


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      = logging.get_logger(__name__)

A      = {"vocab_file": "spm_char.model"}

A      = {
    "vocab_file": {
        "microsoft/speecht5_asr": "https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model",
        "microsoft/speecht5_tts": "https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model",
        "microsoft/speecht5_vc": "https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model",
    }
}

A      = {
    "microsoft/speecht5_asr": 1024,
    "microsoft/speecht5_tts": 1024,
    "microsoft/speecht5_vc": 1024,
}


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  VOCAB_FILES_NAMES
  A__=  PRETRAINED_VOCAB_FILES_MAP
  A__=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A__=  ['input_ids', 'attention_mask']






  def __init__(					self      :					Dict		,						_lowercase      :					List[str]		,						_lowercase      :					Any="<s>"		,						_lowercase      :					Union[str, Any]="</s>"		,						_lowercase      :					Tuple="<unk>"		,						_lowercase      :					int="<pad>"		,						_lowercase      :					Optional[Dict[str, Any]] = None		,						**_lowercase      :					List[str]		,						):




         """simple docstring"""







         UpperCAmelCase__										=     {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=_lowercase		,						eos_token=_lowercase		,						unk_token=_lowercase		,						pad_token=_lowercase		,						sp_model_kwargs=self.sp_model_kwargs		,						**_lowercase		,						)

         UpperCAmelCase__										=     vocab_file

         UpperCAmelCase__										=     spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(_lowercase )






  @property
  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         return self.sp_model.get_piece_size()






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         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      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     self.__dict__.copy()
         UpperCAmelCase__										=     None
         return state






  def __setstate__(					self      :					List[Any]		,						_lowercase      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     d

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

         UpperCAmelCase__										=     spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(self.vocab_file )






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					str ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Tuple		,						_lowercase      :					Optional[Any] ):




         """simple docstring"""







         return self.sp_model.piece_to_id(_lowercase )






  def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     self.sp_model.IdToPiece(_lowercase )
         return token






  def 			_UpperCAmelCase  (					self      :					Tuple		,						_lowercase      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     []
         UpperCAmelCase__										=     ""
         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(_lowercase ) + token
                       UpperCAmelCase__										=     []
                else:
                       current_sub_tokens.append(_lowercase )
         out_string += self.sp_model.decode(_lowercase )
         return out_string.strip()






  def 			_UpperCAmelCase  (					self      :					Tuple		,						_lowercase      :					List[str]		,						_lowercase      :					Optional[int]=None ):




         """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 			_UpperCAmelCase  (					self      :					Tuple		,						_lowercase      :					List[int]		,						_lowercase      :					Optional[List[int]] = None		,						_lowercase      :					bool = False ):




         """simple docstring"""







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

         UpperCAmelCase__										=     [1]
         if token_ids_a is None:
                return ([0] * len(_lowercase )) + suffix_ones
         return ([0] * len(_lowercase )) + ([0] * len(_lowercase )) + suffix_ones






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					str		,						_lowercase      :					Optional[str] = None ):




         """simple docstring"""







         if not os.path.isdir(_lowercase ):
                logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
                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 ) and os.path.isfile(self.vocab_file ):
                copyfile(self.vocab_file		,						_lowercase )
         elif not os.path.isfile(self.vocab_file ):
                with open(_lowercase		,						"wb" ) as fi:
                       UpperCAmelCase__										=     self.sp_model.serialized_model_proto()
                       fi.write(_lowercase )

         return (out_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     lowercase__  (					unittest.TestCase		):






  @property
  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







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






  @property
  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """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=_lowercase		,						feature_extractor=_lowercase		,						provider=self.gpu_provider		,						sess_options=self.gpu_options		,						)
         pipe.set_progress_bar_config(disable=_lowercase )

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

         UpperCAmelCase__										=     np.random.RandomState(0 )
         UpperCAmelCase__										=     pipe(
             prompt=_lowercase		,						image=_lowercase		,						mask_image=_lowercase		,						strength=0.7_5		,						guidance_scale=7.5		,						num_inference_steps=15		,						generator=_lowercase		,						output_type="np"		,						)

         UpperCAmelCase__										=     output.images[0]

         assert image.shape == (5_12, 5_12, 3)
         assert np.abs(expected_image - image ).max() < 1E-2





