

from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFDebertaForMaskedLM,
     TFDebertaForQuestionAnswering,
     TFDebertaForSequenceClassification,
     TFDebertaForTokenClassification,
     TFDebertaModel,
 )



class 		a_	:



      """simple docstring"""







      def __init__(				self					:							Any      ,snake_case					:							Union[str, Any]      ,snake_case					:							Tuple=13      ,snake_case					:							Optional[Any]=7      ,snake_case					:							Tuple=True      ,snake_case					:							List[str]=True      ,snake_case					:							int=True      ,snake_case					:							Union[str, Any]=True      ,snake_case					:							List[str]=99      ,snake_case					:							str=32      ,snake_case					:							List[Any]=2      ,snake_case					:							str=4      ,snake_case					:							Union[str, Any]=37      ,snake_case					:							Tuple="gelu"      ,snake_case					:							Tuple=0.1      ,snake_case					:							Tuple=0.1      ,snake_case					:							Optional[int]=512      ,snake_case					:							List[str]=16      ,snake_case					:							List[Any]=2      ,snake_case					:							Union[str, Any]=0.02      ,snake_case					:							Optional[Any]=3      ,snake_case					:							int=4      ,snake_case					:							Any=None      ,):
           SCREAMING_SNAKE_CASE										=parent
           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										=32
           SCREAMING_SNAKE_CASE										=2
           SCREAMING_SNAKE_CASE										=4
           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										=False
           SCREAMING_SNAKE_CASE										=-1
           SCREAMING_SNAKE_CASE										=True
           SCREAMING_SNAKE_CASE										=2
           SCREAMING_SNAKE_CASE										=0.02
           SCREAMING_SNAKE_CASE										=3
           SCREAMING_SNAKE_CASE										=4
           SCREAMING_SNAKE_CASE										=None
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size    )

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

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

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

           SCREAMING_SNAKE_CASE										=DebertaConfig(
               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      ,relative_attention=self.relative_attention      ,max_relative_positions=self.max_relative_positions      ,position_biased_input=self.position_biased_input      ,initializer_range=self.initializer_range      ,return_dict=snake_case      ,)

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def 	_lowerCAmelCase					(				self					:							Optional[int]      ,snake_case					:							Any      ,snake_case					:							Optional[int]      ,snake_case					:							Union[str, Any]      ,snake_case					:							List[str]      ,snake_case					:							Union[str, Any]      ,snake_case					:							str      ,snake_case					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE										=TFDebertaModel(config=snake_case    )
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

           SCREAMING_SNAKE_CASE										=[input_ids, input_mask]
           SCREAMING_SNAKE_CASE										=model(snake_case    )

           SCREAMING_SNAKE_CASE										=model(snake_case    )

           self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)    )
      def 	_lowerCAmelCase					(				self					:							Optional[Any]      ,snake_case					:							Optional[Any]      ,snake_case					:							Union[str, Any]      ,snake_case					:							Optional[int]      ,snake_case					:							List[Any]      ,snake_case					:							Tuple      ,snake_case					:							List[Any]      ,snake_case					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=TFDebertaForMaskedLM(config=snake_case    )
           SCREAMING_SNAKE_CASE										={
               'input_ids': input_ids,
               'attention_mask': input_mask,
               'token_type_ids': token_type_ids,
           }
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size)    )
      def 	_lowerCAmelCase					(				self					:							List[Any]      ,snake_case					:							int      ,snake_case					:							Union[str, Any]      ,snake_case					:							int      ,snake_case					:							int      ,snake_case					:							Union[str, Any]      ,snake_case					:							Tuple      ,snake_case					:							int    ):
           SCREAMING_SNAKE_CASE										=self.num_labels
           SCREAMING_SNAKE_CASE										=TFDebertaForSequenceClassification(config=snake_case    )
           SCREAMING_SNAKE_CASE										={
               'input_ids': input_ids,
               'attention_mask': input_mask,
               'token_type_ids': token_type_ids,
           }

           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)    )
      def 	_lowerCAmelCase					(				self					:							Optional[int]      ,snake_case					:							int      ,snake_case					:							Tuple      ,snake_case					:							Tuple      ,snake_case					:							Any      ,snake_case					:							Dict      ,snake_case					:							Optional[int]      ,snake_case					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE										=self.num_labels
           SCREAMING_SNAKE_CASE										=TFDebertaForTokenClassification(config=snake_case    )
           SCREAMING_SNAKE_CASE										={
               'input_ids': input_ids,
               'attention_mask': input_mask,
               'token_type_ids': token_type_ids,
           }
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels)    )
      def 	_lowerCAmelCase					(				self					:							int      ,snake_case					:							Union[str, Any]      ,snake_case					:							Union[str, Any]      ,snake_case					:							Optional[Any]      ,snake_case					:							List[str]      ,snake_case					:							str      ,snake_case					:							int      ,snake_case					:							str    ):
           SCREAMING_SNAKE_CASE										=TFDebertaForQuestionAnswering(config=snake_case    )
           SCREAMING_SNAKE_CASE										={
               'input_ids': input_ids,
               'attention_mask': input_mask,
               'token_type_ids': token_type_ids,
           }

           SCREAMING_SNAKE_CASE										=model(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 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=self.prepare_config_and_inputs()
           (
               (
               SCREAMING_SNAKE_CASE
           )						,  (
               SCREAMING_SNAKE_CASE
           )						,  (
               SCREAMING_SNAKE_CASE
           )						,  (
               SCREAMING_SNAKE_CASE
           )						,  (
               SCREAMING_SNAKE_CASE
           )						,  (
               SCREAMING_SNAKE_CASE
           )						,  (
               SCREAMING_SNAKE_CASE
           )						,  
           )										=config_and_inputs
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
           return config, inputs_dict







@require_tf
class 		a_	(							lowerCamelCase_			,      lowerCamelCase_			,      unittest.TestCase					):



      """simple docstring"""







      __UpperCAmelCase      		=				(
          (
              TFDebertaModel,
              TFDebertaForMaskedLM,
              TFDebertaForQuestionAnswering,
              TFDebertaForSequenceClassification,
              TFDebertaForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      __UpperCAmelCase      		=				(
          {
              'feature-extraction': TFDebertaModel,
              'fill-mask': TFDebertaForMaskedLM,
              'question-answering': TFDebertaForQuestionAnswering,
              'text-classification': TFDebertaForSequenceClassification,
              'token-classification': TFDebertaForTokenClassification,
              'zero-shot': TFDebertaForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )

      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				False
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=TFDebertaModelTester(self    )
           SCREAMING_SNAKE_CASE										=ConfigTester(self      ,config_class=snake_case      ,hidden_size=37    )
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           self.config_tester.run_common_tests()
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							Any    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*snake_case    )


      @slow
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=TFDebertaModel.from_pretrained('kamalkraj/deberta-base'    )
           self.assertIsNotNone(snake_case    )







@require_tf
class 		a_	(							unittest.TestCase					):



      """simple docstring"""







      @unittest.skip(reason='Model not available yet'    )
      def 	_lowerCAmelCase					(				self					:							int    ):
           pass


      @slow
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										=TFDebertaModel.from_pretrained('kamalkraj/deberta-base'    )
           SCREAMING_SNAKE_CASE										=tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]    )
           SCREAMING_SNAKE_CASE										=tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
           SCREAMING_SNAKE_CASE										=model(snake_case      ,attention_mask=snake_case    )[0]

           SCREAMING_SNAKE_CASE										=tf.constant(
               [
                   [
                       [-0.59_855_896, -0.80_552_566, -0.8_462_135],
                       [1.4_484_025, -0.93_483_794, -0.80_593_085],
                       [0.3_122_741, 0.00_316_059, -1.4_131_377],
                   ]
               ]    )
           tf.debugging.assert_near(output[:, 1:4, 1:4]      ,snake_case      ,atol=1e-4    )




import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]      )
     SCREAMING_SNAKE_CASE										=np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]      )

     SCREAMING_SNAKE_CASE										=np.abs(img_convolve(lowerCAmelCase_,   lowerCAmelCase_      )      )
     SCREAMING_SNAKE_CASE										=np.abs(img_convolve(lowerCAmelCase_,   lowerCAmelCase_      )      )
     # modify the pix within [0, 255]
     SCREAMING_SNAKE_CASE										=dst_x * 255 / np.max(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=dst_y * 255 / np.max(lowerCAmelCase_      )

     SCREAMING_SNAKE_CASE										=np.sqrt((np.square(lowerCAmelCase_      )) + (np.square(lowerCAmelCase_      ))      )
     SCREAMING_SNAKE_CASE										=dst_xy * 255 / np.max(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=dst_xy.astype(np.uinta      )

     SCREAMING_SNAKE_CASE										=np.arctana(lowerCAmelCase_,   lowerCAmelCase_      )
     return dst, theta


if __name__ == "__main__":
 # read original image
 _lowerCamelCase								=imread("../image_data/lena.jpg")
 # turn image in gray scale value
 _lowerCamelCase								=cvtColor(img, COLOR_BGR2GRAY)

 _lowerCamelCase	,			_lowerCamelCase								=sobel_filter(gray)

 # show result images
 imshow("sobel filter", sobel_grad)
 imshow("sobel theta", sobel_theta)
 waitKey(0)

