





"""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    :						Optional[Any]       =						np.full((len(UpperCAmelCase_  ), sequence_length, 2)  ,			UpperCAmelCase_  )
  else:
    __UpperCAmelCase    :						str       =						np.full((len(UpperCAmelCase_  ), sequence_length)  ,			UpperCAmelCase_  )

  for i, tensor in enumerate(UpperCAmelCase_  ):
    if padding_side == "right":
      if isinstance(UpperCAmelCase_  ,			UpperCAmelCase_  ):
        __UpperCAmelCase    :						List[str]       =						tensor[:sequence_length]
      else:
        __UpperCAmelCase    :						List[str]       =						tensor[:sequence_length]
    else:
      if isinstance(UpperCAmelCase_  ,			UpperCAmelCase_  ):
        __UpperCAmelCase    :						Tuple       =						tensor[:sequence_length]
      else:
        __UpperCAmelCase    :						Union[str, Any]       =						tensor[:sequence_length]

  return out_tensor.tolist()


def       lowerCamelCase_       (   UpperCAmelCase_  )						->str:





  """simple docstring"""


  __UpperCAmelCase    :						Dict       =						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    :						List[str]       =						unicodedata.category(UpperCAmelCase_  )
  if cat.startswith('''P'''  ):
    return True
  return False







@dataclass
class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       PreTrainedTokenizerBase
      _A     :       Union[bool, str, PaddingStrategy]         =       True
      _A     :       Optional[int]         =       None
      _A     :       Optional[int]         =       None
      _A     :       int         =       -1_00
      _A     :       str         =       "pt"

      def       A_					(							self       :      Optional[Any]						,				__lowercase       :      int ):


        '''simple docstring'''




        import torch

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

        return batch






"""simple docstring"""





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


lowercase__				:int				   =       logging.get_logger(__name__)

lowercase__				:List[str]				   =       {
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       List[Any]         =       'gpt_neox'

      def __init__(							self       :      Union[str, Any]						,				__lowercase       :      Any=50_432						,				__lowercase       :      int=6_144						,				__lowercase       :      List[str]=44						,				__lowercase       :      List[Any]=64						,				__lowercase       :      int=24_576						,				__lowercase       :      List[str]="gelu"						,				__lowercase       :      Optional[int]=0.2_5						,				__lowercase       :      Any=10_000						,				__lowercase       :      Union[str, Any]=0.0						,				__lowercase       :      List[str]=0.0						,				__lowercase       :      Union[str, Any]=0.1						,				__lowercase       :      Optional[int]=2_048						,				__lowercase       :      Union[str, Any]=0.0_2						,				__lowercase       :      Optional[int]=1e-5						,				__lowercase       :      str=True						,				__lowercase       :      str=0						,				__lowercase       :      Tuple=2						,				__lowercase       :      Tuple=False						,				__lowercase       :      int=True						,				__lowercase       :      Any=None						,				**__lowercase       :      List[str]						,				):


        '''simple docstring'''




        super().__init__(bos_token_id=__lowercase						,				eos_token_id=__lowercase						,				**__lowercase )
        __UpperCAmelCase    :						int       =						vocab_size
        __UpperCAmelCase    :						Union[str, Any]       =						max_position_embeddings
        __UpperCAmelCase    :						List[str]       =						hidden_size
        __UpperCAmelCase    :						Tuple       =						num_hidden_layers
        __UpperCAmelCase    :						Any       =						num_attention_heads
        __UpperCAmelCase    :						Union[str, Any]       =						intermediate_size
        __UpperCAmelCase    :						str       =						hidden_act
        __UpperCAmelCase    :						str       =						rotary_pct
        __UpperCAmelCase    :						str       =						rotary_emb_base
        __UpperCAmelCase    :						Dict       =						attention_dropout
        __UpperCAmelCase    :						str       =						hidden_dropout
        __UpperCAmelCase    :						Any       =						classifier_dropout
        __UpperCAmelCase    :						Tuple       =						initializer_range
        __UpperCAmelCase    :						Any       =						layer_norm_eps
        __UpperCAmelCase    :						Optional[int]       =						use_cache
        __UpperCAmelCase    :						List[Any]       =						tie_word_embeddings
        __UpperCAmelCase    :						Any       =						use_parallel_residual
        __UpperCAmelCase    :						List[str]       =						rope_scaling
        self._rope_scaling_validation()

        if self.hidden_size % self.num_attention_heads != 0:
          raise ValueError(
              '''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )







      def       A_					(							self       :      int ):


        '''simple docstring'''




        if self.rope_scaling is None:
          return

        if not isinstance(self.rope_scaling						,				__lowercase ) or len(self.rope_scaling ) != 2:
          raise ValueError(
              '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
              f'''got {self.rope_scaling}''' )
        __UpperCAmelCase    :						Union[str, Any]       =						self.rope_scaling.get('''type'''						,				__lowercase )
        __UpperCAmelCase    :						List[Any]       =						self.rope_scaling.get('''factor'''						,				__lowercase )
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
          raise ValueError(
              f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''' )
        if rope_scaling_factor is None or not isinstance(__lowercase						,				__lowercase ) or rope_scaling_factor <= 1.0:
          raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )