






import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
       FlaubertForMultipleChoice,
       FlaubertForQuestionAnswering,
       FlaubertForQuestionAnsweringSimple,
       FlaubertForSequenceClassification,
       FlaubertForTokenClassification,
       FlaubertModel,
       FlaubertWithLMHeadModel,
   )
   from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST


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


     def __init__(				self       :	Dict					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Optional[int]=13					, UpperCAmelCase_       :	List[str]=7					, UpperCAmelCase_       :	int=True					, UpperCAmelCase_       :	Tuple=True					, UpperCAmelCase_       :	int=True					, UpperCAmelCase_       :	List[Any]=True					, UpperCAmelCase_       :	List[Any]=True					, UpperCAmelCase_       :	str=False					, UpperCAmelCase_       :	str=False					, UpperCAmelCase_       :	Any=False					, UpperCAmelCase_       :	Any=2					, UpperCAmelCase_       :	Dict=99					, UpperCAmelCase_       :	List[str]=0					, UpperCAmelCase_       :	Optional[int]=32					, UpperCAmelCase_       :	Optional[int]=5					, UpperCAmelCase_       :	Tuple=4					, UpperCAmelCase_       :	Dict=0.1					, UpperCAmelCase_       :	Any=0.1					, UpperCAmelCase_       :	List[str]=512					, UpperCAmelCase_       :	Optional[Any]=12					, UpperCAmelCase_       :	List[str]=2					, UpperCAmelCase_       :	Union[str, Any]=0.02					, UpperCAmelCase_       :	str=3					, UpperCAmelCase_       :	str=4					, UpperCAmelCase_       :	Optional[int]="last"					, UpperCAmelCase_       :	List[str]=None					, UpperCAmelCase_       :	Any=None					, ):
       SCREAMING_SNAKE_CASE       :      Dict							=    parent
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    batch_size
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    seq_length
       SCREAMING_SNAKE_CASE       :      str							=    is_training
       SCREAMING_SNAKE_CASE       :      int							=    use_input_lengths
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    use_token_type_ids
       SCREAMING_SNAKE_CASE       :      Any							=    use_labels
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    gelu_activation
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    sinusoidal_embeddings
       SCREAMING_SNAKE_CASE       :      str							=    causal
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    asm
       SCREAMING_SNAKE_CASE       :      List[str]							=    n_langs
       SCREAMING_SNAKE_CASE       :      List[str]							=    vocab_size
       SCREAMING_SNAKE_CASE       :      Tuple							=    n_special
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    hidden_size
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    num_hidden_layers
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE       :      str							=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE       :      Tuple							=    max_position_embeddings
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    type_vocab_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    type_sequence_label_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    initializer_range
       SCREAMING_SNAKE_CASE       :      Tuple							=    num_labels
       SCREAMING_SNAKE_CASE       :      str							=    num_choices
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    summary_type
       SCREAMING_SNAKE_CASE       :      int							=    use_proj
       SCREAMING_SNAKE_CASE       :      int							=    scope


     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size      )
       SCREAMING_SNAKE_CASE       :      str							=    random_attention_mask([self.batch_size, self.seq_length]      )

       SCREAMING_SNAKE_CASE       :      Dict							=    None
       if self.use_input_lengths:
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    (
             ids_tensor([self.batch_size]					, vocab_size=2      ) + self.seq_length - 2
         )  # small variation of seq_length

       SCREAMING_SNAKE_CASE       :      Dict							=    None
       if self.use_token_type_ids:
         SCREAMING_SNAKE_CASE       :      List[Any]							=    ids_tensor([self.batch_size, self.seq_length]					, self.n_langs      )

       SCREAMING_SNAKE_CASE       :      Dict							=    None
       SCREAMING_SNAKE_CASE       :      Tuple							=    None
       SCREAMING_SNAKE_CASE       :      List[Any]							=    None
       if self.use_labels:
         SCREAMING_SNAKE_CASE       :      List[Any]							=    ids_tensor([self.batch_size]					, self.type_sequence_label_size      )
         SCREAMING_SNAKE_CASE       :      Tuple							=    ids_tensor([self.batch_size, self.seq_length]					, self.num_labels      )
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    ids_tensor([self.batch_size]					, 2      ).float()
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    ids_tensor([self.batch_size]					, self.num_choices      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    self.get_config()

       return (
           config,
           input_ids,
           token_type_ids,
           input_lengths,
           sequence_labels,
           token_labels,
           is_impossible_labels,
           choice_labels,
           input_mask,
       )


     def 				_A       (				self       :	Union[str, Any]      ):
       return FlaubertConfig(
           vocab_size=self.vocab_size					, n_special=self.n_special					, emb_dim=self.hidden_size					, n_layers=self.num_hidden_layers					, n_heads=self.num_attention_heads					, dropout=self.hidden_dropout_prob					, attention_dropout=self.attention_probs_dropout_prob					, gelu_activation=self.gelu_activation					, sinusoidal_embeddings=self.sinusoidal_embeddings					, asm=self.asm					, causal=self.causal					, n_langs=self.n_langs					, max_position_embeddings=self.max_position_embeddings					, initializer_range=self.initializer_range					, summary_type=self.summary_type					, use_proj=self.use_proj					, )


     def 				_A       (				self       :	Tuple					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	int					, ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    FlaubertModel(config=UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    model(UpperCAmelCase_					, lengths=UpperCAmelCase_					, langs=UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Any							=    model(UpperCAmelCase_					, langs=UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    model(UpperCAmelCase_      )
       self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size)      )


     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Optional[Any]					, ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    FlaubertWithLMHeadModel(UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()

       SCREAMING_SNAKE_CASE       :      Any							=    model(UpperCAmelCase_					, token_type_ids=UpperCAmelCase_					, labels=UpperCAmelCase_      )
       self.parent.assertEqual(result.loss.shape					, ()      )
       self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.vocab_size)      )


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Union[str, Any]					, ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    FlaubertForQuestionAnsweringSimple(UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()

       SCREAMING_SNAKE_CASE       :      List[Any]							=    model(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    model(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       :	Optional[int]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	int					, ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    FlaubertForQuestionAnswering(UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()

       SCREAMING_SNAKE_CASE       :      Any							=    model(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    model(
           UpperCAmelCase_					, start_positions=UpperCAmelCase_					, end_positions=UpperCAmelCase_					, cls_index=UpperCAmelCase_					, is_impossible=UpperCAmelCase_					, p_mask=UpperCAmelCase_					, )

       SCREAMING_SNAKE_CASE       :      Dict							=    model(
           UpperCAmelCase_					, start_positions=UpperCAmelCase_					, end_positions=UpperCAmelCase_					, cls_index=UpperCAmelCase_					, is_impossible=UpperCAmelCase_					, )

       (SCREAMING_SNAKE_CASE		)       :      Optional[int]							=    result_with_labels.to_tuple()

       SCREAMING_SNAKE_CASE       :      str							=    model(UpperCAmelCase_					, start_positions=UpperCAmelCase_					, end_positions=UpperCAmelCase_      )

       (SCREAMING_SNAKE_CASE		)       :      Optional[int]							=    result_with_labels.to_tuple()

       self.parent.assertEqual(result_with_labels.loss.shape					, ()      )
       self.parent.assertEqual(result.start_top_log_probs.shape					, (self.batch_size, model.config.start_n_top)      )
       self.parent.assertEqual(result.start_top_index.shape					, (self.batch_size, model.config.start_n_top)      )
       self.parent.assertEqual(
           result.end_top_log_probs.shape					, (self.batch_size, model.config.start_n_top * model.config.end_n_top)      )
       self.parent.assertEqual(
           result.end_top_index.shape					, (self.batch_size, model.config.start_n_top * model.config.end_n_top)      )
       self.parent.assertEqual(result.cls_logits.shape					, (self.batch_size,)      )


     def 				_A       (				self       :	str					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Union[str, Any]					, ):
       SCREAMING_SNAKE_CASE       :      Dict							=    FlaubertForSequenceClassification(UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    model(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    model(UpperCAmelCase_					, labels=UpperCAmelCase_      )

       self.parent.assertEqual(result.loss.shape					, ()      )
       self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.type_sequence_label_size)      )


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	List[str]					, ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.num_labels
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    FlaubertForTokenClassification(UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model(UpperCAmelCase_					, attention_mask=UpperCAmelCase_					, labels=UpperCAmelCase_      )
       self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.num_labels)      )


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Dict					, ):
       SCREAMING_SNAKE_CASE       :      str							=    self.num_choices
       SCREAMING_SNAKE_CASE       :      Dict							=    FlaubertForMultipleChoice(config=UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE       :      int							=    input_ids.unsqueeze(1      ).expand(-1					, self.num_choices					, -1      ).contiguous()
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    token_type_ids.unsqueeze(1      ).expand(-1					, self.num_choices					, -1      ).contiguous()
       SCREAMING_SNAKE_CASE       :      List[str]							=    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       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.prepare_config_and_inputs()
       (
           SCREAMING_SNAKE_CASE		
       )       :      Tuple							=    config_and_inputs
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    {
           "input_ids": input_ids,
           "token_type_ids": token_type_ids,
           "lengths": input_lengths,
           "attention_mask": input_mask,
       }
       return config, inputs_dict





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


     UpperCamelCase_							:		str			=	(
         (
             FlaubertModel,
             FlaubertWithLMHeadModel,
             FlaubertForQuestionAnswering,
             FlaubertForQuestionAnsweringSimple,
             FlaubertForSequenceClassification,
             FlaubertForTokenClassification,
             FlaubertForMultipleChoice,
         )
         if is_torch_available()
         else ()
     )
     UpperCamelCase_							:		Any			=	(
         {
             '''feature-extraction''': FlaubertModel,
             '''fill-mask''': FlaubertWithLMHeadModel,
             '''question-answering''': FlaubertForQuestionAnsweringSimple,
             '''text-classification''': FlaubertForSequenceClassification,
             '''token-classification''': FlaubertForTokenClassification,
             '''zero-shot''': FlaubertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )


     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Optional[Any]      ):
       if (
           pipeline_test_casse_name == "QAPipelineTests"
           and tokenizer_name is not None
           and not tokenizer_name.endswith("Fast"      )
       ):
         # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
         # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
         # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
         return True

       return False


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	str=False      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    super()._prepare_for_class(UpperCAmelCase_					, UpperCAmelCase_					, return_labels=UpperCAmelCase_      )

       if return_labels:
         if model_class.__name__ == "FlaubertForQuestionAnswering":
           SCREAMING_SNAKE_CASE       :      str							=    torch.zeros(
               self.model_tester.batch_size					, dtype=torch.long					, device=UpperCAmelCase_      )
           SCREAMING_SNAKE_CASE       :      Dict							=    torch.zeros(
               self.model_tester.batch_size					, dtype=torch.long					, device=UpperCAmelCase_      )

       return inputs_dict


     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    FlaubertModelTester(self      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    ConfigTester(self					, config_class=UpperCAmelCase_					, emb_dim=37      )


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


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


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


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


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


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


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


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


     @slow
     def 				_A       (				self       :	List[str]      ):
       for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         SCREAMING_SNAKE_CASE       :      List[Any]							=    FlaubertModel.from_pretrained(UpperCAmelCase_      )
         self.assertIsNotNone(UpperCAmelCase_      )






     @slow
     @require_torch_gpu
     def 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      int							=    self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
         # FlauBertForMultipleChoice behaves incorrectly in JIT environments.
         if model_class == FlaubertForMultipleChoice:
           return

         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    True
         SCREAMING_SNAKE_CASE       :      Any							=    model_class(config=UpperCAmelCase_      )

         SCREAMING_SNAKE_CASE       :      Tuple							=    self._prepare_for_class(UpperCAmelCase_					, UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      int							=    torch.jit.trace(
             UpperCAmelCase_					, (inputs_dict["input_ids"].to("cpu"      ), inputs_dict["attention_mask"].to("cpu"      ))      )

         with tempfile.TemporaryDirectory() as tmp:
           torch.jit.save(UpperCAmelCase_					, os.path.join(UpperCAmelCase_					, "traced_model.pt"      )      )
           SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.jit.load(os.path.join(UpperCAmelCase_					, "traced_model.pt"      )					, map_location=UpperCAmelCase_      )
           loaded(inputs_dict["input_ids"].to(UpperCAmelCase_      )					, inputs_dict["attention_mask"].to(UpperCAmelCase_      )      )





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


     @slow
     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    FlaubertModel.from_pretrained("flaubert/flaubert_base_cased"      )
       SCREAMING_SNAKE_CASE       :      Any							=    torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]      )
       with torch.no_grad():
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model(UpperCAmelCase_      )[0]
       SCREAMING_SNAKE_CASE       :      int							=    torch.Size((1, 11, 768)      )
       self.assertEqual(output.shape					, UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.tensor(
           [[[-2.6_251, -1.4_298, -0.0_227], [-2.8_510, -1.6_387, 0.2_258], [-2.8_114, -1.1_832, -0.3_066]]]      )

       self.assertTrue(torch.allclose(output[:, :3, :3]					, UpperCAmelCase_					, atol=1E-4      )      )














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",
         ]






