



"""simple docstring"""





import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class 				lowercase__       (   SCREAMING_SNAKE_CASE    , unittest.TestCase				):







       '''simple docstring'''


       UpperCamelCase						=					PhobertTokenizer
       UpperCamelCase						=					False



       def 					lowercase__    (							self      :  List[Any]   )			->						Any:


             '''simple docstring'''




             super().setUp()

             # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
             UpperCAmelCase_      			=							["T@@", "i", "I", "R@@", "r", "e@@"]
             UpperCAmelCase_      			=							dict(zip(_UpperCAmelCase		,    range(len(_UpperCAmelCase   )   )   )   )
             UpperCAmelCase_      			=							["#version: 0.2", "l à</w>"]
             UpperCAmelCase_      			=							{"unk_token": "<unk>"}

             UpperCAmelCase_      			=							os.path.join(self.tmpdirname		,    VOCAB_FILES_NAMES["vocab_file"]   )
             UpperCAmelCase_      			=							os.path.join(self.tmpdirname		,    VOCAB_FILES_NAMES["merges_file"]   )

             with open(self.vocab_file		,    "w"		,    encoding="utf-8"   ) as fp:
                   for token in vocab_tokens:
                         fp.write(F"""{token} {vocab_tokens[token]}\n"""   )
             with open(self.merges_file		,    "w"		,    encoding="utf-8"   ) as fp:
                   fp.write("\n".join(_UpperCAmelCase   )   )



       def 					lowercase__    (							self      :  Optional[Any]		,    **_UpperCAmelCase      :  Dict   )			->						List[Any]:


             '''simple docstring'''




             kwargs.update(self.special_tokens_map   )
             return PhobertTokenizer.from_pretrained(self.tmpdirname		,    **_UpperCAmelCase   )



       def 					lowercase__    (							self      :  Optional[int]		,    _UpperCAmelCase      :  str   )			->						List[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							"Tôi là VinAI Research"
             UpperCAmelCase_      			=							"T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
             return input_text, output_text







       def 					lowercase__    (							self      :  Union[str, Any]   )			->						Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							PhobertTokenizer(self.vocab_file		,    self.merges_file		,    **self.special_tokens_map   )
             UpperCAmelCase_      			=							"Tôi là VinAI Research"
             UpperCAmelCase_      			=							"T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
             UpperCAmelCase_      			=							tokenizer.tokenize(_UpperCAmelCase   )
             print(_UpperCAmelCase   )
             self.assertListEqual(_UpperCAmelCase		,    _UpperCAmelCase   )

             UpperCAmelCase_      			=							tokens + [tokenizer.unk_token]

             UpperCAmelCase_      			=							[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase   )		,    _UpperCAmelCase   )









"""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 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       @slow
       def 					lowercase__    (							self      :  Tuple   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
             UpperCAmelCase_      			=							torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]   )
             # The dog is cute and lives in the garden house

             UpperCAmelCase_      			=							torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
             UpperCAmelCase_      			=							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_      			=							model(_UpperCAmelCase   )["last_hidden_state"].detach()
             self.assertEqual(output.shape		,    _UpperCAmelCase   )
             # compare the actual values for a slice of last dim
             self.assertTrue(torch.allclose(output[:, :, -1]		,    _UpperCAmelCase		,    atol=1e-3   )   )







       @slow
       def 					lowercase__    (							self      :  List[Any]   )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_      			=							XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
             UpperCAmelCase_      			=							torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]   )
             # The dog is cute and lives in the garden house

             UpperCAmelCase_      			=							torch.Size((1, 12, 1024)   )  # batch_size, sequence_length, embedding_vector_dim
             UpperCAmelCase_      			=							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_      			=							model(_UpperCAmelCase   )["last_hidden_state"].detach()
             self.assertEqual(output.shape		,    _UpperCAmelCase   )
             # compare the actual values for a slice of last dim
             self.assertTrue(torch.allclose(output[:, :, -1]		,    _UpperCAmelCase		,    atol=1e-3   )   )




