'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import MPNetConfig, 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.models.mpnet.modeling_tf_mpnet import (
						    TFMPNetForMaskedLM,
						    TFMPNetForMultipleChoice,
						    TFMPNetForQuestionAnswering,
						    TFMPNetForSequenceClassification,
						    TFMPNetForTokenClassification,
						    TFMPNetModel,
						)
class 							lowercase_    :







				def __init__(	self       ,     UpperCamelCase__       ,     UpperCamelCase__=1_3       ,     UpperCamelCase__=7       ,     UpperCamelCase__=True       ,     UpperCamelCase__=True       ,     UpperCamelCase__=False       ,     UpperCamelCase__=True       ,     UpperCamelCase__=9_9       ,     UpperCamelCase__=6_4       ,     UpperCamelCase__=2       ,     UpperCamelCase__=4       ,     UpperCamelCase__=6_4       ,     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       ,     )				->       Any:





					"""simple docstring"""

					UpperCAmelCase_				       =					parent
					UpperCAmelCase_				       =					batch_size
					UpperCAmelCase_				       =					seq_length
					UpperCAmelCase_				       =					is_training
					UpperCAmelCase_				       =					use_input_mask
					UpperCAmelCase_				       =					use_token_type_ids
					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   lowerCamelCase_    (	self				)				->       Dict:





					"""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
					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_				       =					MPNetConfig(
					    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       ,     initializer_range=self.initializer_range       ,     )
					return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







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





					"""simple docstring"""

					UpperCAmelCase_				       =					TFMPNetModel(config=lowerCAmelCase_				)
					UpperCAmelCase_				       =					{"input_ids": input_ids, "attention_mask": input_mask}
					UpperCAmelCase_				       =					model(lowerCAmelCase_				)
					UpperCAmelCase_				       =					[input_ids, input_mask]
					UpperCAmelCase_				       =					model(lowerCAmelCase_				)
					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__				)				->       List[str]:





					"""simple docstring"""

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







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





					"""simple docstring"""

					UpperCAmelCase_				       =					TFMPNetForQuestionAnswering(config=lowerCAmelCase_				)
					UpperCAmelCase_				       =					{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					}
					UpperCAmelCase_				       =					model(lowerCAmelCase_				)
					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       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__				)				->       int:





					"""simple docstring"""

					UpperCAmelCase_				       =					self.num_labels
					UpperCAmelCase_				       =					TFMPNetForSequenceClassification(lowerCAmelCase_				)
					UpperCAmelCase_				       =					{"input_ids": input_ids, "attention_mask": input_mask}
					UpperCAmelCase_				       =					model(lowerCAmelCase_				)
					self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)				)







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





					"""simple docstring"""

					UpperCAmelCase_				       =					self.num_choices
					UpperCAmelCase_				       =					TFMPNetForMultipleChoice(lowerCAmelCase_				)
					UpperCAmelCase_				       =					tf.tile(tf.expand_dims(lowerCAmelCase_       ,     1				)       ,     (1, self.num_choices, 1)				)
					UpperCAmelCase_				       =					tf.tile(tf.expand_dims(lowerCAmelCase_       ,     1				)       ,     (1, self.num_choices, 1)				)
					UpperCAmelCase_				       =					{
					    "input_ids": multiple_choice_inputs_ids,
					    "attention_mask": multiple_choice_input_mask,
					}
					UpperCAmelCase_				       =					model(lowerCAmelCase_				)
					self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_choices)				)







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





					"""simple docstring"""

					UpperCAmelCase_				       =					self.num_labels
					UpperCAmelCase_				       =					TFMPNetForTokenClassification(lowerCAmelCase_				)
					UpperCAmelCase_				       =					{"input_ids": input_ids, "attention_mask": input_mask}
					UpperCAmelCase_				       =					model(lowerCAmelCase_				)
					self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.num_labels)				)







				def   lowerCamelCase_    (	self				)				->       Tuple:





					"""simple docstring"""

					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_tf
class 							lowercase_    (     lowerCAmelCase__ ,  lowerCAmelCase__ ,  unittest.TestCase						):
				a_             =   (
				    (
				        TFMPNetForMaskedLM,
				        TFMPNetForMultipleChoice,
				        TFMPNetForQuestionAnswering,
				        TFMPNetForSequenceClassification,
				        TFMPNetForTokenClassification,
				        TFMPNetModel,
				    )
				    if is_tf_available()
				    else ()
				)
				a_             =   (
				    {
				        """feature-extraction""": TFMPNetModel,
				        """fill-mask""": TFMPNetForMaskedLM,
				        """question-answering""": TFMPNetForQuestionAnswering,
				        """text-classification""": TFMPNetForSequenceClassification,
				        """token-classification""": TFMPNetForTokenClassification,
				        """zero-shot""": TFMPNetForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				a_             =   False
				a_             =   False







				def   lowerCamelCase_    (	self				)				->       str:





					"""simple docstring"""

					UpperCAmelCase_				       =					TFMPNetModelTester(self				)
					UpperCAmelCase_				       =					ConfigTester(self       ,     config_class=lowerCAmelCase_       ,     hidden_size=3_7				)







				def   lowerCamelCase_    (	self				)				->       int:





					"""simple docstring"""

					self.config_tester.run_common_tests()







				def   lowerCamelCase_    (	self				)				->       int:





					"""simple docstring"""

					UpperCAmelCase_				       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mpnet_model(*lowerCAmelCase_				)







				def   lowerCamelCase_    (	self				)				->       Tuple:





					"""simple docstring"""

					UpperCAmelCase_				       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mpnet_for_masked_lm(*lowerCAmelCase_				)







				def   lowerCamelCase_    (	self				)				->       Any:





					"""simple docstring"""

					UpperCAmelCase_				       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mpnet_for_question_answering(*lowerCAmelCase_				)







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





					"""simple docstring"""

					UpperCAmelCase_				       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowerCAmelCase_				)







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





					"""simple docstring"""

					UpperCAmelCase_				       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowerCAmelCase_				)







				def   lowerCamelCase_    (	self				)				->       Any:





					"""simple docstring"""

					UpperCAmelCase_				       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mpnet_for_token_classification(*lowerCAmelCase_				)







				@slow
				def   lowerCamelCase_    (	self				)				->       Optional[Any]:





					"""simple docstring"""

					for model_name in ["microsoft/mpnet-base"]:
						UpperCAmelCase_				       =					TFMPNetModel.from_pretrained(lowerCAmelCase_				)
						self.assertIsNotNone(lowerCAmelCase_				)


@require_tf
class 							lowercase_    (     unittest.TestCase						):







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





					"""simple docstring"""

					UpperCAmelCase_				       =					TFMPNetModel.from_pretrained("microsoft/mpnet-base"				)
					UpperCAmelCase_				       =					tf.constant([[0, 1, 2, 3, 4, 5]]				)
					UpperCAmelCase_				       =					model(lowerCAmelCase_				)[0]

					UpperCAmelCase_				       =					[1, 6, 7_6_8]
					self.assertEqual(output.shape       ,     lowerCAmelCase_				)

					UpperCAmelCase_				       =					tf.constant(
					    [
					        [
					            [-0.1067172, 0.08216473, 0.0024543],
					            [-0.03465879, 0.8354118, -0.03252288],
					            [-0.06569476, -0.12424111, -0.0494436],
					        ]
					    ]				)
					tf.debugging.assert_near(output[:, :3, :3]       ,     lowerCAmelCase_       ,     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''')
