




"""simple docstring"""

import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    LiltForQuestionAnswering,
							    LiltForSequenceClassification,
							    LiltForTokenClassification,
							    LiltModel,
							)
							from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class 	lowerCamelCase__			:


			def __init__(   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE=13 ,       SCREAMING_SNAKE_CASE=7 ,       SCREAMING_SNAKE_CASE=True ,       SCREAMING_SNAKE_CASE=True ,       SCREAMING_SNAKE_CASE=True ,       SCREAMING_SNAKE_CASE=True ,       SCREAMING_SNAKE_CASE=99 ,       SCREAMING_SNAKE_CASE=24 ,       SCREAMING_SNAKE_CASE=2 ,       SCREAMING_SNAKE_CASE=6 ,       SCREAMING_SNAKE_CASE=37 ,       SCREAMING_SNAKE_CASE="gelu" ,       SCREAMING_SNAKE_CASE=0.1 ,       SCREAMING_SNAKE_CASE=0.1 ,       SCREAMING_SNAKE_CASE=512 ,       SCREAMING_SNAKE_CASE=16 ,       SCREAMING_SNAKE_CASE=2 ,       SCREAMING_SNAKE_CASE=0.02 ,       SCREAMING_SNAKE_CASE=3 ,       SCREAMING_SNAKE_CASE=None ,       SCREAMING_SNAKE_CASE=1_000 ,       ):



					"""simple docstring"""







					snake_case						:  Dict    	= parent
					snake_case						:  Dict    	= batch_size
					snake_case						:  Tuple    	= seq_length
					snake_case						:  List[str]    	= is_training
					snake_case						:  Optional[int]    	= use_input_mask
					snake_case						:  Any    	= use_token_type_ids
					snake_case						:  List[Any]    	= use_labels
					snake_case						:  Dict    	= vocab_size
					snake_case						:  Optional[Any]    	= hidden_size
					snake_case						:  Any    	= num_hidden_layers
					snake_case						:  Union[str, Any]    	= num_attention_heads
					snake_case						:  List[str]    	= intermediate_size
					snake_case						:  List[Any]    	= hidden_act
					snake_case						:  int    	= hidden_dropout_prob
					snake_case						:  List[Any]    	= attention_probs_dropout_prob
					snake_case						:  Optional[int]    	= max_position_embeddings
					snake_case						:  Union[str, Any]    	= type_vocab_size
					snake_case						:  int    	= type_sequence_label_size
					snake_case						:  Optional[Any]    	= initializer_range
					snake_case						:  Union[str, Any]    	= num_labels
					snake_case						:  List[Any]    	= scope
					snake_case						:  List[str]    	= range_bbox


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  Dict    	= ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size							)

					snake_case						:  Optional[Any]    	= ids_tensor([self.batch_size, self.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						:  List[str]    	= bbox[i, j, 3]
											snake_case						:  List[Any]    	= bbox[i, j, 1]
											snake_case						:  List[Any]    	= t
									if bbox[i, j, 2] < bbox[i, j, 0]:
											snake_case						:  List[str]    	= bbox[i, j, 2]
											snake_case						:  Tuple    	= bbox[i, j, 0]
											snake_case						:  Tuple    	= t

					snake_case						:  Any    	= None
					if self.use_input_mask:
							snake_case						:  Tuple    	= ids_tensor([self.batch_size, self.seq_length] ,       vocab_size=2							)

					snake_case						:  Any    	= None
					if self.use_token_type_ids:
							snake_case						:  str    	= ids_tensor([self.batch_size, self.seq_length] ,       self.type_vocab_size							)

					snake_case						:  Optional[Any]    	= None
					snake_case						:  Tuple    	= None
					if self.use_labels:
							snake_case						:  List[Any]    	= ids_tensor([self.batch_size] ,       self.type_sequence_label_size							)
							snake_case						:  Optional[Any]    	= ids_tensor([self.batch_size, self.seq_length] ,       self.num_labels							)

					snake_case						:  Tuple    	= self.get_config()

					return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					return LiltConfig(
					    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 ,       )


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       ):



					"""simple docstring"""







					snake_case						:  List[str]    	= LiltModel(config=UpperCAmelCase__							)
					model.to(UpperCAmelCase__							)
					model.eval()
					snake_case						:  List[str]    	= model(UpperCAmelCase__ ,       bbox=UpperCAmelCase__ ,       attention_mask=UpperCAmelCase__ ,       token_type_ids=UpperCAmelCase__							)
					snake_case						:  List[Any]    	= model(UpperCAmelCase__ ,       bbox=UpperCAmelCase__ ,       token_type_ids=UpperCAmelCase__							)
					snake_case						:  Any    	= model(UpperCAmelCase__ ,       bbox=UpperCAmelCase__							)

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


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       ):



					"""simple docstring"""







					snake_case						:  str    	= self.num_labels
					snake_case						:  List[str]    	= LiltForTokenClassification(config=UpperCAmelCase__							)
					model.to(UpperCAmelCase__							)
					model.eval()
					snake_case						:  Dict    	= model(
					    UpperCAmelCase__ ,       bbox=UpperCAmelCase__ ,       attention_mask=UpperCAmelCase__ ,       token_type_ids=UpperCAmelCase__ ,       labels=UpperCAmelCase__							)
					self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.seq_length, self.num_labels)							)


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       ):



					"""simple docstring"""







					snake_case						:  List[str]    	= LiltForQuestionAnswering(config=UpperCAmelCase__							)
					model.to(UpperCAmelCase__							)
					model.eval()
					snake_case						:  str    	= model(
					    UpperCAmelCase__ ,       bbox=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							):



					"""simple docstring"""







					snake_case						:  List[str]    	= self.prepare_config_and_inputs()
					(
					    snake_case  
					)						:  Optional[Any]    	= config_and_inputs
					snake_case						:  str    	= {
					    """input_ids""": input_ids,
					    """bbox""": bbox,
					    """token_type_ids""": token_type_ids,
					    """attention_mask""": input_mask,
					}
					return config, inputs_dict







@require_torch
class 	lowerCamelCase__			(     snake_case_ ,     snake_case_ ,     snake_case_ ,     unittest.TestCase	):
			a__       :					List[Any]	    =   (
			    (
			        LiltModel,
			        LiltForSequenceClassification,
			        LiltForTokenClassification,
			        LiltForQuestionAnswering,
			    )
			    if is_torch_available()
			    else ()
			)
			a__       :					Tuple	    =   (
			    {
			        '''feature-extraction''': LiltModel,
			        '''question-answering''': LiltForQuestionAnswering,
			        '''text-classification''': LiltForSequenceClassification,
			        '''token-classification''': LiltForTokenClassification,
			        '''zero-shot''': LiltForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			a__       :					Dict	    =   False
			a__       :					Union[str, Any]	    =   False


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return True


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  Dict    	= LiltModelTester(self							)
					snake_case						:  Optional[Any]    	= ConfigTester(self ,       config_class=UpperCAmelCase__ ,       hidden_size=37							)


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					self.config_tester.run_common_tests()


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  Dict    	= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCAmelCase__							)


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  Optional[int]    	= self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
							snake_case						:  str    	= type
							self.model_tester.create_and_check_model(*UpperCAmelCase__							)


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  Optional[Any]    	= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__							)


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  List[str]    	= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__							)



			@slow
			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case						:  List[Any]    	= LiltModel.from_pretrained(UpperCAmelCase__							)
							self.assertIsNotNone(UpperCAmelCase__							)







@require_torch
@slow
class 	lowerCamelCase__			(     unittest.TestCase	):


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  Tuple    	= LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base"							).to(UpperCAmelCase__							)

					snake_case						:  Union[str, Any]    	= torch.tensor([[1, 2]] ,       device=UpperCAmelCase__							)
					snake_case						:  List[Any]    	= torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] ,       device=UpperCAmelCase__							)

					# forward pass
					with torch.no_grad():
							snake_case						:  Union[str, Any]    	= model(input_ids=UpperCAmelCase__ ,       bbox=UpperCAmelCase__							)

					snake_case						:  str    	= torch.Size([1, 2, 768]							)
					snake_case						:  Any    	= torch.tensor(
					    [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]] ,       device=UpperCAmelCase__ ,       )

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









"""simple docstring"""




import cva
import numpy as np






class        UpperCamelCase			:


 def __init__(     self		:						Optional[int]							,						UpperCAmelCase__		:						float							,						UpperCAmelCase__		:						int							)					->       Dict:

   if k in (0.0_4, 0.0_6):
     _a  :       List[str]             =	k
     _a  :       List[Any]             =	window_size
   else:
     raise ValueError("""invalid k value"""							)


 def __str__(     self		:						Dict							)					->       str:
   return str(self.k							)







 def _lowercase	(     self		:						int							,						UpperCAmelCase__		:						str							)					->       tuple[cva.Mat, list[list[int]]]:
   _a  :       Dict             =	cva.imread(UpperCAmelCase__							,						0							)
   _a      ,  _a  :       List[Any]             =	img.shape
   _a  :       list[list[int]]             =	[]
   _a  :       List[Any]             =	img.copy()
   _a  :       int             =	cva.cvtColor(UpperCAmelCase__							,						cva.COLOR_GRAY2RGB							)
   _a      ,  _a  :       Any             =	np.gradient(UpperCAmelCase__							)
   _a  :       Tuple             =	dx**2
   _a  :       Union[str, Any]             =	dy**2
   _a  :       Union[str, Any]             =	dx * dy
   _a  :       int             =	0.0_4
   _a  :       List[str]             =	self.window_size // 2
   for y in range(UpperCAmelCase__							,						h - offset							):
     for x in range(UpperCAmelCase__							,						w - offset							):
       _a  :       str             =	ixx[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()
       _a  :       List[Any]             =	iyy[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()
       _a  :       Tuple             =	ixy[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()

       _a  :       Any             =	(wxx * wyy) - (wxy**2)
       _a  :       Tuple             =	wxx + wyy
       _a  :       Any             =	det - k * (trace**2)
       # Can change the value
       if r > 0.5:
         corner_list.append([x, y, r]							)
         color_img.itemset((y, x, 0)							,						0							)
         color_img.itemset((y, x, 1)							,						0							)
         color_img.itemset((y, x, 2)							,						255							)
   return color_img, corner_list


if __name__ == "__main__":
 _snake_case          =   HarrisCorner(0.04, 3)
 _snake_case      , _snake_case          =   edge_detect.detect('path_to_image')
 cva.imwrite('detect.png', color_img)

