



"""simple docstring"""

from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, 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 (
	    TFConvBertForMaskedLM,
	    TFConvBertForMultipleChoice,
	    TFConvBertForQuestionAnswering,
	    TFConvBertForSequenceClassification,
	    TFConvBertForTokenClassification,
	    TFConvBertModel,
	)




class SCREAMING_SNAKE_CASE__					:



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





													'''simple docstring'''

													__a		:		Any									=       parent
													__a		:		Union[str, Any]									=       13
													__a		:		Optional[int]									=       7
													__a		:		Optional[int]									=       True
													__a		:		Dict									=       True
													__a		:		Optional[Any]									=       True
													__a		:		Optional[Any]									=       True
													__a		:		Union[str, Any]									=       99
													__a		:		str									=       384
													__a		:		Optional[Any]									=       2
													__a		:		str									=       4
													__a		:		List[str]									=       37
													__a		:		Optional[Any]									=       """gelu"""
													__a		:		str									=       0.1
													__a		:		Union[str, Any]									=       0.1
													__a		:		List[Any]									=       512
													__a		:		List[Any]									=       16
													__a		:		Dict									=       2
													__a		:		Dict									=       0.02
													__a		:		str									=       3
													__a		:		Any									=       4
													__a		:		str									=       128
													__a		:		str									=       2
													__a		:		int									=       9
													__a		:		Union[str, Any]									=       1
													__a		:		Optional[int]									=       None



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		int									=       ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size							)

													__a		:		Tuple									=       None
													if self.use_input_mask:
																				__a		:		Optional[int]									=       random_attention_mask([self.batch_size, self.seq_length]							)

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

													__a		:		Tuple									=       None
													__a		:		Tuple									=       None
													__a		:		Optional[int]									=       None
													if self.use_labels:
																				__a		:		Union[str, Any]									=       ids_tensor([self.batch_size]						,     self.type_sequence_label_size							)
																				__a		:		Dict									=       ids_tensor([self.batch_size, self.seq_length]						,     self.num_labels							)
																				__a		:		List[str]									=       ids_tensor([self.batch_size]						,     self.num_choices							)

													__a		:		Tuple									=       ConvBertConfig(
													    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						,     initializer_range=self.initializer_range						,     return_dict=A_						,     )

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



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       TFConvBertModel(config=A_							)
													__a		:		Optional[int]									=       {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

													__a		:		int									=       [input_ids, input_mask]
													__a		:		Tuple									=       model(A_							)

													__a		:		str									=       model(A_							)

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



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Optional[int]									=       TFConvBertForMaskedLM(config=A_							)
													__a		:		str									=       {
													    """input_ids""": input_ids,
													    """attention_mask""": input_mask,
													    """token_type_ids""": token_type_ids,
													}
													__a		:		int									=       model(A_							)
													self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.seq_length, self.vocab_size)							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Any									=       self.num_labels
													__a		:		int									=       TFConvBertForSequenceClassification(config=A_							)
													__a		:		Optional[Any]									=       {
													    """input_ids""": input_ids,
													    """attention_mask""": input_mask,
													    """token_type_ids""": token_type_ids,
													}

													__a		:		str									=       model(A_							)
													self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.num_labels)							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       self.num_choices
													__a		:		Union[str, Any]									=       TFConvBertForMultipleChoice(config=A_							)
													__a		:		Union[str, Any]									=       tf.tile(tf.expand_dims(A_						,     1							)						,     (1, self.num_choices, 1)							)
													__a		:		Optional[Any]									=       tf.tile(tf.expand_dims(A_						,     1							)						,     (1, self.num_choices, 1)							)
													__a		:		Dict									=       tf.tile(tf.expand_dims(A_						,     1							)						,     (1, self.num_choices, 1)							)
													__a		:		Any									=       {
													    """input_ids""": multiple_choice_inputs_ids,
													    """attention_mask""": multiple_choice_input_mask,
													    """token_type_ids""": multiple_choice_token_type_ids,
													}
													__a		:		int									=       model(A_							)
													self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.num_choices)							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		List[str]									=       self.num_labels
													__a		:		List[str]									=       TFConvBertForTokenClassification(config=A_							)
													__a		:		str									=       {
													    """input_ids""": input_ids,
													    """attention_mask""": input_mask,
													    """token_type_ids""": token_type_ids,
													}
													__a		:		Optional[int]									=       model(A_							)
													self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.seq_length, self.num_labels)							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Tuple									=       TFConvBertForQuestionAnswering(config=A_							)
													__a		:		str									=       {
													    """input_ids""": input_ids,
													    """attention_mask""": input_mask,
													    """token_type_ids""": token_type_ids,
													}

													__a		:		str									=       model(A_							)
													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							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       self.prepare_config_and_inputs()
													(
													    (
													    __a						
													)							,     (
													    __a						
													)							,     (
													    __a						
													)							,     (
													    __a						
													)							,     (
													    __a						
													)							,     (
													    __a						
													)							,     (
													    __a						
													)							,     						
													)		:		List[str]									=       config_and_inputs
													__a		:		List[str]									=       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
													return config, inputs_dict







@require_tf
class SCREAMING_SNAKE_CASE__					(		_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					unittest.TestCase	):
						_lowerCAmelCase							=					(
						    (
						        TFConvBertModel,
						        TFConvBertForMaskedLM,
						        TFConvBertForQuestionAnswering,
						        TFConvBertForSequenceClassification,
						        TFConvBertForTokenClassification,
						        TFConvBertForMultipleChoice,
						    )
						    if is_tf_available()
						    else ()
						)
						_lowerCAmelCase							=					(
						    {
						        "feature-extraction": TFConvBertModel,
						        "fill-mask": TFConvBertForMaskedLM,
						        "question-answering": TFConvBertForQuestionAnswering,
						        "text-classification": TFConvBertForSequenceClassification,
						        "token-classification": TFConvBertForTokenClassification,
						        "zero-shot": TFConvBertForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						_lowerCAmelCase							=					False
						_lowerCAmelCase							=					False
						_lowerCAmelCase							=					False



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Tuple									=       TFConvBertModelTester(self							)
													__a		:		Any									=       ConfigTester(self						,     config_class=A_						,     hidden_size=37							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													self.config_tester.run_common_tests()



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*A_							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Dict									=       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*A_							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[int]									=       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_multiple_choice(*A_							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		str									=       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*A_							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*A_							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*A_							)



						@slow
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a							,     __a		:		Optional[Any]									=       self.model_tester.prepare_config_and_inputs_for_common()
													__a		:		str									=       True
													__a		:		Union[str, Any]									=       True

													if hasattr(A_						,     """use_cache"""							):
																				__a		:		Optional[int]									=       True

													__a		:		int									=       getattr(self.model_tester						,     """encoder_seq_length"""						,     self.model_tester.seq_length							)
													__a		:		int									=       getattr(self.model_tester						,     """key_length"""						,     A_							)

													for model_class in self.all_model_classes:
																				__a		:		List[Any]									=       self._prepare_for_class(A_						,     A_							)
																				__a		:		Union[str, Any]									=       model_class(A_							)
																				__a		:		Tuple									=       len(model(A_							)							)

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											model.save_pretrained(A_						,     saved_model=A_							)
																											__a		:		int									=       os.path.join(A_						,     """saved_model"""						,     """1"""							)
																											__a		:		Tuple									=       tf.keras.models.load_model(A_							)
																											__a		:		int									=       model(A_							)

																											if self.is_encoder_decoder:
																																		__a		:		str									=       outputs["""encoder_hidden_states"""]
																																		__a		:		Tuple									=       outputs["""encoder_attentions"""]
																											else:
																																		__a		:		List[str]									=       outputs["""hidden_states"""]
																																		__a		:		Any									=       outputs["""attentions"""]

																											self.assertEqual(len(A_							)						,     A_							)

																											__a		:		Optional[Any]									=       getattr(
																											    self.model_tester						,     """expected_num_hidden_layers"""						,     self.model_tester.num_hidden_layers + 1							)

																											self.assertEqual(len(A_							)						,     A_							)
																											self.assertListEqual(
																											    list(output_hidden_states[0].shape[-2:]							)						,     [self.model_tester.seq_length, self.model_tester.hidden_size]						,     )

																											self.assertEqual(len(A_							)						,     self.model_tester.num_hidden_layers							)
																											self.assertListEqual(
																											    list(output_attentions[0].shape[-3:]							)						,     [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]						,     )



						@slow
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""							)
													self.assertIsNotNone(A_							)





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a							,     __a		:		List[Any]									=       self.model_tester.prepare_config_and_inputs_for_common()
													__a		:		Dict									=       True
													__a		:		Any									=       getattr(self.model_tester						,     """decoder_seq_length"""						,     self.model_tester.seq_length							)
													__a		:		Any									=       getattr(self.model_tester						,     """encoder_seq_length"""						,     self.model_tester.seq_length							)
													__a		:		str									=       getattr(self.model_tester						,     """key_length"""						,     A_							)
													__a		:		Optional[int]									=       getattr(self.model_tester						,     """key_length"""						,     A_							)

													def check_decoder_attentions_output(_lowercase							):
																				__a		:		int									=       len(A_							)
																				self.assertEqual(out_len % 2						,     0							)
																				__a		:		Optional[int]									=       outputs.decoder_attentions
																				self.assertEqual(len(A_							)						,     self.model_tester.num_hidden_layers							)
																				self.assertListEqual(
																				    list(decoder_attentions[0].shape[-3:]							)						,     [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]						,     )

													def check_encoder_attentions_output(_lowercase							):
																				__a		:		Dict									=       [
																				    t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
																				]
																				self.assertEqual(len(A_							)						,     self.model_tester.num_hidden_layers							)
																				self.assertListEqual(
																				    list(attentions[0].shape[-3:]							)						,     [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]						,     )

													for model_class in self.all_model_classes:
																				__a		:		Dict									=       True
																				__a		:		List[Any]									=       False
																				__a		:		Optional[int]									=       model_class(A_							)
																				__a		:		Tuple									=       model(self._prepare_for_class(A_						,     A_							)							)
																				__a		:		Optional[int]									=       len(A_							)
																				self.assertEqual(config.output_hidden_states						,     A_							)
																				check_encoder_attentions_output(A_							)

																				if self.is_encoder_decoder:
																											__a		:		Tuple									=       model_class(A_							)
																											__a		:		List[Any]									=       model(self._prepare_for_class(A_						,     A_							)							)
																											self.assertEqual(config.output_hidden_states						,     A_							)
																											check_decoder_attentions_output(A_							)

																				# Check that output attentions can also be changed via the config
																				del inputs_dict["output_attentions"]
																				__a		:		Union[str, Any]									=       True
																				__a		:		Union[str, Any]									=       model_class(A_							)
																				__a		:		Union[str, Any]									=       model(self._prepare_for_class(A_						,     A_							)							)
																				self.assertEqual(config.output_hidden_states						,     A_							)
																				check_encoder_attentions_output(A_							)

																				# Check attention is always last and order is fine
																				__a		:		int									=       True
																				__a		:		int									=       True
																				__a		:		Optional[int]									=       model_class(A_							)
																				__a		:		Union[str, Any]									=       model(self._prepare_for_class(A_						,     A_							)							)

																				self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)						,     len(A_							)							)
																				self.assertEqual(model.config.output_hidden_states						,     A_							)
																				check_encoder_attentions_output(A_							)







@require_tf
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase	):



						@slow
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Any									=       TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""							)
													__a		:		Union[str, Any]									=       tf.constant([[0, 1, 2, 3, 4, 5]]							)
													__a		:		int									=       model(A_							)[0]

													__a		:		Tuple									=       [1, 6, 768]
													self.assertEqual(output.shape						,     A_							)

													__a		:		Union[str, Any]									=       tf.constant(
													    [
													        [
													            [-0.0347_5493, -0.468_6034, -0.3063_8832],
													            [0.2263_7248, -0.2698_8646, -0.742_3424],
													            [0.1032_4868, -0.4501_3508, -0.5828_0784],
													        ]
													    ]							)
													tf.debugging.assert_near(output[:, :3, :3]						,     A_						,     atol=1e-4							)






"""simple docstring"""

import math
import sys

import cva
import numpy as np





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: float					):
							# For applying gaussian function for each element in matrix.
							__a		:		int									=       math.sqrt(_lowerCamelCase					)
							__a		:		Any									=       1 / (sigma * math.sqrt(2 * math.pi					))
							return cons * np.exp(-((img / sigma) ** 2) * 0.5					)





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int					):
							__a		:		Any									=       kernel_size // 2
							return img[x - half : x + half + 1, y - half : y + half + 1]





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: float					):
							# Creates a gaussian kernel of given dimension.
							__a		:		int									=       np.zeros((kernel_size, kernel_size)					)
							for i in range(0  ,       _lowerCamelCase					):
														for j in range(0  ,       _lowerCamelCase					):
																					__a		:		Any									=       math.sqrt(
																					    abs(i - kernel_size // 2					) ** 2 + abs(j - kernel_size // 2					) ** 2					)
							return vec_gaussian(_lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: int  ,       ):
							__a		:		Tuple									=       np.zeros(img.shape					)
							__a		:		Optional[int]									=       get_gauss_kernel(_lowerCamelCase  ,       _lowerCamelCase					)
							__a							,     __a		:		int									=       img.shape
							for i in range(kernel_size // 2  ,       size_x - kernel_size // 2					):
														for j in range(kernel_size // 2  ,       size_y - kernel_size // 2					):
																					__a		:		List[str]									=       get_slice(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Any									=       img_s - img_s[kernel_size // 2, kernel_size // 2]
																					__a		:		Optional[Any]									=       vec_gaussian(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Optional[Any]									=       np.multiply(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Any									=       np.multiply(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Tuple									=       np.sum(_lowerCamelCase					) / np.sum(_lowerCamelCase					)
																					__a		:		Optional[Any]									=       val
							return imga





def       __magic_name__    (   _lowerCamelCase				: list					):
							__a		:		Optional[Any]									=       args[1] if args[1:] else """../image_data/lena.jpg"""
							__a		:		Union[str, Any]									=       float(args[2]					) if args[2:] else 1.0
							__a		:		Optional[int]									=       float(args[3]					) if args[3:] else 1.0
							if args[4:]:
														__a		:		Any									=       int(args[4]					)
														__a		:		Any									=       kernel_size + abs(kernel_size % 2 - 1					)
							else:
														__a		:		Optional[int]									=       5
							return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
	lowercase__   ,   lowercase__   ,   lowercase__   ,   lowercase__			   =  parse_args(sys.argv)
	lowercase__			   =  cva.imread(filename, 0)
	cva.imshow("input image", img)

	lowercase__			   =  img / 255
	lowercase__			   =  out.astype("float32")
	lowercase__			   =  bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
	lowercase__			   =  out * 255
	lowercase__			   =  np.uinta(out)
	cva.imshow("output image", out)
	cva.waitKey(0)
	cva.destroyAllWindows()

