






'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


a_					= logging.getLogger(__name__)






@dataclass
class      __SCREAMING_SNAKE_CASE      :
 snake_case_  							=							42
 snake_case_  							=							42
 snake_case_  							=							42






@dataclass
class      __SCREAMING_SNAKE_CASE      :
 snake_case_  							=							42
 snake_case_  							=							42
 snake_case_  							=							None
 snake_case_  							=							None






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							"""train"""
 snake_case_  							=							"""dev"""
 snake_case_  							=							"""test"""






class      __SCREAMING_SNAKE_CASE      :




 @staticmethod
 def      __magic_name__	(      __lowercase	:							Union[str, Any]					,      __lowercase	:							Union[Split, str]      )	->						List[InputExample]:
       raise NotImplementedError




 @staticmethod
 def      __magic_name__	(      __lowercase	:							str      )	->						List[str]:
       raise NotImplementedError




 @staticmethod
 def      __magic_name__	(      __lowercase	:							List[InputExample]					,      __lowercase	:							List[str]					,      __lowercase	:							int					,      __lowercase	:							PreTrainedTokenizer					,      __lowercase	:							Union[str, Any]=False					,      __lowercase	:							str="[CLS]"					,      __lowercase	:							List[str]=1					,      __lowercase	:							Any="[SEP]"					,      __lowercase	:							Optional[Any]=False					,      __lowercase	:							Optional[Any]=False					,      __lowercase	:							Tuple=0					,      __lowercase	:							Optional[int]=0					,      __lowercase	:							List[str]=-1_00					,      __lowercase	:							List[Any]=0					,      __lowercase	:							List[Any]=True					,      )	->						List[InputFeatures]:
       SCREAMING_SNAKE_CASE__     :				Any									={label: i for i, label in enumerate(__lowercase      )}

       SCREAMING_SNAKE_CASE__     :				Any									=[]
       for ex_index, example in enumerate(__lowercase      ):
             if ex_index % 1_00_00 == 0:
                   logger.info('''Writing example %d of %d'''					,      __lowercase					,      len(__lowercase      )      )

             SCREAMING_SNAKE_CASE__     :				Optional[int]									=[]
             SCREAMING_SNAKE_CASE__     :				str									=[]
             for word, label in zip(example.words					,      example.labels      ):
                   SCREAMING_SNAKE_CASE__     :				int									=tokenizer.tokenize(__lowercase      )

                   # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                   if len(__lowercase      ) > 0:
                         tokens.extend(__lowercase      )
                         # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                         label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__lowercase      ) - 1)      )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=tokenizer.num_special_tokens_to_add()
             if len(__lowercase      ) > max_seq_length - special_tokens_count:
                   SCREAMING_SNAKE_CASE__     :				List[Any]									=tokens[: (max_seq_length - special_tokens_count)]
                   SCREAMING_SNAKE_CASE__     :				Tuple									=label_ids[: (max_seq_length - special_tokens_count)]

             # The convention in BERT is:
             # (a) For sequence pairs:
             #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
             #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
             # (b) For single sequences:
             #  tokens:   [CLS] the dog is hairy . [SEP]
             #  type_ids:   0   0   0   0  0     0   0
             #
             # Where "type_ids" are used to indicate whether this is the first
             # sequence or the second sequence. The embedding vectors for `type=0` and
             # `type=1` were learned during pre-training and are added to the wordpiece
             # embedding vector (and position vector). This is not *strictly* necessary
             # since the [SEP] token unambiguously separates the sequences, but it makes
             # it easier for the model to learn the concept of sequences.
             #
             # For classification tasks, the first vector (corresponding to [CLS]) is
             # used as the "sentence vector". Note that this only makes sense because
             # the entire model is fine-tuned.
             tokens += [sep_token]
             label_ids += [pad_token_label_id]
             if sep_token_extra:
                   # roberta uses an extra separator b/w pairs of sentences
                   tokens += [sep_token]
                   label_ids += [pad_token_label_id]
             SCREAMING_SNAKE_CASE__     :				List[str]									=[sequence_a_segment_id] * len(__lowercase      )

             if cls_token_at_end:
                   tokens += [cls_token]
                   label_ids += [pad_token_label_id]
                   segment_ids += [cls_token_segment_id]
             else:
                   SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=[cls_token] + tokens
                   SCREAMING_SNAKE_CASE__     :				List[Any]									=[pad_token_label_id] + label_ids
                   SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=[cls_token_segment_id] + segment_ids

             SCREAMING_SNAKE_CASE__     :				int									=tokenizer.convert_tokens_to_ids(__lowercase      )

             # The mask has 1 for real tokens and 0 for padding tokens. Only real
             # tokens are attended to.
             SCREAMING_SNAKE_CASE__     :				Tuple									=[1 if mask_padding_with_zero else 0] * len(__lowercase      )

             # Zero-pad up to the sequence length.
             SCREAMING_SNAKE_CASE__     :				List[Any]									=max_seq_length - len(__lowercase      )
             if pad_on_left:
                   SCREAMING_SNAKE_CASE__     :				Any									=([pad_token] * padding_length) + input_ids
                   SCREAMING_SNAKE_CASE__     :				Tuple									=([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                   SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=([pad_token_segment_id] * padding_length) + segment_ids
                   SCREAMING_SNAKE_CASE__     :				List[Any]									=([pad_token_label_id] * padding_length) + label_ids
             else:
                   input_ids += [pad_token] * padding_length
                   input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                   segment_ids += [pad_token_segment_id] * padding_length
                   label_ids += [pad_token_label_id] * padding_length

             assert len(__lowercase      ) == max_seq_length
             assert len(__lowercase      ) == max_seq_length
             assert len(__lowercase      ) == max_seq_length
             assert len(__lowercase      ) == max_seq_length

             if ex_index < 5:
                   logger.info('''*** Example ***'''      )
                   logger.info('''guid: %s'''					,      example.guid      )
                   logger.info('''tokens: %s'''					,      ''' '''.join([str(__lowercase      ) for x in tokens]      )      )
                   logger.info('''input_ids: %s'''					,      ''' '''.join([str(__lowercase      ) for x in input_ids]      )      )
                   logger.info('''input_mask: %s'''					,      ''' '''.join([str(__lowercase      ) for x in input_mask]      )      )
                   logger.info('''segment_ids: %s'''					,      ''' '''.join([str(__lowercase      ) for x in segment_ids]      )      )
                   logger.info('''label_ids: %s'''					,      ''' '''.join([str(__lowercase      ) for x in label_ids]      )      )

             if "token_type_ids" not in tokenizer.model_input_names:
                   SCREAMING_SNAKE_CASE__     :				Optional[Any]									=None

             features.append(
                 InputFeatures(
                     input_ids=__lowercase					,      attention_mask=__lowercase					,      token_type_ids=__lowercase					,      label_ids=__lowercase      )      )
       return features


if is_torch_available():
  import torch
  from torch import nn
  from torch.utils.data import Dataset






  class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
   snake_case_  							=							42
   snake_case_  							=							nn.CrossEntropyLoss().ignore_index




   def __init__(      self	:							Union[str, Any]					,      __lowercase	:							TokenClassificationTask					,      __lowercase	:							str					,      __lowercase	:							PreTrainedTokenizer					,      __lowercase	:							List[str]					,      __lowercase	:							str					,      __lowercase	:							Optional[int] = None					,      __lowercase	:							Union[str, Any]=False					,      __lowercase	:							Split = Split.train					,      )	->						Dict:
         # Load data features from cache or dataset file
         SCREAMING_SNAKE_CASE__     :				str									=os.path.join(
             __lowercase					,      '''cached_{}_{}_{}'''.format(mode.value					,      tokenizer.__class__.__name__					,      str(__lowercase      )      )					,      )

         # Make sure only the first process in distributed training processes the dataset,
         # and the others will use the cache.
         SCREAMING_SNAKE_CASE__     :				List[str]									=cached_features_file + '''.lock'''
         with FileLock(__lowercase      ):
               if os.path.exists(__lowercase      ) and not overwrite_cache:
                     logger.info(F"Loading features from cached file {cached_features_file}"      )
                     SCREAMING_SNAKE_CASE__     :				Dict									=torch.load(__lowercase      )
               else:
                     logger.info(F"Creating features from dataset file at {data_dir}"      )
                     SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=token_classification_task.read_examples_from_file(__lowercase					,      __lowercase      )
                     # TODO clean up all this to leverage built-in features of tokenizers
                     SCREAMING_SNAKE_CASE__     :				Tuple									=token_classification_task.convert_examples_to_features(
                         __lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      cls_token_at_end=bool(model_type in ['''xlnet''']      )					,      cls_token=tokenizer.cls_token					,      cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0					,      sep_token=tokenizer.sep_token					,      sep_token_extra=__lowercase					,      pad_on_left=bool(tokenizer.padding_side == '''left'''      )					,      pad_token=tokenizer.pad_token_id					,      pad_token_segment_id=tokenizer.pad_token_type_id					,      pad_token_label_id=self.pad_token_label_id					,      )
                     logger.info(F"Saving features into cached file {cached_features_file}"      )
                     torch.save(self.features					,      __lowercase      )




   def __len__(      self	:							Optional[Any]      )	->						Tuple:
         return len(self.features      )




   def __getitem__(      self	:							str					,      __lowercase	:							List[str]      )	->						InputFeatures:
         return self.features[i]


if is_tf_available():
  import tensorflow as tf






  class      __SCREAMING_SNAKE_CASE      :
   snake_case_  							=							42
   snake_case_  							=							-100




   def __init__(      self	:							Union[str, Any]					,      __lowercase	:							TokenClassificationTask					,      __lowercase	:							str					,      __lowercase	:							PreTrainedTokenizer					,      __lowercase	:							List[str]					,      __lowercase	:							str					,      __lowercase	:							Optional[int] = None					,      __lowercase	:							List[str]=False					,      __lowercase	:							Split = Split.train					,      )	->						str:
         SCREAMING_SNAKE_CASE__     :				Any									=token_classification_task.read_examples_from_file(__lowercase					,      __lowercase      )
         # TODO clean up all this to leverage built-in features of tokenizers
         SCREAMING_SNAKE_CASE__     :				Optional[int]									=token_classification_task.convert_examples_to_features(
             __lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      cls_token_at_end=bool(model_type in ['''xlnet''']      )					,      cls_token=tokenizer.cls_token					,      cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0					,      sep_token=tokenizer.sep_token					,      sep_token_extra=__lowercase					,      pad_on_left=bool(tokenizer.padding_side == '''left'''      )					,      pad_token=tokenizer.pad_token_id					,      pad_token_segment_id=tokenizer.pad_token_type_id					,      pad_token_label_id=self.pad_token_label_id					,      )

         def gen():
               for ex in self.features:
                     if ex.token_type_ids is None:
                           yield (
                               {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                               ex.label_ids,
                           )
                     else:
                           yield (
                               {
                                   "input_ids": ex.input_ids,
                                   "attention_mask": ex.attention_mask,
                                   "token_type_ids": ex.token_type_ids,
                               },
                               ex.label_ids,
                           )

         if "token_type_ids" not in tokenizer.model_input_names:
               SCREAMING_SNAKE_CASE__     :				Optional[int]									=tf.data.Dataset.from_generator(
                   __lowercase					,      ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)					,      (
                       {'''input_ids''': tf.TensorShape([None]      ), '''attention_mask''': tf.TensorShape([None]      )},
                       tf.TensorShape([None]      ),
                   )					,      )
         else:
               SCREAMING_SNAKE_CASE__     :				Optional[Any]									=tf.data.Dataset.from_generator(
                   __lowercase					,      ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)					,      (
                       {
                           '''input_ids''': tf.TensorShape([None]      ),
                           '''attention_mask''': tf.TensorShape([None]      ),
                           '''token_type_ids''': tf.TensorShape([None]      ),
                       },
                       tf.TensorShape([None]      ),
                   )					,      )




   def      __magic_name__	(      self	:							Optional[int]      )	->						List[Any]:
         SCREAMING_SNAKE_CASE__     :				Any									=self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features      )      )      )

         return self.dataset




   def __len__(      self	:							List[str]      )	->						Tuple:
         return len(self.features      )




   def __getitem__(      self	:							List[Any]					,      __lowercase	:							Any      )	->						InputFeatures:
         return self.features[i]







'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_					= logging.get_logger(__name__)

a_					= {
    '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      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							"""gpt_neox"""




 def __init__(      self	:							List[Any]					,      __lowercase	:							Union[str, Any]=5_04_32					,      __lowercase	:							int=61_44					,      __lowercase	:							Tuple=44					,      __lowercase	:							List[str]=64					,      __lowercase	:							str=2_45_76					,      __lowercase	:							Dict="gelu"					,      __lowercase	:							Tuple=0.25					,      __lowercase	:							Tuple=1_00_00					,      __lowercase	:							Tuple=0.0					,      __lowercase	:							str=0.0					,      __lowercase	:							List[Any]=0.1					,      __lowercase	:							Dict=20_48					,      __lowercase	:							Any=0.02					,      __lowercase	:							Dict=1e-5					,      __lowercase	:							List[Any]=True					,      __lowercase	:							str=0					,      __lowercase	:							Optional[Any]=2					,      __lowercase	:							Tuple=False					,      __lowercase	:							List[Any]=True					,      __lowercase	:							Optional[Any]=None					,      **__lowercase	:							Any					,      )	->						Dict:
       super().__init__(bos_token_id=__lowercase					,      eos_token_id=__lowercase					,      **__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=vocab_size
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=max_position_embeddings
       SCREAMING_SNAKE_CASE__     :				Any									=hidden_size
       SCREAMING_SNAKE_CASE__     :				str									=num_hidden_layers
       SCREAMING_SNAKE_CASE__     :				Any									=num_attention_heads
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=intermediate_size
       SCREAMING_SNAKE_CASE__     :				Dict									=hidden_act
       SCREAMING_SNAKE_CASE__     :				str									=rotary_pct
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=rotary_emb_base
       SCREAMING_SNAKE_CASE__     :				List[Any]									=attention_dropout
       SCREAMING_SNAKE_CASE__     :				List[Any]									=hidden_dropout
       SCREAMING_SNAKE_CASE__     :				str									=classifier_dropout
       SCREAMING_SNAKE_CASE__     :				Any									=initializer_range
       SCREAMING_SNAKE_CASE__     :				Dict									=layer_norm_eps
       SCREAMING_SNAKE_CASE__     :				Any									=use_cache
       SCREAMING_SNAKE_CASE__     :				Tuple									=tie_word_embeddings
       SCREAMING_SNAKE_CASE__     :				Tuple									=use_parallel_residual
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=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      __magic_name__	(      self	:							Optional[Any]      )	->						Optional[Any]:
       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}"      )
       SCREAMING_SNAKE_CASE__     :				int									=self.rope_scaling.get('''type'''					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=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}"      )