





'''simple docstring'''






import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          MODEL_FOR_PRETRAINING_MAPPING,
          NezhaForMaskedLM,
          NezhaForMultipleChoice,
          NezhaForNextSentencePrediction,
          NezhaForPreTraining,
          NezhaForQuestionAnswering,
          NezhaForSequenceClassification,
          NezhaForTokenClassification,
          NezhaModel,
      )
      from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST




class 					UpperCAmelCase	:




      def __init__(     self     :					List[Any]     ,     __snake_case     :					List[str]     ,     __snake_case     :					Dict=13     ,     __snake_case     :					List[str]=7     ,     __snake_case     :					Union[str, Any]=True     ,     __snake_case     :					Tuple=True     ,     __snake_case     :					Optional[int]=True     ,     __snake_case     :					Dict=True     ,     __snake_case     :					Tuple=99     ,     __snake_case     :					Any=32     ,     __snake_case     :					Tuple=5     ,     __snake_case     :					Optional[int]=4     ,     __snake_case     :					Tuple=37     ,     __snake_case     :					Union[str, Any]="gelu"     ,     __snake_case     :					Optional[int]=0.1     ,     __snake_case     :					int=0.1     ,     __snake_case     :					int=1_28     ,     __snake_case     :					int=32     ,     __snake_case     :					int=16     ,     __snake_case     :					str=2     ,     __snake_case     :					Dict=0.02     ,     __snake_case     :					Any=3     ,     __snake_case     :					Any=4     ,     __snake_case     :					List[Any]=None     ,     )							-> Tuple:
            _lowerCAmelCase					=				parent
            _lowerCAmelCase					=				batch_size
            _lowerCAmelCase					=				seq_length
            _lowerCAmelCase					=				is_training
            _lowerCAmelCase					=				use_input_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_labels
            _lowerCAmelCase					=				num_choices
            _lowerCAmelCase					=				scope




      def 							lowercase__       (     self     :					Dict    )							-> List[str]:
            _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					=				self.get_config()

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




      def 							lowercase__       (     self     :					Dict    )							-> int:
            return NezhaConfig(
                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=__snake_case     ,     initializer_range=self.initializer_range     ,     )




      def 							lowercase__       (     self     :					List[Any]    )							-> List[str]:
            (
                (
                _lowerCAmelCase
            )	,				(
                _lowerCAmelCase
            )	,				(
                _lowerCAmelCase
            )	,				(
                _lowerCAmelCase
            )	,				(
                _lowerCAmelCase
            )	,				(
                _lowerCAmelCase
            )	,				(
                _lowerCAmelCase
            )	,				
            )					=				self.prepare_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,
                input_mask,
                sequence_labels,
                token_labels,
                choice_labels,
                encoder_hidden_states,
                encoder_attention_mask,
            )




      def 							lowercase__       (     self     :					Any     ,     __snake_case     :					Union[str, Any]     ,     __snake_case     :					Optional[Any]     ,     __snake_case     :					Tuple     ,     __snake_case     :					Tuple     ,     __snake_case     :					List[str]     ,     __snake_case     :					str     ,     __snake_case     :					Optional[Any]    )							-> Optional[Any]:
            _lowerCAmelCase					=				NezhaModel(config=__snake_case    )
            model.to(__snake_case    )
            model.eval()
            _lowerCAmelCase					=				model(__snake_case     ,     attention_mask=__snake_case     ,     token_type_ids=__snake_case    )
            _lowerCAmelCase					=				model(__snake_case     ,     token_type_ids=__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 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					Tuple     ,     __snake_case     :					int     ,     __snake_case     :					Tuple     ,     __snake_case     :					Dict     ,     __snake_case     :					Union[str, Any]     ,     __snake_case     :					Any     ,     __snake_case     :					Optional[int]     ,     __snake_case     :					int     ,     __snake_case     :					List[Any]     ,     )							-> List[Any]:
            _lowerCAmelCase					=				True
            _lowerCAmelCase					=				NezhaModel(__snake_case    )
            model.to(__snake_case    )
            model.eval()
            _lowerCAmelCase					=				model(
                __snake_case     ,     attention_mask=__snake_case     ,     token_type_ids=__snake_case     ,     encoder_hidden_states=__snake_case     ,     encoder_attention_mask=__snake_case     ,     )
            _lowerCAmelCase					=				model(
                __snake_case     ,     attention_mask=__snake_case     ,     token_type_ids=__snake_case     ,     encoder_hidden_states=__snake_case     ,     )
            _lowerCAmelCase					=				model(__snake_case     ,     attention_mask=__snake_case     ,     token_type_ids=__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 							lowercase__       (     self     :					List[Any]     ,     __snake_case     :					Dict     ,     __snake_case     :					Tuple     ,     __snake_case     :					Union[str, Any]     ,     __snake_case     :					int     ,     __snake_case     :					Dict     ,     __snake_case     :					int     ,     __snake_case     :					List[str]    )							-> Any:
            _lowerCAmelCase					=				NezhaForMaskedLM(config=__snake_case    )
            model.to(__snake_case    )
            model.eval()
            _lowerCAmelCase					=				model(__snake_case     ,     attention_mask=__snake_case     ,     token_type_ids=__snake_case     ,     labels=__snake_case    )
            self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.seq_length, self.vocab_size)    )




      def 							lowercase__       (     self     :					Any     ,     __snake_case     :					int     ,     __snake_case     :					str     ,     __snake_case     :					Optional[int]     ,     __snake_case     :					Any     ,     __snake_case     :					Dict     ,     __snake_case     :					Optional[int]     ,     __snake_case     :					Optional[int]    )							-> Optional[int]:
            _lowerCAmelCase					=				NezhaForNextSentencePrediction(config=__snake_case    )
            model.to(__snake_case    )
            model.eval()
            _lowerCAmelCase					=				model(
                __snake_case     ,     attention_mask=__snake_case     ,     token_type_ids=__snake_case     ,     labels=__snake_case     ,     )
            self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, 2)    )




      def 							lowercase__       (     self     :					List[Any]     ,     __snake_case     :					Optional[Any]     ,     __snake_case     :					Optional[Any]     ,     __snake_case     :					Optional[Any]     ,     __snake_case     :					Tuple     ,     __snake_case     :					Any     ,     __snake_case     :					Tuple     ,     __snake_case     :					Union[str, Any]    )							-> Any:
            _lowerCAmelCase					=				NezhaForPreTraining(config=__snake_case    )
            model.to(__snake_case    )
            model.eval()
            _lowerCAmelCase					=				model(
                __snake_case     ,     attention_mask=__snake_case     ,     token_type_ids=__snake_case     ,     labels=__snake_case     ,     next_sentence_label=__snake_case     ,     )
            self.parent.assertEqual(result.prediction_logits.shape     ,     (self.batch_size, self.seq_length, self.vocab_size)    )
            self.parent.assertEqual(result.seq_relationship_logits.shape     ,     (self.batch_size, 2)    )




      def 							lowercase__       (     self     :					List[str]     ,     __snake_case     :					List[str]     ,     __snake_case     :					Optional[Any]     ,     __snake_case     :					Union[str, Any]     ,     __snake_case     :					str     ,     __snake_case     :					List[str]     ,     __snake_case     :					Tuple     ,     __snake_case     :					Any    )							-> Optional[Any]:
            _lowerCAmelCase					=				NezhaForQuestionAnswering(config=__snake_case    )
            model.to(__snake_case    )
            model.eval()
            _lowerCAmelCase					=				model(
                __snake_case     ,     attention_mask=__snake_case     ,     token_type_ids=__snake_case     ,     start_positions=__snake_case     ,     end_positions=__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 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					Tuple     ,     __snake_case     :					List[Any]     ,     __snake_case     :					Tuple     ,     __snake_case     :					Any     ,     __snake_case     :					str     ,     __snake_case     :					Tuple     ,     __snake_case     :					List[Any]    )							-> List[str]:
            _lowerCAmelCase					=				self.num_labels
            _lowerCAmelCase					=				NezhaForSequenceClassification(__snake_case    )
            model.to(__snake_case    )
            model.eval()
            _lowerCAmelCase					=				model(__snake_case     ,     attention_mask=__snake_case     ,     token_type_ids=__snake_case     ,     labels=__snake_case    )
            self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.num_labels)    )




      def 							lowercase__       (     self     :					Tuple     ,     __snake_case     :					Dict     ,     __snake_case     :					Any     ,     __snake_case     :					str     ,     __snake_case     :					Optional[Any]     ,     __snake_case     :					Tuple     ,     __snake_case     :					Optional[Any]     ,     __snake_case     :					Tuple    )							-> int:
            _lowerCAmelCase					=				self.num_labels
            _lowerCAmelCase					=				NezhaForTokenClassification(config=__snake_case    )
            model.to(__snake_case    )
            model.eval()
            _lowerCAmelCase					=				model(__snake_case     ,     attention_mask=__snake_case     ,     token_type_ids=__snake_case     ,     labels=__snake_case    )
            self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.seq_length, self.num_labels)    )




      def 							lowercase__       (     self     :					List[str]     ,     __snake_case     :					Optional[int]     ,     __snake_case     :					Optional[int]     ,     __snake_case     :					int     ,     __snake_case     :					Union[str, Any]     ,     __snake_case     :					Union[str, Any]     ,     __snake_case     :					Tuple     ,     __snake_case     :					Union[str, Any]    )							-> Any:
            _lowerCAmelCase					=				self.num_choices
            _lowerCAmelCase					=				NezhaForMultipleChoice(config=__snake_case    )
            model.to(__snake_case    )
            model.eval()
            _lowerCAmelCase					=				input_ids.unsqueeze(1    ).expand(-1     ,     self.num_choices     ,     -1    ).contiguous()
            _lowerCAmelCase					=				token_type_ids.unsqueeze(1    ).expand(-1     ,     self.num_choices     ,     -1    ).contiguous()
            _lowerCAmelCase					=				input_mask.unsqueeze(1    ).expand(-1     ,     self.num_choices     ,     -1    ).contiguous()
            _lowerCAmelCase					=				model(
                __snake_case     ,     attention_mask=__snake_case     ,     token_type_ids=__snake_case     ,     labels=__snake_case     ,     )
            self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.num_choices)    )




      def 							lowercase__       (     self     :					Dict    )							-> Union[str, Any]:
            _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_torch
class 					UpperCAmelCase	(     snake_case_				,						snake_case_				,						snake_case_				,						unittest.TestCase  ):
      _lowercase:     Any			=  (
          (
              NezhaModel,
              NezhaForMaskedLM,
              NezhaForMultipleChoice,
              NezhaForNextSentencePrediction,
              NezhaForPreTraining,
              NezhaForQuestionAnswering,
              NezhaForSequenceClassification,
              NezhaForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      _lowercase:     Tuple			=  (
          {
              '''feature-extraction''': NezhaModel,
              '''fill-mask''': NezhaForMaskedLM,
              '''question-answering''': NezhaForQuestionAnswering,
              '''text-classification''': NezhaForSequenceClassification,
              '''token-classification''': NezhaForTokenClassification,
              '''zero-shot''': NezhaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _lowercase:     Optional[int]			=  True




      def 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					str     ,     __snake_case     :					Optional[int]     ,     __snake_case     :					List[Any]=False    )							-> str:
            _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					=				torch.zeros(
                            (self.model_tester.batch_size, self.model_tester.seq_length)     ,     dtype=torch.long     ,     device=__snake_case    )
                        _lowerCAmelCase					=				torch.zeros(
                            self.model_tester.batch_size     ,     dtype=torch.long     ,     device=__snake_case    )
            return inputs_dict




      def 							lowercase__       (     self     :					Optional[int]    )							-> Any:
            _lowerCAmelCase					=				NezhaModelTester(self    )
            _lowerCAmelCase					=				ConfigTester(self     ,     config_class=__snake_case     ,     hidden_size=37    )




      def 							lowercase__       (     self     :					Tuple    )							-> Any:
            self.config_tester.run_common_tests()




      def 							lowercase__       (     self     :					Optional[int]    )							-> Optional[Any]:
            _lowerCAmelCase					=				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__snake_case    )




      def 							lowercase__       (     self     :					List[Any]    )							-> Tuple:
            _lowerCAmelCase					=				self.model_tester.prepare_config_and_inputs_for_decoder()
            self.model_tester.create_and_check_model_as_decoder(*__snake_case    )




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> str:
            # This regression test was failing with PyTorch < 1.3
            (
                (
                _lowerCAmelCase
            )	,				(
                _lowerCAmelCase
            )	,				(
                _lowerCAmelCase
            )	,				(
                _lowerCAmelCase
            )	,				(
                _lowerCAmelCase
            )	,				(
                _lowerCAmelCase
            )	,				(
                _lowerCAmelCase
            )	,				(
                _lowerCAmelCase
            )	,				(
                _lowerCAmelCase
            )	,				
            )					=				self.model_tester.prepare_config_and_inputs_for_decoder()

            _lowerCAmelCase					=				None

            self.model_tester.create_and_check_model_as_decoder(
                __snake_case     ,     __snake_case     ,     __snake_case     ,     __snake_case     ,     __snake_case     ,     __snake_case     ,     __snake_case     ,     __snake_case     ,     __snake_case     ,     )




      def 							lowercase__       (     self     :					List[Any]    )							-> Dict:
            _lowerCAmelCase					=				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*__snake_case    )




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Tuple:
            _lowerCAmelCase					=				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_multiple_choice(*__snake_case    )




      def 							lowercase__       (     self     :					List[str]    )							-> Dict:
            _lowerCAmelCase					=				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_next_sequence_prediction(*__snake_case    )




      def 							lowercase__       (     self     :					Dict    )							-> Any:
            _lowerCAmelCase					=				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_pretraining(*__snake_case    )




      def 							lowercase__       (     self     :					Optional[Any]    )							-> Union[str, Any]:
            _lowerCAmelCase					=				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*__snake_case    )




      def 							lowercase__       (     self     :					int    )							-> int:
            _lowerCAmelCase					=				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*__snake_case    )




      def 							lowercase__       (     self     :					List[str]    )							-> str:
            _lowerCAmelCase					=				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*__snake_case    )




      @slow
      def 							lowercase__       (     self     :					Optional[Any]    )							-> Optional[int]:
            for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _lowerCAmelCase					=				NezhaModel.from_pretrained(__snake_case    )
                  self.assertIsNotNone(__snake_case    )




      @slow
      @require_torch_gpu
      def 							lowercase__       (     self     :					str    )							-> Optional[Any]:
            _lowerCAmelCase	,				_lowerCAmelCase					=				self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes:
                  # NezhaForMultipleChoice behaves incorrectly in JIT environments.
                  if model_class == NezhaForMultipleChoice:
                        return

                  _lowerCAmelCase					=				True
                  _lowerCAmelCase					=				model_class(config=__snake_case    )

                  _lowerCAmelCase					=				self._prepare_for_class(__snake_case     ,     __snake_case    )
                  _lowerCAmelCase					=				torch.jit.trace(
                      __snake_case     ,     (inputs_dict["""input_ids"""].to("""cpu"""    ), inputs_dict["""attention_mask"""].to("""cpu"""    ))    )

                  with tempfile.TemporaryDirectory() as tmp:
                        torch.jit.save(__snake_case     ,     os.path.join(__snake_case     ,     """bert.pt"""    )    )
                        _lowerCAmelCase					=				torch.jit.load(os.path.join(__snake_case     ,     """bert.pt"""    )     ,     map_location=__snake_case    )
                        loaded(inputs_dict["""input_ids"""].to(__snake_case    )     ,     inputs_dict["""attention_mask"""].to(__snake_case    )    )




@require_torch
class 					UpperCAmelCase	(     unittest.TestCase  ):




      @slow
      def 							lowercase__       (     self     :					int    )							-> int:
            _lowerCAmelCase					=				NezhaModel.from_pretrained("""sijunhe/nezha-cn-base"""    )
            _lowerCAmelCase					=				torch.tensor([[0, 1, 2, 3, 4, 5]]    )
            _lowerCAmelCase					=				torch.tensor([[0, 1, 1, 1, 1, 1]]    )
            with torch.no_grad():
                  _lowerCAmelCase					=				model(__snake_case     ,     attention_mask=__snake_case    )[0]
            _lowerCAmelCase					=				torch.Size((1, 6, 7_68)    )
            self.assertEqual(output.shape     ,     __snake_case    )
            _lowerCAmelCase					=				torch.tensor([[[0.06_85, 0.24_41, 0.11_02], [0.06_00, 0.19_06, 0.13_49], [0.02_21, 0.08_19, 0.05_86]]]    )

            self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,     __snake_case     ,     atol=1E-4    )    )




      @slow
      def 							lowercase__       (     self     :					int    )							-> Tuple:
            _lowerCAmelCase					=				NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base"""    )
            _lowerCAmelCase					=				torch.tensor([[0, 1, 2, 3, 4, 5]]    )
            _lowerCAmelCase					=				torch.tensor([[1, 1, 1, 1, 1, 1]]    )
            with torch.no_grad():
                  _lowerCAmelCase					=				model(__snake_case     ,     attention_mask=__snake_case    )[0]
            _lowerCAmelCase					=				torch.Size((1, 6, 2_11_28)    )
            self.assertEqual(output.shape     ,     __snake_case    )
            _lowerCAmelCase					=				torch.tensor(
                [[-2.79_39, -1.79_02, -2.21_89], [-2.85_85, -1.89_08, -2.37_23], [-2.64_99, -1.77_50, -2.25_58]]    )

            self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,     __snake_case     ,     atol=1E-4    )    )













'''simple docstring'''






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A__							:  List[str]    =imread(r'''digital_image_processing/image_data/lena_small.jpg''')
A__							:  Union[str, Any]    =cvtColor(img, COLOR_BGR2GRAY)
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				cn.convert_to_negative(lowerCAmelCase    )
      # assert negative_img array for at least one True
      assert negative_img.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      with Image.open("""digital_image_processing/image_data/lena_small.jpg"""    ) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(lowerCAmelCase				,      1_10    )    ).startswith(
                """<PIL.Image.Image image mode=RGB size=100x100 at"""    )
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				canny.gen_gaussian_kernel(9				,      sigma=1.4    )
      # Assert ambiguous array
      assert resp.all()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				imread("""digital_image_processing/image_data/lena_small.jpg"""				,      0    )
      # assert ambiguous array for all == True
      assert canny_img.all()
      _lowerCAmelCase					=				canny.canny(lowerCAmelCase    )
      # assert canny array for at least one True
      assert canny_array.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      assert gg.gaussian_filter(lowerCAmelCase				,      5				,      sigma=0.9    ).all()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]    )
      _lowerCAmelCase					=				conv.img_convolve(lowerCAmelCase				,      lowerCAmelCase    ).astype(lowerCAmelCase    )
      assert res.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      assert med.median_filter(lowerCAmelCase				,      3    ).any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase	,				_lowerCAmelCase					=				sob.sobel_filter(lowerCAmelCase    )
      assert grad.any() and theta.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				sp.make_sepia(lowerCAmelCase				,      20    )
      assert sepia.all()
def   UpperCamelCase__ (						lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg"    ):





      """simple docstring"""


      _lowerCAmelCase					=				bs.Burkes(imread(lowerCAmelCase				,      1    )				,      1_20    )
      burkes.process()
      assert burkes.output_img.any()
def   UpperCamelCase__ (						lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg"				,      ):





      """simple docstring"""


      _lowerCAmelCase					=				rs.NearestNeighbour(imread(lowerCAmelCase				,      1    )				,      4_00				,      2_00    )
      nn.process()
      assert nn.output.any()





def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				"""digital_image_processing/image_data/lena.jpg"""

      # Reading the image and converting it to grayscale.
      _lowerCAmelCase					=				imread(lowerCAmelCase				,      0    )

      # Test for get_neighbors_pixel function() return not None
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				image[x_coordinate][y_coordinate]

      _lowerCAmelCase					=				lbp.get_neighbors_pixel(
          lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )

      assert neighbors_pixels is not None

      # Test for local_binary_pattern function()
      # Create a numpy array as the same height and width of read image
      _lowerCAmelCase					=				np.zeros((image.shape[0], image.shape[1])    )

      # Iterating through the image and calculating the local binary pattern value
      # for each pixel.
      for i in range(0				,      image.shape[0]    ):
            for j in range(0				,      image.shape[1]    ):
                  _lowerCAmelCase					=				lbp.local_binary_value(lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )

      assert lbp_image.any()






