



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():
      import torch

      from transformers import CamembertModel

@require_torch
@require_sentencepiece
@require_tokenizers
class    _SCREAMING_SNAKE_CASE						(unittest.TestCase			):





     @slow
     def 						__snake_case     (							self			:				Optional[Any]					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     str							=						CamembertModel.from_pretrained("camembert-base"					)
      model.to(UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     str							=						torch.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]]  ,	device=UpperCamelCase  ,	dtype=torch.long  ,	)  # J'aime le camembert !
      with torch.no_grad():
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						model(UpperCamelCase					)["last_hidden_state"]
      __SCREAMING_SNAKE_CASE							:     str							=						torch.Size((1, 1_0, 7_6_8)					)
      self.assertEqual(output.shape  ,	UpperCamelCase					)
      # compare the actual values for a slice.
      __SCREAMING_SNAKE_CASE							:     int							=						torch.tensor(
          [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]  ,	device=UpperCamelCase  ,	dtype=torch.float  ,	)
      # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
      # camembert.eval()
      # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

      self.assertTrue(torch.allclose(output[:, :3, :3]  ,	UpperCamelCase  ,	atol=1E-4					)					)







import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin







class       __snake_case (  lowerCamelCase__       , unittest.TestCase ):
 __lowerCamelCase     : Any 	=   SpeechaTextaTokenizer
 __lowerCamelCase     : Tuple 	=   False
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       super().setUp()

       UpperCAmelCase     :			Union[str, Any]     	='''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''    )
       UpperCAmelCase     :			int     	=['''he re</w> 123''', '''here a 1456''']
       UpperCAmelCase     :			int     	=dict(zip(snake_case__		, range(len(snake_case__    )    )    )    )

       UpperCAmelCase     :			Tuple     	={'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

       UpperCAmelCase     :			Tuple     	=tempfile.mkdtemp()
       UpperCAmelCase     :			Optional[Any]     	=os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES['''vocab_file''']    )
       UpperCAmelCase     :			Dict     	=os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES['''merges_file''']    )

       with open(self.vocab_file		, '''w'''		, encoding='''utf-8'''    ) as fp:
             fp.write(json.dumps(snake_case__    ) + '''\n'''    )

       with open(self.merges_file		, '''w'''    ) as fp:
             fp.write('''\n'''.join(snake_case__    )    )
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=list(self.get_tokenizer().get_vocab().keys()    )

       self.assertEqual(vocab_keys[0]		, '''<s>'''    )
       self.assertEqual(vocab_keys[1]		, '''<pad>'''    )
       self.assertEqual(vocab_keys[-1]		, '''vocab'''    )
       self.assertEqual(len(snake_case__    )		, 14    )
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       self.assertEqual(self.get_tokenizer().vocab_size		, 14    )
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=SpeechaTextaTokenizer.from_pretrained(self.tmpdirname    )

       # make sure @@ is correctly concatenated
       UpperCAmelCase     :			str     	=[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
       UpperCAmelCase     :			Optional[Any]     	=tokenizer.decode(snake_case__    )

       self.assertTrue(output_string == '''herecouple words ofthe'''    )
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	=SpeechaTextaTokenizer.from_pretrained(self.tmpdirname    )

       with tempfile.TemporaryDirectory() as tmp_dirname:
             tokenizer.save_pretrained(snake_case__    )
             os.remove(os.path.join(snake_case__		, '''merges.txt'''    )    )

             # load tokenizer without merges file should not throw an error
             UpperCAmelCase     :			Any     	=SpeechaTextaTokenizer.from_pretrained(snake_case__    )

       with tempfile.TemporaryDirectory() as tmp_dirname:
             # save tokenizer and load again
             tokenizer.save_pretrained(snake_case__    )
             UpperCAmelCase     :			List[str]     	=SpeechaTextaTokenizer.from_pretrained(snake_case__    )

       self.assertIsNotNone(snake_case__    )






 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       if not self.test_slow_tokenizer:
             return

       UpperCAmelCase     :			List[str]     	=inspect.signature(self.tokenizer_class.__init__    )
       UpperCAmelCase     :			Union[str, Any]     	=self.get_tokenizer()

       for parameter_name, parameter in signature.parameters.items():
             if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
                   self.assertIn(snake_case__		, tokenizer.init_kwargs    )
