





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     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     UpperCAmelCase      ,     unittest.TestCase				):
   _UpperCamelCase   :		str			 =	VQModel
   _UpperCamelCase   :		List[Any]			 =	'sample'


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    List[str]=(32, 32)	)->       List[str]:





     """simple docstring"""







     lowercase__           =  4
     lowercase__           =  3

     lowercase__           =  floats_tensor((batch_size, num_channels) + sizes	).to(a	)

     return {"sample": image}


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       Optional[int]:





     """simple docstring"""







     return (3, 32, 32)


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       List[Any]:





     """simple docstring"""







     return (3, 32, 32)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       Dict:





     """simple docstring"""







     lowercase__           =  {
         'block_out_channels': [32, 64],
         'in_channels': 3,
         'out_channels': 3,
         'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
         'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
         'latent_channels': 3,
     }
     lowercase__           =  self.dummy_input
     return init_dict, inputs_dict


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       Dict:





     """simple docstring"""







     pass


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       int:





     """simple docstring"""







     pass


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       Optional[Any]:





     """simple docstring"""







     lowercase__      ,   lowercase__           =  VQModel.from_pretrained('fusing/vqgan-dummy'      ,					output_loading_info=a	)
     self.assertIsNotNone(a	)
     self.assertEqual(len(loading_info['missing_keys']	)      ,					0	)

     model.to(a	)
     lowercase__           =  model(**self.dummy_input	)

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       List[str]:





     """simple docstring"""







     lowercase__           =  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	)

     lowercase__           =  torch.randn(1      ,					model.config.in_channels      ,					model.config.sample_size      ,					model.config.sample_size	)
     lowercase__           =  image.to(a	)
     with torch.no_grad():
       lowercase__           =  model(a	).sample

     lowercase__           =  output[0, -1, -3:, -3:].flatten().cpu()
     # fmt: off
     lowercase__           =  torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]	)
     # fmt: on
     self.assertTrue(torch.allclose(a      ,					a      ,					atol=1E-3	)	)









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_camembert import CamembertTokenizer
else:
      lowercase_          =		None


lowercase_          =		logging.get_logger(__name__)

lowercase_          =		{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase_          =		{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
    },
}

lowercase_          =		{
    """camembert-base""": 512,
}

lowercase_          =		"""▁"""
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		int			 =	VOCAB_FILES_NAMES
   _UpperCamelCase   :		Dict			 =	PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase   :		Dict			 =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase   :		Dict			 =	['input_ids', 'attention_mask']
   _UpperCamelCase   :		Optional[int]			 =	CamembertTokenizer


   def __init__(   self      :    List[Any]      ,					a      :    Union[str, Any]=None      ,					a      :    str=None      ,					a      :    Tuple="<s>"      ,					a      :    Tuple="</s>"      ,					a      :    Optional[int]="</s>"      ,					a      :    int="<s>"      ,					a      :    Dict="<unk>"      ,					a      :    Optional[Any]="<pad>"      ,					a      :    Union[str, Any]="<mask>"      ,					a      :    Any=["<s>NOTUSED", "</s>NOTUSED"]      ,					**a      :    int      ,					)->       str:





     """simple docstring"""







     lowercase__           =  AddedToken(a      ,					lstrip=a      ,					rstrip=a	) if isinstance(a      ,					a	) else mask_token

     super().__init__(
         a      ,					tokenizer_file=a      ,					bos_token=a      ,					eos_token=a      ,					sep_token=a      ,					cls_token=a      ,					unk_token=a      ,					pad_token=a      ,					mask_token=a      ,					additional_special_tokens=a      ,					**a      ,					)

     lowercase__           =  vocab_file
     lowercase__           =  False if not self.vocab_file else True


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    List[int]      ,					a      :    Optional[List[int]] = None	)->       List[int]:





     """simple docstring"""







     if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     lowercase__           =  [self.cls_token_id]
     lowercase__           =  [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    List[int]      ,					a      :    Optional[List[int]] = None	)->       List[int]:





     """simple docstring"""







     lowercase__           =  [self.sep_token_id]
     lowercase__           =  [self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep	) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					a      :    str      ,					a      :    Optional[str] = None	)->       Tuple[str]:





     """simple docstring"""







     if not self.can_save_slow_tokenizer:
       raise ValueError(
           'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
           'tokenizer.'	)

     if not os.path.isdir(a	):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""	)
       return
     lowercase__           =  os.path.join(
         a      ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

     if os.path.abspath(self.vocab_file	) != os.path.abspath(a	):
       copyfile(self.vocab_file      ,					a	)

     return (out_vocab_file,)


