



from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
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 TF_MODEL_FOR_PRETRAINING_MAPPING
	from transformers.models.albert.modeling_tf_albert import (
	    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFAlbertForMaskedLM,
	    TFAlbertForMultipleChoice,
	    TFAlbertForPreTraining,
	    TFAlbertForQuestionAnswering,
	    TFAlbertForSequenceClassification,
	    TFAlbertForTokenClassification,
	    TFAlbertModel,
	)





class       __lowerCAmelCase   :




				def __init__(							self		,       _snake_case		,       _snake_case=13		,       _snake_case=7		,       _snake_case=True		,       _snake_case=True		,       _snake_case=True		,       _snake_case=True		,       _snake_case=99		,       _snake_case=16		,       _snake_case=32		,       _snake_case=2		,       _snake_case=4		,       _snake_case=37		,       _snake_case="gelu"		,       _snake_case=0.1		,       _snake_case=0.1		,       _snake_case=512		,       _snake_case=16		,       _snake_case=2		,       _snake_case=0.02		,       _snake_case=3		,       _snake_case=4		,       _snake_case=None		,       ):



									"""simple docstring"""





									_lowerCAmelCase      			=					parent
									_lowerCAmelCase      			=					13
									_lowerCAmelCase      			=					7
									_lowerCAmelCase      			=					True
									_lowerCAmelCase      			=					True
									_lowerCAmelCase      			=					True
									_lowerCAmelCase      			=					True
									_lowerCAmelCase      			=					99
									_lowerCAmelCase      			=					16
									_lowerCAmelCase      			=					32
									_lowerCAmelCase      			=					2
									_lowerCAmelCase      			=					4
									_lowerCAmelCase      			=					37
									_lowerCAmelCase      			=					"""gelu"""
									_lowerCAmelCase      			=					0.1
									_lowerCAmelCase      			=					0.1
									_lowerCAmelCase      			=					512
									_lowerCAmelCase      			=					16
									_lowerCAmelCase      			=					2
									_lowerCAmelCase      			=					0.02
									_lowerCAmelCase      			=					3
									_lowerCAmelCase      			=					4
									_lowerCAmelCase      			=					None




				def 							snake_case     (							self	):



									"""simple docstring"""





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

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

									_lowerCAmelCase      			=					AlbertConfig(
									    vocab_size=self.vocab_size		,       hidden_size=self.hidden_size		,       embedding_size=self.embedding_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		,       )

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




				def 							snake_case     (							self		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case	):



									"""simple docstring"""





									_lowerCAmelCase      			=					TFAlbertModel(config=_snake_case	)
									# inputs = {'input_ids': input_ids,
									#           'attention_mask': input_mask,
									#           'token_type_ids': token_type_ids}
									# sequence_output, pooled_output = model(**inputs)
									_lowerCAmelCase      			=					{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
									_lowerCAmelCase      			=					model(_snake_case	)

									_lowerCAmelCase      			=					[input_ids, input_mask]
									_lowerCAmelCase      			=					model(_snake_case	)

									_lowerCAmelCase      			=					model(_snake_case	)

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




				def 							snake_case     (							self		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.num_labels
									_lowerCAmelCase      			=					TFAlbertForPreTraining(config=_snake_case	)
									_lowerCAmelCase      			=					{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
									_lowerCAmelCase      			=					model(_snake_case	)
									self.parent.assertEqual(result.prediction_logits.shape		,       (self.batch_size, self.seq_length, self.vocab_size)	)
									self.parent.assertEqual(result.sop_logits.shape		,       (self.batch_size, self.num_labels)	)




				def 							snake_case     (							self		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case	):



									"""simple docstring"""





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




				def 							snake_case     (							self		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.num_labels
									_lowerCAmelCase      			=					TFAlbertForSequenceClassification(config=_snake_case	)
									_lowerCAmelCase      			=					{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
									_lowerCAmelCase      			=					model(_snake_case	)
									self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.num_labels)	)




				def 							snake_case     (							self		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case	):



									"""simple docstring"""





									_lowerCAmelCase      			=					TFAlbertForQuestionAnswering(config=_snake_case	)
									_lowerCAmelCase      			=					{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
									_lowerCAmelCase      			=					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 							snake_case     (							self		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.num_choices
									_lowerCAmelCase      			=					TFAlbertForMultipleChoice(config=_snake_case	)
									_lowerCAmelCase      			=					tf.tile(tf.expand_dims(_snake_case		,       1	)		,       (1, self.num_choices, 1)	)
									_lowerCAmelCase      			=					tf.tile(tf.expand_dims(_snake_case		,       1	)		,       (1, self.num_choices, 1)	)
									_lowerCAmelCase      			=					tf.tile(tf.expand_dims(_snake_case		,       1	)		,       (1, self.num_choices, 1)	)
									_lowerCAmelCase      			=					{
									    """input_ids""": multiple_choice_inputs_ids,
									    """attention_mask""": multiple_choice_input_mask,
									    """token_type_ids""": multiple_choice_token_type_ids,
									}
									_lowerCAmelCase      			=					model(_snake_case	)
									self.parent.assertListEqual(list(result["""logits"""].shape	)		,       [self.batch_size, self.num_choices]	)




				def 							snake_case     (							self		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.num_labels
									_lowerCAmelCase      			=					TFAlbertForTokenClassification(config=_snake_case	)
									_lowerCAmelCase      			=					{
									    """input_ids""": input_ids,
									    """attention_mask""": input_mask,
									    """token_type_ids""": token_type_ids,
									}
									_lowerCAmelCase      			=					model(_snake_case	)
									self.parent.assertListEqual(list(result["""logits"""].shape	)		,       [self.batch_size, self.seq_length, self.num_labels]	)




				def 							snake_case     (							self	):



									"""simple docstring"""





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





@require_tf
class       __lowerCAmelCase   (  UpperCAmelCase_ ,		UpperCAmelCase_ ,		unittest.TestCase ):
				__lowerCamelCase				=       (
				    (
				        TFAlbertModel,
				        TFAlbertForPreTraining,
				        TFAlbertForMaskedLM,
				        TFAlbertForSequenceClassification,
				        TFAlbertForQuestionAnswering,
				        TFAlbertForTokenClassification,
				        TFAlbertForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				__lowerCamelCase				=       (
				    {
				        '''feature-extraction''': TFAlbertModel,
				        '''fill-mask''': TFAlbertForMaskedLM,
				        '''question-answering''': TFAlbertForQuestionAnswering,
				        '''text-classification''': TFAlbertForSequenceClassification,
				        '''token-classification''': TFAlbertForTokenClassification,
				        '''zero-shot''': TFAlbertForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				__lowerCamelCase				=       False
				__lowerCamelCase				=       False




				def 							snake_case     (							self		,       _snake_case		,       _snake_case		,       _snake_case=False	):



									"""simple docstring"""





									_lowerCAmelCase      			=					super()._prepare_for_class(_snake_case		,       _snake_case		,       return_labels=_snake_case	)

									if return_labels:
														if model_class in get_values(_snake_case	):
																			_lowerCAmelCase      			=					tf.zeros(self.model_tester.batch_size		,       dtype=tf.intaa	)

									return inputs_dict




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					TFAlbertModelTester(self	)
									_lowerCAmelCase      			=					ConfigTester(self		,       config_class=_snake_case		,       hidden_size=37	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									self.config_tester.run_common_tests()




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_albert_model(*_snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_albert_for_pretraining(*_snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_albert_for_masked_lm(*_snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_albert_for_multiple_choice(*_snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_albert_for_sequence_classification(*_snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_albert_for_question_answering(*_snake_case	)




				@slow
				def 							snake_case     (							self	):



									"""simple docstring"""





									for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_lowerCAmelCase      			=					TFAlbertModel.from_pretrained(_snake_case	)
														self.assertIsNotNone(_snake_case	)





@require_tf
class       __lowerCAmelCase   (  unittest.TestCase ):




				@slow
				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					TFAlbertForPreTraining.from_pretrained("""albert-base-v2"""	)
									_lowerCAmelCase      			=					tf.constant([[0, 1, 2, 3, 4, 5]]	)
									_lowerCAmelCase      			=					model(_snake_case	)[0]

									_lowerCAmelCase      			=					[1, 6, 30000]
									self.assertEqual(output.shape		,       _snake_case	)

									_lowerCAmelCase      			=					tf.constant(
									    [
									        [
									            [4.59_5668, 0.7446_2754, -1.81_8147],
									            [4.595_4347, 0.745_4184, -1.818_8258],
									            [4.595_4905, 0.744_8235, -1.818_2316],
									        ]
									    ]	)
									tf.debugging.assert_near(output[:, :3, :3]		,       _snake_case		,       atol=1e-4	)








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 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

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


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					np.zeros(snake_case    )

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

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

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

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

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

					return destination


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case			,		snake_case			,		snake_case			,		snake_case    ):





					"""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:
																				_lowerCAmelCase      			=					strong
															elif destination[row, col] <= threshold_low:
																				_lowerCAmelCase      			=					0
															else:
																				_lowerCAmelCase      			=					weak


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					for row in range(1			,		image_shape[0]    ):
										for col in range(1			,		image_shape[1]    ):
															if destination[row, col] == weak:
																				if 2_55 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],
																				):
																									_lowerCAmelCase      			=					strong
																				else:
																									_lowerCAmelCase      			=					0


def 	_UpperCAmelCase       (   snake_case			,		snake_case=15			,		snake_case=30			,		snake_case=1_28			,		snake_case=2_55    ):





					"""simple docstring"""

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

					_lowerCAmelCase      			=					suppress_non_maximum(image.shape			,		snake_case			,		snake_case    )

					detect_high_low_threshold(
					    image.shape			,		snake_case			,		snake_case			,		snake_case			,		snake_case			,		snake_case    )

					track_edge(image.shape			,		snake_case			,		snake_case			,		snake_case    )

					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)



