


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


_A		       =						logging.get_logger(__name__)

_A		       =						'▁'

_A		       =						{'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

_A		       =						{
    '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',
    },
}

_A		       =						{'vinai/bartpho-syllable': 1024}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Any      =  VOCAB_FILES_NAMES
    UpperCAmelCase__						:		int      =  PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__						:		Tuple      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__						:		str      =  ["input_ids", "attention_mask"]






    def __init__(			self					,   A_					,   A_					,   A_="<s>"					,   A_="</s>"					,   A_="</s>"					,   A_="<s>"					,   A_="<unk>"					,   A_="<pad>"					,   A_="<mask>"					,   A_ = None					,   **A_					,   )  ->       None:
         # Mask token behave like a normal word, i.e. include the space before it
         __UpperCamelCase    							=AddedToken(A_					,   lstrip=A_					,   rstrip=A_    ) if isinstance(A_					,   A_    ) else mask_token

         __UpperCamelCase    							={} if sp_model_kwargs is None else sp_model_kwargs

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

         __UpperCamelCase    							=vocab_file
         __UpperCamelCase    							=monolingual_vocab_file
         __UpperCamelCase    							=spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.Load(str(A_    )    )

         # Load the reduced vocab

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

         __UpperCamelCase    							={v: k for k, v in self.fairseq_tokens_to_ids.items()}






    def __getstate__(			self    )  ->       Any:
         __UpperCamelCase    							=self.__dict__.copy()
         __UpperCamelCase    							=None
         __UpperCamelCase    							=self.sp_model.serialized_model_proto()
         return state






    def __setstate__(			self					,   A_    )  ->       List[str]:
         __UpperCamelCase    							=d

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

         __UpperCamelCase    							=spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )






    def _a					(			self					,   A_					,   A_ = None    )  ->       List[int]:

         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __UpperCamelCase    							=[self.cls_token_id]
         __UpperCamelCase    							=[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep






    def _a					(			self					,   A_					,   A_ = None					,   A_ = False    )  ->       List[int]:

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

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






    def _a					(			self					,   A_					,   A_ = None    )  ->       List[int]:
         __UpperCamelCase    							=[self.sep_token_id]
         __UpperCamelCase    							=[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 _a					(			self    )  ->       Any:
         return len(self.fairseq_ids_to_tokens    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							={self.convert_ids_to_tokens(A_    ): i for i in range(self.vocab_size    )}
         vocab.update(self.added_tokens_encoder    )
         return vocab






    def _a					(			self					,   A_    )  ->       List[str]:
         return self.sp_model.encode(A_					,   out_type=A_    )






    def _a					(			self					,   A_    )  ->       str:
         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         else:
              return self.unk_token_id






    def _a					(			self					,   A_    )  ->       int:
         return self.fairseq_ids_to_tokens[index]






    def _a					(			self					,   A_    )  ->       List[Any]:
         __UpperCamelCase    							=''.join(A_    ).replace(A_					,   ' '    ).strip()
         return out_string






    def _a					(			self					,   A_					,   A_ = None    )  ->       Tuple[str]:
         if not os.path.isdir(A_    ):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
              return
         __UpperCamelCase    							=os.path.join(
             A_					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase    							=os.path.join(
             A_					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file']					,   )

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

         if os.path.abspath(self.monolingual_vocab_file    ) != os.path.abspath(
             A_    ) and os.path.isfile(self.monolingual_vocab_file    ):
              copyfile(self.monolingual_vocab_file					,   A_    )
         elif not os.path.isfile(self.monolingual_vocab_file    ):
              with open(A_					,   '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(A_    )} \n'    )

         return out_vocab_file, out_monolingual_vocab_file






"""simple docstring"""






import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
						from PIL import Image
else:
						class 				snake_case       :




											"""simple docstring"""

											@staticmethod
											def 		__lowerCAmelCase						(				*lowerCamelCase__   :	List[Any]    ,**lowerCamelCase__   :	Union[str, Any]			):
															pass



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							hashlib.mda(image.tobytes()       )
				return m.hexdigest()[:1_0]



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							np.array(lowerCamelCase       )
				UpperCAmelCase__             =							npimg.shape
				return {"hash": hashimage(lowerCamelCase       ), "shape": shape}







@is_pipeline_test
@require_vision
@require_torch
class 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = dict(
					    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if MODEL_FOR_MASK_GENERATION_MAPPING else [])				)
					snake_case__				    = dict(
					    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])				)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	Dict    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	str			):
									UpperCAmelCase__             =							MaskGenerationPipeline(model=lowerCamelCase__    ,image_processor=lowerCamelCase__			)
									return image_segmenter, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]    ,lowerCamelCase__   :	Tuple    ,lowerCamelCase__   :	str			):
									pass







					@require_tf
					@unittest.skip('Image segmentation not implemented in TF'			)
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									pass







					@slow
					@require_torch
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							pipeline('mask-generation'    ,model='facebook/sam-vit-huge'			)

									UpperCAmelCase__             =							image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'    ,points_per_batch=256			)

									# Shortening by hashing
									UpperCAmelCase__             =							[]
									for i, o in enumerate(outputs['masks']			):
													new_outupt += [{"mask": mask_to_test_readable(lowerCamelCase__			), "scores": outputs["scores"][i]}]

									# fmt: off
									self.assertEqual(
									    nested_simplify(lowerCamelCase__    ,decimals=4			)    ,[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									        {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9_9_6_7},
									        {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_9_3},
									        {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9_9_0_9},
									        {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9_8_7_9},
									        {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9_8_3_4},
									        {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9_7_1_6},
									        {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9_6_1_2},
									        {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9_5_9_9},
									        {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9_5_5_2},
									        {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9_5_3_2},
									        {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9_5_1_6},
									        {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9_4_9_9},
									        {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9_4_8_3},
									        {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9_4_6_4},
									        {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9_4_0_8},
									        {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9_3_3_5},
									        {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9_3_2_6},
									        {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9_2_6_2},
									        {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8_9_9_9},
									        {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8_9_8_6},
									        {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8_9_8_4},
									        {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8_8_7_3},
									        {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8_8_7_1}
									    ]    ,)
									# fmt: on







					@require_torch
					@slow
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							'facebook/sam-vit-huge'
									UpperCAmelCase__             =							pipeline('mask-generation'    ,model=lowerCamelCase__			)

									UpperCAmelCase__             =							image_segmenter(
									    'http://images.cocodataset.org/val2017/000000039769.jpg'    ,pred_iou_thresh=1    ,points_per_batch=256			)

									# Shortening by hashing
									UpperCAmelCase__             =							[]
									for i, o in enumerate(outputs['masks']			):
													new_outupt += [{"mask": mask_to_test_readable(lowerCamelCase__			), "scores": outputs["scores"][i]}]

									self.assertEqual(
									    nested_simplify(lowerCamelCase__    ,decimals=4			)    ,[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1_0},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									    ]    ,)



