






import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

__A  							=  1_80







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







       """simple docstring"""






       __UpperCAmelCase    							=k_size // 2
       __UpperCAmelCase							,   __UpperCAmelCase    							=np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       __UpperCAmelCase    							=(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(A_		) + np.square(A_		)) / (2 * np.square(A_		))		)
       )
       return g







def 			lowercase__  (				A_:	Union[str, Any]     ,			A_:	int     ,			A_:	Dict		)  ->							Union[str, Any]:







       """simple docstring"""






       __UpperCAmelCase    							=np.zeros(A_		)

       for row in range(1     ,			image_shape[0] - 1		):
              for col in range(1     ,			image_shape[1] - 1		):
                     __UpperCAmelCase    							=gradient_direction[row, col]

                     if (
                         0 <= direction < PI / 8
                         or 15 * PI / 8 <= direction <= 2 * PI
                         or 7 * PI / 8 <= direction <= 9 * PI / 8
                     ):
                            __UpperCAmelCase    							=sobel_grad[row, col - 1]
                            __UpperCAmelCase    							=sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   __UpperCAmelCase    							=sobel_grad[row, col]

                     elif (
                         PI / 8 <= direction < 3 * PI / 8
                         or 9 * PI / 8 <= direction < 11 * PI / 8
                     ):
                            __UpperCAmelCase    							=sobel_grad[row + 1, col - 1]
                            __UpperCAmelCase    							=sobel_grad[row - 1, col + 1]
                            if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                   __UpperCAmelCase    							=sobel_grad[row, col]

                     elif (
                         3 * PI / 8 <= direction < 5 * PI / 8
                         or 11 * PI / 8 <= direction < 13 * PI / 8
                     ):
                            __UpperCAmelCase    							=sobel_grad[row - 1, col]
                            __UpperCAmelCase    							=sobel_grad[row + 1, col]
                            if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                   __UpperCAmelCase    							=sobel_grad[row, col]

                     elif (
                         5 * PI / 8 <= direction < 7 * PI / 8
                         or 13 * PI / 8 <= direction < 15 * PI / 8
                     ):
                            __UpperCAmelCase    							=sobel_grad[row - 1, col - 1]
                            __UpperCAmelCase    							=sobel_grad[row + 1, col + 1]
                            if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                   __UpperCAmelCase    							=sobel_grad[row, col]

       return destination







def 			lowercase__  (				A_:	int     ,			A_:	Tuple     ,			A_:	Tuple     ,			A_:	List[Any]     ,			A_:	Dict     ,			A_:	int		)  ->							Union[str, Any]:







       """simple docstring"""






       for row in range(1     ,			image_shape[0] - 1		):
              for col in range(1     ,			image_shape[1] - 1		):
                     if destination[row, col] >= threshold_high:
                            __UpperCAmelCase    							=strong
                     elif destination[row, col] <= threshold_low:
                            __UpperCAmelCase    							=0
                     else:
                            __UpperCAmelCase    							=weak







def 			lowercase__  (				A_:	Optional[Any]     ,			A_:	Optional[Any]     ,			A_:	Tuple     ,			A_:	List[Any]		)  ->							Optional[int]:







       """simple docstring"""






       for row in range(1     ,			image_shape[0]		):
              for col in range(1     ,			image_shape[1]		):
                     if destination[row, col] == weak:
                            if 255 in (
                                destination[row, col + 1],
                                destination[row, col - 1],
                                destination[row - 1, col],
                                destination[row + 1, col],
                                destination[row - 1, col - 1],
                                destination[row + 1, col - 1],
                                destination[row - 1, col + 1],
                                destination[row + 1, col + 1],
                            ):
                                   __UpperCAmelCase    							=strong
                            else:
                                   __UpperCAmelCase    							=0







def 			lowercase__  (				A_:	Tuple     ,			A_:	str=15     ,			A_:	Dict=30     ,			A_:	int=128     ,			A_:	str=255		)  ->							Optional[Any]:







       """simple docstring"""






       __UpperCAmelCase    							=img_convolve(A_     ,			gen_gaussian_kernel(9     ,			sigma=1.4		)		)
       # get the gradient and degree by sobel_filter
       __UpperCAmelCase							,   __UpperCAmelCase    							=sobel_filter(A_		)
       __UpperCAmelCase    							=PI + np.radadeg(A_		)

       __UpperCAmelCase    							=suppress_non_maximum(image.shape     ,			A_     ,			A_		)

       detect_high_low_threshold(
           image.shape     ,			A_     ,			A_     ,			A_     ,			A_     ,			A_		)

       track_edge(image.shape     ,			A_     ,			A_     ,			A_		)

       return destination


if __name__ == "__main__":
   # read original image in gray mode
   __A  							=  cva.imread(r"../image_data/lena.jpg", 0)
   # canny edge detection
   __A  							=  canny(lena)
   cva.imshow("canny", canny_destination)
   cva.waitKey(0)












import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       ErnieMForInformationExtraction,
       ErnieMForMultipleChoice,
       ErnieMForQuestionAnswering,
       ErnieMForSequenceClassification,
       ErnieMForTokenClassification,
       ErnieMModel,
   )
   from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST



class 							_A :
    """simple docstring"""

    def __init__(							self			:    Any   ,    __SCREAMING_SNAKE_CASE			:    str   ,    __SCREAMING_SNAKE_CASE			:    List[str]=13   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=7   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=True   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=True   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=True   ,    __SCREAMING_SNAKE_CASE			:    str=99   ,    __SCREAMING_SNAKE_CASE			:    int=32   ,    __SCREAMING_SNAKE_CASE			:    str=5   ,    __SCREAMING_SNAKE_CASE			:    int=4   ,    __SCREAMING_SNAKE_CASE			:    List[str]=37   ,    __SCREAMING_SNAKE_CASE			:    int="gelu"   ,    __SCREAMING_SNAKE_CASE			:    Tuple=0.1   ,    __SCREAMING_SNAKE_CASE			:    List[str]=0.1   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=512   ,    __SCREAMING_SNAKE_CASE			:    int=16   ,    __SCREAMING_SNAKE_CASE			:    int=2   ,    __SCREAMING_SNAKE_CASE			:    Any=0.02   ,    __SCREAMING_SNAKE_CASE			:    Dict=3   ,    __SCREAMING_SNAKE_CASE			:    Dict=4   ,    __SCREAMING_SNAKE_CASE			:    str=None   ,    )			->       Optional[Any]:
           __UpperCAmelCase    							=parent
           __UpperCAmelCase    							=batch_size
           __UpperCAmelCase    							=seq_length
           __UpperCAmelCase    							=is_training
           __UpperCAmelCase    							=use_input_mask
           __UpperCAmelCase    							=use_labels
           __UpperCAmelCase    							=vocab_size
           __UpperCAmelCase    							=hidden_size
           __UpperCAmelCase    							=num_hidden_layers
           __UpperCAmelCase    							=num_attention_heads
           __UpperCAmelCase    							=intermediate_size
           __UpperCAmelCase    							=hidden_act
           __UpperCAmelCase    							=hidden_dropout_prob
           __UpperCAmelCase    							=attention_probs_dropout_prob
           __UpperCAmelCase    							=max_position_embeddings
           __UpperCAmelCase    							=type_vocab_size
           __UpperCAmelCase    							=type_sequence_label_size
           __UpperCAmelCase    							=initializer_range
           __UpperCAmelCase    							=num_labels
           __UpperCAmelCase    							=num_choices
           __UpperCAmelCase    							=scope



    def _a       (							self			:    str			)			->       Optional[Any]:
           __UpperCAmelCase    							=ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size			)

           __UpperCAmelCase    							=None
           if self.use_input_mask:
                  __UpperCAmelCase    							=random_attention_mask([self.batch_size, self.seq_length]			)

           __UpperCAmelCase    							=None
           __UpperCAmelCase    							=None
           __UpperCAmelCase    							=None
           if self.use_labels:
                  __UpperCAmelCase    							=ids_tensor([self.batch_size]   ,    self.type_sequence_label_size			)
                  __UpperCAmelCase    							=ids_tensor([self.batch_size, self.seq_length]   ,    self.num_labels			)
                  __UpperCAmelCase    							=ids_tensor([self.batch_size]   ,    self.num_choices			)

           __UpperCAmelCase    							=self.get_config()

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



    def _a       (							self			:    Optional[Any]			)			->       Any:
           __UpperCAmelCase    							=ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size			)

           __UpperCAmelCase    							=None
           if self.use_input_mask:
                  __UpperCAmelCase    							=random_attention_mask([self.batch_size, self.seq_length]			)
           __UpperCAmelCase    							=self.get_config()

           return config, input_ids, input_mask



    def _a       (							self			:    List[str]			)			->       List[str]:
           return ErnieMConfig(
               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 _a       (							self			:    str   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    str   ,    __SCREAMING_SNAKE_CASE			:    Dict			)			->       Optional[int]:
           __UpperCAmelCase    							=ErnieMModel(config=__SCREAMING_SNAKE_CASE			)
           model.to(__SCREAMING_SNAKE_CASE			)
           model.eval()
           __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    return_dict=__SCREAMING_SNAKE_CASE			)
           self.parent.assertEqual(result.last_hidden_state.shape   ,    (self.batch_size, self.seq_length, self.hidden_size)			)



    def _a       (							self			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    Dict			)			->       int:
           __UpperCAmelCase    							=ErnieMForQuestionAnswering(config=__SCREAMING_SNAKE_CASE			)
           model.to(__SCREAMING_SNAKE_CASE			)
           model.eval()
           __UpperCAmelCase    							=model(
               __SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    start_positions=__SCREAMING_SNAKE_CASE   ,    end_positions=__SCREAMING_SNAKE_CASE   ,    )
           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 _a       (							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       Any:
           __UpperCAmelCase    							=ErnieMForInformationExtraction(config=__SCREAMING_SNAKE_CASE			)
           model.to(__SCREAMING_SNAKE_CASE			)
           model.eval()
           __UpperCAmelCase    							=torch.ones_like(__SCREAMING_SNAKE_CASE   ,    dtype=torch.floataa			)
           __UpperCAmelCase    							=model(
               __SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    start_positions=__SCREAMING_SNAKE_CASE   ,    end_positions=__SCREAMING_SNAKE_CASE   ,    )
           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 _a       (							self			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       str:
           __UpperCAmelCase    							=self.num_labels
           __UpperCAmelCase    							=ErnieMForSequenceClassification(__SCREAMING_SNAKE_CASE			)
           model.to(__SCREAMING_SNAKE_CASE			)
           model.eval()
           __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    labels=__SCREAMING_SNAKE_CASE			)
           self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_labels)			)



    def _a       (							self			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Dict			)			->       Dict:
           __UpperCAmelCase    							=self.num_labels
           __UpperCAmelCase    							=ErnieMForTokenClassification(config=__SCREAMING_SNAKE_CASE			)
           model.to(__SCREAMING_SNAKE_CASE			)
           model.eval()
           input_ids.to(__SCREAMING_SNAKE_CASE			)
           input_mask.to(__SCREAMING_SNAKE_CASE			)
           token_labels.to(__SCREAMING_SNAKE_CASE			)

           __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    labels=__SCREAMING_SNAKE_CASE			)

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



    def _a       (							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    int   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       List[Any]:
           __UpperCAmelCase    							=self.num_choices
           __UpperCAmelCase    							=ErnieMForMultipleChoice(config=__SCREAMING_SNAKE_CASE			)
           model.to(__SCREAMING_SNAKE_CASE			)
           model.eval()
           __UpperCAmelCase    							=input_ids.unsqueeze(1			).expand(-1   ,    self.num_choices   ,    -1			).contiguous()
           __UpperCAmelCase    							=input_mask.unsqueeze(1			).expand(-1   ,    self.num_choices   ,    -1			).contiguous()
           __UpperCAmelCase    							=model(
               __SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    labels=__SCREAMING_SNAKE_CASE   ,    )
           self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_choices)			)



    def _a       (							self			:    Any			)			->       List[str]:
           __UpperCAmelCase    							=self.prepare_config_and_inputs()
           (
               (
               __UpperCAmelCase
           )							,   (
               __UpperCAmelCase
           )							,   (
               __UpperCAmelCase
           )							,   (
               __UpperCAmelCase
           )							,   (
               __UpperCAmelCase
           )							,   (
               __UpperCAmelCase
           )							,   
           )    							=config_and_inputs
           __UpperCAmelCase    							={"""input_ids""": input_ids, """attention_mask""": input_mask}
           return config, inputs_dict




@require_torch
class 							_A (    UpperCamelCase				,				UpperCamelCase				,				unittest.TestCase							):
    """simple docstring"""

    lowerCamelCase  :       Union[str, Any]   =   (
        (
            ErnieMModel,
            ErnieMForMultipleChoice,
            ErnieMForQuestionAnswering,
            ErnieMForSequenceClassification,
            ErnieMForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    lowerCamelCase  :       Optional[Any]   =   ()
    lowerCamelCase  :       str   =   (
        {
            'feature-extraction': ErnieMModel,
            'question-answering': ErnieMForQuestionAnswering,
            'text-classification': ErnieMForSequenceClassification,
            'token-classification': ErnieMForTokenClassification,
            'zero-shot': ErnieMForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase  :       Optional[int]   =   False



    def _a       (							self			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    int   ,    __SCREAMING_SNAKE_CASE			:    int			)			->       Optional[int]:
           if pipeline_test_casse_name == "QAPipelineTests":
                  return True

           return False



    def _a       (							self			:    Optional[int]			)			->       List[Any]:
           __UpperCAmelCase    							=ErnieMModelTester(self			)
           __UpperCAmelCase    							=ConfigTester(self   ,    config_class=__SCREAMING_SNAKE_CASE   ,    hidden_size=37			)



    def _a       (							self			:    int			)			->       List[str]:
           self.config_tester.run_common_tests()



    def _a       (							self			:    str			)			->       str:
           __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    Optional[int]			)			->       List[str]:
           __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                  __UpperCAmelCase    							=type
                  self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    Optional[int]			)			->       str:
           __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    List[str]			)			->       Union[str, Any]:
           __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    Any			)			->       str:
           __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    str			)			->       List[Any]:
           __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_information_extraction(*__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    List[Any]			)			->       List[str]:
           __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE			)



    @slow
    def _a       (							self			:    List[str]			)			->       Optional[Any]:
           for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  __UpperCAmelCase    							=ErnieMModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
                  self.assertIsNotNone(__SCREAMING_SNAKE_CASE			)




@require_torch
class 							_A (    unittest.TestCase							):
    """simple docstring"""

    @slow
    def _a       (							self			:    Tuple			)			->       Optional[Any]:
           __UpperCAmelCase    							=ErnieMModel.from_pretrained("""susnato/ernie-m-base_pytorch"""			)
           model.eval()
           __UpperCAmelCase    							=torch.tensor([[0, 1, 2, 3, 4, 5]]			)
           __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE			)[0]

           # TODO Replace vocab size
           __UpperCAmelCase    							=768

           __UpperCAmelCase    							=torch.Size((1, 6, hidden_size)			)
           self.assertEqual(output.shape   ,    __SCREAMING_SNAKE_CASE			)

           __UpperCAmelCase    							=torch.tensor(
               [[[-0.0_012, 0.1_245, -0.0_214], [-0.0_742, 0.0_244, -0.0_771], [-0.0_333, 0.1_164, -0.1_554]]]			)

           self.assertTrue(torch.allclose(output[:, :3, :3]   ,    __SCREAMING_SNAKE_CASE   ,    atol=1e-3			)			)




