


"""simple docstring"""



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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case   			=    '''▁'''

__snake_case   			=    {'''vocab_file''': '''spiece.model'''}

__snake_case   			=    {
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

__snake_case   			=    {
    '''google/pegasus-xsum''': 512,
}


__snake_case   			=    logging.get_logger(__name__)







class _lowerCAmelCase				(      _lowercase	):
   __UpperCAmelCase :       List[str]							  =  VOCAB_FILES_NAMES

   __UpperCAmelCase :       str							  =  VOCAB_FILES_NAMES
   __UpperCAmelCase :       str							  =  PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase :       Dict							  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase :       str							  =  ['''input_ids''', '''attention_mask''']







   def __init__(	self						,	UpperCamelCase__						,	UpperCamelCase__="<pad>"						,	UpperCamelCase__="</s>"						,	UpperCamelCase__="<unk>"						,	UpperCamelCase__="<mask_2>"						,	UpperCamelCase__="<mask_1>"						,	UpperCamelCase__=None						,	UpperCamelCase__=103						,	UpperCamelCase__ = None						,	**UpperCamelCase__						,	)	->		List[str]:

       '''simple docstring'''




       snake_case    :      Dict      					=    offset
       if additional_special_tokens is not None:
           if not isinstance(UpperCamelCase__						,	UpperCamelCase__						):
               raise TypeError(
                   F'additional_special_tokens should be of type {type(UpperCamelCase__						)}, but is'
                   F' {type(UpperCamelCase__						)}'						)

           snake_case    :      Union[str, Any]      					=    (
               ([mask_token_sent] + additional_special_tokens)
               if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
               else additional_special_tokens
           )
           # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
           additional_special_tokens_extended += [
               F'<unk_{i}>' for i in range(len(UpperCamelCase__						)						,	self.offset - 1						)
           ]

           if len(set(UpperCamelCase__						)						) != len(UpperCamelCase__						):
               raise ValueError(
                   "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                   F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'						)
           snake_case    :      Tuple      					=    additional_special_tokens_extended
       else:
           snake_case    :      List[Any]      					=    [mask_token_sent] if mask_token_sent is not None else []
           additional_special_tokens += [F'<unk_{i}>' for i in range(2						,	self.offset						)]

       snake_case    :      Any      					=    {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           eos_token=UpperCamelCase__						,	unk_token=UpperCamelCase__						,	mask_token=UpperCamelCase__						,	pad_token=UpperCamelCase__						,	mask_token_sent=UpperCamelCase__						,	offset=UpperCamelCase__						,	additional_special_tokens=UpperCamelCase__						,	sp_model_kwargs=self.sp_model_kwargs						,	**UpperCamelCase__						,	)
       snake_case    :      Optional[int]      					=    mask_token_sent
       snake_case    :      Optional[Any]      					=    vocab_file
       snake_case    :      Any      					=    spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(UpperCamelCase__						)

       # add special tokens to encoder dict
       snake_case    :      Dict[int, str]      					=    {
           0: self.pad_token,
           1: self.eos_token,
       }

       if self.mask_token_sent is not None:
           self.encoder.update(
               {
                   2: self.mask_token_sent,
                   3: self.mask_token,
               }						)

       if self.offset > 0:
           # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
           # mask_token_sent is already added to list -> so start at 1
           self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1						,	self.offset - 1						)}						)

       snake_case    :      Dict[str, int]      					=    {v: k for k, v in self.encoder.items()}







   @property
   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       return len(self.sp_model						) + self.offset







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    {self.convert_ids_to_tokens(UpperCamelCase__						): i for i in range(self.vocab_size						)}
       vocab.update(self.added_tokens_encoder						)
       return vocab







   def __getstate__(	self						)	->		Dict:

       '''simple docstring'''




       snake_case    :      int      					=    self.__dict__.copy()
       snake_case    :      Tuple      					=    None
       return state







   def __setstate__(	self						,	UpperCamelCase__						)	->		str:

       '''simple docstring'''




       snake_case    :      int      					=    d

       # for backward compatibility
       if not hasattr(self						,	"sp_model_kwargs"						):
           snake_case    :      Dict      					=    {}

       snake_case    :      Union[str, Any]      					=    spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(self.vocab_file						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       return self.sp_model.encode(UpperCamelCase__						,	out_type=UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		List[str]:

       '''simple docstring'''




       if token in self.decoder:
           return self.decoder[token]
       elif token in self.added_tokens_decoder:
           return self.added_tokens_decoder[token]
       snake_case    :      Tuple      					=    self.sp_model.piece_to_id(UpperCamelCase__						)
       return sp_id + self.offset







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		str:

       '''simple docstring'''




       if index in self.encoder:
           return self.encoder[index]
       elif index in self.added_tokens_encoder:
           return self.added_tokens_encoder[index]
       else:
           snake_case    :      List[str]      					=    self.sp_model.IdToPiece(index - self.offset						)
       return token







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Optional[int]:

       '''simple docstring'''




       snake_case    :      Any      					=    []
       snake_case    :      str      					=    """"""
       for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
               out_string += self.sp_model.decode(UpperCamelCase__						) + token
               snake_case    :      List[Any]      					=    []
           else:
               current_sub_tokens.append(UpperCamelCase__						)
       out_string += self.sp_model.decode(UpperCamelCase__						)
       return out_string.strip()







   def  lowerCamelCase		(	self						,	UpperCamelCase__=False						)	->		Tuple:

       '''simple docstring'''




       return 1







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       snake_case    :      List[str]      					=    set(self.all_special_ids						)  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id						)  # <unk> is only sometimes special

       return [1 if x in all_special_ids else 0 for x in seq]







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						,	UpperCamelCase__ = False						)	->		str:

       '''simple docstring'''




       if already_has_special_tokens:
           return self._special_token_mask(UpperCamelCase__						)
       elif token_ids_a is None:
           return self._special_token_mask(UpperCamelCase__						) + [1]
       else:
           return self._special_token_mask(token_ids_a + token_ids_a						) + [1]







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__=None						)	->		Optional[int]:

       '''simple docstring'''




       if token_ids_a is None:
           return token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return token_ids_a + token_ids_a + [self.eos_token_id]







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		int:

       '''simple docstring'''




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

       if os.path.abspath(self.vocab_file						) != os.path.abspath(UpperCamelCase__						) and os.path.isfile(self.vocab_file						):
           copyfile(self.vocab_file						,	UpperCamelCase__						)
       elif not os.path.isfile(self.vocab_file						):
           with open(UpperCamelCase__						,	"wb"						) as fi:
               snake_case    :      List[str]      					=    self.sp_model.serialized_model_proto()
               fi.write(UpperCamelCase__						)

       return (out_vocab_file,)













'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_A	:			Union[str, Any]         =False

class _lowercase	(				unittest.TestCase		):
							pass



@slow
@require_torch_gpu
class _lowercase	(				unittest.TestCase		):




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Tuple         =       VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""     )
														pipe.to(UpperCamelCase__     )
														pipe.set_progress_bar_config(disable=UpperCamelCase__     )

														lowerCamelCase__					:	Optional[int]         =       load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""     )
														lowerCamelCase__					:	List[Any]         =       torch.manual_seed(0     )
														lowerCamelCase__					:	List[Any]         =       pipe(
														    image=UpperCamelCase__			,							generator=UpperCamelCase__			,							guidance_scale=7.5			,							num_inference_steps=50			,							output_type="""numpy"""			,							).images

														lowerCamelCase__					:	List[str]         =       image[0, 253:256, 253:256, -1]

														assert image.shape == (1, 512, 512, 3)
														lowerCamelCase__					:	Tuple         =       np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

														assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2

