





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


__A							   =							logging.get_logger(__name__)

__A							   =							{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A							   =							{
    "vocab_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-german-cased": (
            "https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
        ),
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
    },
}

__A							   =							{
    "distilbert-base-uncased": 512,
    "distilbert-base-uncased-distilled-squad": 512,
    "distilbert-base-cased": 512,
    "distilbert-base-cased-distilled-squad": 512,
    "distilbert-base-german-cased": 512,
    "distilbert-base-multilingual-cased": 512,
}


__A							   =							{
    "distilbert-base-uncased": {"do_lower_case": True},
    "distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
    "distilbert-base-cased": {"do_lower_case": False},
    "distilbert-base-cased-distilled-squad": {"do_lower_case": False},
    "distilbert-base-german-cased": {"do_lower_case": False},
    "distilbert-base-multilingual-cased": {"do_lower_case": False},
}



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     VOCAB_FILES_NAMES
 lowercase_						=     PRETRAINED_VOCAB_FILES_MAP
 lowercase_						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_						=     PRETRAINED_INIT_CONFIGURATION
 lowercase_						=     ["input_ids", "attention_mask"]
 lowercase_						=     DistilBertTokenizer

 def __init__(self							:  Tuple			,					UpperCAmelCase_							:  Tuple=None			,					UpperCAmelCase_							:  int=None			,					UpperCAmelCase_							:  Any=True			,					UpperCAmelCase_							:  List[Any]="[UNK]"			,					UpperCAmelCase_							:  Dict="[SEP]"			,					UpperCAmelCase_							:  Dict="[PAD]"			,					UpperCAmelCase_							:  Optional[int]="[CLS]"			,					UpperCAmelCase_							:  str="[MASK]"			,					UpperCAmelCase_							:  Any=True			,					UpperCAmelCase_							:  List[str]=None			,					**UpperCAmelCase_							:  List[str]			,					)			->str:



   '''simple docstring'''



   super().__init__(
       UpperCAmelCase_			,					tokenizer_file=UpperCAmelCase_			,					do_lower_case=UpperCAmelCase_			,					unk_token=UpperCAmelCase_			,					sep_token=UpperCAmelCase_			,					pad_token=UpperCAmelCase_			,					cls_token=UpperCAmelCase_			,					mask_token=UpperCAmelCase_			,					tokenize_chinese_chars=UpperCAmelCase_			,					strip_accents=UpperCAmelCase_			,					**UpperCAmelCase_			,					)

   lowerCamelCase__: Union[str, Any]         =json.loads(self.backend_tokenizer.normalizer.__getstate__())
   if (
       normalizer_state.get("lowercase"			,					UpperCAmelCase_) != do_lower_case
       or normalizer_state.get("strip_accents"			,					UpperCAmelCase_) != strip_accents
       or normalizer_state.get("handle_chinese_chars"			,					UpperCAmelCase_) != tokenize_chinese_chars
   ):
     lowerCamelCase__: List[str]         =getattr(UpperCAmelCase_			,					normalizer_state.pop("type"))
     lowerCamelCase__: Optional[int]         =do_lower_case
     lowerCamelCase__: int         =strip_accents
     lowerCamelCase__: Any         =tokenize_chinese_chars
     lowerCamelCase__: Any         =normalizer_class(**UpperCAmelCase_)

   lowerCamelCase__: str         =do_lower_case

 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  Union[str, Any]=None)			->Dict:



   '''simple docstring'''



   lowerCamelCase__: str         =[self.cls_token_id] + token_ids_a + [self.sep_token_id]

   if token_ids_a:
     output += token_ids_a + [self.sep_token_id]

   return output

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



   '''simple docstring'''



   lowerCamelCase__: str         =[self.sep_token_id]
   lowerCamelCase__: str         =[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 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int]			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  Optional[str] = None)			->Tuple[str]:



   '''simple docstring'''



   lowerCamelCase__: str         =self._tokenizer.model.save(UpperCAmelCase_			,					name=UpperCAmelCase_)
   return tuple(UpperCAmelCase_)


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__	(		__a   ,						__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Dict	      = VQModel
   lowerCAmelCase   :     List[str]	      = '''sample'''



   @property
   def 	lowerCAmelCase    (						self       :	List[Any]		,	_lowercase       :	Tuple=(32, 32)				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			4
    _UpperCamelCase:  Tuple    						=			3

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

    return {"sample": image}



   @property
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    return (3, 32, 32)



   @property
   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    return (3, 32, 32)



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



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

    model.to(_lowercase				)
    _UpperCamelCase:  Tuple    						=			model(**self.dummy_input				)

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



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			VQModel.from_pretrained('''fusing/vqgan-dummy'''				)
    model.to(_lowercase				).eval()

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

    _UpperCamelCase:  int    						=			torch.randn(1		,	model.config.in_channels		,	model.config.sample_size		,	model.config.sample_size				)
    _UpperCamelCase:  Tuple    						=			image.to(_lowercase				)
    with torch.no_grad():
     _UpperCamelCase:  Optional[int]    						=			model(_lowercase				).sample

    _UpperCamelCase:  List[Any]    						=			output[0, -1, -3:, -3:].flatten().cpu()
    # fmt: off
    _UpperCamelCase:  Any    						=			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(_lowercase		,	_lowercase		,	atol=1E-3				)				)