





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	:str    ,   _UpperCamelCase	:Any    ,   _UpperCamelCase	:Optional[Any]=2    ,   _UpperCamelCase	:List[Any]=3    ,   _UpperCamelCase	:Any=4    ,   _UpperCamelCase	:Optional[Any]=2    ,   _UpperCamelCase	:Union[str, Any]=7    ,   _UpperCamelCase	:Optional[int]=True    ,   _UpperCamelCase	:str=True    ,   _UpperCamelCase	:List[Any]=True    ,   _UpperCamelCase	:Optional[Any]=True    ,   _UpperCamelCase	:Optional[Any]=99    ,   _UpperCamelCase	:List[str]=36    ,   _UpperCamelCase	:str=3    ,   _UpperCamelCase	:int=4    ,   _UpperCamelCase	:int=37    ,   _UpperCamelCase	:str="gelu"    ,   _UpperCamelCase	:Optional[int]=0.1    ,   _UpperCamelCase	:str=0.1    ,   _UpperCamelCase	:Optional[int]=512    ,   _UpperCamelCase	:List[Any]=16    ,   _UpperCamelCase	:int=2    ,   _UpperCamelCase	:Tuple=0.0_2    ,   _UpperCamelCase	:List[str]=6    ,   _UpperCamelCase	:str=6    ,   _UpperCamelCase	:str=3    ,   _UpperCamelCase	:List[str]=4    ,   _UpperCamelCase	:Optional[Any]=None    ,   _UpperCamelCase	:Any=1000    ,   )->					Optional[int]:
         __A          =				parent
         __A          =				batch_size
         __A          =				num_channels
         __A          =				image_size
         __A          =				patch_size
         __A          =				text_seq_length
         __A          =				is_training
         __A          =				use_input_mask
         __A          =				use_token_type_ids
         __A          =				use_labels
         __A          =				vocab_size
         __A          =				hidden_size
         __A          =				num_hidden_layers
         __A          =				num_attention_heads
         __A          =				intermediate_size
         __A          =				hidden_act
         __A          =				hidden_dropout_prob
         __A          =				attention_probs_dropout_prob
         __A          =				max_position_embeddings
         __A          =				type_vocab_size
         __A          =				type_sequence_label_size
         __A          =				initializer_range
         __A          =				coordinate_size
         __A          =				shape_size
         __A          =				num_labels
         __A          =				num_choices
         __A          =				scope
         __A          =				range_bbox

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




   def _lowerCAmelCase      (self	:int )->					Optional[int]:
         __A          =				ids_tensor([self.batch_size, self.text_seq_length]    ,   self.vocab_size )

         __A          =				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]:
                           __A          =				bbox[i, j, 3]
                           __A          =				bbox[i, j, 1]
                           __A          =				t
                     if bbox[i, j, 2] < bbox[i, j, 0]:
                           __A          =				bbox[i, j, 2]
                           __A          =				bbox[i, j, 0]
                           __A          =				t

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

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

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

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

         __A          =				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    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:Dict )->					Optional[Any]:
         __A          =				LayoutLMvaModel(config=_UpperCamelCase )
         model.to(_UpperCamelCase )
         model.eval()

         # text + image
         __A          =				model(_UpperCamelCase    ,   pixel_values=_UpperCamelCase )
         __A          =				model(
             _UpperCamelCase    ,   bbox=_UpperCamelCase    ,   pixel_values=_UpperCamelCase    ,   attention_mask=_UpperCamelCase    ,   token_type_ids=_UpperCamelCase )
         __A          =				model(_UpperCamelCase    ,   bbox=_UpperCamelCase    ,   pixel_values=_UpperCamelCase    ,   token_type_ids=_UpperCamelCase )
         __A          =				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
         __A          =				model(_UpperCamelCase )
         self.parent.assertEqual(
             result.last_hidden_state.shape    ,   (self.batch_size, self.text_seq_length, self.hidden_size) )

         # image only
         __A          =				model(pixel_values=_UpperCamelCase )
         self.parent.assertEqual(
             result.last_hidden_state.shape    ,   (self.batch_size, self.image_seq_length, self.hidden_size) )




   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Optional[Any]    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:Optional[int] )->					Tuple:
         __A          =				self.num_labels
         __A          =				LayoutLMvaForSequenceClassification(_UpperCamelCase )
         model.to(_UpperCamelCase )
         model.eval()
         __A          =				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 _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:Optional[Any]    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:Optional[int] )->					Optional[Any]:
         __A          =				self.num_labels
         __A          =				LayoutLMvaForTokenClassification(config=_UpperCamelCase )
         model.to(_UpperCamelCase )
         model.eval()
         __A          =				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 _lowerCAmelCase      (self	:Tuple    ,   _UpperCamelCase	:Optional[Any]    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:Any )->					Dict:
         __A          =				LayoutLMvaForQuestionAnswering(config=_UpperCamelCase )
         model.to(_UpperCamelCase )
         model.eval()
         __A          =				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 _lowerCAmelCase      (self	:List[Any] )->					Dict:
         __A          =				self.prepare_config_and_inputs()
         (
             (
             __A
         )   ,						(
             __A
         )   ,						(
             __A
         )   ,						(
             __A
         )   ,						(
             __A
         )   ,						(
             __A
         )   ,						(
             __A
         )   ,						(
             __A
         )   ,						
         )          =				config_and_inputs
         __A          =				{
             '''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_					(			_lowerCamelCase ,      _lowerCamelCase ,      unittest.TestCase   ):
   lowerCAmelCase__        = False
   lowerCAmelCase__        = False
   lowerCAmelCase__        = False

   lowerCAmelCase__        = (
       (
           LayoutLMvaModel,
           LayoutLMvaForSequenceClassification,
           LayoutLMvaForTokenClassification,
           LayoutLMvaForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   lowerCAmelCase__        = (
       {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
       if is_torch_available()
       else {}
   )




   def _lowerCAmelCase      (self	:Union[str, Any]    ,   _UpperCamelCase	:Optional[Any]    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:Any    ,   _UpperCamelCase	:Optional[Any]    ,   _UpperCamelCase	:Tuple )->					Tuple:
         # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
         # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
         # the sequence dimension of the text embedding only.
         # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
         return True




   def _lowerCAmelCase      (self	:List[str] )->					Tuple:
         __A          =				LayoutLMvaModelTester(self )
         __A          =				ConfigTester(self    ,   config_class=_UpperCamelCase    ,   hidden_size=37 )




   def _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Dict=False )->					Optional[Any]:
         __A          =				copy.deepcopy(_UpperCamelCase )
         if model_class in get_values(_UpperCamelCase ):
               __A          =				{
                   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 ):
                     __A          =				torch.ones(self.model_tester.batch_size    ,   dtype=torch.long    ,   device=_UpperCamelCase )
               elif model_class in get_values(_UpperCamelCase ):
                     __A          =				torch.zeros(
                         self.model_tester.batch_size    ,   dtype=torch.long    ,   device=_UpperCamelCase )
                     __A          =				torch.zeros(
                         self.model_tester.batch_size    ,   dtype=torch.long    ,   device=_UpperCamelCase )
               elif model_class in [
                   *get_values(_UpperCamelCase ),
               ]:
                     __A          =				torch.zeros(
                         self.model_tester.batch_size    ,   dtype=torch.long    ,   device=_UpperCamelCase )
               elif model_class in [
                   *get_values(_UpperCamelCase ),
               ]:
                     __A          =				torch.zeros(
                         (self.model_tester.batch_size, self.model_tester.text_seq_length)    ,   dtype=torch.long    ,   device=_UpperCamelCase    ,   )

         return inputs_dict




   def _lowerCAmelCase      (self	:Dict )->					List[Any]:
         self.config_tester.run_common_tests()




   def _lowerCAmelCase      (self	:Any )->					Optional[Any]:
         __A          =				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_UpperCamelCase )




   def _lowerCAmelCase      (self	:Optional[Any] )->					List[str]:
         __A          =				self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
               __A          =				type
               self.model_tester.create_and_check_model(*_UpperCamelCase )




   def _lowerCAmelCase      (self	:List[Any] )->					Any:
         __A          =				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase )




   def _lowerCAmelCase      (self	:Tuple )->					Tuple:
         __A          =				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase )




   def _lowerCAmelCase      (self	:Optional[Any] )->					Any:
         __A          =				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase )






   @slow
   def _lowerCAmelCase      (self	:Optional[Any] )->					Any:
         for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A          =				LayoutLMvaModel.from_pretrained(_UpperCamelCase )
               self.assertIsNotNone(_UpperCamelCase )





def _a				(	)	->						List[str]:







      '''simple docstring'''






      __A          =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
      return image







@require_torch
class A_					(			unittest.TestCase   ):




   @cached_property
   def _lowerCAmelCase      (self	:List[Any] )->					Union[str, Any]:
         return LayoutLMvaImageProcessor(apply_ocr=_UpperCamelCase ) if is_vision_available() else None






   @slow
   def _lowerCAmelCase      (self	:List[str] )->					Dict:
         __A          =				LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base''' ).to(_UpperCamelCase )

         __A          =				self.default_image_processor
         __A          =				prepare_img()
         __A          =				image_processor(images=_UpperCamelCase    ,   return_tensors='''pt''' ).pixel_values.to(_UpperCamelCase )

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

         # forward pass
         __A          =				model(
             input_ids=input_ids.to(_UpperCamelCase )    ,   bbox=bbox.to(_UpperCamelCase )    ,   pixel_values=pixel_values.to(_UpperCamelCase )    ,   )

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

         __A          =				torch.tensor(
             [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]] ).to(_UpperCamelCase )

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












import os

snake_case__					:							Any													=     {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}



def _a				(	lowerCamelCase:   str )	->						int:







      '''simple docstring'''






      __A          =				0

      __A          =				0
      while index < len(lowerCamelCase ) - 1:
            __A          =				SYMBOLS[numerals[index]]
            __A          =				SYMBOLS[numerals[index + 1]]
            if current_value < next_value:
                  total_value -= current_value
            else:
                  total_value += current_value
            index += 1
      total_value += SYMBOLS[numerals[index]]

      return total_value



def _a				(	lowerCamelCase:   int )	->						str:







      '''simple docstring'''






      __A          =				''''''

      __A          =				num // 10_00
      numerals += m_count * "M"
      num %= 10_00

      __A          =				num // 1_00
      if c_count == 9:
            numerals += "CM"
            c_count -= 9
      elif c_count == 4:
            numerals += "CD"
            c_count -= 4
      if c_count >= 5:
            numerals += "D"
            c_count -= 5
      numerals += c_count * "C"
      num %= 1_00

      __A          =				num // 10
      if x_count == 9:
            numerals += "XC"
            x_count -= 9
      elif x_count == 4:
            numerals += "XL"
            x_count -= 4
      if x_count >= 5:
            numerals += "L"
            x_count -= 5
      numerals += x_count * "X"
      num %= 10

      if num == 9:
            numerals += "IX"
            num -= 9
      elif num == 4:
            numerals += "IV"
            num -= 4
      if num >= 5:
            numerals += "V"
            num -= 5
      numerals += num * "I"

      return numerals



def _a				(	lowerCamelCase:   str = "/p089_roman.txt" )	->						int:







      '''simple docstring'''






      __A          =				0

      with open(os.path.dirname(lowerCamelCase ) + roman_numerals_filename ) as filea:
            __A          =				filea.readlines()

      for line in lines:
            __A          =				line.strip()
            __A          =				parse_roman_numerals(lowerCamelCase )
            __A          =				generate_roman_numerals(lowerCamelCase )
            savings += len(lowerCamelCase ) - len(lowerCamelCase )

      return savings


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





