





'''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    UpperCAmelCase	:
 def __init__(				self      :Optional[Any]						,       lowercase_      :List[Any]						,       lowercase_      :Tuple=13						,       lowercase_      :Optional[int]=7						,       lowercase_      :Union[str, Any]=True						,       lowercase_      :Dict=True						,       lowercase_      :Optional[Any]=True						,       lowercase_      :Any=True						,       lowercase_      :Tuple=99						,       lowercase_      :str=32						,       lowercase_      :List[Any]=2						,       lowercase_      :int=4						,       lowercase_      :Dict=37						,       lowercase_      :Optional[Any]="gelu"						,       lowercase_      :Dict=0.1						,       lowercase_      :str=0.1						,       lowercase_      :Dict=5_12						,       lowercase_      :Dict=16						,       lowercase_      :str=2						,       lowercase_      :Tuple=0.0_2						,       lowercase_      :str=False						,       lowercase_      :Optional[int]=True						,       lowercase_      :Dict="None"						,       lowercase_      :Optional[Any]=3						,       lowercase_      :int=4						,       lowercase_      :Optional[int]=None						,       )->							int:
     A__				=			parent
     A__				=			batch_size
     A__				=			seq_length
     A__				=			is_training
     A__				=			use_input_mask
     A__				=			use_token_type_ids
     A__				=			use_labels
     A__				=			vocab_size
     A__				=			hidden_size
     A__				=			num_hidden_layers
     A__				=			num_attention_heads
     A__				=			intermediate_size
     A__				=			hidden_act
     A__				=			hidden_dropout_prob
     A__				=			attention_probs_dropout_prob
     A__				=			max_position_embeddings
     A__				=			type_vocab_size
     A__				=			type_sequence_label_size
     A__				=			initializer_range
     A__				=			num_labels
     A__				=			num_choices
     A__				=			relative_attention
     A__				=			position_biased_input
     A__				=			pos_att_type
     A__				=			scope
 def      UpperCAmelCase_							(				self      :int							)->							List[str]:
     A__				=			ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size							)

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

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

     A__				=			None
     A__				=			None
     A__				=			None
     if self.use_labels:
         A__				=			ids_tensor([self.batch_size]						,       self.type_sequence_label_size							)
         A__				=			ids_tensor([self.batch_size, self.seq_length]						,       self.num_labels							)

     A__				=			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=lowercase_						,       )

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 def      UpperCAmelCase_							(				self      :str						,       lowercase_      :Optional[int]						,       lowercase_      :str						,       lowercase_      :List[Any]						,       lowercase_      :Any						,       lowercase_      :Any						,       lowercase_      :Optional[Any]						,       lowercase_      :Dict							)->							Optional[Any]:
     A__				=			TFDebertaVaModel(config=lowercase_							)
     A__				=			{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

     A__				=			[input_ids, input_mask]
     A__				=			model(lowercase_							)

     A__				=			model(lowercase_							)

     self.parent.assertEqual(result.last_hidden_state.shape						,       (self.batch_size, self.seq_length, self.hidden_size)							)
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :List[Any]						,       lowercase_      :Dict						,       lowercase_      :Union[str, Any]						,       lowercase_      :Union[str, Any]						,       lowercase_      :Dict						,       lowercase_      :Dict						,       lowercase_      :Any							)->							Optional[Any]:
     A__				=			TFDebertaVaForMaskedLM(config=lowercase_							)
     A__				=			{
         "input_ids": input_ids,
         "attention_mask": input_mask,
         "token_type_ids": token_type_ids,
     }
     A__				=			model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.seq_length, self.vocab_size)							)
 def      UpperCAmelCase_							(				self      :int						,       lowercase_      :Union[str, Any]						,       lowercase_      :Optional[Any]						,       lowercase_      :Optional[Any]						,       lowercase_      :int						,       lowercase_      :Tuple						,       lowercase_      :int						,       lowercase_      :List[str]							)->							List[str]:
     A__				=			self.num_labels
     A__				=			TFDebertaVaForSequenceClassification(config=lowercase_							)
     A__				=			{
         "input_ids": input_ids,
         "attention_mask": input_mask,
         "token_type_ids": token_type_ids,
     }

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

     A__				=			model(lowercase_							)
     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      :Tuple							)->							List[Any]:
     A__				=			self.prepare_config_and_inputs()
     (
         (
         A__
     ),	(
         A__
     ),	(
         A__
     ),	(
         A__
     ),	(
         A__
     ),	(
         A__
     ),	(
         A__
     ),	
     )				=			config_and_inputs
     A__				=			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
     return config, inputs_dict






@require_tf
class    UpperCAmelCase	(       UpperCamelCase__			,       UpperCamelCase__			,       unittest.TestCase				):
 __lowercase   		=				(
     (
         TFDebertaVaModel,
         TFDebertaVaForMaskedLM,
         TFDebertaVaForQuestionAnswering,
         TFDebertaVaForSequenceClassification,
         TFDebertaVaForTokenClassification,
     )
     if is_tf_available()
     else ()
 )
 __lowercase   		=				(
     {
         """feature-extraction""": TFDebertaVaModel,
         """fill-mask""": TFDebertaVaForMaskedLM,
         """question-answering""": TFDebertaVaForQuestionAnswering,
         """text-classification""": TFDebertaVaForSequenceClassification,
         """token-classification""": TFDebertaVaForTokenClassification,
         """zero-shot""": TFDebertaVaForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )

 __lowercase   		=				False
 __lowercase   		=				False
 def      UpperCAmelCase_							(				self      :Dict							)->							List[Any]:
     A__				=			TFDebertaVaModelTester(self							)
     A__				=			ConfigTester(self						,       config_class=lowercase_						,       hidden_size=37							)
 def      UpperCAmelCase_							(				self      :List[str]							)->							List[str]:
     self.config_tester.run_common_tests()
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Any:
     A__				=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_							)
 def      UpperCAmelCase_							(				self      :str							)->							str:
     A__				=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*lowercase_							)
 def      UpperCAmelCase_							(				self      :str							)->							List[str]:
     A__				=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*lowercase_							)
 def      UpperCAmelCase_							(				self      :Tuple							)->							Dict:
     A__				=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*lowercase_							)
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Union[str, Any]:
     A__				=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*lowercase_							)



 @slow
 def      UpperCAmelCase_							(				self      :Dict							)->							List[Any]:
     A__				=			TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"							)
     self.assertIsNotNone(lowercase_							)






@require_tf
class    UpperCAmelCase	(       unittest.TestCase				):
 @unittest.skip(reason="Model not available yet"							)
 def      UpperCAmelCase_							(				self      :List[str]							)->							Any:
     pass



 @slow
 def      UpperCAmelCase_							(				self      :Any							)->							str:
     A__				=			TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"							)
     A__				=			tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]							)
     A__				=			tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
     A__				=			model(lowercase_						,       attention_mask=lowercase_							)[0]

     A__				=			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]						,       lowercase_						,       atol=1E-4							)










'''simple docstring'''

from __future__ import annotations






class    UpperCAmelCase	:
 def __init__(				self      :Optional[int]						,       lowercase_      :int							)->							None:
     A__				=			order

     # a_{0} ... a_{k}
     A__				=			[1.0] + [0.0] * order
     # b_{0} ... b_{k}
     A__				=			[1.0] + [0.0] * order

     # x[n-1] ... x[n-k]
     A__				=			[0.0] * self.order
     # y[n-1] ... y[n-k]
     A__				=			[0.0] * self.order
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :list[float]						,       lowercase_      :list[float]							)->							None:
     if len(lowercase_							) < self.order:
         A__				=			[1.0, *a_coeffs]

     if len(lowercase_							) != self.order + 1:
         A__				=			(
             F"Expected a_coeffs to have {self.order + 1} elements "
             F"for {self.order}-order filter, got {len(lowercase_							)}"
         )
         raise ValueError(lowercase_							)

     if len(lowercase_							) != self.order + 1:
         A__				=			(
             F"Expected b_coeffs to have {self.order + 1} elements "
             F"for {self.order}-order filter, got {len(lowercase_							)}"
         )
         raise ValueError(lowercase_							)

     A__				=			a_coeffs
     A__				=			b_coeffs



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :float							)->							float:
     A__				=			0.0

     # Start at index 1 and do index 0 at the end.
     for i in range(1						,       self.order + 1							):
         result += (
             self.b_coeffs[i] * self.input_history[i - 1]
             - self.a_coeffs[i] * self.output_history[i - 1]
         )

     A__				=			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

     A__				=			self.input_history[:-1]
     A__				=			self.output_history[:-1]

     A__				=			sample
     A__				=			result

     return result



