

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():
						from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class 	A_		(	unittest.TestCase    ):
							@slow
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    Any 	=			AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'       ,return_dict=__UpperCAmelCase).to(__UpperCAmelCase)
								__lowerCamelCase    :    int 	=			AutoTokenizer.from_pretrained('google/mt5-small')

								__lowerCamelCase    :    Any 	=			tokenizer('Hello there'       ,return_tensors='pt').input_ids
								__lowerCamelCase    :    Union[str, Any] 	=			tokenizer('Hi I am'       ,return_tensors='pt').input_ids

								__lowerCamelCase    :    str 	=			model(input_ids.to(__UpperCAmelCase)       ,labels=labels.to(__UpperCAmelCase)).loss
								__lowerCamelCase    :    int 	=			-(labels.shape[-1] * loss.item())

								__lowerCamelCase    :    Any 	=			-84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1E-4)







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 	A_		(	unittest.TestCase    ):
							@slow
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Tuple 	=			TFCamembertModel.from_pretrained('jplu/tf-camembert-base')

								__lowerCamelCase    :    str 	=			tf.convert_to_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]]       ,dtype=tf.intaa       ,)  # J'aime le camembert !"

								__lowerCamelCase    :    int 	=			model(SCREAMING_SNAKE_CASE__)['last_hidden_state']
								__lowerCamelCase    :    Dict 	=			tf.TensorShape((1, 1_0, 7_6_8))
								self.assertEqual(output.shape       ,SCREAMING_SNAKE_CASE__)
								# compare the actual values for a slice.
								__lowerCamelCase    :    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))




