



'''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


__lowerCAmelCase									=   logging.get_logger(__name__)

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

__lowerCAmelCase									=   {
    '''vocab_file''': {
        '''bert_for_seq_generation''': (
            '''https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'''
        ),
    }
}

__lowerCAmelCase									=   {'''bert_for_seq_generation''': 512}

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Optional[int]   				=					VOCAB_FILES_NAMES
							lowerCAmelCase       :    List[Any]   				=					PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase       :    Union[str, Any]   				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase       :    List[int]   				=					[]
							lowerCAmelCase       :    Optional[Any]   				=					['input_ids', 'attention_mask']







							def __init__(							self					:       Union[str, Any]	,_UpperCAmelCase					:       Any	,_UpperCAmelCase					:       Optional[int]="<s>"	,_UpperCAmelCase					:       Optional[Any]="</s>"	,_UpperCAmelCase					:       Optional[Any]="<unk>"	,_UpperCAmelCase					:       Dict="<pad>"	,_UpperCAmelCase					:       str="<::::>"	,_UpperCAmelCase					:       Optional[Dict[str, Any]] = None	,**_UpperCAmelCase					:       Any	,):
									_a     :      int        =							{} if sp_model_kwargs is None else sp_model_kwargs

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

									_a     :      Dict        =							vocab_file

									_a     :      List[str]        =							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(_UpperCAmelCase		)







							@property
							def 							__lowercase			(							self					:       Tuple		):
									return self.sp_model.get_piece_size()







							def 							__lowercase			(							self					:       str		):
									_a     :      List[Any]        =							{self.convert_ids_to_tokens(_UpperCAmelCase		): i for i in range(self.vocab_size		)}
									vocab.update(self.added_tokens_encoder		)
									return vocab







							def __getstate__(							self					:       Optional[int]		):
									_a     :      Tuple        =							self.__dict__.copy()
									_a     :      str        =							None
									return state







							def __setstate__(							self					:       Optional[Any]	,_UpperCAmelCase					:       Optional[int]		):
									_a     :      Optional[int]        =							d

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

									_a     :      Optional[int]        =							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(self.vocab_file		)







							def 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       str		):
									return self.sp_model.encode(_UpperCAmelCase	,out_type=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[int]	,_UpperCAmelCase					:       Tuple		):
									return self.sp_model.piece_to_id(_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[int]	,_UpperCAmelCase					:       str		):
									_a     :      Union[str, Any]        =							self.sp_model.IdToPiece(_UpperCAmelCase		)
									return token







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       Tuple		):
									_a     :      Any        =							[]
									_a     :      Any        =							''
									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(_UpperCAmelCase		) + token
													_a     :      Any        =							[]
											else:
													current_sub_tokens.append(_UpperCAmelCase		)
									out_string += self.sp_model.decode(_UpperCAmelCase		)
									return out_string.strip()







							def 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Optional[str] = None		):
									if not os.path.isdir(_UpperCAmelCase		):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
											return
									_a     :      List[Any]        =							os.path.join(
									    _UpperCAmelCase	,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

									return (out_vocab_file,)






import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       List[Any]       					=IFInpaintingPipeline
							lowercase_       :       Optional[int]       					=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
							lowercase_       :       Any       					=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowercase_       :       str       					=PipelineTesterMixin.required_optional_params - {'''latents'''}




							def  A__      (				self):
												return self._get_dummy_components()




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)

												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)
												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)

												lowercase							=						{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''image''': image,
												    '''mask_image''': mask_image,
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}

												return inputs




							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()   ,reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,)
							def  A__      (				self):
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)




							def  A__      (				self):
												self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != '''cuda'''   ,reason='''float16 requires CUDA''')
							def  A__      (				self):
												# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
												super().test_save_load_floataa(expected_max_diff=1E-1)




							def  A__      (				self):
												self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)




							def  A__      (				self):
												self._test_save_load_local()





							def  A__      (				self):
												self._test_inference_batch_single_identical(
												    expected_max_diff=1E-2   ,)




