






"""simple docstring"""






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  ["image_processor", "tokenizer"]
      __UpperCamelCase	 =  "Pix2StructImageProcessor"
      __UpperCamelCase	 =  ("T5Tokenizer", "T5TokenizerFast")
      def __init__(						self     :  Any						,	lowercase_     :  Dict						,	lowercase_     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		False
          super().__init__(lowercase_						,	lowercase_)
      def __call__(						self     :  Dict						,	lowercase_     :  Optional[int]=None						,	lowercase_     :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,	lowercase_     :  bool = True						,	lowercase_     :  Union[bool, str, PaddingStrategy] = False						,	lowercase_     :  Union[bool, str, TruncationStrategy] = None						,	lowercase_     :  Optional[int] = None						,	lowercase_     :  Optional[int] = 2048						,	lowercase_     :  int = 0						,	lowercase_     :  Optional[int] = None						,	lowercase_     :  Optional[bool] = None						,	lowercase_     :  bool = False						,	lowercase_     :  bool = False						,	lowercase_     :  bool = False						,	lowercase_     :  bool = False						,	lowercase_     :  bool = False						,	lowercase_     :  bool = True						,	lowercase_     :  Optional[Union[str, TensorType]] = None						,	**lowercase_     :  List[str]						,	):




          '''simple docstring'''


          if images is None and text is None:
              raise ValueError('''You have to specify either images or text.''')

          # Get only text
          if images is None and not self.image_processor.is_vqa:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		self.tokenizer
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.tokenizer(
                  text=lowercase_						,	add_special_tokens=lowercase_						,	padding=lowercase_						,	truncation=lowercase_						,	max_length=lowercase_						,	stride=lowercase_						,	pad_to_multiple_of=lowercase_						,	return_attention_mask=lowercase_						,	return_overflowing_tokens=lowercase_						,	return_special_tokens_mask=lowercase_						,	return_offsets_mapping=lowercase_						,	return_token_type_ids=lowercase_						,	return_length=lowercase_						,	verbose=lowercase_						,	return_tensors=lowercase_						,	**lowercase_						,	)
              return text_encoding

          if not self.image_processor.is_vqa:
              # add pixel_values
              SCREAMING_SNAKE_CASE_      :  Any    						=		self.image_processor(
                  lowercase_						,	return_tensors=lowercase_						,	max_patches=lowercase_						,	**lowercase_)
          else:
              # add pixel_values and bbox
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.image_processor(
                  lowercase_						,	return_tensors=lowercase_						,	max_patches=lowercase_						,	header_text=lowercase_						,	**lowercase_)

          if text is not None and not self.image_processor.is_vqa:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		self.tokenizer(
                  text=lowercase_						,	add_special_tokens=lowercase_						,	padding=lowercase_						,	truncation=lowercase_						,	max_length=lowercase_						,	stride=lowercase_						,	pad_to_multiple_of=lowercase_						,	return_attention_mask=lowercase_						,	return_overflowing_tokens=lowercase_						,	return_special_tokens_mask=lowercase_						,	return_offsets_mapping=lowercase_						,	return_token_type_ids=lowercase_						,	return_length=lowercase_						,	verbose=lowercase_						,	return_tensors=lowercase_						,	**lowercase_						,	)

              if "attention_mask" in text_encoding:
                  SCREAMING_SNAKE_CASE_      :  List[Any]    						=		text_encoding.pop('''attention_mask''')
              if "input_ids" in text_encoding:
                  SCREAMING_SNAKE_CASE_      :  Dict    						=		text_encoding.pop('''input_ids''')
          else:
              SCREAMING_SNAKE_CASE_      :  str    						=		None

          if text_encoding is not None:
              encoding_image_processor.update(lowercase_)

          return encoding_image_processor
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	*lowercase_     :  str						,	**lowercase_     :  List[str]):




          '''simple docstring'''


          return self.tokenizer.batch_decode(*lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]						,	*lowercase_     :  List[Any]						,	**lowercase_     :  Any):




          '''simple docstring'''


          return self.tokenizer.decode(*lowercase_						,	**lowercase_)



      @property
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		self.tokenizer.model_input_names
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))










"""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 							lowerCAmelCase__				:






      '''simple docstring'''

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




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		parent
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		batch_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		seq_length
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		is_training
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		use_input_mask
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		use_token_type_ids
          SCREAMING_SNAKE_CASE_      :  int    						=		use_labels
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		vocab_size
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		hidden_size
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		num_hidden_layers
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		num_attention_heads
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		intermediate_size
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		hidden_act
          SCREAMING_SNAKE_CASE_      :  int    						=		hidden_dropout_prob
          SCREAMING_SNAKE_CASE_      :  str    						=		attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		max_position_embeddings
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		type_vocab_size
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		type_sequence_label_size
          SCREAMING_SNAKE_CASE_      :  Any    						=		initializer_range
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		num_labels
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		scope
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		range_bbox
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size)

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

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		None
          if self.use_input_mask:
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		ids_tensor([self.batch_size, self.seq_length]						,	vocab_size=2)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		None
          if self.use_token_type_ids:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		ids_tensor([self.batch_size, self.seq_length]						,	self.type_vocab_size)

          SCREAMING_SNAKE_CASE_      :  List[str]    						=		None
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		None
          if self.use_labels:
              SCREAMING_SNAKE_CASE_      :  int    						=		ids_tensor([self.batch_size]						,	self.type_sequence_label_size)
              SCREAMING_SNAKE_CASE_      :  Dict    						=		ids_tensor([self.batch_size, self.seq_length]						,	self.num_labels)

          SCREAMING_SNAKE_CASE_      :  Any    						=		self.get_config()

          return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''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 _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	lowercase_     :  Optional[Any]						,	lowercase_     :  Optional[Any]						,	lowercase_     :  str						,	lowercase_     :  Optional[Any]						,	lowercase_     :  int						,	lowercase_     :  Optional[Any]						,	lowercase_     :  int						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		LiltModel(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(lowercase_						,	bbox=lowercase_						,	attention_mask=lowercase_						,	token_type_ids=lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(lowercase_						,	bbox=lowercase_						,	token_type_ids=lowercase_)
          SCREAMING_SNAKE_CASE_      :  int    						=		model(lowercase_						,	bbox=lowercase_)

          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 _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Optional[Any]						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  List[Any]						,	lowercase_     :  int						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self.num_labels
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		LiltForTokenClassification(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		model(
              lowercase_						,	bbox=lowercase_						,	attention_mask=lowercase_						,	token_type_ids=lowercase_						,	labels=lowercase_)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels))
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple						,	lowercase_     :  str						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Any						,	lowercase_     :  Dict						,	lowercase_     :  List[str]						,	lowercase_     :  List[Any]						,	lowercase_     :  str						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		LiltForQuestionAnswering(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		model(
              lowercase_						,	bbox=lowercase_						,	attention_mask=lowercase_						,	token_type_ids=lowercase_						,	start_positions=lowercase_						,	end_positions=lowercase_						,	)
          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     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		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_
          )						,			
          )      :  List[str]    						=		config_and_inputs
          SCREAMING_SNAKE_CASE_      :  str    						=		{
              '''input_ids''': input_ids,
              '''bbox''': bbox,
              '''token_type_ids''': token_type_ids,
              '''attention_mask''': input_mask,
          }
          return config, inputs_dict




@require_torch
class 							lowerCAmelCase__				(			UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__				,		unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  (
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      __UpperCamelCase	 =  (
          {
              "feature-extraction": LiltModel,
              "question-answering": LiltForQuestionAnswering,
              "text-classification": LiltForSequenceClassification,
              "token-classification": LiltForTokenClassification,
              "zero-shot": LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  List[str]						,	lowercase_     :  Optional[int]						,	lowercase_     :  Optional[int]						,	lowercase_     :  str						,	lowercase_     :  str):




          '''simple docstring'''


          return True
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		LiltModelTester(self)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		ConfigTester(self						,	config_class=lowercase_						,	hidden_size=37)
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


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




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		type
              self.model_tester.create_and_check_model(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*lowercase_)



      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		LiltModel.from_pretrained(lowercase_)
              self.assertIsNotNone(lowercase_)




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






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base''').to(lowercase_)

          SCREAMING_SNAKE_CASE_      :  str    						=		torch.tensor([[1, 2]]						,	device=lowercase_)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]						,	device=lowercase_)

          # forward pass
          with torch.no_grad():
              SCREAMING_SNAKE_CASE_      :  Dict    						=		model(input_ids=lowercase_						,	bbox=lowercase_)

          SCREAMING_SNAKE_CASE_      :  str    						=		torch.Size([1, 2, 768])
          SCREAMING_SNAKE_CASE_      :  Dict    						=		torch.tensor(
              [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]						,	device=lowercase_						,	)

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


