


from __future__ import annotations

import unittest

from transformers import DebertaConfig, 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 (
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
  )



class       UpperCAmelCase_		:
       '''simple docstring'''






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




              """simple docstring"""




              UpperCamelCase			:		Any      		=						parent
              UpperCamelCase			:		int      		=						13
              UpperCamelCase			:		str      		=						7
              UpperCamelCase			:		int      		=						True
              UpperCamelCase			:		Dict      		=						True
              UpperCamelCase			:		List[Any]      		=						True
              UpperCamelCase			:		List[str]      		=						True
              UpperCamelCase			:		Any      		=						99
              UpperCamelCase			:		Optional[int]      		=						32
              UpperCamelCase			:		Any      		=						2
              UpperCamelCase			:		List[Any]      		=						4
              UpperCamelCase			:		List[Any]      		=						37
              UpperCamelCase			:		List[Any]      		=						'''gelu'''
              UpperCamelCase			:		List[Any]      		=						0.1
              UpperCamelCase			:		Union[str, Any]      		=						0.1
              UpperCamelCase			:		Optional[int]      		=						512
              UpperCamelCase			:		Dict      		=						16
              UpperCamelCase			:		Union[str, Any]      		=						False
              UpperCamelCase			:		Optional[int]      		=						-1
              UpperCamelCase			:		List[Any]      		=						True
              UpperCamelCase			:		int      		=						2
              UpperCamelCase			:		Optional[Any]      		=						0.02
              UpperCamelCase			:		List[Any]      		=						3
              UpperCamelCase			:		Tuple      		=						4
              UpperCamelCase			:		Optional[Any]      		=						None






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		Any      		=						ids_tensor([self.batch_size, self.seq_length]       ,    self.vocab_size  )

              UpperCamelCase			:		Dict      		=						None
              if self.use_input_mask:
                     UpperCamelCase			:		Dict      		=						random_attention_mask([self.batch_size, self.seq_length]  )

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

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

              UpperCamelCase			:		Union[str, Any]      		=						DebertaConfig(
                  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       ,    max_relative_positions=self.max_relative_positions       ,    position_biased_input=self.position_biased_input       ,    initializer_range=self.initializer_range       ,    return_dict=__SCREAMING_SNAKE_CASE       ,    )

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






       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  ):




              """simple docstring"""




              UpperCamelCase			:		Union[str, Any]      		=						TFDebertaModel(config=__SCREAMING_SNAKE_CASE  )
              UpperCamelCase			:		Optional[Any]      		=						{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

              UpperCamelCase			:		Tuple      		=						[input_ids, input_mask]
              UpperCamelCase			:		Optional[int]      		=						model(__SCREAMING_SNAKE_CASE  )

              UpperCamelCase			:		Tuple      		=						model(__SCREAMING_SNAKE_CASE  )

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






       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  ):




              """simple docstring"""




              UpperCamelCase			:		Dict      		=						TFDebertaForMaskedLM(config=__SCREAMING_SNAKE_CASE  )
              UpperCamelCase			:		Union[str, Any]      		=						{
                  '''input_ids''': input_ids,
                  '''attention_mask''': input_mask,
                  '''token_type_ids''': token_type_ids,
              }
              UpperCamelCase			:		Tuple      		=						model(__SCREAMING_SNAKE_CASE  )
              self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.seq_length, self.vocab_size)  )






       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  ):




              """simple docstring"""




              UpperCamelCase			:		Tuple      		=						self.num_labels
              UpperCamelCase			:		List[Any]      		=						TFDebertaForSequenceClassification(config=__SCREAMING_SNAKE_CASE  )
              UpperCamelCase			:		Optional[int]      		=						{
                  '''input_ids''': input_ids,
                  '''attention_mask''': input_mask,
                  '''token_type_ids''': token_type_ids,
              }

              UpperCamelCase			:		Optional[Any]      		=						model(__SCREAMING_SNAKE_CASE  )
              self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_labels)  )






       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  ):




              """simple docstring"""




              UpperCamelCase			:		str      		=						self.num_labels
              UpperCamelCase			:		List[str]      		=						TFDebertaForTokenClassification(config=__SCREAMING_SNAKE_CASE  )
              UpperCamelCase			:		int      		=						{
                  '''input_ids''': input_ids,
                  '''attention_mask''': input_mask,
                  '''token_type_ids''': token_type_ids,
              }
              UpperCamelCase			:		int      		=						model(__SCREAMING_SNAKE_CASE  )
              self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.seq_length, self.num_labels)  )






       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  ):




              """simple docstring"""




              UpperCamelCase			:		Any      		=						TFDebertaForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
              UpperCamelCase			:		Dict      		=						{
                  '''input_ids''': input_ids,
                  '''attention_mask''': input_mask,
                  '''token_type_ids''': token_type_ids,
              }

              UpperCamelCase			:		int      		=						model(__SCREAMING_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  ):




              """simple docstring"""




              UpperCamelCase			:		List[str]      		=						self.prepare_config_and_inputs()
              (
                  (
                  UpperCamelCase				
              )		,       (
                  UpperCamelCase				
              )		,       (
                  UpperCamelCase				
              )		,       (
                  UpperCamelCase				
              )		,       (
                  UpperCamelCase				
              )		,       (
                  UpperCamelCase				
              )		,       (
                  UpperCamelCase				
              )		,       				
              )			:		int      		=						config_and_inputs
              UpperCamelCase			:		int      		=						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
              return config, inputs_dict




@require_tf
class       UpperCAmelCase_		(							__snake_case,   __snake_case,   unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase					:   Optional[Any]							=    (
           (
               TFDebertaModel,
               TFDebertaForMaskedLM,
               TFDebertaForQuestionAnswering,
               TFDebertaForSequenceClassification,
               TFDebertaForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       __UpperCamelCase					:   Any							=    (
           {
               'feature-extraction': TFDebertaModel,
               'fill-mask': TFDebertaForMaskedLM,
               'question-answering': TFDebertaForQuestionAnswering,
               'text-classification': TFDebertaForSequenceClassification,
               'token-classification': TFDebertaForTokenClassification,
               'zero-shot': TFDebertaForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )

       __UpperCamelCase					:   List[str]							=    False
       __UpperCamelCase					:   Tuple							=    False






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		Tuple      		=						TFDebertaModelTester(self  )
              UpperCamelCase			:		str      		=						ConfigTester(self       ,    config_class=__SCREAMING_SNAKE_CASE       ,    hidden_size=37  )






       def        _lowercase      (   self  ):




              """simple docstring"""




              self.config_tester.run_common_tests()






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		Dict      		=						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE  )






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		int      		=						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE  )






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		Optional[Any]      		=						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE  )






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		Optional[Any]      		=						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE  )






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		Optional[int]      		=						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE  )






       @slow
       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		List[Any]      		=						TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''  )
              self.assertIsNotNone(__SCREAMING_SNAKE_CASE  )




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






       @unittest.skip(reason='''Model not available yet'''  )
       def        _lowercase      (   self  ):




              """simple docstring"""




              pass






       @slow
       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		Dict      		=						TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''  )
              UpperCamelCase			:		Optional[Any]      		=						tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  )
              UpperCamelCase			:		List[Any]      		=						tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
              UpperCamelCase			:		int      		=						model(__SCREAMING_SNAKE_CASE       ,    attention_mask=__SCREAMING_SNAKE_CASE  )[0]

              UpperCamelCase			:		int      		=						tf.constant(
                  [
                      [
                          [-0.59_855_896, -0.80_552_566, -0.8_462_135],
                          [1.4_484_025, -0.93_483_794, -0.80_593_085],
                          [0.3_122_741, 0.00_316_059, -1.4_131_377],
                      ]
                  ]  )
              tf.debugging.assert_near(output[:, 1:4, 1:4]       ,    __SCREAMING_SNAKE_CASE       ,    atol=1e-4  )




def    UpperCamelCase(					lowercase_							)	->				str:





  '''simple docstring'''





  if len(lowercase_							) == 0 or not all(len(lowercase_							) == 2 for pair in coordinates							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  if len({tuple(lowercase_							) for pair in coordinates}							) != len(lowercase_							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  snake_case_       						=      {x for x, _ in coordinates}
  if len(lowercase_							) == 1:
    return f'''x={coordinates[0][0]}'''

  if len(lowercase_							) != len(lowercase_							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  snake_case_       						=      len(lowercase_							)

  snake_case_       						=      0
  snake_case_       						=      []
  # put the x and x to the power values in a matrix
  while count_of_line < x:
    snake_case_       						=      0
    snake_case_       						=      coordinates[count_of_line][0]
    snake_case_       						=      []
    while count_in_line < x:
      count_line.append(a ** (x - (count_in_line + 1))							)
      count_in_line += 1
    matrix.append(lowercase_							)
    count_of_line += 1

  snake_case_       						=      0
  # put the y values into a vector
  snake_case_       						=      []
  while count_of_line < x:
    vector.append(coordinates[count_of_line][1]							)
    count_of_line += 1

  snake_case_       						=      0

  while count < x:
    snake_case_       						=      0
    while zahlen < x:
      if count == zahlen:
        zahlen += 1
      if zahlen == x:
        break
      snake_case_       						=      matrix[zahlen][count] / matrix[count][count]
      for counting_columns, item in enumerate(matrix[count]							):
        # manipulating all the values in the matrix
        matrix[zahlen][counting_columns] -= item * bruch
      # manipulating the values in the vector
      vector[zahlen] -= vector[count] * bruch
      zahlen += 1
    count += 1

  snake_case_       						=      0
  # make solutions
  snake_case_       						=      []
  while count < x:
    solution.append(str(vector[count] / matrix[count][count]							)							)
    count += 1

  snake_case_       						=      0
  snake_case_       						=      """f(x)="""

  while count < x:
    snake_case_       						=      solution[count].split("""E"""							)
    if len(lowercase_							) > 1:
      snake_case_       						=      f'''{remove_e[0]}*10^{remove_e[1]}'''
    solved += f'''x^{x - (count + 1)}*{solution[count]}'''
    if count + 1 != x:
      solved += "+"
    count += 1

  return solved


if __name__ == "__main__":
    print(points_to_polynomial([]))
    print(points_to_polynomial([[]]))
    print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
    print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
    print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
    print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
    print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
    print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
    print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
    print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))