
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__  (       _UpperCAmelCase    ,_UpperCAmelCase    ,unittest.TestCase    ):
   _SCREAMING_SNAKE_CASE      :							Union[str, Any]	       =      VQModel
   _SCREAMING_SNAKE_CASE      :							Union[str, Any]	       =      'sample'
   @property
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					List[Any]=(3_2, 3_2)   ):
    __a :  Tuple						=  4
    __a :  Union[str, Any]						=  3

    __a :  List[Any]						=  floats_tensor((batch_size, num_channels) + sizes   ).to(SCREAMING_SNAKE_CASE_   )

    return {"sample": image}
   @property
   def       lowerCAmelCase			(self :					int   ):
    return (3, 3_2, 3_2)
   @property
   def       lowerCAmelCase			(self :					List[Any]   ):
    return (3, 3_2, 3_2)
   def       lowerCAmelCase			(self :					Any   ):
    __a :  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,
    }
    __a :  Union[str, Any]						=  self.dummy_input
    return init_dict, inputs_dict
   def       lowerCAmelCase			(self :					List[str]   ):
    pass
   def       lowerCAmelCase			(self :					Dict   ):
    pass
   def       lowerCAmelCase			(self :					Optional[int]   ):
    __a :  Union[str, Any]						=  VQModel.from_pretrained('''fusing/vqgan-dummy'''					,				output_loading_info=SCREAMING_SNAKE_CASE_   )
    self.assertIsNotNone(SCREAMING_SNAKE_CASE_   )
    self.assertEqual(len(loading_info['''missing_keys''']   )					,				0   )

    model.to(SCREAMING_SNAKE_CASE_   )
    __a :  List[Any]						=  model(**self.dummy_input   )

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







   def       lowerCAmelCase			(self :					str   ):
    __a :  List[Any]						=  VQModel.from_pretrained('''fusing/vqgan-dummy'''   )
    model.to(SCREAMING_SNAKE_CASE_   ).eval()

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

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

    __a :  List[str]						=  output[0, -1, -3:, -3:].flatten().cpu()
    # fmt: off
    __a :  Union[str, 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(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_					,				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_xlnet import XLNetTokenizer
else:
      A__		:					Dict												=  None


A__		:					List[Any]												=  logging.get_logger(__name__)

A__		:					str												=  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

A__		:					Union[str, Any]												=  {
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
    },
}

A__		:					Any												=  {
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

A__		:					Dict												=  """▁"""

# Segments (not really needed)
A__		:					List[str]												=  0
A__		:					List[Any]												=  1
A__		:					Union[str, Any]												=  2
A__		:					List[Any]												=  3
A__		:					str												=  4





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					str							     =			VOCAB_FILES_NAMES
    lowerCamelCase  :					Dict							     =			PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase  :					Tuple							     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase  :					List[Any]							     =			'left'
    lowerCamelCase  :					Optional[Any]							     =			XLNetTokenizer







    def __init__(							self			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_="<s>"			,     SCREAMING_SNAKE_CASE_="</s>"			,     SCREAMING_SNAKE_CASE_="<unk>"			,     SCREAMING_SNAKE_CASE_="<sep>"			,     SCREAMING_SNAKE_CASE_="<pad>"			,     SCREAMING_SNAKE_CASE_="<cls>"			,     SCREAMING_SNAKE_CASE_="<mask>"			,     SCREAMING_SNAKE_CASE_=["<eop>", "<eod>"]			,     **SCREAMING_SNAKE_CASE_			,     )				->     Union[str, Any]:
         # Mask token behave like a normal word, i.e. include the space before it
         __lowerCamelCase   :	str								=		AddedToken(SCREAMING_SNAKE_CASE_			,     lstrip=SCREAMING_SNAKE_CASE_			,     rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	) else mask_token

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

         __lowerCamelCase   :	Union[str, Any]								=		3
         __lowerCamelCase   :	int								=		do_lower_case
         __lowerCamelCase   :	Optional[Any]								=		remove_space
         __lowerCamelCase   :	int								=		keep_accents
         __lowerCamelCase   :	Any								=		vocab_file
         __lowerCamelCase   :	Any								=		False if not self.vocab_file else True







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







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None	)				->     List[int]:
         __lowerCamelCase   :	int								=		[self.sep_token_id]
         __lowerCamelCase   :	Dict								=		[2]

         if token_ids_a is None:
              return len(token_ids_a + sep	) * [0] + cls_segment_id
         return len(token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1] + cls_segment_id







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = 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(SCREAMING_SNAKE_CASE_	):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
              return
         __lowerCamelCase   :	Tuple								=		os.path.join(
             SCREAMING_SNAKE_CASE_			,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

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

         return (out_vocab_file,)






