





'''simple docstring'''






import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roformer.modeling_flax_roformer import (
	    FlaxRoFormerForMaskedLM,
	    FlaxRoFormerForMultipleChoice,
	    FlaxRoFormerForQuestionAnswering,
	    FlaxRoFormerForSequenceClassification,
	    FlaxRoFormerForTokenClassification,
	    FlaxRoFormerModel,
	)




class  _A (					unittest.TestCase		):

		def __init__(   self					:   Optional[Any]  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   int=13  ,      __magic_name__					:   List[str]=7  ,      __magic_name__					:   int=True  ,      __magic_name__					:   List[Any]=True  ,      __magic_name__					:   Optional[Any]=True  ,      __magic_name__					:   str=True  ,      __magic_name__					:   Union[str, Any]=99  ,      __magic_name__					:   Dict=32  ,      __magic_name__					:   Union[str, Any]=5  ,      __magic_name__					:   str=4  ,      __magic_name__					:   Optional[Any]=37  ,      __magic_name__					:   List[str]="gelu"  ,      __magic_name__					:   Dict=0.1  ,      __magic_name__					:   Optional[int]=0.1  ,      __magic_name__					:   int=5_12  ,      __magic_name__					:   str=16  ,      __magic_name__					:   Any=2  ,      __magic_name__					:   str=0.02  ,      __magic_name__					:   Any=4  ,      )					->    Optional[int]:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     parent
					__snake_case					:      List[str]							      =     batch_size
					__snake_case					:      List[str]							      =     seq_length
					__snake_case					:      Optional[Any]							      =     is_training
					__snake_case					:      Optional[Any]							      =     use_attention_mask
					__snake_case					:      int							      =     use_token_type_ids
					__snake_case					:      Tuple							      =     use_labels
					__snake_case					:      Optional[int]							      =     vocab_size
					__snake_case					:      str							      =     hidden_size
					__snake_case					:      Union[str, Any]							      =     num_hidden_layers
					__snake_case					:      str							      =     num_attention_heads
					__snake_case					:      Union[str, Any]							      =     intermediate_size
					__snake_case					:      Any							      =     hidden_act
					__snake_case					:      List[str]							      =     hidden_dropout_prob
					__snake_case					:      Optional[Any]							      =     attention_probs_dropout_prob
					__snake_case					:      List[Any]							      =     max_position_embeddings
					__snake_case					:      List[Any]							      =     type_vocab_size
					__snake_case					:      Optional[int]							      =     type_sequence_label_size
					__snake_case					:      Tuple							      =     initializer_range
					__snake_case					:      int							      =     num_choices

		def 	lowercase__     (   self					:   Optional[int]			)					->    Tuple:


					"""simple docstring"""

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

					__snake_case					:      int							      =     None
					if self.use_attention_mask:
								__snake_case					:      Dict							      =     random_attention_mask([self.batch_size, self.seq_length]			)

					__snake_case					:      Optional[int]							      =     None
					if self.use_token_type_ids:
								__snake_case					:      str							      =     ids_tensor([self.batch_size, self.seq_length]  ,      self.type_vocab_size			)

					__snake_case					:      Any							      =     RoFormerConfig(
					    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  ,      is_decoder=__magic_name__  ,      initializer_range=self.initializer_range  ,      )

					return config, input_ids, token_type_ids, attention_mask

		def 	lowercase__     (   self					:   Dict			)					->    List[Any]:


					"""simple docstring"""

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





@require_flax
class  _A (					__lowercase						,					unittest.TestCase		):
		lowercase__:   str				     =   True

		lowercase__:   Tuple				     =   (
		    (
		        FlaxRoFormerModel,
		        FlaxRoFormerForMaskedLM,
		        FlaxRoFormerForSequenceClassification,
		        FlaxRoFormerForTokenClassification,
		        FlaxRoFormerForMultipleChoice,
		        FlaxRoFormerForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)

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


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     FlaxRoFormerModelTester(self			)

		@slow
		def 	lowercase__     (   self					:   str			)					->    int:


					"""simple docstring"""

					for model_class_name in self.all_model_classes:
								__snake_case					:      Optional[Any]							      =     model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""  ,      from_pt=__magic_name__			)
								__snake_case					:      Dict							      =     model(np.ones((1, 1)			)			)
								self.assertIsNotNone(__magic_name__			)





@require_flax
class  _A (					unittest.TestCase		):

		@slow
		def 	lowercase__     (   self					:   str			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      str							      =     FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""			)
					__snake_case					:      Optional[Any]							      =     jnp.array([[0, 1, 2, 3, 4, 5]]			)
					__snake_case					:      int							      =     model(__magic_name__			)[0]

					__snake_case					:      Union[str, Any]							      =     5_00_00

					__snake_case					:      Dict							      =     (1, 6, vocab_size)
					self.assertEqual(output.shape  ,      __magic_name__			)

					__snake_case					:      Optional[int]							      =     jnp.array(
					    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]			)

					self.assertTrue(jnp.allclose(output[:, :3, :3]  ,      __magic_name__  ,      atol=1E-4			)			)









'''simple docstring'''






import cva
import numpy as np




class  _A :

		def __init__(   self					:   Any  ,      __magic_name__					:   float  ,      __magic_name__					:   int			)					->    Optional[int]:


					"""simple docstring"""

					if k in (0.04, 0.06):
								__snake_case					:      List[str]							      =     k
								__snake_case					:      int							      =     window_size
					else:
								raise ValueError("""invalid k value"""			)

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


					"""simple docstring"""

					return str(self.k			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   str			)					->    tuple[cva.Mat, list[list[int]]]:


					"""simple docstring"""

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

											__snake_case					:      List[str]							      =     (wxx * wyy) - (wxy**2)
											__snake_case					:      Dict							      =     wxx + wyy
											__snake_case					:      List[str]							      =     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)  ,      2_55			)
					return color_img, corner_list


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


