



'''simple docstring'''




import unittest

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


if is_torch_available():
						import torch

						from transformers import CamembertModel






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


			'''simple docstring'''

			@slow
			def UpperCamelCase__ (							self					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		CamembertModel.from_pretrained("""camembert-base"""						)
					model.to(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.tensor(
					    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]							,					device=lowerCAmelCase__							,					dtype=torch.long							,					)  # J'aime le camembert !
					with torch.no_grad():
							__SCREAMING_SNAKE_CASE	:		int   	=		model(lowerCAmelCase__						)["""last_hidden_state"""]
					__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.Size((1, 1_0, 7_6_8)						)
					self.assertEqual(output.shape							,					lowerCAmelCase__						)
					# compare the actual values for a slice.
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		torch.tensor(
					    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]							,					device=lowerCAmelCase__							,					dtype=torch.float							,					)
					# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
					# camembert.eval()
					# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

					self.assertTrue(torch.allclose(output[:, :3, :3]							,					lowerCAmelCase__							,					atol=1E-4						)						)




'''simple docstring'''




UpperCamelCase__	:     List[Any]    =			10_00


def     lowerCAmelCase_       (     _lowerCamelCase:       float ):
		return x * x


def     lowerCAmelCase_       (     _lowerCamelCase:       Any	,				_lowerCamelCase:       float	,				_lowerCamelCase:       float	,				_lowerCamelCase:       int = 4 ):
		assert callable(
		    _lowerCamelCase ), F"the function(object) passed should be callable your input : {function}"
		assert isinstance(_lowerCamelCase	,				(float, int) ), F"a should be float or integer your input : {a}"
		assert isinstance(function(_lowerCamelCase )	,				(float, int) ), (
		    "the function should return integer or float return type of your function, "
		    F"{type(_lowerCamelCase )}"
		)
		assert isinstance(_lowerCamelCase	,				(float, int) ), F"b should be float or integer your input : {b}"
		assert (
		    isinstance(_lowerCamelCase	,				_lowerCamelCase ) and precision > 0
		), F"precision should be positive integer your input : {precision}"

		# just applying the formula of simpson for approximate integration written in
		# mentioned article in first comment of this file and above this function

		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		(b - a) / N_STEPS
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		function(_lowerCamelCase ) + function(_lowerCamelCase )

		for i in range(1	,				_lowerCamelCase ):
				__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		a + h * i
				result += function(_lowerCamelCase ) * (4 if i % 2 else 2)

		result *= h / 3
		return round(_lowerCamelCase	,				_lowerCamelCase )


if __name__ == "__main__":
						import doctest

						doctest.testmod()