





"""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  __UpperCamelCase       (		a__	, a__	, unittest.TestCase		):
							lowerCamelCase     :      List[str]         =VQModel
							lowerCamelCase     :      List[str]         ="""sample"""






							@property
							def 					__a     (     self   ,      lowerCAmelCase__=(32, 32)   )			->				Any:
								a   :	Dict			=       4
								a   :	str			=       3

								a   :	str			=       floats_tensor((batch_size, num_channels) + sizes   ).to(lowerCAmelCase__   )

								return {"sample": image}






							@property
							def 					__a     (     self   )			->				Union[str, Any]:
								return (3, 32, 32)






							@property
							def 					__a     (     self   )			->				int:
								return (3, 32, 32)






							def 					__a     (     self   )			->				int:
								a   :	str			=       {
								    "block_out_channels": [32, 64],
								    "in_channels": 3,
								    "out_channels": 3,
								    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
								    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
								    "latent_channels": 3,
								}
								a   :	Union[str, Any]			=       self.dummy_input
								return init_dict, inputs_dict






							def 					__a     (     self   )			->				List[Any]:
								pass






							def 					__a     (     self   )			->				Union[str, Any]:
								pass






							def 					__a     (     self   )			->				int:
								a, a   :	Optional[int]			=       VQModel.from_pretrained("fusing/vqgan-dummy"   ,      output_loading_info=lowerCAmelCase__   )
								self.assertIsNotNone(lowerCAmelCase__   )
								self.assertEqual(len(loading_info["missing_keys"]   )   ,      0   )

								model.to(lowerCAmelCase__   )
								a   :	Optional[int]			=       model(**self.dummy_input   )

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






							def 					__a     (     self   )			->				int:
								a   :	List[str]			=       VQModel.from_pretrained("fusing/vqgan-dummy"   )
								model.to(lowerCAmelCase__   ).eval()

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

								a   :	int			=       torch.randn(1   ,      model.config.in_channels   ,      model.config.sample_size   ,      model.config.sample_size   )
								a   :	Union[str, Any]			=       image.to(lowerCAmelCase__   )
								with torch.no_grad():
									a   :	Dict			=       model(lowerCAmelCase__   ).sample

								a   :	Tuple			=       output[0, -1, -3:, -3:].flatten().cpu()
								# fmt: off
								a   :	Tuple			=       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(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )









"""simple docstring"""




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_albert import AlbertTokenizer
else:
		a		:				Tuple  			=    None

a		:				Any  			=    logging.get_logger(__name__)
a		:				List[Any]  			=    {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:				str  			=    {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

a		:				str  			=    {
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

a		:				Union[str, Any]  			=    '''▁'''


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      Union[str, Any]         =VOCAB_FILES_NAMES
							lowerCamelCase     :      Dict         =PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase     :      List[Any]         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase     :      List[Any]         =AlbertTokenizer






							def __init__(     self   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__="[CLS]"   ,      lowerCAmelCase__="[SEP]"   ,      lowerCAmelCase__="<unk>"   ,      lowerCAmelCase__="[SEP]"   ,      lowerCAmelCase__="<pad>"   ,      lowerCAmelCase__="[CLS]"   ,      lowerCAmelCase__="[MASK]"   ,      **lowerCAmelCase__   ,      )			->				Union[str, Any]:
								# Mask token behave like a normal word, i.e. include the space before it and
								# is included in the raw text, there should be a match in a non-normalized sentence.
								a   :	Optional[int]			=       (
								    AddedToken(lowerCAmelCase__   ,      lstrip=lowerCAmelCase__   ,      rstrip=lowerCAmelCase__   ,      normalized=lowerCAmelCase__   )
								    if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   )
								    else mask_token
								)

								super().__init__(
								    lowerCAmelCase__   ,      tokenizer_file=lowerCAmelCase__   ,      do_lower_case=lowerCAmelCase__   ,      remove_space=lowerCAmelCase__   ,      keep_accents=lowerCAmelCase__   ,      bos_token=lowerCAmelCase__   ,      eos_token=lowerCAmelCase__   ,      unk_token=lowerCAmelCase__   ,      sep_token=lowerCAmelCase__   ,      pad_token=lowerCAmelCase__   ,      cls_token=lowerCAmelCase__   ,      mask_token=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )

								a   :	Dict			=       do_lower_case
								a   :	Any			=       remove_space
								a   :	Optional[Any]			=       keep_accents
								a   :	List[str]			=       vocab_file
								a   :	Optional[Any]			=       False if not self.vocab_file else True






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	Optional[Any]			=       [self.sep_token_id]
								a   :	int			=       [self.cls_token_id]
								if token_ids_a is None:
									return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	Optional[Any]			=       [self.sep_token_id]
								a   :	int			=       [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   ) * [0] + len(token_ids_a + sep   ) * [1]






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Tuple[str]:
								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(lowerCAmelCase__   ):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
									return
								a   :	Dict			=       os.path.join(
								    lowerCAmelCase__   ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

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

								return (out_vocab_file,)


