


"""simple docstring"""


from typing import Dict, Optional, Tuple

import torch






def    a__       ( __lowercase      )		->		torch.Tensor:
				_A         =	boundaries[1] - boundaries[0]
				_A         =	boundaries + step / 2
				_A         =	torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1      )]   ,	dim=0      )
				return bin_centers






def    a__       ( __lowercase   ,	__lowercase   ,	)		->		Tuple[torch.Tensor, torch.Tensor]:
				_A         =	_calculate_bin_centers(__lowercase      )
				return (
				    torch.sum(aligned_distance_error_probs * bin_centers   ,	dim=-1      ),
				    bin_centers[-1],
				)






def    a__       ( __lowercase   ,	__lowercase = 31   ,	__lowercase = 64   ,	**__lowercase   ,	)		->		Dict[str, torch.Tensor]:
				_A         =	torch.linspace(0   ,	__lowercase   ,	steps=(no_bins - 1)   ,	device=logits.device      )

				_A         =	torch.nn.functional.softmax(__lowercase   ,	dim=-1      )
				_A     ,						_A         =	_calculate_expected_aligned_error(
				    alignment_confidence_breaks=__lowercase   ,	aligned_distance_error_probs=__lowercase   ,	)

				return {
				    "aligned_confidence_probs": aligned_confidence_probs,
				    "predicted_aligned_error": predicted_aligned_error,
				    "max_predicted_aligned_error": max_predicted_aligned_error,
				}






def    a__       ( __lowercase   ,	__lowercase = None   ,	__lowercase = 31   ,	__lowercase = 64   ,	__lowercase = 1E-8   ,	**__lowercase   ,	)		->		torch.Tensor:
				if residue_weights is None:
								_A         =	logits.new_ones(logits.shape[-2]      )

				_A         =	torch.linspace(0   ,	__lowercase   ,	steps=(no_bins - 1)   ,	device=logits.device      )

				_A         =	_calculate_bin_centers(__lowercase      )
				torch.sum(__lowercase      )
				_A         =	logits.shape[-2]
				_A         =	max(__lowercase   ,	19      )

				_A         =	1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

				_A         =	torch.nn.functional.softmax(__lowercase   ,	dim=-1      )

				_A         =	1.0 / (1 + (bin_centers**2) / (da**2))
				_A         =	torch.sum(probs * tm_per_bin   ,	dim=-1      )

				_A         =	residue_weights / (eps + residue_weights.sum())
				_A         =	torch.sum(predicted_tm_term * normed_residue_mask   ,	dim=-1      )

				_A         =	per_alignment * residue_weights

				_A         =	(weighted == torch.max(__lowercase      )).nonzero()[0]
				return per_alignment[tuple(__lowercase      )]



"""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 snake_case					(     unittest.TestCase):







		@slow
		def        a_       (    self					:				int						)				->  Tuple:





						'''simple docstring'''







						_A         =	CamembertModel.from_pretrained("camembert-base"						)
						model.to(a__						)

						_A         =	torch.tensor(
						    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]			,      device=a__			,      dtype=torch.long			,      )  # J'aime le camembert !
						with torch.no_grad():
										_A         =	model(a__						)["last_hidden_state"]
						_A         =	torch.Size((1, 10, 7_68)						)
						self.assertEqual(output.shape			,      a__						)
						# compare the actual values for a slice.
						_A         =	torch.tensor(
						    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]			,      device=a__			,      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]			,      a__			,      atol=1E-4						)						)