






"""simple docstring"""


import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class 						snake_case  (  UpperCAmelCase   , unittest.TestCase       ):
    __magic_name__         =     BertweetTokenizer
    __magic_name__         =     False





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      a     :			Optional[Any]				=   ['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
      a     :			Union[str, Any]				=   dict(zip(A				,		range(len(A			)			)			)			)
      a     :			List[Any]				=   ['#version: 0.2', 'a m</w>']
      a     :			List[str]				=   {'unk_token': '<unk>'}

      a     :			Dict				=   os.path.join(self.tmpdirname				,		VOCAB_FILES_NAMES['vocab_file']			)
      a     :			Any				=   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(A			)			)





    def 				lowerCamelCase__      (							self	:					str				,		**A	:					int			):


      '''simple docstring'''




      kwargs.update(self.special_tokens_map			)
      return BertweetTokenizer.from_pretrained(self.tmpdirname				,		**A			)





    def 				lowerCamelCase__      (							self	:					List[str]				,		A	:					Tuple			):


      '''simple docstring'''




      a     :			Union[str, Any]				=   'I am VinAI Research'
      a     :			Optional[int]				=   'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
      return input_text, output_text





    def 				lowerCamelCase__      (							self	:					Optional[Any]			):


      '''simple docstring'''




      a     :			Optional[int]				=   BertweetTokenizer(self.vocab_file				,		self.merges_file				,		**self.special_tokens_map			)
      a     :			int				=   'I am VinAI Research'
      a     :			Optional[Any]				=   'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
      a     :			int				=   tokenizer.tokenize(A			)
      self.assertListEqual(A				,		A			)

      a     :			Optional[int]				=   tokens + [tokenizer.unk_token]

      a     :			List[str]				=   [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)				,		A			)












"""simple docstring"""


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 						snake_case  (  unittest.TestCase       ):





    @slow
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      a     :			int				=   CamembertModel.from_pretrained('camembert-base'			)
      model.to(A			)

      a     :			List[Any]				=   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=A				,		dtype=torch.long				,		)  # J'aime le camembert !
      with torch.no_grad():
        a     :			int				=   model(A			)['last_hidden_state']
      a     :			Union[str, Any]				=   torch.Size((1, 1_0, 7_6_8)			)
      self.assertEqual(output.shape				,		A			)
      # compare the actual values for a slice.
      a     :			List[str]				=   torch.tensor(
          [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]				,		device=A				,		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]				,		A				,		atol=1E-4			)			)




