




import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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 (
	    DebertaVaForMaskedLM,
	    DebertaVaForMultipleChoice,
	    DebertaVaForQuestionAnswering,
	    DebertaVaForSequenceClassification,
	    DebertaVaForTokenClassification,
	    DebertaVaModel,
	)
	from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class        __snake_case  (_a		):



				def __init__(      self      :		Optional[Any]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Dict=13		,	_UpperCAmelCase      :		Optional[int]=7		,	_UpperCAmelCase      :		Optional[Any]=True		,	_UpperCAmelCase      :		Optional[Any]=True		,	_UpperCAmelCase      :		Optional[Any]=True		,	_UpperCAmelCase      :		Dict=True		,	_UpperCAmelCase      :		Optional[int]=99		,	_UpperCAmelCase      :		Union[str, Any]=32		,	_UpperCAmelCase      :		str=5		,	_UpperCAmelCase      :		str=4		,	_UpperCAmelCase      :		List[Any]=37		,	_UpperCAmelCase      :		Union[str, Any]="gelu"		,	_UpperCAmelCase      :		Union[str, Any]=0.1		,	_UpperCAmelCase      :		str=0.1		,	_UpperCAmelCase      :		str=512		,	_UpperCAmelCase      :		Tuple=16		,	_UpperCAmelCase      :		List[Any]=2		,	_UpperCAmelCase      :		List[Any]=0.02		,	_UpperCAmelCase      :		Optional[Any]=False		,	_UpperCAmelCase      :		Optional[Any]=True		,	_UpperCAmelCase      :		Tuple="None"		,	_UpperCAmelCase      :		Tuple=3		,	_UpperCAmelCase      :		Union[str, Any]=4		,	_UpperCAmelCase      :		str=None		,	)  ->    Union[str, Any]:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size      )

								_lowerCAmelCase    :    int										=      None
								if self.use_input_mask:
												_lowerCAmelCase    :    int										=      ids_tensor([self.batch_size, self.seq_length]		,	vocab_size=2      )

								_lowerCAmelCase    :    int										=      None
								if self.use_token_type_ids:
												_lowerCAmelCase    :    Dict										=      ids_tensor([self.batch_size, self.seq_length]		,	self.type_vocab_size      )

								_lowerCAmelCase    :    List[Any]										=      None
								_lowerCAmelCase    :    Dict										=      None
								_lowerCAmelCase    :    Union[str, Any]										=      None
								if self.use_labels:
												_lowerCAmelCase    :    Tuple										=      ids_tensor([self.batch_size]		,	self.type_sequence_label_size      )
												_lowerCAmelCase    :    Optional[int]										=      ids_tensor([self.batch_size, self.seq_length]		,	self.num_labels      )
												_lowerCAmelCase    :    Union[str, Any]										=      ids_tensor([self.batch_size]		,	self.num_choices      )

								_lowerCAmelCase    :    List[Any]										=      self.get_config()

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Union[str, Any]:





								'''simple docstring'''




								return DebertaVaConfig(
								    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		,	relative_attention=self.relative_attention		,	position_biased_input=self.position_biased_input		,	pos_att_type=self.pos_att_type		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								self.parent.assertListEqual(list(result.loss.size()      )		,	[]      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		List[Any]		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		str      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      DebertaVaModel(config=_UpperCAmelCase      )
								model.to(_UpperCAmelCase      )
								model.eval()
								_lowerCAmelCase    :    Optional[int]										=      model(_UpperCAmelCase		,	attention_mask=_UpperCAmelCase		,	token_type_ids=_UpperCAmelCase      )[0]
								_lowerCAmelCase    :    int										=      model(_UpperCAmelCase		,	token_type_ids=_UpperCAmelCase      )[0]
								_lowerCAmelCase    :    Any										=      model(_UpperCAmelCase      )[0]

								self.parent.assertListEqual(list(sequence_output.size()      )		,	[self.batch_size, self.seq_length, self.hidden_size]      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		Optional[Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      DebertaVaForMaskedLM(config=_UpperCAmelCase      )
								model.to(_UpperCAmelCase      )
								model.eval()
								_lowerCAmelCase    :    Any										=      model(_UpperCAmelCase		,	attention_mask=_UpperCAmelCase		,	token_type_ids=_UpperCAmelCase		,	labels=_UpperCAmelCase      )

								self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.vocab_size)      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		List[str]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		List[str]		,	_UpperCAmelCase      :		List[str]		,	_UpperCAmelCase      :		Optional[int]      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      self.num_labels
								_lowerCAmelCase    :    Optional[Any]										=      DebertaVaForSequenceClassification(_UpperCAmelCase      )
								model.to(_UpperCAmelCase      )
								model.eval()
								_lowerCAmelCase    :    Dict										=      model(_UpperCAmelCase		,	attention_mask=_UpperCAmelCase		,	token_type_ids=_UpperCAmelCase		,	labels=_UpperCAmelCase      )
								self.parent.assertListEqual(list(result.logits.size()      )		,	[self.batch_size, self.num_labels]      )
								self.check_loss_output(_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		Dict		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		List[str]		,	_UpperCAmelCase      :		List[Any]		,	_UpperCAmelCase      :		List[str]		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		List[Any]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      self.num_labels
								_lowerCAmelCase    :    Union[str, Any]										=      DebertaVaForTokenClassification(config=_UpperCAmelCase      )
								model.to(_UpperCAmelCase      )
								model.eval()
								_lowerCAmelCase    :    List[str]										=      model(_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       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		Any      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      DebertaVaForQuestionAnswering(config=_UpperCAmelCase      )
								model.to(_UpperCAmelCase      )
								model.eval()
								_lowerCAmelCase    :    List[str]										=      model(
								    _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       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		List[str]		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		int      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      DebertaVaForMultipleChoice(config=_UpperCAmelCase      )
								model.to(_UpperCAmelCase      )
								model.eval()
								_lowerCAmelCase    :    str										=      input_ids.unsqueeze(1      ).expand(-1		,	self.num_choices		,	-1      ).contiguous()
								_lowerCAmelCase    :    str										=      token_type_ids.unsqueeze(1      ).expand(-1		,	self.num_choices		,	-1      ).contiguous()
								_lowerCAmelCase    :    List[str]										=      input_mask.unsqueeze(1      ).expand(-1		,	self.num_choices		,	-1      ).contiguous()
								_lowerCAmelCase    :    Any										=      model(
								    _UpperCAmelCase		,	attention_mask=_UpperCAmelCase		,	token_type_ids=_UpperCAmelCase		,	labels=_UpperCAmelCase		,	)
								self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_choices)      )



				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    Tuple:





								'''simple docstring'''




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






@require_torch
class        __snake_case  (_a						,	_a						,	unittest.TestCase		):
				lowerCAmelCase__				=     (
				    (
				        DebertaVaModel,
				        DebertaVaForMaskedLM,
				        DebertaVaForSequenceClassification,
				        DebertaVaForTokenClassification,
				        DebertaVaForQuestionAnswering,
				        DebertaVaForMultipleChoice,
				    )
				    if is_torch_available()
				    else ()
				)
				lowerCAmelCase__				=     (
				    {
				        "feature-extraction": DebertaVaModel,
				        "fill-mask": DebertaVaForMaskedLM,
				        "question-answering": DebertaVaForQuestionAnswering,
				        "text-classification": DebertaVaForSequenceClassification,
				        "token-classification": DebertaVaForTokenClassification,
				        "zero-shot": DebertaVaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)

				lowerCAmelCase__				=     True
				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      DebertaVaModelTester(self      )
								_lowerCAmelCase    :    Dict										=      ConfigTester(self		,	config_class=_UpperCAmelCase		,	hidden_size=37      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Union[str, Any]:





								'''simple docstring'''




								self.config_tester.run_common_tests()



				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_model(*_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_sequence_classification(*_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_masked_lm(*_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_question_answering(*_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_token_classification(*_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_multiple_choice(*_UpperCAmelCase      )



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Optional[int]:





								'''simple docstring'''




								for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_lowerCAmelCase    :    Optional[Any]										=      DebertaVaModel.from_pretrained(_UpperCAmelCase      )
												self.assertIsNotNone(_UpperCAmelCase      )






@require_torch
@require_sentencepiece
@require_tokenizers
class        __snake_case  (unittest.TestCase		):



				@unittest.skip(reason="""Model not available yet"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[str]:





								'''simple docstring'''




								pass



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      DebertaVaModel.from_pretrained("""microsoft/deberta-v2-xlarge"""      )

								_lowerCAmelCase    :    Optional[int]										=      torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]      )
								_lowerCAmelCase    :    Optional[Any]										=      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
								with torch.no_grad():
												_lowerCAmelCase    :    str										=      model(_UpperCAmelCase		,	attention_mask=_UpperCAmelCase      )[0]
								# compare the actual values for a slice.
								_lowerCAmelCase    :    Any										=      torch.tensor(
								    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]      )
								self.assertTrue(torch.allclose(output[:, 1:4, 1:4]		,	_UpperCAmelCase		,	atol=1E-4      )		,	f"{output[:, 1:4, 1:4]}"      )







import cva
import numpy as np
class        __snake_case  :



				def __init__(      self      :		List[str]		,	_UpperCAmelCase      :		float		,	_UpperCAmelCase      :		int      )  ->    int:





								'''simple docstring'''




								if k in (0.04, 0.06):
												_lowerCAmelCase    :    str										=      k
												_lowerCAmelCase    :    Optional[Any]										=      window_size
								else:
												raise ValueError("""invalid k value"""      )



				def __str__(      self      :		int      )  ->    str:





								'''simple docstring'''




								return str(self.k      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		str      )  ->    tuple[cva.Mat, list[list[int]]]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      cva.imread(_UpperCAmelCase		,	0      )
								_lowerCAmelCase					,    _lowerCAmelCase    :    Tuple										=      img.shape
								_lowerCAmelCase    :    list[list[int]]										=      []
								_lowerCAmelCase    :    int										=      img.copy()
								_lowerCAmelCase    :    str										=      cva.cvtColor(_UpperCAmelCase		,	cva.COLOR_GRAY2RGB      )
								_lowerCAmelCase					,    _lowerCAmelCase    :    int										=      np.gradient(_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      dx**2
								_lowerCAmelCase    :    Optional[int]										=      dy**2
								_lowerCAmelCase    :    Optional[Any]										=      dx * dy
								_lowerCAmelCase    :    Dict										=      0.04
								_lowerCAmelCase    :    Tuple										=      self.window_size // 2
								for y in range(_UpperCAmelCase		,	h - offset      ):
												for x in range(_UpperCAmelCase		,	w - offset      ):
																_lowerCAmelCase    :    Optional[int]										=      ixx[
																    y - offset : y + offset + 1, x - offset : x + offset + 1
																].sum()
																_lowerCAmelCase    :    int										=      iyy[
																    y - offset : y + offset + 1, x - offset : x + offset + 1
																].sum()
																_lowerCAmelCase    :    Any										=      ixy[
																    y - offset : y + offset + 1, x - offset : x + offset + 1
																].sum()

																_lowerCAmelCase    :    Dict										=      (wxx * wyy) - (wxy**2)
																_lowerCAmelCase    :    Union[str, Any]										=      wxx + wyy
																_lowerCAmelCase    :    int										=      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__":
	_lowerCamelCase      :  int      						=						HarrisCorner(0.0_4, 3)
	_lowerCamelCase						, _lowerCamelCase      :  Any      						=						edge_detect.detect("path_to_image")
	cva.imwrite("detect.png", color_img)

