"""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     UpperCAmelCase						:



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=3    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=7    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=99    ,			_UpperCAmelCase=36    ,			_UpperCAmelCase=3    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=37    ,			_UpperCAmelCase="gelu"    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=512    ,			_UpperCAmelCase=16    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=0.02    ,			_UpperCAmelCase=6    ,			_UpperCAmelCase=6    ,			_UpperCAmelCase=3    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=1000    ,			):
       lowercase__:   Any					=      parent
       lowercase__:   Any					=      batch_size
       lowercase__:   Optional[Any]					=      num_channels
       lowercase__:   str					=      image_size
       lowercase__:   List[str]					=      patch_size
       lowercase__:   Union[str, Any]					=      text_seq_length
       lowercase__:   Dict					=      is_training
       lowercase__:   Union[str, Any]					=      use_input_mask
       lowercase__:   Optional[int]					=      use_token_type_ids
       lowercase__:   int					=      use_labels
       lowercase__:   Optional[int]					=      vocab_size
       lowercase__:   Optional[int]					=      hidden_size
       lowercase__:   Any					=      num_hidden_layers
       lowercase__:   Any					=      num_attention_heads
       lowercase__:   Optional[int]					=      intermediate_size
       lowercase__:   int					=      hidden_act
       lowercase__:   List[Any]					=      hidden_dropout_prob
       lowercase__:   str					=      attention_probs_dropout_prob
       lowercase__:   str					=      max_position_embeddings
       lowercase__:   List[str]					=      type_vocab_size
       lowercase__:   Dict					=      type_sequence_label_size
       lowercase__:   Optional[int]					=      initializer_range
       lowercase__:   int					=      coordinate_size
       lowercase__:   int					=      shape_size
       lowercase__:   Tuple					=      num_labels
       lowercase__:   Dict					=      num_choices
       lowercase__:   Tuple					=      scope
       lowercase__:   Tuple					=      range_bbox

       # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
       lowercase__:   Optional[int]					=      text_seq_length
       lowercase__:   int					=      (image_size // patch_size) ** 2 + 1
       lowercase__:   Optional[int]					=      self.text_seq_length + self.image_seq_length






    def _snake_case			(   self							):
       lowercase__:   Union[str, Any]					=      ids_tensor([self.batch_size, self.text_seq_length]    ,			self.vocab_size							)

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

       lowercase__:   Union[str, Any]					=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       lowercase__:   Optional[int]					=      None
       if self.use_input_mask:
          lowercase__:   Any					=      random_attention_mask([self.batch_size, self.text_seq_length]							)

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

       lowercase__:   int					=      None
       lowercase__:   Any					=      None
       if self.use_labels:
          lowercase__:   List[str]					=      ids_tensor([self.batch_size]    ,			self.type_sequence_label_size							)
          lowercase__:   str					=      ids_tensor([self.batch_size, self.text_seq_length]    ,			self.num_labels							)

       lowercase__:   List[Any]					=      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 _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   Tuple					=      LayoutLMvaModel(config=__SCREAMING_SNAKE_CASE							)
       model.to(__SCREAMING_SNAKE_CASE							)
       model.eval()

       # text + image
       lowercase__:   int					=      model(__SCREAMING_SNAKE_CASE    ,			pixel_values=__SCREAMING_SNAKE_CASE							)
       lowercase__:   int					=      model(
           __SCREAMING_SNAKE_CASE    ,			bbox=__SCREAMING_SNAKE_CASE    ,			pixel_values=__SCREAMING_SNAKE_CASE    ,			attention_mask=__SCREAMING_SNAKE_CASE    ,			token_type_ids=__SCREAMING_SNAKE_CASE							)
       lowercase__:   List[str]					=      model(__SCREAMING_SNAKE_CASE    ,			bbox=__SCREAMING_SNAKE_CASE    ,			pixel_values=__SCREAMING_SNAKE_CASE    ,			token_type_ids=__SCREAMING_SNAKE_CASE							)
       lowercase__:   List[Any]					=      model(__SCREAMING_SNAKE_CASE    ,			bbox=__SCREAMING_SNAKE_CASE    ,			pixel_values=__SCREAMING_SNAKE_CASE							)

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

       # text only
       lowercase__:   Optional[int]					=      model(__SCREAMING_SNAKE_CASE							)
       self.parent.assertEqual(
           result.last_hidden_state.shape    ,			(self.batch_size, self.text_seq_length, self.hidden_size)							)

       # image only
       lowercase__:   List[str]					=      model(pixel_values=__SCREAMING_SNAKE_CASE							)
       self.parent.assertEqual(
           result.last_hidden_state.shape    ,			(self.batch_size, self.image_seq_length, self.hidden_size)							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   Tuple					=      self.num_labels
       lowercase__:   str					=      LayoutLMvaForSequenceClassification(__SCREAMING_SNAKE_CASE							)
       model.to(__SCREAMING_SNAKE_CASE							)
       model.eval()
       lowercase__:   Optional[Any]					=      model(
           __SCREAMING_SNAKE_CASE    ,			bbox=__SCREAMING_SNAKE_CASE    ,			pixel_values=__SCREAMING_SNAKE_CASE    ,			attention_mask=__SCREAMING_SNAKE_CASE    ,			token_type_ids=__SCREAMING_SNAKE_CASE    ,			labels=__SCREAMING_SNAKE_CASE    ,			)
       self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels)							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   Union[str, Any]					=      self.num_labels
       lowercase__:   Optional[int]					=      LayoutLMvaForTokenClassification(config=__SCREAMING_SNAKE_CASE							)
       model.to(__SCREAMING_SNAKE_CASE							)
       model.eval()
       lowercase__:   Tuple					=      model(
           __SCREAMING_SNAKE_CASE    ,			bbox=__SCREAMING_SNAKE_CASE    ,			pixel_values=__SCREAMING_SNAKE_CASE    ,			attention_mask=__SCREAMING_SNAKE_CASE    ,			token_type_ids=__SCREAMING_SNAKE_CASE    ,			labels=__SCREAMING_SNAKE_CASE    ,			)
       self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.text_seq_length, self.num_labels)							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   Dict					=      LayoutLMvaForQuestionAnswering(config=__SCREAMING_SNAKE_CASE							)
       model.to(__SCREAMING_SNAKE_CASE							)
       model.eval()
       lowercase__:   int					=      model(
           __SCREAMING_SNAKE_CASE    ,			bbox=__SCREAMING_SNAKE_CASE    ,			pixel_values=__SCREAMING_SNAKE_CASE    ,			attention_mask=__SCREAMING_SNAKE_CASE    ,			token_type_ids=__SCREAMING_SNAKE_CASE    ,			start_positions=__SCREAMING_SNAKE_CASE    ,			end_positions=__SCREAMING_SNAKE_CASE    ,			)
       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							):
       lowercase__:   Dict					=      self.prepare_config_and_inputs()
       (
           (
           lowercase__			
       ), (
           lowercase__			
       ), (
           lowercase__			
       ), (
           lowercase__			
       ), (
           lowercase__			
       ), (
           lowercase__			
       ), (
           lowercase__			
       ), (
           lowercase__			
       ), 			
       ):   Any					=      config_and_inputs
       lowercase__:   Optional[int]					=      {
           '''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     UpperCAmelCase						(_UpperCAmelCase    ,_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Any						    = False
    _UpperCAmelCase	:Optional[int]						    = False
    _UpperCAmelCase	:Dict						    = False

    _UpperCAmelCase	:List[Any]						    = (
        (
            LayoutLMvaModel,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    _UpperCAmelCase	:int						    = (
        {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
        if is_torch_available()
        else {}
    )






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       return True






    def _snake_case			(   self							):
       lowercase__:   Optional[int]					=      LayoutLMvaModelTester(self							)
       lowercase__:   Optional[int]					=      ConfigTester(self    ,			config_class=__SCREAMING_SNAKE_CASE    ,			hidden_size=37							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase=False							):
       lowercase__:   Optional[Any]					=      copy.deepcopy(__SCREAMING_SNAKE_CASE							)
       if model_class in get_values(__SCREAMING_SNAKE_CASE							):
          lowercase__:   Dict					=      {
              k: v.unsqueeze(1							).expand(-1    ,			self.model_tester.num_choices    ,			-1							).contiguous()
              if isinstance(__SCREAMING_SNAKE_CASE    ,			torch.Tensor							) and v.ndim > 1
              else v
              for k, v in inputs_dict.items()
          }
       if return_labels:
          if model_class in get_values(__SCREAMING_SNAKE_CASE							):
             lowercase__:   str					=      torch.ones(self.model_tester.batch_size    ,			dtype=torch.long    ,			device=__SCREAMING_SNAKE_CASE							)
          elif model_class in get_values(__SCREAMING_SNAKE_CASE							):
             lowercase__:   List[str]					=      torch.zeros(
                 self.model_tester.batch_size    ,			dtype=torch.long    ,			device=__SCREAMING_SNAKE_CASE							)
             lowercase__:   Dict					=      torch.zeros(
                 self.model_tester.batch_size    ,			dtype=torch.long    ,			device=__SCREAMING_SNAKE_CASE							)
          elif model_class in [
              *get_values(__SCREAMING_SNAKE_CASE							),
          ]:
             lowercase__:   Optional[Any]					=      torch.zeros(
                 self.model_tester.batch_size    ,			dtype=torch.long    ,			device=__SCREAMING_SNAKE_CASE							)
          elif model_class in [
              *get_values(__SCREAMING_SNAKE_CASE							),
          ]:
             lowercase__:   Optional[Any]					=      torch.zeros(
                 (self.model_tester.batch_size, self.model_tester.text_seq_length)    ,			dtype=torch.long    ,			device=__SCREAMING_SNAKE_CASE    ,			)

       return inputs_dict






    def _snake_case			(   self							):
       self.config_tester.run_common_tests()






    def _snake_case			(   self							):
       lowercase__:   Any					=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE							)






    def _snake_case			(   self							):
       lowercase__:   List[Any]					=      self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
          lowercase__:   List[str]					=      type
          self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE							)






    def _snake_case			(   self							):
       lowercase__:   List[Any]					=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE							)






    def _snake_case			(   self							):
       lowercase__:   Union[str, Any]					=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE							)






    def _snake_case			(   self							):
       lowercase__:   Union[str, Any]					=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE							)






    @slow
    def _snake_case			(   self							):




       for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase__:   Tuple					=      LayoutLMvaModel.from_pretrained(__SCREAMING_SNAKE_CASE							)
          self.assertIsNotNone(__SCREAMING_SNAKE_CASE							)



def SCREAMING_SNAKE_CASE__			(   )  ->  Any:
   lowercase__:   Any					=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
   return image




@require_torch
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    @cached_property
    def _snake_case			(   self							):
       return LayoutLMvaImageProcessor(apply_ocr=__SCREAMING_SNAKE_CASE							) if is_vision_available() else None






    @slow
    def _snake_case			(   self							):
       lowercase__:   Optional[Any]					=      LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''							).to(__SCREAMING_SNAKE_CASE							)

       lowercase__:   int					=      self.default_image_processor
       lowercase__:   Tuple					=      prepare_img()
       lowercase__:   str					=      image_processor(images=__SCREAMING_SNAKE_CASE    ,			return_tensors='''pt'''							).pixel_values.to(__SCREAMING_SNAKE_CASE							)

       lowercase__:   Union[str, Any]					=      torch.tensor([[1, 2]]							)
       lowercase__:   Any					=      torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]							).unsqueeze(0							)

       # forward pass
       lowercase__:   str					=      model(
           input_ids=input_ids.to(__SCREAMING_SNAKE_CASE							)    ,			bbox=bbox.to(__SCREAMING_SNAKE_CASE							)    ,			pixel_values=pixel_values.to(__SCREAMING_SNAKE_CASE							)    ,			)

       # verify the logits
       lowercase__:   List[str]					=      torch.Size((1, 199, 768)							)
       self.assertEqual(outputs.last_hidden_state.shape    ,			__SCREAMING_SNAKE_CASE							)

       lowercase__:   Tuple					=      torch.tensor(
           [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]							).to(__SCREAMING_SNAKE_CASE							)

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



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase :       Union[str, Any]			   =     {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase :       Union[str, Any]			   =     [
      'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SEWForCTC',
      'SEWForSequenceClassification',
      'SEWModel',
      'SEWPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sew import (
        SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWForCTC,
        SEWForSequenceClassification,
        SEWModel,
        SEWPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase :       int			   =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






