





import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


snake_case 				=       logging.getLogger(__name__)





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	Optional[Any]						,__A	:	Union[str, Any]=-1  )						->					int:
         # in NER datasets, the last column is usually reserved for NER label
         _lowercase							= label_idx







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	int						,__A	:	Union[Split, str]  )						->					List[InputExample]:
         if isinstance(__A						,__A  ):
           _lowercase							= mode.value
         _lowercase							= os.path.join(__A						,F"""{mode}.txt"""  )
         _lowercase							= 1
         _lowercase							= []
         with open(__A						,encoding='utf-8'  ) as f:
           _lowercase							= []
           _lowercase							= []
           for line in f:
             if line.startswith('-DOCSTART-'  ) or line == "" or line == "\n":
               if words:
                 examples.append(InputExample(guid=F"""{mode}-{guid_index}"""						,words=__A						,labels=__A  )  )
                 guid_index += 1
                 _lowercase							= []
                 _lowercase							= []
             else:
               _lowercase							= line.split(' '  )
               words.append(splits[0]  )
               if len(__A  ) > 1:
                 labels.append(splits[self.label_idx].replace('\n'						,''  )  )
               else:
                 # Examples could have no label for mode = "test"
                 labels.append('O'  )
           if words:
             examples.append(InputExample(guid=F"""{mode}-{guid_index}"""						,words=__A						,labels=__A  )  )
         return examples







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	TextIO						,__A	:	TextIO						,__A	:	List  )						->					Tuple:
         _lowercase							= 0
         for line in test_input_reader:
           if line.startswith('-DOCSTART-'  ) or line == "" or line == "\n":
             writer.write(__A  )
             if not preds_list[example_id]:
               example_id += 1
           elif preds_list[example_id]:
             _lowercase							= line.split()[0] + ' ' + preds_list[example_id].pop(0  ) + '\n'
             writer.write(__A  )
           else:
             logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'						,line.split()[0]  )







       def    __UpperCAmelCase (    self	:	Any						,__A	:	str  )						->					List[str]:
         if path:
           with open(__A						,'r'  ) as f:
             _lowercase							= f.read().splitlines()
           if "O" not in labels:
             _lowercase							= ['O'] + labels
           return labels
         else:
           return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	Optional[int]  )						->					Optional[Any]:
         # in CONLL2003 dataset chunk column is second-to-last
         super().__init__(label_idx=-2  )







       def    __UpperCAmelCase (    self	:	Dict						,__A	:	str  )						->					List[str]:
         if path:
           with open(__A						,'r'  ) as f:
             _lowercase							= f.read().splitlines()
           if "O" not in labels:
             _lowercase							= ['O'] + labels
           return labels
         else:
           return [
               "O",
               "B-ADVP",
               "B-INTJ",
               "B-LST",
               "B-PRT",
               "B-NP",
               "B-SBAR",
               "B-VP",
               "B-ADJP",
               "B-CONJP",
               "B-PP",
               "I-ADVP",
               "I-INTJ",
               "I-LST",
               "I-PRT",
               "I-NP",
               "I-SBAR",
               "I-VP",
               "I-ADJP",
               "I-CONJP",
               "I-PP",
           ]





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	Union[str, Any]						,__A	:	Union[Split, str]  )						->					List[InputExample]:
         if isinstance(__A						,__A  ):
           _lowercase							= mode.value
         _lowercase							= os.path.join(__A						,F"""{mode}.txt"""  )
         _lowercase							= 1
         _lowercase							= []

         with open(__A						,encoding='utf-8'  ) as f:
           for sentence in parse_incr(__A  ):
             _lowercase							= []
             _lowercase							= []
             for token in sentence:
               words.append(token['form']  )
               labels.append(token['upos']  )
             assert len(__A  ) == len(__A  )
             if words:
               examples.append(InputExample(guid=F"""{mode}-{guid_index}"""						,words=__A						,labels=__A  )  )
               guid_index += 1
         return examples







       def    __UpperCAmelCase (    self	:	str						,__A	:	TextIO						,__A	:	TextIO						,__A	:	List  )						->					Any:
         _lowercase							= 0
         for sentence in parse_incr(__A  ):
           _lowercase							= preds_list[example_id]
           _lowercase							= ''
           for token in sentence:
             out += F"""{token["form"]} ({token["upos"]}|{s_p.pop(0  )}) """
           out += "\n"
           writer.write(__A  )
           example_id += 1







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	str  )						->					List[str]:
         if path:
           with open(__A						,'r'  ) as f:
             return f.read().splitlines()
         else:
           return [
               "ADJ",
               "ADP",
               "ADV",
               "AUX",
               "CCONJ",
               "DET",
               "INTJ",
               "NOUN",
               "NUM",
               "PART",
               "PRON",
               "PROPN",
               "PUNCT",
               "SCONJ",
               "SYM",
               "VERB",
               "X",
           ]






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


snake_case 				=       logging.get_logger(__name__)

snake_case 				=       {
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : str      =  '''gptj'''
       SCREAMING_SNAKE_CASE_      : int      =  {
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }







       def __init__(    self	:	str						,__A	:	List[str]=5_0400						,__A	:	Dict=2048						,__A	:	List[str]=4096						,__A	:	Dict=28						,__A	:	Optional[int]=16						,__A	:	Tuple=64						,__A	:	Optional[int]=None						,__A	:	Optional[int]="gelu_new"						,__A	:	Dict=0.0						,__A	:	List[str]=0.0						,__A	:	Optional[int]=0.0						,__A	:	str=1e-5						,__A	:	List[Any]=0.02						,__A	:	str=True						,__A	:	int=5_0256						,__A	:	Union[str, Any]=5_0256						,__A	:	int=False						,**__A	:	List[Any]						,)						->					str:
         _lowercase							= vocab_size
         _lowercase							= n_positions
         _lowercase							= n_embd
         _lowercase							= n_layer
         _lowercase							= n_head
         _lowercase							= n_inner
         _lowercase							= rotary_dim
         _lowercase							= activation_function
         _lowercase							= resid_pdrop
         _lowercase							= embd_pdrop
         _lowercase							= attn_pdrop
         _lowercase							= layer_norm_epsilon
         _lowercase							= initializer_range
         _lowercase							= use_cache

         _lowercase							= bos_token_id
         _lowercase							= eos_token_id

         super().__init__(
             bos_token_id=__A						,eos_token_id=__A						,tie_word_embeddings=__A						,**__A  )





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	Tuple						,__A	:	PretrainedConfig						,__A	:	str = "default"						,__A	:	List[PatchingSpec] = None						,__A	:	bool = False						,)						->					Any:
         super().__init__(__A						,task=__A						,patching_specs=__A						,use_past=__A  )
         if not getattr(self._config						,'pad_token_id'						,__A  ):
           # TODO: how to do that better?
           _lowercase							= 0







       @property
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Mapping[str, Mapping[int, str]]:
         _lowercase							= OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}  )
         if self.use_past:
           self.fill_with_past_key_values_(__A						,direction='inputs'  )
           _lowercase							= {0: 'batch', 1: 'past_sequence + sequence'}
         else:
           _lowercase							= {0: 'batch', 1: 'sequence'}

         return common_inputs







       @property
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					int:
         return self._config.n_layer







       @property
       def    __UpperCAmelCase (    self	:	Tuple  )						->					int:
         return self._config.n_head







       def    __UpperCAmelCase (    self	:	Any						,__A	:	PreTrainedTokenizer						,__A	:	int = -1						,__A	:	int = -1						,__A	:	bool = False						,__A	:	Optional[TensorType] = None						,)						->					Mapping[str, Any]:
         _lowercase							= super(__A						,self  ).generate_dummy_inputs(
             __A						,batch_size=__A						,seq_length=__A						,is_pair=__A						,framework=__A  )

         # We need to order the input in the way they appears in the forward()
         _lowercase							= OrderedDict({'input_ids': common_inputs['input_ids']}  )

         # Need to add the past_keys
         if self.use_past:
           if not is_torch_available():
             raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'  )
           else:
             import torch

             _lowercase						,		_lowercase							= common_inputs['input_ids'].shape
             # Not using the same length for past_key_values
             _lowercase							= seqlen + 2
             _lowercase							= (
                 batch,
                 self.num_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // self.num_attention_heads,
             )
             _lowercase							= [
                 (torch.zeros(__A  ), torch.zeros(__A  )) for _ in range(self.num_layers  )
             ]

         _lowercase							= common_inputs['attention_mask']
         if self.use_past:
           _lowercase							= ordered_inputs['attention_mask'].dtype
           _lowercase							= torch.cat(
               [ordered_inputs['attention_mask'], torch.ones(__A						,__A						,dtype=__A  )]						,dim=1  )

         return ordered_inputs







       @property
       def    __UpperCAmelCase (    self	:	str  )						->					int:
         return 13