
"""simple docstring"""



import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	from transformers.models.roberta.modeling_flax_roberta import (
	    FlaxRobertaForCausalLM,
	    FlaxRobertaForMaskedLM,
	    FlaxRobertaForMultipleChoice,
	    FlaxRobertaForQuestionAnswering,
	    FlaxRobertaForSequenceClassification,
	    FlaxRobertaForTokenClassification,
	    FlaxRobertaModel,
	)
class        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



				def __init__(					self	:			Optional[int]				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			Any=13				,     UpperCAmelCase_	:			Union[str, Any]=7				,     UpperCAmelCase_	:			int=True				,     UpperCAmelCase_	:			Optional[int]=True				,     UpperCAmelCase_	:			Optional[Any]=True				,     UpperCAmelCase_	:			Dict=True				,     UpperCAmelCase_	:			List[str]=99				,     UpperCAmelCase_	:			List[str]=32				,     UpperCAmelCase_	:			Optional[Any]=5				,     UpperCAmelCase_	:			Optional[Any]=4				,     UpperCAmelCase_	:			List[str]=37				,     UpperCAmelCase_	:			List[str]="gelu"				,     UpperCAmelCase_	:			Any=0.1				,     UpperCAmelCase_	:			int=0.1				,     UpperCAmelCase_	:			Optional[Any]=512				,     UpperCAmelCase_	:			int=16				,     UpperCAmelCase_	:			List[str]=2				,     UpperCAmelCase_	:			Optional[int]=0.02				,     UpperCAmelCase_	:			Optional[Any]=4				,     )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			parent
											_lowerCAmelCase  	=			batch_size
											_lowerCAmelCase  	=			seq_length
											_lowerCAmelCase  	=			is_training
											_lowerCAmelCase  	=			use_attention_mask
											_lowerCAmelCase  	=			use_token_type_ids
											_lowerCAmelCase  	=			use_labels
											_lowerCAmelCase  	=			vocab_size
											_lowerCAmelCase  	=			hidden_size
											_lowerCAmelCase  	=			num_hidden_layers
											_lowerCAmelCase  	=			num_attention_heads
											_lowerCAmelCase  	=			intermediate_size
											_lowerCAmelCase  	=			hidden_act
											_lowerCAmelCase  	=			hidden_dropout_prob
											_lowerCAmelCase  	=			attention_probs_dropout_prob
											_lowerCAmelCase  	=			max_position_embeddings
											_lowerCAmelCase  	=			type_vocab_size
											_lowerCAmelCase  	=			type_sequence_label_size
											_lowerCAmelCase  	=			initializer_range
											_lowerCAmelCase  	=			num_choices


				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size  )

											_lowerCAmelCase  	=			None
											if self.use_attention_mask:
																		_lowerCAmelCase  	=			random_attention_mask([self.batch_size, self.seq_length]  )

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

											_lowerCAmelCase  	=			RobertaConfig(
											    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=_A				,     initializer_range=self.initializer_range				,     )

											return config, input_ids, token_type_ids, attention_mask


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







											"""simple docstring"""




											_lowerCAmelCase  	=			self.prepare_config_and_inputs()
											_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			config_and_inputs
											_lowerCAmelCase  	=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
											return config, inputs_dict







				def 				__lowerCamelCase	(					self	:			Tuple  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.prepare_config_and_inputs()
											_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			config_and_inputs

											_lowerCAmelCase  	=			True
											_lowerCAmelCase  	=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
											_lowerCAmelCase  	=			ids_tensor([self.batch_size, self.seq_length]				,     vocab_size=2  )

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)






@require_flax
class        _SCREAMING_SNAKE_CASE  (  __lowercase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[int]							=					True

				SCREAMING_SNAKE_CASE_:							Dict							=					(
				    (
				        FlaxRobertaModel,
				        FlaxRobertaForCausalLM,
				        FlaxRobertaForMaskedLM,
				        FlaxRobertaForSequenceClassification,
				        FlaxRobertaForTokenClassification,
				        FlaxRobertaForMultipleChoice,
				        FlaxRobertaForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)


				def 				__lowerCamelCase	(					self	:			str  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			FlaxRobertaModelTester(self  )







				@slow
				def 				__lowerCamelCase	(					self	:			str  )		->  List[Any]:







											"""simple docstring"""




											for model_class_name in self.all_model_classes:
																		_lowerCAmelCase  	=			model_class_name.from_pretrained('roberta-base'				,     from_pt=_A  )
																		_lowerCAmelCase  	=			model(np.ones((1, 1)  )  )
																		self.assertIsNotNone(_A  )








"""simple docstring"""



from pathlib import Path

import numpy as np
from PIL import Image






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							return (gray > 127) & (gray <= 255)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray     ,  SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase  	=			np.zeros_like(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)						)

							# Copy image to padded image
							_lowerCAmelCase  	=			image

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


if __name__ == "__main__":
	# read original image
	_snake_case          =						Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
	_snake_case          =						np.array(Image.open(lena_path))
	# kernel to be applied
	_snake_case          =						np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
	_snake_case          =						dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
	# Save the output image
	_snake_case          =						Image.fromarray(output).convert('''RGB''')
	pil_img.save('''result_dilation.png''')






