





'''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    __SCREAMING_SNAKE_CASE						:



     '''simple docstring'''






     def __init__(       self    , lowerCamelCase    , lowerCamelCase=2    , lowerCamelCase=3    , lowerCamelCase=4    , lowerCamelCase=2    , lowerCamelCase=7    , lowerCamelCase=True    , lowerCamelCase=True    , lowerCamelCase=True    , lowerCamelCase=True    , lowerCamelCase=99    , lowerCamelCase=36    , lowerCamelCase=3    , lowerCamelCase=4    , lowerCamelCase=37    , lowerCamelCase="gelu"    , lowerCamelCase=0.1    , lowerCamelCase=0.1    , lowerCamelCase=512    , lowerCamelCase=16    , lowerCamelCase=2    , lowerCamelCase=0.02    , lowerCamelCase=6    , lowerCamelCase=6    , lowerCamelCase=3    , lowerCamelCase=4    , lowerCamelCase=None    , lowerCamelCase=1_000    , ):
       _snake_case  		=					parent
       _snake_case  		=					batch_size
       _snake_case  		=					num_channels
       _snake_case  		=					image_size
       _snake_case  		=					patch_size
       _snake_case  		=					text_seq_length
       _snake_case  		=					is_training
       _snake_case  		=					use_input_mask
       _snake_case  		=					use_token_type_ids
       _snake_case  		=					use_labels
       _snake_case  		=					vocab_size
       _snake_case  		=					hidden_size
       _snake_case  		=					num_hidden_layers
       _snake_case  		=					num_attention_heads
       _snake_case  		=					intermediate_size
       _snake_case  		=					hidden_act
       _snake_case  		=					hidden_dropout_prob
       _snake_case  		=					attention_probs_dropout_prob
       _snake_case  		=					max_position_embeddings
       _snake_case  		=					type_vocab_size
       _snake_case  		=					type_sequence_label_size
       _snake_case  		=					initializer_range
       _snake_case  		=					coordinate_size
       _snake_case  		=					shape_size
       _snake_case  		=					num_labels
       _snake_case  		=					num_choices
       _snake_case  		=					scope
       _snake_case  		=					range_bbox

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

     def 						UpperCamelCase(       self						):
       _snake_case  		=					ids_tensor([self.batch_size, self.text_seq_length]    , self.vocab_size						)

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

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

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

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

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

       _snake_case  		=					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 						UpperCamelCase(       self    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase						):
       _snake_case  		=					LayoutLMvaModel(config=lowercase_						)
       model.to(lowercase_						)
       model.eval()

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

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

     def 						UpperCamelCase(       self    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase						):
       _snake_case  		=					self.num_labels
       _snake_case  		=					LayoutLMvaForSequenceClassification(lowercase_						)
       model.to(lowercase_						)
       model.eval()
       _snake_case  		=					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 						UpperCamelCase(       self    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase						):
       _snake_case  		=					self.num_labels
       _snake_case  		=					LayoutLMvaForTokenClassification(config=lowercase_						)
       model.to(lowercase_						)
       model.eval()
       _snake_case  		=					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 						UpperCamelCase(       self    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase						):
       _snake_case  		=					LayoutLMvaForQuestionAnswering(config=lowercase_						)
       model.to(lowercase_						)
       model.eval()
       _snake_case  		=					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 						UpperCamelCase(       self						):
       _snake_case  		=					self.prepare_config_and_inputs()
       (
           (
           _snake_case     
       )		,		(
           _snake_case     
       )		,		(
           _snake_case     
       )		,		(
           _snake_case     
       )		,		(
           _snake_case     
       )		,		(
           _snake_case     
       )		,		(
           _snake_case     
       )		,		(
           _snake_case     
       )		,		     
       )  		=					config_and_inputs
       _snake_case  		=					{
           "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    __SCREAMING_SNAKE_CASE						(    lowercase__       ,     lowercase__       ,     unittest.TestCase      ):



     '''simple docstring'''






     UpperCAmelCase__							:    Union[str, Any]     	= False
     UpperCAmelCase__							:    Union[str, Any]     	= False
     UpperCAmelCase__							:    Optional[int]     	= False

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

     def 						UpperCamelCase(       self    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase						):
       # `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 						UpperCamelCase(       self						):
       _snake_case  		=					LayoutLMvaModelTester(self						)
       _snake_case  		=					ConfigTester(self    , config_class=lowercase_    , hidden_size=37						)

     def 						UpperCamelCase(       self    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase=False						):
       _snake_case  		=					copy.deepcopy(lowercase_						)
       if model_class in get_values(lowercase_						):
         _snake_case  		=					{
             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_						):
           _snake_case  		=					torch.ones(self.model_tester.batch_size    , dtype=torch.long    , device=lowercase_						)
         elif model_class in get_values(lowercase_						):
           _snake_case  		=					torch.zeros(
               self.model_tester.batch_size    , dtype=torch.long    , device=lowercase_						)
           _snake_case  		=					torch.zeros(
               self.model_tester.batch_size    , dtype=torch.long    , device=lowercase_						)
         elif model_class in [
             *get_values(lowercase_						),
         ]:
           _snake_case  		=					torch.zeros(
               self.model_tester.batch_size    , dtype=torch.long    , device=lowercase_						)
         elif model_class in [
             *get_values(lowercase_						),
         ]:
           _snake_case  		=					torch.zeros(
               (self.model_tester.batch_size, self.model_tester.text_seq_length)    , dtype=torch.long    , device=lowercase_    , )

       return inputs_dict

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

     def 						UpperCamelCase(       self						):
       _snake_case  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase_						)

     def 						UpperCamelCase(       self						):
       _snake_case  		=					self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
         _snake_case  		=					type
         self.model_tester.create_and_check_model(*lowercase_						)

     def 						UpperCamelCase(       self						):
       _snake_case  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*lowercase_						)

     def 						UpperCamelCase(       self						):
       _snake_case  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*lowercase_						)

     def 						UpperCamelCase(       self						):
       _snake_case  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*lowercase_						)






     @slow
     def 						UpperCamelCase(       self						):
       for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _snake_case  		=					LayoutLMvaModel.from_pretrained(lowercase_						)
         self.assertIsNotNone(lowercase_						)







def 					snake_case_   (		):







  '''simple docstring'''



  _snake_case  		=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
  return image




@require_torch
class    __SCREAMING_SNAKE_CASE						(    unittest.TestCase      ):



     '''simple docstring'''






     @cached_property
     def 						UpperCamelCase(       self						):
       return LayoutLMvaImageProcessor(apply_ocr=lowercase_						) if is_vision_available() else None






     @slow
     def 						UpperCamelCase(       self						):
       _snake_case  		=					LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"						).to(lowercase_						)

       _snake_case  		=					self.default_image_processor
       _snake_case  		=					prepare_img()
       _snake_case  		=					image_processor(images=lowercase_    , return_tensors="pt"						).pixel_values.to(lowercase_						)

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

       # forward pass
       _snake_case  		=					model(
           input_ids=input_ids.to(lowercase_						)    , bbox=bbox.to(lowercase_						)    , pixel_values=pixel_values.to(lowercase_						)    , )

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

       _snake_case  		=					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						)						)






from __future__ import annotations

from typing import Any




class       lowercase_					:





				def __init__(				self       ,  lowercase_)  -> None:
											a__      =num_of_nodes
											a__      =[]
											a__      ={}





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> None:
											self.m_edges.append([u_node, v_node, weight])





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> int:

											if self.m_component[u_node] == u_node:
																		return u_node
											return self.find_component(self.m_component[u_node])





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> None:

											if self.m_component[u_node] != u_node:
																		for k in self.m_component:
																									a__      =self.find_component(lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> None:

											if component_size[u_node] <= component_size[v_node]:
																		a__      =v_node
																		component_size[v_node] += component_size[u_node]
																		self.set_component(lowercase_)

											elif component_size[u_node] >= component_size[v_node]:
																		a__      =self.find_component(lowercase_)
																		component_size[u_node] += component_size[v_node]
																		self.set_component(lowercase_)





				def 						__UpperCamelCase    (				self)  -> None:
											a__      =[]
											a__      =0

											a__      =[-1] * self.m_num_of_nodes

											# A list of components (initialized to all of the nodes)
											for node in range(self.m_num_of_nodes):
																		self.m_component.update({node: node})
																		component_size.append(1)

											a__      =self.m_num_of_nodes

											while num_of_components > 1:
																		for edge in self.m_edges:
																									a__    ,     a__    ,     a__      =edge

																									a__      =self.m_component[u]
																									a__      =self.m_component[v]

																									if u_component != v_component:

																																for component in (u_component, v_component):
																																							if (
																																							    minimum_weight_edge[component] == -1
																																							    or minimum_weight_edge[component][2] > w
																																							):
																																														a__      =[u, v, w]

																		for edge in minimum_weight_edge:
																									if isinstance(lowercase_       ,  lowercase_):
																																a__    ,     a__    ,     a__      =edge

																																a__      =self.m_component[u]
																																a__      =self.m_component[v]

																																if u_component != v_component:
																																							mst_weight += w
																																							self.union(lowercase_       ,  lowercase_       ,  lowercase_)
																																							print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n""")
																																							num_of_components -= 1

																		a__      =[-1] * self.m_num_of_nodes
											print(F"""The total weight of the minimal spanning tree is: {mst_weight}""")







def   _lowercase(					):
							pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()

