






'''simple docstring'''






import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin




class a_    (    UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							PhobertTokenizer
   _snake_case        =							False







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      __snake_case				:      Optional[int]     							= ['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
      __snake_case				:      Optional[int]     							= dict(zip(__a					,   range(len(__a))))
      __snake_case				:      Any     							= ['#version: 0.2', 'l à</w>']
      __snake_case				:      Tuple     							= {'unk_token': '<unk>'}

      __snake_case				:      Union[str, Any]     							= os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['vocab_file'])
      __snake_case				:      List[str]     							= 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     SCREAMING_SNAKE_CASE__					(self					,   **__a)			->      Dict:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      Any:
      """simple docstring"""






      __snake_case				:      List[str]     							= 'Tôi là VinAI Research'
      __snake_case				:      Optional[int]     							= 'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
      return input_text, output_text







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      __snake_case				:      str     							= PhobertTokenizer(self.vocab_file					,   self.merges_file					,   **self.special_tokens_map)
      __snake_case				:      int     							= 'Tôi là VinAI Research'
      __snake_case				:      Optional[Any]     							= 'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
      __snake_case				:      str     							= tokenizer.tokenize(__a)
      print(__a)
      self.assertListEqual(__a					,   __a)

      __snake_case				:      List[Any]     							= tokens + [tokenizer.unk_token]

      __snake_case				:      List[str]     							= [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(__a)					,   __a)







'''simple docstring'''






import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class a_    (    UpperCamelCase_		,      UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							VQModel
   _snake_case        =							"""sample"""







   @property
   def     SCREAMING_SNAKE_CASE__					(self					,   __a=(3_2, 3_2))			->      str:
      """simple docstring"""






      __snake_case				:      Dict     							= 4
      __snake_case				:      Optional[int]     							= 3

      __snake_case				:      str     							= floats_tensor((batch_size, num_channels) + sizes).to(__a)

      return {"sample": image}







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      return (3, 3_2, 3_2)







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      return (3, 3_2, 3_2)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= {
          'block_out_channels': [3_2, 6_4],
          'in_channels': 3,
          'out_channels': 3,
          'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
          'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
          'latent_channels': 3,
      }
      __snake_case				:      List[Any]     							= self.dummy_input
      return init_dict, inputs_dict







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      __snake_case							,__snake_case				:      List[Any]     							= VQModel.from_pretrained('fusing/vqgan-dummy'					,   output_loading_info=__a)
      self.assertIsNotNone(__a)
      self.assertEqual(len(loading_info['missing_keys'])					,   0)

      model.to(__a)
      __snake_case				:      Any     							= model(**self.dummy_input)

      assert image is not None, "Make sure output is not None"







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= VQModel.from_pretrained('fusing/vqgan-dummy')
      model.to(__a).eval()

      torch.manual_seed(0)
      if torch.cuda.is_available():
         torch.cuda.manual_seed_all(0)

      __snake_case				:      Tuple     							= torch.randn(1					,   model.config.in_channels					,   model.config.sample_size					,   model.config.sample_size)
      __snake_case				:      Optional[int]     							= image.to(__a)
      with torch.no_grad():
         __snake_case				:      List[Any]     							= model(__a).sample

      __snake_case				:      int     							= output[0, -1, -3:, -3:].flatten().cpu()
      # fmt: off
      __snake_case				:      int     							= torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143])
      # fmt: on
      self.assertTrue(torch.allclose(__a					,   __a					,   atol=1E-3))