



"""simple docstring"""


import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


snake_case_				     =       logging.getLogger(__name__)







@dataclass(frozen=_UpperCAmelCase				)
class       A_			:



						"""simple docstring"""
						__UpperCamelCase						=							42
						__UpperCamelCase						=							42
						__UpperCamelCase						=							None
						__UpperCamelCase						=							None
						__UpperCamelCase						=							None







@dataclass(frozen=_UpperCAmelCase				)
class       A_			:



						"""simple docstring"""
						__UpperCamelCase						=							42
						__UpperCamelCase						=							None
						__UpperCamelCase						=							None
						__UpperCamelCase						=							None
						__UpperCamelCase						=							None


if is_torch_available():
				import torch
				from torch.utils.data import Dataset







				class       A_			(     _UpperCAmelCase				):



										"""simple docstring"""
										__UpperCamelCase						=							42






										def __init__(					self :int   ,  lowercase_ :str   ,  lowercase_ :PreTrainedTokenizer   ,  lowercase_ :str   ,  lowercase_ :Optional[int] = None   ,  lowercase_ :List[Any]=False   ,  lowercase_ :bool = False   ,  )				->			int:
																UpperCAmelCase             = hans_processors[task]()

																UpperCAmelCase             = os.path.join(
																    a__   ,  'cached_{}_{}_{}_{}'.format(
																        'dev' if evaluate else 'train'   ,  tokenizer.__class__.__name__   ,  str(a__					)   ,  a__   ,  )   ,  )
																UpperCAmelCase             = processor.get_labels()
																if tokenizer.__class__ in (
																    RobertaTokenizer,
																    RobertaTokenizerFast,
																    XLMRobertaTokenizer,
																    BartTokenizer,
																    BartTokenizerFast,
																):
																						# HACK(label indices are swapped in RoBERTa pretrained model)
																						UpperCAmelCase      ,    UpperCAmelCase             = label_list[2], label_list[1]
																UpperCAmelCase             = label_list

																# Make sure only the first process in distributed training processes the dataset,
																# and the others will use the cache.
																UpperCAmelCase             = cached_features_file + '.lock'
																with FileLock(a__					):
																						if os.path.exists(a__					) and not overwrite_cache:
																												logger.info(f"""Loading features from cached file {cached_features_file}"""					)
																												UpperCAmelCase             = torch.load(a__					)
																						else:
																												logger.info(f"""Creating features from dataset file at {data_dir}"""					)

																												UpperCAmelCase             = (
																												    processor.get_dev_examples(a__					) if evaluate else processor.get_train_examples(a__					)
																												)

																												logger.info('Training examples: %s'   ,  len(a__					)					)
																												UpperCAmelCase             = hans_convert_examples_to_features(a__   ,  a__   ,  a__   ,  a__					)
																												logger.info('Saving features into cached file %s'   ,  a__					)
																												torch.save(self.features   ,  a__					)






										def __len__(					self :int					)				->			Dict:
																return len(self.features					)






										def __getitem__(					self :Dict   ,  lowercase_ :List[Any]					)				->			Dict:
																return self.features[i]






										def 	UpperCAmelCase__     (					self :List[Any]					)				->			List[str]:
																return self.label_list


if is_tf_available():
				import tensorflow as tf







				class       A_			:



										"""simple docstring"""
										__UpperCamelCase						=							42






										def __init__(					self :Tuple   ,  lowercase_ :str   ,  lowercase_ :PreTrainedTokenizer   ,  lowercase_ :str   ,  lowercase_ :Optional[int] = 1_28   ,  lowercase_ :Any=False   ,  lowercase_ :bool = False   ,  )				->			Optional[int]:
																UpperCAmelCase             = hans_processors[task]()
																UpperCAmelCase             = processor.get_labels()
																if tokenizer.__class__ in (
																    RobertaTokenizer,
																    RobertaTokenizerFast,
																    XLMRobertaTokenizer,
																    BartTokenizer,
																    BartTokenizerFast,
																):
																						# HACK(label indices are swapped in RoBERTa pretrained model)
																						UpperCAmelCase      ,    UpperCAmelCase             = label_list[2], label_list[1]
																UpperCAmelCase             = label_list

																UpperCAmelCase             = processor.get_dev_examples(a__					) if evaluate else processor.get_train_examples(a__					)
																UpperCAmelCase             = hans_convert_examples_to_features(a__   ,  a__   ,  a__   ,  a__					)

																def gen():
																						for ex_index, ex in tqdm.tqdm(enumerate(self.features					)   ,  desc='convert examples to features'					):
																												if ex_index % 1_00_00 == 0:
																																		logger.info('Writing example %d of %d' % (ex_index, len(a__					))					)

																												yield (
																												    {
																												        "example_id": 0,
																												        "input_ids": ex.input_ids,
																												        "attention_mask": ex.attention_mask,
																												        "token_type_ids": ex.token_type_ids,
																												    },
																												    ex.label,
																												)

																UpperCAmelCase             = tf.data.Dataset.from_generator(
																    a__   ,  (
																        {
																            'example_id': tf.intaa,
																            'input_ids': tf.intaa,
																            'attention_mask': tf.intaa,
																            'token_type_ids': tf.intaa,
																        },
																        tf.intaa,
																    )   ,  (
																        {
																            'example_id': tf.TensorShape([]					),
																            'input_ids': tf.TensorShape([None, None]					),
																            'attention_mask': tf.TensorShape([None, None]					),
																            'token_type_ids': tf.TensorShape([None, None]					),
																        },
																        tf.TensorShape([]					),
																    )   ,  )






										def 	UpperCAmelCase__     (					self :Union[str, Any]					)				->			str:
																return self.dataset






										def __len__(					self :Dict					)				->			List[Any]:
																return len(self.features					)






										def __getitem__(					self :Any   ,  lowercase_ :Dict					)				->			int:
																return self.features[i]






										def 	UpperCAmelCase__     (					self :str					)				->			Optional[Any]:
																return self.label_list







class       A_			(     _UpperCAmelCase				):



						"""simple docstring"""






						def 	UpperCAmelCase__     (					self :Dict   ,  lowercase_ :Dict					)				->			List[Any]:
												return self._create_examples(self._read_tsv(os.path.join(a__   ,  'heuristics_train_set.txt'					)					)   ,  'train'					)






						def 	UpperCAmelCase__     (					self :Optional[int]   ,  lowercase_ :Tuple					)				->			Optional[int]:
												return self._create_examples(self._read_tsv(os.path.join(a__   ,  'heuristics_evaluation_set.txt'					)					)   ,  'dev'					)






						def 	UpperCAmelCase__     (					self :int					)				->			Optional[Any]:
												return ["contradiction", "entailment", "neutral"]






						def 	UpperCAmelCase__     (					self :Any   ,  lowercase_ :Optional[int]   ,  lowercase_ :List[Any]					)				->			List[Any]:
												UpperCAmelCase             = []
												for i, line in enumerate(a__					):
																		if i == 0:
																								continue
																		UpperCAmelCase             = '%s-%s' % (set_type, line[0])
																		UpperCAmelCase             = line[5]
																		UpperCAmelCase             = line[6]
																		UpperCAmelCase             = line[7][2:] if line[7].startswith('ex'					) else line[7]
																		UpperCAmelCase             = line[0]
																		examples.append(InputExample(guid=a__   ,  text_a=a__   ,  text_b=a__   ,  label=a__   ,  pairID=a__					)					)
												return examples


def  _lowerCAmelCase      (   lowercase_		,	lowercase_		,	lowercase_		,	lowercase_		,	):
						UpperCAmelCase             = {label: i for i, label in enumerate(snake_case_						)}

						UpperCAmelCase             = []
						for ex_index, example in tqdm.tqdm(enumerate(snake_case_						)		,	desc='convert examples to features'						):
												if ex_index % 10000 == 0:
																		logger.info('Writing example %d' % (ex_index)						)

												UpperCAmelCase             = tokenizer(
												    example.text_a		,	example.text_b		,	add_special_tokens=snake_case_		,	max_length=snake_case_		,	padding='max_length'		,	truncation=snake_case_		,	return_overflowing_tokens=snake_case_		,	)

												UpperCAmelCase             = label_map[example.label] if example.label in label_map else 0

												UpperCAmelCase             = int(example.pairID						)

												features.append(InputFeatures(**snake_case_		,	label=snake_case_		,	pairID=snake_case_						)						)

						for i, example in enumerate(examples[:5]						):
												logger.info('*** Example ***'						)
												logger.info(F"""guid: {example}"""						)
												logger.info(F"""features: {features[i]}"""						)

						return features


snake_case_				     =       {
    """hans""": 3,
}






snake_case_				     =       {
    """hans""": HansProcessor,
}












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 		SCREAMING_SNAKE_CASE__				:


      def __init__(self       :      Any				,			a__       :      Union[str, Any]				,			a__       :      int=13				,			a__       :      int=7				,			a__       :      Optional[Any]=True				,			a__       :      Optional[int]=True				,			a__       :      Any=True				,			a__       :      str=True				,			a__       :      List[Any]=99				,			a__       :      Any=24				,			a__       :      List[str]=2				,			a__       :      Optional[int]=6				,			a__       :      int=37				,			a__       :      List[str]="gelu"				,			a__       :      List[Any]=0.1				,			a__       :      Optional[int]=0.1				,			a__       :      Union[str, Any]=512				,			a__       :      List[str]=16				,			a__       :      Optional[int]=2				,			a__       :      Union[str, Any]=0.0_2				,			a__       :      str=3				,			a__       :      Optional[Any]=None				,			a__       :      Any=1000				,			):





         """simple docstring"""



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


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



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

         __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]:
                  __snake_case							       =   bbox[i, j, 3]
                  __snake_case							       =   bbox[i, j, 1]
                  __snake_case							       =   t
               if bbox[i, j, 2] < bbox[i, j, 0]:
                  __snake_case							       =   bbox[i, j, 2]
                  __snake_case							       =   bbox[i, j, 0]
                  __snake_case							       =   t

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

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

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

         __snake_case							       =   self.get_config()

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


      def    a (self       :      List[str]      ):





         """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       :      List[Any]				,			a__       :      List[Any]				,			a__       :      Optional[Any]				,			a__       :      List[str]				,			a__       :      int				,			a__       :      Optional[int]				,			a__       :      str				,			a__       :      Optional[int]				,			):





         """simple docstring"""



         __snake_case							       =   LiltModel(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(a__				,			bbox=a__				,			attention_mask=a__				,			token_type_ids=a__      )
         __snake_case							       =   model(a__				,			bbox=a__				,			token_type_ids=a__      )
         __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       :      Any				,			a__       :      Tuple				,			a__       :      Dict				,			a__       :      Optional[int]				,			a__       :      Dict				,			a__       :      Union[str, Any]				,			a__       :      str				,			a__       :      Tuple				,			):





         """simple docstring"""



         __snake_case							       =   self.num_labels
         __snake_case							       =   LiltForTokenClassification(config=a__      )
         model.to(a__      )
         model.eval()
         __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       :      int				,			a__       :      Optional[Any]				,			a__       :      int				,			a__       :      int				,			a__       :      Optional[Any]				,			a__       :      Tuple				,			a__       :      Union[str, Any]				,			a__       :      str				,			):





         """simple docstring"""



         __snake_case							       =   LiltForQuestionAnswering(config=a__      )
         model.to(a__      )
         model.eval()
         __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       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	
         )							       =   config_and_inputs
         __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 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[Any]							=    (
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      A_						:				Any							=    (
          {
              'feature-extraction': LiltModel,
              'question-answering': LiltForQuestionAnswering,
              'text-classification': LiltForSequenceClassification,
              'token-classification': LiltForTokenClassification,
              'zero-shot': LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      A_						:				Optional[int]							=    False
      A_						:				List[Any]							=    False


      def    a (self       :      Dict				,			a__       :      Tuple				,			a__       :      Tuple				,			a__       :      Tuple				,			a__       :      Union[str, Any]				,			a__       :      Any      ):





         """simple docstring"""



         return True


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   LiltModelTester(self      )
         __snake_case							       =   ConfigTester(self				,			config_class=a__				,			hidden_size=37      )


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         self.config_tester.run_common_tests()


      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__      )


      def    a (self       :      List[Any]      ):





         """simple docstring"""



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


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*a__      )


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __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]      ):





         """simple docstring"""



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






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


      def    a (self       :      Tuple      ):





         """simple docstring"""



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

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

         # forward pass
         with torch.no_grad():
            __snake_case							       =   model(input_ids=a__				,			bbox=a__      )

         __snake_case							       =   torch.Size([1, 2, 768]      )
         __snake_case							       =   torch.tensor(
             [[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]]				,			device=a__				,			)

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

