

import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

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


if is_torch_available():
	import torch

	from transformers import (
	    MODEL_FOR_PRETRAINING_MAPPING,
	    NezhaForMaskedLM,
	    NezhaForMultipleChoice,
	    NezhaForNextSentencePrediction,
	    NezhaForPreTraining,
	    NezhaForQuestionAnswering,
	    NezhaForSequenceClassification,
	    NezhaForTokenClassification,
	    NezhaModel,
	)
	from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST



class 					_a		:


					'''simple docstring'''






					def __init__(				self					, A__					, A__=13					, A__=7					, A__=True					, A__=True					, A__=True					, A__=True					, A__=99					, A__=32					, A__=5					, A__=4					, A__=37					, A__="gelu"					, A__=0.1					, A__=0.1					, A__=128					, A__=32					, A__=16					, A__=2					, A__=0.0_2					, A__=3					, A__=4					, A__=None					, ):
						A__							:					List[Any]       =	parent
						A__							:					int       =	batch_size
						A__							:					Union[str, Any]       =	seq_length
						A__							:					List[str]       =	is_training
						A__							:					Optional[Any]       =	use_input_mask
						A__							:					str       =	use_token_type_ids
						A__							:					Union[str, Any]       =	use_labels
						A__							:					Optional[int]       =	vocab_size
						A__							:					Optional[int]       =	hidden_size
						A__							:					Any       =	num_hidden_layers
						A__							:					Optional[Any]       =	num_attention_heads
						A__							:					List[str]       =	intermediate_size
						A__							:					Any       =	hidden_act
						A__							:					Dict       =	hidden_dropout_prob
						A__							:					str       =	attention_probs_dropout_prob
						A__							:					Any       =	max_position_embeddings
						A__							:					List[str]       =	type_vocab_size
						A__							:					Dict       =	type_sequence_label_size
						A__							:					Optional[int]       =	initializer_range
						A__							:					str       =	num_labels
						A__							:					Any       =	num_choices
						A__							:					Union[str, Any]       =	scope






					def 						__A    (				self    ):
						A__							:					Dict       =	ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size    )

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

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

						A__							:					Tuple       =	None
						A__							:					int       =	None
						A__							:					str       =	None
						if self.use_labels:
							A__							:					List[Any]       =	ids_tensor([self.batch_size]					, self.type_sequence_label_size    )
							A__							:					str       =	ids_tensor([self.batch_size, self.seq_length]					, self.num_labels    )
							A__							:					List[str]       =	ids_tensor([self.batch_size]					, self.num_choices    )

						A__							:					Dict       =	self.get_config()

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






					def 						__A    (				self    ):
						return NezhaConfig(
						    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					, is_decoder=A__					, initializer_range=self.initializer_range					, )






					def 						__A    (				self    ):
						(
						    (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     
						)							:					List[Any]       =	self.prepare_config_and_inputs()

						A__							:					Union[str, Any]       =	True
						A__							:					Tuple       =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
						A__							:					str       =	ids_tensor([self.batch_size, self.seq_length]					, vocab_size=2    )

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_mask,
						    sequence_labels,
						    token_labels,
						    choice_labels,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)






					def 						__A    (				self					, A__					, A__					, A__					, A__					, A__					, A__					, A__    ):
						A__							:					List[Any]       =	NezhaModel(config=A__    )
						model.to(A__    )
						model.eval()
						A__							:					Union[str, Any]       =	model(A__					, attention_mask=A__					, token_type_ids=A__    )
						A__							:					str       =	model(A__					, token_type_ids=A__    )
						A__							:					Optional[int]       =	model(A__    )
						self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size)    )
						self.parent.assertEqual(result.pooler_output.shape					, (self.batch_size, self.hidden_size)    )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, A__					, A__					, A__					, A__					, A__					, ):
						A__							:					List[str]       =	True
						A__							:					List[Any]       =	NezhaModel(A__    )
						model.to(A__    )
						model.eval()
						A__							:					Tuple       =	model(
						    A__					, attention_mask=A__					, token_type_ids=A__					, encoder_hidden_states=A__					, encoder_attention_mask=A__					, )
						A__							:					Optional[Any]       =	model(
						    A__					, attention_mask=A__					, token_type_ids=A__					, encoder_hidden_states=A__					, )
						A__							:					Dict       =	model(A__					, attention_mask=A__					, token_type_ids=A__    )
						self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size)    )
						self.parent.assertEqual(result.pooler_output.shape					, (self.batch_size, self.hidden_size)    )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, A__					, A__					, A__    ):
						A__							:					Any       =	NezhaForMaskedLM(config=A__    )
						model.to(A__    )
						model.eval()
						A__							:					int       =	model(A__					, attention_mask=A__					, token_type_ids=A__					, labels=A__    )
						self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.vocab_size)    )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, A__					, A__					, A__    ):
						A__							:					Optional[Any]       =	NezhaForNextSentencePrediction(config=A__    )
						model.to(A__    )
						model.eval()
						A__							:					int       =	model(
						    A__					, attention_mask=A__					, token_type_ids=A__					, labels=A__					, )
						self.parent.assertEqual(result.logits.shape					, (self.batch_size, 2)    )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, A__					, A__					, A__    ):
						A__							:					str       =	NezhaForPreTraining(config=A__    )
						model.to(A__    )
						model.eval()
						A__							:					Union[str, Any]       =	model(
						    A__					, attention_mask=A__					, token_type_ids=A__					, labels=A__					, next_sentence_label=A__					, )
						self.parent.assertEqual(result.prediction_logits.shape					, (self.batch_size, self.seq_length, self.vocab_size)    )
						self.parent.assertEqual(result.seq_relationship_logits.shape					, (self.batch_size, 2)    )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, A__					, A__					, A__    ):
						A__							:					str       =	NezhaForQuestionAnswering(config=A__    )
						model.to(A__    )
						model.eval()
						A__							:					str       =	model(
						    A__					, attention_mask=A__					, token_type_ids=A__					, start_positions=A__					, end_positions=A__					, )
						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 						__A    (				self					, A__					, A__					, A__					, A__					, A__					, A__					, A__    ):
						A__							:					Optional[int]       =	self.num_labels
						A__							:					Optional[int]       =	NezhaForSequenceClassification(A__    )
						model.to(A__    )
						model.eval()
						A__							:					List[Any]       =	model(A__					, attention_mask=A__					, token_type_ids=A__					, labels=A__    )
						self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_labels)    )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, A__					, A__					, A__    ):
						A__							:					Union[str, Any]       =	self.num_labels
						A__							:					List[Any]       =	NezhaForTokenClassification(config=A__    )
						model.to(A__    )
						model.eval()
						A__							:					List[str]       =	model(A__					, attention_mask=A__					, token_type_ids=A__					, labels=A__    )
						self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.num_labels)    )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, A__					, A__					, A__    ):
						A__							:					int       =	self.num_choices
						A__							:					Optional[int]       =	NezhaForMultipleChoice(config=A__    )
						model.to(A__    )
						model.eval()
						A__							:					Union[str, Any]       =	input_ids.unsqueeze(1    ).expand(-1					, self.num_choices					, -1    ).contiguous()
						A__							:					int       =	token_type_ids.unsqueeze(1    ).expand(-1					, self.num_choices					, -1    ).contiguous()
						A__							:					Tuple       =	input_mask.unsqueeze(1    ).expand(-1					, self.num_choices					, -1    ).contiguous()
						A__							:					int       =	model(
						    A__					, attention_mask=A__					, token_type_ids=A__					, labels=A__					, )
						self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_choices)    )






					def 						__A    (				self    ):
						A__							:					Any       =	self.prepare_config_and_inputs()
						(
						    (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     
						)							:					List[str]       =	config_and_inputs
						A__							:					Optional[Any]       =	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
						return config, inputs_dict



@require_torch
class 					_a		(__magic_name__	,     __magic_name__	,     __magic_name__	,     unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     Any													= (
					    (
					        NezhaModel,
					        NezhaForMaskedLM,
					        NezhaForMultipleChoice,
					        NezhaForNextSentencePrediction,
					        NezhaForPreTraining,
					        NezhaForQuestionAnswering,
					        NezhaForSequenceClassification,
					        NezhaForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					UpperCAmelCase__:     List[Any]													= (
					    {
					        '''feature-extraction''': NezhaModel,
					        '''fill-mask''': NezhaForMaskedLM,
					        '''question-answering''': NezhaForQuestionAnswering,
					        '''text-classification''': NezhaForSequenceClassification,
					        '''token-classification''': NezhaForTokenClassification,
					        '''zero-shot''': NezhaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					UpperCAmelCase__:     List[Any]													= True






					def 						__A    (				self					, A__					, A__					, A__=False    ):
						A__							:					Tuple       =	super()._prepare_for_class(A__					, A__					, return_labels=A__    )

						if return_labels:
							if model_class in get_values(A__    ):
								A__							:					List[Any]       =	torch.zeros(
								    (self.model_tester.batch_size, self.model_tester.seq_length)					, dtype=torch.long					, device=A__    )
								A__							:					Tuple       =	torch.zeros(
								    self.model_tester.batch_size					, dtype=torch.long					, device=A__    )
						return inputs_dict






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	NezhaModelTester(self    )
						A__							:					List[Any]       =	ConfigTester(self					, config_class=A__					, hidden_size=37    )






					def 						__A    (				self    ):
						self.config_tester.run_common_tests()






					def 						__A    (				self    ):
						A__							:					Union[str, Any]       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__    )






					def 						__A    (				self    ):
						A__							:					Dict       =	self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_model_as_decoder(*A__    )






					def 						__A    (				self    ):
						# This regression test was failing with PyTorch < 1.3
						(
						    (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     
						)							:					Tuple       =	self.model_tester.prepare_config_and_inputs_for_decoder()

						A__							:					Optional[Any]       =	None

						self.model_tester.create_and_check_model_as_decoder(
						    A__					, A__					, A__					, A__					, A__					, A__					, A__					, A__					, A__					, )






					def 						__A    (				self    ):
						A__							:					int       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*A__    )






					def 						__A    (				self    ):
						A__							:					int       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*A__    )






					def 						__A    (				self    ):
						A__							:					List[Any]       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_next_sequence_prediction(*A__    )






					def 						__A    (				self    ):
						A__							:					List[str]       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*A__    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*A__    )






					def 						__A    (				self    ):
						A__							:					int       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*A__    )






					def 						__A    (				self    ):
						A__							:					List[str]       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*A__    )






					@slow
					def 						__A    (				self    ):
						for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A__							:					Union[str, Any]       =	NezhaModel.from_pretrained(A__    )
							self.assertIsNotNone(A__    )






					@slow
					@require_torch_gpu
					def 						__A    (				self    ):
						A__     ,     A__							:					Optional[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
							# NezhaForMultipleChoice behaves incorrectly in JIT environments.
							if model_class == NezhaForMultipleChoice:
								return

							A__							:					Optional[Any]       =	True
							A__							:					Dict       =	model_class(config=A__    )

							A__							:					Optional[Any]       =	self._prepare_for_class(A__					, A__    )
							A__							:					List[Any]       =	torch.jit.trace(
							    A__					, (inputs_dict["""input_ids"""].to("""cpu"""    ), inputs_dict["""attention_mask"""].to("""cpu"""    ))    )

							with tempfile.TemporaryDirectory() as tmp:
								torch.jit.save(A__					, os.path.join(A__					, """bert.pt"""    )    )
								A__							:					Optional[Any]       =	torch.jit.load(os.path.join(A__					, """bert.pt"""    )					, map_location=A__    )
								loaded(inputs_dict["""input_ids"""].to(A__    )					, inputs_dict["""attention_mask"""].to(A__    )    )



@require_torch
class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					@slow
					def 						__A    (				self    ):
						A__							:					Optional[int]       =	NezhaModel.from_pretrained("""sijunhe/nezha-cn-base"""    )
						A__							:					List[str]       =	torch.tensor([[0, 1, 2, 3, 4, 5]]    )
						A__							:					Tuple       =	torch.tensor([[0, 1, 1, 1, 1, 1]]    )
						with torch.no_grad():
							A__							:					Any       =	model(A__					, attention_mask=A__    )[0]
						A__							:					str       =	torch.Size((1, 6, 768)    )
						self.assertEqual(output.shape					, A__    )
						A__							:					Optional[Any]       =	torch.tensor([[[0.0_6_8_5, 0.2_4_4_1, 0.1_1_0_2], [0.0_6_0_0, 0.1_9_0_6, 0.1_3_4_9], [0.0_2_2_1, 0.0_8_1_9, 0.0_5_8_6]]]    )

						self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					, A__					, atol=1e-4    )    )






					@slow
					def 						__A    (				self    ):
						A__							:					List[Any]       =	NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base"""    )
						A__							:					Union[str, Any]       =	torch.tensor([[0, 1, 2, 3, 4, 5]]    )
						A__							:					Tuple       =	torch.tensor([[1, 1, 1, 1, 1, 1]]    )
						with torch.no_grad():
							A__							:					List[Any]       =	model(A__					, attention_mask=A__    )[0]
						A__							:					Union[str, Any]       =	torch.Size((1, 6, 2_1128)    )
						self.assertEqual(output.shape					, A__    )
						A__							:					Optional[int]       =	torch.tensor(
						    [[-2.7_9_3_9, -1.7_9_0_2, -2.2_1_8_9], [-2.8_5_8_5, -1.8_9_0_8, -2.3_7_2_3], [-2.6_4_9_9, -1.7_7_5_0, -2.2_5_5_8]]    )

						self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					, A__					, atol=1e-4    )    )






from __future__ import annotations

from collections.abc import Callable

A_ :				List[Any]						 =    list[list[float | int]]

def 		UpperCamelCase      (lowercase_:				Matrix					,   lowercase_:				Matrix      )  ->   Matrix:
	A__							:					int       =	len(lowercase_      )
	A__							:					Matrix       =	[[0 for _ in range(size + 1      )] for _ in range(lowercase_      )]
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					float

	for row in range(lowercase_      ):
		for col in range(lowercase_      ):
			A__							:					List[str]       =	matrix[row][col]

		A__							:					int       =	vector[row][0]

	A__							:					Optional[int]       =	0
	A__							:					str       =	0
	while row < size and col < size:
		# pivoting
		A__							:					int       =	max((abs(augmented[rowa][col]      ), rowa) for rowa in range(lowercase_					,   lowercase_      )      )[
		    1
		]
		if augmented[pivot_row][col] == 0:
			col += 1
			continue
		else:
			A__     ,     A__							:					Union[str, Any]       =	augmented[pivot_row], augmented[row]

		for rowa in range(row + 1					,   lowercase_      ):
			A__							:					List[Any]       =	augmented[rowa][col] / augmented[row][col]
			A__							:					Dict       =	0
			for cola in range(col + 1					,   size + 1      ):
				augmented[rowa][cola] -= augmented[row][cola] * ratio

		row += 1
		col += 1

	# back substitution
	for col in range(1					,   lowercase_      ):
		for row in range(lowercase_      ):
			A__							:					List[str]       =	augmented[row][col] / augmented[col][col]
			for cola in range(lowercase_					,   size + 1      ):
				augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
	return [
	    [round(augmented[row][size] / augmented[row][row]					,   10      )] for row in range(lowercase_      )
	]

def 		UpperCamelCase      (lowercase_:				list[int]      )  ->   Callable[[int], int]:
	A__							:					int       =	len(lowercase_      )
	A__							:					Matrix       =	[[0 for _ in range(lowercase_      )] for _ in range(lowercase_      )]
	A__							:					Matrix       =	[[0] for _ in range(lowercase_      )]
	A__							:					Matrix
	A__							:					int
	A__							:					int
	A__							:					int

	for x_val, y_val in enumerate(lowercase_      ):
		for col in range(lowercase_      ):
			A__							:					Dict       =	(x_val + 1) ** (size - col - 1)
		A__							:					Any       =	y_val

	A__							:					Union[str, Any]       =	solve(lowercase_					,   lowercase_      )

	def interpolated_func(lowercase_:				int      ) -> int:
		return sum(
		    round(coeffs[x_val][0]      ) * (var ** (size - x_val - 1))
		    for x_val in range(lowercase_      )      )

	return interpolated_func

def 		UpperCamelCase      (lowercase_:				int      )  ->   int:
	return (
	    1
	    - variable
	    + variable**2
	    - variable**3
	    + variable**4
	    - variable**5
	    + variable**6
	    - variable**7
	    + variable**8
	    - variable**9
	    + variable**10
	)

def 		UpperCamelCase      (lowercase_:				Callable[[int], int] = question_function					,   lowercase_:				int = 10      )  ->   int:
	A__							:					list[int]       =	[func(lowercase_      ) for x_val in range(1					,   order + 1      )]

	A__							:					list[Callable[[int], int]]       =	[
	    interpolate(data_points[:max_coeff]      ) for max_coeff in range(1					,   order + 1      )
	]

	A__							:					int       =	0
	A__							:					Callable[[int], int]
	A__							:					int

	for poly in polynomials:
		A__							:					List[str]       =	1
		while func(lowercase_      ) == poly(lowercase_      ):
			x_val += 1

		ret += poly(lowercase_      )

	return ret


if __name__ == "__main__":
	print(f'''{solution() = }''')



