

'''simple docstring'''
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         LiltForQuestionAnswering,
         LiltForSequenceClassification,
         LiltForTokenClassification,
         LiltModel,
     )
     from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST







class 							A__    :





  def __init__(		self      :	str							,					_a      :	Any							,					_a      :	List[str]=13							,					_a      :	Union[str, Any]=7							,					_a      :	List[Any]=True							,					_a      :	Optional[int]=True							,					_a      :	Tuple=True							,					_a      :	Optional[Any]=True							,					_a      :	Optional[int]=99							,					_a      :	str=24							,					_a      :	str=2							,					_a      :	Union[str, Any]=6							,					_a      :	Union[str, Any]=37							,					_a      :	Any="gelu"							,					_a      :	Any=0.1							,					_a      :	List[Any]=0.1							,					_a      :	List[str]=512							,					_a      :	Tuple=16							,					_a      :	List[str]=2							,					_a      :	int=0.02							,					_a      :	Optional[int]=3							,					_a      :	Any=None							,					_a      :	int=1000							,					)	->    Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =parent
       _SCREAMING_SNAKE_CASE							    =batch_size
       _SCREAMING_SNAKE_CASE							    =seq_length
       _SCREAMING_SNAKE_CASE							    =is_training
       _SCREAMING_SNAKE_CASE							    =use_input_mask
       _SCREAMING_SNAKE_CASE							    =use_token_type_ids
       _SCREAMING_SNAKE_CASE							    =use_labels
       _SCREAMING_SNAKE_CASE							    =vocab_size
       _SCREAMING_SNAKE_CASE							    =hidden_size
       _SCREAMING_SNAKE_CASE							    =num_hidden_layers
       _SCREAMING_SNAKE_CASE							    =num_attention_heads
       _SCREAMING_SNAKE_CASE							    =intermediate_size
       _SCREAMING_SNAKE_CASE							    =hidden_act
       _SCREAMING_SNAKE_CASE							    =hidden_dropout_prob
       _SCREAMING_SNAKE_CASE							    =attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE							    =max_position_embeddings
       _SCREAMING_SNAKE_CASE							    =type_vocab_size
       _SCREAMING_SNAKE_CASE							    =type_sequence_label_size
       _SCREAMING_SNAKE_CASE							    =initializer_range
       _SCREAMING_SNAKE_CASE							    =num_labels
       _SCREAMING_SNAKE_CASE							    =scope
       _SCREAMING_SNAKE_CASE							    =range_bbox





  def 						A  (		self      :	int )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size )

       _SCREAMING_SNAKE_CASE							    =ids_tensor([self.batch_size, self.seq_length, 4]							,					self.range_bbox )
       # Ensure that bbox is legal
       for i in range(bbox.shape[0] ):
            for j in range(bbox.shape[1] ):
                 if bbox[i, j, 3] < bbox[i, j, 1]:
                      _SCREAMING_SNAKE_CASE							    =bbox[i, j, 3]
                      _SCREAMING_SNAKE_CASE							    =bbox[i, j, 1]
                      _SCREAMING_SNAKE_CASE							    =t
                 if bbox[i, j, 2] < bbox[i, j, 0]:
                      _SCREAMING_SNAKE_CASE							    =bbox[i, j, 2]
                      _SCREAMING_SNAKE_CASE							    =bbox[i, j, 0]
                      _SCREAMING_SNAKE_CASE							    =t

       _SCREAMING_SNAKE_CASE							    =None
       if self.use_input_mask:
            _SCREAMING_SNAKE_CASE							    =ids_tensor([self.batch_size, self.seq_length]							,					vocab_size=2 )

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

       _SCREAMING_SNAKE_CASE							    =None
       _SCREAMING_SNAKE_CASE							    =None
       if self.use_labels:
            _SCREAMING_SNAKE_CASE							    =ids_tensor([self.batch_size]							,					self.type_sequence_label_size )
            _SCREAMING_SNAKE_CASE							    =ids_tensor([self.batch_size, self.seq_length]							,					self.num_labels )

       _SCREAMING_SNAKE_CASE							    =self.get_config()

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





  def 						A  (		self      :	Optional[int] )	->    List[Any]:





       '''simple docstring'''






       return LiltConfig(
           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							,					initializer_range=self.initializer_range							,					)





  def 						A  (		self      :	int							,					_a      :	Any							,					_a      :	str							,					_a      :	Union[str, Any]							,					_a      :	Any							,					_a      :	Tuple							,					_a      :	List[Any]							,					_a      :	Dict							,					)	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =LiltModel(config=_a )
       model.to(_a )
       model.eval()
       _SCREAMING_SNAKE_CASE							    =model(_a							,					bbox=_a							,					attention_mask=_a							,					token_type_ids=_a )
       _SCREAMING_SNAKE_CASE							    =model(_a							,					bbox=_a							,					token_type_ids=_a )
       _SCREAMING_SNAKE_CASE							    =model(_a							,					bbox=_a )

       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[int]							,					_a      :	Tuple							,					_a      :	List[str]							,					_a      :	Optional[Any]							,					_a      :	Any							,					_a      :	Tuple							,					_a      :	Any							,					_a      :	Optional[int]							,					)	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.num_labels
       _SCREAMING_SNAKE_CASE							    =LiltForTokenClassification(config=_a )
       model.to(_a )
       model.eval()
       _SCREAMING_SNAKE_CASE							    =model(
           _a							,					bbox=_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 						A  (		self      :	Tuple							,					_a      :	str							,					_a      :	List[str]							,					_a      :	Optional[Any]							,					_a      :	Union[str, Any]							,					_a      :	Dict							,					_a      :	List[Any]							,					_a      :	str							,					)	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =LiltForQuestionAnswering(config=_a )
       model.to(_a )
       model.eval()
       _SCREAMING_SNAKE_CASE							    =model(
           _a							,					bbox=_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 						A  (		self      :	Dict )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.prepare_config_and_inputs()
       (
           (
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						
       )							    =config_and_inputs
       _SCREAMING_SNAKE_CASE							    ={
           'input_ids': input_ids,
           'bbox': bbox,
           'token_type_ids': token_type_ids,
           'attention_mask': input_mask,
       }
       return config, inputs_dict







@require_torch
class 							A__    (       A__     ,   A__     ,   A__     ,   unittest.TestCase  ):
  A__		 =				(
      (
          LiltModel,
          LiltForSequenceClassification,
          LiltForTokenClassification,
          LiltForQuestionAnswering,
      )
      if is_torch_available()
      else ()
  )
  A__		 =				(
      {
          'feature-extraction': LiltModel,
          'question-answering': LiltForQuestionAnswering,
          'text-classification': LiltForSequenceClassification,
          'token-classification': LiltForTokenClassification,
          'zero-shot': LiltForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  A__		 =				False
  A__		 =				False





  def 						A  (		self      :	Tuple							,					_a      :	Union[str, Any]							,					_a      :	int							,					_a      :	List[Any]							,					_a      :	Optional[int]							,					_a      :	int )	->    Optional[Any]:





       '''simple docstring'''






       return True





  def 						A  (		self      :	List[str] )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =LiltModelTester(self )
       _SCREAMING_SNAKE_CASE							    =ConfigTester(self							,					config_class=_a							,					hidden_size=37 )





  def 						A  (		self      :	Dict )	->    Optional[int]:





       '''simple docstring'''






       self.config_tester.run_common_tests()





  def 						A  (		self      :	Union[str, Any] )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_a )





  def 						A  (		self      :	Optional[int] )	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
            _SCREAMING_SNAKE_CASE							    =type
            self.model_tester.create_and_check_model(*_a )





  def 						A  (		self      :	int )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_a )





  def 						A  (		self      :	List[Any] )	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_a )





  @slow
  def 						A  (		self      :	Optional[int] )	->    Dict:





       '''simple docstring'''






       for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _SCREAMING_SNAKE_CASE							    =LiltModel.from_pretrained(_a )
            self.assertIsNotNone(_a )







@require_torch
@slow
class 							A__    (       unittest.TestCase  ):





  def 						A  (		self      :	Dict )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base' ).to(_a )

       _SCREAMING_SNAKE_CASE							    =torch.tensor([[1, 2]]							,					device=_a )
       _SCREAMING_SNAKE_CASE							    =torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]							,					device=_a )

       # forward pass
       with torch.no_grad():
            _SCREAMING_SNAKE_CASE							    =model(input_ids=_a							,					bbox=_a )

       _SCREAMING_SNAKE_CASE							    =torch.Size([1, 2, 768] )
       _SCREAMING_SNAKE_CASE							    =torch.tensor(
           [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]							,					device=_a							,					)

       self.assertTrue(outputs.last_hidden_state.shape							,					_a )
       self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]							,					_a							,					atol=1e-3 ) )



'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score

import datasets


lowerCamelCase :      List[Any]					 =     "\\n    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n"

lowerCamelCase :      Optional[Any]					 =     "\\n    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n"

lowerCamelCase :      int					 =     "\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n    predictions: list of predictions to score (as int64),\n        except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).\n    references: list of ground truth labels corresponding to the predictions (as int64),\n        except for 'cvit-mkb-clsr' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"precision\": Precision@10\nExamples:\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wnli')  # 'wnli' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')\n    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'precision@10': 1.0}\n\n"


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[str]							, _UpperCamelCase						:     Tuple				)      ->			List[Any]:
     """simple docstring"""




     return float((preds == labels).mean()				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Any				)      ->			List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =simple_accuracy(_UpperCamelCase							, _UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =float(fa_score(y_true=_UpperCamelCase							, y_pred=_UpperCamelCase				)				)
     return {
         "accuracy": acc,
         "f1": fa,
     }


def        _lowerCAmelCase					(						_UpperCamelCase						:     Any							, _UpperCamelCase						:     int				)      ->			str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =np.array(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =np.array(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =en_sentvecs.shape[0]

     # mean centering
     _SCREAMING_SNAKE_CASE							    =en_sentvecs - np.mean(_UpperCamelCase							, axis=0				)
     _SCREAMING_SNAKE_CASE							    =in_sentvecs - np.mean(_UpperCamelCase							, axis=0				)

     _SCREAMING_SNAKE_CASE							    =cdist(_UpperCamelCase							, _UpperCamelCase							, 'cosine'				)
     _SCREAMING_SNAKE_CASE							    =np.array(range(_UpperCamelCase				)				)
     _SCREAMING_SNAKE_CASE							    =sim.argsort(axis=1				)[:, :10]
     _SCREAMING_SNAKE_CASE							    =np.any(preds == actual[:, None]							, axis=1				)
     return float(matches.mean()				)







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





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





       '''simple docstring'''






       if self.config_name not in [
           "wnli",
           "copa",
           "sna",
           "csqa",
           "wstp",
           "inltkh",
           "bbca",
           "cvit-mkb-clsr",
           "iitp-mr",
           "iitp-pr",
           "actsa-sc",
           "md",
           "wiki-ner",
       ]:
            raise KeyError(
                'You should supply a configuration name selected in '
                '["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
                '"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
                '"wiki-ner"]' )
       return datasets.MetricInfo(
           description=_DESCRIPTION							,					citation=_CITATION							,					inputs_description=_KWARGS_DESCRIPTION							,					features=datasets.Features(
               {
                   'predictions': datasets.Value('int64' )
                   if self.config_name != 'cvit-mkb-clsr'
                   else datasets.Sequence(datasets.Value('float32' ) ),
                   'references': datasets.Value('int64' )
                   if self.config_name != 'cvit-mkb-clsr'
                   else datasets.Sequence(datasets.Value('float32' ) ),
               } )							,					codebase_urls=[]							,					reference_urls=[]							,					format='numpy' if self.config_name != 'cvit-mkb-clsr' else None							,					)





  def 						A  (		self      :	List[str]							,					_a      :	Tuple							,					_a      :	Optional[int] )	->    int:





       '''simple docstring'''






       if self.config_name == "cvit-mkb-clsr":
            return {"precision@10": precision_at_aa(_a							,					_a )}
       elif self.config_name in ["wiki-ner"]:
            return acc_and_fa(_a							,					_a )
       elif self.config_name in [
           "wnli",
           "copa",
           "sna",
           "csqa",
           "wstp",
           "inltkh",
           "bbca",
           "iitp-mr",
           "iitp-pr",
           "actsa-sc",
           "md",
       ]:
            return {"accuracy": simple_accuracy(_a							,					_a )}
       else:
            raise KeyError(
                'You should supply a configuration name selected in '
                '["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
                '"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
                '"wiki-ner"]' )
