





from __future__ import annotations

import unittest

from transformers import MPNetConfig, 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.models.mpnet.modeling_tf_mpnet import (
						    TFMPNetForMaskedLM,
						    TFMPNetForMultipleChoice,
						    TFMPNetForQuestionAnswering,
						    TFMPNetForSequenceClassification,
						    TFMPNetForTokenClassification,
						    TFMPNetModel,
						)

class SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					def __init__(			self						,	_lowerCAmelCase						,	_lowerCAmelCase=13						,	_lowerCAmelCase=7						,	_lowerCAmelCase=True						,	_lowerCAmelCase=True						,	_lowerCAmelCase=False						,	_lowerCAmelCase=True						,	_lowerCAmelCase=99						,	_lowerCAmelCase=64						,	_lowerCAmelCase=2						,	_lowerCAmelCase=4						,	_lowerCAmelCase=64						,	_lowerCAmelCase="gelu"						,	_lowerCAmelCase=0.1						,	_lowerCAmelCase=0.1						,	_lowerCAmelCase=512						,	_lowerCAmelCase=16						,	_lowerCAmelCase=2						,	_lowerCAmelCase=0.02						,	_lowerCAmelCase=3						,	_lowerCAmelCase=4						,	_lowerCAmelCase=None						,	):
										lowerCamelCase__      =							parent
										lowerCamelCase__      =							batch_size
										lowerCamelCase__      =							seq_length
										lowerCamelCase__      =							is_training
										lowerCamelCase__      =							use_input_mask
										lowerCamelCase__      =							use_token_type_ids
										lowerCamelCase__      =							use_labels
										lowerCamelCase__      =							vocab_size
										lowerCamelCase__      =							hidden_size
										lowerCamelCase__      =							num_hidden_layers
										lowerCamelCase__      =							num_attention_heads
										lowerCamelCase__      =							intermediate_size
										lowerCamelCase__      =							hidden_act
										lowerCamelCase__      =							hidden_dropout_prob
										lowerCamelCase__      =							attention_probs_dropout_prob
										lowerCamelCase__      =							max_position_embeddings
										lowerCamelCase__      =							type_vocab_size
										lowerCamelCase__      =							type_sequence_label_size
										lowerCamelCase__      =							initializer_range
										lowerCamelCase__      =							num_labels
										lowerCamelCase__      =							num_choices
										lowerCamelCase__      =							scope





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size						)

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

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

										lowerCamelCase__      =							MPNetConfig(
										    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						,	initializer_range=self.initializer_range						,	)
										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							TFMPNetModel(config=_lowerCAmelCase						)
										lowerCamelCase__      =							{"input_ids": input_ids, "attention_mask": input_mask}
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										lowerCamelCase__      =							[input_ids, input_mask]
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							TFMPNetForMaskedLM(config=_lowerCAmelCase						)
										lowerCamelCase__      =							{"input_ids": input_ids, "attention_mask": input_mask}
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							TFMPNetForQuestionAnswering(config=_lowerCAmelCase						)
										lowerCamelCase__      =							{
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										}
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										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        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							self.num_labels
										lowerCamelCase__      =							TFMPNetForSequenceClassification(_lowerCAmelCase						)
										lowerCamelCase__      =							{"input_ids": input_ids, "attention_mask": input_mask}
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							self.num_choices
										lowerCamelCase__      =							TFMPNetForMultipleChoice(_lowerCAmelCase						)
										lowerCamelCase__      =							tf.tile(tf.expand_dims(_lowerCAmelCase						,	1						)						,	(1, self.num_choices, 1)						)
										lowerCamelCase__      =							tf.tile(tf.expand_dims(_lowerCAmelCase						,	1						)						,	(1, self.num_choices, 1)						)
										lowerCamelCase__      =							{
										    "input_ids": multiple_choice_inputs_ids,
										    "attention_mask": multiple_choice_input_mask,
										}
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							self.num_labels
										lowerCamelCase__      =							TFMPNetForTokenClassification(_lowerCAmelCase						)
										lowerCamelCase__      =							{"input_ids": input_ids, "attention_mask": input_mask}
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)						)






					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.prepare_config_and_inputs()
										((lowerCamelCase__)     ,		(lowerCamelCase__)     ,		(lowerCamelCase__)     ,		(lowerCamelCase__)     ,		(lowerCamelCase__)     ,		(lowerCamelCase__))      =							config_and_inputs
										lowerCamelCase__      =							{"input_ids": input_ids, "attention_mask": input_mask}
										return config, inputs_dict

@require_tf
class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_				,							unittest.TestCase ):





					"""simple docstring"""


					A__								=     (
					    (
					        TFMPNetForMaskedLM,
					        TFMPNetForMultipleChoice,
					        TFMPNetForQuestionAnswering,
					        TFMPNetForSequenceClassification,
					        TFMPNetForTokenClassification,
					        TFMPNetModel,
					    )
					    if is_tf_available()
					    else ()
					)
					A__								=     (
					    {
					        "feature-extraction": TFMPNetModel,
					        "fill-mask": TFMPNetForMaskedLM,
					        "question-answering": TFMPNetForQuestionAnswering,
					        "text-classification": TFMPNetForSequenceClassification,
					        "token-classification": TFMPNetForTokenClassification,
					        "zero-shot": TFMPNetForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					A__								=     False
					A__								=     False





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFMPNetModelTester(self						)
										lowerCamelCase__      =							ConfigTester(self						,	config_class=_lowerCAmelCase						,	hidden_size=37						)





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





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_model(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_masked_lm(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_question_answering(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_sequence_classification(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_multiple_choice(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_token_classification(*_lowerCAmelCase						)






					@slow
					def        __magic_name__			(			self						):
										for model_name in ["microsoft/mpnet-base"]:
															lowerCamelCase__      =							TFMPNetModel.from_pretrained(_lowerCAmelCase						)
															self.assertIsNotNone(_lowerCAmelCase						)

@require_tf
class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					@slow
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFMPNetModel.from_pretrained("microsoft/mpnet-base"						)
										lowerCamelCase__      =							tf.constant([[0, 1, 2, 3, 4, 5]]						)
										lowerCamelCase__      =							model(_lowerCAmelCase						)[0]

										lowerCamelCase__      =							[1, 6, 768]
										self.assertEqual(output.shape						,	_lowerCAmelCase						)

										lowerCamelCase__      =							tf.constant(
										    [
										        [
										            [-0.1_067_172, 0.08_216_473, 0.0_024_543],
										            [-0.03_465_879, 0.8_354_118, -0.03_252_288],
										            [-0.06_569_476, -0.12_424_111, -0.0_494_436],
										        ]
										    ]						)
										tf.debugging.assert_near(output[:, :3, :3]						,	_lowerCAmelCase						,	atol=1E-4						)








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

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

					lowerCamelCase__      =							{x for x, _ in coordinates}
					if len(a) == 1:
										return f"x={coordinates[0][0]}"

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

					lowerCamelCase__      =							len(a)

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

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

					lowerCamelCase__      =							0

					while count < x:
										lowerCamelCase__      =							0
										while zahlen < x:
															if count == zahlen:
																				zahlen += 1
															if zahlen == x:
																				break
															lowerCamelCase__      =							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

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

					lowerCamelCase__      =							0
					lowerCamelCase__      =							"f(x)="

					while count < x:
										lowerCamelCase__      =							solution[count].split("E")
										if len(a) > 1:
															lowerCamelCase__      =							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, 1_1]]))
						print(points_to_polynomial([[1, -3], [2, -6], [3, -1_1]]))
						print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))

