




'''simple docstring'''







import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 (
       MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
       MODEL_FOR_QUESTION_ANSWERING_MAPPING,
       MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
       LayoutLMvaConfig,
       LayoutLMvaForQuestionAnswering,
       LayoutLMvaForSequenceClassification,
       LayoutLMvaForTokenClassification,
       LayoutLMvaModel,
   )
   from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
   from PIL import Image

   from transformers import LayoutLMvaImageProcessor


class     _a						:
       def __init__(			self		:			Optional[Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			List[Any]=2     ,       lowercase		:			str=3     ,       lowercase		:			Tuple=4     ,       lowercase		:			Optional[Any]=2     ,       lowercase		:			List[str]=7     ,       lowercase		:			Union[str, Any]=True     ,       lowercase		:			str=True     ,       lowercase		:			Optional[Any]=True     ,       lowercase		:			str=True     ,       lowercase		:			Any=99     ,       lowercase		:			Any=36     ,       lowercase		:			Dict=3     ,       lowercase		:			int=4     ,       lowercase		:			Any=37     ,       lowercase		:			Tuple="gelu"     ,       lowercase		:			Any=0.1     ,       lowercase		:			Tuple=0.1     ,       lowercase		:			Dict=512     ,       lowercase		:			Optional[Any]=16     ,       lowercase		:			int=2     ,       lowercase		:			str=0.02     ,       lowercase		:			Union[str, Any]=6     ,       lowercase		:			Union[str, Any]=6     ,       lowercase		:			List[Any]=3     ,       lowercase		:			Optional[int]=4     ,       lowercase		:			Optional[int]=None     ,       lowercase		:			Optional[int]=1_000     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	parent
           UpperCAmelCase     =	batch_size
           UpperCAmelCase     =	num_channels
           UpperCAmelCase     =	image_size
           UpperCAmelCase     =	patch_size
           UpperCAmelCase     =	text_seq_length
           UpperCAmelCase     =	is_training
           UpperCAmelCase     =	use_input_mask
           UpperCAmelCase     =	use_token_type_ids
           UpperCAmelCase     =	use_labels
           UpperCAmelCase     =	vocab_size
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	num_hidden_layers
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	intermediate_size
           UpperCAmelCase     =	hidden_act
           UpperCAmelCase     =	hidden_dropout_prob
           UpperCAmelCase     =	attention_probs_dropout_prob
           UpperCAmelCase     =	max_position_embeddings
           UpperCAmelCase     =	type_vocab_size
           UpperCAmelCase     =	type_sequence_label_size
           UpperCAmelCase     =	initializer_range
           UpperCAmelCase     =	coordinate_size
           UpperCAmelCase     =	shape_size
           UpperCAmelCase     =	num_labels
           UpperCAmelCase     =	num_choices
           UpperCAmelCase     =	scope
           UpperCAmelCase     =	range_bbox

           # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
           UpperCAmelCase     =	text_seq_length
           UpperCAmelCase     =	(image_size // patch_size) ** 2 + 1
           UpperCAmelCase     =	self.text_seq_length + self.image_seq_length
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	ids_tensor([self.batch_size, self.text_seq_length]     ,       self.vocab_size       )

           UpperCAmelCase     =	ids_tensor([self.batch_size, self.text_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]:
                       UpperCAmelCase     =	bbox[i, j, 3]
                       UpperCAmelCase     =	bbox[i, j, 1]
                       UpperCAmelCase     =	t
                   if bbox[i, j, 2] < bbox[i, j, 0]:
                       UpperCAmelCase     =	bbox[i, j, 2]
                       UpperCAmelCase     =	bbox[i, j, 0]
                       UpperCAmelCase     =	t

           UpperCAmelCase     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

           UpperCAmelCase     =	None
           if self.use_input_mask:
               UpperCAmelCase     =	random_attention_mask([self.batch_size, self.text_seq_length]       )

           UpperCAmelCase     =	None
           if self.use_token_type_ids:
               UpperCAmelCase     =	ids_tensor([self.batch_size, self.text_seq_length]     ,       self.type_vocab_size       )

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

           UpperCAmelCase     =	LayoutLMvaConfig(
               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     ,       coordinate_size=self.coordinate_size     ,       shape_size=self.shape_size     ,       input_size=self.image_size     ,       patch_size=self.patch_size     ,       )

           return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
       def 					A   (			self		:			Optional[Any]     ,       lowercase		:			str     ,       lowercase		:			List[Any]     ,       lowercase		:			Tuple     ,       lowercase		:			List[Any]     ,       lowercase		:			List[str]     ,       lowercase		:			Optional[Any]     ,       lowercase		:			List[str]     ,       lowercase		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	LayoutLMvaModel(config=UpperCamelCase__       )
           model.to(UpperCamelCase__       )
           model.eval()

           # text + image
           UpperCAmelCase     =	model(UpperCamelCase__     ,       pixel_values=UpperCamelCase__       )
           UpperCAmelCase     =	model(
               UpperCamelCase__     ,       bbox=UpperCamelCase__     ,       pixel_values=UpperCamelCase__     ,       attention_mask=UpperCamelCase__     ,       token_type_ids=UpperCamelCase__       )
           UpperCAmelCase     =	model(UpperCamelCase__     ,       bbox=UpperCamelCase__     ,       pixel_values=UpperCamelCase__     ,       token_type_ids=UpperCamelCase__       )
           UpperCAmelCase     =	model(UpperCamelCase__     ,       bbox=UpperCamelCase__     ,       pixel_values=UpperCamelCase__       )

           self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)       )

           # text only
           UpperCAmelCase     =	model(UpperCamelCase__       )
           self.parent.assertEqual(
               result.last_hidden_state.shape     ,       (self.batch_size, self.text_seq_length, self.hidden_size)       )

           # image only
           UpperCAmelCase     =	model(pixel_values=UpperCamelCase__       )
           self.parent.assertEqual(
               result.last_hidden_state.shape     ,       (self.batch_size, self.image_seq_length, self.hidden_size)       )
       def 					A   (			self		:			Optional[Any]     ,       lowercase		:			int     ,       lowercase		:			List[Any]     ,       lowercase		:			Any     ,       lowercase		:			Optional[Any]     ,       lowercase		:			Optional[Any]     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			Dict     ,       lowercase		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.num_labels
           UpperCAmelCase     =	LayoutLMvaForSequenceClassification(UpperCamelCase__       )
           model.to(UpperCamelCase__       )
           model.eval()
           UpperCAmelCase     =	model(
               UpperCamelCase__     ,       bbox=UpperCamelCase__     ,       pixel_values=UpperCamelCase__     ,       attention_mask=UpperCamelCase__     ,       token_type_ids=UpperCamelCase__     ,       labels=UpperCamelCase__     ,       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_labels)       )
       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			List[str]     ,       lowercase		:			List[Any]     ,       lowercase		:			str     ,       lowercase		:			str     ,       lowercase		:			Any     ,       lowercase		:			Optional[int]     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.num_labels
           UpperCAmelCase     =	LayoutLMvaForTokenClassification(config=UpperCamelCase__       )
           model.to(UpperCamelCase__       )
           model.eval()
           UpperCAmelCase     =	model(
               UpperCamelCase__     ,       bbox=UpperCamelCase__     ,       pixel_values=UpperCamelCase__     ,       attention_mask=UpperCamelCase__     ,       token_type_ids=UpperCamelCase__     ,       labels=UpperCamelCase__     ,       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.text_seq_length, self.num_labels)       )
       def 					A   (			self		:			Tuple     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			Dict     ,       lowercase		:			Dict     ,       lowercase		:			Optional[int]     ,       lowercase		:			Optional[int]     ,       lowercase		:			int     ,       lowercase		:			List[Any]     ,       lowercase		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	LayoutLMvaForQuestionAnswering(config=UpperCamelCase__       )
           model.to(UpperCamelCase__       )
           model.eval()
           UpperCAmelCase     =	model(
               UpperCamelCase__     ,       bbox=UpperCamelCase__     ,       pixel_values=UpperCamelCase__     ,       attention_mask=UpperCamelCase__     ,       token_type_ids=UpperCamelCase__     ,       start_positions=UpperCamelCase__     ,       end_positions=UpperCamelCase__     ,       )
           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		:			List[Any]       ):



           '''simple docstring'''

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






@require_torch
class     _a						(			SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							unittest.TestCase				):
       __a       :  Any            =      False
       __a       :  int            =      False
       __a       :  List[str]            =      False

       __a       :  Tuple            =      (
           (
               LayoutLMvaModel,
               LayoutLMvaForSequenceClassification,
               LayoutLMvaForTokenClassification,
               LayoutLMvaForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       __a       :  str            =      (
           {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
           if is_torch_available()
           else {}
       )
       def 					A   (			self		:			str     ,       lowercase		:			List[Any]     ,       lowercase		:			List[str]     ,       lowercase		:			Optional[int]     ,       lowercase		:			int     ,       lowercase		:			List[str]       ):



           '''simple docstring'''

           return True
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	LayoutLMvaModelTester(self       )
           UpperCAmelCase     =	ConfigTester(self     ,       config_class=UpperCamelCase__     ,       hidden_size=37       )
       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			Optional[Any]     ,       lowercase		:			Tuple     ,       lowercase		:			Optional[Any]=False       ):



           '''simple docstring'''

           UpperCAmelCase     =	copy.deepcopy(UpperCamelCase__       )
           if model_class in get_values(UpperCamelCase__       ):
               UpperCAmelCase     =	{
                   k: v.unsqueeze(1       ).expand(-1     ,       self.model_tester.num_choices     ,       -1       ).contiguous()
                   if isinstance(UpperCamelCase__     ,       torch.Tensor       ) and v.ndim > 1
                   else v
                   for k, v in inputs_dict.items()
               }
           if return_labels:
               if model_class in get_values(UpperCamelCase__       ):
                   UpperCAmelCase     =	torch.ones(self.model_tester.batch_size     ,       dtype=torch.long     ,       device=UpperCamelCase__       )
               elif model_class in get_values(UpperCamelCase__       ):
                   UpperCAmelCase     =	torch.zeros(
                       self.model_tester.batch_size     ,       dtype=torch.long     ,       device=UpperCamelCase__       )
                   UpperCAmelCase     =	torch.zeros(
                       self.model_tester.batch_size     ,       dtype=torch.long     ,       device=UpperCamelCase__       )
               elif model_class in [
                   *get_values(UpperCamelCase__       ),
               ]:
                   UpperCAmelCase     =	torch.zeros(
                       self.model_tester.batch_size     ,       dtype=torch.long     ,       device=UpperCamelCase__       )
               elif model_class in [
                   *get_values(UpperCamelCase__       ),
               ]:
                   UpperCAmelCase     =	torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.text_seq_length)     ,       dtype=torch.long     ,       device=UpperCamelCase__     ,       )

           return inputs_dict
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           self.config_tester.run_common_tests()
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase__       )
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
               UpperCAmelCase     =	type
               self.model_tester.create_and_check_model(*UpperCamelCase__       )
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__       )
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__       )
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__       )







       @slow
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase     =	LayoutLMvaModel.from_pretrained(UpperCamelCase__       )
               self.assertIsNotNone(UpperCamelCase__       )






def 		snake_case_							():
    UpperCAmelCase     =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
    return image







@require_torch
class     _a						(			unittest.TestCase				):
       @cached_property
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           return LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase__       ) if is_vision_available() else None







       @slow
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''       ).to(UpperCamelCase__       )

           UpperCAmelCase     =	self.default_image_processor
           UpperCAmelCase     =	prepare_img()
           UpperCAmelCase     =	image_processor(images=UpperCamelCase__     ,       return_tensors='''pt'''       ).pixel_values.to(UpperCamelCase__       )

           UpperCAmelCase     =	torch.tensor([[1, 2]]       )
           UpperCAmelCase     =	torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]       ).unsqueeze(0       )

           # forward pass
           UpperCAmelCase     =	model(
               input_ids=input_ids.to(UpperCamelCase__       )     ,       bbox=bbox.to(UpperCamelCase__       )     ,       pixel_values=pixel_values.to(UpperCamelCase__       )     ,       )

           # verify the logits
           UpperCAmelCase     =	torch.Size((1, 199, 768)       )
           self.assertEqual(outputs.last_hidden_state.shape     ,       UpperCamelCase__       )

           UpperCAmelCase     =	torch.tensor(
               [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]       ).to(UpperCamelCase__       )

           self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,       UpperCamelCase__     ,       atol=1E-4       )       )










'''simple docstring'''







import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class     _a						(			__a							,							unittest.TestCase				):
       __a       :  Any            =      MgpstrTokenizer
       __a       :  Optional[Any]            =      False
       __a       :  str            =      {}
       __a       :  Optional[int]            =      False
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           super().setUp()

           # fmt: off
           UpperCAmelCase     =	['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
           # fmt: on
           UpperCAmelCase     =	dict(zip(lowercase     ,       range(len(lowercase       )       )       )       )

           UpperCAmelCase     =	os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['''vocab_file''']       )
           with open(self.vocab_file     ,       '''w'''     ,       encoding='''utf-8'''       ) as fp:
               fp.write(json.dumps(lowercase       ) + '''\n'''       )
       def 					A   (			self		:			int     ,       **lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,       **lowercase       )
       def 					A   (			self		:			int     ,       lowercase		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	'''tester'''
           UpperCAmelCase     =	'''tester'''
           return input_text, output_text
       @unittest.skip('''MGP-STR always lower cases letters.'''       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           pass
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_tokenizers(do_lower_case=lowercase       )
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                   UpperCAmelCase     =	'''[SPECIAL_TOKEN]'''

                   tokenizer.add_special_tokens({'''cls_token''': special_token}       )
                   UpperCAmelCase     =	tokenizer.encode([special_token]     ,       add_special_tokens=lowercase       )
                   self.assertEqual(len(lowercase       )     ,       1       )

                   UpperCAmelCase     =	tokenizer.decode(lowercase     ,       skip_special_tokens=lowercase       )
                   self.assertTrue(special_token not in decoded       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_tokenizers()
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                   UpperCAmelCase ,  UpperCAmelCase     =	self.get_input_output_texts(lowercase       )

                   UpperCAmelCase     =	tokenizer.tokenize(lowercase       )
                   UpperCAmelCase     =	tokenizer.convert_tokens_to_ids(lowercase       )
                   UpperCAmelCase     =	tokenizer.encode(lowercase     ,       add_special_tokens=lowercase       )
                   self.assertListEqual(lowercase     ,       lowercase       )

                   UpperCAmelCase     =	tokenizer.convert_ids_to_tokens(lowercase       )
                   self.assertNotEqual(len(lowercase       )     ,       0       )
                   UpperCAmelCase     =	tokenizer.decode(lowercase       )
                   self.assertIsInstance(lowercase     ,       lowercase       )

                   self.assertEqual(text_a.replace(''' '''     ,       ''''''       )     ,       lowercase       )
       @unittest.skip('''MGP-STR tokenizer only handles one sequence.'''       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           pass







       @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''       )
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           pass




