



"""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        _UpperCAmelCase       :

 def __init__(     self		:       List[Any]  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       Any=13  ,     lowercase_		:       Dict=7  ,     lowercase_		:       List[Any]=True  ,     lowercase_		:       Optional[int]=True  ,     lowercase_		:       str=True  ,     lowercase_		:       Tuple=True  ,     lowercase_		:       Any=99  ,     lowercase_		:       Union[str, Any]=24  ,     lowercase_		:       Any=2  ,     lowercase_		:       Optional[int]=6  ,     lowercase_		:       str=37  ,     lowercase_		:       Dict="gelu"  ,     lowercase_		:       List[Any]=0.1  ,     lowercase_		:       str=0.1  ,     lowercase_		:       List[str]=512  ,     lowercase_		:       List[str]=16  ,     lowercase_		:       Dict=2  ,     lowercase_		:       Optional[int]=0.02  ,     lowercase_		:       int=3  ,     lowercase_		:       Any=None  ,     lowercase_		:       Any=1000  ,     ):
     snake_case_ :     Optional[Any]				=	parent
     snake_case_ :     Optional[Any]				=	batch_size
     snake_case_ :     List[str]				=	seq_length
     snake_case_ :     List[Any]				=	is_training
     snake_case_ :     Optional[int]				=	use_input_mask
     snake_case_ :     Tuple				=	use_token_type_ids
     snake_case_ :     List[Any]				=	use_labels
     snake_case_ :     Optional[int]				=	vocab_size
     snake_case_ :     Optional[int]				=	hidden_size
     snake_case_ :     Union[str, Any]				=	num_hidden_layers
     snake_case_ :     Optional[Any]				=	num_attention_heads
     snake_case_ :     Optional[int]				=	intermediate_size
     snake_case_ :     Tuple				=	hidden_act
     snake_case_ :     Union[str, Any]				=	hidden_dropout_prob
     snake_case_ :     str				=	attention_probs_dropout_prob
     snake_case_ :     Union[str, Any]				=	max_position_embeddings
     snake_case_ :     Optional[int]				=	type_vocab_size
     snake_case_ :     int				=	type_sequence_label_size
     snake_case_ :     Optional[int]				=	initializer_range
     snake_case_ :     Optional[int]				=	num_labels
     snake_case_ :     Optional[Any]				=	scope
     snake_case_ :     List[Any]				=	range_bbox

 def 						_snake_case					(     self		:       Tuple				):
     snake_case_ :     Optional[int]				=	ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size				)

     snake_case_ :     Union[str, Any]				=	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_ :     Optional[int]				=	bbox[i, j, 3]
                 snake_case_ :     Dict				=	bbox[i, j, 1]
                 snake_case_ :     Dict				=	t
             if bbox[i, j, 2] < bbox[i, j, 0]:
                 snake_case_ :     Union[str, Any]				=	bbox[i, j, 2]
                 snake_case_ :     List[str]				=	bbox[i, j, 0]
                 snake_case_ :     str				=	t

     snake_case_ :     Any				=	None
     if self.use_input_mask:
         snake_case_ :     Union[str, Any]				=	ids_tensor([self.batch_size, self.seq_length]  ,     vocab_size=2				)

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

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

     snake_case_ :     Union[str, Any]				=	self.get_config()

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

 def 						_snake_case					(     self		:       List[str]				):
     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 						_snake_case					(     self		:       Dict  ,     lowercase_		:       List[str]  ,     lowercase_		:       Tuple  ,     lowercase_		:       Tuple  ,     lowercase_		:       Any  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       Optional[int]  ,     ):
     snake_case_ :     Optional[Any]				=	LiltModel(config=_A				)
     model.to(_A				)
     model.eval()
     snake_case_ :     Tuple				=	model(_A  ,     bbox=_A  ,     attention_mask=_A  ,     token_type_ids=_A				)
     snake_case_ :     List[Any]				=	model(_A  ,     bbox=_A  ,     token_type_ids=_A				)
     snake_case_ :     Dict				=	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 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       List[Any]  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       Tuple  ,     lowercase_		:       List[str]  ,     lowercase_		:       List[str]  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       Any  ,     ):
     snake_case_ :     Optional[int]				=	self.num_labels
     snake_case_ :     Any				=	LiltForTokenClassification(config=_A				)
     model.to(_A				)
     model.eval()
     snake_case_ :     List[str]				=	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 						_snake_case					(     self		:       int  ,     lowercase_		:       str  ,     lowercase_		:       Optional[Any]  ,     lowercase_		:       List[Any]  ,     lowercase_		:       Any  ,     lowercase_		:       List[str]  ,     lowercase_		:       Dict  ,     lowercase_		:       Tuple  ,     ):
     snake_case_ :     Dict				=	LiltForQuestionAnswering(config=_A				)
     model.to(_A				)
     model.eval()
     snake_case_ :     Optional[Any]				=	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 						_snake_case					(     self		:       Tuple				):
     snake_case_ :     List[Any]				=	self.prepare_config_and_inputs()
     (
         snake_case_						
     ) :     Optional[int]				=	config_and_inputs
     snake_case_ :     int				=	{
         '''input_ids''': input_ids,
         '''bbox''': bbox,
         '''token_type_ids''': token_type_ids,
         '''attention_mask''': input_mask,
     }
     return config, inputs_dict





@require_torch
class        _UpperCAmelCase       (			__a    ,     __a    ,     __a    ,     unittest.TestCase):
 _lowerCAmelCase   :			Union[str, Any]							=		(
     (
         LiltModel,
         LiltForSequenceClassification,
         LiltForTokenClassification,
         LiltForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 _lowerCAmelCase   :			List[Any]							=		(
     {
         """feature-extraction""": LiltModel,
         """question-answering""": LiltForQuestionAnswering,
         """text-classification""": LiltForSequenceClassification,
         """token-classification""": LiltForTokenClassification,
         """zero-shot""": LiltForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _lowerCAmelCase   :			Dict							=		False
 _lowerCAmelCase   :			Union[str, Any]							=		False

 def 						_snake_case					(     self		:       List[str]  ,     lowercase_		:       Any  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       int  ,     lowercase_		:       Tuple  ,     lowercase_		:       List[Any]				):
     return True

 def 						_snake_case					(     self		:       Union[str, Any]				):
     snake_case_ :     List[Any]				=	LiltModelTester(self				)
     snake_case_ :     Optional[int]				=	ConfigTester(self  ,     config_class=_A  ,     hidden_size=37				)

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

 def 						_snake_case					(     self		:       Union[str, Any]				):
     snake_case_ :     Optional[int]				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_A				)

 def 						_snake_case					(     self		:       Dict				):
     snake_case_ :     Optional[Any]				=	self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
         snake_case_ :     Dict				=	type
         self.model_tester.create_and_check_model(*_A				)

 def 						_snake_case					(     self		:       List[Any]				):
     snake_case_ :     Any				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*_A				)

 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     Optional[int]				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*_A				)






 @slow
 def 						_snake_case					(     self		:       List[str]				):


     for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_ :     Any				=	LiltModel.from_pretrained(_A				)
         self.assertIsNotNone(_A				)





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

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     Any				=	LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''				).to(_A				)

     snake_case_ :     Any				=	torch.tensor([[1, 2]]  ,     device=_A				)
     snake_case_ :     str				=	torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]  ,     device=_A				)

     # forward pass
     with torch.no_grad():
         snake_case_ :     str				=	model(input_ids=_A  ,     bbox=_A				)

     snake_case_ :     Dict				=	torch.Size([1, 2, 768]				)
     snake_case_ :     Tuple				=	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 json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 			lowerCamelCase_   (	__a     ,       unittest.TestCase      ):
      lowerCAmelCase__		     =     LEDTokenizer
      lowerCAmelCase__		     =     LEDTokenizerFast
      lowerCAmelCase__		     =     True






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          super().setUp()
          UpperCAmelCase__			:		List[Any]													=				[
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''\u0120''',
              '''\u0120l''',
              '''\u0120n''',
              '''\u0120lo''',
              '''\u0120low''',
              '''er''',
              '''\u0120lowest''',
              '''\u0120newer''',
              '''\u0120wider''',
              '''<unk>''',
          ]
          UpperCAmelCase__			:		Optional[Any]													=				dict(zip(_A						,						range(len(_A ) ) ) )
          UpperCAmelCase__			:		Union[str, Any]													=				['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
          UpperCAmelCase__			:		Any													=				{'''unk_token''': '''<unk>'''}

          UpperCAmelCase__			:		Dict													=				os.path.join(self.tmpdirname						,						VOCAB_FILES_NAMES['''vocab_file'''] )
          UpperCAmelCase__			:		Tuple													=				os.path.join(self.tmpdirname						,						VOCAB_FILES_NAMES['''merges_file'''] )
          with open(self.vocab_file						,						'''w'''						,						encoding='''utf-8''' ) as fp:
              fp.write(json.dumps(_A ) + '''\n''' )
          with open(self.merges_file						,						'''w'''						,						encoding='''utf-8''' ) as fp:
              fp.write('''\n'''.join(_A ) )






      def lowercase_							(					self				:    Optional[int]						,						**_A				:    Any ):


          '''simple docstring'''


          kwargs.update(self.special_tokens_map )
          return self.tokenizer_class.from_pretrained(self.tmpdirname						,						**_A )






      def lowercase_							(					self				:    Union[str, Any]						,						**_A				:    Optional[Any] ):


          '''simple docstring'''


          kwargs.update(self.special_tokens_map )
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,						**_A )






      def lowercase_							(					self				:    Tuple						,						_A				:    List[str] ):


          '''simple docstring'''


          return "lower newer", "lower newer"






      @cached_property
      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          return LEDTokenizer.from_pretrained('''allenai/led-base-16384''' )






      @cached_property
      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384''' )






      @require_torch
      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          UpperCAmelCase__			:		Dict													=				[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__			:		Union[str, Any]													=				tokenizer(_A						,						max_length=len(_A )						,						padding=_A						,						return_tensors='''pt''' )
              self.assertIsInstance(_A						,						_A )

              self.assertEqual((2, 9)						,						batch.input_ids.shape )
              self.assertEqual((2, 9)						,						batch.attention_mask.shape )
              UpperCAmelCase__			:		int													=				batch.input_ids.tolist()[0]
              self.assertListEqual(_A						,						_A )






      @require_torch
      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__			:		List[str]													=				tokenizer(_A						,						padding=_A						,						return_tensors='''pt''' )
              self.assertIn('''input_ids'''						,						_A )
              self.assertIn('''attention_mask'''						,						_A )
              self.assertNotIn('''labels'''						,						_A )
              self.assertNotIn('''decoder_attention_mask'''						,						_A )






      @require_torch
      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				[
              '''Summary of the text.''',
              '''Another summary.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__			:		Optional[Any]													=				tokenizer(text_target=_A						,						max_length=32						,						padding='''max_length'''						,						return_tensors='''pt''' )
              self.assertEqual(32						,						targets['''input_ids'''].shape[1] )






      @require_torch
      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__			:		Any													=				tokenizer(
                  ['''I am a small frog''' * 1_024, '''I am a small frog''']						,						padding=_A						,						truncation=_A						,						return_tensors='''pt''' )
              self.assertIsInstance(_A						,						_A )
              self.assertEqual(batch.input_ids.shape						,						(2, 5_122) )






      @require_torch
      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				['''A long paragraph for summarization.''']
          UpperCAmelCase__			:		List[Any]													=				[
              '''Summary of the text.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__			:		Optional[Any]													=				tokenizer(_A						,						return_tensors='''pt''' )
              UpperCAmelCase__			:		int													=				tokenizer(text_target=_A						,						return_tensors='''pt''' )
              UpperCAmelCase__			:		str													=				inputs['''input_ids''']
              UpperCAmelCase__			:		Tuple													=				targets['''input_ids''']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )






      @require_torch
      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__			:		Tuple													=				['''Summary of the text.''', '''Another summary.''']
              UpperCAmelCase__			:		List[str]													=				[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

              UpperCAmelCase__			:		List[str]													=				tokenizer(_A						,						padding=_A )
              UpperCAmelCase__			:		str													=				[[0] * len(_A ) for x in encoded_output['''input_ids''']]
              UpperCAmelCase__			:		Any													=				tokenizer.pad(_A )
              self.assertSequenceEqual(outputs['''global_attention_mask''']						,						_A )






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          pass






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
                  UpperCAmelCase__			:		Optional[int]													=				self.rust_tokenizer_class.from_pretrained(_A						,						**_A )
                  UpperCAmelCase__			:		int													=				self.tokenizer_class.from_pretrained(_A						,						**_A )
                  UpperCAmelCase__			:		Any													=				'''A, <mask> AllenNLP sentence.'''
                  UpperCAmelCase__			:		Dict													=				tokenizer_r.encode_plus(_A						,						add_special_tokens=_A						,						return_token_type_ids=_A )
                  UpperCAmelCase__			:		Optional[int]													=				tokenizer_p.encode_plus(_A						,						add_special_tokens=_A						,						return_token_type_ids=_A )
                  self.assertEqual(sum(tokens_r['''token_type_ids'''] )						,						sum(tokens_p['''token_type_ids'''] ) )
                  self.assertEqual(
                      sum(tokens_r['''attention_mask'''] ) / len(tokens_r['''attention_mask'''] )						,						sum(tokens_p['''attention_mask'''] ) / len(tokens_p['''attention_mask'''] )						,						)

                  UpperCAmelCase__			:		str													=				tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
                  UpperCAmelCase__			:		Tuple													=				tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''] )
                  self.assertSequenceEqual(tokens_p['''input_ids''']						,						[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
                  self.assertSequenceEqual(tokens_r['''input_ids''']						,						[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )

                  self.assertSequenceEqual(
                      _A						,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
                  self.assertSequenceEqual(
                      _A						,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
