






import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_PRETRAINING_MAPPING,
       MegatronBertForCausalLM,
       MegatronBertForMaskedLM,
       MegatronBertForMultipleChoice,
       MegatronBertForNextSentencePrediction,
       MegatronBertForPreTraining,
       MegatronBertForQuestionAnswering,
       MegatronBertForSequenceClassification,
       MegatronBertForTokenClassification,
       MegatronBertModel,
   )


class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	Optional[int]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Optional[int]=13					, UpperCAmelCase_       :	str=7					, UpperCAmelCase_       :	Union[str, Any]=True					, UpperCAmelCase_       :	str=True					, UpperCAmelCase_       :	Tuple=True					, UpperCAmelCase_       :	Union[str, Any]=True					, UpperCAmelCase_       :	Tuple=99					, UpperCAmelCase_       :	Tuple=64					, UpperCAmelCase_       :	Dict=32					, UpperCAmelCase_       :	List[Any]=5					, UpperCAmelCase_       :	str=4					, UpperCAmelCase_       :	Dict=37					, UpperCAmelCase_       :	str="gelu"					, UpperCAmelCase_       :	Optional[Any]=0.1					, UpperCAmelCase_       :	str=0.1					, UpperCAmelCase_       :	Optional[int]=512					, UpperCAmelCase_       :	Dict=16					, UpperCAmelCase_       :	int=2					, UpperCAmelCase_       :	List[Any]=0.02					, UpperCAmelCase_       :	List[str]=3					, UpperCAmelCase_       :	Union[str, Any]=4					, UpperCAmelCase_       :	List[Any]=None					, ):
       SCREAMING_SNAKE_CASE       :      Any							=    parent
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    batch_size
       SCREAMING_SNAKE_CASE       :      int							=    seq_length
       SCREAMING_SNAKE_CASE       :      List[Any]							=    is_training
       SCREAMING_SNAKE_CASE       :      Tuple							=    use_input_mask
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    use_token_type_ids
       SCREAMING_SNAKE_CASE       :      List[str]							=    use_labels
       SCREAMING_SNAKE_CASE       :      int							=    vocab_size
       SCREAMING_SNAKE_CASE       :      List[str]							=    hidden_size
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    embedding_size
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    num_hidden_layers
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    intermediate_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    hidden_act
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE       :      List[Any]							=    max_position_embeddings
       SCREAMING_SNAKE_CASE       :      str							=    type_vocab_size
       SCREAMING_SNAKE_CASE       :      Any							=    type_sequence_label_size
       SCREAMING_SNAKE_CASE       :      Tuple							=    initializer_range
       SCREAMING_SNAKE_CASE       :      int							=    num_labels
       SCREAMING_SNAKE_CASE       :      int							=    num_choices
       SCREAMING_SNAKE_CASE       :      Tuple							=    scope


     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      int							=    ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size      )

       SCREAMING_SNAKE_CASE       :      Any							=    None
       if self.use_input_mask:
         SCREAMING_SNAKE_CASE       :      Tuple							=    random_attention_mask([self.batch_size, self.seq_length]      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    None
       if self.use_token_type_ids:
         SCREAMING_SNAKE_CASE       :      Any							=    ids_tensor([self.batch_size, self.seq_length]					, self.type_vocab_size      )

       SCREAMING_SNAKE_CASE       :      Dict							=    None
       SCREAMING_SNAKE_CASE       :      int							=    None
       SCREAMING_SNAKE_CASE       :      Any							=    None
       if self.use_labels:
         SCREAMING_SNAKE_CASE       :      List[Any]							=    ids_tensor([self.batch_size]					, self.type_sequence_label_size      )
         SCREAMING_SNAKE_CASE       :      List[Any]							=    ids_tensor([self.batch_size, self.seq_length]					, self.num_labels      )
         SCREAMING_SNAKE_CASE       :      Dict							=    ids_tensor([self.batch_size]					, self.num_choices      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


     def 				_A       (				self       :	Tuple      ):
       return MegatronBertConfig(
           vocab_size=self.vocab_size					, hidden_size=self.hidden_size					, num_hidden_layers=self.num_hidden_layers					, num_attention_heads=self.num_attention_heads					, intermediate_size=self.intermediate_size					, embedding_size=self.embedding_size					, hidden_act=self.hidden_act					, hidden_dropout_prob=self.hidden_dropout_prob					, attention_probs_dropout_prob=self.attention_probs_dropout_prob					, max_position_embeddings=self.max_position_embeddings					, type_vocab_size=self.type_vocab_size					, is_decoder=UpperCAmelCase_					, initializer_range=self.initializer_range					, )


     def 				_A       (				self       :	int					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	str      ):
       SCREAMING_SNAKE_CASE       :      int							=    MegatronBertModel(config=UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    model(UpperCAmelCase_					, attention_mask=UpperCAmelCase_					, token_type_ids=UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    model(UpperCAmelCase_					, token_type_ids=UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    model(UpperCAmelCase_      )

       self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size)      )
       self.parent.assertEqual(result.pooler_output.shape					, (self.batch_size, self.hidden_size)      )


     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    MegatronBertForMaskedLM(config=UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    model(UpperCAmelCase_					, attention_mask=UpperCAmelCase_					, token_type_ids=UpperCAmelCase_					, labels=UpperCAmelCase_      )
       self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.vocab_size)      )


     def 				_A       (				self       :	Tuple					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    MegatronBertForCausalLM(config=UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    model(UpperCAmelCase_					, attention_mask=UpperCAmelCase_					, token_type_ids=UpperCAmelCase_					, labels=UpperCAmelCase_      )
       self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.vocab_size)      )


     def 				_A       (				self       :	str					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Any      ):
       SCREAMING_SNAKE_CASE       :      int							=    MegatronBertForNextSentencePrediction(config=UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE       :      int							=    model(
           UpperCAmelCase_					, attention_mask=UpperCAmelCase_					, token_type_ids=UpperCAmelCase_					, labels=UpperCAmelCase_					, )
       self.parent.assertEqual(result.logits.shape					, (self.batch_size, 2)      )


     def 				_A       (				self       :	int					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Any      ):
       SCREAMING_SNAKE_CASE       :      int							=    MegatronBertForPreTraining(config=UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE       :      str							=    model(
           UpperCAmelCase_					, attention_mask=UpperCAmelCase_					, token_type_ids=UpperCAmelCase_					, labels=UpperCAmelCase_					, next_sentence_label=UpperCAmelCase_					, )
       self.parent.assertEqual(result.prediction_logits.shape					, (self.batch_size, self.seq_length, self.vocab_size)      )
       self.parent.assertEqual(result.seq_relationship_logits.shape					, (self.batch_size, 2)      )


     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	str      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    MegatronBertForQuestionAnswering(config=UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE       :      Any							=    model(
           UpperCAmelCase_					, attention_mask=UpperCAmelCase_					, token_type_ids=UpperCAmelCase_					, start_positions=UpperCAmelCase_					, end_positions=UpperCAmelCase_					, )
       self.parent.assertEqual(result.start_logits.shape					, (self.batch_size, self.seq_length)      )
       self.parent.assertEqual(result.end_logits.shape					, (self.batch_size, self.seq_length)      )


     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.num_labels
       SCREAMING_SNAKE_CASE       :      Dict							=    MegatronBertForSequenceClassification(UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE       :      Any							=    model(UpperCAmelCase_					, attention_mask=UpperCAmelCase_					, token_type_ids=UpperCAmelCase_					, labels=UpperCAmelCase_      )
       self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_labels)      )


     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.num_labels
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    MegatronBertForTokenClassification(config=UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE       :      int							=    model(UpperCAmelCase_					, attention_mask=UpperCAmelCase_					, token_type_ids=UpperCAmelCase_					, labels=UpperCAmelCase_      )
       self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.num_labels)      )


     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.num_choices
       SCREAMING_SNAKE_CASE       :      Dict							=    MegatronBertForMultipleChoice(config=UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE       :      str							=    input_ids.unsqueeze(1      ).expand(-1					, self.num_choices					, -1      ).contiguous()
       SCREAMING_SNAKE_CASE       :      Tuple							=    token_type_ids.unsqueeze(1      ).expand(-1					, self.num_choices					, -1      ).contiguous()
       SCREAMING_SNAKE_CASE       :      Dict							=    input_mask.unsqueeze(1      ).expand(-1					, self.num_choices					, -1      ).contiguous()
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model(
           UpperCAmelCase_					, attention_mask=UpperCAmelCase_					, token_type_ids=UpperCAmelCase_					, labels=UpperCAmelCase_					, )
       self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_choices)      )






     def 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    self.prepare_config_and_inputs()
       (
           SCREAMING_SNAKE_CASE  
       )       :      int							=    config_and_inputs
       SCREAMING_SNAKE_CASE       :      str							=    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict





@require_torch
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		List[Any]			=	(
         (
             MegatronBertModel,
             MegatronBertForMaskedLM,
             MegatronBertForCausalLM,
             MegatronBertForMultipleChoice,
             MegatronBertForNextSentencePrediction,
             MegatronBertForPreTraining,
             MegatronBertForQuestionAnswering,
             MegatronBertForSequenceClassification,
             MegatronBertForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     UpperCamelCase_							:		int			=	(
         {
             '''feature-extraction''': MegatronBertModel,
             '''fill-mask''': MegatronBertForMaskedLM,
             '''question-answering''': MegatronBertForQuestionAnswering,
             '''text-classification''': MegatronBertForSequenceClassification,
             '''text-generation''': MegatronBertForCausalLM,
             '''token-classification''': MegatronBertForTokenClassification,
             '''zero-shot''': MegatronBertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     UpperCamelCase_							:		Optional[Any]			=	True
     # test_resize_embeddings = False
     UpperCamelCase_							:		Any			=	False


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	str=False      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    super()._prepare_for_class(UpperCAmelCase_					, UpperCAmelCase_					, return_labels=UpperCAmelCase_      )

       if return_labels:
         if model_class in get_values(UpperCAmelCase_      ):
           SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.zeros(
               (self.model_tester.batch_size, self.model_tester.seq_length)					, dtype=torch.long					, device=UpperCAmelCase_      )
           SCREAMING_SNAKE_CASE       :      Tuple							=    torch.zeros(
               self.model_tester.batch_size					, dtype=torch.long					, device=UpperCAmelCase_      )
       return inputs_dict


     def 				_A       (				self       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    MegatronBertModelTester(self      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    ConfigTester(self					, config_class=UpperCAmelCase_					, hidden_size=37      )


     def 				_A       (				self       :	List[str]      ):
       self.config_tester.run_common_tests()


     def 				_A       (				self       :	int      ):
       SCREAMING_SNAKE_CASE       :      int							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_model(*UpperCAmelCase_      )


     def 				_A       (				self       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Any							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_masked_lm(*UpperCAmelCase_      )


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*UpperCAmelCase_      )


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*UpperCAmelCase_      )


     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      str							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_pretraining(*UpperCAmelCase_      )


     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      str							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_question_answering(*UpperCAmelCase_      )


     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*UpperCAmelCase_      )






     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_token_classification(*UpperCAmelCase_      )





def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  return torch.tensor(
      lowercase						,     dtype=torch.long						,     device=lowercase						,     )


snake_case    				=		1e-4


@require_torch
@require_sentencepiece
@require_tokenizers
class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     @slow
     @unittest.skip("Model is not available."      )
     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      Any							=    "nvidia/megatron-bert-uncased-345m"
       if "MYDIR" in os.environ:
         SCREAMING_SNAKE_CASE       :      Tuple							=    os.path.join(os.environ["MYDIR"]					, UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      str							=    MegatronBertModel.from_pretrained(UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.half()
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]      )
       with torch.no_grad():
         SCREAMING_SNAKE_CASE       :      Any							=    model(UpperCAmelCase_      )[0]
       SCREAMING_SNAKE_CASE       :      Tuple							=    torch.Size((1, 9, 1024)      )
       self.assertEqual(output.shape					, UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    [-0.6_040, -0.2_517, -0.1_025, 0.3_420, -0.6_758, -0.0_017, -0.1_089, -0.1_990, 0.5_728]
       for ii in range(3      ):
         for jj in range(3      ):
           SCREAMING_SNAKE_CASE       :      Any							=    output[0, ii, jj]
           SCREAMING_SNAKE_CASE       :      Dict							=    expected[3 * ii + jj]
           SCREAMING_SNAKE_CASE       :      Any							=    "ii={} jj={} a={} b={}".format(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_      )
           self.assertTrue(math.isclose(UpperCAmelCase_					, UpperCAmelCase_					, rel_tol=UpperCAmelCase_					, abs_tol=UpperCAmelCase_      )					, msg=UpperCAmelCase_      )














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  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


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


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Union[Split, str]      ):
       if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    mode.value
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    os.path.join(UpperCAmelCase_					, f'''{mode}.txt'''      )
       SCREAMING_SNAKE_CASE       :      int							=    1
       SCREAMING_SNAKE_CASE       :      Any							=    []
       with open(UpperCAmelCase_					, encoding="utf-8"      ) as f:
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    []
         SCREAMING_SNAKE_CASE       :      List[str]							=    []
         for line in f:
           if line.startswith("-DOCSTART-"      ) or line == "" or line == "\n":
             if words:
               examples.append(InputExample(guid=f'''{mode}-{guid_index}'''					, words=UpperCAmelCase_					, labels=UpperCAmelCase_      )      )
               guid_index += 1
               SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    []
               SCREAMING_SNAKE_CASE       :      Optional[int]							=    []
           else:
             SCREAMING_SNAKE_CASE       :      str							=    line.split(" "      )
             words.append(splits[0]      )
             if len(UpperCAmelCase_      ) > 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=UpperCAmelCase_					, labels=UpperCAmelCase_      )      )
       return examples


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






     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	str      ):
       if path:
         with open(UpperCAmelCase_					, "r"      ) as f:
           SCREAMING_SNAKE_CASE       :      List[Any]							=    f.read().splitlines()
         if "O" not in labels:
           SCREAMING_SNAKE_CASE       :      Tuple							=    ["O"] + labels
         return labels
       else:
         return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


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






     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	str      ):
       if path:
         with open(UpperCAmelCase_					, "r"      ) as f:
           SCREAMING_SNAKE_CASE       :      Dict							=    f.read().splitlines()
         if "O" not in labels:
           SCREAMING_SNAKE_CASE       :      str							=    ["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  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Union[Split, str]      ):
       if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      str							=    mode.value
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    os.path.join(UpperCAmelCase_					, f'''{mode}.txt'''      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    1
       SCREAMING_SNAKE_CASE       :      str							=    []

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


     def 				_A       (				self       :	str					, UpperCAmelCase_       :	TextIO					, UpperCAmelCase_       :	TextIO					, UpperCAmelCase_       :	List      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    0
       for sentence in parse_incr(UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    preds_list[example_id]
         SCREAMING_SNAKE_CASE       :      Any							=    ""
         for token in sentence:
           out += f'''{token['form']} ({token['upos']}|{s_p.pop(0      )}) '''
         out += "\n"
         writer.write(UpperCAmelCase_      )
         example_id += 1






     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	str      ):
       if path:
         with open(UpperCAmelCase_					, "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",
         ]






