





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    lowerCAmelCase							(					__UpperCamelCase,  unittest.TestCase			):
 # FIXME: add fast tests
 pass


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

     lowerCamelCase__    :      List[Any]					 =		'A red cat sitting on a park bench'

     lowerCamelCase__    :      Dict					 =		np.random.RandomState(0	)
     lowerCamelCase__    :      Optional[int]					 =		pipe(
         prompt=UpperCAmelCase			,     image=UpperCAmelCase			,     mask_image=UpperCAmelCase			,     guidance_scale=7.5			,     num_inference_steps=10			,     generator=UpperCAmelCase			,     output_type='np'			,     )
     lowerCamelCase__    :      Any					 =		output.images
     lowerCamelCase__    :      Union[str, Any]					 =		images[0, 255:258, 255:258, -1]

     assert images.shape == (1, 512, 512, 3)
     lowerCamelCase__    :      Union[str, Any]					 =		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      A_	(      self  :      Optional[Any]	)			->	Dict:
     lowerCamelCase__    :      Any					 =		load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
     lowerCamelCase__    :      Optional[Any]					 =		load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
     lowerCamelCase__    :      Tuple					 =		LMSDiscreteScheduler.from_pretrained(
         'runwayml/stable-diffusion-inpainting'			,     subfolder='scheduler'			,     revision='onnx'	)
     lowerCamelCase__    :      str					 =		OnnxStableDiffusionInpaintPipeline.from_pretrained(
         'runwayml/stable-diffusion-inpainting'			,     revision='onnx'			,     scheduler=UpperCAmelCase			,     safety_checker=UpperCAmelCase			,     feature_extractor=UpperCAmelCase			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
     pipe.set_progress_bar_config(disable=UpperCAmelCase	)

     lowerCamelCase__    :      str					 =		'A red cat sitting on a park bench'

     lowerCamelCase__    :      Tuple					 =		np.random.RandomState(0	)
     lowerCamelCase__    :      int					 =		pipe(
         prompt=UpperCAmelCase			,     image=UpperCAmelCase			,     mask_image=UpperCAmelCase			,     guidance_scale=7.5			,     num_inference_steps=20			,     generator=UpperCAmelCase			,     output_type='np'			,     )
     lowerCamelCase__    :      str					 =		output.images
     lowerCamelCase__    :      Tuple					 =		images[0, 255:258, 255:258, -1]

     assert images.shape == (1, 512, 512, 3)
     lowerCamelCase__    :      Dict					 =		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










import os
import unicodedata
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 SPIECE_UNDERLINE, logging


A__								=						logging.get_logger(__name__)

A__								=						{'''vocab_file''': '''spiece.model'''}

A__								=						{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}


class       a							(			__lowerCamelCase				):



		def __init__(     self     :Union[str, Any]   ,__lowercase     :Optional[Any]   ,__lowercase     :int=False   ,__lowercase     :int=True   ,__lowercase     :Optional[int]=False   ,__lowercase     :List[str]="<s>"   ,__lowercase     :str="</s>"   ,__lowercase     :Dict="<unk>"   ,__lowercase     :Optional[int]="<sep>"   ,__lowercase     :Tuple="<pad>"   ,__lowercase     :Union[str, Any]="<cls>"   ,__lowercase     :Dict="<mask>"   ,__lowercase     :List[Any]=["<eop>", "<eod>"]   ,__lowercase     :Optional[Dict[str, Any]] = None   ,**__lowercase     :int   ,):
						snake_case__	:      Any     		=	AddedToken(__lowercase   ,lstrip=__lowercase   ,rstrip=__lowercase  ) if isinstance(__lowercase   ,__lowercase  ) else mask_token

						snake_case__	:      Tuple     		=	{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    do_lower_case=__lowercase   ,remove_space=__lowercase   ,keep_accents=__lowercase   ,bos_token=__lowercase   ,eos_token=__lowercase   ,unk_token=__lowercase   ,sep_token=__lowercase   ,pad_token=__lowercase   ,cls_token=__lowercase   ,mask_token=__lowercase   ,additional_special_tokens=__lowercase   ,sp_model_kwargs=self.sp_model_kwargs   ,**__lowercase   ,)

						snake_case__	:      Optional[Any]     		=	3

						snake_case__	:      List[str]     		=	do_lower_case
						snake_case__	:      Union[str, Any]     		=	remove_space
						snake_case__	:      Tuple     		=	keep_accents
						snake_case__	:      List[str]     		=	vocab_file

						snake_case__	:      Optional[Any]     		=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
						self.sp_model.Load(__lowercase  )

						try:
										import jieba
						except ModuleNotFoundError as error:
										raise error.__class__(
										    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
										    '''See https://pypi.org/project/jieba/ for installation.'''  )
						snake_case__	:      List[Any]     		=	jieba
						snake_case__	:      Union[str, Any]     		=	str.maketrans(''' \n'''   ,'''\u2582\u2583'''  )



		@property
		# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						return len(self.sp_model  )



		def 	__lowerCamelCase			(     self     :Any  ):
						snake_case__	:      Optional[Any]     		=	{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     :List[str]  ):
						snake_case__	:      Optional[Any]     		=	self.__dict__.copy()
						snake_case__	:      Optional[int]     		=	None
						return state



		def __setstate__(     self     :int   ,__lowercase     :str  ):
						snake_case__	:      Tuple     		=	d

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

						snake_case__	:      List[Any]     		=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
						self.sp_model.Load(self.vocab_file  )



		def 	__lowerCamelCase			(     self     :Dict   ,__lowercase     :Optional[int]  ):
						if self.remove_space:
										snake_case__	:      int     		=	''' '''.join(inputs.strip().split()  )
						else:
										snake_case__	:      Tuple     		=	inputs
						snake_case__	:      List[Any]     		=	outputs.replace('''``'''   ,'''"'''  ).replace('''\'\''''   ,'''"'''  )

						if not self.keep_accents:
										snake_case__	:      Any     		=	unicodedata.normalize('''NFKD'''   ,__lowercase  )
										snake_case__	:      Dict     		=	''''''.join([c for c in outputs if not unicodedata.combining(__lowercase  )]  )
						if self.do_lower_case:
										snake_case__	:      str     		=	outputs.lower()

						return outputs



		def 	__lowerCamelCase			(     self     :Optional[Any]   ,__lowercase     :str  ):
						snake_case__	:      Dict     		=	self.preprocess_text(__lowercase  )
						snake_case__	:      Any     		=	self.sp_model.encode(__lowercase   ,out_type=__lowercase  )
						snake_case__	:      List[str]     		=	[]
						for piece in pieces:
										if len(__lowercase  ) > 1 and piece[-1] == str(''','''  ) and piece[-2].isdigit():
														snake_case__	:      Union[str, Any]     		=	self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowercase   ,''''''  )  )
														if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																		if len(cur_pieces[0]  ) == 1:
																						snake_case__	:      Optional[Any]     		=	cur_pieces[1:]
																		else:
																						snake_case__	:      Optional[Any]     		=	cur_pieces[0][1:]
														cur_pieces.append(piece[-1]  )
														new_pieces.extend(__lowercase  )
										else:
														new_pieces.append(__lowercase  )

						return new_pieces



		def 	__lowerCamelCase			(     self     :Union[str, Any]   ,__lowercase     :Dict  ):
						return self.sp_model.PieceToId(__lowercase  )



		def 	__lowerCamelCase			(     self     :Tuple   ,__lowercase     :Optional[int]  ):
						return self.sp_model.IdToPiece(__lowercase  )



		def 	__lowerCamelCase			(     self     :Tuple   ,__lowercase     :List[Any]  ):
						snake_case__	:      Union[str, Any]     		=	''''''.join(__lowercase  ).replace(__lowercase   ,''' '''  ).strip()
						return out_string



		def 	__lowerCamelCase			(     self     :List[Any]   ,__lowercase     :List[int]   ,__lowercase     :Optional[List[int]] = None  ):
						snake_case__	:      Optional[Any]     		=	[self.sep_token_id]
						snake_case__	:      str     		=	[self.cls_token_id]
						if token_ids_a is None:
										return token_ids_a + sep + cls
						return token_ids_a + sep + token_ids_a + sep + cls



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

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

						if token_ids_a is not None:
										return ([0] * len(__lowercase  )) + [1] + ([0] * len(__lowercase  )) + [1, 1]
						return ([0] * len(__lowercase  )) + [1, 1]



		def 	__lowerCamelCase			(     self     :List[str]   ,__lowercase     :List[int]   ,__lowercase     :Optional[List[int]] = None  ):
						snake_case__	:      List[Any]     		=	[self.sep_token_id]
						snake_case__	:      Optional[Any]     		=	[2]

						if token_ids_a is None:
										return len(token_ids_a + sep  ) * [0] + cls_segment_id
						return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1] + cls_segment_id



		def 	__lowerCamelCase			(     self     :Optional[int]   ,__lowercase     :str   ,__lowercase     :Optional[str] = None  ):
						if not os.path.isdir(__lowercase  ):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
										return
						snake_case__	:      Union[str, Any]     		=	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:
														snake_case__	:      str     		=	self.sp_model.serialized_model_proto()
														fi.write(__lowercase  )

						return (out_vocab_file,)






		def 	__lowerCamelCase			(     self     :Union[str, Any]   ,*__lowercase     :Optional[int]   ,**__lowercase     :Dict  ):
						snake_case__	:      Dict     		=	super()._decode(*__lowercase   ,**__lowercase  )
						snake_case__	:      List[Any]     		=	text.replace(''' '''   ,''''''  ).replace('''\u2582'''   ,''' '''  ).replace('''\u2583'''   ,'''\n'''  )
						return text

