


'''simple docstring'''






import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort




class 	a       (       _SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 # FIXME: add fast tests
 pass





@nightly
@require_onnxruntime
@require_torch_gpu
class 	a       (       unittest.TestCase    ):
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      return (
          "CUDAExecutionProvider",
          {
              "gpu_mem_limit": "15000000000",  # 15GB
              "arena_extend_strategy": "kSameAsRequested",
          },
      )
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							ort.SessionOptions()
      _a      =							False
      return options
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/in_paint/overture-creations-5sI6fQgYIuo.png'  )
      _a      =							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'  )
      _a      =							OnnxStableDiffusionInpaintPipeline.from_pretrained(
          'runwayml/stable-diffusion-inpainting' ,			revision='onnx' ,			safety_checker=__magic_name__ ,			feature_extractor=__magic_name__ ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
      pipe.set_progress_bar_config(disable=__magic_name__  )

      _a      =							'A red cat sitting on a park bench'

      _a      =							np.random.RandomState(0  )
      _a      =							pipe(
          prompt=__magic_name__ ,			image=__magic_name__ ,			mask_image=__magic_name__ ,			guidance_scale=7.5 ,			num_inference_steps=10 ,			generator=__magic_name__ ,			output_type='np' ,			)
      _a      =							output.images
      _a      =							images[0, 2_55:2_58, 2_55:2_58, -1]

      assert images.shape == (1, 5_12, 5_12, 3)
      _a      =							np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3




 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/in_paint/overture-creations-5sI6fQgYIuo.png'  )
      _a      =							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'  )
      _a      =							LMSDiscreteScheduler.from_pretrained(
          'runwayml/stable-diffusion-inpainting' ,			subfolder='scheduler' ,			revision='onnx'  )
      _a      =							OnnxStableDiffusionInpaintPipeline.from_pretrained(
          'runwayml/stable-diffusion-inpainting' ,			revision='onnx' ,			scheduler=__magic_name__ ,			safety_checker=__magic_name__ ,			feature_extractor=__magic_name__ ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
      pipe.set_progress_bar_config(disable=__magic_name__  )

      _a      =							'A red cat sitting on a park bench'

      _a      =							np.random.RandomState(0  )
      _a      =							pipe(
          prompt=__magic_name__ ,			image=__magic_name__ ,			mask_image=__magic_name__ ,			guidance_scale=7.5 ,			num_inference_steps=20 ,			generator=__magic_name__ ,			output_type='np' ,			)
      _a      =							output.images
      _a      =							images[0, 2_55:2_58, 2_55:2_58, -1]

      assert images.shape == (1, 5_12, 5_12, 3)
      _a      =							np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3





'''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


a_   :      Union[str, Any]				    =      logging.get_logger(__name__)

a_   :      Union[str, Any]				    =      {"vocab_file": "spiece.model"}

a_   :      List[Any]				    =      {
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

a_   :      Dict				    =      {"bert_for_seq_generation": 5_1_2}




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		VOCAB_FILES_NAMES
 _lowerCAmelCase	   =		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase	   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase	   =		[]
 _lowerCAmelCase	   =		["""input_ids""", """attention_mask"""]
 def __init__(	self ,			__magic_name__ ,			__magic_name__="<s>" ,			__magic_name__="</s>" ,			__magic_name__="<unk>" ,			__magic_name__="<pad>" ,			__magic_name__="<::::>" ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							None:
      _a      =							{} if sp_model_kwargs is None else sp_model_kwargs

      # Add extra_ids to the special token list
      super().__init__(
          bos_token=__magic_name__ ,			eos_token=__magic_name__ ,			unk_token=__magic_name__ ,			pad_token=__magic_name__ ,			sep_token=__magic_name__ ,			sp_model_kwargs=self.sp_model_kwargs ,			**__magic_name__ ,			)

      _a      =							vocab_file

      _a      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(__magic_name__  )
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      return self.sp_model.get_piece_size()
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							{self.convert_ids_to_tokens(__magic_name__  ): i for i in range(self.vocab_size  )}
      vocab.update(self.added_tokens_encoder  )
      return vocab
 def __getstate__(	self  )   ->							Any:
      _a      =							self.__dict__.copy()
      _a      =							None
      return state
 def __setstate__(	self ,			__magic_name__  )   ->							int:
      _a      =							d

      # for backward compatibility
      if not hasattr(self ,			'sp_model_kwargs'  ):
           _a      =							{}

      _a      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(self.vocab_file  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      return self.sp_model.encode(__magic_name__ ,			out_type=__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      return self.sp_model.piece_to_id(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      _a      =							self.sp_model.IdToPiece(__magic_name__  )
      return token
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Optional[Any]:
      _a      =							[]
      _a      =							''
      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(__magic_name__  ) + token
                _a      =							[]
           else:
                current_sub_tokens.append(__magic_name__  )
      out_string += self.sp_model.decode(__magic_name__  )
      return out_string.strip()




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							Tuple[str]:
      if not os.path.isdir(__magic_name__  ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'  )
           return
      _a      =							os.path.join(
          __magic_name__ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

      if os.path.abspath(self.vocab_file  ) != os.path.abspath(__magic_name__  ) and os.path.isfile(self.vocab_file  ):
           copyfile(self.vocab_file ,			__magic_name__  )
      elif not os.path.isfile(self.vocab_file  ):
           with open(__magic_name__ ,			'wb'  ) as fi:
                _a      =							self.sp_model.serialized_model_proto()
                fi.write(__magic_name__  )

      return (out_vocab_file,)

