



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 		snake_case						(	unittest.TestCase  ):



   '''simple docstring'''






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

    """simple docstring"""







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

   @property
   def      UpperCamelCase_  (					self							:			Any)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  List[Any]					=					ort.SessionOptions()
    _snake_case    :  Optional[int]					=					False
    return options


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

    """simple docstring"""







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

    _snake_case    :  Dict					=					"""A red cat sitting on a park bench"""

    _snake_case    :  int					=					np.random.RandomState(0)
    _snake_case    :  Optional[Any]					=					pipe(
        prompt=lowerCAmelCase				,		image=lowerCAmelCase				,		mask_image=lowerCAmelCase				,		strength=0.75				,		guidance_scale=7.5				,		num_inference_steps=15				,		generator=lowerCAmelCase				,		output_type="""np"""				,		)

    _snake_case    :  Optional[int]					=					output.images[0]

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





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_camembert import CamembertTokenizer
else:
  a__    =	None


a__    =	logging.get_logger(__name__)

a__    =	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

a__    =	{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
    },
}

a__    =	{
    """camembert-base""": 5_12,
}

a__    =	"""▁"""







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: List[str]          =		VOCAB_FILES_NAMES
   snake_case_					: str          =		PRETRAINED_VOCAB_FILES_MAP
   snake_case_					: Optional[Any]          =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case_					: List[Any]          =		["""input_ids""", """attention_mask"""]
   snake_case_					: int          =		CamembertTokenizer

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

    """simple docstring"""







    _snake_case    :  Dict					=					AddedToken(lowerCAmelCase				,		lstrip=lowerCAmelCase				,		rstrip=lowerCAmelCase) if isinstance(lowerCAmelCase				,		lowerCAmelCase) else mask_token

    super().__init__(
        lowerCAmelCase				,		tokenizer_file=lowerCAmelCase				,		bos_token=lowerCAmelCase				,		eos_token=lowerCAmelCase				,		sep_token=lowerCAmelCase				,		cls_token=lowerCAmelCase				,		unk_token=lowerCAmelCase				,		pad_token=lowerCAmelCase				,		mask_token=lowerCAmelCase				,		additional_special_tokens=lowerCAmelCase				,		**lowerCAmelCase				,		)

    _snake_case    :  List[str]					=					vocab_file
    _snake_case    :  Optional[Any]					=					False if not self.vocab_file else True

   def      UpperCamelCase_  (					self							:			Optional[int]				,		lowerCAmelCase							:			List[int]				,		lowerCAmelCase							:			Optional[List[int]] = None)				->				List[int]:

    """simple docstring"""







    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    _snake_case    :  Optional[Any]					=					[self.cls_token_id]
    _snake_case    :  List[Any]					=					[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep

   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			List[int]				,		lowerCAmelCase							:			Optional[List[int]] = None)				->				List[int]:

    """simple docstring"""







    _snake_case    :  str					=					[self.sep_token_id]
    _snake_case    :  List[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]


   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Optional[str] = None)				->				Tuple[str]:

    """simple docstring"""







    if not self.can_save_slow_tokenizer:
     raise ValueError(
         """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
         """tokenizer.""")

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

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

    return (out_vocab_file,)
