






"""simple docstring"""



import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
 import optax
 from flax.training.common_utils import onehot

 from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
 from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @slow
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"							)
        UpperCAmelCase__						:    str							=							AutoTokenizer.from_pretrained("google/mt5-small"							)

        UpperCAmelCase__						:    str							=							tokenizer("Hello there"							,			return_tensors="np"							).input_ids
        UpperCAmelCase__						:    Optional[Any]							=							tokenizer("Hi I am"							,			return_tensors="np"							).input_ids

        UpperCAmelCase__						:    Any							=							shift_tokens_right(snake_case__							,			model.config.pad_token_id							,			model.config.decoder_start_token_id							)

        UpperCAmelCase__						:    Any							=							model(snake_case__							,			decoder_input_ids=snake_case__							).logits
        UpperCAmelCase__						:    Any							=							optax.softmax_cross_entropy(snake_case__							,			onehot(snake_case__							,			logits.shape[-1]							)							).mean()

        UpperCAmelCase__						:    List[str]							=							-(labels.shape[-1] * loss.item())

        UpperCAmelCase__						:    Dict							=							-84.9127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1e-4							)










"""simple docstring"""



import unittest

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


if is_torch_available():
 import torch

 from transformers import XLMRobertaModel




@require_sentencepiece
@require_tokenizers
@require_torch
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @slow
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							XLMRobertaModel.from_pretrained("xlm-roberta-base"							)
        UpperCAmelCase__						:    str							=							torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]							)
        # The dog is cute and lives in the garden house

        UpperCAmelCase__						:    Tuple							=							torch.Size((1, 1_2, 7_6_8)							)  # batch_size, sequence_length, embedding_vector_dim
        UpperCAmelCase__						:    Optional[int]							=							torch.tensor(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]							)
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
             UpperCAmelCase__						:    int							=							model(snake_case__							)["last_hidden_state"].detach()
        self.assertEqual(output.shape							,			snake_case__							)
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]							,			snake_case__							,			atol=1e-3							)							)




   @slow
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							XLMRobertaModel.from_pretrained("xlm-roberta-large"							)
        UpperCAmelCase__						:    Tuple							=							torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]							)
        # The dog is cute and lives in the garden house

        UpperCAmelCase__						:    Union[str, Any]							=							torch.Size((1, 1_2, 1_0_2_4)							)  # batch_size, sequence_length, embedding_vector_dim
        UpperCAmelCase__						:    Optional[Any]							=							torch.tensor(
            [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]							)
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
             UpperCAmelCase__						:    Optional[Any]							=							model(snake_case__							)["last_hidden_state"].detach()
        self.assertEqual(output.shape							,			snake_case__							)
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]							,			snake_case__							,			atol=1e-3							)							)


