





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
		import numpy as np
		import tensorflow as tf

		from transformers import TFCamembertModel


@require_tf
@require_sentencepiece
@require_tokenizers
class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						@slow
						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										_snake_case :			int  						=   TFCamembertModel.from_pretrained('jplu/tf-camembert-base'					)

										_snake_case :			List[str]  						=   tf.convert_to_tensor(
										    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] ,				dtype=tf.intaa ,				)  # J'aime le camembert !"

										_snake_case :			str  						=   model(lowerCamelCase_					)["last_hidden_state"]
										_snake_case :			List[Any]  						=   tf.TensorShape((1, 10, 7_68)					)
										self.assertEqual(output.shape ,				lowerCamelCase_					)
										# compare the actual values for a slice.
										_snake_case :			Optional[Any]  						=   tf.convert_to_tensor(
										    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] ,				dtype=tf.floataa ,				)
										# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
										# camembert.eval()
										# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

										self.assertTrue(np.allclose(output[:, :3, :3].numpy() ,				expected_slice.numpy() ,				atol=1e-4					)					)













import torch

from transformers import AutoModel


class 			lowercase				(							torch.nn.Module     ):



						"""simple docstring"""

						def __init__(    self			:		Tuple ,				lowerCamelCase_			:		Dict="sayef/fsner-bert-base-uncased"					):





										'''simple docstring'''







										super(lowerCamelCase_ ,				self					).__init__()

										_snake_case :			Optional[Any]  						=   AutoModel.from_pretrained(lowerCamelCase_ ,				return_dict=lowerCamelCase_					)
										_snake_case :			str  						=   torch.nn.CosineSimilarity(3 ,				1e-08					)
										_snake_case :			str  						=   torch.nn.Softmax(dim=1					)







						def 		__UpperCAmelCase       (    self			:		int ,				**lowerCamelCase_			:		List[str]					):





										'''simple docstring'''







										return self.bert(**lowerCamelCase_					).last_hidden_state







						def 		__UpperCAmelCase       (    self			:		List[Any] ,				lowerCamelCase_			:		Union[str, Any]					):





										'''simple docstring'''







										return token_embeddings.sum(2 ,				keepdim=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		Dict ,				lowerCamelCase_			:		Tuple ,				lowerCamelCase_			:		Optional[Any]=1					):





										'''simple docstring'''







										return self.softmax(T * self.cos(lowerCamelCase_ ,				lowerCamelCase_					)					)







						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		Any ,				lowerCamelCase_			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			int  						=   W_supports['sizes'].tolist()
										_snake_case :			int  						=   W_supports['start_token_id'].item()
										_snake_case :			List[str]  						=   W_supports['end_token_id'].item()

										del W_supports["sizes"]
										del W_supports["start_token_id"]
										del W_supports["end_token_id"]

										_snake_case :			Optional[int]  						=   self.BERT(**lowerCamelCase_					)
										_snake_case :			Optional[Any]  						=   self.BERT(**lowerCamelCase_					)

										_snake_case :			Optional[int]  						=   None
										_snake_case :			Optional[int]  						=   None

										_snake_case :			List[str]  						=   W_supports['input_ids'] == start_token_id
										_snake_case :			Union[str, Any]  						=   W_supports['input_ids'] == end_token_id

										for i, size in enumerate(lowerCamelCase_					):
														if i == 0:
																		_snake_case :			str  						=   0
														else:
																		_snake_case :			Union[str, Any]  						=   support_sizes[i - 1]

														_snake_case :			Tuple  						=   S[s : s + size][start_token_masks[s : s + size]]
														_snake_case :			Optional[int]  						=   S[s : s + size][end_token_masks[s : s + size]]

														_snake_case :			Tuple  						=   torch.matmul(q[i] ,				s_start.T					).sum(1					).softmax(0					)
														_snake_case :			Union[str, Any]  						=   torch.matmul(q[i] ,				s_end.T					).sum(1					).softmax(0					)

														if p_starts is not None:
																		_snake_case :			Optional[Any]  						=   torch.vstack((p_starts, p_start)					)
																		_snake_case :			List[str]  						=   torch.vstack((p_ends, p_end)					)
														else:
																		_snake_case :			Union[str, Any]  						=   p_start
																		_snake_case :			Any  						=   p_end

										return p_starts, p_ends






