



"""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_rembert import RemBertTokenizer
else:
  lowerCamelCase       	=						None

lowerCamelCase       	=						logging.get_logger(__name__)
lowerCamelCase       	=						{"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase       	=						{
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
    """tokenizer_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase       	=						{
    """google/rembert""": 256,
}

lowerCamelCase       	=						"""▁"""
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					VOCAB_FILES_NAMES
       UpperCamelCase						=					PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase						=					RemBertTokenizer



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


             '''simple docstring'''




             UpperCAmelCase_      			=							AddedToken(_UpperCAmelCase		,    lstrip=_UpperCAmelCase		,    rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase		,    _UpperCAmelCase   ) else mask_token

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

             UpperCAmelCase_      			=							do_lower_case
             UpperCAmelCase_      			=							remove_space
             UpperCAmelCase_      			=							keep_accents
             UpperCAmelCase_      			=							vocab_file
             UpperCAmelCase_      			=							False if not self.vocab_file else True



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


             '''simple docstring'''




             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 					lowercase__    (							self      :  Optional[int]		,    _UpperCAmelCase      :  List[int]		,    _UpperCAmelCase      :  Optional[List[int]] = None		,    _UpperCAmelCase      :  bool = False   )			->						List[int]:


             '''simple docstring'''




             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(_UpperCAmelCase   )) + [1] + ([0] * len(_UpperCAmelCase   )) + [1]
             return [1] + ([0] * len(_UpperCAmelCase   )) + [1]



       def 					lowercase__    (							self      :  Tuple		,    _UpperCAmelCase      :  List[int]		,    _UpperCAmelCase      :  Optional[List[int]] = None   )			->						List[int]:


             '''simple docstring'''




             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 					lowercase__    (							self      :  List[Any]		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  Optional[str] = None   )			->						Tuple[str]:


             '''simple docstring'''




             if not os.path.isdir(_UpperCAmelCase   ):
                   logger.error("Vocabulary path ({}) should be a directory".format(_UpperCAmelCase   )   )
                   return
             UpperCAmelCase_      			=							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   ):
                   copyfile(self.vocab_file		,    _UpperCAmelCase   )

             return (out_vocab_file,)









"""simple docstring"""





import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
  import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       @property
       def 					lowercase__    (							self      :  Tuple   )			->						Any:


             '''simple docstring'''




             return (
                 "CUDAExecutionProvider",
                 {
                     "gpu_mem_limit": "15000000000",  # 15GB
                     "arena_extend_strategy": "kSameAsRequested",
                 },
             )



       @property
       def 					lowercase__    (							self      :  List[str]   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							ort.SessionOptions()
             UpperCAmelCase_      			=							False
             return options







       def 					lowercase__    (							self      :  Dict   )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/overture-creations-5sI6fQgYIuo.png"   )
             UpperCAmelCase_      			=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"   )
             UpperCAmelCase_      			=							load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"   )

             # using the PNDM scheduler by default
             UpperCAmelCase_      			=							OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
                 "CompVis/stable-diffusion-v1-4"		,    revision="onnx"		,    safety_checker=_UpperCAmelCase		,    feature_extractor=_UpperCAmelCase		,    provider=self.gpu_provider		,    sess_options=self.gpu_options		,    )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

             UpperCAmelCase_      			=							"A red cat sitting on a park bench"

             UpperCAmelCase_      			=							np.random.RandomState(0   )
             UpperCAmelCase_      			=							pipe(
                 prompt=_UpperCAmelCase		,    image=_UpperCAmelCase		,    mask_image=_UpperCAmelCase		,    strength=0.75		,    guidance_scale=7.5		,    num_inference_steps=15		,    generator=_UpperCAmelCase		,    output_type="np"		,    )

             UpperCAmelCase_      			=							output.images[0]

             assert image.shape == (512, 512, 3)
             assert np.abs(expected_image - image   ).max() < 1e-2




