





"""simple docstring"""




import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCamelCase_	 ="""\
@inproceedings{ramshaw-marcus-1995-text,
    title = \"Text Chunking using Transformation-Based Learning\",
    author = \"Ramshaw, Lance  and
      Marcus, Mitch\",
    booktitle = \"Third Workshop on Very Large Corpora\",
    year = \"1995\",
    url = \"https://www.aclweb.org/anthology/W95-0107\",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
"""

UpperCamelCase_	 ="""\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
"""

UpperCamelCase_	 ="""
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode=\"strict\". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        \"warn\". \"warn\" acts as 0, but the warning is raised.

Returns:
    'scores': dict. Summary of the scores for overall and per type
        Overall:
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure,
        Per type:
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> seqeval = datasets.load_metric(\"seqeval\")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']
    >>> print(results[\"overall_f1\"])
    0.5
    >>> print(results[\"PER\"][\"f1\"])
    1.0
"""




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,							_KWARGS_DESCRIPTION							)
class 					_a		( datasets.Metric							):






				def 							snake_case      (					self						:       List[Any]				)      -> List[str]:




										'''simple docstring'''



										return datasets.MetricInfo(
										    description=_DESCRIPTION,					citation=_CITATION,					homepage='''https://github.com/chakki-works/seqeval''',					inputs_description=_KWARGS_DESCRIPTION,					features=datasets.Features(
										        {
										            '''predictions''': datasets.Sequence(datasets.Value('''string''',					id='''label'''				),					id='''sequence'''				),
										            '''references''': datasets.Sequence(datasets.Value('''string''',					id='''label'''				),					id='''sequence'''				),
										        }				),					codebase_urls=['''https://github.com/chakki-works/seqeval'''],					reference_urls=['''https://github.com/chakki-works/seqeval'''],					)






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       bool = False,					lowerCAmelCase__						:       Optional[str] = None,					lowerCAmelCase__						:       Optional[str] = None,					lowerCAmelCase__						:       Optional[List[int]] = None,					lowerCAmelCase__						:       Union[str, int] = "warn",					)      -> List[str]:




										'''simple docstring'''



										if scheme is not None:
																try:
																						_UpperCamelCase     :   Tuple      =       importlib.import_module('''seqeval.scheme'''				)
																						_UpperCamelCase     :   Union[str, Any]      =       getattr(lowerCAmelCase__,					lowerCAmelCase__				)
																except AttributeError:
																						raise ValueError(f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""				)
										_UpperCamelCase     :   int      =       classification_report(
										    y_true=lowerCAmelCase__,					y_pred=lowerCAmelCase__,					suffix=lowerCAmelCase__,					output_dict=lowerCAmelCase__,					scheme=lowerCAmelCase__,					mode=lowerCAmelCase__,					sample_weight=lowerCAmelCase__,					zero_division=lowerCAmelCase__,					)
										report.pop('''macro avg'''				)
										report.pop('''weighted avg'''				)
										_UpperCamelCase     :   Any      =       report.pop('''micro avg'''				)

										_UpperCamelCase     :   int      =       {
										    type_name: {
										        '''precision''': score['''precision'''],
										        '''recall''': score['''recall'''],
										        '''f1''': score['''f1-score'''],
										        '''number''': score['''support'''],
										    }
										    for type_name, score in report.items()
										}
										_UpperCamelCase     :   Dict      =       overall_score['''precision''']
										_UpperCamelCase     :   int      =       overall_score['''recall''']
										_UpperCamelCase     :   Tuple      =       overall_score['''f1-score''']
										_UpperCamelCase     :   List[Any]      =       accuracy_score(y_true=lowerCAmelCase__,					y_pred=lowerCAmelCase__				)

										return scores











'''simple docstring'''






import unittest

from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
      import torch

      from transformers import (
          YosoForMaskedLM,
          YosoForMultipleChoice,
          YosoForQuestionAnswering,
          YosoForSequenceClassification,
          YosoForTokenClassification,
          YosoModel,
      )
      from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST




class a_     :







   def __init__(self       , __a       , __a=1_3       , __a=7       , __a=True       , __a=True       , __a=True       , __a=True       , __a=9_9       , __a=3_2       , __a=5       , __a=4       , __a=3_7       , __a="gelu"       , __a=0.1       , __a=0.1       , __a=5_1_2       , __a=1_6       , __a=2       , __a=0.02       , __a=3       , __a=4       , __a=None       , )      ->   int:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			parent
      __snake_case					:   Dict  				=			batch_size
      __snake_case					:   Union[str, Any]  				=			seq_length
      __snake_case					:   str  				=			is_training
      __snake_case					:   Optional[Any]  				=			use_input_mask
      __snake_case					:   Dict  				=			use_token_type_ids
      __snake_case					:   Union[str, Any]  				=			use_labels
      __snake_case					:   Optional[Any]  				=			vocab_size
      __snake_case					:   Optional[Any]  				=			hidden_size
      __snake_case					:   int  				=			num_hidden_layers
      __snake_case					:   Any  				=			num_attention_heads
      __snake_case					:   Optional[Any]  				=			intermediate_size
      __snake_case					:   List[str]  				=			hidden_act
      __snake_case					:   List[Any]  				=			hidden_dropout_prob
      __snake_case					:   Optional[Any]  				=			attention_probs_dropout_prob
      __snake_case					:   str  				=			max_position_embeddings
      __snake_case					:   str  				=			type_vocab_size
      __snake_case					:   Dict  				=			type_sequence_label_size
      __snake_case					:   int  				=			initializer_range
      __snake_case					:   Dict  				=			num_labels
      __snake_case					:   Optional[int]  				=			num_choices
      __snake_case					:   Dict  				=			scope







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[Any]:
      """simple docstring"""






      __snake_case					:   Optional[int]  				=			ids_tensor([self.batch_size, self.seq_length]       , self.vocab_size)

      __snake_case					:   Optional[Any]  				=			None
      if self.use_input_mask:
         __snake_case					:   Union[str, Any]  				=			random_attention_mask([self.batch_size, self.seq_length])

      __snake_case					:   Tuple  				=			None
      if self.use_token_type_ids:
         __snake_case					:   Union[str, Any]  				=			ids_tensor([self.batch_size, self.seq_length]       , self.type_vocab_size)

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

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

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







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      return YosoConfig(
          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       , 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=__a       , initializer_range=self.initializer_range       , )







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      __snake_case					:   Tuple  				=			self.get_config()
      __snake_case					:   Tuple  				=			3_0_0
      return config







   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      (
          (
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,
      )					:   Optional[Any]  				=			self.prepare_config_and_inputs()

      __snake_case					:   Optional[int]  				=			True
      __snake_case					:   Optional[int]  				=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
      __snake_case					:   Dict  				=			ids_tensor([self.batch_size, self.seq_length]       , vocab_size=2)

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   Dict:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			YosoModel(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Optional[Any]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a)
      __snake_case					:   int  				=			model(__a       , token_type_ids=__a)
      __snake_case					:   Optional[Any]  				=			model(__a)
      self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a       , __a       , __a       , )      ->   int:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			True
      __snake_case					:   Dict  				=			YosoModel(__a)
      model.to(__a)
      model.eval()
      __snake_case					:   List[str]  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , encoder_hidden_states=__a       , encoder_attention_mask=__a       , )
      __snake_case					:   List[Any]  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , encoder_hidden_states=__a       , )
      __snake_case					:   List[Any]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a)
      self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			YosoForMaskedLM(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Union[str, Any]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a       , labels=__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.vocab_size))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   int:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			YosoForQuestionAnswering(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   str  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , start_positions=__a       , end_positions=__a       , )
      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      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   int:
      """simple docstring"""






      __snake_case					:   str  				=			self.num_labels
      __snake_case					:   Dict  				=			YosoForSequenceClassification(__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Optional[int]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a       , labels=__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_labels))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   Dict:
      """simple docstring"""






      __snake_case					:   str  				=			self.num_labels
      __snake_case					:   Dict  				=			YosoForTokenClassification(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Tuple  				=			model(__a       , attention_mask=__a       , token_type_ids=__a       , labels=__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.num_labels))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   str:
      """simple docstring"""






      __snake_case					:   int  				=			self.num_choices
      __snake_case					:   Tuple  				=			YosoForMultipleChoice(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Any  				=			input_ids.unsqueeze(1).expand(-1       , self.num_choices       , -1).contiguous()
      __snake_case					:   Tuple  				=			token_type_ids.unsqueeze(1).expand(-1       , self.num_choices       , -1).contiguous()
      __snake_case					:   List[str]  				=			input_mask.unsqueeze(1).expand(-1       , self.num_choices       , -1).contiguous()
      __snake_case					:   Optional[int]  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , labels=__a       , )
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_choices))







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      __snake_case					:   int  				=			self.prepare_config_and_inputs()
      (
          (
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,
      )					:   Union[str, Any]  				=			config_and_inputs
      __snake_case					:   Optional[int]  				=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict




@require_torch
class a_     (     UpperCamelCase_		,   UpperCamelCase_		,   unittest.TestCase				):
   _snake_case	   =				(
       (
           YosoModel,
           YosoForMaskedLM,
           YosoForMultipleChoice,
           YosoForQuestionAnswering,
           YosoForSequenceClassification,
           YosoForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   _snake_case	   =				False
   _snake_case	   =				False
   _snake_case	   =				False

   _snake_case	   =				()
   _snake_case	   =				(
       {
           """feature-extraction""": YosoModel,
           """fill-mask""": YosoForMaskedLM,
           """question-answering""": YosoForQuestionAnswering,
           """text-classification""": YosoForSequenceClassification,
           """token-classification""": YosoForTokenClassification,
           """zero-shot""": YosoForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			YosoModelTester(self)
      __snake_case					:   List[Any]  				=			ConfigTester(self       , config_class=__a       , hidden_size=3_7)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      self.config_tester.run_common_tests()







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Tuple  				=			self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         __snake_case					:   List[Any]  				=			type
         self.model_tester.create_and_check_model(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   Optional[int]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   str  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      __snake_case					:   Optional[int]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   List[str]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__a)







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __snake_case					:   Tuple  				=			YosoModel.from_pretrained(__a)
         self.assertIsNotNone(__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Any:
      """simple docstring"""






      return




@require_torch
class a_     (     unittest.TestCase				):







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   str  				=			YosoModel.from_pretrained('uw-madison/yoso-4096')
      __snake_case					:   str  				=			torch.tensor([[0, 1, 2, 3, 4, 5]])

      with torch.no_grad():
         __snake_case					:   str  				=			model(__a)[0]

      __snake_case					:   Union[str, Any]  				=			torch.Size((1, 6, 7_6_8))
      self.assertEqual(output.shape       , __a)

      __snake_case					:   Dict  				=			torch.tensor(
          [[[-0.0_611, 0.1_242, 0.0_840], [0.0_280, -0.0_048, 0.1_125], [0.0_106, 0.0_226, 0.0_751]]])

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







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096')
      __snake_case					:   Tuple  				=			torch.tensor([[0, 1, 2, 3, 4, 5]])

      with torch.no_grad():
         __snake_case					:   Any  				=			model(__a)[0]

      __snake_case					:   List[str]  				=			5_0_2_6_5

      __snake_case					:   List[str]  				=			torch.Size((1, 6, vocab_size))
      self.assertEqual(output.shape       , __a)

      __snake_case					:   str  				=			torch.tensor(
          [[[-2.1_313, -3.7_285, -2.2_407], [-2.7_047, -3.3_314, -2.6_408], [0.0_629, -2.5_166, -0.3_356]]])

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







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   Dict  				=			YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096')
      __snake_case					:   Optional[Any]  				=			torch.arange(4_0_9_6).unsqueeze(0)

      with torch.no_grad():
         __snake_case					:   Dict  				=			model(__a)[0]

      __snake_case					:   List[str]  				=			5_0_2_6_5

      __snake_case					:   List[Any]  				=			torch.Size((1, 4_0_9_6, vocab_size))
      self.assertEqual(output.shape       , __a)

      __snake_case					:   Tuple  				=			torch.tensor(
          [[[-2.3_914, -4.3_742, -5.0_956], [-4.0_988, -4.2_384, -7.0_406], [-3.1_427, -3.7_192, -6.6_800]]])

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