


"""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:
 UpperCAmelCase      		=			None

UpperCAmelCase      		=			logging.get_logger(__name__)
UpperCAmelCase      		=			{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase      		=			{
    '''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''',
    },
}

UpperCAmelCase      		=			{
    '''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,
}

UpperCAmelCase      		=			'''▁'''
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      int        =   VOCAB_FILES_NAMES
       __A			:      int        =   PRETRAINED_VOCAB_FILES_MAP
       __A			:      str        =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __A			:      Union[str, Any]        =   AlbertTokenizer






       def __init__(   self  :    Tuple  ,     snake_case__  :    Optional[int]=None  ,     snake_case__  :    Dict=None  ,     snake_case__  :    Optional[Any]=True  ,     snake_case__  :    Dict=True  ,     snake_case__  :    List[Any]=False  ,     snake_case__  :    str="[CLS]"  ,     snake_case__  :    Tuple="[SEP]"  ,     snake_case__  :    List[Any]="<unk>"  ,     snake_case__  :    Optional[int]="[SEP]"  ,     snake_case__  :    Any="<pad>"  ,     snake_case__  :    Dict="[CLS]"  ,     snake_case__  :    Dict="[MASK]"  ,     **snake_case__  :    Any  ,     ):

              '''simple docstring'''






              lowercase		:List[str]       							=				(
                  AddedToken(snake_case__  ,     lstrip=snake_case__  ,     rstrip=snake_case__  ,     normalized=snake_case__				)
                  if isinstance(snake_case__  ,     snake_case__				)
                  else mask_token
              )

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

              lowercase		:List[Any]       							=				do_lower_case
              lowercase		:int       							=				remove_space
              lowercase		:Optional[Any]       							=				keep_accents
              lowercase		:Optional[int]       							=				vocab_file
              lowercase		:Optional[int]       							=				False if not self.vocab_file else True






       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    List[int]  ,     snake_case__  :    Optional[List[int]] = None				):

              '''simple docstring'''






              lowercase		:List[str]       							=				[self.sep_token_id]
              lowercase		:str       							=				[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  __snake_case						(   self  :    Dict  ,     snake_case__  :    List[int]  ,     snake_case__  :    Optional[List[int]] = None				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				[self.sep_token_id]
              lowercase		: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  __snake_case						(   self  :    Optional[int]  ,     snake_case__  :    str  ,     snake_case__  :    Optional[str] = None				):

              '''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(snake_case__				):
                     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
                     return
              lowercase		:Optional[int]       							=				os.path.join(
                  snake_case__  ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

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

              return (out_vocab_file,)





"""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 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      List[str]        =   VQModel
       __A			:      Any        =   "sample"






       @property
       def  __snake_case						(   self  :    int  ,     snake_case__  :    int=(3_2, 3_2)				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				4
              lowercase		:Tuple       							=				3

              lowercase		:List[str]       							=				floats_tensor((batch_size, num_channels) + sizes				).to(snake_case__				)

              return {"sample": image}






       @property
       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              return (3, 3_2, 3_2)






       @property
       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              return (3, 3_2, 3_2)






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				{
                  '''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,
              }
              lowercase		:Dict       							=				self.dummy_input
              return init_dict, inputs_dict






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              pass






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              pass






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase							,  lowercase		:Optional[int]       							=				VQModel.from_pretrained('''fusing/vqgan-dummy'''  ,     output_loading_info=snake_case__				)
              self.assertIsNotNone(snake_case__				)
              self.assertEqual(len(loading_info['''missing_keys''']				)  ,     0				)

              model.to(snake_case__				)
              lowercase		:List[str]       							=				model(**self.dummy_input				)

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







       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				VQModel.from_pretrained('''fusing/vqgan-dummy'''				)
              model.to(snake_case__				).eval()

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

              lowercase		:List[Any]       							=				torch.randn(1  ,     model.config.in_channels  ,     model.config.sample_size  ,     model.config.sample_size				)
              lowercase		:int       							=				image.to(snake_case__				)
              with torch.no_grad():
                     lowercase		:str       							=				model(snake_case__				).sample

              lowercase		:List[Any]       							=				output[0, -1, -3:, -3:].flatten().cpu()
              # fmt: off
              lowercase		:Tuple       							=				torch.tensor([-0.01_53, -0.40_44, -0.18_80, -0.51_61, -0.24_18, -0.40_72, -0.16_12, -0.06_33, -0.01_43]				)
              # fmt: on
              self.assertTrue(torch.allclose(snake_case__  ,     snake_case__  ,     atol=1e-3				)				)

