
import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
   import torch

   from transformers import (
       XLMForMultipleChoice,
       XLMForQuestionAnswering,
       XLMForQuestionAnsweringSimple,
       XLMForSequenceClassification,
       XLMForTokenClassification,
       XLMModel,
       XLMWithLMHeadModel,
   )
   from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST






class   UpperCAmelCase		:



   def __init__(self		:					Optional[Any]    ,						snake_case__		:					str    ,						snake_case__		:					Optional[Any]=13    ,						snake_case__		:					List[str]=7    ,						snake_case__		:					Dict=True    ,						snake_case__		:					str=True    ,						snake_case__		:					Optional[Any]=True    ,						snake_case__		:					Tuple=True    ,						snake_case__		:					Union[str, Any]=True    ,						snake_case__		:					Optional[int]=False    ,						snake_case__		:					Tuple=False    ,						snake_case__		:					Tuple=False    ,						snake_case__		:					List[str]=2    ,						snake_case__		:					List[Any]=99    ,						snake_case__		:					List[Any]=0    ,						snake_case__		:					List[Any]=32    ,						snake_case__		:					Tuple=5    ,						snake_case__		:					int=4    ,						snake_case__		:					str=0.1    ,						snake_case__		:					Dict=0.1    ,						snake_case__		:					Optional[Any]=5_12    ,						snake_case__		:					Optional[Any]=2    ,						snake_case__		:					Tuple=0.02    ,						snake_case__		:					Optional[int]=2    ,						snake_case__		:					Union[str, Any]=4    ,						snake_case__		:					int="last"    ,						snake_case__		:					str=True    ,						snake_case__		:					Dict=None    ,						snake_case__		:					int=0    ,						)		->	List[Any]:


          '''simple docstring'''


          snake_case       :   Tuple							=      parent
          snake_case       :   List[str]							=      batch_size
          snake_case       :   Dict							=      seq_length
          snake_case       :   List[str]							=      is_training
          snake_case       :   Dict							=      use_input_lengths
          snake_case       :   List[str]							=      use_token_type_ids
          snake_case       :   Dict							=      use_labels
          snake_case       :   List[Any]							=      gelu_activation
          snake_case       :   Any							=      sinusoidal_embeddings
          snake_case       :   Tuple							=      causal
          snake_case       :   str							=      asm
          snake_case       :   Tuple							=      n_langs
          snake_case       :   int							=      vocab_size
          snake_case       :   Any							=      n_special
          snake_case       :   List[Any]							=      hidden_size
          snake_case       :   Optional[int]							=      num_hidden_layers
          snake_case       :   List[Any]							=      num_attention_heads
          snake_case       :   Any							=      hidden_dropout_prob
          snake_case       :   Tuple							=      attention_probs_dropout_prob
          snake_case       :   Dict							=      max_position_embeddings
          snake_case       :   int							=      type_sequence_label_size
          snake_case       :   List[str]							=      initializer_range
          snake_case       :   str							=      num_labels
          snake_case       :   Optional[Any]							=      num_choices
          snake_case       :   Union[str, Any]							=      summary_type
          snake_case       :   Any							=      use_proj
          snake_case       :   int							=      scope
          snake_case       :   Optional[Any]							=      bos_token_id



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size )
          snake_case       :   Optional[int]							=      random_attention_mask([self.batch_size, self.seq_length] )

          snake_case       :   Tuple							=      None
          if self.use_input_lengths:
                 snake_case       :   List[str]							=      (
                     ids_tensor([self.batch_size]    ,						vocab_size=2 ) + self.seq_length - 2
                 )  # small variation of seq_length

          snake_case       :   Any							=      None
          if self.use_token_type_ids:
                 snake_case       :   str							=      ids_tensor([self.batch_size, self.seq_length]    ,						self.n_langs )

          snake_case       :   Optional[int]							=      None
          snake_case       :   Optional[int]							=      None
          snake_case       :   Union[str, Any]							=      None
          if self.use_labels:
                 snake_case       :   Optional[Any]							=      ids_tensor([self.batch_size]    ,						self.type_sequence_label_size )
                 snake_case       :   int							=      ids_tensor([self.batch_size, self.seq_length]    ,						self.num_labels )
                 snake_case       :   Union[str, Any]							=      ids_tensor([self.batch_size]    ,						2 ).float()
                 snake_case       :   int							=      ids_tensor([self.batch_size]    ,						self.num_choices )

          snake_case       :   List[Any]							=      self.get_config()

          return (
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              choice_labels,
              input_mask,
          )



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


          '''simple docstring'''


          return XLMConfig(
              vocab_size=self.vocab_size    ,						n_special=self.n_special    ,						emb_dim=self.hidden_size    ,						n_layers=self.num_hidden_layers    ,						n_heads=self.num_attention_heads    ,						dropout=self.hidden_dropout_prob    ,						attention_dropout=self.attention_probs_dropout_prob    ,						gelu_activation=self.gelu_activation    ,						sinusoidal_embeddings=self.sinusoidal_embeddings    ,						asm=self.asm    ,						causal=self.causal    ,						n_langs=self.n_langs    ,						max_position_embeddings=self.max_position_embeddings    ,						initializer_range=self.initializer_range    ,						summary_type=self.summary_type    ,						use_proj=self.use_proj    ,						num_labels=self.num_labels    ,						bos_token_id=self.bos_token_id    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					Any    ,						snake_case__		:					List[str]    ,						snake_case__		:					str    ,						snake_case__		:					List[Any]    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					int    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					str    ,						)		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   str							=      XLMModel(config=snake_case__ )
          model.to(snake_case__ )
          model.eval()
          snake_case       :   Dict							=      model(snake_case__    ,						lengths=snake_case__    ,						langs=snake_case__ )
          snake_case       :   Optional[Any]							=      model(snake_case__    ,						langs=snake_case__ )
          snake_case       :   Union[str, Any]							=      model(snake_case__ )
          self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					Tuple    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					List[str]    ,						snake_case__		:					Any    ,						snake_case__		:					Any    ,						snake_case__		:					List[Any]    ,						)		->	str:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      XLMWithLMHeadModel(snake_case__ )
          model.to(snake_case__ )
          model.eval()

          snake_case       :   str							=      model(snake_case__    ,						token_type_ids=snake_case__    ,						labels=snake_case__ )
          self.parent.assertEqual(result.loss.shape    ,						() )
          self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.vocab_size) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Any    ,						snake_case__		:					List[str]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Tuple    ,						snake_case__		:					str    ,						snake_case__		:					int    ,						snake_case__		:					int    ,						)		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   int							=      XLMForQuestionAnsweringSimple(snake_case__ )
          model.to(snake_case__ )
          model.eval()

          snake_case       :   int							=      model(snake_case__ )

          snake_case       :   Optional[int]							=      model(snake_case__    ,						start_positions=snake_case__    ,						end_positions=snake_case__ )
          snake_case       :   Tuple							=      outputs
          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 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					int    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Dict    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					Any    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Dict    ,						snake_case__		:					Tuple    ,						)		->	str:


          '''simple docstring'''


          snake_case       :   int							=      XLMForQuestionAnswering(snake_case__ )
          model.to(snake_case__ )
          model.eval()

          snake_case       :   int							=      model(snake_case__ )

          snake_case       :   Optional[int]							=      model(
              snake_case__    ,						start_positions=snake_case__    ,						end_positions=snake_case__    ,						cls_index=snake_case__    ,						is_impossible=snake_case__    ,						p_mask=snake_case__    ,						)

          snake_case       :   int							=      model(
              snake_case__    ,						start_positions=snake_case__    ,						end_positions=snake_case__    ,						cls_index=snake_case__    ,						is_impossible=snake_case__    ,						)

          ((snake_case)				,					)       :   List[Any]							=      result_with_labels.to_tuple()

          snake_case       :   Optional[Any]							=      model(snake_case__    ,						start_positions=snake_case__    ,						end_positions=snake_case__ )

          ((snake_case)				,					)       :   int							=      result_with_labels.to_tuple()

          self.parent.assertEqual(result_with_labels.loss.shape    ,						() )
          self.parent.assertEqual(result.start_top_log_probs.shape    ,						(self.batch_size, model.config.start_n_top) )
          self.parent.assertEqual(result.start_top_index.shape    ,						(self.batch_size, model.config.start_n_top) )
          self.parent.assertEqual(
              result.end_top_log_probs.shape    ,						(self.batch_size, model.config.start_n_top * model.config.end_n_top) )
          self.parent.assertEqual(
              result.end_top_index.shape    ,						(self.batch_size, model.config.start_n_top * model.config.end_n_top) )
          self.parent.assertEqual(result.cls_logits.shape    ,						(self.batch_size,) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					Dict    ,						snake_case__		:					Any    ,						snake_case__		:					Any    ,						snake_case__		:					int    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					int    ,						snake_case__		:					str    ,						snake_case__		:					Dict    ,						)		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      XLMForSequenceClassification(snake_case__ )
          model.to(snake_case__ )
          model.eval()

          snake_case       :   Dict							=      model(snake_case__ )
          snake_case       :   List[str]							=      model(snake_case__    ,						labels=snake_case__ )
          self.parent.assertEqual(result.loss.shape    ,						() )
          self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					Any    ,						snake_case__		:					Any    ,						snake_case__		:					int    ,						snake_case__		:					str    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					str    ,						snake_case__		:					List[str]    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Optional[int]    ,						)		->	Tuple:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      self.num_labels
          snake_case       :   Tuple							=      XLMForTokenClassification(snake_case__ )
          model.to(snake_case__ )
          model.eval()

          snake_case       :   Dict							=      model(snake_case__    ,						attention_mask=snake_case__    ,						labels=snake_case__ )
          self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.num_labels) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Any    ,						snake_case__		:					str    ,						snake_case__		:					Tuple    ,						snake_case__		:					Tuple    ,						snake_case__		:					Tuple    ,						snake_case__		:					Tuple    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Union[str, Any]    ,						)		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   List[str]							=      self.num_choices
          snake_case       :   str							=      XLMForMultipleChoice(config=snake_case__ )
          model.to(snake_case__ )
          model.eval()
          snake_case       :   Dict							=      input_ids.unsqueeze(1 ).expand(-1    ,						self.num_choices    ,						-1 ).contiguous()
          snake_case       :   int							=      token_type_ids.unsqueeze(1 ).expand(-1    ,						self.num_choices    ,						-1 ).contiguous()
          snake_case       :   Tuple							=      input_mask.unsqueeze(1 ).expand(-1    ,						self.num_choices    ,						-1 ).contiguous()
          snake_case       :   Union[str, Any]							=      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 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Any:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      self.prepare_config_and_inputs()
          (
              (
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					
          )       :   Dict							=      config_and_inputs
          snake_case       :   Dict							=      {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
          return config, inputs_dict






@require_torch
class   UpperCAmelCase		(						A_       ,A_       ,A_       ,unittest.TestCase ):
   A__					:		Dict						     =     (
       (
           XLMModel,
           XLMWithLMHeadModel,
           XLMForQuestionAnswering,
           XLMForSequenceClassification,
           XLMForQuestionAnsweringSimple,
           XLMForTokenClassification,
           XLMForMultipleChoice,
       )
       if is_torch_available()
       else ()
   )
   A__					:		Any						     =     (
       (XLMWithLMHeadModel,) if is_torch_available() else ()
   )  # TODO (PVP): Check other models whether language generation is also applicable
   A__					:		str						     =     (
       {
           "feature-extraction": XLMModel,
           "fill-mask": XLMWithLMHeadModel,
           "question-answering": XLMForQuestionAnsweringSimple,
           "text-classification": XLMForSequenceClassification,
           "text-generation": XLMWithLMHeadModel,
           "token-classification": XLMForTokenClassification,
           "zero-shot": XLMForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					Tuple    ,						snake_case__		:					int    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Dict    ,						snake_case__		:					List[str] )		->	int:


          '''simple docstring'''


          if (
              pipeline_test_casse_name == "QAPipelineTests"
              and tokenizer_name is not None
              and not tokenizer_name.endswith("Fast" )
          ):
                 # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                 # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                 # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                 return True

          return False



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Dict    ,						snake_case__		:					str=False )		->	int:


          '''simple docstring'''


          snake_case       :   Tuple							=      super()._prepare_for_class(snake_case__    ,						snake_case__    ,						return_labels=snake_case__ )

          if return_labels:
                 if model_class.__name__ == "XLMForQuestionAnswering":
                        snake_case       :   Tuple							=      torch.zeros(
                            self.model_tester.batch_size    ,						dtype=torch.long    ,						device=snake_case__ )
                        snake_case       :   Optional[int]							=      torch.zeros(
                            self.model_tester.batch_size    ,						dtype=torch.long    ,						device=snake_case__ )

          return inputs_dict



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	int:


          '''simple docstring'''


          snake_case       :   Any							=      XLMModelTester(self )
          snake_case       :   List[str]							=      ConfigTester(self    ,						config_class=snake_case__    ,						emb_dim=37 )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Optional[Any]:


          '''simple docstring'''


          self.config_tester.run_common_tests()



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Any:


          '''simple docstring'''


          snake_case       :   int							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_model(*snake_case__ )



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


          '''simple docstring'''


          snake_case       :   str							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_lm_head(*snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Dict:


          '''simple docstring'''


          snake_case       :   List[str]							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_simple_qa(*snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Tuple:


          '''simple docstring'''


          snake_case       :   List[str]							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_qa(*snake_case__ )



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


          '''simple docstring'''


          snake_case       :   str							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_sequence_classif(*snake_case__ )



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


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_token_classif(*snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Any:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					str    ,						snake_case__		:					List[Any]    ,						snake_case__		:					int    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Optional[Any]=False    ,						snake_case__		:					int=1 )		->	Optional[int]:


          '''simple docstring'''


          self.assertIsInstance(snake_case__    ,						snake_case__ )
          self.assertListEqual(
              [isinstance(snake_case__    ,						snake_case__ ) for iter_attentions in attentions]    ,						[True] * len(snake_case__ ) )
          self.assertEqual(len(snake_case__ )    ,						(max_length - min_length) * num_beam_groups )

          for idx, iter_attentions in enumerate(snake_case__ ):
                 # adds PAD dummy token
                 snake_case       :   int							=      min_length + idx + 1
                 snake_case       :   Any							=      min_length + idx + 1

                 snake_case       :   str							=      (
                     batch_size * num_beam_groups,
                     config.num_attention_heads,
                     tgt_len,
                     src_len,
                 )
                 # check attn size
                 self.assertListEqual(
                     [layer_attention.shape for layer_attention in iter_attentions]    ,						[expected_shape] * len(snake_case__ ) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					List[str]    ,						snake_case__		:					Dict    ,						snake_case__		:					Tuple    ,						snake_case__		:					Dict    ,						snake_case__		:					Optional[int]=False    ,						snake_case__		:					str=1 )		->	Dict:


          '''simple docstring'''


          self.assertIsInstance(snake_case__    ,						snake_case__ )
          self.assertListEqual(
              [isinstance(snake_case__    ,						snake_case__ ) for iter_hidden_states in hidden_states]    ,						[True] * len(snake_case__ )    ,						)
          self.assertEqual(len(snake_case__ )    ,						(max_length - min_length) * num_beam_groups )

          for idx, iter_hidden_states in enumerate(snake_case__ ):
                 # adds PAD dummy token
                 snake_case       :   str							=      min_length + idx + 1
                 snake_case       :   Tuple							=      (batch_size * num_beam_groups, seq_len, config.hidden_size)
                 # check hidden size
                 self.assertListEqual(
                     [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]    ,						[expected_shape] * len(snake_case__ )    ,						)
          pass



   @slow
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	List[str]:


          '''simple docstring'''


          for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case       :   Dict							=      XLMModel.from_pretrained(snake_case__ )
                 self.assertIsNotNone(snake_case__ )






@require_torch
class   UpperCAmelCase		(						unittest.TestCase ):



   @slow
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   int							=      XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048" )
          model.to(snake_case__ )
          snake_case       :   Any							=      torch.tensor([[14, 4_47]]    ,						dtype=torch.long    ,						device=snake_case__ )  # the president
          snake_case       :   Dict							=      [
              14,
              4_47,
              14,
              4_47,
              14,
              4_47,
              14,
              4_47,
              14,
              4_47,
              14,
              4_47,
              14,
              4_47,
              14,
              4_47,
              14,
              4_47,
              14,
              4_47,
          ]  # the president the president the president the president the president the president the president the president the president the president
          # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
          snake_case       :   Optional[int]							=      model.generate(snake_case__    ,						do_sample=snake_case__ )
          self.assertListEqual(output_ids[0].cpu().numpy().tolist()    ,						snake_case__ )













"""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

lowercase_				     =     imread(R'digital_image_processing/image_data/lena_small.jpg')
lowercase_				     =     cvtColor(img, COLOR_BGR2GRAY)



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= cn.convert_to_negative(__UpperCamelCase )
  # assert negative_img array for at least one True
  assert negative_img.any()



def 					lowerCAmelCase				(    ):




  """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(__UpperCamelCase		,       1_1_0 ) ).startswith(
        '''<PIL.Image.Image image mode=RGB size=100x100 at''' )



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= canny.gen_gaussian_kernel(9		,       sigma=1.4 )
  # Assert ambiguous array
  assert resp.all()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= imread('''digital_image_processing/image_data/lena_small.jpg'''		,       0 )
  # assert ambiguous array for all == True
  assert canny_img.all()
  __A									= canny.canny(__UpperCamelCase )
  # assert canny array for at least one True
  assert canny_array.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  assert gg.gaussian_filter(__UpperCamelCase		,       5		,       sigma=0.9 ).all()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
  __A									= conv.img_convolve(__UpperCamelCase		,       __UpperCamelCase ).astype(__UpperCamelCase )
  assert res.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  assert med.median_filter(__UpperCamelCase		,       3 ).any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A			,    __A									= sob.sobel_filter(__UpperCamelCase )
  assert grad.any() and theta.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= sp.make_sepia(__UpperCamelCase		,       2_0 )
  assert sepia.all()



def 					lowerCAmelCase				(    __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg" ):




  """simple docstring"""







  __A									= bs.Burkes(imread(__UpperCamelCase		,       1 )		,       1_2_0 )
  burkes.process()
  assert burkes.output_img.any()



def 					lowerCAmelCase				(    __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"		,       ):




  """simple docstring"""







  __A									= rs.NearestNeighbour(imread(__UpperCamelCase		,       1 )		,       4_0_0		,       2_0_0 )
  nn.process()
  assert nn.output.any()





def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= '''digital_image_processing/image_data/lena.jpg'''

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

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

  __A									= lbp.get_neighbors_pixel(
      __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase )

  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
  __A									= 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] ):
      __A									= lbp.local_binary_value(__UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase )

  assert lbp_image.any()




