

import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
			import torch

			from transformers import (
			    MPNetForMaskedLM,
			    MPNetForMultipleChoice,
			    MPNetForQuestionAnswering,
			    MPNetForSequenceClassification,
			    MPNetForTokenClassification,
			    MPNetModel,
			)






class _UpperCamelCase				:
			'''simple docstring'''







			def __init__(      self				:			Union[str, Any]   ,   a				:			Tuple   ,   a				:			List[str]=13   ,   a				:			List[str]=7   ,   a				:			Union[str, Any]=True   ,   a				:			List[Any]=True   ,   a				:			Any=False   ,   a				:			List[str]=True   ,   a				:			str=99   ,   a				:			Union[str, Any]=64   ,   a				:			Any=5   ,   a				:			Dict=4   ,   a				:			List[Any]=64   ,   a				:			Optional[Any]="gelu"   ,   a				:			Tuple=0.1   ,   a				:			Union[str, Any]=0.1   ,   a				:			Dict=512   ,   a				:			List[str]=16   ,   a				:			Tuple=2   ,   a				:			Optional[int]=0.02   ,   a				:			Tuple=3   ,   a				:			List[Any]=4   ,   a				:			Any=None   ,   )				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							parent
				SCREAMING_SNAKE_CASE :   List[Any]					  =							batch_size
				SCREAMING_SNAKE_CASE :   Any					  =							seq_length
				SCREAMING_SNAKE_CASE :   Tuple					  =							is_training
				SCREAMING_SNAKE_CASE :   List[Any]					  =							use_input_mask
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							use_token_type_ids
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							use_labels
				SCREAMING_SNAKE_CASE :   str					  =							vocab_size
				SCREAMING_SNAKE_CASE :   int					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   List[Any]					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							hidden_act
				SCREAMING_SNAKE_CASE :   Dict					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Dict					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   List[str]					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   str					  =							type_sequence_label_size
				SCREAMING_SNAKE_CASE :   List[Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   Dict					  =							num_labels
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							num_choices
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							scope
			def 				__UpperCamelCase			(      self				:			int						)				->							List[str]:

				"""simple docstring"""

				return MPNetConfig.from_pretrained("microsoft/mpnet-base"						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							int:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							None
				if self.use_input_mask:
					SCREAMING_SNAKE_CASE :   List[Any]					  =							random_attention_mask([self.batch_size, self.seq_length]						)

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

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.get_config()
				return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Dict:

				"""simple docstring"""

				return 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   ,   )
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			Union[str, Any]   ,   a				:			str   ,   a				:			int   ,   a				:			List[str]   ,   a				:			Union[str, Any]   ,   a				:			List[str]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							MPNetModel(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   Tuple					  =							model(a   ,   a						)
				SCREAMING_SNAKE_CASE :   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 				__UpperCamelCase			(      self				:			List[str]   ,   a				:			Any   ,   a				:			Union[str, Any]   ,   a				:			Dict   ,   a				:			int   ,   a				:			List[Any]   ,   a				:			Dict						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							MPNetForQuestionAnswering(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							model(
				    a   ,   attention_mask=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 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			Union[str, Any]   ,   a				:			int   ,   a				:			Optional[int]   ,   a				:			Dict   ,   a				:			Any   ,   a				:			List[str]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.num_labels
				SCREAMING_SNAKE_CASE :   Dict					  =							MPNetForSequenceClassification(a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   List[Any]					  =							model(a   ,   attention_mask=a   ,   labels=a						)
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_labels)						)
			def 				__UpperCamelCase			(      self				:			List[str]   ,   a				:			Any   ,   a				:			Optional[Any]   ,   a				:			Dict   ,   a				:			Optional[Any]   ,   a				:			int   ,   a				:			Tuple						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.num_choices
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							MPNetForMultipleChoice(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							input_ids.unsqueeze(1						).expand(-1   ,   self.num_choices   ,   -1						).contiguous()
				SCREAMING_SNAKE_CASE :   Tuple					  =							input_mask.unsqueeze(1						).expand(-1   ,   self.num_choices   ,   -1						).contiguous()
				SCREAMING_SNAKE_CASE :   List[Any]					  =							model(
				    a   ,   attention_mask=a   ,   labels=a   ,   )
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_choices)						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			str   ,   a				:			Tuple   ,   a				:			Optional[int]   ,   a				:			List[Any]   ,   a				:			Tuple   ,   a				:			int						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							self.num_labels
				SCREAMING_SNAKE_CASE :   List[str]					  =							MPNetForTokenClassification(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   List[Any]					  =							model(a   ,   attention_mask=a   ,   labels=a						)
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.num_labels)						)






			def 				__UpperCamelCase			(      self				:			Dict						)				->							Optional[Any]:

				"""simple docstring"""

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






@require_torch
class _UpperCamelCase				(				__A				,						__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =(
			    (
			        MPNetForMaskedLM,
			        MPNetForMultipleChoice,
			        MPNetForQuestionAnswering,
			        MPNetForSequenceClassification,
			        MPNetForTokenClassification,
			        MPNetModel,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__							     =(
			    {
			        'feature-extraction': MPNetModel,
			        'fill-mask': MPNetForMaskedLM,
			        'question-answering': MPNetForQuestionAnswering,
			        'text-classification': MPNetForSequenceClassification,
			        'token-classification': MPNetForTokenClassification,
			        'zero-shot': MPNetForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__							     =False
			lowerCamelCase__							     =True
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							MPNetModelTester(self						)
				SCREAMING_SNAKE_CASE :   str					  =							ConfigTester(self   ,   config_class=a   ,   hidden_size=37						)
			def 				__UpperCamelCase			(      self				:			int						)				->							List[str]:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_mpnet_model(*a						)
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_mpnet_for_sequence_classification(*a						)
			def 				__UpperCamelCase			(      self				:			Dict						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_mpnet_for_multiple_choice(*a						)
			def 				__UpperCamelCase			(      self				:			Any						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_mpnet_for_token_classification(*a						)






			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_mpnet_for_question_answering(*a						)






@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@slow
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							MPNetModel.from_pretrained("microsoft/mpnet-base"						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							model(a						)[0]
				SCREAMING_SNAKE_CASE :   Tuple					  =							torch.Size((1, 11, 768)						)
				self.assertEqual(output.shape   ,   a						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							torch.tensor(
				    [[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]]						)
				# compare the actual values for a slice.
				self.assertTrue(torch.allclose(output[:, :3, :3]   ,   a   ,   atol=1e-4						)						)
'''simple docstring'''


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean





def    lowerCAmelCase       (		UpperCamelCase__					: int							):


	"""simple docstring"""

	# A local function to see if a dot lands in the circle.
	def is_in_circle(UpperCamelCase__					: float     ,       UpperCamelCase__					: float							) -> bool:
		__UpperCAmelCase      					=      sqrt((x**2) + (y**2)							)
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	__UpperCAmelCase      					=      mean(
	    int(is_in_circle(uniform(-1.0     ,       1.0							)     ,       uniform(-1.0     ,       1.0							)							)							)
	    for _ in range(UpperCamelCase__							)							)
	# The ratio of the area for circle to square is pi/4.
	__UpperCAmelCase      					=      proportion * 4
	print(f"""The estimated value of pi is {pi_estimate}"""							)
	print(f"""The numpy value of pi is {pi}"""							)
	print(f"""The total error is {abs(pi - pi_estimate							)}"""							)





def    lowerCAmelCase       (		UpperCamelCase__					: int     ,       UpperCamelCase__					: Callable[[float], float]     ,       UpperCamelCase__					: float = 0.0     ,       UpperCamelCase__					: float = 1.0     ,       ):


	"""simple docstring"""
	return mean(
	    function_to_integrate(uniform(UpperCamelCase__     ,       UpperCamelCase__							)							) for _ in range(UpperCamelCase__							)							) * (max_value - min_value)





def    lowerCAmelCase       (		UpperCamelCase__					: int     ,       UpperCamelCase__					: float = 0.0     ,       UpperCamelCase__					: float = 1.0							):


	"""simple docstring"""

	def identity_function(UpperCamelCase__					: float							) -> float:
		return x

	__UpperCAmelCase      					=      area_under_curve_estimator(
	    UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
	__UpperCAmelCase      					=      (max_value * max_value - min_value * min_value) / 2

	print('''******************'''							)
	print(f"""Estimating area under y=x where x varies from {min_value} to {max_value}"""							)
	print(f"""Estimated value is {estimated_value}"""							)
	print(f"""Expected value is {expected_value}"""							)
	print(f"""Total error is {abs(estimated_value - expected_value							)}"""							)
	print('''******************'''							)





def    lowerCAmelCase       (		UpperCamelCase__					: int							):


	"""simple docstring"""

	def function_to_integrate(UpperCamelCase__					: float							) -> float:
		return sqrt(4.0 - x * x							)

	__UpperCAmelCase      					=      area_under_curve_estimator(
	    UpperCamelCase__     ,       UpperCamelCase__     ,       0.0     ,       2.0							)

	print('''******************'''							)
	print('''Estimating pi using area_under_curve_estimator'''							)
	print(f"""Estimated value is {estimated_value}"""							)
	print(f"""Expected value is {pi}"""							)
	print(f"""Total error is {abs(estimated_value - pi							)}"""							)
	print('''******************'''							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
