstance(_UpperCamelCase ,_UpperCamelCase							):
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length, 2) ,_UpperCamelCase							)
   else:
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length) ,_UpperCamelCase							)

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

   return out_tensor.tolist()





def 		a__			(		_UpperCamelCase			: Dict							):
   __lowerCamelCase													=    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
   __lowerCamelCase													=    unicodedata.category(_UpperCamelCase							)
   if cat.startswith('''P'''							):
      return True
   return False





@dataclass
class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  42
 lowerCAmelCase__       					=  True
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  -1_0_0
 lowerCAmelCase__       					=  "pt"




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    import torch

    __lowerCamelCase													=    '''label''' if '''label''' in features[0].keys() else '''labels'''
    __lowerCamelCase													=    [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __lowerCamelCase													=    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

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

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

    return batch






