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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase					:			Any			      =		logging.get_logger(__name__)

__lowerCAmelCase					:			str			      =		"""▁"""

__lowerCAmelCase					:			Dict			      =		{"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

__lowerCAmelCase					:			Dict			      =		{
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

__lowerCAmelCase					:			Optional[int]			      =		{"""vinai/bartpho-syllable""": 1_024}


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			Tuple        =    VOCAB_FILES_NAMES
      A__	:			Tuple        =    PRETRAINED_VOCAB_FILES_MAP
      A__	:			Union[str, Any]        =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A__	:			List[Any]        =    ["input_ids", "attention_mask"]




      def __init__(      self		:       Optional[Any]				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       Optional[int]				,       _snake_case		:       int="<s>"				,       _snake_case		:       Dict="</s>"				,       _snake_case		:       Dict="</s>"				,       _snake_case		:       str="<s>"				,       _snake_case		:       Optional[int]="<unk>"				,       _snake_case		:       str="<pad>"				,       _snake_case		:       List[Any]="<mask>"				,       _snake_case		:       Optional[Dict[str, Any]] = None				,       **_snake_case		:       Optional[int]				,       ):
          __lowercase		:			Optional[int]	    =       AddedToken(_snake_case				,       lstrip=_snake_case				,       rstrip=_snake_case				) if isinstance(_snake_case				,       _snake_case				) else mask_token

          __lowercase		:			str	    =       {} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=_snake_case				,       eos_token=_snake_case				,       unk_token=_snake_case				,       sep_token=_snake_case				,       cls_token=_snake_case				,       pad_token=_snake_case				,       mask_token=_snake_case				,       sp_model_kwargs=self.sp_model_kwargs				,       **_snake_case				,       )

          __lowercase		:			Dict	    =       vocab_file
          __lowercase		:			List[str]	    =       monolingual_vocab_file
          __lowercase		:			Tuple	    =       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
          self.sp_model.Load(str(_snake_case				)				)

          # Load the reduced vocab

          # Keep order of special tokens for backward compatibility
          __lowercase		:			str	    =       {}
          __lowercase		:			List[str]	    =       0
          for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
              if str(_snake_case				) not in self.fairseq_tokens_to_ids:
                  __lowercase		:			Optional[Any]	    =       cnt
                  cnt += 1
          with open(_snake_case				,       '''r'''				,       encoding='''utf-8'''				) as f:
              for line in f.readlines():
                  __lowercase		:			Tuple	    =       line.strip().split()[0]
                  __lowercase		:			Any	    =       len(self.fairseq_tokens_to_ids				)
          if str(_snake_case				) not in self.fairseq_tokens_to_ids:
              __lowercase		:			str	    =       len(self.fairseq_tokens_to_ids				)

          __lowercase		:			List[str]	    =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}




      def __getstate__(      self		:       Union[str, Any]				):
          __lowercase		:			Optional[int]	    =       self.__dict__.copy()
          __lowercase		:			str	    =       None
          __lowercase		:			Tuple	    =       self.sp_model.serialized_model_proto()
          return state




      def __setstate__(      self		:       int				,       _snake_case		:       str				):
          __lowercase		:			List[str]	    =       d

          # for backward compatibility
          if not hasattr(self				,       '''sp_model_kwargs'''				):
              __lowercase		:			List[Any]	    =       {}

          __lowercase		:			Dict	    =       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)




      def 		snake_case_			(      self		:       List[str]				,       _snake_case		:       List[int]				,       _snake_case		:       Optional[List[int]] = None				):




          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __lowercase		:			List[Any]	    =       [self.cls_token_id]
          __lowercase		:			Union[str, Any]	    =       [self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep




      def 		snake_case_			(      self		:       str				,       _snake_case		:       List[int]				,       _snake_case		:       Optional[List[int]] = None				,       _snake_case		:       bool = False				):




          if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=_snake_case				,       token_ids_a=_snake_case				,       already_has_special_tokens=_snake_case				)

          if token_ids_a is None:
              return [1] + ([0] * len(_snake_case				)) + [1]
          return [1] + ([0] * len(_snake_case				)) + [1, 1] + ([0] * len(_snake_case				)) + [1]




      def 		snake_case_			(      self		:       Tuple				,       _snake_case		:       List[int]				,       _snake_case		:       Optional[List[int]] = None				):
          __lowercase		:			Optional[int]	    =       [self.sep_token_id]
          __lowercase		:			str	    =       [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 + sep + token_ids_a + sep				) * [0]




      @property
      def 		snake_case_			(      self		:       Any				):
          return len(self.fairseq_ids_to_tokens				)




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			Union[str, Any]	    =       {self.convert_ids_to_tokens(_snake_case				): i for i in range(self.vocab_size				)}
          vocab.update(self.added_tokens_encoder				)
          return vocab




      def 		snake_case_			(      self		:       Any				,       _snake_case		:       str				):
          return self.sp_model.encode(_snake_case				,       out_type=_snake_case				)




      def 		snake_case_			(      self		:       Union[str, Any]				,       _snake_case		:       Union[str, Any]				):




          if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
          else:
              return self.unk_token_id




      def 		snake_case_			(      self		:       Tuple				,       _snake_case		:       List[str]				):
          return self.fairseq_ids_to_tokens[index]




      def 		snake_case_			(      self		:       List[Any]				,       _snake_case		:       Optional[Any]				):
          __lowercase		:			List[Any]	    =       ''''''.join(_snake_case				).replace(_snake_case				,       ''' '''				).strip()
          return out_string




      def 		snake_case_			(      self		:       List[Any]				,       _snake_case		:       str				,       _snake_case		:       Optional[str] = None				):




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

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

          if os.path.abspath(self.monolingual_vocab_file				) != os.path.abspath(
              _snake_case				) and os.path.isfile(self.monolingual_vocab_file				):
              copyfile(self.monolingual_vocab_file				,       _snake_case				)
          elif not os.path.isfile(self.monolingual_vocab_file				):
              with open(_snake_case				,       '''w'''				,       encoding='''utf-8'''				) as fp:
                  for token in self.fairseq_tokens_to_ids:
                      if token not in self.all_special_tokens:
                          fp.write(F'{str(_snake_case				)} \n'				)

          return out_vocab_file, out_monolingual_vocab_file




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 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      @property
      def 		snake_case_			(      self		:       Union[str, Any]				):
          return (
              "CUDAExecutionProvider",
              {
                  "gpu_mem_limit": "15000000000",  # 15GB
                  "arena_extend_strategy": "kSameAsRequested",
              },
          )




      @property
      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Tuple	    =       ort.SessionOptions()
          __lowercase		:			List[Any]	    =       False
          return options




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			int	    =       load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
          __lowercase		:			int	    =       load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
          __lowercase		:			Optional[Any]	    =       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
          __lowercase		:			Union[str, Any]	    =       OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
              '''CompVis/stable-diffusion-v1-4'''				,       revision='''onnx'''				,       safety_checker=_snake_case				,       feature_extractor=_snake_case				,       provider=self.gpu_provider				,       sess_options=self.gpu_options				,       )
          pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			Tuple	    =       '''A red cat sitting on a park bench'''

          __lowercase		:			int	    =       np.random.RandomState(0				)
          __lowercase		:			int	    =       pipe(
              prompt=_snake_case				,       image=_snake_case				,       mask_image=_snake_case				,       strength=0.75				,       guidance_scale=7.5				,       num_inference_steps=15				,       generator=_snake_case				,       output_type='''np'''				,       )

          __lowercase		:			Dict	    =       output.images[0]

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


