'''simple docstring'''

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 							lowercase_    :







				def __init__(	self       ,     UpperCamelCase__       ,     UpperCamelCase__=1_3       ,     UpperCamelCase__=7       ,     UpperCamelCase__=True       ,     UpperCamelCase__=True       ,     UpperCamelCase__=True       ,     UpperCamelCase__=True       ,     UpperCamelCase__=9_9       ,     UpperCamelCase__=3_2       ,     UpperCamelCase__=2       ,     UpperCamelCase__=4       ,     UpperCamelCase__=3_7       ,     UpperCamelCase__="gelu"       ,     UpperCamelCase__=0.1       ,     UpperCamelCase__=0.1       ,     UpperCamelCase__=5_1_2       ,     UpperCamelCase__=1_6       ,     UpperCamelCase__=2       ,     UpperCamelCase__=0.02       ,     UpperCamelCase__=3       ,     UpperCamelCase__=4       ,     UpperCamelCase__=None       ,     )				->       Tuple:





					"""simple docstring"""

					UpperCAmelCase_				       =					parent
					UpperCAmelCase_				       =					1_3
					UpperCAmelCase_				       =					7
					UpperCAmelCase_				       =					True
					UpperCAmelCase_				       =					True
					UpperCAmelCase_				       =					True
					UpperCAmelCase_				       =					True
					UpperCAmelCase_				       =					9_9
					UpperCAmelCase_				       =					3_2
					UpperCAmelCase_				       =					2
					UpperCAmelCase_				       =					4
					UpperCAmelCase_				       =					3_7
					UpperCAmelCase_				       =					"gelu"
					UpperCAmelCase_				       =					0.1
					UpperCAmelCase_				       =					0.1
					UpperCAmelCase_				       =					5_1_2
					UpperCAmelCase_				       =					1_6
					UpperCAmelCase_				       =					False
					UpperCAmelCase_				       =					-1
					UpperCAmelCase_				       =					True
					UpperCAmelCase_				       =					2
					UpperCAmelCase_				       =					0.02
					UpperCAmelCase_				       =					3
					UpperCAmelCase_				       =					4
					UpperCAmelCase_				       =					None







				def   lowerCamelCase_    (	self				)				->       List[Any]:





					"""simple docstring"""

					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
					if self.use_token_type_ids:
						UpperCAmelCase_				       =					ids_tensor([self.batch_size, self.seq_length]       ,     self.type_vocab_size				)

					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_				       =					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=UpperCamelCase__       ,     )

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







				def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__				)				->       Any:





					"""simple docstring"""

					UpperCAmelCase_				       =					TFDebertaModel(config=UpperCamelCase__				)
					UpperCAmelCase_				       =					{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

					UpperCAmelCase_				       =					[input_ids, input_mask]
					UpperCAmelCase_				       =					model(UpperCamelCase__				)

					UpperCAmelCase_				       =					model(UpperCamelCase__				)

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







				def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__				)				->       int:





					"""simple docstring"""

					UpperCAmelCase_				       =					TFDebertaForMaskedLM(config=UpperCamelCase__				)
					UpperCAmelCase_				       =					{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					    "token_type_ids": token_type_ids,
					}
					UpperCAmelCase_				       =					model(UpperCamelCase__				)
					self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)				)







				def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__				)				->       List[str]:





					"""simple docstring"""

					UpperCAmelCase_				       =					self.num_labels
					UpperCAmelCase_				       =					TFDebertaForSequenceClassification(config=UpperCamelCase__				)
					UpperCAmelCase_				       =					{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					    "token_type_ids": token_type_ids,
					}

					UpperCAmelCase_				       =					model(UpperCamelCase__				)
					self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)				)







				def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__				)				->       Optional[int]:





					"""simple docstring"""

					UpperCAmelCase_				       =					self.num_labels
					UpperCAmelCase_				       =					TFDebertaForTokenClassification(config=UpperCamelCase__				)
					UpperCAmelCase_				       =					{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					    "token_type_ids": token_type_ids,
					}
					UpperCAmelCase_				       =					model(UpperCamelCase__				)
					self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.num_labels)				)







				def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__				)				->       Optional[Any]:





					"""simple docstring"""

					UpperCAmelCase_				       =					TFDebertaForQuestionAnswering(config=UpperCamelCase__				)
					UpperCAmelCase_				       =					{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					    "token_type_ids": token_type_ids,
					}

					UpperCAmelCase_				       =					model(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				)				->       List[str]:





					"""simple docstring"""

					UpperCAmelCase_				       =					self.prepare_config_and_inputs()
					(
					    (
					    UpperCAmelCase_ 
					)       ,       (
					    UpperCAmelCase_ 
					)       ,       (
					    UpperCAmelCase_ 
					)       ,       (
					    UpperCAmelCase_ 
					)       ,       (
					    UpperCAmelCase_ 
					)       ,       (
					    UpperCAmelCase_ 
					)       ,       (
					    UpperCAmelCase_ 
					)       ,        
					)				       =					config_and_inputs
					UpperCAmelCase_				       =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
					return config, inputs_dict


@require_tf
class 							lowercase_    (     snake_case_ ,  snake_case_ ,  unittest.TestCase						):
				a_             =   (
				    (
				        TFDebertaModel,
				        TFDebertaForMaskedLM,
				        TFDebertaForQuestionAnswering,
				        TFDebertaForSequenceClassification,
				        TFDebertaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				a_             =   (
				    {
				        """feature-extraction""": TFDebertaModel,
				        """fill-mask""": TFDebertaForMaskedLM,
				        """question-answering""": TFDebertaForQuestionAnswering,
				        """text-classification""": TFDebertaForSequenceClassification,
				        """token-classification""": TFDebertaForTokenClassification,
				        """zero-shot""": TFDebertaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				a_             =   False
				a_             =   False







				def   lowerCamelCase_    (	self				)				->       Tuple:





					"""simple docstring"""

					UpperCAmelCase_				       =					TFDebertaModelTester(self				)
					UpperCAmelCase_				       =					ConfigTester(self       ,     config_class=UpperCamelCase__       ,     hidden_size=3_7				)







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





					"""simple docstring"""

					self.config_tester.run_common_tests()







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





					"""simple docstring"""

					UpperCAmelCase_				       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCamelCase__				)







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





					"""simple docstring"""

					UpperCAmelCase_				       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__				)







				def   lowerCamelCase_    (	self				)				->       str:





					"""simple docstring"""

					UpperCAmelCase_				       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__				)







				def   lowerCamelCase_    (	self				)				->       List[str]:





					"""simple docstring"""

					UpperCAmelCase_				       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__				)







				def   lowerCamelCase_    (	self				)				->       List[str]:





					"""simple docstring"""

					UpperCAmelCase_				       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__				)







				@slow
				def   lowerCamelCase_    (	self				)				->       Tuple:





					"""simple docstring"""

					UpperCAmelCase_				       =					TFDebertaModel.from_pretrained("kamalkraj/deberta-base"				)
					self.assertIsNotNone(UpperCamelCase__				)


@require_tf
class 							lowercase_    (     unittest.TestCase						):







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





					"""simple docstring"""

					pass







				@slow
				def   lowerCamelCase_    (	self				)				->       str:





					"""simple docstring"""

					UpperCAmelCase_				       =					TFDebertaModel.from_pretrained("kamalkraj/deberta-base"				)
					UpperCAmelCase_				       =					tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]				)
					UpperCAmelCase_				       =					tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
					UpperCAmelCase_				       =					model(UpperCamelCase__       ,     attention_mask=UpperCamelCase__				)[0]

					UpperCAmelCase_				       =					tf.constant(
					    [
					        [
					            [-0.59855896, -0.80552566, -0.8462135],
					            [1.4484025, -0.93483794, -0.80593085],
					            [0.3122741, 0.00316059, -1.4131377],
					        ]
					    ]				)
					tf.debugging.assert_near(output[:, 1:4, 1:4]       ,     UpperCamelCase__       ,     atol=1e-4				)

'''simple docstring'''

import numpy as np
from PIL import Image



def lowerCamelCase__						(       A_       ):
 UpperCAmelCase_       ,       UpperCAmelCase_       ,       UpperCAmelCase_				       =					rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
 return 0.2989 * r + 0.5870 * g + 0.1140 * b



def lowerCamelCase__						(       A_       ):
 return (gray > 127) & (gray <= 255)



def lowerCamelCase__						(       A_       ,			A_       ):
 UpperCAmelCase_				       =					np.zeros_like(A_       )
 UpperCAmelCase_				       =					np.zeros(
     (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)       )

 # Copy image to padded image
 UpperCAmelCase_				       =					image

 # Iterate over image & apply kernel
 for x in range(image.shape[1]       ):
  for y in range(image.shape[0]       ):
   UpperCAmelCase_				       =					(
       kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
   ).sum()
   UpperCAmelCase_				       =					int(summation == 5       )
 return output


# kernel to be applied
__snake_case       :   List[str]  		=    np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      __snake_case       :   int  		=    np.array(Image.open(R'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      __snake_case       :   str  		=    erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      __snake_case       :   List[Any]  		=    Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')
