





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   a_ :

						@staticmethod
						def      lowerCAmelCase(   *UpperCAmelCase__   :  Optional[Any]     , **UpperCAmelCase__   :  Tuple						):






										"""simple docstring"""
										pass
def 							a_	(    __magic_name__   )				->			Union[str, Any]:




				"""simple docstring"""






				snake_case   :					Dict    						=				hashlib.mda(image.tobytes()   )
				return m.hexdigest()[:10]
def 							a_	(    __magic_name__   )				->			Optional[Any]:




				"""simple docstring"""






				snake_case   :					Any    						=				np.array(_SCREAMING_SNAKE_CASE   )
				snake_case   :					List[str]    						=				npimg.shape
				return {"hash": hashimage(_SCREAMING_SNAKE_CASE   ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class   a_ (			unittest.TestCase      ):
			A__      :			Optional[int]       	=  dict(
			    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])      )
			A__      :			Tuple       	=  dict(
			    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])      )

			def      lowerCAmelCase(   self   :  str     , UpperCAmelCase__   :  int     , UpperCAmelCase__   :  Optional[int]     , UpperCAmelCase__   :  List[Any]						):






							"""simple docstring"""
							snake_case   :					Union[str, Any]    						=				MaskGenerationPipeline(model=__UpperCamelCase     , image_processor=__UpperCamelCase						)
							return image_segmenter, [
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							]

			def      lowerCAmelCase(   self   :  List[Any]     , UpperCAmelCase__   :  int     , UpperCAmelCase__   :  Tuple						):






							"""simple docstring"""
							pass

			@require_tf
			@unittest.skip('''Image segmentation not implemented in TF'''						)
			def      lowerCAmelCase(   self   :  List[str]						):






							"""simple docstring"""
							pass

			@slow
			@require_torch
			def      lowerCAmelCase(   self   :  List[Any]						):






							"""simple docstring"""
							snake_case   :					Optional[int]    						=				pipeline('''mask-generation'''     , model='''facebook/sam-vit-huge'''						)

							snake_case   :					Optional[int]    						=				image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg'''     , points_per_batch=256						)

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

							# fmt: off
							self.assertEqual(
							    nested_simplify(__UpperCamelCase     , decimals=4						)     , [
							        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0444},
							        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.021},
							        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0167},
							        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0132},
							        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0053},
							        {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (480, 640)}, '''scores''': 0.9967},
							        {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (480, 640)}, '''scores''': 0.993},
							        {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (480, 640)}, '''scores''': 0.9909},
							        {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (480, 640)}, '''scores''': 0.9879},
							        {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (480, 640)}, '''scores''': 0.9834},
							        {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (480, 640)}, '''scores''': 0.9716},
							        {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (480, 640)}, '''scores''': 0.9612},
							        {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (480, 640)}, '''scores''': 0.9599},
							        {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (480, 640)}, '''scores''': 0.9552},
							        {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (480, 640)}, '''scores''': 0.9532},
							        {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (480, 640)}, '''scores''': 0.9516},
							        {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (480, 640)}, '''scores''': 0.9499},
							        {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (480, 640)}, '''scores''': 0.9483},
							        {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (480, 640)}, '''scores''': 0.9464},
							        {'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (480, 640)}, '''scores''': 0.943},
							        {'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (480, 640)}, '''scores''': 0.943},
							        {'''mask''': {'''hash''': '''c749b25868''', '''shape''': (480, 640)}, '''scores''': 0.9408},
							        {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (480, 640)}, '''scores''': 0.9335},
							        {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (480, 640)}, '''scores''': 0.9326},
							        {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (480, 640)}, '''scores''': 0.9262},
							        {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (480, 640)}, '''scores''': 0.8999},
							        {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (480, 640)}, '''scores''': 0.8986},
							        {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (480, 640)}, '''scores''': 0.8984},
							        {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (480, 640)}, '''scores''': 0.8873},
							        {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (480, 640)}, '''scores''': 0.8871}
							    ]     , )
							# fmt: on



			@require_torch
			@slow
			def      lowerCAmelCase(   self   :  Dict						):






							"""simple docstring"""
							snake_case   :					int    						=				"""facebook/sam-vit-huge"""
							snake_case   :					List[Any]    						=				pipeline('''mask-generation'''     , model=__UpperCamelCase						)

							snake_case   :					Union[str, Any]    						=				image_segmenter(
							    '''http://images.cocodataset.org/val2017/000000039769.jpg'''     , pred_iou_thresh=1     , points_per_batch=256						)

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

							self.assertEqual(
							    nested_simplify(__UpperCamelCase     , decimals=4						)     , [
							        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0444},
							        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0210},
							        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0167},
							        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0132},
							        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0053},
							    ]     , )

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


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	"▁"

SCREAMING_SNAKE_CASE		:     Dict         =	{"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE		:     List[Any]         =	{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

SCREAMING_SNAKE_CASE		:     Any         =	{
    "google/reformer-crime-and-punishment": 524288,
}

class        UpperCamelCase						(				__a				):
						a__		:Optional[Any]      =			VOCAB_FILES_NAMES
						a__		:List[Any]      =			PRETRAINED_VOCAB_FILES_MAP
						a__		:Union[str, Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__		:List[Any]      =			['''input_ids''', '''attention_mask''']





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase="</s>"			,     __UpperCamelCase="<unk>"			,     __UpperCamelCase=[]			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				None:
												UpperCamelCase_		:							List[str]  =		{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    eos_token=__UpperCamelCase			,     unk_token=__UpperCamelCase			,     additional_special_tokens=__UpperCamelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **__UpperCamelCase			,     )

												UpperCamelCase_		:							int  =		vocab_file
												UpperCamelCase_		:							str  =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
												self.sp_model.Load(__UpperCamelCase						)





						@property
						def        A_							(self						)				->				Dict:
												return self.sp_model.get_piece_size()





						def        A_							(self						)				->				Dict[str, int]:
												UpperCamelCase_		:							str  =		{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						)				->				Any:
												UpperCamelCase_		:							Dict  =		self.__dict__.copy()
												UpperCamelCase_		:							List[str]  =		None
												return state





						def __setstate__(self			,     __UpperCamelCase						)				->				List[Any]:
												UpperCamelCase_		:							List[Any]  =		d

												# for backward compatibility
												if not hasattr(self			,     """sp_model_kwargs"""						):
																		UpperCamelCase_		:							Any  =		{}

												UpperCamelCase_		:							Dict  =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
												self.sp_model.Load(self.vocab_file						)





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





						def        A_							(self			,     __UpperCamelCase						)				->				Union[str, Any]:
												return self.sp_model.piece_to_id(__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase						)				->				Dict:
												if index < self.sp_model.get_piece_size():
																		UpperCamelCase_		:							Union[str, Any]  =		self.sp_model.IdToPiece(__UpperCamelCase						)
												return token





						def        A_							(self			,     __UpperCamelCase						)				->				int:
												UpperCamelCase_		:							Dict  =		[]
												UpperCamelCase_		:							Union[str, 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
																								UpperCamelCase_		:							Optional[Any]  =		[]
																		else:
																								current_sub_tokens.append(__UpperCamelCase						)
												out_string += self.sp_model.decode(__UpperCamelCase						)
												return out_string.strip()





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				Tuple[str]:
												if not os.path.isdir(__UpperCamelCase						):
																		logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
																		return
												UpperCamelCase_		:							Tuple  =		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:
																								UpperCamelCase_		:							int  =		self.sp_model.serialized_model_proto()
																								fi.write(__UpperCamelCase						)

												return (out_vocab_file,)






