





"""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  _SCREAMING_SNAKE_CASE:




         @staticmethod
         def    _UpperCamelCase					(						*SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)      ->				Dict:





               """simple docstring"""






               pass



def 					__lowerCamelCase       (							a_					:		Image							)      ->				str:
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  hashlib.mda(image.tobytes()							)
      return m.hexdigest()[:10]



def 					__lowerCamelCase       (							a_					:		Image							)      ->				Dict:
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  np.array(a_							)
      __SCREAMING_SNAKE_CASE       :Tuple		    =  npimg.shape
      return {"hash": hashimage(a_							), "shape": shape}







@is_pipeline_test
@require_vision
@require_torch
class  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_       :				str     						=  dict(
           (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()  ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])  )
       SCREAMING_SNAKE_CASE_       :				int     						=  dict(
           (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()  ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])  )




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  MaskGenerationPipeline(model=SCREAMING_SNAKE_CASE__		,image_processor=SCREAMING_SNAKE_CASE__							)
             return image_segmenter, [
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
             ]




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






             pass




       @require_tf
       @unittest.skip('''Image segmentation not implemented in TF'''							)
       def    _UpperCamelCase					(						self							)      ->				List[Any]:





             """simple docstring"""






             pass




       @slow
       @require_torch
       def    _UpperCamelCase					(						self							)      ->				Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  pipeline('''mask-generation'''		,model='''facebook/sam-vit-huge'''							)

             __SCREAMING_SNAKE_CASE       :int		    =  image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg'''		,points_per_batch=2_56							)

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

             # fmt: off
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__		,decimals=4							)		,[
                     {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_4_4_4},
                     {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_2_1},
                     {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_1_6_7},
                     {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_1_3_2},
                     {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_0_5_3},
                     {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_9_6_7},
                     {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_9_3},
                     {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_9_0_9},
                     {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_8_7_9},
                     {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_8_3_4},
                     {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_7_1_6},
                     {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_6_1_2},
                     {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_5_9_9},
                     {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_5_5_2},
                     {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_5_3_2},
                     {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_5_1_6},
                     {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_4_9_9},
                     {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_4_8_3},
                     {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_4_6_4},
                     {'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_4_3},
                     {'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_4_3},
                     {'''mask''': {'''hash''': '''c749b25868''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_4_0_8},
                     {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_3_3_5},
                     {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_3_2_6},
                     {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_2_6_2},
                     {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8_9_9_9},
                     {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8_9_8_6},
                     {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8_9_8_4},
                     {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8_8_7_3},
                     {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8_8_7_1}
                 ]		,)
             # fmt: on




       @require_torch
       @slow
       def    _UpperCamelCase					(						self							)      ->				Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  '''facebook/sam-vit-huge'''
             __SCREAMING_SNAKE_CASE       :str		    =  pipeline('''mask-generation'''		,model=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :int		    =  image_segmenter(
                 '''http://images.cocodataset.org/val2017/000000039769.jpg'''		,pred_iou_thresh=1		,points_per_batch=2_56							)

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

             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__		,decimals=4							)		,[
                     {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_4_4_4},
                     {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_2_1_0},
                     {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_1_6_7},
                     {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_1_3_2},
                     {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_0_5_3},
                 ]		,)






"""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": 5_1_2}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				str     						=  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_       :				Optional[int]     						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_       :				List[int]     						=  []
       SCREAMING_SNAKE_CASE_       :				List[Any]     						=  ['''input_ids''', '''attention_mask''']




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__="<s>"		,SCREAMING_SNAKE_CASE__="</s>"		,SCREAMING_SNAKE_CASE__="<unk>"		,SCREAMING_SNAKE_CASE__="<pad>"		,SCREAMING_SNAKE_CASE__="<::::>"		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__		,)      ->				None:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  {} if sp_model_kwargs is None else sp_model_kwargs

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

             __SCREAMING_SNAKE_CASE       :Dict		    =  vocab_file

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




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





             """simple docstring"""






             return self.sp_model.get_piece_size()




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__							): i for i in range(self.vocab_size							)}
             vocab.update(self.added_tokens_encoder							)
             return vocab




       def __getstate__(						self							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self.__dict__.copy()
             __SCREAMING_SNAKE_CASE       :Dict		    =  None
             return state




       def __setstate__(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  d

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

             __SCREAMING_SNAKE_CASE       :str		    =  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.Load(self.vocab_file							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				List[str]:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






             return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__							)
             return token




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  []
             __SCREAMING_SNAKE_CASE       :List[str]		    =  ''''''
             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(SCREAMING_SNAKE_CASE__							) + token
                         __SCREAMING_SNAKE_CASE       :Dict		    =  []
                   else:
                         current_sub_tokens.append(SCREAMING_SNAKE_CASE__							)
             out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__							)
             return out_string.strip()




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)      ->				Tuple[str]:





             """simple docstring"""






             if not os.path.isdir(SCREAMING_SNAKE_CASE__							):
                   logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
                   return
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  os.path.join(
                 SCREAMING_SNAKE_CASE__		,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

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

             return (out_vocab_file,)