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  __magic_name__  :





   """simple docstring"""




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



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= parent
    _UpperCamelCase:     Union[str, Any]							= batch_size
    _UpperCamelCase:     Any							= num_channels
    _UpperCamelCase:     str							= image_size
    _UpperCamelCase:     str							= patch_size
    _UpperCamelCase:     Dict							= text_seq_length
    _UpperCamelCase:     Optional[int]							= is_training
    _UpperCamelCase:     Any							= use_input_mask
    _UpperCamelCase:     Dict							= use_token_type_ids
    _UpperCamelCase:     Optional[int]							= use_labels
    _UpperCamelCase:     Optional[int]							= vocab_size
    _UpperCamelCase:     Union[str, Any]							= hidden_size
    _UpperCamelCase:     str							= num_hidden_layers
    _UpperCamelCase:     int							= num_attention_heads
    _UpperCamelCase:     int							= intermediate_size
    _UpperCamelCase:     Union[str, Any]							= hidden_act
    _UpperCamelCase:     str							= hidden_dropout_prob
    _UpperCamelCase:     Optional[int]							= attention_probs_dropout_prob
    _UpperCamelCase:     Tuple							= max_position_embeddings
    _UpperCamelCase:     List[str]							= type_vocab_size
    _UpperCamelCase:     Optional[int]							= type_sequence_label_size
    _UpperCamelCase:     Optional[int]							= initializer_range
    _UpperCamelCase:     str							= coordinate_size
    _UpperCamelCase:     Optional[Any]							= shape_size
    _UpperCamelCase:     Optional[int]							= num_labels
    _UpperCamelCase:     Any							= num_choices
    _UpperCamelCase:     List[Any]							= scope
    _UpperCamelCase:     List[str]							= range_bbox

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



   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    _UpperCamelCase:     str							= ids_tensor([self.batch_size, self.text_seq_length]		,  self.vocab_size							)

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

    _UpperCamelCase:     int							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

    _UpperCamelCase:     Any							= None
    if self.use_input_mask:
     _UpperCamelCase:     Dict							= random_attention_mask([self.batch_size, self.text_seq_length]							)

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

    _UpperCamelCase:     str							= None
    _UpperCamelCase:     str							= None
    if self.use_labels:
     _UpperCamelCase:     Optional[int]							= ids_tensor([self.batch_size]		,  self.type_sequence_label_size							)
     _UpperCamelCase:     int							= ids_tensor([self.batch_size, self.text_seq_length]		,  self.num_labels							)

    _UpperCamelCase:     Tuple							= 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   lowerCAmelCase						(	self					:  Tuple		,  _lowercase					:  Dict		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  Optional[Any]		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  Optional[int]		,  _lowercase					:  str		,  _lowercase					:  Dict		,  _lowercase					:  Optional[int]							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= LayoutLMvaModel(config=_lowercase							)
    model.to(_lowercase							)
    model.eval()

    # text + image
    _UpperCamelCase:     Dict							= model(_lowercase		,  pixel_values=_lowercase							)
    _UpperCamelCase:     Optional[Any]							= model(
        _lowercase		,  bbox=_lowercase		,  pixel_values=_lowercase		,  attention_mask=_lowercase		,  token_type_ids=_lowercase							)
    _UpperCamelCase:     Dict							= model(_lowercase		,  bbox=_lowercase		,  pixel_values=_lowercase		,  token_type_ids=_lowercase							)
    _UpperCamelCase:     Union[str, Any]							= model(_lowercase		,  bbox=_lowercase		,  pixel_values=_lowercase							)

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

    # text only
    _UpperCamelCase:     Tuple							= model(_lowercase							)
    self.parent.assertEqual(
        result.last_hidden_state.shape		,  (self.batch_size, self.text_seq_length, self.hidden_size)							)

    # image only
    _UpperCamelCase:     Optional[Any]							= model(pixel_values=_lowercase							)
    self.parent.assertEqual(
        result.last_hidden_state.shape		,  (self.batch_size, self.image_seq_length, self.hidden_size)							)



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  Tuple		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  Optional[int]		,  _lowercase					:  Dict		,  _lowercase					:  Optional[int]		,  _lowercase					:  List[str]		,  _lowercase					:  Optional[int]							):



    """simple docstring"""



    _UpperCamelCase:     int							= self.num_labels
    _UpperCamelCase:     int							= LayoutLMvaForSequenceClassification(_lowercase							)
    model.to(_lowercase							)
    model.eval()
    _UpperCamelCase:     Any							= model(
        _lowercase		,  bbox=_lowercase		,  pixel_values=_lowercase		,  attention_mask=_lowercase		,  token_type_ids=_lowercase		,  labels=_lowercase		,  )
    self.parent.assertEqual(result.logits.shape		,  (self.batch_size, self.num_labels)							)



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  Optional[Any]		,  _lowercase					:  List[Any]		,  _lowercase					:  Dict		,  _lowercase					:  Dict		,  _lowercase					:  int		,  _lowercase					:  int		,  _lowercase					:  Tuple		,  _lowercase					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Any							= self.num_labels
    _UpperCamelCase:     Any							= LayoutLMvaForTokenClassification(config=_lowercase							)
    model.to(_lowercase							)
    model.eval()
    _UpperCamelCase:     Dict							= model(
        _lowercase		,  bbox=_lowercase		,  pixel_values=_lowercase		,  attention_mask=_lowercase		,  token_type_ids=_lowercase		,  labels=_lowercase		,  )
    self.parent.assertEqual(result.logits.shape		,  (self.batch_size, self.text_seq_length, self.num_labels)							)



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  List[str]		,  _lowercase					:  List[str]		,  _lowercase					:  Optional[int]		,  _lowercase					:  List[Any]		,  _lowercase					:  Optional[int]		,  _lowercase					:  int		,  _lowercase					:  Optional[int]		,  _lowercase					:  int							):



    """simple docstring"""



    _UpperCamelCase:     str							= LayoutLMvaForQuestionAnswering(config=_lowercase							)
    model.to(_lowercase							)
    model.eval()
    _UpperCamelCase:     Any							= model(
        _lowercase		,  bbox=_lowercase		,  pixel_values=_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   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= self.prepare_config_and_inputs()
    (
        (
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							(
        _UpperCamelCase
    )	,							
    ):     List[str]							= config_and_inputs
    _UpperCamelCase:     Any							= {
        '''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  __magic_name__  (		__a      ,	__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[Any]  			=   False
   lowerCAmelCase      :    Any  			=   False
   lowerCAmelCase      :    str  			=   False

   lowerCAmelCase      :    str  			=   (
       (
           LayoutLMvaModel,
           LayoutLMvaForSequenceClassification,
           LayoutLMvaForTokenClassification,
           LayoutLMvaForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   lowerCAmelCase      :    Dict  			=   (
       {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
       if is_torch_available()
       else {}
   )



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  str		,  _lowercase					:  Dict		,  _lowercase					:  List[str]		,  _lowercase					:  Any		,  _lowercase					:  Dict							):



    """simple docstring"""



    return True



   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= LayoutLMvaModelTester(self							)
    _UpperCamelCase:     Optional[int]							= ConfigTester(self		,  config_class=_lowercase		,  hidden_size=37							)



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  str		,  _lowercase					:  Dict		,  _lowercase					:  Union[str, Any]=False							):



    """simple docstring"""



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

    return inputs_dict



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    self.config_tester.run_common_tests()



   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_lowercase							)



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



    """simple docstring"""



    _UpperCamelCase:     Tuple							= self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
     _UpperCamelCase:     int							= type
     self.model_tester.create_and_check_model(*_lowercase							)



   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*_lowercase							)



   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*_lowercase							)



   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*_lowercase							)



   @slow
   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _UpperCamelCase:     Optional[int]							= LayoutLMvaModel.from_pretrained(_lowercase							)
     self.assertIsNotNone(_lowercase							)




def       lowerCAmelCase_     (		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     int							= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
 return image





@require_torch
class  __magic_name__  (		unittest.TestCase    ):





   """simple docstring"""




   @cached_property
   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



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



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



    """simple docstring"""



    _UpperCamelCase:     str							= LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''							).to(_lowercase							)

    _UpperCamelCase:     Optional[int]							= self.default_image_processor
    _UpperCamelCase:     str							= prepare_img()
    _UpperCamelCase:     Tuple							= image_processor(images=_lowercase		,  return_tensors='''pt'''							).pixel_values.to(_lowercase							)

    _UpperCamelCase:     int							= torch.tensor([[1, 2]]							)
    _UpperCamelCase:     List[Any]							= torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]							).unsqueeze(0							)

    # forward pass
    _UpperCamelCase:     Optional[int]							= model(
        input_ids=input_ids.to(_lowercase							)		,  bbox=bbox.to(_lowercase							)		,  pixel_values=pixel_values.to(_lowercase							)		,  )

    # verify the logits
    _UpperCamelCase:     Union[str, Any]							= torch.Size((1, 199, 768)							)
    self.assertEqual(outputs.last_hidden_state.shape		,  _lowercase							)

    _UpperCamelCase:     int							= torch.tensor(
        [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]							).to(_lowercase							)

    self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]		,  _lowercase		,  atol=1E-4							)							)
def       lowerCAmelCase_     (		lowercase:							int = 600_851_475_143		)    ->				int:







 '''simple docstring'''

 try:
  _UpperCamelCase:     Optional[int]							= int(lowercase		)
 except (TypeError, ValueError):
  raise TypeError('''Parameter n must be int or castable to int.'''		)
 if n <= 0:
  raise ValueError('''Parameter n must be greater than or equal to one.'''		)
 _UpperCamelCase:     Dict							= 1
 _UpperCamelCase:     Tuple							= 2
 while i * i <= n:
  while n % i == 0:
   _UpperCamelCase:     List[Any]							= i
   n //= i
  i += 1
 if n > 1:
  _UpperCamelCase:     Any							= n
 return int(lowercase		)


if __name__ == "__main__":
  print(f"""{solution() = }""")