



'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 (
     TFDebertaVaForMaskedLM,
     TFDebertaVaForQuestionAnswering,
     TFDebertaVaForSequenceClassification,
     TFDebertaVaForTokenClassification,
     TFDebertaVaModel,
 )






class 	lowerCAmelCase_:
  '''simple docstring'''




  def __init__(   self  ,__UpperCAmelCase  ,__UpperCAmelCase=13  ,__UpperCAmelCase=7  ,__UpperCAmelCase=True  ,__UpperCAmelCase=True  ,__UpperCAmelCase=True  ,__UpperCAmelCase=True  ,__UpperCAmelCase=99  ,__UpperCAmelCase=32  ,__UpperCAmelCase=2  ,__UpperCAmelCase=4  ,__UpperCAmelCase=37  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=512  ,__UpperCAmelCase=16  ,__UpperCAmelCase=2  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=False  ,__UpperCAmelCase=True  ,__UpperCAmelCase="None"  ,__UpperCAmelCase=3  ,__UpperCAmelCase=4  ,__UpperCAmelCase=None  ,)   ->		Tuple:
      lowerCAmelCase__			:  Optional[int]											= parent
      lowerCAmelCase__			:  int											= batch_size
      lowerCAmelCase__			:  Optional[int]											= seq_length
      lowerCAmelCase__			:  Union[str, Any]											= is_training
      lowerCAmelCase__			:  Union[str, Any]											= use_input_mask
      lowerCAmelCase__			:  Optional[int]											= use_token_type_ids
      lowerCAmelCase__			:  List[Any]											= use_labels
      lowerCAmelCase__			:  Optional[Any]											= vocab_size
      lowerCAmelCase__			:  Optional[int]											= hidden_size
      lowerCAmelCase__			:  int											= num_hidden_layers
      lowerCAmelCase__			:  str											= num_attention_heads
      lowerCAmelCase__			:  int											= intermediate_size
      lowerCAmelCase__			:  List[Any]											= hidden_act
      lowerCAmelCase__			:  List[str]											= hidden_dropout_prob
      lowerCAmelCase__			:  int											= attention_probs_dropout_prob
      lowerCAmelCase__			:  Union[str, Any]											= max_position_embeddings
      lowerCAmelCase__			:  List[Any]											= type_vocab_size
      lowerCAmelCase__			:  Any											= type_sequence_label_size
      lowerCAmelCase__			:  Dict											= initializer_range
      lowerCAmelCase__			:  Any											= num_labels
      lowerCAmelCase__			:  Union[str, Any]											= num_choices
      lowerCAmelCase__			:  int											= relative_attention
      lowerCAmelCase__			:  str											= position_biased_input
      lowerCAmelCase__			:  List[Any]											= pos_att_type
      lowerCAmelCase__			:  int											= scope




  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      lowerCAmelCase__			:  str											= ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size				)

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

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

      lowerCAmelCase__			:  Tuple											= None
      lowerCAmelCase__			:  Tuple											= None
      lowerCAmelCase__			:  int											= None
      if self.use_labels:
          lowerCAmelCase__			:  Optional[Any]											= ids_tensor([self.batch_size]  ,self.type_sequence_label_size				)
          lowerCAmelCase__			:  Tuple											= ids_tensor([self.batch_size, self.seq_length]  ,self.num_labels				)

      lowerCAmelCase__			:  int											= DebertaVaConfig(
          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  ,relative_attention=self.relative_attention  ,position_biased_input=self.position_biased_input  ,initializer_range=self.initializer_range  ,return_dict=__UpperCAmelCase  ,)

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




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Optional[int]:
      lowerCAmelCase__			:  Any											= TFDebertaVaModel(config=__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

      lowerCAmelCase__			:  Optional[Any]											= [input_ids, input_mask]
      lowerCAmelCase__			:  Optional[int]											= model(__UpperCAmelCase				)

      lowerCAmelCase__			:  Dict											= model(__UpperCAmelCase				)

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




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Any:
      lowerCAmelCase__			:  Tuple											= TFDebertaVaForMaskedLM(config=__UpperCAmelCase				)
      lowerCAmelCase__			:  Optional[Any]											= {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }
      lowerCAmelCase__			:  Optional[int]											= model(__UpperCAmelCase				)
      self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.vocab_size)				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Optional[Any]:
      lowerCAmelCase__			:  Dict											= self.num_labels
      lowerCAmelCase__			:  Union[str, Any]											= TFDebertaVaForSequenceClassification(config=__UpperCAmelCase				)
      lowerCAmelCase__			:  Optional[int]											= {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }

      lowerCAmelCase__			:  Dict											= model(__UpperCAmelCase				)
      self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Dict:
      lowerCAmelCase__			:  Any											= self.num_labels
      lowerCAmelCase__			:  str											= TFDebertaVaForTokenClassification(config=__UpperCAmelCase				)
      lowerCAmelCase__			:  int											= {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }
      lowerCAmelCase__			:  Optional[int]											= model(__UpperCAmelCase				)
      self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.num_labels)				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		List[Any]:
      lowerCAmelCase__			:  Dict											= TFDebertaVaForQuestionAnswering(config=__UpperCAmelCase				)
      lowerCAmelCase__			:  str											= {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }

      lowerCAmelCase__			:  Any											= model(__UpperCAmelCase				)
      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 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__			:  Union[str, Any]											= self.prepare_config_and_inputs()
      (
          (
          lowerCAmelCase__
      )				,       (
          lowerCAmelCase__
      )				,       (
          lowerCAmelCase__
      )				,       (
          lowerCAmelCase__
      )				,       (
          lowerCAmelCase__
      )				,       (
          lowerCAmelCase__
      )				,       (
          lowerCAmelCase__
      )				,       
      )			:  Tuple											= config_and_inputs
      lowerCAmelCase__			:  Optional[Any]											= {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
      return config, inputs_dict







@require_tf
class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				int           =				(
      (
          TFDebertaVaModel,
          TFDebertaVaForMaskedLM,
          TFDebertaVaForQuestionAnswering,
          TFDebertaVaForSequenceClassification,
          TFDebertaVaForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  __lowercase							:				Union[str, Any]           =				(
      {
          '''feature-extraction''': TFDebertaVaModel,
          '''fill-mask''': TFDebertaVaForMaskedLM,
          '''question-answering''': TFDebertaVaForQuestionAnswering,
          '''text-classification''': TFDebertaVaForSequenceClassification,
          '''token-classification''': TFDebertaVaForTokenClassification,
          '''zero-shot''': TFDebertaVaForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )

  __lowercase							:				List[str]           =				False
  __lowercase							:				Dict           =				False




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  str											= TFDebertaVaModelTester(self				)
      lowerCAmelCase__			:  Union[str, Any]											= ConfigTester(self  ,config_class=__UpperCAmelCase  ,hidden_size=37				)




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      self.config_tester.run_common_tests()




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__			:  List[Any]											= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__			:  int											= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__			:  int											= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__			:  Optional[Any]											= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      lowerCAmelCase__			:  Dict											= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase				)






  @slow
  def 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__			:  Any											= TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""				)
      self.assertIsNotNone(__UpperCAmelCase				)







@require_tf
class 	lowerCAmelCase_(    unittest.TestCase					):
  '''simple docstring'''




  @unittest.skip(reason="""Model not available yet"""				)
  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      pass






  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__			:  Optional[Any]											= TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""				)
      lowerCAmelCase__			:  Union[str, Any]											= tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]				)
      lowerCAmelCase__			:  str											= tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
      lowerCAmelCase__			:  Dict											= model(__UpperCAmelCase  ,attention_mask=__UpperCAmelCase				)[0]

      lowerCAmelCase__			:  List[str]											= tf.constant(
          [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]				)
      tf.debugging.assert_near(output[:, 1:4, 1:4]  ,__UpperCAmelCase  ,atol=1E-4				)







'''simple docstring'''





from PIL import Image
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__				,       lowerCAmelCase__			:  Dict											= image.size
    lowerCAmelCase__			:  int											= 0
    lowerCAmelCase__			:  int											= image.load()
    for i in range(UpperCamelCase      ):
        for j in range(UpperCamelCase      ):
            lowerCAmelCase__			:  int											= pixels[j, i]
            mean += pixel
    mean //= width * height

    for j in range(UpperCamelCase      ):
        for i in range(UpperCamelCase      ):
            lowerCAmelCase__			:  Dict											= 255 if pixels[i, j] > mean else 0
    return image


if __name__ == "__main__":
 _lowerCAmelCase										=     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
 image.save('''output_image_path''')


