


import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    DebertaVaForMaskedLM,
						    DebertaVaForMultipleChoice,
						    DebertaVaForQuestionAnswering,
						    DebertaVaForSequenceClassification,
						    DebertaVaForTokenClassification,
						    DebertaVaModel,
						)
						from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict=1_3     ,    SCREAMING_SNAKE_CASE	:		str=7     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=True     ,    SCREAMING_SNAKE_CASE	:		str=True     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=True     ,    SCREAMING_SNAKE_CASE	:		Dict=True     ,    SCREAMING_SNAKE_CASE	:		Dict=9_9     ,    SCREAMING_SNAKE_CASE	:		str=3_2     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=5     ,    SCREAMING_SNAKE_CASE	:		Dict=4     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=3_7     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]="gelu"     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=0.1     ,    SCREAMING_SNAKE_CASE	:		int=0.1     ,    SCREAMING_SNAKE_CASE	:		int=5_1_2     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1_6     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=2     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=0.02     ,    SCREAMING_SNAKE_CASE	:		str=False     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=True     ,    SCREAMING_SNAKE_CASE	:		Tuple="None"     ,    SCREAMING_SNAKE_CASE	:		List[str]=3     ,    SCREAMING_SNAKE_CASE	:		str=4     ,    SCREAMING_SNAKE_CASE	:		List[str]=None     ,    )			->	Tuple:




											'''simple docstring'''

											__snake_case				=		parent
											__snake_case				=		batch_size
											__snake_case				=		seq_length
											__snake_case				=		is_training
											__snake_case				=		use_input_mask
											__snake_case				=		use_token_type_ids
											__snake_case				=		use_labels
											__snake_case				=		vocab_size
											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		num_attention_heads
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		attention_probs_dropout_prob
											__snake_case				=		max_position_embeddings
											__snake_case				=		type_vocab_size
											__snake_case				=		type_sequence_label_size
											__snake_case				=		initializer_range
											__snake_case				=		num_labels
											__snake_case				=		num_choices
											__snake_case				=		relative_attention
											__snake_case				=		position_biased_input
											__snake_case				=		pos_att_type
											__snake_case				=		scope


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		ids_tensor([self.batch_size, self.seq_length]     ,    self.vocab_size						)

											__snake_case				=		None
											if self.use_input_mask:
																	__snake_case				=		ids_tensor([self.batch_size, self.seq_length]     ,    vocab_size=2						)

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

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

											__snake_case				=		self.get_config()

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	int:




											'''simple docstring'''

											return 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     ,    initializer_range=self.initializer_range     ,    relative_attention=self.relative_attention     ,    position_biased_input=self.position_biased_input     ,    pos_att_type=self.pos_att_type     ,    )


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Any:




											'''simple docstring'''

											self.parent.assertListEqual(list(result.loss.size()						)     ,    []						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		str						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		DebertaVaModel(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE						)[0]
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE						)[0]
											__snake_case				=		model(SCREAMING_SNAKE_CASE						)[0]

											self.parent.assertListEqual(list(sequence_output.size()						)     ,    [self.batch_size, self.seq_length, self.hidden_size]						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]						)			->	str:




											'''simple docstring'''

											__snake_case				=		DebertaVaForMaskedLM(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE     ,    labels=SCREAMING_SNAKE_CASE						)

											self.parent.assertEqual(result.logits.shape     ,    (self.batch_size, self.seq_length, self.vocab_size)						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Tuple						)			->	int:




											'''simple docstring'''

											__snake_case				=		self.num_labels
											__snake_case				=		DebertaVaForSequenceClassification(SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE     ,    labels=SCREAMING_SNAKE_CASE						)
											self.parent.assertListEqual(list(result.logits.size()						)     ,    [self.batch_size, self.num_labels]						)
											self.check_loss_output(SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		self.num_labels
											__snake_case				=		DebertaVaForTokenClassification(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE     ,    labels=SCREAMING_SNAKE_CASE						)
											self.parent.assertEqual(result.logits.shape     ,    (self.batch_size, self.seq_length, self.num_labels)						)


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




											'''simple docstring'''

											__snake_case				=		DebertaVaForQuestionAnswering(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(
											    SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE     ,    start_positions=SCREAMING_SNAKE_CASE     ,    end_positions=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 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		DebertaVaForMultipleChoice(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		input_ids.unsqueeze(1						).expand(-1     ,    self.num_choices     ,    -1						).contiguous()
											__snake_case				=		token_type_ids.unsqueeze(1						).expand(-1     ,    self.num_choices     ,    -1						).contiguous()
											__snake_case				=		input_mask.unsqueeze(1						).expand(-1     ,    self.num_choices     ,    -1						).contiguous()
											__snake_case				=		model(
											    SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE     ,    labels=SCREAMING_SNAKE_CASE     ,    )
											self.parent.assertEqual(result.logits.shape     ,    (self.batch_size, self.num_choices)						)






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




											'''simple docstring'''

											__snake_case				=		self.prepare_config_and_inputs()
											(
											    (
											    __snake_case					
											)		,					(
											    __snake_case					
											)		,					(
											    __snake_case					
											)		,					(
											    __snake_case					
											)		,					(
											    __snake_case					
											)		,					(
											    __snake_case					
											)		,					(
											    __snake_case					
											)		,										
											)				=		config_and_inputs
											__snake_case				=		{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
											return config, inputs_dict






@require_torch
class     UpperCamelCase(  _a     ,							_a     ,							unittest.TestCase   ):
					snake_case_		:				Union[str, Any]										=	(
					    (
					        DebertaVaModel,
					        DebertaVaForMaskedLM,
					        DebertaVaForSequenceClassification,
					        DebertaVaForTokenClassification,
					        DebertaVaForQuestionAnswering,
					        DebertaVaForMultipleChoice,
					    )
					    if is_torch_available()
					    else ()
					)
					snake_case_		:				Optional[Any]										=	(
					    {
					        """feature-extraction""": DebertaVaModel,
					        """fill-mask""": DebertaVaForMaskedLM,
					        """question-answering""": DebertaVaForQuestionAnswering,
					        """text-classification""": DebertaVaForSequenceClassification,
					        """token-classification""": DebertaVaForTokenClassification,
					        """zero-shot""": DebertaVaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)

					snake_case_		:				Dict										=	True
					snake_case_		:				Union[str, Any]										=	False
					snake_case_		:				Tuple										=	False
					snake_case_		:				List[str]										=	False
					snake_case_		:				List[Any]										=	False


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		DebertaVaModelTester(self						)
											__snake_case				=		ConfigTester(self     ,    config_class=SCREAMING_SNAKE_CASE     ,    hidden_size=3_7						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Dict:




											'''simple docstring'''

											self.config_tester.run_common_tests()


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_model(*SCREAMING_SNAKE_CASE						)


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




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_sequence_classification(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_masked_lm(*SCREAMING_SNAKE_CASE						)


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




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_question_answering(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_token_classification(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_multiple_choice(*SCREAMING_SNAKE_CASE						)






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




											'''simple docstring'''

											for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__snake_case				=		DebertaVaModel.from_pretrained(SCREAMING_SNAKE_CASE						)
																	self.assertIsNotNone(SCREAMING_SNAKE_CASE						)






@require_torch
@require_sentencepiece
@require_tokenizers
class     UpperCamelCase(  unittest.TestCase   ):


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




											'''simple docstring'''

											pass






					@slow
					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		DebertaVaModel.from_pretrained("microsoft/deberta-v2-xlarge"						)

											__snake_case				=		torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]						)
											__snake_case				=		torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
											with torch.no_grad():
																	__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE						)[0]
											# compare the actual values for a slice.
											__snake_case				=		torch.tensor(
											    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]						)
											self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,    SCREAMING_SNAKE_CASE     ,    atol=1e-4						)     ,    f'''{output[:, 1:4, 1:4]}'''						)









from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase = 10**-10 )						->	float:


						'''simple docstring'''






						__snake_case				=		a
						while True:
												__snake_case				=		Decimal(_lowerCAmelCase ) - (
												    Decimal(eval(_lowerCAmelCase ) ) / Decimal(eval(str(diff(_lowerCAmelCase ) ) ) )  # noqa: S307
												)
												# This number dictates the accuracy of the answer
												if abs(eval(_lowerCAmelCase ) ) < precision:  # noqa: S307
																		return float(_lowerCAmelCase )


# Let's Execute
if __name__ == "__main__":
	# Find root of trigonometric function
	# Find value of pi
	print(f'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')
	# Find root of polynomial
	print(f'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
	# Find Square Root of 5
	print(f'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
	# Exponential Roots
	print(f'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')





