import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 	__snake_case	( __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       MgpstrTokenizer
      _snake_case    		=       False
      _snake_case    		=       {}
      _snake_case    		=       False



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Dict:


          """simple docstring"""


          super().setUp()

          # fmt: off
          _lowerCamelCase     :		Any 						=						["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
          # fmt: on
          _lowerCamelCase     :		Optional[int] 						=						dict(zip(_UpperCamelCase    ,  range(len(_UpperCamelCase))))

          _lowerCamelCase     :		str 						=						os.path.join(self.tmpdirname    ,  VOCAB_FILES_NAMES["""vocab_file"""])
          with open(self.vocab_file    ,  """w"""    ,  encoding="""utf-8""") as fp:
              fp.write(json.dumps(_UpperCamelCase) + """\n""")



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  **_UpperCamelCase     :				List[Any])				->int:


          """simple docstring"""


          return MgpstrTokenizer.from_pretrained(self.tmpdirname    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				List[str])				->str:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						"""tester"""
          _lowerCamelCase     :		int 						=						"""tester"""
          return input_text, output_text



      @unittest.skip("""MGP-STR always lower cases letters.""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Any:


          """simple docstring"""


          pass



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


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						self.get_tokenizers(do_lower_case=_UpperCamelCase)
          for tokenizer in tokenizers:
              with self.subTest(F"""{tokenizer.__class__.__name__}"""):
                  _lowerCamelCase     :		Optional[Any] 						=						"""[SPECIAL_TOKEN]"""

                  tokenizer.add_special_tokens({"""cls_token""": special_token})
                  _lowerCamelCase     :		Optional[Any] 						=						tokenizer.encode([special_token]    ,  add_special_tokens=_UpperCamelCase)
                  self.assertEqual(len(_UpperCamelCase)    ,  1)

                  _lowerCamelCase     :		Any 						=						tokenizer.decode(_UpperCamelCase    ,  skip_special_tokens=_UpperCamelCase)
                  self.assertTrue(special_token not in decoded)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->str:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F"""{tokenizer.__class__.__name__}"""):
                  _lowerCamelCase						,							_lowerCamelCase     :		List[Any] 						=						self.get_input_output_texts(_UpperCamelCase)

                  _lowerCamelCase     :		Dict 						=						tokenizer.tokenize(_UpperCamelCase)
                  _lowerCamelCase     :		Optional[int] 						=						tokenizer.convert_tokens_to_ids(_UpperCamelCase)
                  _lowerCamelCase     :		Any 						=						tokenizer.encode(_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase)
                  self.assertListEqual(_UpperCamelCase    ,  _UpperCamelCase)

                  _lowerCamelCase     :		Optional[Any] 						=						tokenizer.convert_ids_to_tokens(_UpperCamelCase)
                  self.assertNotEqual(len(_UpperCamelCase)    ,  0)
                  _lowerCamelCase     :		str 						=						tokenizer.decode(_UpperCamelCase)
                  self.assertIsInstance(_UpperCamelCase    ,  _UpperCamelCase)

                  self.assertEqual(text_a.replace(""" """    ,  """""")    ,  _UpperCamelCase)



      @unittest.skip("""MGP-STR tokenizer only handles one sequence.""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Dict:


          """simple docstring"""


          pass




      @unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->int:


          """simple docstring"""


          pass





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 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






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


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						XLMRobertaModel.from_pretrained("""xlm-roberta-base""")
          _lowerCamelCase     :		Optional[int] 						=						torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]])
          # The dog is cute and lives in the garden house

          _lowerCamelCase     :		Optional[Any] 						=						torch.Size((1, 12, 768))  # batch_size, sequence_length, embedding_vector_dim
          _lowerCamelCase     :		str 						=						torch.tensor(
              [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]])
          #  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():
              _lowerCamelCase     :		List[str] 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						XLMRobertaModel.from_pretrained("""xlm-roberta-large""")
          _lowerCamelCase     :		Optional[Any] 						=						torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]])
          # The dog is cute and lives in the garden house

          _lowerCamelCase     :		str 						=						torch.Size((1, 12, 1024))  # batch_size, sequence_length, embedding_vector_dim
          _lowerCamelCase     :		Union[str, Any] 						=						torch.tensor(
              [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]])
          #  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():
              _lowerCamelCase     :		int 						=						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))




