

"""simple docstring"""




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

import sentencepiece as spm

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


_lowercase   :	Optional[Any]												=						logging.get_logger(__name__)

_lowercase   :	Union[str, Any]												=						{'vocab_file': 'sentencepiece.model'}

_lowercase   :	Tuple												=						{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

_lowercase   :	List[str]												=						{
    'google/rembert': 2_56,
}
class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: Union[str, Any]					=					VOCAB_FILES_NAMES
	a__		: List[Any]					=					PRETRAINED_VOCAB_FILES_MAP
	a__		: Optional[int]					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



	def __init__(  self     :       Optional[int]			,    _lowercase     :       Optional[Any]			,    _lowercase     :       Optional[Any]=False			,    _lowercase     :       Tuple=True			,    _lowercase     :       str=True			,    _lowercase     :       str="[CLS]"			,    _lowercase     :       Dict="[SEP]"			,    _lowercase     :       Union[str, Any]="[UNK]"			,    _lowercase     :       Any="[SEP]"			,    _lowercase     :       Union[str, Any]="[PAD]"			,    _lowercase     :       Tuple="[CLS]"			,    _lowercase     :       Optional[Any]="[MASK]"			,    **_lowercase     :       str			,    ):
		super().__init__(
		    do_lower_case=_lowercase			,    remove_space=_lowercase			,    keep_accents=_lowercase			,    bos_token=_lowercase			,    eos_token=_lowercase			,    unk_token=_lowercase			,    sep_token=_lowercase			,    pad_token=_lowercase			,    cls_token=_lowercase			,    mask_token=_lowercase			,    **_lowercase			,    )

		__UpperCAmelCase      		= do_lower_case
		__UpperCAmelCase      		= remove_space
		__UpperCAmelCase      		= keep_accents
		__UpperCAmelCase      		= vocab_file

		__UpperCAmelCase      		= spm.SentencePieceProcessor()
		self.sp_model.Load(_lowercase						)



	@property
	def        a  (  self     :       int						):
		return len(self.sp_model						)



	def        a  (  self     :       Tuple						):
		__UpperCAmelCase      		= {self.convert_ids_to_tokens(_lowercase						): i for i in range(self.vocab_size						)}
		vocab.update(self.added_tokens_encoder						)
		return vocab



	def __getstate__(  self     :       Tuple						):
		__UpperCAmelCase      		= self.__dict__.copy()
		__UpperCAmelCase      		= None
		return state



	def __setstate__(  self     :       Tuple			,    _lowercase     :       str						):
		__UpperCAmelCase      		= d
		__UpperCAmelCase      		= spm.SentencePieceProcessor()
		self.sp_model.Load(self.vocab_file						)



	def        a  (  self     :       Tuple			,    _lowercase     :       Optional[int]			,    _lowercase     :       List[Any]=False						):
		__UpperCAmelCase      		= self.sp_model.EncodeAsPieces(_lowercase						)
		return pieces



	def        a  (  self     :       int			,    _lowercase     :       List[str]						):
		return self.sp_model.PieceToId(_lowercase						)



	def        a  (  self     :       List[str]			,    _lowercase     :       str						):
		return self.sp_model.IdToPiece(_lowercase						)



	def        a  (  self     :       Any			,    _lowercase     :       Dict						):
		__UpperCAmelCase      		= self.sp_model.decode_pieces(_lowercase						)
		return out_string



	def        a  (  self     :       str			,    _lowercase     :       List[int]			,    _lowercase     :       Optional[List[int]] = None						):
		__UpperCAmelCase      		= [self.sep_token_id]
		__UpperCAmelCase      		= [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     :       Optional[Any]			,    _lowercase     :       List[int]			,    _lowercase     :       Optional[List[int]] = None			,    _lowercase     :       bool = False						):

		if already_has_special_tokens:
			if token_ids_a is not None:
				raise ValueError(
				    '''You should not supply a second sequence if the provided sequence of '''
				    '''ids is already formatted with special tokens for the model.'''						)
			return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

		if token_ids_a is not None:
			return [1] + ([0] * len(_lowercase						)) + [1] + ([0] * len(_lowercase						)) + [1]
		return [1] + ([0] * len(_lowercase						)) + [1]



	def        a  (  self     :       Tuple			,    _lowercase     :       List[int]			,    _lowercase     :       Optional[List[int]] = None						):
		__UpperCAmelCase      		= [self.sep_token_id]
		__UpperCAmelCase      		= [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     :       Union[str, Any]			,    _lowercase     :       str			,    _lowercase     :       Optional[str] = None						):
		if not os.path.isdir(_lowercase						):
			logger.error('''Vocabulary path ({}) should be a directory'''.format(_lowercase						)						)
			return
		__UpperCAmelCase      		= os.path.join(
		    _lowercase			,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

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

		return (out_vocab_file,)





'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class __A     (    A  ,  A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			List[Any]		=			IFInpaintingSuperResolutionPipeline
 __lowerCamelCase       :			Tuple		=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
 __lowerCamelCase       :			Optional[Any]		=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}		)
 __lowerCamelCase       :			str		=			PipelineTesterMixin.required_optional_params - {'latents'}



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      return self._get_superresolution_dummy_components()



 def    a__    (self					,  A					,  A=0			)       ->	List[Any]:


      """simple docstring"""



      if str(A			).startswith('''mps'''			):
           _a           =    torch.manual_seed(A			)
      else:
           _a           =    torch.Generator(device=A			).manual_seed(A			)

      _a           =    floats_tensor((1, 3, 16, 16)					,  rng=random.Random(A			)			).to(A			)
      _a           =    floats_tensor((1, 3, 32, 32)					,  rng=random.Random(A			)			).to(A			)
      _a           =    floats_tensor((1, 3, 32, 32)					,  rng=random.Random(A			)			).to(A			)

      _a           =    {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': image,
          '''original_image''': original_image,
          '''mask_image''': mask_image,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''output_type''': '''numpy''',
      }

      return inputs



 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()					,  reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,  )
 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      self._test_save_load_optional_components()



 @unittest.skipIf(torch_device != '''cuda'''					,  reason='''float16 requires CUDA'''			)
 def    a__    (self			)       ->	str:


      """simple docstring"""



      super().test_save_load_floataa(expected_max_diff=1E-1			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)



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


      """simple docstring"""



      self._test_save_load_local()



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2					,  )



